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Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin
Journal of Hydrology ( IF 6.4 ) Pub Date : 2022-08-06 , DOI: 10.1016/j.jhydrol.2022.128295
Shoaib Ali , Dong Liu , Qiang Fu , Muhammad Jehanzeb Masud Cheema , Subodh Chandra Pal , Arfan Arshad , Quoc Bao Pham , Liangliang Zhang

The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25° from 1°. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and self-calibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely, NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions.



中文翻译:

基于机器学习的 GRACE 卫星数据在印度河流域构建时空高分辨率地下水干旱

降水不足引发的复杂现象是影响水资源和人类生活的干旱。由于时空尺度上的地下水观测稀少,传统的地下水干旱事件评估方法受到了阻碍。印度河流域灌溉系统(IBIS)研究区的这些地下水干旱事件没有得到很好的整体研究。本研究将四种机器学习模型应用于重力恢复和气候实验 (GRACE) 陆地蓄水 (TWS) 和地下水蓄水 (GWS) 数据的训练数据集,以将分辨率从 1° 提高到 0.25°。Extreme Gradient Boosting (XGBoost) 模型优于四个模型,结果显示 Pearson 相关性 (R) (0.99)、Nash Sutcliff 效率 (NSE) (0.99)、均方根误差 (RMSE) (5.22 mm) 和平均绝对误差(MAE)(2。75 毫米)。GRACE 地下水干旱指数 (GGDI) 是通过标准化 XGBoost 缩减的 GWS 来计算的。分析了2003-2016年整个IBIS中GGDI的趋势特征、时间演化和空间分布,采用小波相干法评价遥相关因子与GGDI的关系。XGBoost 降尺度模型可以准确再现当地地下水行为,验证系数值的相关性可接受(范围从 0.02 到 0.84)。使用 1、3 和 6 个月的累积标准化降水蒸散指数 (SPEI) 和自校准的帕尔默干旱严重程度指数 (sc-PDSI) 来验证 GGDI。研究结果表明,GGDI 具有与 SPEI-3 和 SPEI-6 和 sc-PDSI 相当的干旱模式。遥相关因子对小波相干技术显示的 GGDI 有显着影响。在 IBIS 的其他遥相关因素中,观察到海面温度指数(即 NINO3.4)对 GGDI 的影响显着较高。拟议的框架可以作为干旱监测的有用工具,更好地了解 IBIS 和其他类似气候区域的极端水文气候条件。

更新日期:2022-08-06
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