Recent improvements to the Australian Water Resources Assessment Landscape Model (version 7)

2021 ◽  
Author(s):  
Ashkan Shokri ◽  
Ali Azarnivand ◽  
Katayoon Bahramian ◽  
Greg Keir ◽  
Andrew Frost
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Terrestrial Water Storage ◽  
Australian Water ◽  
Wide Range ◽  
Terrestrial Water ◽  
Spatial Calibration ◽  
Calibration Approach ◽  
Resources Assessment

<p>The Australian Water Resources Assessment Landscape (AWRA-L) model is a continental gridded, daily time-step, water balance model, developed over the last decade by CSIRO and the Australian Bureau of Meteorology for a range of hydrological applications. The model outputs (including soil moisture, evapotranspiration, runoff and deep drainage; available through www.bom.gov.au/water/landscape) have found wide application for monitoring purposes (e.g. for flood and fire risk, drought monitoring), water reporting (eg. National Water Accounts), and in analysing trends in water balance outputs including streamflow. In addition to these historical/monitoring applications, AWRA-L is being further used for production of 10-day forecasts, seasonal forecasts, and long-term projections of hydrological outputs out to the end of the century.</p><p> </p><p>This study details recent development of AWRA-L for improved performance across the water balance for use in monitoring through to long term projections. Changes are implemented across three broad areas: improved static and dynamic inputs, altered conceptual structure (additional urban component and baseflow ephemerality), and altered calibration approach. In particular, a new spatial calibration approach is applied across the nation using over 300 catchments. To do so model pixel output values are compared against spatially distributed satellite data for soil moisture, evapotranspiration (ET), and two new components including fraction of vegetation (F<sub>veg</sub>) and terrestrial water storage (TWS). In the previous versions of the model lumped catchment average values of evapotranspiration and soil moisture were used. In addition to comparing to a wide range of national datasets (streamflow observations, flux tower observations, soil moisture network observations, recharge observations), the model performance was compared for drought analysis (reproducing 2-state rainfall-runoff behaviour observed in parts of Australia) and flood analysis (correlating with operationally used flood forecasting parameters). Overall, the modified AWRA-L outperformed previous versions in terms of water balance estimation according to a wide range of validation data. The successful application of the spatial calibration method can potentially pave the path for more frequent application of complex calibration methods for large scale simulations. Furthermore, consideration of a terrestrial water storage component in the objective function highlights the importance of this factor in capturing more accurate simulation of other water balance components, particularly streamflow. The improved streamflow performance demonstrates the enhanced functionality of the model in capturing intermittency and streamflow shifts in seasonally dry and groundwater dependent catchments, further demonstrated in the drought analysis. Finally, the flood study demonstrates the application and value of the model for real time flood-monitoring and forecasting purposes. This study shows the potential of AWRA-L model and associated spatial calibration approach for accurate simulation of water balance variables for use in continental-scale studies.</p>

scholarly journals Seasonal Variations in Terrestrial Water Storage for Major Midlatitude River Basins

2006 ◽  
Vol 7 (1) ◽  
pp. 39-60 ◽  
Author(s):  
Martin Hirschi ◽  
Sonia I. Seneviratne ◽  
Christoph Schär
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Snow Cover ◽  
Water Storage ◽  
River Basins ◽  
Interannual Variations ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Seasonal And Interannual Variations

Abstract This paper presents a new diagnostic dataset of monthly variations in terrestrial water storage for 37 midlatitude river basins in Europe, Asia, North America, and Australia. Terrestrial water storage is the sum of all forms of water storage on land surfaces, and its seasonal and interannual variations are in principle determined by soil moisture, groundwater, snow cover, and surface water. The dataset is derived with the combined atmospheric and terrestrial water-balance approach using conventional streamflow measurements and atmospheric moisture convergence data from the ECMWF 40-yr Re-Analysis (ERA-40). A recent study for the Mississippi River basin (Seneviratne et al. 2004) has demonstrated the validity of this diagnostic approach and found that it agreed well with in situ observations in Illinois. The present study extends this previous analysis to other regions of the midlatitudes. A systematic analysis is presented of the slow drift that occurs with the water-balance approach. It is shown that the drift not only depends on the size of the catchment under consideration, but also on the geographical region and the underlying topography. The drift is in general not constant in time, but artificial inhomogeneities may result from changes in the global observing system used in the 44 yr of the reanalysis. To remove this time-dependent drift, a simple high-pass filter is applied. Validation of the results is conducted for several catchments with an appreciable coverage of in situ soil moisture and snow cover depth observations in the former Soviet Union, Mongolia, and China. Although the groundwater component is not accounted for in these observations, encouraging correlations are found between diagnostic and in situ estimates of terrestrial water storage, both for seasonal and interannual variations. Comparisons conducted against simulated ERA-40 terrestrial water storage variations suggest that the reanalysis substantially underestimates the amplitude of the seasonal cycle. The basin-scale water-balance (BSWB) dataset is available for download over the Internet. It constitutes a useful tool for the validation of climate models, large-scale land surface data assimilation systems, and indirect observations of terrestrial water storage variations.

scholarly journals Reconstructing Terrestrial Water Storage Variations from 1980 to 2015 in the Beishan Area of China

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjie Yin ◽  
Litang Hu ◽  
Shin-Chan Han ◽  
Menglin Zhang ◽  
Yanguo Teng
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Storage ◽  
Balance Method ◽  
Disposal Site ◽  
Terrestrial Water Storage ◽  
Water Balance Method ◽  
Terrestrial Water ◽  
Beishan Area

Terrestrial water storage (TWS) is a key element in the global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided a highly valuable dataset, which allows the study of TWS over larger river basins worldwide. However, the lifetime of GRACE is too short to demonstrate long-term variability in TWS. In the Beishan area of northwestern China, which is selected as the most prospective site for high-level radioactive waste (HLRW) disposal, the assessment of long-term TWS changes is crucial to understand disposal safety. Monthly and annual TWS changes during the past 35 years are reconstructed using GRACE data, other remote sensing products, and the water balance method. Hydrological flux outputs from multisource remote sensing products are analyzed and compared to select appropriate data sources. The results show that a decreasing trend is found for GRACE-filtered and Center for Space Research (CSR) mascon solutions from 2003 to 2015, with slopes of −2.30 ± 0.52 and −1.52 ± 0.24 mm/year, respectively. TWS variations independently computed from the water balance method also show a similar decreasing trend with the GRACE observations, with a slope of −0.94 mm/year over the same period. Overall, the TWS anomalies in the Beishan area change seasonally within 10 mm and have been decreasing since 1980, keeping a desirable dry condition as a HLRW disposal site.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Recent changes in terrestrial water storage in the Upper Nile Basin: an evaluation of commonly used gridded GRACE products

2017 ◽  
Vol 21 (9) ◽  
pp. 4533-4549 ◽  
Author(s):  
Mohammad Shamsudduha ◽  
Richard G. Taylor ◽  
Darren Jones ◽  
Laurent Longuevergne ◽  
Michael Owor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lake Victoria ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Nile Basin ◽  
Lake Victoria Basin ◽  
Groundwater Storage ◽  
Terrestrial Water ◽  
Substantial Uncertainty

Abstract. GRACE (Gravity Recovery and Climate Experiment) satellite data monitor large-scale changes in total terrestrial water storage (ΔTWS), providing an invaluable tool where in situ observations are limited. Substantial uncertainty remains, however, in the amplitude of GRACE gravity signals and the disaggregation of TWS into individual terrestrial water stores (e.g. groundwater storage). Here, we test the phase and amplitude of three GRACE ΔTWS signals from five commonly used gridded products (i.e. NASA's GRCTellus: CSR, JPL, GFZ; JPL-Mascons; GRGS GRACE) using in situ data and modelled soil moisture from the Global Land Data Assimilation System (GLDAS) in two sub-basins (LVB: Lake Victoria Basin; LKB: Lake Kyoga Basin) of the Upper Nile Basin. The analysis extends from January 2003 to December 2012, but focuses on a large and accurately observed reduction in ΔTWS of 83 km3 from 2003 to 2006 in the Lake Victoria Basin. We reveal substantial variability in current GRACE products to quantify the reduction of ΔTWS in Lake Victoria that ranges from 80 km3 (JPL-Mascons) to 69 and 31 km3 for GRGS and GRCTellus respectively. Representation of the phase in TWS in the Upper Nile Basin by GRACE products varies but is generally robust with GRGS, JPL-Mascons, and GRCTellus (ensemble mean of CSR, JPL, and GFZ time-series data), explaining 90, 84, and 75 % of the variance respectively in "in situ" or "bottom-up" ΔTWS in the LVB. Resolution of changes in groundwater storage (ΔGWS) from GRACE ΔTWS is greatly constrained by both uncertainty in changes in soil-moisture storage (ΔSMS) modelled by GLDAS LSMs (CLM, NOAH, VIC) and the low annual amplitudes in ΔGWS (e.g. 1.8–4.9 cm) observed in deeply weathered crystalline rocks underlying the Upper Nile Basin. Our study highlights the substantial uncertainty in the amplitude of ΔTWS that can result from different data-processing strategies in commonly used, gridded GRACE products; this uncertainty is disregarded in analyses of ΔTWS and individual stores applying a single GRACE product.

scholarly journals Trend Dynamics of GRACE Terrestrial Water Storage in the Nile River Basin

2019 ◽  
Author(s):  
Emad Hasan ◽  
Aondover Tarhule
Keyword(s):  
Soil Moisture ◽  
Population Density ◽  
River Basin ◽  
Goodness Of Fit ◽  
Water Storage ◽  
Least Square ◽  
Nile River ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Nile River Basin

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

scholarly journals Recent changes in terrestrial water storage in the Upper Nile Basin: an evaluation of commonly used gridded GRACE products

2017 ◽  
Author(s):  
Mohammad Shamsudduha ◽  
Richard G. Taylor ◽  
Darren Jones ◽  
Laurent Longuevergne ◽  
Michael Owor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lake Victoria ◽  
Water Storage ◽  
Series Data ◽  
Terrestrial Water Storage ◽  
Nile Basin ◽  
Groundwater Storage ◽  
Terrestrial Water ◽  
Substantial Uncertainty

Abstract. GRACE (Gravity Recovery and Climate Experiment) satellite data monitor large-scale changes in total terrestrial water storage (ΔTWS) providing an invaluable tool where in situ observations are limited. Substantial uncertainty remains, however, in the amplitude of GRACE gravity signals and the disaggregation of ΔTWS into individual terrestrial water stores (e.g. groundwater storage). Here, we test the phase and amplitude of GRACE ΔTWS signals from 5 commonly-used gridded products (i.e., NASA's GRCTellus: CSR, JPL GFZ; JPL-Mascons; GRGS GRACE) using in situ data and modelled soil-moisture from the Global Land Data Assimilation System (GLDAS). The focus of this analysis is a large and accurately observed reduction in ΔTWS of 75 km3 from 2004 to 2006 in Lake Victoria in the Upper Nile Basin. We reveal substantial variability in current GRACE products to quantify the reduction of ΔTWS in Lake Victoria that ranges from 68 km3 (GRGS) to 50 km3 and 26 km3 for JPL-Mascons and GRCTellus, respectively. Representation of the phase in ΔTWS in the Upper Nile Basin by GRACE products varies but is generally robust with GRGS, JPL-Mascons and GRCTellus (ensemble mean of CSR, JPL and GFZ time-series data) explaining 91 %, 85 %, and 77 % of the variance, respectively, in in-situ ΔTWS. Resolution of changes in groundwater storage (ΔGWS) from GRACE ΔTWS is greatly constrained by both uncertainty in modelled changes in soil-moisture storage (ΔSMS) and the low annual amplitudes in ΔGWS (e.g., 3.5 to 4.4 cm) observed in deeply weathered crystalline rocks underlying the Upper Nile Basin. Our study highlights the substantial uncertainty in the amplitude of ΔTWS that can result from different data-processing strategies in commonly used, gridded GRACE products.

scholarly journals The importance of vegetation to understand terrestrial water storage variations

2021 ◽  
Author(s):  
Tina Trautmann ◽  
Sujan Koirala ◽  
Nuno Carvalhais ◽  
Andreas Güntner ◽  
Martin Jung
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Water Storage ◽  
Model Parameters ◽  
Intermediate Water ◽  
Model Structure ◽  
Hydrological Models ◽  
Terrestrial Water Storage ◽  
Terrestrial Water

Abstract. So far, various studies aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way how vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including vegetation on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment with vegetation parameters varying in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but including vegetation data led to even better performance and more physically plausible parameter values. Largest improvements regarding TWS and ET were seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of snow and different soil water storage components to the TWS variations, with the dominance of an intermediate water pool that interacts with the fast plant accessible soil moisture and the delayed water storage. The findings indicate the important role of deeper moisture storages as well as groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.

scholarly journals Hydrological Control of Vegetation Greenness Dynamics in Africa: A Multivariate Analysis Using Satellite Observed Soil Moisture, Terrestrial Water Storage and Precipitation

Land ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 15 ◽  
Author(s):  
Sabastine Ugbaje ◽  
Thomas Bishop
Keyword(s):  
Soil Moisture ◽  
Water Availability ◽  
Water Storage ◽  
Important Variable ◽  
Delayed Response ◽  
Terrestrial Water Storage ◽  
Vegetation Greenness ◽  
Vegetation Activity ◽  
Terrestrial Water ◽  
Hydrological Variables

Vegetation activity in many parts of Africa is constrained by dynamics in the hydrologic cycle. Using satellite products, the relative importance of soil moisture, rainfall, and terrestrial water storage (TWS) on vegetation greenness seasonality and anomaly over Africa were assessed for the period between 2003 and 2015. The possible delayed response of vegetation to water availability was considered by including 0–6 and 12 months of the hydrological variables lagged in time prior to the vegetation greenness observations. Except in the drylands, the relationship between vegetation greenness seasonality and the hydrological measures was generally strong across Africa. Contrarily, anomalies in vegetation greenness were generally less coupled to anomalies in water availability, except in some parts of eastern and southern Africa where a moderate relationship was evident. Soil moisture was the most important variable driving vegetation greenness in more than 50% of the areas studied, followed by rainfall when seasonality was considered, and by TWS when the monthly anomalies were used. Soil moisture and TWS were generally concurrent or lagged vegetation by 1 month, whereas precipitation lagged vegetation by 1–2 months. Overall, the results underscore the pre-eminence of soil moisture as an indicator of vegetation greenness among satellite measured hydrological variables.

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