Spatial and temporal characteristics of water conservation services and rapid response framework for water yield in key ecological zones of the Yiluo River basin

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies Pub Date : 2025-06-24 DOI:10.1016/j.ejrh.2025.102542

Junqiang Xu , Fan Wang , Chao Ren , Jianmin Bian , Tao Li , Zikai Ping

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引用次数: 0

Abstract

Study region

Yiluo River basin, an important water source and ecological barrier in China.

Study focus

In this study, we analyzed the spatial and temporal patterns of water yield and the main influencing factors of the Yiluo River Basin based on the water yield response framework constructed by the SWAT model and the intelligent optimization algorithm.

New hydrological insights for the region

The results indicated that the average annual total of water-source containment per unit area in the district was 330.03 mm from 2019 to 2023, with a Nash-Sutcliffe Efficiency (NSE) of 0.77, based on the average of two hydrological sites in the SWAT model. The high value of water conservation goes mainly in the forested mountainous areas of the upper reaches of the Yi River and concentrated in July–October, seasonal differences in the amount of water conservation are mainly influenced by precipitation (correlation of 0.79), and potential evapotranspiration determines its lower limit value. Urban land uses and riparian areas with high levels of hydraulic erosion are areas with low water yield concentration. The artificial neural network-based prediction framework achieved high performance with Pearson correlation coefficients exceeding 0.90 across all datasets. The average relative error was 1.31 % (training), 1.39 % (validation), and 1.24 % (test), with MAPE values below 2 %. This approach allows flexible scenario modeling and has been successfully applied to seven cases, offering valuable early-warning insights for regional ecological planning and water resource management.

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宜罗江流域重点生态区保水服务时空特征及产水量快速响应框架

研究区域为中国重要的水源和生态屏障——漯河流域。本研究基于SWAT模型和智能优化算法构建的水量响应框架，分析了沂罗河流域水量的时空格局及其主要影响因素。结果表明，基于SWAT模型中两个水文点的平均值，2019 - 2023年，该地区单位面积年平均水源围护总量为330.03 mm， Nash-Sutcliffe效率（NSE）为0.77。水分涵养量的高值区主要出现在沂河上游的森林山区，集中在7 - 10月，水分涵养量的季节差异主要受降水的影响（相关系数为0.79），其下限由潜在蒸散量决定。水力侵蚀程度高的城市用地和河岸地区是产水量集中度低的地区。基于人工神经网络的预测框架在所有数据集上的Pearson相关系数均超过0.90，实现了高性能预测。平均相对误差为1.31 %（训练），1.39 %（验证）和1.24 %（测试），MAPE值低于2 %。该方法允许灵活的情景建模，并已成功应用于七个案例，为区域生态规划和水资源管理提供了有价值的预警见解。

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.