1980-2022 年科罗拉多河上游流域地表水引水点和日取水量数据库。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Samuel F Lopez, Jacob E Knight, Fred D Tilman, Melissa D Masbruch, Daniel R Wise, Casey J Jones, Matthew P Miller
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引用次数: 0

摘要

科罗拉多河的流域面积约占美国本土总面积的 8%,为 4000 万人供水,是世界上水量分配最合理的河流之一。据估计,科罗拉多河流域上游 (UCOL) 的天然溪流占流域总溪流的 92%,因此了解 UCOL 的地表水取水位置和数量对于管理科罗拉多河系统非常重要。由于 UCOL 包括五个州的部分地区,用水数据分散在众多联邦和州机构的数据库中,没有一个集中的数据集以精细的空间和时间分辨率记录整个 UCOL 的地表水使用情况。本文列出了从 UCOL 引出地表水的 1,358 个主要结构,以及从 1980 年到 2022 年的相应每日时间序列取水记录。文中记录了数据编纂工作、处理方法以及该引水数据库的内容,并提供了摘要信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Database of surface water diversion sites and daily withdrawals for the Upper Colorado River Basin, 1980-2022.

The Colorado River drains about 8% of the conterminous United States, provides water for 40 million people, and is one of the most overallocated rivers in the world. As the upper Colorado River Basin (UCOL) contributes an estimated 92% of the total basin natural streamflow, knowledge of the location and amount of surface water withdrawals in the UCOL is important for managing the Colorado River system. Since the UCOL encompasses portions of five states, water use data are dispersed among numerous federal and state agency databases, and there is no centralized dataset that documents surface water use within the entire UCOL at a fine spatial and temporal resolution. This article presents an inventory of 1,358 major structures that divert surface water from and within the UCOL with corresponding daily time series withdrawal records from 1980 through 2022. Data compilation efforts, processing methods, and contents of this diversion database are documented, and summary information is provided.

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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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