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一个多时间尺度框架,用于全面量化和归因人类对排水的影响

Chunchen Xia^1,2, Lingna Zhang^1,2, Zekai Zhu^1,2

¹College of Civil Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Scientific reports

|December 11, 2025

概括

这项研究为分析流水变化提供了一个新的框架,确定降水是关键驱动因素,人类活动是重要影响因素. 它优化时间尺度和模型,以准确管理水资源.

科学领域:

水文和水资源管理水文和水资源管理
环境科学环境科学
地理空间分析是什么

背景情况:

流量变化归因对于水资源的保护和管理至关重要.
之前的研究经常使用单一规模的方法,忽视了多对线性.
归因分析仅限于单因素评估.

研究的目的:

建立一个流水重建,量化和贡献率分析的方法框架.
为了确定最佳的时间尺度和解释变量,用于流失模型.
整合多个来源的数据,以便全面地归因人类驱动因素.

主要方法:

跨多个时间尺度的水文气象变量的聚合.
斯皮尔曼相关性和差异通胀因子 (VIF) 分析用于变量优化.
使用随机森林回归模型 (RFRM) 和土壤和水资源评估工具 (SWAT) 进行流水重建.
远程传感和统计数据的整合用于人类驾驶员识别.

主要成果:

降水被确定为主要的气象驱动因素 (赛季规模的斯皮尔曼p=0.90).
每月和每两个月的尺度被确定为最佳的建模.
RFRM和SWAT表现出互补的优势,SWAT在高流量模拟方面表现出色.
多源数据整合揭示了人类活动在流水过程中的双重作用.

结论:

开发的框架为时间尺度和流量归因中的模型选择提供了方法指导.
该方法提供了一种可转移的方法,用于定量归因流失变化的流失变化.
这些发现支持改善水资源保护和管理战略.

关键词:

水文模型水文模型机器学习模型机器学习模型多时间尺度的多时间尺度.遥感是一种远程传感.流水归因流失的归因

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