Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)
文献类型：期刊论文
| 作者 | Ali, Shoaib; Khorrami, Behnam2; Jehanzaib, Muhammad3; Tariq, Aqil4,5; Ajmal, Muhammad6; Arshad, Arfan7; Shafeeque, Muhammad8; Dilawar, Adil9,10; Basit, Iqra11; Zhang, Liangliang |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 15期号:4页码:873 |
| 关键词 | Indus Basin Irrigation System GRACE TWS machine learning models downscaling drought monitoring |
| DOI | 10.3390/rs15040873 |
| 文献子类 | Article |
| 英文摘要 | Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1 degrees) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1 degrees to 0.25 degrees. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of -1.20 and -1.28 and total WSD of -496.99 mm and -734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS. |
| WOS关键词 | TERRESTRIAL WATER STORAGE ; SATELLITE-OBSERVATIONS ; MOISTURE CHANGES ; LAND ; PRECIPITATION ; GROUNDWATER ; CLIMATE ; RAINFALL ; INDEXES ; TRENDS |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000940714500001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200761]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.New Mex State Univ, Dept Plant & Environm Sci, 3170S Espina Str, Las Cruces, NM 88003 USA 2.Northeast Agr Univ, Sch Water Conservancy & Civil Engn, Harbin 150030, Peoples R China 3.Dokuz Eylul Univ, Grad Sch Appl & Nat Sci, Dept GIS, TR-35220 Izmir, Turkey 4.Hanyang Univ, Res Inst Engn & Technol, Ansan 15588, South Korea 5.Mississippi State Univ, Coll Forest Resources, Dept Wildlife Fisheries & Aquaculture, 775 Stone Blvd, Starkville, MS 39762 USA 6.Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan 430079, Peoples R China 7.Univ Engn & Technol, Dept Agr Engn, Peshawar 25120, Pakistan 8.Oklahoma State Univ, Dept Biosyst & Agr Engn, Stillwater, OK 74078 USA 9.Univ Bremen, Inst Geog, D-28359 Bremen, Germany 10.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resource & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ali, Shoaib,Khorrami, Behnam,Jehanzaib, Muhammad,et al. Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)[J]. REMOTE SENSING,2023,15(4):873. |
| APA | Ali, Shoaib.,Khorrami, Behnam.,Jehanzaib, Muhammad.,Tariq, Aqil.,Ajmal, Muhammad.,...&Khan, Shahid Nawaz.(2023).Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS).REMOTE SENSING,15(4),873. |
| MLA | Ali, Shoaib,et al."Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)".REMOTE SENSING 15.4(2023):873. |
入库方式： OAI收割
来源：地理科学与资源研究所
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