Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management.
|Number of pages||10|
|Journal||Journal of Hydrology|
|Publication status||Published - 2019 Aug|
Bibliographical noteFunding Information:
This study was funded by NASA SERVIR Applied Sciences Program in Earth Science Division ( NNH15ZDA001N-SERVIR ). Computing resources were supported by the NASA Center for Climate Simulation (NCCS). GRACE land mascon solutions were downloaded from UT-CSR mascon website ( http://www2.csr.utexas.edu/grace ). The ASCAT data are provided by the NOAA Soil Moisture Operational Products System (SMOPS). The SMOS data are provided by the Centre Aval de Traitement des donnéees SMOS ( https://www.catds.fr ). The SMAP data are publicly available at the NASA National Snow and Ice Data Center Distributed Active Archive Center ( https://nsidc.org/data/smap/smap-data.html ).
© 2019 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Water Science and Technology