TY - JOUR
T1 - Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data
AU - Forootan, Ehsan
AU - Kusche, Jürgen
AU - Loth, Ina
AU - Schuh, Wolf Dieter
AU - Eicker, Annette
AU - Awange, Joseph
AU - Longuevergne, Laurent
AU - Diekkrüger, Bernd
AU - Schmidt, Michael
AU - Shum, C. K.
N1 - Funding Information:
Acknowledgments The authors would like to thank M. J. Rycroft (Editor in Chief) and anonymous reviewers for their useful comments, which considerably improved this paper. We also thank S. Nahmani (LAboratoire de Recherche en Géodésie, France) for his detailed comments on the earlier version of this study. We are grateful for the GRACE, WGHM, TRMM, and SST data, as well as climate indices used in this study. E. Forootan and J. Kusche are grateful for the supports by the German Research Foundation (DFG), under the project DFG BAYES-G. The Ohio State University component of the research is supported by the NASA’s Advanced Concepts in Space Geodesy Program (Grant No. NNX12AK28G) and by the Chinese Academy of Sciences/SAFEA International Partnership Program for Creative Research Teams (Grant No. KZZD-EW-TZ-05). The authors are grateful for the data used in this study. This is a TIGeR Publication no. 510.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management. Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162 days as well as a-hopefully-limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near-real-time GRACE forecast over the regions that exhibit strong teleconnections.
AB - West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management. Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162 days as well as a-hopefully-limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near-real-time GRACE forecast over the regions that exhibit strong teleconnections.
KW - Autoregressive model
KW - GRACE gap filling
KW - Independent Component Analysis
KW - Predicting GRACE-TWS
KW - West Africa
U2 - 10.1007/s10712-014-9292-0
DO - 10.1007/s10712-014-9292-0
M3 - Journal Article
AN - SCOPUS:84903899520
SN - 0169-3298
VL - 35
SP - 913
EP - 940
JO - Surveys in Geophysics
JF - Surveys in Geophysics
IS - 4
ER -
