Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis
The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions have provided valuable data for monitoring global terrestrial water storage anomalies (TWSA) over the past two decades. However, the nearly one-year gap between these missions pose challenges for long-term TWSA me...
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000045 |
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| author | Jaydeo K. Dharpure Ian M. Howat Saurabh Kaushik Bryan G. Mark |
| author_facet | Jaydeo K. Dharpure Ian M. Howat Saurabh Kaushik Bryan G. Mark |
| author_sort | Jaydeo K. Dharpure |
| collection | DOAJ |
| description | The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions have provided valuable data for monitoring global terrestrial water storage anomalies (TWSA) over the past two decades. However, the nearly one-year gap between these missions pose challenges for long-term TWSA measurements and various applications. Unlike previous studies, we use a combination of Machine Learning (ML) methods—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Deep Neural Network (DNN), and Stacked Long-Short Term Memory (SLSTM)—to identify and efficiently bridge the gap between GRACE and GFO by using the best-performing ML model to estimate TWSA at each grid cell. The models were trained using six hydroclimatic variables (temperature, precipitation, runoff, evapotranspiration, ERA5-Land derived TWSA, and cumulative water storage change), as well as a vegetation index and timing variables, to reconstruct global land TWSA at 0.5° grid resolution. We evaluated the performance of each model using Nash-Sutcliffe Efficiency (NSE), Pearson's Correlation Coefficient (PCC), and Root Mean Square Error (RMSE). Our results demonstrate test accuracy with area weighted average NSE, PCC, and RMSE of 0.51 ± 0.31, 0.71 ± 0.23, and 4.75 ± 3.63 cm, respectively. The model's performance was further compared across five climatic zones, with two previously reconstructed products (Li and Humphrey methods) at 26 major river basins, during flood/drought events, and for sea-level rise. Our results showcase the model's superior performance and its capability to accurately predict data gaps at both grid and basin scales globally. |
| format | Article |
| id | doaj-art-67f7a388f9ca4022b9e6d0e194ab3bd8 |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-67f7a388f9ca4022b9e6d0e194ab3bd82025-02-08T05:01:10ZengElsevierScience of Remote Sensing2666-01722025-06-0111100198Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysisJaydeo K. Dharpure0Ian M. Howat1Saurabh Kaushik2Bryan G. Mark3Byrd Polar and Climate Research Center, The Ohio State University, Columbus, 43210, USA; Corresponding author.Byrd Polar and Climate Research Center, The Ohio State University, Columbus, 43210, USA; School of Earth Sciences, The Ohio State University, Columbus, 43210, USAByrd Polar and Climate Research Center, The Ohio State University, Columbus, 43210, USA; School of Earth Sciences, The Ohio State University, Columbus, 43210, USA; School of Geography, Development & Environment, University of Arizona, Tucson, 85721, USAByrd Polar and Climate Research Center, The Ohio State University, Columbus, 43210, USA; Department of Geography, The Ohio State University, Columbus, 43210, USAThe Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions have provided valuable data for monitoring global terrestrial water storage anomalies (TWSA) over the past two decades. However, the nearly one-year gap between these missions pose challenges for long-term TWSA measurements and various applications. Unlike previous studies, we use a combination of Machine Learning (ML) methods—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Deep Neural Network (DNN), and Stacked Long-Short Term Memory (SLSTM)—to identify and efficiently bridge the gap between GRACE and GFO by using the best-performing ML model to estimate TWSA at each grid cell. The models were trained using six hydroclimatic variables (temperature, precipitation, runoff, evapotranspiration, ERA5-Land derived TWSA, and cumulative water storage change), as well as a vegetation index and timing variables, to reconstruct global land TWSA at 0.5° grid resolution. We evaluated the performance of each model using Nash-Sutcliffe Efficiency (NSE), Pearson's Correlation Coefficient (PCC), and Root Mean Square Error (RMSE). Our results demonstrate test accuracy with area weighted average NSE, PCC, and RMSE of 0.51 ± 0.31, 0.71 ± 0.23, and 4.75 ± 3.63 cm, respectively. The model's performance was further compared across five climatic zones, with two previously reconstructed products (Li and Humphrey methods) at 26 major river basins, during flood/drought events, and for sea-level rise. Our results showcase the model's superior performance and its capability to accurately predict data gaps at both grid and basin scales globally.http://www.sciencedirect.com/science/article/pii/S2666017225000045GRACEGap fillingMass changeMachine learningDeep learning |
| spellingShingle | Jaydeo K. Dharpure Ian M. Howat Saurabh Kaushik Bryan G. Mark Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis Science of Remote Sensing GRACE Gap filling Mass change Machine learning Deep learning |
| title | Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis |
| title_full | Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis |
| title_fullStr | Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis |
| title_full_unstemmed | Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis |
| title_short | Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis |
| title_sort | combining machine learning algorithms for bridging gaps in grace and grace follow on missions using era5 land reanalysis |
| topic | GRACE Gap filling Mass change Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000045 |
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