Revealing the causal response in landslide hydrology with MT-InSAR and spatial-temporal CCM: A case study in Jinsha River

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software Pub Date : 2025-03-20 DOI:10.1016/j.envsoft.2025.106434

Xiao Ling , Dongping Ming , Zhi Zhang , Jianao Cai , Wenyi Zhao , Mingzhi Zhang , Yongshuang Zhang , Bingbo Gao

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引用次数: 0

Abstract

Convergent Cross Mapping (CCM) is a powerful tool for analyzing causality in complex dynamic systems. However, standard CCM and Geographical CCM (GCCM) focus exclusively on temporal or spatial attributes, failing to integrate both dimensions. This study introduces a spatial-temporal CCM that quantifies the state of convergence to enable batched analyses of large-scale spatial datasets. The proposed method captures variations in causality and delayed responses across different spatial locations, thereby enhancing spatial-temporal data utility and the efficiency of causal inference. Using this model, we analyzed the relationship between landslides and hydrology. The results revealed that Areas with High Displacement (AHDs) responded more rapidly to hydrological factors than stable regions, with deep-layer soil moisture (100–289 cm depth) exhibiting the strongest causality and the fastest response. Building on these findings, we identified zones of minimal instability within each AHD (areas that displayed the quickest response to hydrological changes).

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基于MT-InSAR和时空CCM的滑坡水文成因响应研究——以金沙江为例

收敛交叉映射（CCM）是分析复杂动态系统因果关系的有力工具。然而，标准CCM和地理CCM （GCCM）只关注时间或空间属性，未能整合这两个维度。本研究引入了一种时空CCM，该CCM量化了收敛状态，从而能够对大规模空间数据集进行批量分析。该方法捕获了不同空间位置的因果关系和延迟响应的变化，从而提高了时空数据的效用和因果推理的效率。利用该模型分析了滑坡与水文的关系。结果表明：高位移区对水文因子的响应比稳定区更快，其中深层土壤水分（100 ~ 289 cm）的因果关系最强，响应最快；在这些发现的基础上，我们确定了每个AHD中最小的不稳定区域（对水文变化反应最快的区域）。

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

Environmental Modelling & Software 工程技术-工程：环境

CiteScore

9.30

自引率

8.20%

发文量

241

审稿时长

60 days

期刊介绍： Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.