Aon Suvro, Nandi Subimal, Sen Shoubhik, Biswas Sujata
Department of Civil Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India.
Department of Civil Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India.
Sci Total Environ. 2024 Nov 15;951:175666. doi: 10.1016/j.scitotenv.2024.175666. Epub 2024 Aug 20.
The Ganga River Basin which is the home of almost half a billion people have plunged into groundwater drought due to anthropogenic activities. Hence groundwater drought assessment and its propagation from the meteorological drought is highly required for the Ganga River Basin. This paper focuses on the evaluation of historical groundwater drought using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) dataset, further to obtain the drought propagation times. Traditionally the drought propagation time is obtained from the correlation between groundwater drought time series and various scales of meteorological time series. However, the GRACE-derived groundwater drought index (GGDI) showed lesser correlation with the Standardized Precipitation Index (SPI) / Standardized Precipitation Evapotranspiration Index (SPEI) due to the presence of consistent trend in the GGDI series. Hence, a novel quantitative approach using Cross Wavelet Transform (XWT) is introduced to determine the drought propagation time which can devoid of the contribution of anthropogenic activities. Extracting the significant power area of XWT of GGDI with SPI/SPEI of different scales led to the determination of groundwater drought propagation time. The results showed Groundwater Storage Anomaly (GWSA) has a steep downtrend for Upper Gangetic Basin (UGB) (-26.2 mm/year) and Yamuna Chambal Basin (YCB) (-21.8 mm/year). It was observed that UGB and YCB faced groundwater drought from 2017 to 2022. The wavelet analysis showed that the drought propagation time of YCB is 14 months, UGB is 17 months, and the Lower Gangetic Basin (LGB) is 21 months. The frequency domain analysis of the drought signals suggested YCB had a faster response to the meteorological forcing, and LGB had the slowest response.
恒河流域居住着近5亿人口,由于人为活动,该流域已陷入地下水干旱状态。因此,恒河流域迫切需要进行地下水干旱评估及其从气象干旱的演变研究。本文重点利用重力恢复与气候实验(GRACE)和全球陆地数据同化系统(GLDAS)数据集评估历史地下水干旱情况,进而获取干旱演变时间。传统上,干旱演变时间是通过地下水干旱时间序列与不同尺度气象时间序列之间的相关性来确定的。然而,由于GRACE得出的地下水干旱指数(GGDI)序列存在一致趋势,其与标准化降水指数(SPI)/标准化降水蒸散指数(SPEI)的相关性较低。因此,引入了一种使用交叉小波变换(XWT)的新型定量方法来确定干旱演变时间,该方法可以消除人为活动的影响。通过提取不同尺度的GGDI与SPI/SPEI的XWT显著功率区域,确定了地下水干旱演变时间。结果表明,恒河上游流域(UGB)(-26.2毫米/年)和亚穆纳河-昌巴尔河流域(YCB)(-21.8毫米/年)的地下蓄水异常(GWSA)呈急剧下降趋势。据观察,UGB和YCB在2017年至2022年期间面临地下水干旱。小波分析表明,YCB的干旱演变时间为14个月,UGB为17个月,恒河下游流域(LGB)为21个月。干旱信号的频域分析表明,YCB对气象强迫的响应较快,而LGB的响应最慢。