ORCID Profile
0000-0003-0450-4292
Current Organisations
Macquarie University Faculty of Science and Engineering
,
Macquarie University
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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 | Water Resources Engineering | Surfacewater Hydrology | Natural Resource Management | Applied Statistics | Statistics | Knowledge Representation and Machine Learning | Surfacewater Hydrology
ANZSRC Socio-Economic Objective (SEO)
Water Allocation and Quantification | Ecosystem Assessment and Management of Fresh, Ground and Surface Water Environments | Environmental Management Systems | Land and water management | Land and water management | Expanding Knowledge in the Earth Sciences | Precious (Noble) Metal Ore Exploration | Mining Land and Water Management | Land and water management |
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
Using field data to inform and evaluate a new model of catchment hydrologic connectivity
Publisher: American Geophysical Union (AGU)
Date: 10-2013
DOI: 10.1002/WRCR.20546
Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networks
Publisher: Elsevier BV
Date: 04-2023
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.
Improving probabilistic monthly water quantity and quality predictions using a simplified residual-based modeling approach
Publisher: Elsevier BV
Date: 10-2022
Assessing the Potential Robustness of Conceptual Rainfall‐Runoff Models Under a Changing Climate
Publisher: American Geophysical Union (AGU)
Date: 07-2018
DOI: 10.1029/2018WR022636
Hydrologic multi-model ensemble predictions using variational Bayesian deep learning
Publisher: Elsevier BV
Date: 2022
Calibrating and assessing uncertainty in coastal numerical models
Publisher: Elsevier BV
Date: 07-2017
Insights on the impact of systematic model errors on data assimilation performance in changing catchments
Publisher: Elsevier BV
Date: 03-2018
Explaining changes in rainfall-runoff relationships during and after Australia's Millennium Drought: a community perspective
Publisher: Copernicus GmbH
Date: 20-04-2022
Abstract: Abstract. The Millennium Drought lasted more than a decade, and is notable for causing persistent shifts in the relationship between rainfall and runoff in many south-east Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents a range of possible process explanations of flow response, and then evaluates these hypotheses against available evidence. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (eg. why was the Millennium Drought different to previous droughts?) and spatially (eg. why did rainfall-runoff relationships shift in some catchments but not in others?). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including: declines in groundwater storage, reduced recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and interception of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change.
A Beta Regression Model for Improved Solar Radiation Predictions
Publisher: American Meteorological Society
Date: 08-2013
Abstract: Predicting global solar radiation is an integral part of much environmental modeling. There are several approaches for predicting global solar radiation at a site where no instrumentation exists. One popular approach uses the difference between daily high and low temperature, typically using a nonlinear equation to express the relationship between change in temperature and estimated global solar radiation. Additional variables are usually included in successive steps creating a hierarchy of analysis. The authors propose an alternative beta regression approach to modeling global solar radiation, allowing for the inclusion of multiple environmental predictor variables and strata into one flexible model. The model is applied to several case studies, and results are compared with recently proposed empirical solar radiation models. Beta regression provides a robust, flexible modeling approach for predicting global solar radiation that allows for the addition and removal of independent variables as appropriate and can be interpreted using standard inferential statistics. In addition, the beta regression model provides estimates of uncertainty that can be incorporated into subsequent models and calculations.
Investigating strategies to improve hydrologic model performance in a changing climate
Publisher: Elsevier BV
Date: 12-1212
A quantitative approach for integrating multiple lines of evidence for the evaluation of environmental health risks
Publisher: PeerJ
Date: 15-01-2015
DOI: 10.7717/PEERJ.730
Spatial Variation in Catchment Response to Climate Change Depends on Lateral Moisture Transport and Nutrient Dynamics
Publisher: American Geophysical Union (AGU)
Date: 10-2022
DOI: 10.1029/2021WR030577
Abstract: Future shifts in rainfall, temperature and carbon dioxide (CO 2 ) will impact hydrologic and ecosystem behavior. These changes are expected to vary in space because water and nutrient availability vary with terrain and soil properties, with feedbacks on vegetation and canopy adjustment. However, within‐basin patterns and spatial dependencies of ecohydrologic dynamics have often been ignored in future scenario modeling. We used a distributed process‐based ecohydrologic model, the Regional Hydro‐Ecological Simulation System, as a virtual catchment to examine spatial and temporal variability in climate change response. We found spatial heterogeneity in Leaf Area Index, transpiration and soil saturation trends, with some scenarios even showing opposite trends in different locations. For ex le, in a drying scenario, decreased vegetation productivity in water‐limited upslope areas enhanced downslope nutrient subsidies so that productivity increased in the nutrient‐limited riparian zone. In scenarios with both warming and rising CO 2 , lifying feedbacks between mineralization, vegetation water use efficiency and litter fall led to large increases in growth that were often strongest in the riparian area (depending on the coincident rainfall change). Modeled transpiration trends were determined by the competing effects of vegetation growth and changing water use efficiency. Overall, the riparian zone experienced substantially different (and even opposing) ecohydrologic trends compared to the rest of the catchment, which is important because productive riparian areas often contribute a disproportionate amount of vegetation growth, transpiration and nutrient consumption to catchment totals. Models that are spatially lumped, lack key ecosystem‐driving dynamics, or ignore lateral transport could misrepresent the complex ecohydrologic changes catchments could experience in the future.
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
Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network
Publisher: Elsevier BV
Date: 12-2021
Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric
Publisher: Elsevier BV
Date: 06-2018
It takes a community to raise a hydrologist: the Modular Curriculum for Hydrologic Advancement (MOCHA)
Publisher: Copernicus GmbH
Date: 16-09-2013
DOI: 10.5194/HESS-16-3405-2012
Abstract: Abstract. Protection from hydrological extremes and the sustainable supply of hydrological services in the presence of changing climate and lifestyles as well as rocketing population pressure in many parts of the world are the defining societal challenges for hydrology in the 21st century. A review of the existing literature shows that these challenges and their educational consequences for hydrology were foreseeable and were even predicted by some. However, surveys of the current educational basis for hydrology also clearly demonstrate that hydrology education is not yet ready to prepare students to deal with these challenges. We present our own vision of the necessary evolution of hydrology education, which we implemented in the Modular Curriculum for Hydrologic Advancement (MOCHA). The MOCHA project is directly aimed at developing a community-driven basis for hydrology education. In this paper we combine literature review, community survey, discussion and assessment to provide a holistic baseline for the future of hydrology education. The ultimate objective of our educational initiative is to enable educators to train a new generation of "renaissance hydrologists," who can master the holistic nature of our field and of the problems we encounter.
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.
Quantifying the Effects of Sea Level Rise on Estuarine Drainage Systems
Publisher: Wiley
Date: 24-11-2021
Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling
Publisher: Elsevier BV
Date: 12-2012
Quantifying the Effects of Sea Level Rise on Estuarine Drainage Systems
Publisher: Wiley
Date: 20-05-2022
A comparison of methods for discretizing continuous variables in Bayesian Networks
Publisher: Elsevier BV
Date: 10-2018
The influence of data transformations in simulating Total Suspended Solids using Bayesian inference
Publisher: Elsevier BV
Date: 11-2019
International capacity building to achieve SDG6: insights from longitudinal analysis of five water operator partnerships
Publisher: Informa UK Limited
Date: 22-09-2022
Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest
Publisher: Informa UK Limited
Date: 23-07-2013
Modelling precipitation uncertainties in a multi-objective Bayesian ecohydrological setting
Publisher: Elsevier BV
Date: 2019
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
Linking Changes in Land Cover and Land Use of the Lower Mekong Basin to Instream Nitrate and Total Suspended Solids Variations
Publisher: MDPI AG
Date: 08-04-2020
DOI: 10.3390/SU12072992
Abstract: Population growth and economic development are driving changes in land use/land cover (LULC) of the transboundary Lower Mekong River Basin (LMB), posing a serious threat to the integrity of the river system. Using data collected on a monthly basis over 30 years (1985–2015) at 14 stations located along the Lower Mekong river, this study explores whether spatiotemporal relationships exist between LULC changes and instream concentrations of total suspended solids (TSS) and nitrate—as proxies of water quality. The results show seasonal influences where temporal patterns of instream TSS and nitrate concentrations mirror patterns detected for discharge. Changes in LULC influenced instream TSS and nitrate levels differently over time and space. The seasonal Mann–Kendall (SMK) confirmed significant reduction of instream TSS concentrations at six stations (p 0.05), while nitrate levels increased at five stations (p 0.05), predominantly in stations located in the upper section of the basin where forest areas and mountainous topography dominate the landscape. Temporal correlation analyses point to the conversion of grassland (r = −0.61, p 0.01) to paddy fields (r = 0.63, p 0.01) and urban areas (r = 0.44, p 0.05) as the changes in LULC that mostly impact instream nitrate contents. The reduction of TSS appears influenced by increased forest land cover (r = −0.72, p 0.01) and by the development and operation of hydropower projects in the upper Mekong River. Spatial correlation analyses showed positive associations between forest land cover and instream concentrations of TSS (r = 0.64, p = 0.01) and nitrate (r = 0.54, p 0.05), indicating that this type of LULC was heavily disturbed and harvested, resulting in soil erosion and runoff of nitrate rich sediment during the Wet season. Our results show that enhanced understanding of how LULC changes influence instream water quality at spatial and temporal scales is vital for assessing potential impacts of future land and water resource development on freshwater resources of the LMB.
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.
Volatilization of Potentially Harmful Trace Elements in Coal Pyrite during Heat Treatment in a Tube Furnace at 573–1473 K
Publisher: American Chemical Society (ACS)
Date: 15-08-2023
Verifying model performance using validation of Pareto solutions
Publisher: Elsevier BV
Date: 06-2023
Landscape changes and their hydrologic effects: Interactions and feedbacks across scales
Publisher: Elsevier BV
Date: 2021
Thermal transformation of coal pyrite with different structural types during heat treatment in air at 573–1473 K
Publisher: Elsevier BV
Date: 11-2022
Taking the pulse of hydrology education
Publisher: Wiley
Date: 2007
DOI: 10.1002/HYP.6766
ESTIMATING THERMAL REGIMES OF BULL TROUT AND ASSESSING THE POTENTIAL EFFECTS OF CLIMATE WARMING ON CRITICAL HABITATS
Publisher: Wiley
Date: 08-01-2013
DOI: 10.1002/RRA.2638
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
Quantifying the Effects of Sea Level Rise on Estuarine Drainage Systems
Publisher: American Geophysical Union (AGU)
Date: 06-2022
DOI: 10.1029/2021WR031405
Abstract: Much of the development of the low elevation coastal zone has involved the reclamation of low‐lying floodplains and wetlands through the construction of flood mitigation and drainage systems. These systems function throughout the tidal range, protecting from high tides while draining excess catchment flows to the low tide. However, drainage can only be achieved under gravity when water levels in the catchment drains are higher than those in the estuary. Changes to the tidal range and to the duration of the rising and falling tides that occur throughout estuarine waters will result in dynamic variations in the window of opportunity for gravity discharge within and between different catchments and under sea level rise (SLR). Existing concerns regarding SLR impacts have focused on the acute effects of higher water levels, but SLR will affect the full tidal range, and drainage systems will be particularly vulnerable to changes in the low tide. This study introduces the concept of the drainage window to address this limitation by assessing how the present‐day and future SLR tidal regimes may influence the drainage of different estuarine floodplains. Applying the drainage window to two different estuaries indicated that SLR may substantially reduce the opportunity for discharging many estuarine floodplain drainage systems. Reduced drainage creates a host of chronic problems that may necessitate changes to existing land uses. A holistic assessment of future changes to all water levels (including low tide levels and extended flood recession periods) is required to inform strategic land use planning and estuarine management.
Which Rainfall Errors Can Hydrologic Models Handle? Implications for Using Satellite‐Derived Products in Sparsely Gauged Catchments
Publisher: American Geophysical Union (AGU)
Date: 08-2022
DOI: 10.1029/2020WR029331
Abstract: There is interest in applying satellite‐derived rainfall products for water management in data‐sparse areas. However, questions remain around how uncertainties in different products interact with hydrologic models to determine simulation skill. Most related work uses performance statistics that inherently combine rainfall magnitude, timing and persistence, making it unclear which product improvements should be prioritized. We applied six satellite‐derived rainfall products in a conceptual hydrologic model (GR4J) across four Australian catchments with dense gauge data for comparison. We found that GR4J's inherent flexibility allowed it to filter errors in rainfall magnitude and variance through parameterization. Therefore, when rainfall observations for bias correction are unavailable, calibration of a flexible model could prove a useful alternative. However, the model was less able to compensate for errors in rainfall occurrence. In fact, the Probability of Detection score explained 59% of the variance in calibration performance (26% for validation), while overall bias explained just 14% (8% for validation). All products underestimated rainfall state persistence, but this had less influence on model skill. We then removed gauges from the observed data set to mimic data sparsity, finding that even a few gauges could reproduce rainfall occurrence and outperform satellite‐derived products. Two data‐sparse catchments in Vietnam were modeled to check whether the same learnings applied. The gauge data also performed best in Vietnam, and performance of most satellite‐derived products was comparable to the Australian case. Efforts to increase the spatial and temporal resolution of satellite observations, which could improve rainfall detection, will enhance satellite‐derived precipitation for hydrologic modeling.
A watershed-scale assessment of a process soil CO2production and efflux model
Publisher: American Geophysical Union (AGU)
Date: 28-05-2011
DOI: 10.1029/2010WR009941
Modeling Water Quality in Watersheds: From Here to the Next Generation
Publisher: American Geophysical Union (AGU)
Date: 11-2020
DOI: 10.1029/2020WR027721
Approximate Bayesian computation and Bayes' linear analysis: Toward high- dimensional ABC
Publisher: Informa UK Limited
Date: 02-01-2014
Modelling and understanding the hierarchy in a mixture of experts using multiple catchment descriptors
Publisher: Elsevier BV
Date: 12-2013
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
Characterising uncertainty in precipitation downscaling using a Bayesian approach
Publisher: Elsevier BV
Date: 07-2019
Daily time series of river water levels derived from a seasonal linear model using multisource satellite products under uncertainty
Publisher: Elsevier BV
Date: 11-2021
A coupled metabolic‐hydraulic model and calibration scheme for estimating whole‐river metabolism during dynamic flow conditions
Publisher: Wiley
Date: 28-09-2017
DOI: 10.1002/LOM3.10204
A conceptual model for simulating streamflow in a changing snow-covered catchment: application to the data-sparse upper Brahmaputra River basin
Publisher: Informa UK Limited
Date: 10-08-2022
Spatial Heterogeneity of Snow Density and Its Influence on Snow Water Equivalence Estimates in a Large Mountainous Basin
Publisher: MDPI AG
Date: 12-01-2016
Landscape structure and climate influences on hydrologic response
Publisher: American Geophysical Union (AGU)
Date: 12-2011
DOI: 10.1029/2011WR011161
Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada
Publisher: Springer Science and Business Media LLC
Date: 05-09-2017
Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management
Publisher: Elsevier BV
Date: 11-2012
DOI: 10.1016/J.SCITOTENV.2012.07.059
Abstract: One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.
Transboundary river basins: Scenarios of hydropower development and operation under extreme climate conditions
Publisher: Elsevier BV
Date: 2022
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.
Mixtures of experts for understanding model discrepancy in dynamic computer models
Publisher: Elsevier BV
Date: 03-2014
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
Explaining changes in rainfall–runoff relationships during and after Australia's Millennium Drought: a community perspective
Publisher: Copernicus GmbH
Date: 06-12-2022
DOI: 10.5194/HESS-26-6073-2022
Abstract: Abstract. The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and harvesting of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change.
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.
A metric for attributing variability in modelled streamflows
Publisher: Elsevier BV
Date: 10-2016
Using 3D robust smoothing to fill land surface temperature gaps at the continental scale
Publisher: Elsevier BV
Date: 10-2019
Modeling the catchment via mixtures: Issues of model specification and validation
Publisher: American Geophysical Union (AGU)
Date: 11-2006
DOI: 10.1029/2005WR004613
Creativity, Uncertainty, and Automated Model Building
Publisher: Wiley
Date: 03-07-2017
DOI: 10.1111/GWAT.12552
Functional models for longitudinal data with covariate dependent smoothness
Publisher: Institute of Mathematical Statistics
Date: 2016
DOI: 10.1214/16-EJS1113
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.
A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data
Publisher: Elsevier BV
Date: 06-2018
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.
Fire and flood expand the floodplain shifting habitat mosaic concept
Publisher: University of Chicago Press
Date: 12-2015
DOI: 10.1086/684016
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.
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).
Can examining past variability help us understand catchment response to future climate change?
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-3319
Abstract: & & It is common to test hydrologic models under contrasting historical periods as an indicator of likely performance under climate change. For ex le, a model calibrated under average conditions may be tested under increasingly dry subsets of the observational record. Any decline in performance as the testing conditions deviate further from the calibration conditions is then assumed to represent likely performance degradation under climate change scenarios with comparable rainfall decreases. Many studies have inherently applied the assumption that past rainfall variability can be used as a proxy for future climate change, but the analogy may be flawed for three main reasons:& & & ul& & li& Due to lagged hydrologic response to meteorological shifts, catchment behaviour under long-term wetting or drying may not be fully represented over shorter wet or dry periods.& /li& & li& Subsets of the past record selected based on rainfall are unlikely to reflect future temperature increases.& /li& & li& Past observations do not include expected increases in carbon dioxide levels.& /li& & /ul& & & If any of these factors substantially impacts catchment response, subsets of the historical record with equivalent rainfall will not be accurate proxies for future climate scenarios. We tested the impact of each factor using the ecohydrologic model RHESSys. RHESSys dynamically simulates vegetation growth, subsurface flow and nutrient cycling and is thus able to capture the key processes that could drive nonstationary catchment response in the future. We found that all three future climate factors (rainfall change persistence, temperature, and carbon dioxide) altered catchment response substantially, especially for drier future scenarios. For our study catchment, persistence of dry conditions over many decades led to different subsurface water storage levels than the same rainfall experienced over shorter timeframes, leading to different streamflow. The impacts of increased temperature and carbon dioxide concentrations on vegetation further altered runoff behaviour. This means that long-term climate change effects will not necessarily emerge over short historical periods with equivalent rainfall. In our ex le, ignoring persistence in rainfall changes, rising temperatures, and higher carbon dioxide levels could lead us to underestimate model performance degradation in terms of Nash-Sutcliffe efficiency by as much as 0.41. Therefore, the uncertainty introduced in hydrologic models by future climate change has probably been underestimated in the current literature.& &
Quantifying watershed sensitivity to spatially variable N loading and the relative importance of watershed N retention mechanisms
Publisher: American Geophysical Union (AGU)
Date: 08-2011
DOI: 10.1029/2010WR009738
Tools for investigating the prior distribution in Bayesian hydrology
Publisher: Elsevier BV
Date: 07-2016
Global distribution and epidemiologic associations of escherichia coli clonal group A, 1998-2007
Publisher: Centers for Disease Control and Prevention (CDC)
Date: 11-2011
Abstract: Escherichia coli clonal group A (CGA) was first reported in 2001 as an emerging multidrug-resistant extraintestinal pathogen. Because CGA has considerable implications for public health, we examined the trends of its global distribution, clinical associations, and temporal prevalence for the years 1998-2007. We characterized 2,210 E. coli extraintestinal clinical isolates from 32 centers on 6 continents by CGA status for comparison with trimethoprim/sulfamethoxazole (TMP/SMZ) phenotype, specimen type, inpatient/outpatient source, and adult/child host we adjusted for clustering by center. CGA prevalence varied greatly by center and continent, was strongly associated with TMP/SMZ resistance but not with other epidemiologic variables, and exhibited no temporal prevalence trend. Our findings indicate that CGA is a prominent, primarily TMP/SMZ-resistant extraintestinal pathogen concentrated within the Western world, with considerable pathogenic versatility. The stable prevalence of CGA over time suggests full emergence by the late 1990s, followed by variable endemicity worldwide as an antimicrobial drug-resistant public health threat.
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.
Quantifying input error in hydrologic modeling using the Bayesian error analysis with reordering (BEAR) approach
Publisher: Elsevier BV
Date: 07-2021
Implications of future climate change for event-based hydrologic models
Publisher: Elsevier BV
Date: 09-2018
Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework
Publisher: Elsevier BV
Date: 06-2010
The ensemble Kalman filter is an ABC algorithm
Publisher: Springer Science and Business Media LLC
Date: 23-11-2011
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.
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.& &
Adoption of cropping sequences in northeast Montana: A spatio-temporal analysis
Publisher: Elsevier BV
Date: 12-2014
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.& &
Simulation of streamflow and instream loads of total suspended solids and nitrate in a large transboundary river basin using Source model and geospatial analysis
Publisher: Elsevier BV
Date: 11-2020
Modelling climate change impacts on the Brahmaputra streamflow resulting from changes in snowpack attributes
Publisher: Elsevier BV
Date: 12-2021
Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation
Publisher: Elsevier BV
Date: 08-2016
Typecasting catchments: Classification, directionality, and the pursuit of universality
Publisher: Elsevier BV
Date: 02-2018
Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective
Publisher: Wiley
Date: 06-2022
DOI: 10.1002/HYP.14596
Abstract: Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co‐creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high‐dimensional mapping, while remaining faithful to process‐based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.
Diagnostic calibration and cross-catchment transferability of a simple process-consistent hydrologic model
Publisher: Wiley
Date: 30-08-2016
DOI: 10.1002/HYP.10955
Transboundary river catchment areas of developing countries: Potential and limitations of watershed models for the simulation of sediment and nutrient loads. A review
Publisher: Elsevier BV
Date: 08-2019
Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2018WR022627
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.
A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling
Publisher: American Geophysical Union (AGU)
Date: 02-2004
DOI: 10.1029/2003WR002378
Hydrological model selection: A Bayesian alternative
Publisher: American Geophysical Union (AGU)
Date: 10-2005
DOI: 10.1029/2004WR003719
Modeling residual hydrologic errors with Bayesian inference
Publisher: Elsevier BV
Date: 09-2015
Investigating controls on the thermal sensitivity of Pennsylvania streams
Publisher: Wiley
Date: 27-07-2011
DOI: 10.1002/HYP.8186
A novel approach for deriving river discharge using passive microwaves
Publisher: Wiley
Date: 28-09-2022
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
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
Water Sensitive Urban Design (WSUD) Spatial Prioritisation through Global Sensitivity Analysis for Effective Urban Pluvial Flood Mitigation
Publisher: Elsevier BV
Date: 05-2023
Generalized likelihood uncertainty estimation (GLUE) and approximate Bayesian computation: What's the connection?
Publisher: American Geophysical Union (AGU)
Date: 12-2012
DOI: 10.1029/2011WR011128
Bayesian Networks in coastal engineering: Distinguishing descriptive and predictive applications
Publisher: Elsevier BV
Date: 05-2018
Is Past Variability a Suitable Proxy for Future Change? A Virtual Catchment Experiment
Publisher: American Geophysical Union (AGU)
Date: 02-2020
DOI: 10.1029/2019WR026275
Abstract: To estimate the robustness of hydrologic models under projected future climate change, researchers test transferability between climatically contrasting observed periods. This approach can only assess the performance changes induced by altered precipitation and related environmental dynamics (e.g., greening under wet conditions), since the instrumental record does not contain temperatures or carbon dioxide levels that are similar to future climate change projections. Additionally, there is an inherent assumption that long‐term persistence of changes in precipitation will not further impact catchment response. In this study, we undertake a series of virtual catchment experiments using an ecohydrologic model that simulates dynamic vegetation growth, nutrient cycling, and subsurface hydrology. These experiments explore a number of climate change scenarios. We compare simulations based on persistent altered climate states against simulations designed to represent historical periods with the same precipitation but limited time for ecohydrologic adaptation. We find that persistence of precipitation changes as well as increased temperature and elevated carbon dioxide levels can all substantially impact streamflow under drier future conditions. For wetter future scenarios, simulated differences in the flow regime were smaller, but there was still notable ergence in modeled low flows and other hydrologic variables. The results suggest that historical periods with equivalent precipitation statistics cannot necessarily be used as proxies for future climate change when examining catchment runoff response and/or model performance. The current literature likely underestimates the potential for nonstationarity in hydrologic assessments, especially for drier future scenarios.
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.
Hydrologic connectivity between landscapes and streams: Transferring reach- and plot-scale understanding to the catchment scale
Publisher: American Geophysical Union (AGU)
Date: 04-2009
DOI: 10.1029/2008WR007225
Does accounting for state uncertainty improve hydrologic model predictions?
Publisher: Wiley
Date: 16-09-2020
Revisiting Pan Evaporation Trends in Australia a Decade on
Publisher: American Geophysical Union (AGU)
Date: 27-10-2018
DOI: 10.1029/2018GL079332
Abstract: Decreases in pan evaporation ( E pan ) have been reported around the world despite increasing air temperatures this was attributed to reductions in wind speed and solar radiation. Using 42 years (1975–2016) of Australian E pan data, we reexamined E pan trends, adding over a decade of observations to previous analyses. Flexible local linear regression models showed that many previously reported decreasing E pan trends have plateaued or reversed. Attribution analysis confirmed that 1975–1994 E pan decreases in southern/western Australia were chiefly driven by decreasing wind speeds. Increasing vapor pressure deficit subsequently became dominant, resulting in 1994–2016 E pan increases. Climate trend analyses should consider applying flexible statistical models to qualitatively understand temporal dynamics, complementing linear models that are able to provide quantitative assessments, especially when multiple drivers are involved.
Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework
Publisher: Wiley
Date: 2007
DOI: 10.1002/HYP.6294
Calibrating hydrologic models in flow-corrected time
Publisher: American Geophysical Union (AGU)
Date: 2014
DOI: 10.1002/2013WR014635
Related Organisations
Macquarie University Faculty Of Science And Engineering
Location: Australia
Related Funding Activities
Discovery Projects - Grant ID: DP0879218
Start Date: 03-2008
End Date: 03-2012
Amount: $355,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP170103959
Start Date: 2017
End Date: 12-2022
Amount: $244,988.00
Funder: Australian Research Council
View Funded ActivityARC Future Fellowships - Grant ID: FT120100269
Start Date: 06-2013
End Date: 04-2019
Amount: $574,428.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC190100031
Start Date: 08-2020
End Date: 08-2025
Amount: $3,973,202.00
Funder: Australian Research Council
View Funded Activity