ORCID Profile
0000-0002-6758-0519
Current Organisation
University of New South Wales
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Physical Geography and Environmental Geoscience | Surfacewater Hydrology | Water Resources Engineering | Surfacewater Hydrology | Civil Engineering | Climate Change Processes | Water Quality Engineering | Environmental Science and Management | Infrastructure Engineering and Asset Management | Applied Hydrology (Drainage, Flooding, Irrigation, Quality, Etc.) | Natural Resource Management | Climatology (excl. Climate Change Processes) | Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.) | Agricultural Engineering | Water And Sanitary Engineering | Natural Resource Management | Landscape Ecology | Water Treatment Processes | Hydrology Not Elsewhere Classified |
ANZSRC Socio-Economic Objective (SEO)
Land and water management | Land and water management | Climate change | Water Allocation and Quantification | Natural Hazards in Fresh, Ground and Surface Water Environments | Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Climate Change Adaptation Measures | Land and water management | Water services and utilities | Expanding Knowledge in Engineering | Climate Variability (excl. Social Impacts) | Land and water management | Integrated (ecosystem) assessment and management | Water Services and Utilities | Physical and Chemical Conditions of Water in Fresh, Ground and Surface Water Environments (excl. Urban and Industrial Use) | Global climate change adaptation measures | Health Protection and/or Disaster Response | Rural Water Evaluation (incl. Water Quality)
Related Links
Publications
A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments
Publisher: Elsevier BV
Date: 07-2014
Causes of the Widespread 2019–2020 Australian Bushfire Season
Publisher: American Geophysical Union (AGU)
Date: 11-2020
DOI: 10.1029/2020EF001671
Assessing Goodness of Fit for Verifying Probabilistic Forecasts
Publisher: MDPI AG
Date: 27-10-2021
Abstract: The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a probabilistic prediction or forecast is that predicted probability distributions for a target variable are not stationary in time, meaning one observation alone exists to quantify goodness of fit for each prediction issued. Therefore, we suggest an additional dissociation that can dissociate target information from the other time variant part—the target to be verified in this study is the alignment of observations to the predicted probability distribution. For this dissociation, the probability integral transformation is used. To measure the goodness of fit for the predicted probability distributions, this study uses the root mean squared deviation metric. If the observations after the dissociation can be assumed to be independent, the mean square deviation metric becomes a chi-square test statistic, which enables statistically testing the hypothesis regarding whether the observations are from the same population as the predicted probability distributions. An illustration of our proposed rationale is provided using the multi-model ensemble prediction for El Niño–Southern Oscillation.
Correcting systematic biases across multiple atmospheric variables in the frequency domain
Publisher: Springer Science and Business Media LLC
Date: 30-03-2018
A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations
Publisher: Elsevier BV
Date: 07-2017
Can Bias correction of regional climate model lateral boundary conditions improve low-frequency rainfall variability?
Publisher: American Meteorological Society
Date: 12-2017
Abstract: Global climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of s le moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation.
Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts
Publisher: Elsevier BV
Date: 08-2015
A New Framework for Modeling Future Hydrologic Extremes: Nested Bias Correction as a Precursor to Stochastic Rainfall Downscaling
Publisher: American Society of Civil Engineers
Date: 07-02-2013
Multisite seasonal forecast of arid river flows using a dynamic model combination approach
Publisher: American Geophysical Union (AGU)
Date: 10-2009
DOI: 10.1029/2008WR007510
Addressing the mischaracterization of extreme rainfall in regional climate model simulations – A synoptic pattern based bias correction approach
Publisher: Elsevier BV
Date: 2018
Consideration of Trends in Evaluating Inter-basin Water Transfer Alternatives within a Fuzzy Decision Making Framework
Publisher: Springer Science and Business Media LLC
Date: 24-03-2009
Determination of the spatial scaling relationship of rainfall extremes using radar data
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-14968
Abstract: Climate models and their future projections, are normally provided in coarse spatial resolutions which makes them an imprecise source of information for certain hydrological purposes. Finding the proficient means of downscaling such data is one of the open questions of climate research. Previous research has shown that, the rainfall extremes show self-similarity in time and that a relatively similar behavior exists in regard to the spatial scale as well (Veneziano et al 2002). This study aims at determining the spatial scaling relationship of the rainfall extremes by using fine grids of radar datasets and upscaling them. In an empirical manner by aggregating the radar rainfall cells in space and for different cell sizes with a = 1, 2, 3, & #8230 km and for different durations of d = 5 min, 15 min 30 min, 1 hr, 2 hr, 4 hr, & #8230 , 24 hr the Annual Maximum Series are extracted. Using the AMS of different spatial and temporal scales and applying the Koutsoyiannis et at. 1998 method for rainfall extreme value analysis, the probability distribution function is fitted. Assessing the changes of the PDF parameters with the scale, with a logarithmic transformation on both variables ln(parameter) vs. ln(scale), can show the sought relationship. The preliminary results of the study show definable non-linear relationships for location and scale parameters of the GEV distribution and the eta parameter of the Koutsoyiannis et al. 1998 parametrization.& Koutsoyiannis, D. Kozonis, and A. Manetas, A mathematical framework for studying rainfall intensity-duration-frequency relationships, Journal of Hydrology, 206 (1-2), 118& #8211 , doi:10.1016/S0022-1694(98)00097-3, 1998.Veneziano, Daniele Furcolo, Pierluigi (2002): Multifractality of rainfall and scaling of intensity-duration-frequency curves. In Water Resour. Res. 38 (12), 42-1-42-12. DOI: 10.1029/2001WR000372.
A comparison of three stochastic multi-site precipitation occurrence generators
Publisher: Elsevier BV
Date: 11-2006
Applicability of Hydrologic Response Units in low topographic relief catchments and evaluation using high resolution aerial photograph analysis
Publisher: Elsevier BV
Date: 07-2016
Merging radar and in situ rainfall measurements: An assessment of different combination algorithms
Publisher: American Geophysical Union (AGU)
Date: 10-2016
DOI: 10.1002/2015WR018441
Author comments/responses
Publisher: Copernicus GmbH
Date: 11-09-2017
Application of Scaling in Radar Reflectivity for Correcting Range-Dependent Bias in Climatological Radar Rainfall Estimates
Publisher: American Meteorological Society
Date: 10-2004
Author comments/responses
Publisher: Copernicus GmbH
Date: 11-09-2017
Is there an upper extent to systematic bias correction of climate model simulations? Application to low-frequency variability within the Niño3.4 region
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-579
Abstract: Most systematic bias correction approaches which are developed based on the bias of the statistical properties of interest perform well to bias correct the current climate simulations with respect to observations. However, the significance of the application of systematic bias correction approaches on the raw output of climate model simulations remains a debate due to the unavailability of future climate observation to validate the approach.The output of a recent ultra-high resolution climate model simulation, UHR-CESM, demonstrates the best performance to simulate variability of sea surface temperature (SST) in the tropical Pacific with an exception of a small bias in mean. This knowledge encouraged us to use the outputs of the model to represent the truth both in current and future climates. We use the output of the model in response to the current climate CO2 concentration as the representative of the current climate. While the outputs of model simulation in response to doubling and quadrupling CO2 concentrations are used as the representative of the truth of future climates.We bias correct monthly SST simulations for 8 (eight) Coupled Model Intercomparison Project 6 (CMIP6) over the Ni& #241 o 3.4 region having the same CO2 concentration as our reference model using a novel time-frequency continuous wavelet-based bias correction (CWBC). The results show a nearly perfect correction of distributional, trend, and spectral attributes biases in the 8 (eight) climate model simulations in the current climate and a consistent reduction of the biases in the model simulation in response to doubled CO2 concentration. Although the overall quality of the statistical attributes is improved after the application of bias correction in response to the more extreme change of quadrupled CO2 concentration, a degradation in the spectral attributes is observed. It shows that a systematic bias correction approach has its upper limit. Therefore, while the application of bias correction approaches is recommended prior to the further use of raw climate model simulations, up to what extent future climate simulations are reliably bias corrected should be handled carefully.
A dimension range representation (DRR) measure for self-organizing maps
Publisher: Elsevier BV
Date: 05-2016
Assessing GCM Convergence for India Using the Variable Convergence Score
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2014
Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric
Publisher: Elsevier BV
Date: 06-2018
A global comparison of alternate AMSR2 soil moisture products: Why do they differ?
Publisher: Elsevier BV
Date: 05-2015
Addressing Climatic Non-Stationarity in the Assessment of Flood Risk
Publisher: Informa UK Limited
Date: 2010
Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach
Publisher: Elsevier BV
Date: 07-2006
Do Longer Dry Spells Associated With Warmer Years Compound the Stress on Global Water Resources?
Publisher: American Geophysical Union (AGU)
Date: 02-2022
DOI: 10.1029/2021EF002392
Abstract: Availability of water resources is significantly affected by changes in seasonal rainfall, with water often in short supply when most needed. The majority of current research focuses on the impacts of multiyear drought, using monthly or annual average rainfall to investigate impacts to water resources. Here, we use daily rainfall to evaluate changes in dry spells lengths, defined as the continuous number of days without rain, and investigate how these changes may impart stresses on water resources in warmer summer seasons globally. We use over 100 years of precipitation and temperature data across the world, arranged into warm and cold groups of years on the basis of mean summer temperature. These warm and cold groups are then compared to demonstrate an overwhelming tendency for warmer summers to contain longer dry spells globally. This difference in dry spell length in warmer summer seasons is argued to have far reaching ramifications in warmer summers. For some of the largest cities in the world, vulnerability of water resources, which is a measure of the magnitude or severity of the water availability deficit, is shown to be on average 30% higher due to longer seasonal dry spells in warmer summers. Such an impact points to a need to reassess water resources plans and policies to include the impacts of seasonal dry spells, especially relating to large urban populations around the world.
A new method for verification of delineated channel networks
Publisher: American Geophysical Union (AGU)
Date: 03-2014
DOI: 10.1002/2013WR014290
Improved water allocation utilizing probabilistic climate forecasts: Short‐term water contracts in a risk management framework
Publisher: American Geophysical Union (AGU)
Date: 11-2009
DOI: 10.1029/2009WR007821
Can integrated green spaces and storage facilities absorb the increased risk of flooding due to climate change in developed urban environments?
Publisher: Elsevier BV
Date: 12-2019
Can sub-daily multivariate bias correction of regional climate model boundary conditions improve simulation of the diurnal precipitation cycle?
Publisher: Authorea, Inc.
Date: 08-05-2023
DOI: 10.22541/ESSOAR.168351202.25973894/V1
Abstract: The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and litude. While improvements in the regional climate model (RCM) with bias-corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias-corrected lateral and lower boundary conditions. Results show that the RCMs with bias-corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub-daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short-term intense precipitation occurs.
Continuous rainfall simulation: 2. A regionalized daily rainfall generation approach
Publisher: American Geophysical Union (AGU)
Date: 2012
DOI: 10.1029/2011WR010490
Resolving Inconsistencies in Extreme Precipitation‐Temperature Sensitivities
Publisher: American Geophysical Union (AGU)
Date: 10-09-2020
DOI: 10.1029/2020GL089723
Abstract: Extreme precipitation events are intensifying with increasing temperatures. However, observed extreme precipitation‐temperature sensitivities have been found to vary significantly across the globe. Here we show that negative sensitivities found in previous studies are the result of limited consideration of within‐day temperature variations due to precipitation. We find that short‐duration extreme precipitation can be better described by subdaily atmospheric conditions before the start of storm events, resulting in positive sensitivities with increased consistency with the Clausius‐Clapeyron relation across a wide range of climatic regions. Contrary to previous studies that advocate that dew point temperature drives precipitation, dry‐bulb temperature is found to be a sufficient descriptor of precipitation variability. We argue that analysis methods for estimating extreme precipitation‐temperature sensitivities should account for the strong and prolonged cooling effect of intense precipitation, as well as for the intermittent nature of precipitation.
Characterising errors using satellite metadata for eco‐hydrological model calibration
Publisher: American Geophysical Union (AGU)
Date: 21-08-2023
DOI: 10.1029/2022WR033978
Abstract: Understanding the origins of errors between model predictions and catchment observations is a critical element in hydrologic model calibration and uncertainty estimation. Difficulties arise because there are a variety of error sources but only one measure of the total residual error between model predictions and catchment observations. One promising approach is to collect extra information a priori to characterise the data error before calibration. We implement here a new model calibration strategy for an ecohydrological model, using the satellite metadata information as a means to inform the model priors, to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that 1) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error 2) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance 3) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.
Rectifying low-frequency variability in future climate sea surface temperature simulations: are corrections for extreme change scenarios realistic?
Publisher: IOP Publishing
Date: 05-2023
Abstract: Most procedures for redressing systematic bias in climate modeling are calibrated using current climate observations, and perform well. However, their performance in the future climate remains uncertain as no observations exist to compare against. In this context, we use the current and future climate outputs of an ultra-high resolution of Community Earth System Model (UHR-CESM) as the representative truth and bias correct monthly sea surface temperature (SST) simulations of eight Coupled Model Intercomparison Project 6 models over the Niño 3.4 region. A time-frequency bias correction approach is used to correct for bias in distributional, trend, and spectral attributes present in the models. This results in a near perfect power spectrum of the bias corrected current climate model simulations. Considering all correction procedures remain unchanged into the future, the overall representation of the corrected SST simulations shows improvement with consistency across models for the doubled CO 2 scenario, but higher variability and lower consistency in the quadrupled CO 2 concentration scenario.
Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling
Publisher: Elsevier BV
Date: 12-2012
An assessment of CMIP5 multi-model decadal hindcasts over Australia from a hydrological viewpoint
Publisher: Elsevier BV
Date: 11-2014
Correcting lateral boundary biases in regional climate modelling: the effect of the relaxation zone
Publisher: Springer Science and Business Media LLC
Date: 08-2020
A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling
Publisher: American Meteorological Society
Date: 28-04-2016
Abstract: A novel multivariate quantile-matching nesting bias correction approach is developed to remove systematic biases in general circulation model (GCM) outputs over multiple time scales. This is a significant advancement over typical quantile-matching alternatives available for bias correction, as they implicitly assume that correction of in idual variable attributes will lead to correction of dependence biases between multiple variables. Furthermore, existing approaches perform bias correction at a given time scale (e.g., daily), whereas applications often require biases to be addressed at more than one time scale (such as annual in the case of most water resources planning projects). The proposed approach addresses all these issues, and additionally attempts to correct for lag-1 dependence (and cross-dependence) attributes across multiple time scales. The approach is called multivariate recursive quantile nesting bias correction (MRQNBC). The fidelity of the approach is demonstrated by applying it to a vector of CSIRO Mk3 GCM atmospheric variables and comparing the results with the commonly used quantile-matching approach. Following this, the implications of the approach in hydrology- and water resources–related applications are demonstrated by feeding the bias-corrected data to a rainfall downscaling model and comparing the downscaled rainfall attributes for current and future climate. The proposed approach is shown to represent the variability and persistence related attributes better and can thus be expected to have important consequences for the simulation of occurrence and intensity of extreme events such as floods and droughts in downscaled simulations, of importance in various climate impact assessment applications.
What are the impacts of bias correction on future drought projections?
Publisher: Elsevier BV
Date: 06-2015
The influence of data transformations in simulating Total Suspended Solids using Bayesian inference
Publisher: Elsevier BV
Date: 11-2019
Real-Time Updating of Rainfall Threshold Curves for Flood Forecasting
Publisher: American Society of Civil Engineers (ASCE)
Date: 04-2015
Nonlinear manifold representation in natural systems: The SOMersault
Publisher: Elsevier BV
Date: 03-2017
Evaluating the effect of climate change on areal reduction factors using regional climate model projections
Publisher: Elsevier BV
Date: 09-2015
An information theoretic alternative to model a natural system using observational information alone
Publisher: American Geophysical Union (AGU)
Date: 2014
DOI: 10.1002/2013WR013845
Improving the Combination of Satellite Soil Moisture Data Sets by Considering Error Cross Correlation: A Comparison Between Triple Collocation (TC) and Extended Double Instrumental Variable (EIVD) Alt
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Continuous rainfall simulation: 1. A regionalized subdaily disaggregation approach
Publisher: American Geophysical Union (AGU)
Date: 2012
DOI: 10.1029/2011WR010489
Estimating design hydrologic extremes in a warming climate: alternatives, uncertainties and the way forward
Publisher: The Royal Society
Date: 03-2021
Abstract: It is now well established that our warming planet is experiencing changes in extreme storms and floods, resulting in a need to better specify hydrologic design guidelines that can be projected into the future. This paper attempts to summarize the nature of changes occurring and the impact they are having on the design flood magnitude, with a focus on the urban catchments that we will increasingly reside in as time goes on. Two lines of reasoning are used to assess and model changes in design hydrology. The first of these involves using observed storms and soil moisture conditions and projecting how these may change into the future. The second involves using climate model simulations of the future and using them as inputs into hydrologic models to assess the changed design estimates. We discuss here the limitations in both and suggest that the two are, in fact, linked, as climate model projections for the future are needed in the first approach to form meaningful projections for the future. Based on the author's experience with both lines of reasoning, this invited commentary presents a theoretical narrative linking these two and identifying factors and assumptions that need to be validated before implementation in practice. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.
Building a Flood-Warning Framework for Ungauged Locations Using Low Resolution, Open-Access Remotely Sensed Surface Soil Moisture, Precipitation, Soil, and Topographic Information
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Evaluating regional climate models for simulating sub-daily rainfall extremes
Publisher: Springer Science and Business Media LLC
Date: 28-11-2015
Quantifying input uncertainty in the calibration of water quality models: reshuffling errors via the secant method
Publisher: Copernicus GmbH
Date: 12-11-2020
Abstract: Abstract. Uncertainty in inputs can significantly impair parameter estimation in water quality modeling, necessitating accurate quantification of input errors. However, decomposing input error from model residual error is still challenging. This study develops a new algorithm, referred to as Bayesian error analysis with reshuffling (BEAR), to address this problem. The basic approach requires s ling errors from a pre-estimated error distribution and then reshuffling them with their inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSS) simulation via a conceptual water quality model. Based on case studies using synthetic data, the BEAR method successfully isolates the input error and parameter error. The results of a real case study demonstrate that even with the presence of model structural error and output data error, the BEAR method can approximate the true input and bring a better model fit through an effective input modification. However, its effectiveness is limited by the assumption that the input uncertainty should be dominant and that the prior information of the input error model can be estimated. The application of the BEAR method in TSS simulation is effective for understanding a range of water quality conditions and the further developed algorithm can be extended to other water quality predictions.
A software for correcting systematic biases in RCM input boundary conditions
Publisher: Elsevier BV
Date: 10-2023
A Nearest Neighbor Bootstrap For Resampling Hydrologic Time Series
Publisher: American Geophysical Union (AGU)
Date: 03-1996
DOI: 10.1029/95WR02966
Reducing Model Structural Uncertainty in Climate Model Projections—A Rank-Based Model Combination Approach
Publisher: American Meteorological Society
Date: 12-2017
Abstract: Future changes in monthly precipitation are typically evaluated by estimating the shift in the long-term mean/variability or based on the change in the marginal distribution. General circulation model (GCM) precipitation projections deviate across various models and emission scenarios and hence provide no consensus on the expected future change. The current study proposes a rank ercentile-based multimodel combination approach to account for the fact that alternate model projections do not share a common time indexing. The approach is evaluated using 10 GCM historical runs for the current period and is validated by comparing with two approaches: equal weighting and a non-percentile-based optimal weighting. The percentile-based optimal combination exhibits lower values of RMSE in estimating precipitation terciles. Future (2000–49) multimodel projections show that January and July precipitation exhibit an increase in simulated monthly extremes (25th and 75th percentiles) over many climate regions of the conterminous United States.
Catchment scale simulations of soil moisture dynamics using an equivalent cross-section based hydrological modelling approach
Publisher: Elsevier BV
Date: 09-2018
Global seasonal precipitation forecasts using improved sea surface temperature predictions
Publisher: American Geophysical Union (AGU)
Date: 02-05-2017
DOI: 10.1002/2016JD025953
A Resampling Approach for Correcting Systematic Spatiotemporal Biases for Multiple Variables in a Changing Climate
Publisher: American Geophysical Union (AGU)
Date: 2019
DOI: 10.1029/2018WR023270
Abstract: Systematic biases in climate model simulations are commonly addressed using univariate bias correction algorithms that involve matching of mean, variance, and quantiles. These approaches work well for a single variable and location and effectively mimic the observed temporal structure in the corrected series. The intervariable, interspace, and high‐ or low‐frequency temporal dependencies that characterize observed hydrological records are often left untouched and lead to substantial biases in applications such as catchment modeling where their correct representation is critical. In the approach presented here, changes in the dependence attributes are ascertained by res ling of the historical ranks into what these might resemble in the future. The proposed approach is not limited in terms of the number of variables, grid points in space, and the time scale considered. Most importantly, it maintains the shift in dependence and other attributes between the current and the future climate as ascertained by a climate model. The approach is illustrated using daily time series of temperature, precipitation, relative humidity, and wind speed simulated by a regional climate model at 8,910 grid points over Australia. Spatial, temporal, and cross‐variable dependence attributes of the corrected simulations at daily and aggregated time scales are compared against quantile mapping and substantial improvements in performance identified. Res ling of corrected ranks offers a very simple, flexible, and effective general purpose multivariate, multitime, and multilocation bias correction alternative for current and future climate. As the approach works in three dimensions, space, time, and variables, it is denoted as 3DBC, or three‐dimensional bias correction.
Characterizing Drought Using the Reliability-Resilience-Vulnerability Concept
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2013
Multivariate streamflow forecasting using independent component analysis
Publisher: American Geophysical Union (AGU)
Date: 02-2008
DOI: 10.1029/2007WR006104
Can the variability in precipitation simulations across GCMs be reduced through sensible bias correction?
Publisher: Springer Science and Business Media LLC
Date: 24-01-2017
Assessing future rainfall projections using multiple GCMs and a multi-site stochastic downscaling model
Publisher: Elsevier BV
Date: 04-2013
Modeling multivariable hydrological series: Principal component analysis or independent component analysis?
Publisher: American Geophysical Union (AGU)
Date: 06-2007
DOI: 10.1029/2006WR005617
Residual Mass Severity Index (RMSI) – a duration free method to characterise droughts
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-6317
Abstract: & & The complexity of representing droughts has led to many drought indices being developed. A common aspect for many of these indices, however, is the need to adopt a predefined time period, over which a drought is characterized. Therefore, to declare a catchment as drought-impacted, 6, 12 or 24-month SPI are required. Actual water allocations, however, are required at all times and are thus duration free a concept well described by the well-known residual mass curve. Here we propose a new framework to characterize drought, termed as the Residual Mass Severity Index (RMSI). As the name suggests, the RMSI defines drought based on the magnitude of the residual mass in any location which is calculated by performing a water balance using a prescribed demand. Demand here is adopted as the median monthly precipitation for the region. Water shortages only become significant when there is a sustained deficit compared to this demand. The above described residual mass is standardized to formulate the RMSI across Australia. The new RMSI has been validated against established drought indices (such as the SPI) to highlight the advantages of a duration-free drought index.& & & & RMSI provides a simple method of assessing sustained and severe drought anomalies which is important with expected increases in water scarcity due to anthropogenic climate change. We demonstrate that RMSI can be used as a tool to evaluate the performance of General Circulation Models (GMCs) in representing the sustainability of water resource systems as a product of resilience, reliability, and vulnerability (RRV) of the system. Future projections of drought from GCMs which perform well in representing RMSI in the RRV context in the historical climate are then compared to drought projections from the full CMIP5 ensemble.& & & & Keywords: Drought, Residual Mass Curve, SPI, RRV, Climate Change, CMIP5 GCMs& &
Can antecedent moisture conditions modulate the increase in flood risk due to climate change in urban catchments?
Publisher: Elsevier BV
Date: 04-2019
Modelling and understanding the hierarchy in a mixture of experts using multiple catchment descriptors
Publisher: Elsevier BV
Date: 12-2013
How does the Interdecadal Pacific Oscillation affect design floods in Australia?
Publisher: American Geophysical Union (AGU)
Date: 05-2011
DOI: 10.1029/2010WR009420
Long-Range Niño-3.4 Predictions Using Pairwise Dynamic Combinations of Multiple Models
Publisher: American Meteorological Society
Date: 02-2009
Abstract: The interest in climate prediction has seen a rise in the number of modeling alternatives in recent years. One way to reduce the predictive uncertainty from any such modeling procedure is to combine or average the modeled outputs. Multiple model results can be combined such that the combination weights may either be static or vary over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting. The authors mix models on a pairwise basis using importance weights that vary in time, reflecting the persistence of in idual model skills. Such an approach is referred to here as a dynamic pairwise combination tree and is presented as an improvement over the case where the importance weights are static or constant over time. The pairwise importance weight is modeled as a product of a “mixture ratio” and a “bias direction,” the former representing the fraction of the absolute residual error associated with each of the paired models, and the latter representing an indicator of the sign of the two residual errors. The mixture ratio is modeled using a generalized autoregressive model and the bias direction using ordered logistic regression. The method is applied to combine three climate models, the variables of interest being the monthly sea surface temperature anomalies averaged over the Niño-3.4 region from 1956 to 2001. The authors test the combined model skill using a “leave ± 6 months out cross-validation” approach along with validation in 10-yr blocks. This study attained a small but consistent improvement of the predictive skill of the dynamically combined models compared to the existing practice of static weight combination.
Continental-scale bias-corrected climate and hydrological projections for Australia
Publisher: Copernicus GmbH
Date: 03-2023
DOI: 10.5194/GMD-2023-7
Abstract: Abstract. The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. With the focus on hydrological change assessment, the NHP service aims at being complementary to climate projections work carried out by many federal and state governments, universities, and other organisations across Australia. The projections comprise an ensemble of application-ready bias-corrected climate model data and derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099 and two emission scenarios (RCP 4.5 and RCP 8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs) and one to output from a regional climate model forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation and surface winds. These variables are required inputs to the Bureau's landscape water balance hydrological model (AWRA-L) which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root-zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation and ex les of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (0.25914/6130680dc5a51) and a user interface is accessible at awo.bom.gov.au roducts rojection/.
Evaluating the non-stationarity of Australian annual maximum flood
Publisher: Elsevier BV
Date: 06-2013
An operational approach for classifying storms in real-time radar rainfall estimation
Publisher: Elsevier BV
Date: 12-2008
Correcting Systematic Bias in Climate Model Simulations in the Time‐Frequency Domain
Publisher: American Geophysical Union (AGU)
Date: 29-09-2022
DOI: 10.1029/2022GL100550
Abstract: Systematic biases in General Circulation Model (GCM) simulations require some adjustment before their use in change assessment and adaptation management studies. GCM simulations of the Coupled Model Intercomparison Project 6, although outperform the previous generations of GCMs, exhibit persistent biases in magnitude, variability, and frequency across a range of variables of interest. Here, we propose a novel continuous wavelet‐based bias correction (CWBC) approach to address such biases in the time‐frequency domain. The correction focuses on the magnitude and frequency of the modeled time series, as interpreted via the time‐varying spectrum ascertained using the continuous wavelet transform. The approach is applied to correct systematic biases in the time series of Niño 3.4 sea surface temperature and Arctic sea‐ice extent. The application of CWBC successfully reproduces observed attributes in the bias‐corrected time series of both climate variables for the current climate simulation along with providing a sensible projection for the future.
Deriving daily water levels from satellite altimetry and land surface temperature for sparsely gauged catchments: A case study for the Mekong River
Publisher: Elsevier BV
Date: 06-2018
A cascade approach to continuous rainfall data generation at point locations
Publisher: Springer Science and Business Media LLC
Date: 13-04-2007
A conditional disaggregation algorithm for generating fine time-scale rainfall data in a warmer climate
Publisher: Elsevier BV
Date: 02-2013
Representing low‐frequency variability in continuous rainfall simulations: A hierarchical random B artlett L ewis continuous rainfall generation model
Publisher: American Geophysical Union (AGU)
Date: 12-2015
DOI: 10.1002/2015WR017469
Merging radar and gauge information within a dynamical model combination framework for precipitation estimation in cold climates
Publisher: Elsevier BV
Date: 09-2019
Jointly Calibrating Hydrologic Model Parameters and State Adjustments
Publisher: American Geophysical Union (AGU)
Date: 08-2021
DOI: 10.1029/2020WR028499
Abstract: A method is presented to address model state uncertainty in hydrologic model simulation. This is achieved by introducing tuneable parameters that allow adjustments to the model states. Excessive dimensionality is avoided by introducing only a limited number of parameters that control the index (timing) and size of the state adjustments. The method is designed to compensate for issues with hydrologic model structures, particularly those relevant to the soil moisture state in a rainfall‐runoff model. In the context of water resource planning and management, errors in the model states have often been overlooked as an important source of uncertainty and have the potential to significantly degrade model simulations. A synthetic study shows that a classical parameter estimation approach will produce biased distributions when state errors exist, and that the proposed state and parameter uncertainty estimation (SPUE) can remove the bias in parameter estimates for improved model simulations. In a real case study, SPUE and the classical approach are implemented in 46 sites around Australia. The results show that hydrologic parameter distributions for a selected conceptual model can be significantly different when accounting for state uncertainty. This has large implications for scenario modeling since it puts into dispute how to determine appropriate parameters for such studies. SPUE outperforms the classical approach in a range of calibration and validation metrics, particularly for sites that contain zero flows. Future work involves testing SPUE with different hydrologic models and likelihood formulations, and enhancing rigor by explicitly accounting for observational data uncertainty.
Assessing atmospheric bias correction for dynamical consistency using potential vorticity
Publisher: IOP Publishing
Date: 12-2014
Increase in urban flood risk resulting from climate change – The role of storm temporal patterns
Publisher: Copernicus GmbH
Date: 30-06-2017
Abstract: Abstract. Warming temperatures are causing extreme rainfall to intensify resulting in increased risk of flooding in developed areas. Quantifying this increased risk is of critical importance for the protection of life and property as well as for infrastructure planning and design. The study presented in this manuscript uses a comprehensive hydrologic and hydraulic model of a fully developed urban/suburban catchment to explore two primary questions related to climate change impacts on flood risk: (1) How does climate change effects on storm temporal patterns and rainfall volumes impact flooding in a developed complex watershed? (2) Is the storm temporal pattern as critical as the total volume of rainfall when evaluating urban flood risk? The updated NOAA Atlas 14 intensity–duration–frequency (IDF) relationships and temporal patterns, widely used in design and planning modelling in the USA, form the basis of the assessment reported here. Current literature shows that a rise in temperature will result in intensification of rainfall. These impacts are not explicitly included in the NOAA temporal patterns, which can have consequences on the design and planning of adaptation measures. We use the expected increase in temperature for the RCP8.5 scenario for 2081–2100, to project temporal patterns and rainfall volumes to reflect future climatic change. The modelling analysis for a 22 km2 developed watershed show that temporal patterns cause substantial variability in flood depths during a storm event. The changes in the projected temporal patterns alone increase the risk of flood magnitude between 1 to 35 % with the cumulative impacts of temperature rise on temporal pattern and the storm volume increasing flood risk by between 10 to 170 % across the locations that were referenced for a 50 year return period storm. The variability in catchment response to temporal patterns show that regional storage facilities are sensitive to rainfall patterns that are loaded at the latter part of the storm duration while the short duration extremely intense storms will cause extensive flooding at all locations. This study shows that changes in temporal patterns will have a significant impact on urban/suburban catchment response and need to be carefully considered and adjusted to account for climate change when used for design and planning future stormwater systems.
Specifying a hierarchical mixture of experts for hydrologic modeling: Gating function variable selection
Publisher: American Geophysical Union (AGU)
Date: 05-2013
DOI: 10.1002/WRCR.20150
Beyond river discharge gauging: hydrologic predictions using remote sensing alone
Publisher: IOP Publishing
Date: 17-02-2023
Abstract: This study suggests a radical approach to hydrologic predictions in ungauged basins, addressing the long standing challenge of issuing predictions when in-situ river discharge does not exist. A simple but powerful rationale for measuring and modeling river discharge is proposed, using coupled advances in hydrologic modeling and satellite remote sensing. Our approach presents a Surrogate River discharge driven Model (SRM) that infers Surrogate River discharge (SR) from remotely sensed microwave signals with the ability to mimic river discharge in varying topographies and vegetation cover, which is then used to calibrate a hydrological model enabling physical realism in the resulting river discharge profile by adding an estimated mean of river discharge via the Budyko framework. The strength of SRM comes from the fact that it only uses remotely sensed data in prediction. The approach is demonstrated for 130 catchments in the Murray Darling Basin (MDB) in Australia, a region of high economic and environmental importance. The newly proposed SR (SR L , representing L-band microwave) boosts the Nash-Sutcliffe Efficiency (NSE) of modeled flow, showing a mean NSE of 0.54, with 70% of catchments exceeding NSE 0.4. We conclude that SRM effectively predicts high-flow and low-flow events related to flood and drought. Overall, this new approach will significantly improve catchment simulation capacity, enhancing water security and flood forecasting capability not only in the MDB but also worldwide.
Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2017WR021293
Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 2 — Predictor identification of quarterly rainfall using ocean-atmosphere information
Publisher: Elsevier BV
Date: 12-2000
Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework
Publisher: Copernicus GmbH
Date: 30-10-2014
DOI: 10.5194/HESSD-11-12063-2014
Abstract: Abstract. Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.
Radar rainfall error variance and its impact on radar rainfall calibration
Publisher: Elsevier BV
Date: 2003
Rethinking Satellite Data Merging: From Averaging to SNR Optimization
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Coping with model structural uncertainty in medium-term hydro-climatic forecasting
Publisher: IWA Publishing
Date: 04-2011
DOI: 10.2166/NH.2011.104
Abstract: This paper reviews two alternatives for reducing structural uncertainty in medium-term hydro-climatic forecasting. The first is a static ensemble average, illustrated here using the Multiple Reservoir Inflow Forecasting System, a nonparametric probabilistic forecasting model that relates streamflow to climate predictors, and generates monthly sequences of multi-site flow from the present for the coming 12 months. Instead of forming a single predictive relationship, multiple constituent models, each having their own unique predictor variable sets, are formed. A weighted probabilistic combination of these constituent models completes the static ensemble average. The second alternative is a dynamic ensemble average that allows constituent models to change importance with time, model weights evolving as a function of these weights at preceding time steps. Dynamic model combination is demonstrated here for first combining multiple sea surface temperature anomaly forecasts to produce a global sea surface temperature anomaly field, and then using the dynamically combined sea surface temperature anomaly (SSTA) field to concurrently ascertain inflows at multiple locations in a semi-arid Australian catchment. The paper concludes by identifying scenarios under which one would expect to see improvements as a result of static or dynamic model combination, and provides suggestions for further research in this area.
A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data
Publisher: Elsevier BV
Date: 06-2018
Mitigating parameter bias in hydrological modelling due to uncertainty in covariates
Publisher: Elsevier BV
Date: 07-2007
A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations
Publisher: American Geophysical Union (AGU)
Date: 2012
DOI: 10.1029/2011WR010464
A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions With Sparse Ground Data Networks
Publisher: American Geophysical Union (AGU)
Date: 18-11-2021
DOI: 10.1029/2021GL095729
Abstract: This study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regional climate models (RCMs), coupled to the proposed QDM‐spatial bias‐correction, in which the distribution parameters are first interpolated onto a fine grid (rather than the observed daily rainfall). The proposed model is validated through a cross‐validatory (CV) evaluation using rainfall data from a set of weather stations in South Korea and climate change scenarios simulated by three alternate RCMs. The results demonstrate the efficacy of the proposed model to simulate the bias‐corrected daily rainfall sequences over large regions at fine resolutions. A discussion of the potential use of the proposed approach in the field of hydrometeorology is also offered.
Should flood regimes change in a warming climate? The role of antecedent moisture conditions
Publisher: American Geophysical Union (AGU)
Date: 26-07-2016
DOI: 10.1002/2016GL069448
Sampling biases in CMIP5 decadal forecasts
Publisher: American Geophysical Union (AGU)
Date: 14-04-2016
DOI: 10.1002/2016JD024804
Development and Application of a Multisite Rainfall Stochastic Downscaling Framework for Climate Change Impact Assessment
Publisher: American Geophysical Union (AGU)
Date: 07-2010
DOI: 10.1029/2009WR008423
Effectiveness of CMIP5 Decadal Experiments for Interannual Rainfall Prediction Over Australia
Publisher: American Geophysical Union (AGU)
Date: 08-2019
DOI: 10.1029/2018WR024462
Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models
Publisher: American Geophysical Union (AGU)
Date: 09-2021
DOI: 10.1029/2021WR029772
Abstract: Significant attention has recently been paid to deep learning as a method for improved catchment modeling. Compared with process‐based models, deep learning is often criticized for its lack of interpretability. One solution is to combine a process‐based hydrological model with a residual error model based on deep learning to give full scope to their respective advantages. In classical residual error models, Bayesian inference via Markov chain Monte Carlo (MCMC) is commonly used to provide an estimation of the uncertainty. However, deep neural networks tend to have excessively large numbers of parameters, making MCMC an unsuitable approach. Here, we introduce an alternative to Bayesian MCMC s ling called stochastic variational inference (SVI) which has recently been developed for Bayesian deep learning in Natural Language Processing. We implement SVI in a Long Short‐Term Memory (LSTM) network and construct residual error models in process‐based hydrological models. This approach is examined in the contrasting geographical and climatic characteristics of two catchments from China, the Tangnaihai catchment and the Shiquan catchment. Compared with the Bayesian linear regression model, the Bayesian LSTM provides better uncertainty estimates. Specifically, the proposed method improves the Continuous Ranked Probability Score (CRPS) by over 10% in both two catchments. In the Tangnaihai catchment, it provides more than 10% narrower uncertainty intervals in terms of Sharpness with slightly superior Reliability. In the Shiquan catchment, it provides comparable uncertainty intervals with better Reliability. Further, our study highlights the scalability of SVI to high‐dimensional parameter spaces in hydrological applications (e.g., distributed hydrological models, groundwater models).
A volume law for specification of linear channel storage for estimation of large floods
Publisher: American Geophysical Union (AGU)
Date: 02-2000
DOI: 10.1029/2000WR900045
Patterns and comparisons of human-induced changes on river flood impacts in cities
Publisher: Copernicus GmbH
Date: 29-05-2017
Abstract: Abstract. This study investigates patterns of current conditions and anticipated future changes in city-level flood impacts driven by urbanisation and climate change. Global patterns relating urban river flood impacts to socioeconomic development and changing hydrologic conditions are established, and world cities are matched to these patterns. Comparisons are provided between 98 in idual cities. We use a novel adaption of the self-organizing map method to establish and present patterns in the nonlinearly-related environmental and social variables. Spatiotemporal output maps of prevalent patterns compare baseline and changing trends of city-specific exposures of population and property to river flooding, revealing relationships between the cities based on their relative map placements. Cities experiencing high (or low) baseline flood impacts on population and/or property that are expected to improve (or worsen), as a result of anticipated climate change and development, are identified and compared. This paper condenses and conveys large amounts of information through visual communication to accelerate the understanding of relationships between local urban conditions and global processes, and to potentially motivate knowledge transfer between decision makers facing similar circumstances.
Using second-order approximation to incorporate GCM uncertainty in climate change impact assessments
Publisher: Springer Science and Business Media LLC
Date: 18-03-2017
Defining the Z–R Relationship Using Gauge Rainfall with Coarse Temporal Resolution: Implications for Flood Forecasting
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2014
A single model ensemble versus a dynamic modeling platform: Semi-distributed rainfall runoff modeling in a Hierarchical Mixtures of Experts framework
Publisher: American Geophysical Union (AGU)
Date: 12-01-2007
DOI: 10.1029/2006GL028054
Modelling daily transmission losses in basin‐scale river system models under changing hydrological regimes
Publisher: Wiley
Date: 06-2022
DOI: 10.1002/HYP.14625
Abstract: With rising concerns for water security and increasing interest in water resource development, accounting for river transmission losses in arid/semi‐arid region water budgets is a crucial yet challenging task. Transmission losses are usually confounded with many different processes and exacerbated by hydrologic and climatic non‐stationarity. A common deficiency in existing basin‐scale river system loss models is the poor representation of dynamic river losses into groundwater. To address this, a new conceptual model was developed to represent relevant river processes including rainfall to runoff transformation, river loss/gain to groundwater, river rainfall/evaporation and routing on a reach‐by‐reach basis. A critical process of the model is the exchange of water between the river and a groundwater store, called the river bed/bank store. The exchange to groundwater typically occurs with relatively high river volumes. Conversely, the store can discharge water back to the river when river volumes are relatively low. The model is designed to be transposable between different time‐periods, such as pre‐ and post‐development scenarios, assuming sufficient data exist during calibration. Using explicit Bayesian formulations, calibrations were performed against observed streamflow and remotely sensed evapotranspiration data. The new loss model was investigated using a test case in the Cooper Creek, Australia. The model performed overall better than a benchmark (flow vs. loss) model in a range of calibration/validation metrics. The new model provides river state and flux terms typically not available in basin‐scale models, and thus, is expected to be valuable during calibration/validation by allowing the use of alternative observed data types, for ex le, actual evapotranspiration or groundwater observations. Moreover, these extra terms could be very beneficial to water budget and ecological assessment studies.
The opposing factors controlling change in warming related future flood extremes
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4277
Abstract: & & It is well accepted that warmer temperatures lead to greater moisture holding capacity for the atmosphere, resulting in bigger downpours, creating larger design precipitation intensities and possibly less secure flood infrastructure. It is also known that higher temperatures increase evaporation rates and hence dry soils quicker than before. This presentation discusses the role each of these controls plays in natural and urbanised catchments. It is shown that one of these two tends to dominate depending on a range of factors, including catchment attributes, as well as how extreme the design problem is. This presentation uses ex les from four urban catchments spread across three continents as well as over 200 natural catchments representing various climatic zones in Australia to form its conclusions.& &
Effects of Rain Gauge Temporal Resolution on the Specification of a Z–R Relationship
Publisher: American Meteorological Society
Date: 07-2009
Abstract: The weather radar is an efficient alternative for measuring spatially varying rainfall covering a large area at a high temporal resolution. This paper studies the impact of rainfall gauge temporal resolution on optimal relationships between radar reflectivity (Z) and rainfall rate (R). Four datasets of radar reflectivity and corresponding rain gauge rainfall data from Sydney and Brisbane, Australia, and one dataset from Bangkok, Thailand, were used in the analysis. Climatological Z–R relationships were calibrated using rainfall aggregated over 1–24 h to investigate the evidence of temporal scaling in the Z–R calibrated parameters. This analysis points to an increase in the multiplicative term (the A parameter) of the Z–R relationship as temporal resolutions become finer. This pattern is repeated in all the datasets analyzed. Thereafter, a simple scaling hypothesis was proposed to develop transformations that could scale the A parameter in the Z–R relation across a range of temporal resolutions. This scaling relationship was found to be suitable, with the scaling exponent attaining values close to 0.055 across all the datasets analyzed. The proposed relationship has a significant role in radar rainfall estimation studies, especially in regions where subdaily gauge rainfall measurements are not readily available to ascertain optimal Z–R parameters.
Forecasting streamflows in Australia using the tropical Indo-Pacific thermocline as predictor
Publisher: Elsevier BV
Date: 08-2007
A comparison of alternatives for daily to sub-daily rainfall disaggregation
Publisher: Elsevier BV
Date: 11-2012
An Assessment of GCM Skill in Simulating Persistence across Multiple Time Scales
Publisher: American Meteorological Society
Date: 15-07-2011
Abstract: Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and litude of drought under a future climate, require that the year-to-year persistence in a range of hydrological variables such as catchment average rainfall be properly represented. This persistence is often attributable to low-frequency variability in the global sea surface temperature (SST) field and other large-scale climate variables through a complex sequence of teleconnections. To evaluate the capacity of general circulation models (GCMs) to accurately represent this low-frequency variability, a set of wavelet-based skill measures has been developed to compare GCM performance in representing interannual variability with the observed global SST data, as well as to assess the extent to which this variability is imparted in precipitation and surface pressure anomaly fields. A validation of the derived skill measures is performed using GCM precipitation as an input in a reservoir storage context, with the accuracy of reservoir storage estimates shown to be improved by using GCM outputs that correctly represent the observed low-frequency variability. Significant differences in the performance of different GCMs is demonstrated, suggesting that judicious selection of models is required if the climate impact assessment is sensitive to low-frequency variability. The two GCMs that were found to exhibit the most appropriate representation of global low-frequency variability for in idual variables assessed were the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4 and L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) when considering all three variables, the Max Planck Institute (MPI) ECHAM5 performed well. Importantly, models that represented interannual variability well for SST also performed well for the other two variables, while models that performed poorly for SST also had consistently low skill across the remaining variables.
correlation biases paper review
Publisher: Copernicus GmbH
Date: 29-10-2018
Linking Total Precipitable Water to Precipitation Extremes Globally
Publisher: American Geophysical Union (AGU)
Date: 02-2022
DOI: 10.1029/2021EF002473
Abstract: The relationship between extreme precipitation ( EP ) and precipitable water ( W ) is useful to assess design extremes and speculate on their expected changes with rising global temperatures. This study investigates the relationship between daily and longer‐duration EP and corresponding W at a global scale by analyzing remote‐sensed and reanalysis data sets from 2003 to 2019. An assessment of the consistency in the temporal trend across various W data sets reveals a consistent statistically significant upward trend during the period. This upward trend, while predominant worldwide, is especially significant over tropical land regions. W is found to generally be positively correlated with surface (dew point) temperature, suggesting a rise in temperature will cause a greater W over time. To assess whether EP s occur coincident with extreme W , the Concurrent Extremes Index (CEI) is proposed, which compares the cumulative distribution functions between the two variables and assumes a value of unity if ranks of the EP series are identical to that of the coincident W series, and zero with no correspondence. For EP (defined as the five largest 1‐day events per year on average), a high CEI is pronounced across the tropics, except for rainforests. The W ‐ EP relationship is noticeably weakened in nontropics, except the inland regions of North America and East Asia. An assessment indicates that as the duration of the EP becomes longer, the influence of W on EP decreases. However, the contrast in the W ‐ EP relationship between the tropics and nontropics is found to become more pronounced as longer‐duration EP s are considered.
A programming tool to generate multi-site daily rainfall using a two-stage semi parametric model
Publisher: Elsevier BV
Date: 2015
The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments
Publisher: Informa UK Limited
Date: 26-04-2019
Influence of reanalysis datasets on dynamically downscaling the recent past
Publisher: Springer Science and Business Media LLC
Date: 05-10-2017
An error estimation method for precipitation and temperature projections for future climates
Publisher: American Geophysical Union (AGU)
Date: 17-11-2012
DOI: 10.1029/2012JD018062
Improved Extreme Rainfall Frequency Analysis Using a Two‐Step Kappa Approach
Publisher: American Geophysical Union (AGU)
Date: 04-2023
DOI: 10.1029/2021WR031854
Abstract: Accurate estimation of annual exceedance probabilities (AEPs) of extreme rainfalls through rainfall frequency analysis is a critical step in engineering design for flood mitigation and disaster response. Here we show how the estimation of rainfall frequency curves can be improved by fitting a four‐parameter Kappa distribution to a peaks‐over‐threshold (POT) series. To fit the Kappa distribution to POT data we present a two‐step fitting approach based on maximum likelihood estimation which separately models storm intensity and the arrival frequency. First, a Generalized Pareto distribution (GPA) describing storm intensity is fitted, followed by a Binomial distribution for storm arrivals. We compare the performance of this two‐step Kappa approach to an analogous two‐step Generalized Extreme Value (GEV) approach and to Kappa and GEV distributions fitted to annual maxima series (AMS), using both synthetic and real‐world data representative of Australian climatic conditions. The experiments show that leveraging additional information from the POT series in the two‐step Kappa approach dramatically improves quantile estimation and reduces uncertainty compared to fitting either the GEV or the Kappa distributions to AMS, particularly for rare quantiles. When skew–kurtosis properties of extreme rainfalls are well represented by the Kappa but not the GEV, the two‐step Kappa approach yields unbiased quantiles under extrapolation, while the use of GEV can lead to highly biased estimates. From these results, we believe the two‐step Kappa approach is suitable for both at‐site and regional rainfall frequency analyses as it can accommodate distributions ranging from GPA to GEV without encountering over‐fitting problems generally associated with four‐parameter distributions.
A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM
Publisher: Elsevier BV
Date: 04-2017
A novel streamflow estimation using L-band passive microwave
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-315
Abstract: & & We present a new basis for measuring river discharge in ungauged catchments worldwide, which is a prerequisite for any flood or drought mitigation system to be effective. Surrogate runoff (SR) is created from remotely sensed data to make up for the absence of in-situ streamflow. Because of its widespread availability and global coverage, SR derived from remotely sensed data offers an attractive streamflow alternative. & br& Specifically, the satellite-derived measurement-calibration ratio (MC ratio, also known as C/M ratio) is an appealing option because of its positive correlation with the observed streamflow and its physical property to detect floods. However, challenges in using the C/M ratio to predict streamflow dynamics have been identified because of its limited penetration skill and assumptions in the SR calculation. A signal with a longer wavelength is a possible alternative with better penetration, but the key assumptions for deriving SR are hard to satisfy with a coarser signal. Thus, a new approach to making an SR is required to use a longer wavelength sensor, such as the L-band microwave, which allows advanced data quality. The proposed SR formulation in our study alternates or reduces assumptions in SR calculation to use a coarse grid. The improved performance of the new SR is presented for 467 Australian Hydrologic Reference Stations, which can be considered free from anthropogenic effects and have distinct attributes. Results show significant enhancements in the Pearson linear correlation (R) between SR and in-situ streamflow: 44% of the study areas show R higher than 0.4 with the new approach, whereas only 13% of the study areas show R higher than 0.4 with the previous approach (C/M ratio). Overall, SR is dramatically improved by using the newly designed SR with the L-band microwave signal.& &
Rainfall generation
Publisher: American Geophysical Union
Date: 2010
DOI: 10.1029/2010GM000973
An improved standardization procedure to remove systematic low frequency variability biases in GCM simulations
Publisher: American Geophysical Union (AGU)
Date: 12-2012
DOI: 10.1029/2012WR012446
Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs
Publisher: Elsevier BV
Date: 2018
Bayesian model calibration in ungauged catchments via satellite data
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-313
Abstract: & & We launch a novel approach for hydrologic modeling in ungauged basins using satellite data. Due to universal availability, satellite data is an attractive option to fill the absence of in-situ data to calibrate a hydrologic model in ungauged or poorly gauged basins. The one specific satellite-derived calibration-measurement ratio (C/M ratio) is useful because of its physical property to indicate the water extent and its demonstrated correlation with observed streamflow. However, there are challenges in calibrating a hydrologic model using the C/M ratio because it has a different dimension to streamflow. Therefore, a new approach is required to use the modeled surrogate streamflow instead of the raw C/M ratio in place of streamflow. A new Bayesian approach is introduced here to identify parameters of surrogate streamflow in the absence of a time series of streamflow. This approach calibrates the conjugated probability of the parameter set of a hydrologic model and a surrogate streamflow model. Specifically, the proposed likelihood includes supplementary information, such as an estimated mean value of streamflow, to join the information of streamflow volume and dynamics of the modeled flow. Our new approach is assessed for multiple Australian Hydrologic Stations with distinct attributes. The strength in the new method is demonstrated with high Nash-Sutcliffe Efficiency values (0.535 ~ 0.781), and the uncertainties in the new model calibration are quantified via Markov Chain Monte Carlo s ling. The errors of the surrogate streamflow model and the hydrologic model are analyzed, and the predictive intervals are assessed with the benchmark model derived from the in-situ streamflow. Overall, our work improves previous studies on the hydrological predictions using the C/M ratio. Furthermore, it enables surrogate data to be highly correlated to the actual data, regardless of their dimensions.& &
Merging gauge and satellite rainfall with specification of associated uncertainty across Australia
Publisher: Elsevier BV
Date: 08-2013
Impact of bias-corrected RCM lateral boundary conditions on precipitation extremes
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-312
Abstract: & & Hydro-climatological applications often require global climate models (GCMs) outputs to assess the impacts of climate change. However, it is well known that the direct use of GCM simulations is limited as their spatial and temporal resolution are insufficient to provide output at the regional scale required in assessing changes in extreme rainfall. Although regional climate models (RCMs) forced with GCM data are widely used to resolve finer resolutions, their application is hindered by systematic biases contained in large-scale circulation patterns from driving GCM data. To deal with these considerable biases, recent studies have suggested the bias correction of the input boundary conditions of RCM.& & & & This study focuses on the impact of bias corrections in the input boundary conditions of RCM on extreme rainfall events. Three bias correction methods are used: mean, mean and variance, and nested bias correction (NBC) that corrects lag-1 autocorrelations. RCM used here is the Weather Research and Forecasting model (WRF), and the European Center for Medium-Range Weather Forecast& #8217 s (ECMWF) ERA-Interim (ERA-I) reanalysis model is used as an & #8220 observational& #8221 reference for bias correction. The downscaling is performed over the Australasian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain.& & & & Two quantitative measures are used to evaluate the impact of bias correction on the RCM output: root-mean-square errors (RMSE) and bias. Indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used to evaluate bias correction performance on extreme rainfall.& & & & It is clear from the statistics used here that bias correction on the input boundary condition produces a noticeable improvement in daily precipitation percentile indices. The results also show that the sophisticated method representing rainfall variability and long-term persistence corrects details in simulating extreme rainfall.& &
Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation
Publisher: Elsevier BV
Date: 08-2016
Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification
Publisher: Elsevier BV
Date: 12-2000
Changing Storm Temporal Patterns with Increasing Temperatures across Australia
Publisher: American Meteorological Society
Date: 15-09-2023
Abstract: It is well known that as a consequence of climate change, higher temperatures are causing extreme precipitation events to intensify, leading to greater flooding. However, the relationship between temperature and the temporal distribution of precipitation within storms is not well understood, with limited research focus on precipitation event loading or where the bulk of the precipitation occurs within the storm duration. Here, we investigate the relationship between temperature and the temporal pattern of precipitation, with a focus on event loading. Historical trend analyses based on station observations reveal that precipitation events have become increasingly front loaded (i.e., a greater percentage of precipitation falling earlier in the storm) across Australia over the past six decades. This increased frontal loading of precipitation events coincides with increasing trends in representative storm temperatures, with higher temperatures associated with a greater proportion of short-duration convective events. Linking these precipitation events with the representative storm temperatures shows that precipitation events become more front loaded with increasing temperature across nearly all event durations and intensities, with the emphasis on shorter duration ( h) events in the tropics. There is a clear systematic shift toward more front-loaded temporal patterns of precipitation with increasing temperature, coupled with intensification of embedded bursts. These results have implications for potentially increased flooding, with hydrological applications needing to account for nonstationarity in the temporal pattern of precipitation. To date, there is very little understanding of how temporal patterns of precipitation events change with increasing temperatures. Here, we investigate the relationship between temperature and the temporal pattern of precipitation events with a focus on the timing of when the bulk of precipitation occurs (termed event loading ). Our results indicate a clear systematic shift toward more front-loaded temporal patterns of precipitation with increasing temperature, coupled with intensification of embedded bursts. Greater shifts in temporal patterns of precipitation are observed for shorter-duration precipitation events, particularly in the tropics. The impact of changing temporal patterns of precipitation on flood estimation will require careful examination due to the risk of increased flood peaks.
Quantification of Uncertainty in Projections of Extreme Daily Precipitation
Publisher: American Geophysical Union (AGU)
Date: 08-2020
DOI: 10.1029/2019EA001052
Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2018WR022627
Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques
Publisher: Elsevier BV
Date: 05-2016
Bayesian Model Calibration Using Surrogate Streamflow in Ungauged Catchments
Publisher: American Geophysical Union (AGU)
Date: 2022
DOI: 10.1029/2021WR031287
Abstract: We present a novel approach for modeling streamflow in ungauged catchments. Because of their widespread availability and global coverage, remotely sensed data provide an attractive alternative to supplement the absence of streamflow data in hydrological model calibration. One observable signal holds particular appeal the satellite‐derived calibration ratio‐measurement (C/M ratio) has been widely studied as a direct measurement of streamflow because of its physical relationship to streamflow and demonstrated correlation with in situ streamflow. This study identifies the challenges in calibrating a hydrological model using a satellite‐derived C/M ratio, presenting a rationale designed to account for the limitations that these data pose. A new Bayesian calibration approach is developed that uses the surrogate streamflow derived from the C/M ratio in place of direct streamflow observations. We assess and demonstrate our approach for three Australian Hydrologic Reference Stations, which can be considered free from anthropogenic effects, with distinct attributes. The results indicate the competency of the proposed approach, showing model performance with 0.54 ∼ 0.78 Nash‐Sutcliffe efficiency values, with the uncertainties in the model calibration quantified via Markov Chain Monte Carlo s ling. Overall, our study finds the new model calibration promising for predictions in ungauged basins (PUB), as the global data coverage of satellite data and the suitability of the approach suggest significant improvements over traditional approaches to PUB.
Modeling residual hydrologic errors with Bayesian inference
Publisher: Elsevier BV
Date: 09-2015
A novel approach for deriving river discharge using passive microwaves
Publisher: Wiley
Date: 28-09-2022
Time varying parameter models for catchments with land use change: the importance of model structure
Publisher: Copernicus GmbH
Date: 04-07-2017
Abstract: Abstract. Rapid population and economic growth in South-East-Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modelling methodologies capable of handling changing land use conditions are therefore becoming ever more important, and are receiving increasing attention from hydrologists. A recently developed Data Assimilation based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium sized catchment (2880 km2) in Northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of time varying parameter methods. The framework was utilized with two conceptual models (HBV and HyMOD) that gave good quality streamflow predictions during pre-change conditions. Although both time varying parameter models gave improved streamflow predictions under changed conditions compared to the time invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time varying parameter framework successfully models streamflow under changed land cover conditions. It also serves as an effective tool for separating the influence of climatic and land use change in retrospective studies where the lack of a paired control catchment precludes such an assessment.
An Upper Limit to Seasonal Rainfall Predictability?
Publisher: American Meteorological Society
Date: 15-06-2010
Abstract: The asymptotic predictability of global land surface precipitation is estimated empirically at the seasonal time scale with lead times from 0 to 12 months. Predictability is defined as the unbiased estimate of predictive skill using a given model structure assuming that all relevant predictors are included, thus representing an upper bound to the predictive skill for seasonal forecasting applications. To estimate predictability, a simple linear regression model is formulated based on the assumption that land surface precipitation variability can be ided into a component forced by low-frequency variability in the global sea surface temperature anomaly (SSTA) field and that can theoretically be predicted one or more seasons into the future, and a “weather noise” component that originates from nonlinear dynamical instabilities in the atmosphere and is not predictable beyond ~10 days. Asymptotic predictability of global precipitation was found to be 14.7% of total precipitation variance using 1900–2007 data, with only minor increases in predictability using shorter and presumably less error-prone records. This estimate was derived based on concurrent SSTA–precipitation relationships and therefore constitutes the maximum skill achievable assuming perfect forecasts of the evolution of the SSTA field. Imparting lags on the SSTA–precipitation relationship, the 3-, 6-, 9-, and 12-month predictability of global precipitation was estimated to be 7.3%, 5.4%, 4.2%, and 3.7%, respectively, demonstrating the comparative gains that can be achieved by developing improved SSTA forecasts compared to developing improved SSTA–precipitation relationships. Finally, the actual average cross-validated predictive skill was found to be 2.1% of the total precipitation variance using the full 1900–2007 dataset and was dominated by the El Niño–Southern Oscillation (ENSO) phenomenon. This indicates that there is still significant potential for increases in predictive skill through improved parameter estimates, the use of longer and/or more reliable datasets, and the use of larger spatial fields to substitute for limited temporal records.
Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments
Publisher: American Geophysical Union (AGU)
Date: 12-2010
DOI: 10.1029/2010WR009514
Future aridity under conditions of global climate change
Publisher: Elsevier BV
Date: 11-2017
Measuring the spatial connectivity of extreme rainfall
Publisher: Elsevier BV
Date: 11-2020
Water Supply Sustainability Revisited: Assessment Methodology for Multiple Water Resources
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2023
An Objective Multiattribute Analysis Approach for Allocation of Scarce Irrigation Water Resources1
Publisher: Wiley
Date: 29-01-2010
Application of an improved linear storage routing model for the estimation of large floods
Publisher: Elsevier BV
Date: 02-2002
Quantifying the sources of uncertainty in upper air climate variables
Publisher: American Geophysical Union (AGU)
Date: 22-04-2016
DOI: 10.1002/2015JD024341
A global assessment of the timing of extreme rainfall from TRMM and GPM for improving hydrologic design
Publisher: IOP Publishing
Date: 25-04-2016
Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework
Publisher: Copernicus GmbH
Date: 27-08-2015
DOI: 10.5194/HESS-19-3695-2015
Abstract: Abstract. Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.
On the non-stationarity of hydrological response in anthropogenically unaffected catchments: An Australian perspective
Publisher: Copernicus GmbH
Date: 29-07-2016
Abstract: Abstract. Increases in greenhouse gas concentrations are expected to impact the terrestrial hydrologic cycle through changes in radiative forcings and plant physiological and structural responses. Here we investigate the nature and frequency of non-stationary hydrological response as evidenced through water balance studies over 166 anthropogenically unaffected catchments in Australia. Non-stationarity of hydrologic response is investigated through analysis of long term trend in annual runoff ratio (1984–2005). Results indicate that a significant trend (p
A software toolkit for correcting systematic biases in climate model simulations
Publisher: Elsevier BV
Date: 06-2018
review of correlation paper
Publisher: Copernicus GmbH
Date: 26-10-2018
A nonparametric stochastic downscaling framework for daily rainfall at multiple locations
Publisher: American Geophysical Union (AGU)
Date: 02-08-2006
DOI: 10.1029/2005JD006637
Does accounting for state uncertainty improve hydrologic model predictions?
Publisher: Wiley
Date: 16-09-2020
Atmospheric Moisture Measurements Explain Increases in Tropical Rainfall Extremes
Publisher: American Geophysical Union (AGU)
Date: 02-02-2019
DOI: 10.1029/2018GL080833
Abstract: Anthropogenic climate change is increasing extreme rainfall as a result of an increased water‐holding capacity of the atmosphere due to higher temperatures. However, observed rainfall‐temperature scaling relationships often differ from the theorized increases in moisture‐holding capacity. This discrepancy is most evident in the tropics, where higher surface temperatures show a marked decrease in extreme rainfall intensity despite observed increases in extreme rainfall. Here we use atmospheric moisture measurements from the National Aeronautics and Space Administration's Atmospheric Infrared Sounder with surface data to investigate the tropical rainfall‐temperature scaling relationship. We show rainfall intensity scales positively with integrated water vapor in all regions. Further, integrated water vapor does not consistently scale positively with surface air temperature and its dependence on background temperature offers a physical explanation for the apparent negative scaling. We conclude that the inconsistent relationship between surface air temperature and moisture is the reason for the “apparent” negative scaling consistently found in the tropics.
Observed relationships between extreme sub‐daily precipitation, surface temperature, and relative humidity
Publisher: American Geophysical Union (AGU)
Date: 11-2010
DOI: 10.1029/2010GL045081
Simulating rainfall time-series: how to account for statistical variability at multiple scales?
Publisher: Springer Science and Business Media LLC
Date: 11-04-2017
Effect of solar variability on atmospheric moisture storage
Publisher: American Geophysical Union (AGU)
Date: 02-2009
DOI: 10.1029/2008GL036310
Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework
Publisher: Wiley
Date: 2007
DOI: 10.1002/HYP.6294
A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting
Publisher: Elsevier BV
Date: 2000
Impact of atmospheric moisture in a rainfall downscaling framework for catchment‐scale climate change impact assessment
Publisher: Wiley
Date: 22-02-2011
DOI: 10.1002/JOC.2067
Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis
Publisher: Springer Science and Business Media LLC
Date: 15-12-2015
Delineating hydrologic response units in large upland catchments and its evaluation using soil moisture simulations
Publisher: Elsevier BV
Date: 08-2013
Spatiotemporal variation of long‐term drought propensity through reliability‐resilience‐vulnerability based D rought M anagement I ndex
Publisher: American Geophysical Union (AGU)
Date: 10-2014
DOI: 10.1002/2014WR015703
Could the 2019-20 Australia bushfires or 2020-22 floods be predicted using CMIP decadal prediction?
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10096
Abstract: Over the past six years, Australia has experienced significant fluctuations in rainfall, including prolonged dry conditions and extensive bushfires, followed by two consecutive years of heavy rainfall in the east. Could such anomalies be predicted many years in advance is the question this study hopes to answer. A prediction framework that combines empirical and physically-based approaches using CMIP decadal prediction, and a novel spectral transformation approach is presented. When tested in a hindcast experiment, this framework shows significant prediction skill for rainfall up to five years in the future across all regions and climate zones in Australia. This framework was used to project from 2018 to 2022, covering the years of bushfires and extreme floods in Australia, as an added blindfolded validation of the prediction approach used. Following this, a blind projection of the precipitation anomalies over the continent for the coming five years is presented, to assess whether the anomalies for the past five years were, indeed, anomalies, or part of a pattern of what can be expected into the future. It is shown that this decadal framework has great potential for predicting whether the next few years will be wetter or drier, extending the predictive accuracy beyond a few months into the future. This can be valuable for managing water resources, prioritizing demands, protecting vulnerable systems, and reducing uncertainty in hydrological decision-making.
Comparison of two nonparametric alternatives for stochastic generation of monthly rainfall
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2006
Insights on the impact of systematic model errors on data assimilation performance in changing catchments
Publisher: Elsevier BV
Date: 03-2018
How is spatial homogeneity in precipitation extremes changing globally?
Publisher: Wiley
Date: 27-10-2022
Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments
Publisher: IWA Publishing
Date: 12-06-2023
DOI: 10.2166/WCC.2023.230
Abstract: Quantifying climate change impact on water resources systems at regional or catchment scales is important in water resources planning and management. General circulation models (GCMs) represent our main source of knowledge about future climate change. However, several key limitations restrict the direct use of GCM simulations for water resource assessments. In particular, the presence of systematic bias and the need for its correction is an essential pre-processing step that improves the quality of GCM simulations, making climate change impact assessments more robust and believable. What exactly is systematic bias? Can systematic bias be quantified if the model is asynchronous with observations or other model simulations? Should model bias be sub-categorized to focus on in idual attributes of interest or aggregated to focus on lower moments alone? How would one address bias in multiple attributes without making the correction model complex? How could one be confident that corrected simulations for the yet-to-be-seen future bear a closer resemblance to the truth? How can one meaningfully extrapolate correction to multiple dimensions, without being impacted by the ‘Curse of Dimensionality’? These are some of the questions we attempt to address in the paper.
Identifying Sustained Drought Anomalies in Hydrological Records: A Wavelet Approach
Publisher: American Geophysical Union (AGU)
Date: 27-07-2018
DOI: 10.1029/2018JD028455
Impact of mid-Pacific Ocean thermocline on the prediction of Australian rainfall
Publisher: Elsevier BV
Date: 02-2006
Patterns and comparisons of human-induced changes in river flood impacts in cities
Publisher: Copernicus GmbH
Date: 13-03-2018
DOI: 10.5194/HESS-22-1793-2018
Abstract: Abstract. In this study, information extracted from the first global urban fluvial flood risk data set (Aqueduct) is investigated and visualized to explore current and projected city-level flood impacts driven by urbanization and climate change. We use a novel adaption of the self-organizing map (SOM) method, an artificial neural network proficient at clustering, pattern extraction, and visualization of large, multi-dimensional data sets. Prevalent patterns of current relationships and anticipated changes over time in the nonlinearly-related environmental and social variables are presented, relating urban river flood impacts to socioeconomic development and changing hydrologic conditions. Comparisons are provided between 98 in idual cities. Output visualizations compare baseline and changing trends of city-specific exposures of population and property to river flooding, revealing relationships between the cities based on their relative map placements. Cities experiencing high (or low) baseline flood impacts on population and/or property that are expected to improve (or worsen), as a result of anticipated climate change and development, are identified and compared. This paper condenses and conveys large amounts of information through visual communication to accelerate the understanding of relationships between local urban conditions and global processes.
Improvements of satellite observations through data merging: status and challenges
Publisher: Wiley
Date: 30-11-2020
Correcting of real-time radar rainfall bias using a Kalman filtering approach
Publisher: Elsevier BV
Date: 02-2006
A nonparametric nonhomogeneous hidden Markov model for downscaling of multisite daily rainfall occurrences
Publisher: American Geophysical Union (AGU)
Date: 27-08-2005
DOI: 10.1029/2004JD005677
Statistics in Hydrology
Publisher: MDPI AG
Date: 13-05-2022
DOI: 10.3390/W14101571
Abstract: Statistical methods have a long history in the analysis of hydrological data for designing, planning, infilling, forecasting, and specifying better models to assess scenarios of land use and climate change in catchments [...]
On the divergence of potential and actual evapotranspiration trends: An assessment across alternate global datasets
Publisher: American Geophysical Union (AGU)
Date: 09-2017
DOI: 10.1002/2016EF000499
A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights
Publisher: Elsevier BV
Date: 09-2016
A Bayesian alternative for multi-objective ecohydrological model specification
Publisher: Elsevier BV
Date: 2018
Hydrologic modeling in dynamic catchments: A data assimilation approach
Publisher: American Geophysical Union (AGU)
Date: 05-2016
DOI: 10.1002/2015WR017192
Sensitivity of Australian roof drainage structures to design rainfall variability and climatic change
Publisher: Elsevier BV
Date: 08-2019
How does increasing temperature affect the sub-annual distribution of monthly rainfall?
Publisher: IOP Publishing
Date: 06-02-2023
Abstract: This paper investigates the relationship between temperature and sub-annual rainfall patterns using long-term monthly rainfall and temperature data from 1920 to 2018 in Australia. A parameter ( τ ) is used to measure the evenness of temporal rainfall distribution within each year, with τ = 0 indicating a uniform pattern. The study examines the relationship between τ and temperature for each year, considering whether it was warmer or cooler than average across five climate zones (CZs) in Australia, including tropical, arid, and three temperate climate classes. This study discovered a considerable association between annual maximum temperature and the distribution of monthly rainfall, with high temperatures resulting in greater variation (as represented by larger τ values) in the sub-annual distribution of monthly rainfall throughout all CZs, particularly in arid regions with τ values ranging from 0.27 to 0.52. In contrast, regions with temperate climates without dry seasons had a lower and narrower range of τ , from 0.15 to 0.26. This variability in rainfall distribution makes managing water resources more challenging in arid regions in Australia.
Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns
Publisher: Copernicus GmbH
Date: 29-03-2018
DOI: 10.5194/HESS-22-2041-2018
Abstract: Abstract. The effects of climate change are causing more frequent extreme rainfall events and an increased risk of flooding in developed areas. Quantifying this increased risk is of critical importance for the protection of life and property as well as for infrastructure planning and design. The updated National Oceanic and Atmospheric Administration (NOAA) Atlas 14 intensity–duration–frequency (IDF) relationships and temporal patterns are widely used in hydrologic and hydraulic modeling for design and planning in the United States. Current literature shows that rising temperatures as a result of climate change will result in an intensification of rainfall. These impacts are not explicitly included in the NOAA temporal patterns, which can have consequences on the design and planning of adaptation and flood mitigation measures. In addition there is a lack of detailed hydraulic modeling when assessing climate change impacts on flooding. The study presented in this paper uses a comprehensive hydrologic and hydraulic model of a fully developed urban/suburban catchment to explore two primary questions related to climate change impacts on flood risk. (1) How do climate change effects on storm temporal patterns and rainfall volumes impact flooding in a developed complex watershed? (2) Is the storm temporal pattern as critical as the total volume of rainfall when evaluating urban flood risk? We use the NOAA Atlas 14 temporal patterns, along with the expected increase in temperature for the RCP8.5 scenario for 2081–2100, to project temporal patterns and rainfall volumes to reflect future climatic change. The model results show that different rainfall patterns cause variability in flood depths during a storm event. The changes in the projected temporal patterns alone increase the risk of flood magnitude up to 35 %, with the cumulative impacts of temperature rise on temporal patterns and the storm volume increasing flood risk from 10 to 170 %. The results also show that regional storage facilities are sensitive to rainfall patterns that are loaded in the latter part of the storm duration, while extremely intense short-duration storms will cause flooding at all locations. This study shows that changes in temporal patterns will have a significant impact on urban/suburban flooding and need to be carefully considered and adjusted to account for climate change when used for the design and planning of future storm water systems.
Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map
Publisher: Elsevier BV
Date: 03-2015
Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach
Publisher: MDPI AG
Date: 21-06-2016
DOI: 10.3390/RS8060518
Continuous rainfall generation for a warmer climate using observed temperature sensitivities
Publisher: Elsevier BV
Date: 2017
Twenty-three unsolved problems in hydrology (UPH) – a community perspective
Publisher: Informa UK Limited
Date: 02-07-2019
Forecasting drought revisited – the importance of spectral transformations to dominant atmospheric predictor variables
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-12334
Abstract: & & As we write this abstract, Australia is experiencing widespread forest fires, Sydney has declared significant water restriction measures curtailing demand, and the entire country is experiencing a drought that is amongst the worst on record. Formulating a stable and practical approach for predicting drought into the future is being realised as an important need, as we enter an era of warmer climates that complicate this problem to an even greater extent. This study presents a novel basis for forecasting drought into the future. Use is made of a recently developed wavelets based methodology for transforming predictor variables so as to force greater consistency in spectral attributes with the response being modelled. Using a commonly adopted drought index, we demonstrate how the wavelets transformed predictor variables can be used to model the response with greater accuracy than otherwise. These transformed predictor variables are then used in conjunction with CMIP5 decadal climate forecasts to demonstrate the accuracy attainable at longer lead times than is currently possible. While our application focusses on the Australian mainland, the method is generic and can be adopted anywhere.& &
Tools for enhancing the application of self-organizing maps in water resources research and engineering
Publisher: Elsevier BV
Date: 09-2020
Preserving low-frequency variability in generated daily rainfall sequences
Publisher: Elsevier BV
Date: 10-2007
Anthropogenic intensification of short-duration rainfall extremes
Publisher: Springer Science and Business Media LLC
Date: 15-01-2021
Assessing the extent of non-stationary biases in GCMs
Publisher: Elsevier BV
Date: 06-2017
Assessing Countermeasure Effectiveness in Controlling Cyanobacterial Exceedance in Riverine Systems Using Probabilistic Forecasting Alternatives
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2021
A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis
Publisher: American Geophysical Union (AGU)
Date: 03-2020
DOI: 10.1029/2019WR025910
The Impact of Climate Change on Operational Probable Maximum Precipitation Estimates
Publisher: American Geophysical Union (AGU)
Date: 11-2022
DOI: 10.1029/2022WR032247
Abstract: The safety of high‐risk water infrastructure, such as dams and nuclear power plants, is often assessed by reference to their ability to accommodate floods derived from the Probable Maximum Precipitation (PMP). However, a key shortcoming of traditional PMP estimates is the assumption of a stationary climate, with evidence indicating that key meteorological conditions related to the magnitudes of extreme storms, such as atmospheric moisture, are changing in a warming climate. Due to the pragmatic nature of PMP methods derived for design purposes, inferring potential changes in PMP estimates based solely on trends or projections of atmospheric variables can ignore PMP method complexities and constraints. Here we explore how different traditional PMP methods will respond to a potential increase in atmospheric moisture. We find that increases in persisting dewpoint will lead to increases in PMP estimates, and the nature of this impact depends on whether the moisture maximization step is based on local or transposed regional information. An historical trend analysis reveals annual maximum persisting dewpoint temperatures have increased continuously over Australia over the past 60 years, with further increases predicted over the coming decades for all Shared Socioeconomic Pathways (SSPs). PMP estimates across Australia are predicated to increase by an average value of 13% by 2100 based on the conservative SSP1‐2.6, compared to 33% for SSP5‐8.5. We conclude PMP methods will require regular updating to account for changing persisting dewpoints and likely progressive increases in PMP, and the ensuing flood estimates.
A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation
Publisher: American Geophysical Union (AGU)
Date: 18-08-2015
DOI: 10.1002/2015GL064981
Interpreting variability in global SST data using independent component analysis and principal component analysis
Publisher: Wiley
Date: 2010
DOI: 10.1002/JOC.1888
A non-parametric bootstrapping framework embedded in a toolkit for assessing water quality model performance
Publisher: Elsevier BV
Date: 09-2018
Spatial, Temporal, and Multivariate Bias in Regional Climate Model Simulations
Publisher: American Geophysical Union (AGU)
Date: 10-06-2021
DOI: 10.1029/2020GL092058
Abstract: Correction of atmospheric variables to remove systematic biases in global climate model (GCM) simulations before downscaling offers a means of improving climate simulation accuracy in climate change impact assessments. Various mathematical approaches have been used to correct the lateral and lower boundary conditions of regional climate models (RCMs). Most of these techniques correct only the magnitude of each variable in idually over time without regard to spatial and multivariate bias. Here, we investigate how well an RCM is able to reproduce the dependence of an observed variable based on three aspects: temporal, spatial, and multivariate. Results show that the RCM simulations with univariate bias‐corrected GCM boundary conditions perform well in capturing both temporal and spatial dependence. However, all RCM simulations do not show improvement in the representation of dependence between variables, indicating the need for alternatives that correct systematic biases in multivariate dependence in both lateral and lower boundary conditions.
Steeper temporal distribution of rain intensity at higher temperatures within Australian storms
Publisher: Springer Science and Business Media LLC
Date: 08-06-2015
DOI: 10.1038/NGEO2456
Correcting for systematic biases in GCM simulations in the frequency domain
Publisher: Elsevier BV
Date: 07-2016
A Comparison of Australian Open Water Body Evaporation Trends for Current and Future Climates Estimated from Class A Evaporation Pans and General Circulation Models
Publisher: American Meteorological Society
Date: 02-2010
Abstract: Trends of decreasing pan evaporation around the world have renewed interest in evaporation and its behavior in a warming world. Observed pan evaporation around Australia has been modeled to attribute changes in its constituent variables. It is found that wind speed decreases have generally led to decreases in pan evaporation. Trends were also calculated from reanalysis and general circulation model (GCM) outputs. The reanalysis reflected the general pattern and magnitude of the observed station trends across Australia. However, unlike the station trends, the reanalysis trends are mainly driven by vapor pressure deficit changes than wind speed changes. Some of the GCMs modeled the trends well, but most showed an average positive trend for Australia. Half the GCMs analyzed show increasing wind speed trends, and most show larger changes in vapor pressure deficit than would be expected based on the station data. Future changes to open water body evaporation have also been assessed using projections for two emission scenarios. Averaged across Australia, the models show a 5% increase in open water body evaporation by 2070 compared to 1990 levels. There is considerable variability in the model projections, particularly for the aerodynamic component of evaporation. Assumptions of increases in evaporation in a warming world need to be considered in light of the variability in the parameters that affect evaporation.
Global sea surface temperature forecasts using an improved multimodel approach
Publisher: American Meteorological Society
Date: 09-05-2014
DOI: 10.1175/JCLI-D-13-00486.1
Abstract: With the availability of hindcasts or real-time forecasts from a number of coupled climate models, multimodel ensemble forecasting systems have gained popularity in recent years. However, many models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Assigning equal weights to each model in space and time may result in a biased forecast with narrower confidence limits than is appropriate. Although methods for combining forecasts that take into consideration differences in model accuracy over space and time exist, they suffer from a lack of consideration of the intermodel dependence that may exist. This study proposes an approach that considers the dependence among models while combining multimodel ensemble forecast. The approach is evaluated by combining sea surface temperature (SST) forecasts from five climate models for the period 1960–2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, is predicted three months in advance using the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for all the seasons over the majority of grid points compared to the case in which the dependence among the models is ignored. Consequently, the proposed approach of combining multiple models, taking into account the interdependence that exists, provides an attractive strategy to develop improved SST forecasts.
A Model to Evaluate the Impact of Headway Variation and Vehicle Size on the Reliability of Public Transit
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 3 — A nonparametric probabilistic forecast model
Publisher: Elsevier BV
Date: 12-2000
Accounting for interannual variability: A comparison of options for water resources climate change impact assessments
Publisher: American Geophysical Union (AGU)
Date: 04-2011
DOI: 10.1029/2010WR009272
Do Derived Drought Indices Better Characterize Future Drought Change?
Publisher: American Geophysical Union (AGU)
Date: 27-06-2023
DOI: 10.1029/2022EF003350
Abstract: Current methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments.
Dominant modes of interannual variability in Australian rainfall analyzed using wavelets
Publisher: American Geophysical Union (AGU)
Date: 02-03-2006
DOI: 10.1029/2005JD005996
Quantile regression for investigating scaling of extreme precipitation with temperature
Publisher: American Geophysical Union (AGU)
Date: 04-2014
DOI: 10.1002/2013WR015194
Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms
Publisher: IWA Publishing
Date: 2020
Abstract: The present understanding of how changes in climate conditions will impact the flux of natural organic matter (NOM) from the terrestrial to aquatic environments and thus aquatic dissolved organic carbon (DOC) concentrations is limited. In this study, three machine learning algorithms were used to predict variations in DOC concentrations in an Australian drinking water catchment as a function of climate, catchment and physical water quality data. Four independent variables including precipitation, temperature, leaf area index and turbidity (n = 5,540) were selected from a large dataset to develop and train each machine learning model. The accuracy of the multivariable linear regression, support vector regression (SVR) and Gaussian process regression algorithms with different kernel functions was determined using adjusted R-squared (adj. R2), root-mean-squared error (RMSE) and mean absolute error (MAE). Model accuracy was very sensitive to the time interval used to average climate observations prior to pairing with DOC observations. The SVR model with a quadratic kernel function and a 12-day time interval between climate and water quality observations outperformed the other machine learning algorithms (adj. R2 = 0.71, RMSE = 1.9, MAE = 1.35). The area under the receiver operating characteristic curve method (AUC) confirmed that the SVR model could predict 92% of the elevated DOC observations however, it was not possible to estimate DOC values at specific s ling sites in the catchment, probably due to the complex local geological and hydrological changes in the sites that directly surround and feed each s ling point. Further research is required to establish potential relationships between climatological data and NOM concentration in other water catchments – especially in the face of a changing climate.
Selection of a kernel bandwidth for measuring dependence in hydrologic time series using the mutual information criterion
Publisher: Springer Science and Business Media LLC
Date: 08-2001
Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models
Publisher: American Geophysical Union (AGU)
Date: 05-01-2016
DOI: 10.1002/2015JD023719
An Assessment of Drift Correction Alternatives for CMIP5 Decadal Predictions
Publisher: American Geophysical Union (AGU)
Date: 14-10-2017
DOI: 10.1002/2017JD026900
Development of a computationally efficient semi-distributed hydrologic modeling application for soil moisture, lateral flow and runoff simulation
Publisher: Elsevier BV
Date: 11-2016
Predicting hydrologic response through a hierarchical catchment knowledgebase: A Bayes empirical Bayes approach
Publisher: American Geophysical Union (AGU)
Date: 02-2014
DOI: 10.1002/2013WR015079
Impact of climate change on floods in the Brahmaputra basin using CMIP5 decadal predictions
Publisher: Elsevier BV
Date: 08-2015
Spatial Disaggregation of Coarse Soil Moisture Data by Using High-Resolution Remotely Sensed Vegetation Products
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI)
Publisher: Elsevier BV
Date: 07-2015
Evaluation of downscaled daily rainfall hindcasts over Sydney, Australia using statistical and dynamical downscaling approaches
Publisher: IWA Publishing
Date: 03-07-2014
DOI: 10.2166/NH.2013.094
Abstract: The assessment of local and regional impacts of climate change often requires downscaling of general circulation model (GCM) projections from coarser GCM-scale to finer local- or catchment-scale spatial resolution. This paper provides an assessment of two downscaling approaches for simulation of daily rainfall over Sydney, Australia. The two downscaling alternatives compared include a multivariate multisite statistical downscaling model based on semi-parametric conditional simulation and a dynamical downscaling approach that uses the National Center for Atmospheric Research (NCAR) weather research and forecasting (WRF) model. The two approaches are evaluated for their ability to reproduce important at-site rainfall statistics at a network of 45 raingauge stations and regional statistics over the catchment area of the Warragamba Dam (9,050 km2). The results indicate that the simulations from these approaches capture many regionally observed climate features, including the simulated seasonal and annual means and daily extreme rainfall values. Further analyses suggest that the statistical downscaling approach provides improved simulations of attributes related to point rainfall, spell lengths and amounts, whereas the dynamical approach is well-suited for applications where regionally averaged rainfall is of primary concern.
Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework
Publisher: Elsevier BV
Date: 11-2016
Assessing Severe Drought and Wet Events over India in a Future Climate Using a Nested Bias-Correction Approach
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2013
Why continuous simulation? The role of antecedent moisture in design flood estimation
Publisher: American Geophysical Union (AGU)
Date: 06-2012
DOI: 10.1029/2011WR010997
Intensification of Convective Rain Cells at Warmer Temperatures Observed from High-Resolution Weather Radar Data
Publisher: American Meteorological Society
Date: 04-2018
Abstract: This study contributes to the understanding of the relationship between air temperature and convection by analyzing the characteristics of rainfall at the storm and convective rain cell scales. High spatial–temporal resolution (1 km, 5 min) estimates from a uniquely long weather radar record (24 years) were coupled with near-surface air temperature over Mediterranean and semiarid regions in the eastern Mediterranean. In the examined temperature range (5°–25°C), the peak intensity of in idual convective rain cells was found to increase with temperature, but at a lower rate than the 7%°C−1 scaling expected from the Clausius–Clapeyron relation, while the area of the in idual convective rain cells slightly decreases or, at most, remains unchanged. At the storm scale, the areal convective rainfall was found to increase with warmer temperatures, whereas the areal nonconvective rainfall and the stormwide area decrease. This suggests an enhanced moisture convergence from the stormwide extent toward the convective rain cells. Results indicate a reduction in the total rainfall amounts and an increased heterogeneity of the spatial structure of the storm rainfall for temperatures increasing up to 25°C. Thermodynamic conditions, analyzed using convective available potential energy, were determined to be similar between Mediterranean and semiarid regions. Limitations in the atmospheric moisture availability when shifting from Mediterranean to semiarid climates were detected and explain the suppression of the intensity of the convective rain cells when moving toward drier regions. The relationships obtained in this study are relevant for nearby regions characterized by Mediterranean and semiarid climates.
A wavelet-based tool to modulate variance in predictors: An application to predicting drought anomalies
Publisher: Elsevier BV
Date: 2021
Characterising uncertainty in precipitation downscaling using a Bayesian approach
Publisher: Elsevier BV
Date: 07-2019
Investigation of factors leading to extreme floods by space-time simulation of rainfall and runoff
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-9947
Abstract: Extreme floods are caused by special meteorological conditions matching critical space-time scales of flood generation processes in a catchment. Fortunately for most of the floods these conditions are not meet. However, it is hypothesized that for many events reasonable changes on the flood producing storms would lead to exceptional floods. In order to investigate which factors are most relevant for the maximisation potential of floods a simulation study has been carried out. Event based conditional space-time simulation of short-time step rainfall is applied. So, mainly the space-time patterns and the rainfall intensities are modified conditioned on observed rainfall data. The space-time rainfall is generated by sequential Gaussian simulation with and without considering temporal correlation and advection. The intensities are modified considering the observed saturation deficit. Many space-time rainfall realisations are produced and used as input for a rainfall-runoff model with varying initial conditions. This case study uses data from the Mulde river catchment in Germany and applies the methodology to a set of selected large flood events. The results will reveal how extreme the floods could have become and how much increased rainfall intensities, pattern modification or initial catchment conditions contribute each to the total maximisation potential of the floods.
Constraining continuous rainfall simulations for derived design flood estimation
Publisher: Elsevier BV
Date: 11-2016
Impacts of the tropical trans-basin variability on Australian rainfall
Publisher: Springer Science and Business Media LLC
Date: 22-10-2016
Integrating multiple scales of hydraulic conductivity measurements in training image-based stochastic models
Publisher: American Geophysical Union (AGU)
Date: 2015
DOI: 10.1002/2014WR016150
Evaluating global reanalysis datasets for provision of boundary conditions in regional climate modelling
Publisher: Springer Science and Business Media LLC
Date: 23-01-2016
Disaggregating Soil Moisture to Finer Spatial Resolutions: A Comparison of Alternatives
Publisher: American Geophysical Union (AGU)
Date: 11-2018
DOI: 10.1029/2018WR022575
Comparison of statistical downscaling techniques for multisite daily rainfall conditioned on atmospheric variables for the Sydney region
Publisher: Informa UK Limited
Date: 2009
Characterising errors using satellite metadata for eco-hydrological modelling
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-5100
Abstract: Understanding the origin of errors in model predictions is a critical element in hydrologic model calibration and uncertainty estimation. While there exist a variety of plausible error sources, only one measure of the total residual error can be ascertained when the observed response is known. Here we show that collecting extra information a priori to characterise the data error before calibration can assist in improved model calibration and uncertainty estimation. A new model calibration strategy using the satellite metadata information is proposed as a means to inform the model prior, and subsequently to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that 1) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error 2) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance 3) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.
Correcting bias in radar Z – R relationships due to uncertainty in point rain gauge networks
Publisher: Elsevier BV
Date: 11-2014
A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes
Publisher: Elsevier BV
Date: 03-2019
Estimating radar precipitation in cold climates: The role of air temperature within a non-parametric framework
Publisher: Copernicus GmbH
Date: 19-12-2018
DOI: 10.5194/HESS-22-6533-2018
Abstract: Abstract. The use of ground-based precipitation measurements in radar precipitation estimation is well known in radar hydrology. However, the approach of using gauged precipitation and near-surface air temperature observations to improve radar precipitation estimates in cold climates is much less common. In cold climates, precipitation is in the form of snow, rain or a mixture of the two phases. Air temperature is intrinsic to the phase of the precipitation and could therefore be a possible covariate in the models used to ascertain radar precipitation estimates. In the present study, we investigate the use of air temperature within a non-parametric predictive framework to improve radar precipitation estimation for cold climates. A non-parametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables and gauge precipitation as an observed response using a k nearest neighbour (k-nn) regression estimator. The relative importance of the two predictors is ascertained using an information theory-based weighting. Four years (2011–2015) of hourly radar precipitation rates from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 68 gauges and gridded observational air temperatures were used to formulate the predictive model, hence making our investigation possible. Gauged precipitation data were corrected for wind-induced under-catch before using them as true observed response. The predictive model with air temperature as an added covariate reduces root-mean-square error (RMSE) by up to 15 % compared to the model that uses radar precipitation rate as the sole predictor. More than 80 % of gauge locations in the study area showed improvement with the new method. Further, the associated impact of air temperature became insignificant at more than 85 % of gauge locations when the near-surface air temperature was warmer than 10 ∘C, which indicates that the partial dependence of precipitation on air temperature is most useful for colder temperatures.
A metric for attributing variability in modelled streamflows
Publisher: Elsevier BV
Date: 10-2016
How Is Spatial Homogeneity in Precipitation Extremes Changing Globally?
Publisher: American Geophysical Union (AGU)
Date: 19-08-2023
DOI: 10.1029/2023GL103233
Abstract: The effect of climate change on precipitation intensity is well documented. However, findings regarding changes in spatial extent of extreme precipitation events are still ambiguous as previous studies focused on particular regions and time domains. This study addresses this ambiguity by investigating the pattern of changes in the spatial extent of short duration extreme precipitation events globally. A grid‐based indicator termed Spatial‐Homogeneity is proposed and used to assess the changes of spatial extent in Global Precipitation Measurement records. This study shows that (a) rising temperature causes significant shrinking of precipitation extent in tropics, but an expansion of precipitation extent in arid regions, (b) storms with higher precipitation intensity show a faster decrease in spatial extent, and (c) larger spatial extent storms are associated with higher total precipitable water. Results imply that in a warming climate, tropics may experience severe floods as storms may become more intense and spatially concentrated.
The human dimension of water availability: Influence of management rules on water supply for irrigated agriculture and the environment
Publisher: Elsevier BV
Date: 09-2020
Modeling the catchment via mixtures: Issues of model specification and validation
Publisher: American Geophysical Union (AGU)
Date: 11-2006
DOI: 10.1029/2005WR004613
Assessment of Climate Change Impacts on Reservoir Storage Reliability, Resilience, and Vulnerability Using a Multivariate Frequency Bias Correction Approach
Publisher: American Geophysical Union (AGU)
Date: 02-2020
DOI: 10.1029/2019WR026022
Abstract: Raw simulations of global or regional climate models are rarely used in catchment scale hydrological impact assessment or subsequent reservoir storage change assessment studies. Keeping this in mind, this study uses a frequency bias correction alternative for multiple variables to evaluate the impact of climate change on reservoir storage reliability, resilience, and vulnerability across Australia. The bias‐corrected time series of daily rainfall and temperature are used as inputs to a hydrological model to derive flows and assess change to reservoir storage attributes. A total of six fifth phase of the Coupled Model Intercomparison Project climate models dynamically downscaled using the Conformal Cubic Atmospheric Model are used. Streamflow data of 222 high‐quality catchments in near‐natural conditions across Australia are used and change ascertained. The results for the historical climate show that the multivariate frequency bias correction approach outperforms the traditional quantile matching alternative in representing the runoff characteristics related to reservoir storage. For the future climate, the results suggest decrease in the annual mean runoff for most catchments. The proposed approach leads to a smaller decrease in the standard deviation of annual runoff and a reduction in the water supply capability, as indicated by a reduction in reliability and resilience and an increase in vulnerability, to meet the demand in comparison to both raw and quantile matching ‐ based climate simulations across for most catchments. Overall, a reduction in water supply capability to meet a given demand in the future for most regional climate models and catchments is projected.
Using all data to improve seasonal sea surface temperature predictions: A combination‐based model forecast with unequal observation lengths
Publisher: Wiley
Date: 23-03-2018
DOI: 10.1002/JOC.5494
Incorporating multiple observational uncertainties in water quality model calibration
Publisher: Wiley
Date: 2022
DOI: 10.1002/HYP.14452
Abstract: The presence of error in water quality and hydrologic variables can significantly impair the calibration of water quality models. Precise and reliable identification of observational errors can have a significant impact on improving model parameter estimation. This study develops the Bayesian error analysis with reordering (BEAR) method to accommodate multiple sources of observational errors in the calibration of a water quality model. It realizes this goal by s ling the errors for input and output data from their respective error distributions and reordering them with inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSS) simulated via a conceptual water quality model. Based on case studies using synthetic data, the new algorithm successfully quantifies one source of observational error when the error model of another source of observational error can be estimated accurately in advance. The results of a real case study also illustrate that considering observational errors in both model inputs and outputs, rather than in just the inputs or outputs, can improve the parameter calibration and error characterization. The improvements depend on the precision of the prior information for the error model. The application of this new algorithm in TSS simulation can be an ex le to understand how the BEAR algorithm works in other water quality models. The core idea of the BEAR algorithm is flexible and can be extended to the calibration of other environmental models.
Ecohydrologic Error Models for Improved Bayesian Inference in Remotely Sensed Catchments
Publisher: American Geophysical Union (AGU)
Date: 06-2019
DOI: 10.1029/2019WR025055
Abstract: Leaf area index (LAI) is an important vegetation indicator widely used for simulating vegetation dynamics and quantifying biomass production. Spatial and temporal variability of LAI are often characterized using satellite remote sensing products. However, these types of satellite products often have relatively low quality when compared to in situ measurements. This work presents an approach for characterizing Moderate Resolution Imaging Spectroradiometer LAI observation errors in a Bayesian ecohydrological modeling framework using Moderate Resolution Imaging Spectroradiometer quality flags data. We introduce a novel ecohydrologic error model, which partitions observation and model residual error according to the estimated retrieval uncertainty of LAI and the quality flags for each pixel. We examine our approach in two study catchments in Australia with varying degrees of good and poor quality satellite LAI data. Results show improved LAI predictions and less model residual error for both catchments when accounting for satellite observational uncertainties in a Bayesian framework.
Time-varying parameter models for catchments with land use change: the importance of model structure
Publisher: Copernicus GmbH
Date: 16-05-2018
DOI: 10.5194/HESS-22-2903-2018
Abstract: Abstract. Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.
How reliable are GCM simulations for different atmospheric variables?
Publisher: Springer Science and Business Media LLC
Date: 14-10-2017
How well do general circulation models represent low-frequency rainfall variability?
Publisher: American Geophysical Union (AGU)
Date: 03-2014
DOI: 10.1002/2012WR013085
Comparison of two approaches for downscaling synoptic atmospheric patterns to multisite precipitation occurrence
Publisher: American Geophysical Union (AGU)
Date: 27-07-2004
DOI: 10.1029/2004JD004823
On the predictability of SSTA indices from CMIP5 decadal experiments
Publisher: IOP Publishing
Date: 07-2015
Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations
Publisher: American Geophysical Union (AGU)
Date: 08-2017
DOI: 10.1002/2017MS001003
Multi-model drought predictions using temporally aggregated climate indicators
Publisher: Elsevier BV
Date: 02-2020
Measurement of GCM Skill in Predicting Variables Relevant for Hydroclimatological Assessments
Publisher: American Meteorological Society
Date: 15-08-2009
Abstract: Simulations from general circulation models are now being used for a variety of studies and purposes. With up to 23 different GCMs now available, it is desirable to determine whether a specific variable from a particular model is representative of the ensemble mean, which is often assumed to indicate the likely state of that variable in the future. The answers are important for decision makers and researchers using selective model outputs for follow-on studies such as statistical downscaling, which currently assume all model outputs are simulated with equal reliability. A skill score, termed the variable convergence score (VCS), has been derived that can be used to rank variables based on the coefficient of variation of the ensemble. The key benefit is the development of a simple methodology that allows for a quantitative assessment between different hydroclimatic variables. The VCS methodology has been applied to the outputs of nine GCMs for eight different variables and two emission scenarios to provide a relative ranking of the variables averaged across Australia and over different climatic regions of the country. The methodology, however, would be applicable for any region or any variable of interest from GCMs. It was found that the surface variables with the highest scores are pressure, temperature, and humidity. Regionally in Australia, models again show the best agreement in the surface pressure projections. The tropical and southwestern temperate zones show the overall highest variable convergence when all variables are considered. The desert zone shows relatively low model agreement, particularly in the projections of precipitation and specific humidity.
Tools for investigating the prior distribution in Bayesian hydrology
Publisher: Elsevier BV
Date: 07-2016
Does improved SSTA prediction ensure better seasonal rainfall forecasts?
Publisher: American Geophysical Union (AGU)
Date: 05-2015
DOI: 10.1002/2014WR015997
Does storm duration modulate the extreme precipitation-temperature scaling relationship?
Publisher: American Geophysical Union (AGU)
Date: 28-10-2015
DOI: 10.1002/2015GL066274
Preliminary Analysis of Trends in Australian Flood Data
Publisher: American Society of Civil Engineers
Date: 14-05-2010
DOI: 10.1061/41114(371)14
A multi-objective assessment of alternate conceptual ecohydrological models
Publisher: Elsevier BV
Date: 10-2015
A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature
Publisher: American Geophysical Union (AGU)
Date: 08-2015
DOI: 10.1002/2014WR016729
A comparison of methods to estimate future sub-daily design rainfall
Publisher: Elsevier BV
Date: 12-2017
Relationship of Drought and Engineered Water Supply: Multivariate Index for Quantifying Sustained Water Stress in Anthropogenically Affected Subbasins
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2019
Integrating Ocean Subsurface Temperatures in Statistical ENSO Forecasts
Publisher: American Meteorological Society
Date: 09-2005
DOI: 10.1175/JCLI3477.1
Abstract: Subsurface characteristics of oceans have recently become of interest to climate modelers. Here subsurface information has been linked to the evolution of the El Niño–Southern Oscillation (ENSO) in a simple statistical formulation. The hypothesis proposed is that the inclusion of subsurface ocean heat content in a persistence-based representation of ENSO results in an increase in prediction skill. The subsurface temperature field is represented by anomalies in the 20°C isotherm (Z20) in the Indian and Pacific Oceans. Using a cross-validation approach, the first two empirical orthogonal functions (EOFs) of the Z20 anomalies are derived, but only the second EOF is used as a predictor. The first EOF is found to be representative of the mature ENSO signal while the second EOF shows characteristics that are precursory to an ENSO event. When included in a persistence-based prediction scheme, the second EOF enhances the skill of ENSO hindcasts up to a lead time of 15 months. Results are compared with another model that uses the second EOF of the SST anomalies in the tropical Pacific Ocean and persistence as predictors. Cross-validated hindcasts from the isotherm-based scheme are generally more skillful than those obtained from the persistence and SST-based prediction schemes. Hindcasts of cold events are particularly close to the observed values even at long lags. Major improvements occur for predictions made during boreal winter and spring months when the addition of subsurface information resulted in predictions that are not greatly affected by the d ing effect of the “spring barrier.”
How Do Terrestrial Determinants Impact the Response of Water Quality to Climate Drivers?—An Elasticity Perspective on the Water–Land–Climate Nexus
Publisher: MDPI AG
Date: 17-11-2017
DOI: 10.3390/SU9112118
An application of artificial neural networks for rainfall forecasting
Publisher: Elsevier BV
Date: 03-2001
An Integrated Approach to Error Correction for Real-Time Radar-Rainfall Estimation
Publisher: American Meteorological Society
Date: 2006
DOI: 10.1175/JTECH1832.1
Abstract: A procedure for estimating radar rainfall in real time consists of three main steps: 1) the measurement of reflectivity and removal of known sources of errors, 2) the conversion of the reflectivity to a rainfall rate (Z–R conversion), and 3) the adjustment of the mean field bias as assessed using a rain gauge network. Error correction is associated with the first two steps and incorporates removing erroneous measurements and correcting biases in the Z–R conversion. This paper investigates the relative importance of error correction and the mean field bias–adjustment processes. In addition to the correction for ground clutter, the bright band, and hail, the two error correction strategies considered here are 1) a scale transformation function to remove range-dependent bias in measured reflectivity resulting from an increase in observation volume with range, and 2) the classification of storm types to account for the variation in Z–R relationships for convective and stratiform rainfall. The mean field bias is removed using two alternatives: 1) estimation of the bias at each time step based on the s le of observations available, and 2) use of a Kalman filter to estimate the bias under assumptions of a Markovian dependence structure. A 7-month record of radar and rain gauge rainfall for Sydney, Australia, were used in this study. The results show a stepwise decrease in the root-mean-square error (rmse) of radar rainfall with added levels of error correction using either of the two mean field bias–adjustment methods considered in our study. It was found that although the effects of the two error correction strategies were small compared to bias adjustment, they do form an important step of radar-rainfall estimation.
Assessing the sensitivity of hydro-climatological change detection methods to model uncertainty and bias
Publisher: Elsevier BV
Date: 12-2019
Supplementary material to "On the non-stationarity of hydrological response in anthropogenically unaffected catchments: An Australian perspective"
Publisher: Copernicus GmbH
Date: 29-07-2016
Improved spatial prediction: A combinatorial approach
Publisher: American Geophysical Union (AGU)
Date: 07-2013
DOI: 10.1002/WRCR.20290
A global assessment of climate–water quality relationships in large rivers: An elasticity perspective
Publisher: Elsevier BV
Date: 2014
DOI: 10.1016/J.SCITOTENV.2013.09.002
Abstract: To uncover climate-water quality relationships in large rivers on a global scale, the present study investigates the climate elasticity of river water quality (CEWQ) using long-term monthly records observed at 14 large rivers. Temperature and precipitation elasticities of 12 water quality parameters, highlighted by N- and P-nutrients, are assessed. General observations on elasticity values show the usefulness of this approach to describe the magnitude of stream water quality responses to climate change, which improves that of simple statistical correlation. Sensitivity type, intensity and variability rank of CEWQ are reported and specific characteristics and mechanism of elasticity of nutrient parameters are also revealed. Among them, the performance of ammonia, total phosphorus-air temperature models, and nitrite, orthophosphorus-precipitation models are the best. Spatial and temporal assessment shows that precipitation elasticity is more variable in space than temperature elasticity and that seasonal variation is more evident for precipitation elasticity than for temperature elasticity. Moreover, both anthropogenic activities and environmental factors are found to impact CEWQ for select variables. The major relationships that can be inferred include: (1) human population has a strong linear correlation with temperature elasticity of turbidity and total phosphorus and (2) latitude has a strong linear correlation with precipitation elasticity of turbidity and N nutrients. As this work improves our understanding of the relation between climate factors and surface water quality, it is potentially helpful for investigating the effect of climate change on water quality in large rivers, such as on the long-term change of nutrient concentrations.
Predicting cyanobacteria occurrence using climatological and environmental controls
Publisher: Elsevier BV
Date: 05-2020
An Improved Covariate for Projecting Future Rainfall Extremes?
Publisher: American Geophysical Union (AGU)
Date: 08-2020
DOI: 10.1029/2019WR026924
Abstract: Projection of extreme rainfall under climate change remains an area of considerable uncertainty. In the absence of geographically consistent simulations of extreme rainfall for the future, alternatives relying on physical relationships between a warmer atmosphere and its moisture carrying capacity are projected, scaling with a known atmospheric covariate. The most common atmospheric covariate adopted is surface air temperature, as it exhibits great consistency across climate model simulations into the future and, as per the Clausius‐Clapeyron relationship, has a well‐established link to atmospheric moisture capacity. However, empirical assessments of this relationship show that it varies with latitude, surface temperature, atmospheric temperature, and other factors, suggesting there may be more stable “global” atmospheric covariates that could be used instead. We argue that a better‐suited covariate would be one that captures the relationship between extreme rainfall and temperature but exhibits greater consistency in the relationship across regions as well as climatic zones. Our analysis identifies plausible atmospheric indicators of changes to future extreme rainfall, which now proliferate literature and compare their suitability based on the variability they exhibit across multiple geographical, topographic, and climatic zones within Australia. It is shown that surface air temperature exhibits a regionally inconsistent relationship with extreme rainfall and hence is not suitable for projecting to future conditions. The study identified integrated water vapor and surface dew point temperature as promising alternatives, with the former showing greater consistency in space but at the cost of reduced temporal coverage.
Comparison of two approaches for generation of daily rainfall data
Publisher: Springer Science and Business Media LLC
Date: 08-02-2005
Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method
Publisher: Copernicus GmbH
Date: 04-03-2022
DOI: 10.5194/HESS-26-1203-2022
Abstract: Abstract. Uncertainty in input can significantly impair parameter estimation in water quality modeling, necessitating the accurate quantification of input errors. However, decomposing the input error from the model residual error is still challenging. This study develops a new algorithm, referred to as the Bayesian Error Analysis with Reordering (BEAR), to address this problem. The basic approach requires s ling errors from a pre-estimated error distribution and then reordering them with their inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSSs) simulation via a conceptual water quality model. Based on case studies using synthetic data, the BEAR method successfully improves the input error identification and parameter estimation by introducing the error rank estimation and the error position reordering. The results of a real case study demonstrate that, even with the presence of model structural error and output data error, the BEAR method can approximate the true input and bring a better model fit through an effective input modification. However, its effectiveness depends on the accuracy and selection of the input error model. The application of the BEAR method in TSS simulation can be extended to other water quality models.
Hydrological sustainability of international virtual water trade
Publisher: IOP Publishing
Date: 31-10-2023
Kernel bandwidth selection for a first order nonparametric streamflow simulation model
Publisher: Springer Science and Business Media LLC
Date: 30-04-1998
On the non-stationarity of hydrological response in anthropogenically unaffected catchments: an Australian perspective
Publisher: Copernicus GmbH
Date: 12-01-2017
Abstract: Abstract. Increases in greenhouse gas concentrations are expected to impact the terrestrial hydrologic cycle through changes in radiative forcings and plant physiological and structural responses. Here, we investigate the nature and frequency of non-stationary hydrological response as evidenced through water balance studies over 166 anthropogenically unaffected catchments in Australia. Non-stationarity of hydrologic response is investigated through analysis of long-term trend in annual runoff ratio (1984–2005). Results indicate that a significant trend (p 0.01) in runoff ratio is evident in 20 catchments located in three main ecoregions of the continent. Runoff ratio decreased across the catchments with non-stationary hydrologic response with the exception of one catchment in northern Australia. Annual runoff ratio sensitivity to annual fractional vegetation cover was similar to or greater than sensitivity to annual precipitation in most of the catchments with non-stationary hydrologic response indicating vegetation impacts on streamflow. We use precipitation–productivity relationships as the first-order control for ecohydrologic catchment classification. A total of 12 out of 20 catchments present a positive precipitation–productivity relationship possibly enhanced by CO2 fertilization effect. In the remaining catchments, biogeochemical and edaphic factors may be impacting productivity. Results suggest vegetation dynamics should be considered in exploring causes of non-stationary hydrologic response.
Reconstructing hydro-climatological data using dynamical downscaling of reanalysis products in data-sparse regions – Application to the Limpopo catchment in southern Africa
Publisher: Elsevier BV
Date: 08-2017
Should radar precipitation depend on incident air temperature? A new estimation algorithm for cold climates
Publisher: Copernicus GmbH
Date: 22-12-2017
Abstract: Abstract. In cold climates, the form of precipitation (snow or rain or mixture of snow and rain) results in uncertainty in radar precipitation estimation. Estimation often proceeds without distinguishing the state of precipitation which can be reliably specified as a function of associated air temperature. In the present study, we hypothesise that incident air temperature is related to the phase of the precipitation and ensuing reflectivity measurement, and therefore could be used in prediction models to improve radar precipitation estimates in cold climates. This is the first study to our knowledge that assesses the dependence of radar precipitation on incident air temperature and presents a procedure that can be used for taking it into consideration. We use a data based nonparametric statistical approach for this assessment. A nonparametric predictive model is constructed with radar rain rate and air temperature as predictor variables and gauge precipitation as observed response using a k-nearest neighbour (k-nn) regression estimator. A partial information theoretic technique is used to ascertain the relative importance of the two predictors. Six years (2011–2017) of hourly radar rain rate from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 88 raingauges and gridded observational air temperature were used to formulate the predictive model and hence evaluate our hypothesis. The predictive model with temperature as an additional covariate reduces root mean squared error (RMSE) up to 15 % compared to the predictive model with radar rain rate as the sole predictor. More than 80 % of the raingauge locations in the study area showed improvement with the new method. Further, the estimated partial weight for air temperature assumed a zero value for more than 85 % of gauge locations when temperature was above 10 °C, which indicates that the partial dependence of precipitation on air temperature is most important for colder climates.
A quasi‐global assessment of changes in remotely sensed rainfall extremes with temperature
Publisher: American Geophysical Union (AGU)
Date: 28-12-2016
DOI: 10.1002/2016GL071354
The Role of Floodplain Topography in Deriving Basin Discharge Using Passive Microwave Remote Sensing
Publisher: American Geophysical Union (AGU)
Date: 02-2019
DOI: 10.1029/2018WR023627
If Precipitation Extremes Are Increasing, Why Aren't Floods?
Publisher: American Geophysical Union (AGU)
Date: 11-2018
DOI: 10.1029/2018WR023749
A Signal Processing Approach to Correct Systematic Bias in Trend and Variability in Climate Model Simulations
Publisher: American Geophysical Union (AGU)
Date: 08-07-2021
DOI: 10.1029/2021GL092953
Abstract: Bias correction of General Circulation Model (GCM) is now an essential part of climate change studies. However, the climate change trend has been overlooked in majority of bias correction approaches. Here, a novel signal processing‐based approach for correcting systematic biases in the time‐varying trend of GCM simulations is proposed. The approach corrects for systematic deviations in spectral attributes of raw GCM simulations using discrete wavelet transforms. The order one and two moments of the underlying trend represented by the lowest frequency of wavelet component are corrected to ensure continuity in the corrected time series from the current to the future simulation period. The approach is applied to correct two data sets that exhibit opposite time‐varying trends representing the global mean sea level (GMSL) and the Arctic sea‐ice extent. Results indicate that bias in trend is corrected, while continuity in time and observed variability at all frequencies in current climate simulations are maintained.
Impact of atmospheric circulation on the rainfall-temperature relationship in Australia
Publisher: IOP Publishing
Date: 07-09-2020
A simulation tool for managing environmental flows in regulated rivers
Publisher: Elsevier BV
Date: 11-2015
Impact of the El Niño–Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode on Daily to Subdaily Rainfall Characteristics in East Australia
Publisher: American Meteorological Society
Date: 05-2012
Abstract: The relationship between seasonal aggregate rainfall and large-scale climate modes, particularly the El Niño–Southern Oscillation (ENSO), has been the subject of a significant and ongoing research effort. However, relatively little is known about how the character of in idual rainfall events varies as a function of each of these climate modes. This study investigates the change in rainfall occurrence, intensity, and storm interevent time at both daily and subdaily time scales in east Australia, as a function of indices for ENSO, the Indian Ocean dipole (IOD), and the southern annular mode (SAM), with a focus on the cool season months. Long-record datasets have been used to s le a large variety of climate events for better statistical significance. Results using both the daily and subdaily rainfall datasets consistently show that it is the occurrence of rainfall events, rather than the average intensity of rainfall during the events, which is most strongly influenced by each of the climate modes. This is shown to be most likely associated with changes to the time between wet spells. Furthermore, it is found that despite the recent attention in the research literature on other climate modes, ENSO remains the leading driver of rainfall variability over east Australia, particularly farther inland during the winter and spring seasons. These results have important implications for how water resources are managed, as well as how the implications of large-scale climate modes are included in rainfall models to best capture interannual and longer-scale variability.
Refining Predictor Spectral Representation Using Wavelet Theory for Improved Natural System Modeling
Publisher: American Geophysical Union (AGU)
Date: 03-2020
DOI: 10.1029/2019WR026962
Hydrological model selection: A Bayesian alternative
Publisher: American Geophysical Union (AGU)
Date: 10-2005
DOI: 10.1029/2004WR003719
Evaluation of volcanic aerosol impacts on atmospheric water vapor using CMIP3 and CMIP5 simulations
Publisher: American Geophysical Union (AGU)
Date: 27-05-2013
DOI: 10.1002/JGRD.50420
Global assessment of flood and storm extremes with increased temperatures
Publisher: Springer Science and Business Media LLC
Date: 11-08-2017
DOI: 10.1038/S41598-017-08481-1
Abstract: There is overwhelming consensus that the intensity of heavy precipitation events is increasing in a warming world. It is generally expected such increases will translate to a corresponding increase in flooding. Here, using global data sets for non-urban catchments, we investigate the sensitivity of extreme daily precipitation and streamflow to changes in daily temperature. We find little evidence to suggest that increases in heavy rainfall events at higher temperatures result in similar increases in streamflow, with most regions throughout the world showing decreased streamflow with higher temperatures. To understand why this is the case, we assess the impact of the size of the catchment and the rarity of the event. As the precipitation event becomes more extreme and the catchment size becomes smaller, characteristics such as the initial moisture in the catchment become less relevant, leading to a more consistent response of precipitation and streamflow extremes to temperature increase. Our results indicate that only in the most extreme cases, for smaller catchments, do increases in precipitation at higher temperatures correspond to increases in streamflow.
Increases in temperature do not translate to increased flooding
Publisher: Springer Science and Business Media LLC
Date: 12-12-2019
Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes
Publisher: The Royal Society
Date: 03-2021
Abstract: A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling towards understanding mechanisms of change the usefulness of temperature-scaling relations towards detecting and attributing extreme rainfall change and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K −1 ), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.
An equivalent cross-sectional basis for semidistributed hydrological modeling
Publisher: American Geophysical Union (AGU)
Date: 05-2014
DOI: 10.1002/2013WR014741
Correcting biases in regional climate model boundary variables for improved simulation of high-impact compound events
Publisher: Elsevier BV
Date: 09-2023
A new framework for monitoring flood inundation using readily available satellite data
Publisher: American Geophysical Union (AGU)
Date: 16-03-2016
DOI: 10.1002/2016GL068192
Fluvial Flood Losses in the Contiguous United States Under Climate Change
Publisher: American Geophysical Union (AGU)
Date: 02-2023
DOI: 10.1029/2022EF003328
Abstract: Flooding is one of the most devastating natural disasters causing significant economic losses. One of the dominant drivers of flood losses is heavy precipitation, with other contributing factors such as built environments and socio‐economic conditions superimposed to it. To better understand the risk profile associated with this hazard, we develop probabilistic models to quantify the future likelihood of fluvial flood‐related property damage exceeding a critical threshold (i.e., high property damage) at the state level across the conterminous United States. The model is conditioned on indicators representing heavy precipitation amount and frequency derived from observed and downscaled precipitation. The likelihood of high property damage is estimated from the conditional probability distribution of annual total property damage, which is derived from the joint probability of the property damage and heavy precipitation indicators. Our results indicate an increase in the probability of high property damage (i.e., exceedance of 70th percentile of observed annual property damage for each state) in the future. Higher probability of high property damage is projected to be clustered in the states across the western and south‐western United States, and parts of the U.S. Northwest and the northern Rockies and Plains. Depending on the state, the mean annual probability of high property damage in these regions could range from 38% to 80% and from 46% to 95% at the end of the century (2090s) under RCP4.5 and RCP8.5 scenarios, respectively. This is equivalent to 20%–40% increase in the probability compared to the historical period 1996–2005. Results show that uncertainty in the projected probability of high property damage ranges from 14% to 35% across the states. The spatio‐temporal variability of the uncertainty across the states and three future decades (i.e., 2050s, 2070s, and 2090s) exhibits nonstationarity, which is driven by the uncertainty associated with the probabilistic prediction models and climate change scenarios.
Bayesian calibration and uncertainty analysis of hydrological models: A comparison of adaptive Metropolis and sequential Monte Carlo samplers
Publisher: American Geophysical Union (AGU)
Date: 07-2011
DOI: 10.1029/2010WR010217
Comparing and Combining Remotely Sensed Land Surface Temperature Products for Improved Hydrological Applications
Publisher: MDPI AG
Date: 20-02-2016
DOI: 10.3390/RS8020162
A framework to quantify GCM uncertainties for use in impact assessment studies
Publisher: Elsevier BV
Date: 11-2014
Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach
Publisher: American Meteorological Society
Date: 04-2011
Abstract: This paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958–2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.
Probabilistic Estimation of Multivariate Streamflow Using Independent Component Analysis and Climate Information
Publisher: American Meteorological Society
Date: 12-2009
Abstract: A statistical estimation approach is presented and applied to multiple reservoir inflow series that form part of Sydney’s water supply system. The approach involves first identifying sources of interannual and interdecadal climate variability using a combination of correlation- and wavelet-based methods, then using this information to construct probabilistic, multivariate seasonal estimates using a method based on independent component analysis (ICA). The attraction of the ICA-based approach is that, by transforming the multivariate dataset into a set of independent time series, it is possible to maintain the parsimony of univariate statistical methods while ensuring that both the spatial and temporal dependencies are accurately captured. Based on a correlation analysis of the reservoir inflows with the original sea surface temperature anomaly data, the principal sources of variability in Sydney’s reservoir inflows appears to be a combination of the El Niño–Southern Oscillation (ENSO) phenomenon and the Pacific decadal oscillation (PDO). A multivariate ICA-based estimation model was then used to capture this variability, and it was shown that this approach performed well in maintaining the temporal dependence while also accurately maintaining the spatial dependencies that exist in the 11-dimensional historical reservoir inflow dataset.
Multi-objective assessment of three remote sensing vegetation products for streamflow prediction in a conceptual ecohydrological model
Publisher: Elsevier BV
Date: 12-2016
Implications for engineering design of shorter more extreme rainfalls and increased flood variability
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1224
Abstract: Increases in extreme rainfall intensities as a result of climate change pose a great risk due to the possible increases in pluvial flooding. But evidence is emerging that the observed increases in extreme rainfall are not resulting in universal increases in flooding. Here, we begin by presenting historical evidence for changes in extreme rainfalls and floods discussing the underlying mechanisms for the changes, before examining the implications of climate change projections on engineering design.Extreme rainfall is intensifying universally across the globe with more extreme events experiencing larger degrees of intensification. Simultaneously, and somewhat paradoxically, the magnitude of frequent floods (those expected to occur on average once per year) are in general decreasing, particularly in the tropical and arid regions of the world. We suggest this is likely due to the dominance of drying antecedent soil moisture conditions and shorter storm durations at higher temperatures offsetting any increases in rainfall intensity. However, for rare magnitude floods (those expected, on average, to occur less than once every twenty years) the increase in rainfall appears to outweigh any decrease in soil moisture or change in the temporal pattern of the storm.Climate model projections, downscaled through a continental scale water balance model and locally calibrated rainfall-runoff models, show that future projections of flood responses follow historical trends & #8211 with the rarer the flood, the more likely it is to be increasing. To deepen our understanding, we focus our analysis on event runoff coefficients as an indicator of future runoff changes. Across Australia we find runoff coefficients are projected to decrease, that is, reduced runoff resulting from the same amount of rainfall. These results indicate drier conditions and a compounding of the reduced average rainfall and drier conditions already being experiences in many arid parts of the world.With these historical changes and projections in mind we conclude with some insights and implications on how best to incorporate the additional uncertainty due to climate change when estimating floods for planning and design purposes. As floods constitute a large portion of the inflows into reservoirs, we suggest that future water resources planning will need to account for reduced runoff yields. To assess the potential impacts of future climate change for planning and design purposes we need to consider how changes to rainfall intensity vary with both storm duration and storm rarity, as well as how antecedent conditions influence the proportion of rainfall that appears as runoff. There remains significant work in adapting our current flood guidance to reflect these historical and projected changes.
Closure to “Comparison of Two Nonparametric Alternatives for Stochastic Generation of Monthly Rainfall” by R. Srikanthan, A. Sharma, and T. A. McMahon
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2007
Can Regional Climate Modeling Capture the Observed Changes in Spatial Organization of Extreme Storms at Higher Temperatures?
Publisher: American Geophysical Union (AGU)
Date: 12-05-2018
DOI: 10.1029/2018GL077716
Reduced spatial extent of extreme storms at higher temperatures
Publisher: American Geophysical Union (AGU)
Date: 25-04-2016
DOI: 10.1002/2016GL068509
Projected Changes in the Tibetan Plateau Snowpack Resulting From Rising Global Temperatures
Publisher: American Geophysical Union (AGU)
Date: 23-03-2022
DOI: 10.1029/2021JD036201
Abstract: The snowpack is a critical component of the hydrologic cycle in cold regions, the change in which becomes important for proper planning and management of water. The Tibetan Plateau provides significant amount of water to most Asian rivers, and consequently the downstream population is dependent on its availability. Despite its importance, potential change in snowpack in this region due to climate change is poorly understood to date, largely because of remoteness and the orographic complexity of the area. This study inspects the impact of climate change on snowpack change over the Tibetan Plateau considering historical simulations (1981–2004), near future projections (2041–2064), and far future projections (2071–2094) from global climate models (GCMs) and regional climate models (RCMs) of derived temperature, precipitation, and snow water equivalent (SWE). A multivariate nesting bias correction approach (MRNBC) was employed to correct possible biases in GCM and RCM derived temperature, precipitation, and SWE jointly over multiple time scales to preserve interdependencies among the variables while enabling simulation of year‐to‐year persistence, which is of importance in water security assessments. MRNBC reduced the bias in model simulations significantly and improved projections of the snow climatology. The results indicate that the annual maximum spell of snow‐free days will increase over the region whereas the snowy day fraction will decrease in the future compared to the historical period. In addition, annual SWE are noted to be decreasing in both the near future and far future with respect to historical averages. Changes in SWE will result from warming temperatures and also from changes in precipitation, which will lead to more rainfall than snowfall thus affecting snowmelt processes.
Estimating Radar Precipitation in Cold Climates: The role of Air Temperature within a Nonparametric Framework
Publisher: Copernicus GmbH
Date: 28-06-2018
Abstract: Abstract. In cold climates, the form of precipitation (snow or rain or a mixture of snow and rain) results in uncertainty in radar precipitation estimation. Estimation often proceeds without distinguishing the state of precipitation which is known to impact the radar reflectivity–precipitation relationship. In the present study, we investigate the use of air temperature within a nonparametric predictive framework to improve radar precipitation estimation for cold climates. Compared to radar reflectivity–gauge relationships, this approach uses gauge precipitation and air temperature observations to estimate radar precipitation. A nonparametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables, and gauge precipitation as an observed response using a k-nearest neighbour (k-nn) regression estimator. The relative importance of the two predictors is ascertained using an information theory-based rationale. Four years (2011–2015) of hourly radar precipitation rate from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 68 gauges, and gridded observational air temperature were used to formulate the predictive model and hence make our investigation possible. Gauged precipitation data were corrected for wind induced catch error before using them as true observed response. The predictive model with air temperature as an added covariate reduces root mean squared error (RMSE) by up to 15 % compared to the model that uses radar precipitation rate as the sole predictor. More than 80 % of gauge locations in the study area showed improvement with the new method. Further, the associated impact of air temperature became insignificant at more than 85 % of gauge locations when the temperature was above 10 degrees Celsius, which indicates that the partial dependence of precipitation on air temperature is most important for colder climates alone.
Correcting for systematic biases in multiple raw GCM variables across a range of timescales
Publisher: Elsevier BV
Date: 2015
Evaluating spatio-temporal representations in daily rainfall sequences from three stochastic multi-site weather generation approaches
Publisher: Elsevier BV
Date: 06-2009
Can annual streamflow volumes be characterised by flood events alone?
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10210
Abstract: Can total annual streamflow in any given year be largely characterised by a relatively small number of high flow events? A comprehensive assessment of this is of high value as there is evidence to suggest that as flood events increase in rarity a more consistent response between streamflow extremes and temperature increases can be established & #8212 providing greater reliability in projections of rare events. We propose here a novel methodology to characterise streamflow regimes in the context of total annual streamflow for water supply. Using the Australian Bureau of Meteorology& #8217 s Hydrologic Reference Station database, we developed annual event flow distributions that standardise the relationship between total annual streamflow and event flows. It was found that the annual event flow distributions are primarily a function of local climate and catchment size and were largely insensitive to interannual variability represented by the El Nino Southern Oscillation Index, mean annual temperature, or total annual rainfall volume. Statistically significant trends were found in the timeseries of annual event flow distribution values, signalling a move to a less even distribution in the southern latitudes and a more even distribution in the northern latitudes. Our results show that total annual streamflows can be characterised by a small number of high flow events. This suggests that for Australia& #8217 s most critical surface drinking water supply catchments the streamflow yields can be represented by changes in a few, high flow events, independent of interannual variability. As these relationships are non-stationary, they may provide a basis for understanding changes in water supply into the future.
Phase randomisation paper by Brunner et al.
Publisher: Copernicus GmbH
Date: 29-04-2019
A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability
Publisher: Elsevier BV
Date: 03-2007
Correction to “Effect of solar variability on atmospheric moisture storage”
Publisher: American Geophysical Union (AGU)
Date: 07-08-2009
DOI: 10.1029/2009GL039124
Assessment of global aridity change
Publisher: Elsevier BV
Date: 2015
A bulk queue model for the evaluation of impact of headway variations and passenger waiting behavior on public transit performance
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
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