Hyper-resolution naturalized streamflow data for Geum River in South Korea (1951-2020).

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Byeong-Hee Kim, Young-Oh Kim, Jonghun Kam
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引用次数: 0

Abstract

Long-term streamflow data at a hyper-resolution (less than 1 km) is essential for hydroclimatic extreme and ecological assessment, which is not available over a river basin where rapid socioeconomic growth have been experienced. Here, we use the Variable Infiltration Capacity-River Routing Model (VIC-RRM) to reconstruct naturalized daily streamflow at 90-meter resolution for the Geum River, one of South Korea's major rivers, over 1951-2020. VIC-RRM demonstrates high temporal consistency with a correlation coefficient exceeding 0.6 for observed streamflow seasonality at over 60% of the 90 gauge stations along the Geum River. However, 36% of the stations show low modified Kling-Gupta Efficiency (0.2-0.4), primarily due to uncertainties in runoff data and human disturbance impacts like irrigation and reservoir storage. Our simulated naturalized data reveal decadal variability in the 1990s and an increase in day-to-day variability of the Geum River in the 2010s compared to those in the 1970s. This dataset provides physically consistent naturalized streamflow data for reference data to evaluate climate change-driven changes in streamflow for the Geum River.

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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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