Quality controlled, reliable groundwater level data with corresponding specific yield over India.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Satish Kumar Kuruva, Maya Raghunath Suryawanshi, Amin Shakya, Chethan Va, Balaram Shaw, Vandana Sukumaran, Retinder Kour, Aayushi Kochar, Shard Chander, Bhaskar R Nikam, Nagesh Kumar Dasika, Bramha Dutt Vishwakarma
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引用次数: 0

Abstract

Groundwater is a vital resource for domestic, agricultural, and industrial use, with its demand growing due to population growth and climate change. Several studies have identified groundwater depleting in India at unsustainable rate over North-west part, but a contrasting trend is observed in the southern India. To better study groundwater dynamics quality-controlled and reliable well data is essential, which is missing. Here we process seasonal groundwater levels from 32,299 wells across India to obtain reliable well data and provide respective specific yields. Initially, wells with no data and negative values are removed. Later three-sigma method is imposed on each well to eliminate outliers. Finally, wells with at least two values per year, with no value repeating more than twice consecutively, are retained, resulting in 2,759 reliable wells. We used vectorization-based method to classify aquifer types and estimated specific yields based on hydrogeological map. We also provide open access to data and scripts so that researchers can study groundwater variations, compare GRACE and model-based groundwater estimates against in-situ well data.

质量控制,可靠的地下水位数据与相应的具体产量在印度。
地下水是家庭、农业和工业使用的重要资源,由于人口增长和气候变化,对地下水的需求不断增长。几项研究已经确定,印度西北部的地下水正在以不可持续的速度枯竭,但在印度南部观察到相反的趋势。为了更好地研究地下水动力学,质量控制和可靠的井数据是必不可少的,而这正是目前所缺乏的。在这里,我们处理了印度32,299口井的季节性地下水水位,以获得可靠的井数据,并提供各自的具体产量。最初,没有数据和负值的井被移除。然后对每口井采用三西格玛方法来消除异常值。最后,保留每年至少有两个值的井,没有连续重复超过两次的值,从而产生2,759口可靠的井。采用矢量化方法对含水层类型进行分类,并根据水文地质图估算含水层的具体产量。我们还提供数据和脚本的开放访问,以便研究人员可以研究地下水变化,将GRACE和基于模型的地下水估计与原位井数据进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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