Abstract
Physically-based or process-based hydrologic models play a critical role in hydrologic forecasting [...]
Highlights
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Jadidoleslam et al [5] assimilated satellite-based soil moisture estimates including Soil Moisture Active Passive (SMAP) [7] and Soil Moisture and Ocean Salinity (SMOS) [8] for real-time streamflow predictions. They used a distributed hydrologic model called the Hillslope Link Model (HLM) and the same baseline parameter set that was determined a priori to isolate the impact of three different data assimilation approaches on streamflow predictions
Using the Kling–Gupta Efficiency (KGE) and Peak Difference Ratio (PDR), they found that the EnKFV, ensemble Kalman filter (EnKF), and hard update resulted in the most, intermediate, and least significant improvements, respectively
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The main interest of this Special Issue is on how to improve hydrological modeling predictions by assimilating data from multiple sources. Data assimilation is a procedure in which data observed from a system are mathematically or statistically analyzed and integrated into models to improve their predictive performance.
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