Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?
文献类型：期刊论文
| 作者 | Sun, Alexander Y.1; Scanlon, Bridget R.1; Zhang, Zizhan2; Walling, David3; Bhanja, Soumendra N.4; Mukherjee, Abhijit4; Zhong, Zhi1 |
| 刊名 | WATER RESOURCES RESEARCH
 |
| 出版日期 | 2019-02-01 |
| 卷号 | 55期号:2页码:1179-1195 |
| 关键词 | deep learning GRACE GLDAS Unet transfer learning CNN |
| ISSN号 | 0043-1397 |
| DOI | 10.1029/2018WR023333 |
| 英文摘要 | Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH-simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow-on mission, GRACE-FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country-average correlation coefficient of 0.94 and Nash-Sutcliff efficient of 0.87, or 14% and 52% improvement, respectively, over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region. Plain Language Summary Global hydrological models are increasingly being used to assess water availability and sea level rise. Deficiencies in the conceptualization and parameterization in these models may introduce significant uncertainty in model predictions. GRACE satellite senses total water storage at the regional/continental scales. In this study, we applied deep learning to learn the spatial and temporal patterns of mismatch or residual between model simulation and GRACE observations. This hybrid learning approach leverages strengths of data science and hypothesis-driven physical modeling. We show, through three different types of convolution neural network-based deep learning models, that deep learning is a viable approach for improving model-GRACE match. The method can also be used to fill in data gaps between GRACE missions. |
| 资助项目 | Jackson School of Geosciences, UT Austin ; National Natural Science Foundation of China[41874098] |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000461858900017 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://202.127.146.157/handle/2RYDP1HH/7078]  |
| 专题 | 中国科学院武汉植物园 |
| 通讯作者 | Sun, Alexander Y. |
| 作者单位 | 1.Univ Texas Austin, Jackson Sch Geosci, Bur Econ Geol, Austin, TX 78712 USA 2.Chinese Acad Sci, Inst Geodesy & Geophys, State Key Lab Geodesy & Earths Dynam, Wuhan, Hubei, Peoples R China 3.Univ Texas Austin, Texas Adv Comp Ctr, Austin, TX 78712 USA 4.Indian Inst Technol Kharagpur, Dept Geol & Geophys, Kharagpur, W Bengal, India |
| 推荐引用方式 GB/T 7714 | Sun, Alexander Y.,Scanlon, Bridget R.,Zhang, Zizhan,et al. Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?[J]. WATER RESOURCES RESEARCH,2019,55(2):1179-1195. |
| APA | Sun, Alexander Y..,Scanlon, Bridget R..,Zhang, Zizhan.,Walling, David.,Bhanja, Soumendra N..,...&Zhong, Zhi.(2019).Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?.WATER RESOURCES RESEARCH,55(2),1179-1195. |
| MLA | Sun, Alexander Y.,et al."Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?".WATER RESOURCES RESEARCH 55.2(2019):1179-1195. |
入库方式： OAI收割
来源：武汉植物园
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