Improving the performance of remote sensing-based water budget components across mid- and small- scale basins

Abstract

In the last few decades, many global basins have been threatened by rapid urban growth and global warming, resulting in changes in their climate regime. Climate change has increased the incidence of extreme weather events, uncertain water availability, water scarcity, and water pollution. Remote sensing (RS) has emerged as a powerful technique that provides estimations with high spatiotemporal resolution and broad spatial coverage. In recent years, the efficacy of RS products for water budget (WB) analysis has been widely tested and implemented in global and regional basins. Although RS products provide high temporal and spatial resolution images with a near-global coverage, uncertainty is still a significant problem. The main goal of this study is to utilize two different approaches to minimize the uncertainty of the products and to improve RS-based WB estimations in mid- and small- scale basins. The first approach aims to improve the efficacy of water WB estimations from various hydrological data products in the Sakarya basin by; (1) Evaluating the uncertainties of hydrological data products, (2) Merging four precipitation (P) and six evapotranspiration (ET) products using the error variances, and (3) Employing the Constrained Kalman Filter (CKF) method to distribute residual errors (r) among WB components based on their relative uncertainties. The results showed that applying bias correction before the merging process improved estimations of P products with decreasing root mean square error (RMSE), except PERSIANN. VIC and bias-corrected CMORPH products outperformed other ET and bias-corrected P products, respectively, in terms of mean merging weights. The terrestrial water storage change (ΔS) is the primary reason for non-closure errors. This is mainly caused by the two facts. First, the Sakarya basin is a relatively small basin that GRACE can not simply resolve. Second, while P, ET, and Q mostly describe the surface water dynamics, ΔS includes both the surface water and ground water. It is well known that surface water and ground water have completely different dynamic behaviors. The change in surface water is much faster than the change in groundwater. The CKF results were insensitive to variations in uncertainties of runoff (Q). P derived from the CKF was the best output, with the highest correlation coefficient (CC) and the smallest root mean square deviation (RMSD). In the second approach, the annual r in the WB equation arising from the uncertainties of the RS products was minimized by applying fuzzy correction coefficients to each WB component. For analysis, three different fuzzy linear regression (FLR) models with fourteen different sub-models were used in the two basins having different spatial characteristics, namely Sakarya and Cyprus basins. The performance of sub-models is better in the Sakarya basin than that in the Cyprus basin, which has a higher leakage error due to across ocean/land boundary. Moreover, the Cyprus basin is too small for some low-resolution RS-based products to resolve. The Zeng and Hojati sub-models outperformed Tanaka sub-models in the Sakarya basin, whereas Zeng Case-I, Zeng Case-II, and Hojati (degree of fitting index (h) =0.9) sub-models showed the best performance in the Cyprus basin. The best fuzzy sub-models reduced the error up to 68% and 52% in terms of mean absolute error compared to non-fuzzy model in the Sakarya and Cyprus basins, respectively. Further evaluations showed that the best sub-model P well captured the temporal patterns of gauge observations in both basins. Moreover, they have the best consistency with gauge observations in terms of RMSE, Kling-Gupta efficiency (KGE), and percent bias (PBIAS) in the both basins. The results proved that the second approach will provide valuable insights into WB analysis in ungauged basins by incorporating the fuzzy logic approach into hydrological RS products. In general, the FLR and CKF derived P, ET, and Q showed similar seasonal variation with peak and bottom values appeared in nearly the same years. In terms of CC, RMSE, and bias, fuzzy outputs show closest agreement with CKF outputs for Q, with slightly less agreement for P and ET, and much less agreement for ΔS. It can be concluded that the majority of the errors in the second approach are caused by fuzzy ΔS.