气候变化领域集成服务门户(CCPortal): Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

CCPortal

DOI	10.3390/rs15040873
	Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)
	Ali, Shoaib; Khorrami, Behnam; Jehanzaib, Muhammad; Tariq, Aqil; Ajmal, Muhammad; Arshad, Arfan; Shafeeque, Muhammad; Dilawar, Adil; Basit, Iqra; Zhang, Liangliang; Sadri, Samira; Niaz, Muhammad Ahmad; Jamil, Ahsan; Khan, Shahid Nawaz
发表日期	2023
EISSN	2072-4292
卷号	15 期号:4
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.
英文关键词	Indus Basin Irrigation System; GRACE; TWS; machine learning models; downscaling; drought monitoring
语种	英语
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000940714500001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281272
作者单位	Northeast Agricultural University - China; Dokuz Eylul University; Hanyang University; Mississippi State University; Wuhan University; University of Engineering & Technology Peshawar; Oklahoma State University System; Oklahoma State University - Stillwater; University of Bremen; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Punjab; Shahid Chamran University of Ahvaz; New Mexico State University; South Dakota State University
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],2023,15(4).
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).
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).