Improving Streamflow Prediction Using Remotely-Sensed Soil Moisture and Snow Depth

Abstract

Abstract:The monitoring of both cold and warm season hydrologic processes in headwater watersheds is critical for accurate water resource monitoring in many alpine regions. This work presents a new method that explores the simultaneous use of remotely sensed surface soil moisture (SM) and snow depth (SD) retrievals to improve hydrological modeling in such areas. In particular, remotely sensed SM and SD retrievals are applied to filter errors present in both solid and liquid phase precipitation accumulation products acquired from satellite remote sensing. Simultaneously, SM and SD retrievals are also used to correct antecedent SM and SD states within a hydrological model. In synthetic data assimilation experiments, results suggest that the simultaneous correction of both precipitation forcing and SM/SD antecedent conditions is more efficient at improving streamflow simulation than data assimilation techniques which focus solely on the constraint of antecedent SM or SD conditions. In a real assimilation case, results demonstrate the potential benefits of remotely sensed SM and SD retrievals for improving the representation of hydrological processes in a headwater basin. In particular, it is demonstrated that dual precipitation/state correction represents an efficient strategy for improving the simulation of cold-region hydrological processes.

Component Analysis of Errors in Four GPM-Based Precipitation Estimations over Mainland China

“…Meanwhile, GSMaP was identified to be the best performed precipitation estimation over the east and south of mainland China in spite of the performance reduction over the arid northwest. In order to improve the quality of precipitation, more research methods should be explored, such as the integration of multi-source precipitation information (e.g., satellite-based precipitation estimations, ground gauge/radar observations, reanalysis precipitation products, climate model products, and so on) and the data assimilation method using precipitation related geophysical variables (e.g., soil moisture [59] and snow depth [60]). Besides, further investigations should be also carried out to assess IMERG Level-2 retrieval algorithms and thus provide the underlying insights of how the uncertainty propagates to the IMERG Level-3 precipitation products.…”

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions

“…Remote sensing can play a direct role in enhancing hydrological predictions via updating of initial conditions. Data needed to initialize forecast models (e.g., soil moisture, snow, and river levels/discharge) can be derived from remote sensing and many studies have shown the benefits of updating hydrological forecasts (Hirpa et al, ; Lü et al, ) particularly increasing predictability in snow‐dominated and dry regions of the world (Shukla et al, 2013). Several experimental, operational systems that provide satellite‐based monitoring and initialization of hydrological forecasts include the LACFDM (), African Flood and Drought Monitor (Sheffield et al, ), and the European Flood Alert System (Thielen et al, ).…”

On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation

“…The variability of the results in our study is not exceptional in the SSM−DA literature. Studies of this type have reported a variety of results, ranging from successful improvements (Lievens et al, 2015;López López et al, 2016), to slight improvements (Pauwels et al, 2002), no significant differences (Lü et al, 2016) or even moderate decreases in model performance Matgen et al, 2012a). Studies on the topic cover a wide range of catchment sizes, climate conditions, remote sensing sources, model conceptualizations and assimilation methods.…”