Water Resources Assessment System Research Articles

Integrating satellite soil-moisture estimates and hydrological model products over Australia

Accurate soil-moisture monitoring is essential for water-resource management and agricultural applications, and is now widely undertaken using satellite remote sensing or terrestrial hydrological models’ products. While both methods have limitations, e.g. the limited soil depth resolution of space-borne data and data deficiencies in models, data-assimilation techniques can provide an alternative approach. Here, we use the recently developed data-driven Kalman–Takens approach to integrate satellite soil-moisture products with those of the Australian Water Resources Assessment system Landscape (AWRA-L) model. This is done to constrain the model’s soil-moisture simulations over Australia with those observed from the Advanced Microwave Scanning Radiometer-Earth Observing System and Soil-Moisture and Ocean Salinity between 2002 and 2017. The main objective is to investigate the ability of the integration framework to improve AWRA-L simulations of soil moisture. The improved estimates are then used to investigate spatiotemporal soil-moisture variations. The results show that the proposed model-satellite data integration approach improves the continental soil-moisture estimates by increasing their correlation to independent in situ measurements (∼10% relative to the non-assimilation estimates).HighlightsSatellite soil-moisture measurements are used to improve model simulation.A data-driven approach based on Kalman–Takens is applied.The applied data-integration approach improves soil-moisture estimates.

Grey Clustering Evaluation of Water Resources Carrying Capacity Based on Triangle Whitening Weight Function

Water resources are an important component of the resource environment. The sustainable carrying capacity of water resources is an essential indicator for the comprehensive evaluation of regional sustainable development. This paper will select 17 water resources carrying capacity impact factors to establish a sustainable water resources assessment system. The grey clustering evaluation based on the triangle whitening weight function is used to obtain the objective evaluation results of water resources carrying capacity.

Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization

Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations. In this study, we assess the application of local analysis (LA) to maximize the contribution of GRACE TWS in hydrological data assimilation. For this, we assimilate GRACE TWS into the World-Wide Water Resources Assessment system (W3RA) over the Australian continent while applying LA and accounting for existing spatial correlations using the full error covariance matrix. GRACE TWS data is applied with different spatial resolutions including 1° to 5° grids, as well as basin averages. The ensemble-based sequential filtering technique of the Square Root Analysis (SQRA) is applied to assimilate TWS data into W3RA. For each spatial scale, the performance of the data assimilation is assessed through comparison with independent in-situ ground water and soil moisture observations. Overall, the results demonstrate that LA is able to stabilize the inversion process (within the implementation of the SQRA filter) leading to less errors for all spatial scales considered with an average RMSE improvement of 54% (e.g., 52.23 mm down to 26.80 mm) for all the cases with respect to groundwater in-situ measurements. Validating the assimilated results with groundwater observations indicates that LA leads to 13% better (in terms of RMSE) assimilation results compared to the cases with Gaussian errors assumptions. This highlights the great potential of LA and the use of the full error covariance matrix of GRACE TWS estimates for improved data assimilation results.

Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment

Summary A framework was developed for the continental assimilation of satellite soil moisture (SM) into an operational water balance modelling system. The ensemble Kalman filter (EnKF) was implemented to assimilate AMSR-E and ASCAT-derived SM products into the landscape model of the Australian Water Resources Assessment system (AWRA-L) and generate ensembles of daily top-layer and shallow root-zone soil moisture analyses for the continent at 0.05° resolution. We evaluated the AWRA-L SM estimates with and without assimilation against in situ moisture measurements in southeast Australia (OzNet), as well as against a new network of cosmic-ray moisture probes (CosmOz) spread across the country. Results show that AWRA-L root-zone moisture estimates are improved though the assimilation of satellite SM: model estimates of 0–30 cm moisture content improved for more than 90% of OzNet sites, with an increase in average correlation from 0.68 (before assimilation) to 0.73 (after assimilation); while estimates 0–90 cm moisture improved for 60% of sites with increased average correlation from 0.56 to 0.65. The assimilation of AMSR-E and ASCAT appeared to yield similar performance gains for the top-layer, however ASCAT data assimilation improved root-zone estimation for more sites. Poor performance of one data set was compensated by the other through joint assimilation. The most significant improvements in AWRA-L root-zone moisture estimation (with increases in correlation as high as 90%) occurred for sites where both the assimilation of satellite soil moisture improved top-layer SM accuracy and the open-loop deep-layer storage estimates were reasonably good. CosmOz SM measurements exhibited highest correlation with AWRA-L estimates for modelled root-zones layer thicknesses ranging from 20 cm to 1 m. Slight improvements through satellite data assimilation were observed for only 2 of 7 CosmOz sites, but the comparison was affected by a short data overlap period. The location of some of the CosmOz probes was not optimal for evaluation of satellite SM assimilation, but their utility is demonstrated and the observations may become suitable for assimilation themselves in future.

Conceptual evaluation of continental land-surface model behaviour

Continental land-surface models, such as the landscape component of the Australian Water Resources Assessment System (AWRA-L), aim to simulate the water balance over a wide variety of climates, land forms and land uses. To accommodate this range of hydrological conditions, model conceptualisation has to be flexible, while at the same time robust and parsimonious to allow for calibration using sparse data sets.In this study a Monte Carlo sensitivity analysis of the AWRA-L system is carried out as a step preceding calibration in which the hyperspace formed by parameters and initial conditions is explored using Latin Hypercube Sampling. The main goal is to test whether the model behaviour is in accordance with current understanding of Australian hydrology and to guide calibration. To visualise and analyse the high-dimensionality of the output space and the complex, non-linear interactions between processes and parameters, we used Self Organizing Maps, a non-parametric neural network.The results show that the main cause of non-linear model behaviour can be attributed to the ratio of rainfall over potential evaporation ratio, which determines which processes will dominate the water balance and the persistence of initial conditions. The model behaviour corresponds well to the current understanding of the hydrology of the Australian continent.

Land cover and water yield: inference problems when comparing catchments with mixed land cover

Abstract. Controlled experiments provide strong evidence that changing land cover (e.g. deforestation or afforestation) can affect mean catchment streamflow (Q). By contrast, a similarly strong influence has not been found in studies that interpret Q from multiple catchments with mixed land cover. One possible reason is that there are methodological issues with the way in which the Budyko framework was used in the latter type studies. We examined this using Q data observed in 278 Australian catchments and by making inferences from synthetic Q data simulated by a hydrological process model (the Australian Water Resources Assessment system Landscape model). The previous contrasting findings could be reproduced. In the synthetic experiment, the land cover influence was still present but not accurately detected with the Budyko- framework. Likely sources of interpretation bias demonstrated include: (i) noise in land cover, precipitation and Q data; (ii) additional catchment climate characteristics more important than land cover; and (iii) covariance between Q and catchment attributes. These methodological issues caution against the use of a Budyko framework to quantify a land cover influence in Q data from mixed land-cover catchments. Importantly, however, our findings do not rule out that there may also be physical processes that modify the influence of land cover in mixed land-cover catchments. Process model simulations suggested that lateral water redistribution between vegetation types and recirculation of intercepted rainfall may be important.

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5×20m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model.In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates.The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling.

Use of Gravity Recovery and Climate Experiment terrestrial water storage retrievals to evaluate model estimates by the Australian water resources assessment system

Terrestrial water storage (TWS) estimates retrieved from the Gravity Recovery and Climate Experiment (GRACE) satellite mission were compared to TWS modeled by the Australian Water Resources Assessment (AWRA) system. The aim was to test whether differences could be attributed and used to identify model deficiencies. Data for 2003–2010 were decomposed into the seasonal cycle, linear trends and the remaining de‐trended anomalies before comparing. AWRA tended to have smaller seasonal amplitude than GRACE. GRACE showed a strong (>15 mm yr−1) drying trend in northwest Australia that was associated with a preceding period of unusually wet conditions, whereas weaker drying trends in the southern Murray Basin and southwest Western Australia were associated with relatively dry conditions. AWRA estimated trends were less negative for these regions, while a more positive trend was estimated for areas affected by cyclone Charlotte in 2009. For 2003–2009, a decrease of 7–8 mm yr−1 (50–60 km3 yr−1) was estimated from GRACE, enough to explain 6%–7% of the contemporary rate of global sea level rise. This trend was not reproduced by the model. Agreement between model and data suggested that the GRACE retrieval error estimates are biased high. A scaling coefficient applied to GRACE TWS to reduce the effect of signal leakage appeared to degrade quantitative agreement for some regions. Model aspects identified for improvement included a need for better estimation of rainfall in northwest Australia, and more sophisticated treatment of diffuse groundwater discharge processes and surface‐groundwater connectivity for some regions.

Water Management Reform in Poland: A Step Toward Ecodevelopment

A framework for water management reform is included in Poland's new Water Law, which is currently under discussion in the lower house of Parliament. The new system will decentralize water management in Poland, create more efficient control and enforcement structures, establish a new water resources assessment system, and allow for increased public involvement in water-related decision making. The core of the new structure will be river basin-based Water Management Boards with broad authorities, controlled by the Water Councils with public participation. The Boards' water management responsi bilities will be separate from control and enforcement. To meet expectations associated with the reform, the new system has to integrate surface and groundwater resources management and different competencies in administering waters. The reform's water protection mechanisms must include pollu tion prevention. The public must have a means of playing an active role in local and regional water-related decision making.