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.
Highlights
Satellite-based surface soil moisture (SM) or/and snow depth (SD) retrievals data are commonly used to improve streamflow and/or flood forecasts [1,2,3,4,5]
In the analysis presented below, it is applied in all instances that a precipitation time series is required except for the calibration of the Soil Moisture Analysis Research Tool (SMART) and Snow Depth Analysis Rainfall Tool (SDART) λ factors described in Sections 2.4 and 2.5
All analysis is based on calibration of the Hydrologiska Byrans Vattenbalansavdelning (HBV) model against outlet streamflow observations acquired for both the Tuotuo River and Ganzi Basins (Section 3.1)
Summary
Satellite-based surface soil moisture (SM) or/and snow depth (SD) retrievals data are commonly used to improve streamflow and/or flood forecasts [1,2,3,4,5]. This potential is likely to receive greater attention in the coming decade as additional remotely sensed soil moisture data products reach maturity. Longer-term surface relative soil saturation products generated by the Advanced Scatterometer (ASCAT) onboard of the Meteorological Operational (METOP) Satellite have been found to accurately reproduce the temporal dynamics of in-situ and ground modeled SM observations across different sites in Europe [3,10]. Based on the heritage of the ERS scatterometer, the ASCAT sensor has provided the basis of an operational, global-scale SM product since January
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