ORCID Profile
0000-0002-5335-6209
Current Organisation
University of Melbourne
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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)
Environmental Monitoring | Surfacewater Hydrology | Environmental Science and Management | Physical Geography and Environmental Geoscience | Natural Resource Management | Agricultural Spatial Analysis and Modelling | Water Resources Engineering | Photogrammetry and Remote Sensing | Environmental Engineering Modelling | Civil Engineering | Environmental Engineering Modelling | Geomatic Engineering | Farm Management, Rural Management And Agribusiness | Meteorology | Surfacewater Hydrology | Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.) |
ANZSRC Socio-Economic Objective (SEO)
Weather | Climate Change Mitigation Strategies | Field crops | Climate Variability (excl. Social Impacts) | Management of Greenhouse Gas Emissions from Animal Production | Land and water management | Climate change | Management of Water Consumption by Plant Production | Natural Hazards in Fresh, Ground and Surface Water Environments | Management of Greenhouse Gas Emissions from Plant Production | Control of Plant Pests, Diseases and Exotic Species in Farmland, Arable Cropland and Permanent Cropland Environments | Physical and Chemical Conditions of Water in Fresh, Ground and Surface Water Environments (excl. Urban and Industrial Use) | Farmland, Arable Cropland and Permanent Cropland Water Management | Rural Water Evaluation (incl. Water Quality)
Related Links
Publications
A data-based predictive model for spatiotemporal variability in stream water quality
Publisher: Copernicus GmbH
Date: 24-02-2020
Abstract: Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit) and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.
Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia
Publisher: Elsevier BV
Date: 07-2013
Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing
Publisher: Wiley
Date: 06-01-2022
DOI: 10.1002/JOC.7515
Abstract: Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend‐aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30‐year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30‐year trends is to robustly represent long‐term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large s ling variability. Calibration is applied to 20‐year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day‐of‐year climatology, raw week‐1 forecasts reproduce temperature trends of the 20‐year observations in many regions while raw week‐4 forecasts do not exhibit the 20‐year observed trends. After trend‐aware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week‐1 forecasts show apparent trends consistent with the 20‐year observations, as the calibration transfers forecast skill and embeds the 20‐year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week‐4 forecasts exhibit the 30‐year observed trends, as the calibration reverts the forecasts to the 30‐year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend‐aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend‐aware method can be applied to deliver high‐quality subseasonal forecasts and support decision‐making in a changing climate.
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.
Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence
Publisher: MDPI AG
Date: 09-10-2019
DOI: 10.3390/RS11202336
Abstract: The soil chronosequence is a useful method for investigating pedological theories. Soil chemical, physical and mineralogical properties in chronosequences change over time and exhibit systematic and time-dependent trends, which can be used to analyze the rates and directions of pedogenic changes. The potential of soil spectroscopy as an emerging, rapid and cost-effective technique for predicting soil properties has been widely accepted and has motivated the application of spectroscopic techniques to the analysis of soil chronosequence. We present a soil chronosequence derived from 1000-year-old calcareous marine sediments and examine changes in six soil properties over this period. We evaluated the utility of a soil spectroscopic method to detect soil property changes and to predict the pedogenic properties and soil ages of the chronosequence. The results show that some soil pedogenic processes, such as soil organic matter accumulation, CaCO3 leaching and clay migration, can be identified in the millennium chronosequence. Power chronofunctions are formulated for soil organic matter (SOM) and Logarithmic chronofunctions are fitted for clay, CaCO3 and pH. These pedogenic processes are identified in the reflectance intensity and absorption features of soil spectroscopy, and pedogenic properties can be calibrated via soil reflectance spectroscopy. Profile ages can also be predicted via pseudo multi-depth spectra of soil profiles, and soil spectral curves for 0–30 cm generated the best prediction results (RPD = 1.85). We conclude that soil properties, changing due to weathering and soil formation, act as a bridge linking spectroscopy and weathering levels edogenic processes. The results imply that applying spectroscopy techniques to chronosequence study and mapping the degree of soil development in certain areas should be possible.
Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems
Publisher: MDPI AG
Date: 05-03-2022
DOI: 10.3390/RS14051286
Abstract: Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
Calibration of an impedance probe for estimation of surface soil water content over large regions
Publisher: Elsevier BV
Date: 09-2005
Upscaling sparse ground‐based soil moisture observations for the validation of coarse‐resolution satellite soil moisture products
Publisher: American Geophysical Union (AGU)
Date: 19-04-2012
DOI: 10.1029/2011RG000372
Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0
Publisher: Copernicus GmbH
Date: 11-08-2020
DOI: 10.5194/GMD-2020-241
Abstract: Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5 (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding these crop functional types (CFT) to the list of actively managed crops in CLM5 (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is a common agricultural management technique in humid and sub-humid regions. We compared simulation results with field data and found that both the parameterization of the CFTs, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes, leaf area index (LAI), net ecosystem exchange (RMSE reduction for latent and sensible heat by up to 57 % and 59 % respectively) and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI curve and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). We anticipate that our model modifications offer opportunities to improve yield predictions, to study the effects of large-scale cover cropping on energy fluxes, soil carbon and nitrogen pools, and soil water storage in future studies with CLM5.
Comparison of KOMPSAT-5 and Sentinel-1 Radar Data for Soil Moisture Estimations Using a New Semi-Empirical Model
Publisher: MDPI AG
Date: 19-08-2022
DOI: 10.3390/RS14164042
Abstract: X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm3/cm3 and 0.032 cm3/cm3 at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.
Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery
Publisher: MDPI AG
Date: 18-02-2022
DOI: 10.3390/RS14040997
Abstract: Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training s le pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort.
A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments
Publisher: Elsevier BV
Date: 11-2021
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.
Upscaling of field-scale soil moisture measurements using distributed land surface modeling
Publisher: Elsevier BV
Date: 2005
A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data
Publisher: MDPI AG
Date: 31-12-2020
DOI: 10.3390/RS13010122
Abstract: The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R2) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R2 can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.
Drift of Earth's Pole Confirms Groundwater Depletion as a Significant Contributor to Global Sea Level Rise 1993–2010
Publisher: American Geophysical Union (AGU)
Date: 15-06-2023
DOI: 10.1029/2023GL103509
Abstract: Climate model estimates show significant groundwater depletion during the 20th century, consistent with global mean sea level (GMSL) budget analysis. However, prior to the Argo float era, in the early 2000’s, there is little information about steric sea level contributions to GMSL, making the role of groundwater depletion in this period less certain. We show that a useful constraint is found in observed polar motion (PM). In the period 1993–2010, we find that predicted PM excitation trends estimated from various sources of surface mass loads and the estimated glacial isostatic adjustment agree very well with the observed. Among many contributors to the PM excitation trend, groundwater storage changes are estimated to be the second largest (4.36 cm/yr) toward 64.16°E. Neglecting groundwater effects, the predicted trend differs significantly from the observed. PM observations may also provide a tool for studying historical continental scale water storage variations.
An airborne simulation of the SMAP data stream
Publisher: IEEE
Date: 07-2012
Key Factors Affecting Temporal Variability in Stream Water Quality
Publisher: American Geophysical Union (AGU)
Date: 2019
DOI: 10.1029/2018WR023370
Abstract: Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NO x ), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NO x and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.
Assessing Near‐Surface Soil Moisture Assimilation Impacts on Modeled Root‐Zone Moisture for an Australian Agricultural Landscape
Publisher: Wiley
Date: 31-10-2014
Evaluation of post-retrieval de-noising of active and passive microwave satellite soil moisture
Publisher: Elsevier BV
Date: 06-2015
Soil Salinity Impacts on L-Band Remote Sensing of Soil Moisture
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
Sensitivity of multi-parameter soil moisture retrievals to incidence angle configuration
Publisher: Elsevier BV
Date: 03-2014
Beyond triple collocation: Applications to soil moisture monitoring
Publisher: American Geophysical Union (AGU)
Date: 10-06-2014
DOI: 10.1002/2013JD021043
Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery
Publisher: MDPI AG
Date: 04-2021
DOI: 10.3390/IJGI10040211
Abstract: There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) res led at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.
Supplementary material to Seasonal crop yield prediction with SEAS5 long-range meteorological forecasts in a land surface modelling approach
Publisher: Copernicus GmbH
Date: 27-02-2023
The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0
Publisher: Copernicus GmbH
Date: 28-01-2021
Abstract: Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5 (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5 and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
Quantifying the contribution of biophysical and environmental factors in uncertainty of modeling canopy conductance
Publisher: Elsevier BV
Date: 2021
Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections
Publisher: American Geophysical Union (AGU)
Date: 05-2020
DOI: 10.1029/2019WR025286
Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard
Publisher: MDPI AG
Date: 14-07-2021
DOI: 10.3390/RS13142775
Abstract: Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing c aigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.
Estimating Annual Water Storage Variations Using Microwave-based Soil Moisture Retrievals
Publisher: Copernicus GmbH
Date: 21-11-2016
Abstract: Abstract. Due to their shallow vertical support, remotely-sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (S). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying annual variations in S via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2,000–10,000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain significant information concerning relative inter-annual variations in S. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing to enhance the spatial resolution of S estimates acquired from gravity remote sensing. However, challenging calibration issues regarding the relationship between S and surface soil moisture must be resolved before the approach can be used for absolute water budget closure.
An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting
Publisher: Elsevier BV
Date: 11-2014
Reply to comment by H. Vereecken et al. on “Field observations of soil moisture variability across scales”
Publisher: American Geophysical Union (AGU)
Date: 12-2008
DOI: 10.1029/2008WR007323
What Are the Key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality?
Publisher: American Geophysical Union (AGU)
Date: 10-2018
DOI: 10.1029/2017WR022172
Abstract: This study uses water‐quality data collected over 20 years, from 102 predominantly rural sites across Victoria, Australia, to further our understanding of spatial variability in riverine water quality. We focus on concentrations of total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate/nitrite (NO x ), and electrical conductivity. We used an exhaustive search approach to identify the linear models that best link catchment characteristics to time‐averaged constituent concentrations. We ran additional analyses to (1) assess the performance of these models under drought conditions, and (2) understand the key drivers of site‐level variability (standard deviations) of constituent concentrations. Natural catchment characteristics appear to have a greater effect on spatial differences in average constituent concentrations. Performance of the statistical models of time‐averaged constituent concentrations varied, and spatial variability in mean electrical conductivity levels could be more readily explained by catchment characteristics compared to more reactive nutrients. Notwithstanding, the models performed relatively well under varying hydrologic conditions for most constituents. As such, these models provide an insight into the key factors affecting spatial variability in average stream water‐quality conditions. We also identified that hydrologic, climatic, and topographic characteristics of the catchment helped explain the spatial variability in temporal changes in constituents. After calibration and validation, these models of both average water quality and variability in water quality could be used to forecast stream water‐quality responses to future land use, climate, or soil and land management changes.
L‐band microwave observations over land surface using a two‐dimensional synthetic aperture radiometer
Publisher: American Geophysical Union (AGU)
Date: 07-2007
DOI: 10.1029/2007GL030098
Partitioning of Precipitation Into Terrestrial Water Balance Components Under a Drying Climate
Publisher: American Geophysical Union (AGU)
Date: 05-2023
DOI: 10.1029/2022WR033538
Abstract: To accurately project future water availability under a drying climate, it is important to understand how precipitation is partitioned into other terrestrial water balance components, such as fluxes (evaporation, transpiration, runoff) and changes in storage (soil moisture, groundwater). Many studies have reported unexpected large runoff reductions during drought, particularly for multi‐year events, and some studies report a persistent change in partitioning even after the meteorological drought has ended. This study focused on understanding how actual evapotranspiration (AET) and change in subsurface storage (Δ S ) respond to climate variability and change, examining Australia's Millennium Drought (MD, 1997–2009). The study initially conducted a catchment‐scale water balance analysis to investigate interactions between Δ S and AET. Then the water balance analysis was extended to regional scale to investigate Δ S using interpolated rainfall and discharge with remotely sensed AET. Lastly, we evaluated conceptual rainfall‐runoff model performance of two commonly used models against these water balance estimates. The evaluation of water‐balance‐derived Δ S against Gravity Recovery and Climate Experiment (GRACE) estimates shows a significant multiyear storage decline however, with different rates. In contrast, AET rates (annualized) remained approximately constant before and during the MD, contrasting with some reports of evapotranspiration enhancement elsewhere. Overall, given AET remained approximately constant, drought‐induced precipitation reductions were partitioned into Δ S and streamflow. The employed conceptual rainfall‐runoff models failed to realistically represent AET during the MD, suggesting the need for improved conceptualization of processes. This study provides useful implications for explaining future hydrological changes if similar AET behavior is observed under a drying climate.
Soil Moisture Retrieval Using an L-Band Synthetic Aperture Radiometer During the Soil Moisture Experiments 2003 (SMEX03) and 2004 (SMEX04)
Publisher: IEEE
Date: 2008
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
Publisher: Copernicus GmbH
Date: 29-08-2023
DOI: 10.5194/HESS-27-3143-2023
Abstract: Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.
Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes
Publisher: Copernicus GmbH
Date: 09-04-2015
DOI: 10.5194/HESS-19-1659-2015
Abstract: Abstract. Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large ( 40 000 km2) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation by explicitly correcting bias in soil moisture and streamflow in the ensemble generation process, and for seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided a more accurate streamflow prediction (Nash–Sutcliffe efficiency, NSE = 0.77) than the lumped model (NSE = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments (two of them with NSE below 0.3). After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24%, respectively the false alarm ratio was reduced by 9% in both cases the peak volume error was reduced by 58 and 1%, respectively the ensemble skill was improved (evidenced by 12 and 13% reductions in the continuous ranked probability scores, respectively) and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed satellite SM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving some characteristics of the streamflow ensemble prediction however, the updated prediction is still poor since SM-DA does not address the systematic errors found in the model prior to assimilation.
A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments
Publisher: Copernicus GmbH
Date: 20-05-2021
DOI: 10.5194/HESS-25-2663-2021
Abstract: Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of (1) identifying the influential environmental drivers of temporal variation in flow event concentrations and (2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g. runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations and between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.
Dual forcing and state correction via soil moisture assimilation for improved rainfall-runoff modeling
Publisher: American Meteorological Society
Date: 25-09-2014
Abstract: Uncertainties in precipitation forcing and prestorm soil moisture states represent important sources of error in streamflow predictions obtained from a hydrologic model. An earlier synthetic twin experiment has demonstrated that error in both antecedent soil moisture states and rainfall forcing can be filtered by assimilating remotely sensed surface soil moisture retrievals. This opens up the possibility of applying satellite soil moisture estimates to address both key sources of error in hydrologic model predictions. Here, in an attempt to extend the synthetic analysis into a real-data environment, two satellite-based surface soil moisture products—based on both passive and active microwave remote sensing—are assimilated using the same dual forcing/state correction approach. A bias correction scheme is implemented to remove bias in background forecasts caused by synthetic perturbations in the ensemble filtering routines, and a triple collocation–based technique is adopted to derive rescaled observations and observation error variances. Results are largely in agreement with the earlier synthetic analysis. That is, the correction of satellite-derived rainfall forcing is able to improve streamflow prediction, especially during relatively high-flow periods. In contrast, prestorm soil moisture state correction is more efficient in improving the base flow component of streamflow. When rainfall and soil moisture state corrections are combined, the RMSE of both the high- and low-flow components of streamflow can be reduced by ~40% and ~30%, respectively. However, an unresolved issue is that soil moisture data assimilation also leads to underprediction of very intense precipitation/high-flow events.
Using short-term ensemble weather forecast to evaluate outcomes of irrigation
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4210
Abstract: & & Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving water productivity by at least 10%. In practice, scheduling decisions are often needed several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs such as soil water, weather and crop conditions. Since each input can be subject to its own uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions.& & & & This study aims to evaluate the outcomes of alternative irrigation scheduling decisions under uncertainty, with a focus on the uncertainties arising from short-term weather forecast. To achieve this, we performed a model-based study to simulate crop root-zone soil water content, in which we comprehensively explored different combinations of ensemble short-term rainfall forecast and alternative decisions of irrigation scheduling. This modelling produced an ensemble of soil water contents to enable quantification of risks of over- and under-irrigation these ensemble estimates were summarized to inform optimal timing of next irrigation event to minimize both the risks of stressing crop and wasting water. With inclusion of other sources of uncertainty (e.g. soil water observation, crop factor), this approach shows good potential to be extended to a comprehensive framework to support practical irrigation decision-making for farmers.& &
Multi-scale spatial correlation and scaling behavior of surface soil moisture
Publisher: American Geophysical Union (AGU)
Date: 04-2006
DOI: 10.1029/2006GL025831
Investigation into sustainable water use in India using combined large-scale earth system-based modelling and census-based statistical data
Publisher: Elsevier BV
Date: 08-2020
Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals
Publisher: Copernicus GmbH
Date: 29-03-2017
DOI: 10.5194/HESS-21-1849-2017
Abstract: Abstract. Due to their shallow vertical support, remotely sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (dS ∕ dt). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying dS ∕ dt via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2000–10 000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain statistically significant information concerning dS ∕ dt. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing products to enhance the spatial resolution of dS ∕ dt estimates acquired from gravity remote sensing.
Clarifications on the “Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S.”
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments
Publisher: Copernicus GmbH
Date: 12-01-2021
Abstract: Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programs have collected a large amount of data that provide a good basis to investigate the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provides an opportunity to further our understanding of water quality dynamics in sub-tropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of: 1) identifying the influential environmental drivers of temporal variation in flow event concentrations and 2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g., runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations, as well as between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.
Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags
Publisher: American Geophysical Union (AGU)
Date: 04-2013
DOI: 10.1002/WRCR.20169
An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts
Publisher: Springer Science and Business Media LLC
Date: 14-07-2023
DOI: 10.1007/S00271-022-00807-W
Abstract: Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.
Normalized Temperature Drought Index (NTDI) for Soil Moisture Monitoring Using MODIS and Landsat-8 Data
Publisher: MDPI AG
Date: 29-05-2023
DOI: 10.3390/RS15112830
Abstract: As the fundamental regulator of energy exchange in the vegetation–soil–atmosphere circulation system, soil moisture is a key parameter for drought monitoring and is indispensable to the land surface hydrological processes. In order to overcome the constraints of the Perpendicular Drought Index, PDI (performs poorly over the fields with dense vegetation and hard to construct the soil line), and the Temperature Vegetation Drought Index, TVDI (requires similar atmospheric forcing and large enough dimension of mapping area), in soil moisture monitoring, a new drought index (Normalized Temperature Drought Index, NTDI) is proposed to explore the spatiotemporal changes of soil moisture by substituting red and near-infrared reflectances with vegetation index and normalized land surface temperature on the basis of the PDI framework. Victoria, Australia, was selected as the study area as it experiences many severe droughts and has been affected for more than ten years. Time series of satellite-based data were applied to evaluate the effectiveness and applicability of the NTDI at the regional scale. Results indicated that the expression of the soil line representing the water condition of the bare soil is easier to obtain in the new trapezoid framework and has good fits with the coefficients of determination (R2) of more than 0.8. Compared with PDI, TVDI and Modified PDI (MPDI) at the cropping sites, NTDI exhibits a relatively better performance in soil moisture monitoring for most days where the R2 achieved can reach to more than 0.7 on DOY 242, 254 and 272. Meanwhile, spatial–temporal mappings of the four drought indices from satellite data were conducted, and the NTDI presented the slightly seasonal variation and effectively described the real spatial characteristics of regional drought. Overall, the NTDI seems to a viable approach and can provide insight into spatial and temporal soil moisture monitoring at different scales.
A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4725
Abstract: & & & & & & & & Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km& sup& & /sup& . The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO& sub& x& /sub& ), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO& sub& x& /sub& ) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.& / & & / & & / & & / &
Irrigation in the Earth system
Publisher: Springer Science and Business Media LLC
Date: 20-06-2023
Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes
Publisher: Copernicus GmbH
Date: 23-09-2014
DOI: 10.5194/HESSD-11-10635-2014
Abstract: Abstract. Assimilation of remotely sensed soil moisture data (SM–DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM–DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia ( 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM–DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash–Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM–DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively the NS of the ensemble mean increased by 7 and 38%, respectively the false alarm ratio was reduced by 15 and 25%, respectively and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM–DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM–DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM–DA does not address systematic errors in the model.
Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard
Publisher: MDPI AG
Date: 30-01-2018
DOI: 10.3390/S18020397
De‐noising of passive and active microwave satellite soil moisture time series
Publisher: American Geophysical Union (AGU)
Date: 26-07-2013
DOI: 10.1002/GRL.50695
Role of vegetation responses in hydrological shifts under multiyear droughts
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10029
Abstract: Drought-induced vegetation responses are often hypothesized as one of the key drivers of hydrological changes under multiyear droughts. However, until now, this hypothesis has not been systematically tested on areas that experienced significant drought-induced reductions in streamflow generation. Our results do not support this hypothesis and suggest that vegetation changes are unlikely to be the main driver of observed hydrological changes.We employed multiple remotely sensed vegetation indices (AVHRR NDVI & fPAR, MODIS NDVI & EVI, and Ku-VOD from multiple microwave satellite sensors) and rainfall-runoff shift indicators to investigate vegetation responses and their influences on streamflow generation during the Millennium Drought (from 1997 to 2009) in 156 catchments in Victoria, Australia. Many of these catchments experienced significant shifts in their rainfall-runoff relationship by severely reducing streamflow generation during the Millennium Drought. However, we show that vegetation indices are statistically similar or higher in many catchments during the Millennium Drought compared to pre-drought, consistent with published literature. Moreover, the spatial pattern of increase in vegetation indices does not match the spatial distribution of hydrological shifts, measured by significant streamflow reductions for a given rainfall. We argue that vegetation response is unlikely to be a primary driver of the observed hydrological shifts, although they are regarded as crucial in determining hydrological behaviour more generally. This finding has important implications for better understanding and modelling hydrological responses under future climate changes.
Correcting unintended perturbation biases in hydrologic data assimilation
Publisher: American Meteorological Society
Date: 06-2009
Abstract: Hydrologic data assimilation has become an important tool for improving hydrologic model predictions by using observations from ground, aircraft, and satellite sensors. Among existing data assimilation methods, the ensemble Kalman filter (EnKF) provides a robust framework for optimally updating nonlinear model predictions using observations. In the EnKF, background prediction uncertainty is obtained using a Monte Carlo approach where state variables, parameters, and forcing data for the model are synthetically perturbed to explicitly simulate the error-prone representation of hydrologic processes in the model. However, it is shown here that, owing to the nonlinear nature of these processes, an ensemble of model forecasts perturbed by mean-zero Gaussian noise can produce biased background predictions. This ensemble perturbation bias in soil moisture states can lead to significant mass balance errors and degrade the performance of the EnKF analysis in deeper soil layers. Here, a simple method of bias correction is introduced in which such perturbation bias is corrected using an unperturbed model simulation run in parallel with the EnKF analysis. The proposed bias-correction scheme effectively removes biases in soil moisture and reduces soil water mass balance errors. The performance of the EnKF is improved in deeper layers when the filter is applied with the bias-correction scheme. The interplay of nonlinear hydrologic processes is discussed in the context of perturbation biases, and implications of the bias correction for real-data assimilation cases are presented.
Seasonal forecast of soil moisture over Mediterranean-climate forest catchments using a machine learning approach
Publisher: Elsevier BV
Date: 04-2023
Evaluating the role of solar-induced fluorescence (SIF) and plant physiological traits for leaf nitrogen assessment in almond using airborne hyperspectral imagery
Publisher: Elsevier BV
Date: 09-2022
Multi-scale analysis of bias correction of soil moisture
Publisher: Copernicus GmbH
Date: 06-01-2015
Abstract: Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatio-temporal variability of the target variable, inter-sensor differences with variable measurement supports. One such ex le is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that is increasingly being used to address this issue, but is only applicable to the linear regime, whereas the non-linear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatio-temporal scales, we propose a multi-timescale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data-adaptive, non-linear de-noising via wavelet thresholding.
Assimilation of stream discharge for flood forecasting
Publisher: American Geophysical Union (AGU)
Date: 05-2015
DOI: 10.1002/2014WR016667
A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques
Publisher: Elsevier BV
Date: 02-2023
Towards an ensemble-based short-term flood forecasting using an event-based flood model- incorporating catchment-average estimates of soil moisture
Publisher: Elsevier BV
Date: 02-2021
Copernicus satellites for supporting irrigation and water management in Australia: the COALA H2020 Project.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-7379
Abstract: & & COALA is a project funded by the Horizon 2020 program of the European Union with the aim of developing Copernicus Earth Observation-based information services for irrigation and nutrient management in Australia, building on consolidated experience of past EU projects and existing operational irrigation advisory services. Earth Observation-based services can provide & #8220 diagnostic& #8221 data and information relevant for integrated input management of irrigation water and nutrients, from subplot level to irrigation scheme or river basin levels.& & & & COALA, started on January 2020, is developing Copernicus-based information service for the Australian agricultural systems, based on strong collaboration with Academic Australian institutions and business players. COALA services will provide to farmers, irrigation organisation and basin authorities information about crops development, water and nutrient status, irrigated areas by means of innovative algorithms based on Sentinel Earth Observation data, which will be accessed by means of the new cloud platforms (DIAS) of Copernicus. In-situ and other source of data, such as ground soil moisture probes, meteorological stations and Numerical Weather Prediction models, will be used to improve the information provided to the final users.& & & & The advancements beyond the state of art of COALA methodologies for managing irrigation are:& & & & COALA will demonstrate that Copernicus data and new DIAS infrastructure can greatly improve the availability of a multi-scale information product shared by the different levels of users. The innovative approach achieves a & quot converging loop procedure& quot between water authority, irrigation infrastructure operation and farmers, enabling transparency in all the decision taken at all levels and improving the accuracy of estimation of actual water use.& & & & & strong& www.coalaproject.eu/& /strong& & &
Estimating evapotranspiration by using canopy conductance models with Sentinel-2 data in irrigated crops in California and Australia
Publisher: Copernicus GmbH
Date: 08-05-2023
DOI: 10.5194/EGUSPHERE-GC8-HYDRO-58
Abstract: & & Deriving evapotranspiration is crucial for determining the water requirements of crops and for efficiently allocating water resources for irrigation. Various experiments and methods have proven that earth observation (EO) is a useful tool for estimating evapotranspiration and supporting irrigation and water resource management at different scales.& & & & This study presents a framework based on the Penman-Monteith big leaf model and Shuttleworth-Wallace sparse canopy model for estimating the evapotranspiration in irrigated crops with partial and full-canopy conditions.& & & & The approach fully utilizes the high-resolution and multi-spectral capabilities of the Sentinel-2 (S2) sensors for the derivation of surface parameters such as hemispherical shortwave albedo(& #945 ), Leaf Area Index (LAI), and the water status of the soil-canopy ensemble by using the OPTRAM model. Proposed by Sadeghi [1], the OPTRAM model uses the pixel distribution in the Shortwave Infrared Transformed Reflectance (STR)-NDVI space, where the water content of the soil-canopy system is linearly correlated to the STR index.& & & & In detail, the proposed approach estimates the contributions of soil and canopy to the total evapotranspiration by incorporating the OPRAM model to assess the water status of the surface and adjust the resistance terms in the combination equation [2]& & & & The results are validated by using Eddy Covariance data collected during the GRAPEX (Grape Remote Sensing Atmospheric Profile Evapotranspiration eXperiment) project [3], T-REX (Tree crop Remote sensing of Evapotranspiration eXperiment) project, and COALA (COpernicus Applications and services for Low impact agriculture in Australia) project [4]. These projects are conducted respectively in irrigated vineyards and almond orchards in California, and in irrigated maize and alfalfa in Australia.& & & & [1] Sadeghi, Morteza, Scott B. Jones, and William D. Philpot.: A linear physically-based model for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment 164, 66-76 (2015).& & & & [2] D& #8217 Urso, G., Bolognesi, S. F., Kustas, W. P., Knipper, K. R., Anderson, M. C., Alsina, M. M., ... & Belfiore, O. R.: Determining evapotranspiration by using combination equation models with sentinel-2 data and comparison with thermal-based energy balance in a California irrigated Vineyard. Remote Sensing, 13(18), 3720 (2021).& & & & [3] Kustas, W.P., Anderson, M.C., Alfieri, J.G., Knipper, K., Torres-Rua, A., Parry, C.K., Nieto, H., Agam, N., White, W.A., Gao, F. The grape remote sensing atmospheric profile and evapotranspiration experiment. Bulletin of the American Meteorological Society 2018, 99, 1791-1812.& & & & [4] COALA project. www.coalaproject.eu/& &
Modeling Vegetation Water Stress over the Forest from Space: Temperature Vegetation Water Stress Index (TVWSI)
Publisher: MDPI AG
Date: 17-11-2021
DOI: 10.3390/RS13224635
Abstract: The conventional Land Surface Temperature (LST)–Normalized Difference Vegetation Index (NDVI) trapezoid model has been widely used to retrieve vegetation water stress. However, it has two inherent limitations: (1) its complex and computationally intensive parameterization for multi-temporal observations and (2) deficiency in canopy water content information. We tested the hypothesis that an improved water stress index could be constructed by the representation of canopy water content information to the LST–NDVI trapezoid model. Therefore, this study proposes a new index that combines three indicators associated with vegetation water stress: canopy temperature through LST, canopy water content through Surface Water Content Index (SWCI), and canopy fractional cover through NDVI in one temporally transferrable index. Firstly, a new optical space of SWCI–NDVI was conceptualized based on the linear physical relationship between shortwave infrared (SWIR) and soil moisture. Secondly, the SWCI–NDVI feature space was parameterized, and an index d(SWCI, NDVI) was computed based on the distribution of the observations in the SWCI–NDVI spectral space. Finally, standardized LST (LST/long term mean of LST) was combined to d(SWCI, NDVI) to give a new water stress index, Temperature Vegetation Water Stress Index (TVWSI). The modeled soil moisture from the Australian Water Resource Assessment—Landscape (AWRA-L) and Soil Water Fraction (SWF) from four FLUXNET sites across Victoria and New South Wales were used to evaluate TVWSI. The index TVWSI exhibited a high correlation with AWRA-L soil moisture (R2 of 0.71 with p 0.001) and the ground-based SWF (R2 of 0.25–0.51 with p 0.001). TVWSI predicted soil moisture more accurately with RMSE of 21.82 mm (AWRA-L) and 0.02–0.04 (SWF) compared to the RMSE ranging 28.98–36.68 mm (AWRA-L) and 0.03–0.05 (SWF) were obtained for some widely used water stress indices. The TVWSI could also be a useful input parameter for other environmental models.
Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-10915
Abstract: & & The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5 (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding the two crop functional types (CFT) for sugar beet and potatoes to the list of actively managed crops in CLM5 (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity to reduce erosion and improve soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop specific parameterization, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes (RMSE reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes and seasonal cycle of LAI, and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water and energy fluxes.& &
Supplementary material to "A predictive model for spatio-temporal variability in stream water quality"
Publisher: Copernicus GmbH
Date: 23-07-2019
Two-dimensional synthetic aperture radiometry over land surface during soil moisture experiment in 2003 (SMEX03)
Publisher: IEEE
Date: 2007
Optical Sensing of Vegetation Water Content: A Synthesis Study
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
Publisher: MDPI AG
Date: 27-09-2021
DOI: 10.3390/RS13193865
Abstract: Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation (2) rainfall monitoring and prediction (3) flood simulations and predictions and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions, on how to make the most out of the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world.
Author Correction: Irrigation in the Earth system
Publisher: Springer Science and Business Media LLC
Date: 18-10-2023
Characterization of footprint-scale surface soil moisture variability using Gaussian and beta distribution functions during the Southern Great Plains 1997 (SGP97) hydrology experiment
Publisher: American Geophysical Union (AGU)
Date: 12-2005
DOI: 10.1029/2004WR003835
Improving root-zone soil moisture estimations using dynamic root growth and crop phenology
Publisher: Elsevier BV
Date: 12-2015
Simulating regional inter-annual crop yield variability over multiple decades with the Community Land Model (CLM5)
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-4818
Abstract: Global climate change with a predicted increase in weather extremes entails vulnerability and new challenges to regional agriculture. While the general impacts of climate change on global food security are a much studied topic, the implications for regional inter-annual yield variability remain unclear. In this study, we analysed the effects of weather trends on regional crop productivity within two agriculturally managed regions in different climate zones, simulated with the latest version of the Community Land Model (version 5.0) over two decades (1999-2019). We evaluated the models& #8217 potential to represent the inter-annual variability of crop yield in comparison to recorded yield variability and different weather indicators, e.g., drought index and growing season length and evaluated which variables (i.e., temperature, precipitation, initial soil moisture content) dominantly drive changes in CLM5-predicted yield variability. The simulation results were able to reproduce the sign of crop yield anomalies, and thus provide a basis on which to study the effects of different weather patterns on inter-annual yield variability. However, the simulations showed limitations in correctly capturing inter-annual differences of crop yield in terms of total magnitudes (up to 10 times lower than in official records). Our results indicate that these limitation arise mainly from uncertainties in the representation of the subsurface soil moisture regime and a corresponding lack of sensitivity towards drought stress. Insights from this work were used to summarize implications for future analysis of CLM5-BGC simulation results over agriculturally managed land and allowed us to discuss and investigate possible technical model improvements.
A Laboratory Study on Non-Invasive Soil Water Content Estimation Using Capacitive Based Sensors
Publisher: MDPI AG
Date: 05-02-2019
DOI: 10.3390/S19030651
Abstract: Soil water content is an important parameter in many engineering, agricultural and environmental applications. In practice, there exists a need to measure this parameter rather frequently in both time and space. However, common measurement techniques are typically invasive, time-consuming and labour-intensive, or rely on potentially risky (although highly regulated) nuclear-based methods, making frequent measurements of soil water content impractical. Here we investigate in the laboratory the effectiveness of four new low-cost non-invasive sensors to estimate the soil water content of a range of soil types. While the results of each of the four sensors are promising, one of the sensors, herein called the “AOGAN” sensor, exhibits superior performance, as it was designed based on combining the best geometrical and electronic features of the other three sensors. The performance of the sensors is, however, influenced by the quality of the sensor-soil coupling and the soil surface roughness. Accuracy was found to be within 5% of volumetric water content, considered sufficient to enable higher spatiotemporal resolution contrast for mapping of soil water content.
Multi-scale analysis of bias correction of soil moisture
Publisher: Copernicus GmbH
Date: 29-07-2014
DOI: 10.5194/HESSD-11-8995-2014
Abstract: Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatiotemporal variability of the target variable, inter-sensor differences with variable measurement supports. One such ex le is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that increasingly being used to address this issue but is only applicable to the linear regime, whereas nonlinear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatiotemporal scales, we propose a multi-time-scale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely-sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data adaptive, nonlinear de-noising via wavelet thresholding.
GRACE and AMSR-E-based estimates of winter season solid precipitation accumulation in the Arctic drainage region
Publisher: American Geophysical Union (AGU)
Date: 29-10-2010
DOI: 10.1029/2009JD013504
Soil Moisture Retrieval Using a Two-Dimensional L-Band Synthetic Aperture Radiometer in a Semiarid Environment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2010
Stand-alone error characterisation of microwave satellite soil moisture using a Fourier method
Publisher: Elsevier BV
Date: 11-2014
Towards Understanding Evapotranspiration Shifts Under a Drying Climate
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13013
Abstract: & & Around 60 percent of terrestrial precipitation on the global average transforms into evapotranspiration. However, reliable estimation of actual evapotranspiration (AET) is challenging as it depends on multiple climatic and biophysical factors. Despite developments such as remotely sensed AET products, AET responses to prolonged drought is still poorly understood. Therefore, this study focuses on understanding long-term changes and variability of AET prior to and during the Millennium Drought in Victoria, Australia. We also investigate the capability of commonly used rainfall-runoff models to simulate AET under multiyear droughts. Therefore, we employ simple sensitivity analysis to examine four different water balance approaches between pre-drought and drought periods in six different study catchments in Victoria. The first water balance approach is the simplest long-term water balance approach, partitioning long-term precipitation into evapotranspiration and runoff. The second water balance approach adopts a long-term change in storage to the water balance during the Millennium Drought by employing regional-scale change in GRACE estimates derived from Fowler et al. (2020). The third and fourth water balances are based on simulations from SIMHYD and SACRAMENTO. Surprisingly, the adoption of long-term change in storage during the Millennium Drought indicates that the annual rates of pre-drought AET were largely maintained throughout the drought i.e. the rate was relatively constant with time. This suggests that AET gets priority over streamflow following a drying shift in precipitation partitioning resulting in a relatively constant AET under multiyear drought. In contrast, the rainfall-runoff models underestimated AET during the drought compared to both water balance approaches. These results broadly acknowledge the need for model improvements to provide more realistic AET estimates under future drying climates and provide a new perspective on recent hydrological phenomena such as changing rainfall-runoff relationships in these regions. Furthermore, this sensitivity analysis was augmented and confirmed by a regional-scale water balance approach.& & & & Keywords: Catchment water balance, Evapotranspiration, Change in storage, Rainfall-runoff models& & & & References:& Fowler, K., Knoben, W., Peel, M., Peterson, T., Ryu, D., Saft, M., Seo, K.W., Western, A., 2020. Many Commonly Used Rainfall-Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections. Water Resour. Res. 56. 0.1029/2019WR025286& & & & & & &
In-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4987
Abstract: & & Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User& #8217 s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.& & & & Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest& &
Improving scheduling, benchmarking and forecasting to boost irrigation productivity
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-6275
Abstract: & & Irrigation water is an expensive and limited resource, and optimized water use is beneficial to saving water while boosting productivity. This project aims to develop integrated irrigation scheduling, benchmarking and forecasting capabilities to inform optimal irrigation practices and the suitable tools and information required for this. To achieve this, we designed a three-year project which combines simulations and field-scale monitoring. One aspect of this project is to develop a comprehensive uncertainty framework to better understand the uncertainty in scheduling, which is informed by soil water models, along with multiple sources of information such as soil, crop, weather and field management. Besides, we are also conducting large-scale benchmarking study to identify better irrigation practices across multiple farms, fields, crop types and seasons. The project outcomes will be integrated with our partner, Rubicon& #8217 s water ordering portal and adopted by most Australian irrigation farmers, with significant long-term benefits expected in agricultural production and water conservation.& & &
Negligible Impact on Precipitation From a Permanent Inland Lake in Central Australia
Publisher: American Geophysical Union (AGU)
Date: 22-08-2023
DOI: 10.1029/2023GL103913
Abstract: For over a century, numerous proposals for increasing available water in central Australia have been raised, inspired in part by the natural occurrence of the ephemeral lake, Kati Thanda‐Lake Eyre. It has also been proposed that additional rainfall generated by the lake would spread beyond the lake itself, potentially opening up large tracts of uncultivated land to dryland agriculture. Here we use a climate model to examine how adding a permanent lake to Australia's arid center might influence local and regional precipitation. Locally, evaporative cooling from the lake increases low‐level ergence, suppressing precipitation. Regionally, additional moisture from the lake is spread thinly over the Australian continent, resulting in little change to total precipitation. Overall, our results do not support the assertion that maintaining a large inland lake like Kati Thanda‐Lake Eyre in central Australia would significantly increase precipitation, either locally or regionally.
Seasonal crop yield prediction with SEAS5 long-range meteorological forecasts in a land surface modelling approach
Publisher: Copernicus GmbH
Date: 27-02-2023
DOI: 10.5194/HESS-2023-28
Abstract: Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications that usually require high resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia and the Australian state of Victoria. We found that, after pre-processing of the forecast products (temporal downscaling of precipitation and incoming shortwave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to generate a model system response very close to reference simulation results forced by reanalysis data. Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual variability in crop yield across the Australian domain (approximately 50 % inter-annual variability in recorded yields and up to 17 % in simulated yields) compared to the German domain (approximately 15 % inter-annual variability in recorded yields and up to 5 % in simulated yields). The high and low yield seasons (2020 and 2018) among the four simulated years were clearly reproduced in forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the German domain. However, the simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic over- and underestimations in both the forecast and the reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency's (ESA CCI). These observed biases of soil moisture as well as the low inter-annual variability of simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.
Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2012
A new Remote Sensing-based vegetation water stress index: -Temperature Vegetation Water Stress Index (TVWSI)
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-12308
Abstract: & & Remote sensing techniques are widely used to evaluate the biophysical status of vegetation, including water stress caused by soil water deficit. Based on the nominal links between water stress condition, transpiration and canopy temperature in the vegetation, numerous studies have used a trapezoidal relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) over vegetated surfaces to develop the water stress metric, in which the level of stress could be identified by the spatial location of the pixels on the spectral space (Goetz and Goetz 1997 Lambin, Lambin, and Ehrlich 1996 Nemani et al. 1993 Nemani and Running 1989 Price 1990 Sandholt, Rasmussen, and Andersen 2002). However, the amount of change in canopy temperature could also vary spatially by the canopy water status at that time. Thus, LST-NDVI alone cannot construct an efficient metric to see the spatial patterns of water stress at ecosystem level unless they are coupled with water status of vegetation at that moment. This study hypothesizes that a metric which can combine LST-NDVI information with an indicator for canopy water status could give more accurate estimations of the real-time vegetation water stress. The remotely sensed plant canopy water status indicator (a metric based on canopy reflection in the Short-Wave Infrared region (SWIR)) could add the canopy water status information to the LST-NDVI based indices, which may better explain spatial/temporal water stress condition in the plants especially in densely forested areas where signal saturation is a major issue. In this study, the third-dimensional information of SWIR has been combined with LST-NDVI spectral space to create a new remotely sensed vegetation water stress index, TVWSI (Temperature Vegetation Water Stress Index) which seems to be more realistic to capture stress dynamics at large scale.& & & & & Sixty grids (2 km X 2 km) each containing 16 pixels of daily MODIS-reflectance (band 1 & #8211 band 7, 500 m spatial resolution) and 4 pixels of daily MODIS-LST (1 km spatial resolution) were chosen over forested areas in Victoria representing most of the bioregions as classified by the Interim Biogeographic Regionalisation for Australia (IBRA7). From 2002 to 2018 daily TVWSI values of each grid were evaluated against the modelled daily available soil moisture content in the top 1 m of the soil profile, and rainfall data, from the Australian Bureau of Meteorology (BOM). TVWSI performed better than other dryness indices mentioned in the literature. A high correlation was obtained between TVWSI vs. soil moisture and TVWSI vs. rainfall with a coefficient of determination value of 0.6 (p& .001) and 0.61 (p& .001) respectively when data were combined spatially and temporally. Even improved correlations ranging (0.4-0.7, p& .001) were obtained for in idual grids over the mentioned period. While correlation ranging (0.15-0.48, p& .001) were obtained using dryness indices like Perpendicular Drought Index (PDI), Modified PDI (MPDI), Temperature Vegetation Dryness Index (TVDI) and Vegetation Supply Water Index (VSWI). The result shows that the TVWSI can capture real-time ecosystem water stress well and the metric could be an efficient input parameter for many hydrological, drought and fire prediction models.& & & & & & &
A predictive model for spatio-temporal variability in stream water quality
Publisher: Copernicus GmbH
Date: 23-07-2019
Abstract: Abstract. Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream water quality can be highly variable both over space and time. To develop effective management strategies for riverine water quality, it is critical to be able to predict these spatio-temporal variabilities. However, our current capacity to model stream water quality is limited, particularly at large spatial scales across multiple catchments. This is due to a lack of understanding of the key controls that drive spatio-temporal variabilities of stream water quality. To address this, we developed a Bayesian hierarchical statistical model to analyse the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed based on monthly water quality monitoring data collected at 102 sites over 21 years. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). Among the six constituents, the models explained varying proportions of variation in water quality. EC was the most predictable constituent (88.6 % variability explained) and FRP had the lowest predictive performance (19.9 % variability explained). The models were validated for multiple sets of calibration/validation sites and showed robust performance. Temporal validation revealed a systematic change in the TSS model performance across most catchments since an extended drought period in the study region, highlighting potential shifts in TSS dynamics over the drought. Further improvements in model performance need to focus on: (1) alternative statistical model structures to improve fitting for the low concentration data, especially records below the detection limit and (2) better representation of non-conservative constituents by accounting for important biogeochemical processes. We also recommend future improvements in water quality monitoring programs which can potentially enhance the model capacity, via: (1) improving the monitoring and assimilation of high-frequency water quality data and (2) improving the availability of data to capture land use and management changes over time.
Field observations of soil moisture variability across scales
Publisher: American Geophysical Union (AGU)
Date: 2008
DOI: 10.1029/2006WR005804
Multidecadal Trend of Basin‐Scale Evapotranspiration Estimated Using AVHRR Data in the Krishna River Basin, India
Publisher: Wiley
Date: 08-03-2013
DOI: 10.2136/VZJ2012.0118
Possible Increase of Air Temperature by Irrigation
Publisher: American Geophysical Union (AGU)
Date: 19-10-2022
DOI: 10.1029/2022GL100427
Abstract: Irrigation cools near surface air temperature by increasing evapotranspiration from wetter soil. However, elevated evapotranspiration can also increase atmospheric albedo and enhance the local greenhouse effect via increased atmospheric water vapor. Their net effects on daily air temperature remains controversial. Here we show that in several considered regions, Northwest India and Central Valley of California, irrigation could result in warmer air temperature if night‐time warming is stronger than daytime cooling by irrigation. During the daytime, air temperature reduces through evaporative cooling and reduced solar radiation from increased atmospheric albedo outweighing the local greenhouse effect. At night‐time, the increased atmospheric water vapor by irrigation tends to make a stronger local greenhouse effect that increases night‐time temperature. Our results highlight the possible increase of air temperature by irrigation and the importance of considering sub‐daily processes when assessing the impact of irrigation on daily air temperature and temperature related socioeconomic phenomena.
Related Organisations
USDA Agricultural Research Service
Location: United States of America
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