ExtendinG SUb-DAily River Discharge data over INdia (GUARDIAN).

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-10-18 DOI:10.1038/s41597-024-03923-8

Girish Patidar, J Indu, Subhankar Karmakar

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Abstract

River discharge information is crucial for various applications, but the measurement process often remains impeded by factors that hinder near real-time (NRT) data availability in India. However, leveraging telemetry-based water surface elevation (WSE) data across the country provides an opportunity to convert it into river discharge. This conversion is made possible by utilizing rating curves (RCs) derived from historical collocated measurements of WSE and discharge. In this study, NRT WSE from the Central Water Commission (CWC) flood portal is obtained via the web-scraping tool. Through the application of RCs, discharge data is extended across 210 gauging stations in India from the year 2020 to the present, encompassing sub-daily discharge during the non-monsoon and hourly discharge series during monsoon (June-September) across the Indian rivers. Annually, the study generated over 800,000 discharge data points for Indian rivers, accounting for more than 4000 discharge measurements per station annually. These comprehensive datasets provide valuable insights for water resource and flood management research, offering NRT access to WSE, and discharge, along with the local RCs.

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将 SUb-DAily 河流排水数据扩展到印度（GUARDIAN）。

河流排泄量信息对各种应用都至关重要，但在印度，测量过程经常会受到各种因素的阻碍，这些因素妨碍了近实时（NRT）数据的可用性。不过，利用全国各地基于遥测的水面高程（WSE）数据，就有机会将其转换为河流排水量。这种转换可以利用从历史上的 WSE 和排水量同位测量中得出的额定曲线 (RC)。在本研究中，通过网络抓取工具从中央水利委员会（CWC）洪水门户网站获取了 NRT WSE。通过应用 RC，从 2020 年至今，印度 210 个测量站的排泄量数据得到了扩展，包括印度河流非季风期间的次日排泄量和季风期间（6 月至 9 月）的小时排泄量序列。这项研究每年为印度河流生成 80 多万个排泄量数据点，每个测站每年测量的排泄量超过 4000 个。这些全面的数据集为水资源和洪水管理研究提供了宝贵的见解，为国家研究机构提供了获取 WSE 和排水量以及当地 RC 的途径。

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来源期刊

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.