Improving estimates of water resources in a semi-arid region by assimilating GRACE data into the PCR-GLOBWB hydrological model

Abstract

Abstract. An accurate estimation of water resources dynamics is crucial for proper management of both agriculture and the local ecology, particularly in semi-arid regions. Imperfections in model physics, uncertainties in model land parameters and meteorological data, as well as the human impact on land changes often limit the accuracy of hydrological models in estimating water storages. To mitigate this problem, this study investigated the assimilation of terrestrial water storage variation (TWSV) estimates derived from the Gravity Recovery And Climate Experiment (GRACE) data using an ensemble Kalman filter (EnKF) approach. The region considered was the Hexi Corridor in northern China. The hydrological model used for the analysis was PCR-GLOBWB, driven by satellite-based forcing data from April 2002 to December 2010. The impact of the GRACE data assimilation (DA) scheme was evaluated in terms of the TWSV, as well as the variation of individual hydrological storage estimates. The capability of GRACE DA to adjust the storage level was apparent not only for the entire TWSV but also for the groundwater component. In this study, spatially correlated errors in GRACE data were taken into account, utilizing the full error variance-covariance matrices provided as a part of the GRACE data product. The benefits of this approach were demonstrated by comparing the EnKF results obtained with and without taking into account error correlations. The results were validated against in situ groundwater data from five well sites. On average, the experiments showed that GRACE DA improved the accuracy of groundwater storage estimates by as much as 25 %. The inclusion of error correlations provided an equal or greater improvement in the estimates. In contrast, a validation against in situ streamflow data from two river gauges showed no significant benefits of GRACE DA. This is likely due to the limited spatial and temporal resolution of GRACE observations. Finally, results of the GRACE DA study were used to assess the status of water resources over the Hexi Corridor over the considered 9-year time interval. Areally averaged values revealed that TWS, soil moisture, and groundwater storages over the region decreased with an average rate of approximately 0.2, 0

On the use of the GRACE normal equation of inter-satellite tracking data for estimation of soil moisture and groundwater in Australia

“…It is clearly seen that GWS values from App 1 are overestimated while the one from App 2 fits the ground data significantly better. This behaviour was also seen in Tangdamrongsub et al (2017), where the water storage estimates tend to be overestimated when error components such as spatial correlation error were neglected as in App 1. GWS from App 2 shows clear improvements in terms of NS coefficients in both networks.…”

“…This method is called Approach 1 (App 1). In Approach 2 (App 2) as in Tangdamrongsub et al (2017), by accounting for the uncertainty in model outputs and GRACE data, the water storage states are updated through a Kalman filter:…”

“…Furthermore, GRACE error and sensitivity is dependent on latitudes due to the orbit convergence toward poles and any post-processing filters will alter the GRACE data and their error information. Rigorous statistical error information is of equal importance to derivation of TWS for data assimilation and model calibration (Tangdamrongsub et al, 2017;Schumacher et al, 2016Schumacher et al, , 2018. TWS and its uncertainty estimates should be formulated directly from L1B data considering the complete statistical information.…”

“…It is apparent that the use of post-processed GRACE products often requires data tuning, leading possibly to an integration of the altered gravity information into the data assimilation system. Some recent studies began to employ the full variance-covariance information in the data assimilation scheme to enhance the quality of the estimates (Eicker et al, 2014;Schumacher et al, 2016;Tangdamrongsub et al, 2017;Khaki et al, 2017a, b).…”

Characterizing Drought and Flood Events over the Yangtze River Basin Using the HUST-Grace2016 Solution and Ancillary Data

“…Two kinds of hydrological models were used in this study: PCR-GLOBWB [15][16][17] and GLDAS [18]. The PCR-GLOBWB model provides monthly TWS estimates as the sum of surface water, soil moisture and groundwater.…”