Improving surface soil moisture estimates in West Africa through GRACE data assimilation

Hahn Chul Jung; Augusto Getirana; Kristi R. Arsenault; Sujay Kumar; Issoufou Maigary

doi:10.1016/j.jhydrol.2019.05.042

Improving surface soil moisture estimates in West Africa through GRACE data assimilation

Hahn Chul Jung, Augusto Getirana, Kristi R. Arsenault, Sujay Kumar, Issoufou Maigary

Department of Earth System Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	192-201
Number of pages	10
Journal	Journal of Hydrology
Volume	575
DOIs	https://doi.org/10.1016/j.jhydrol.2019.05.042
Publication status	Published - 2019 Aug

Bibliographical note

Funding 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 ).

Publisher Copyright:
© 2019 Elsevier B.V.

All Science Journal Classification (ASJC) codes

Water Science and Technology

Access to Document

10.1016/j.jhydrol.2019.05.042

Cite this

@article{2f4a62847a5040279d46e94b3c3ab2e9,

title = "Improving surface soil moisture estimates in West Africa through GRACE data assimilation",

abstract = "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.",

author = "Jung, {Hahn Chul} and Augusto Getirana and Arsenault, {Kristi R.} and Sujay Kumar and Issoufou Maigary",

note = "Funding 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 donn{\'e}ees 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 ). Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

year = "2019",

month = aug,

doi = "10.1016/j.jhydrol.2019.05.042",

language = "English",

volume = "575",

pages = "192--201",

journal = "Journal of Hydrology",

issn = "0022-1694",

publisher = "Elsevier",

}

TY - JOUR

T1 - Improving surface soil moisture estimates in West Africa through GRACE data assimilation

AU - Jung, Hahn Chul

AU - Getirana, Augusto

AU - Arsenault, Kristi R.

AU - Kumar, Sujay

AU - Maigary, Issoufou

N1 - Funding 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 ). Publisher Copyright: © 2019 Elsevier B.V.

PY - 2019/8

Y1 - 2019/8

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85066089986&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066089986&partnerID=8YFLogxK

U2 - 10.1016/j.jhydrol.2019.05.042

DO - 10.1016/j.jhydrol.2019.05.042

M3 - Article

AN - SCOPUS:85066089986

SN - 0022-1694

VL - 575

SP - 192

EP - 201

JO - Journal of Hydrology

JF - Journal of Hydrology

ER -

Improving surface soil moisture estimates in West Africa through GRACE data assimilation

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this