Management watershed identification of sub-watersheds using spatial pattern analysis of watershed morphometric variables

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies Pub Date : 2025-09-25 DOI:10.1016/j.ejrh.2025.102807

Seon Yeon Choi, Chang Dae Jo, Heon Gak Kwon

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引用次数: 0

Abstract

Study Region

This study was conducted across 195 sub-watersheds within the Nakdong River Basin in South Korea. The basin encompasses diverse topographic and hydrological characteristics, making it a suitable for morphometric and spatial analysis.

Study Focus

Sixteen morphometric parameters were derived using a geographic information system (GIS). Principal component analysis (PCA) was applied to extract eight key variables: drainage density (D_d), drainage intensity (D_i), stream frequency (F_s), infiltration number (I_f), length of overland flow (L_of), circularity ratio (R_c), relative relief (R_r), and compactness constant (C_c). Spatial autocorrelation and clustering were assessed using Local Moran’s I and Local Geary’s C to identify localized patterns and anomalies in watershed morphology.

New Hydrological Insights for the Region

Watersheds with high D_d and F_s values exhibited dense stream networks and were prone to rapid runoff. Elevated I_f, D_i, and R_r indicated high hydrologic reactivity and erosion susceptibility. High L_of values suggested greater potential for pollutant retention, while R_c and C_c reflected circular basin shapes associated with concentrated runoff and flood risk. Spatial clusters of these variables were found in the upper and middle Nakdong reaches, highlighting zones vulnerable to environmental stress. Local Geary’s C revealed morphological similarities in northeastern and central sub-watersheds, while dissimilarities were interpreted as hydrological anomalies. The integrated PCA–LISA framework offers a robust approach for identifying water environment vulnerability and guiding targeted watershed management.

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基于流域形态计量变量空间格局分析的流域管理识别

研究区域本研究在韩国洛东江流域的195个子流域进行。该盆地包含不同的地形和水文特征，使其适合于形态计量学和空间分析。使用地理信息系统（GIS）导出16个形态计量学参数。采用主成分分析（PCA）提取了8个关键变量：排水密度（Dd）、排水强度（Di）、水流频率（Fs）、入渗数（If）、坡面流长度（Lof）、圆度比（Rc）、相对起伏度（Rr）和紧实度常数（Cc）。利用Local Moran’s I和Local Geary’s C评估空间自相关和聚类，以识别流域形态的局部模式和异常。高Dd和Fs值的区域流域具有密集的水系，且容易发生快速径流。If、Di和Rr的升高表明了较高的水文反应性和侵蚀敏感性。高Lof值表明污染物滞留潜力更大，而Rc和Cc反映了与集中径流和洪水风险相关的圆形流域形状。这些变量在洛东上游和中游呈空间聚集，突出了易受环境压力影响的区域。局部Geary’s C揭示了东北和中部子流域的形态相似性，而差异被解释为水文异常。综合PCA-LISA框架为识别水环境脆弱性和指导有针对性的流域管理提供了强有力的方法。

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

Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)

CiteScore

6.70

自引率

8.50%

发文量

284

审稿时长

60 days

期刊介绍： Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.