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Open AccessArticle

A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa

1
School of Earth Sciences and Engineering, Hohai University, Jiangning Campus, Nanjing 211100, China
2
Department of Geomatic Engineering, Kwame Nkrumah University of Science and Technology, Kumasi AK000-AK911, Ghana
*
Author to whom correspondence should be addressed.
Water 2019, 11(2), 401; https://doi.org/10.3390/w11020401
Received: 5 January 2019 / Revised: 6 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Remotely sensed terrestrial water storage changes (TWSC) from the past Gravity Recovery and Climate Experiment (GRACE) mission cover a relatively short period (≈15 years). This short span presents challenges for long-term studies (e.g., drought assessment) in data-poor regions like West Africa (WA). Thus, we developed a Nonlinear Autoregressive model with eXogenous input (NARX) neural network to backcast GRACE-derived TWSC series to 1979 over WA. We trained the network to simulate TWSC based on its relationship with rainfall, evaporation, surface temperature, net-precipitation, soil moisture, and climate indices. The reconstructed TWSC series, upon validation, indicate high skill performance with a root-mean-square error (RMSE) of 11.83 mm/month and coefficient correlation of 0.89. The validation was performed considering only 15% of the available TWSC data not used to train the network. More so, we used the total water content changes (TWCC) synthesized from Noah driven global land data assimilation system in a simulation under the same condition as the GRACE data. The results based on this simulation show the feasibility of the NARX networks in hindcasting TWCC with RMSE of 8.06 mm/month and correlation coefficient of 0.88. The NARX network proved robust to adequately reconstruct GRACE-derived TWSC estimates back to 1979. View Full-Text
Keywords: artificial neural network; GRACE; terrestrial water storage artificial neural network; GRACE; terrestrial water storage
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MDPI and ACS Style

Ferreira, V.G.; Andam-Akorful, S.A.; Dannouf, R.; Adu-Afari, E. A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa. Water 2019, 11, 401.

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