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

Ferreira, Vagner G.; Andam-Akorful, Samuel A.; Dannouf, Ramia; Adu-Afari, Emmanuel

doi:10.3390/w11020401

Cited by 19 publications

(9 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding regional studies, our results are close to the results of Humphrey et al (2017), with a CC value of 0.96, but better than the results in Becker et al (2011), with a CC value of 0.9 in the Amazon basin. The DNN modeled results in our study are close to the results in Ferreira et al (2019) in the Niger (NSE = 0.91) and Volta (NSE = 0.89) basins but are better in the Senegal (NSE = 0.78) basin and also better than the results in Ahmed et al (2019) in the Nile (NSE = 0.60), Limpopo (NSE = 0.59), and Volta (NSE = 0.90) basin. In addition, we obtained somewhat improved results than Sun et al (2019) in India from the DNN and SARIMAX models, with mean NSE/CC values of 0.93/0.96 in our study versus 0.87/0.94 in Sun et al (2019).…”

Section: Discussionsupporting

confidence: 91%

“…Precipitation and temperature indeed strongly affect water storage changes in most of the global land surface; however, other factors would also be highly correlated to TWSA, like Noah TWS used in this work and in Sun et al (2019). On the other hand, more forcing data do not necessarily mean better performance of the data‐driven models (Ahmed et al, 2019; Ferreira et al, 2019). It is not just the input data that determine the performance of models, but also the parameters and hyperparameters of the model itself.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Sun

Long

Yang

et al. 2020

Water Resources Research

171

View full text Add to dashboard Cite

Launched in May 2018, the Gravity Recovery and Climate Experiment Follow-On mission (GRACE-FO)-the successor of the erstwhile GRACE mission-monitors changes in total water storage, which is a critical state variable of the regional and global hydrologic cycles. However, the gap between data of the two missions is breaking the continuity of the observations and limiting its further application. In this study, we used three learning-based models, that is, deep neural network, multiple linear regression (MLR), and seasonal autoregressive integrated moving average with exogenous variables, and six GRACE solutions (i.e., Jet Propulsion Laboratory spherical harmonics (JPL-SH), Center for Space Research SH (CSR-SH), GeoforschungsZentrum Potsdam SH (GFZ-SH), JPL mass concentration blocks (mascons) (JPL-M), CSR mascons (CSR-M), and Goddard Space Flight Center mascons (GSFC-M)) to reconstruct the missing monthly data at a grid cell scale. Evaluation showed that the three learning-based models were reliable for the reconstruction of GRACE data in areas with humid and no/low human interventions. The deep neural network models slightly outperformed the seasonal autoregressive integrated moving average with exogenous variables models and significantly outperformed the multiple linear regression models in most of 60 basins studied. The three GRACE mascon data sets performed better than the SH data sets at the basin scale. The models with SH solutions showed similar performance, but the models with the mascon solutions varied markedly in some basins. Results of this study are expected to provide a reference for bridging the data gaps between the GRACE and GRACE-FO satellites and for selecting suitable GRACE solutions for regional hydrologic studies.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Sun

Long

Yang

et al. 2020

Water Resources Research

171

View full text Add to dashboard Cite

show abstract

“…(3) the use of empirical orthogonal function decomposition to reconstruct TWS GRACE data over the Amazon basin by examining the correlation between TWS GRACE and water levels over inter-annual and multi-decadal time periods [54]; (4) the use of statistical models to reconstruct global natural-varying TWS GRACE using rainfall and temperature data [55]; and (5) the use of artificial intelligence to predict the TWS GRACE over a large karst plateau in Southwest China using in situ precipitation, monthly mean temperature, and GLDAS soil moisture [56] and to predict the TWS GRACE over West Africa using rainfall, evaporation, surface temperature, net-precipitation, soil moisture, and climate indices [57].…”

Section: Introductionmentioning

confidence: 99%

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

et al. 2019

View full text Add to dashboard Cite

The GRACE-derived terrestrial water storage (TWS GRACE ) provides measurements of the mass exchange and transport between continents, oceans, and ice sheets. In this study, a statistical approach was used to forecast TWS GRACE data using 10 major African watersheds as test sites. The forecasted TWS GRACE was then used to predict drought events in the examined African watersheds. Using a nonlinear autoregressive with exogenous input (NARX) model, relationships were derived between TWS GRACE data and the controlling and/or related variables (rainfall, temperature, evapotranspiration, and Normalized Difference Vegetation Index). The performance of the model was found to be "very good" (Nash-Sutcliffe (NSE) > 0.75; scaled root mean square error (R*) < 0.5) for 60% of the investigated watersheds, "good" (NSE > 0.65; R* < 0.6) for 10%, and "satisfactory" (NSE > 0.50; R* < 0.7) for the remaining 30% of the watersheds. During the forecasted period, no drought events were predicted over the Niger basin, the termination of the latest (March-October 2015) drought event was observed over the Zambezi basin, and the onset of a drought event (January-March 2016) over the Lake Chad basin was correctly predicted. Adopted methodologies generate continuous and uninterrupted TWS GRACE records, provide predictive tools to address environmental and hydrological problems, and help bridge the current gap between GRACE missions.

show abstract

“…The precipitation and temperature [64], [65] are used the most, for at least 40% of the TWSA could be reconstructed only by using the two variability [66]. The other explanatory variables, such as humidity, ground pressure, and NDVI (normalized difference vegetation index) as well as WS, NT, ET, and RS used in this study are also widely used by the researchers [27]. According to Sun et al [55], it is not just the parameters and hyperparameters of the model itself that determine the performance of models, but also the input data would influence the performance.…”

Section: B Impact Of the Different Climate Drivers And Comparison With Previous Studiesmentioning

confidence: 99%

“…Recent developments of machine learning provide new avenues in data reconstruction (before April 2002) and prediction (after June 2017) [27]. Many studies have introduced learning-based approaches to reconstruct and predict TWSA by constructing relationships between TWSA and related climatic and hydrological variables.…”

Section: Introductionmentioning

confidence: 99%

Improving the Reliability of the Prediction of Terrestrial Water Storage in Yunnan Using the Artificial Neural Network Selective Joint Prediction Model

2021

View full text Add to dashboard Cite

Although Gravity Recovery and Climate Experiment (GRACE) can provide accurate estimates in water storage, there are about 11 months gaps between GRACE and its successor GRACE-Follow On (GRACE-FO). To improve the accuracy of bridging the gaps, this study combines the partial least squares regression (PLSR) model with the learning-based models for the first time, and construct an artificial neural network selective joint prediction model. To this end, the performance of combination of PLSR and nonlinear autoregressive with exogenous input (NARX) models, the back propagation (BP) models, and multiply linear regression (MLR) models are compared on both grid and region scales. Results indicate the proportion of ''qualified'' and above predicted by PLSR combined with NARX on grid scale increased by 32.71% and 24.65% compared to the other two models. Finally, PLSR joint NARX model performs the best against GRACE observations, then employed to predict and bridge the TWSA gaps from July 2017 to December 2019 in northwest, southwest and central of Yunnan. The NSE values between predicted TWSA (terrestrial water storage anomalies) and GRACE-FO are increased by 55.10%, 93.10% and 133.33% compared to modeled TWSA and GRACE. This study provides a new perspective for filling the GRACE gap, and for selecting optimal variables in other relative hydrologic studies.INDEX TERMS GRACE/GRACE-FO mission, learning-based models, artificial neural network selective joint prediction model, TWSA.

show abstract

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

Cited by 19 publications

References 40 publications

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

Improving the Reliability of the Prediction of Terrestrial Water Storage in Yunnan Using the Artificial Neural Network Selective Joint Prediction Model

Contact Info

Product

Resources

About