Mohammed, K.; Leconte, R.; Trudel, M. Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation. Water2023, 15, 321.
Mohammed, K.; Leconte, R.; Trudel, M. Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation. Water 2023, 15, 321.
Mohammed, K.; Leconte, R.; Trudel, M. Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation. Water2023, 15, 321.
Mohammed, K.; Leconte, R.; Trudel, M. Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation. Water 2023, 15, 321.
Abstract
Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal dimensions. A set of synthetic experiments were designed in this study to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM as well as streamflow. Results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but not so much in the bottom soil layer. The updating of model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments.
Keywords
data assimilation; soil moisture; EnKF; SMAP; WRF-Hydro
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
