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气候变化背景下降雨滑坡风险评估研究进展

黄雨, 何正迎

黄雨,何正迎. 气候变化背景下降雨滑坡风险评估研究进展[J]. 中国地质灾害与防治学报,2025,36(2): 13-27. DOI: 10.16031/j.cnki.issn.1003-8035.202501002
引用本文: 黄雨,何正迎. 气候变化背景下降雨滑坡风险评估研究进展[J]. 中国地质灾害与防治学报,2025,36(2): 13-27. DOI: 10.16031/j.cnki.issn.1003-8035.202501002
HUANG Yu,HE Zhengying. Research progress on rainfall-triggered landslide risk assessment under the context of climate change[J]. The Chinese Journal of Geological Hazard and Control,2025,36(2): 13-27. DOI: 10.16031/j.cnki.issn.1003-8035.202501002
Citation: HUANG Yu,HE Zhengying. Research progress on rainfall-triggered landslide risk assessment under the context of climate change[J]. The Chinese Journal of Geological Hazard and Control,2025,36(2): 13-27. DOI: 10.16031/j.cnki.issn.1003-8035.202501002

气候变化背景下降雨滑坡风险评估研究进展

基金项目: 国家重点研发计划项目(2024YFC3012600)
详细信息
    作者简介:

    黄 雨(1973—),男,江苏南京人,工学博士,教授,主要从事工程地质教学与科研工作。E-mail:yhuang@tongji.edu.cn

  • 中图分类号: P642.22

Research progress on rainfall-triggered landslide risk assessment under the context of climate change

  • 摘要:

    随着全球气候变化的加剧,极端降雨事件日益频繁,导致降雨型滑坡灾害频发,造成了巨大的人员伤亡与经济损失。文章系统回顾了气候变化背景下降雨型滑坡风险评估的研究进展,重点讨论了以下三个关键方面:(1)考虑气候变化的降雨作用下边坡可靠度评估;(2)考虑降雨模式不确定性的边坡易损性评估;(3)基于机器学习方法的降雨型滑坡危险性评估。在此基础上,文章进一步分析了气候变化背景下降雨型滑坡风险评估所面临的多维挑战,包括气候变化带来的不确定性、高时空分辨率地质气象数据缺乏以及模型跨区域的适应性等。最后,文章从精细的地质调查、多因素孕灾机理、基于韧性的风险评估等角度,展望了实现降雨型滑坡灾害韧性防灾的未来研究方向。研究旨在为降雨型滑坡灾害的防灾减灾工作提供理论支持和方法参考,促进滑坡灾害风险管理的科学化与精细化发展。

    Abstract:

    With the intensification of global climate change, extreme rainfall events have become increasingly frequent, leading to recurrent rainfall-triggered landslides and causing significant casualties and economic losses. With the context of climate change, this study systematically reviews the research progress on advancements in probabilistic risk assessment of rainfall-triggered landslides, focusing on three key aspects: (1) slope reliability assessment under rainfall conditions considering climate change; (2) vulnerability assessment of slopes considering the uncertainty of rainfall patterns; and (3) rainfall-induced landslide hazard assessment based on machine learning methods. On this basis, this study further analyzes the multidimensional challenges faced by rainfall-triggered landslide risk assessment under climate change, including uncertainties associated with climate change, the lack of high spatio-temporal resolution geological and meteorological data, and the adaptability of models across different regions. Finally, from the perspectives of detailed geological surveys, multi-factor disaster gestation mechanisms, this study looks towards future research directions for enhancing resilience in rainfall-induced landslide disaster prevention, from landslide mechanisms under multiple factors, to resilience-based risk assessment. This study aims to provide theoretical support and methodological references for the disaster prevention and mitigation work of rainfall-triggered landslides, promoting the scientific, systematic, and refined development of landslide risk management.

  • 随着全球气候变化,极端降雨事件日益频繁,导致降雨型滑坡灾害频发,对全球范围内的人员生命安全和经济发展构成了严重威胁和巨大损失[14]。例如,2014年1月,巴西伊塔奥卡市强降雨引发了365处山体滑坡,受灾面积达33 km2,92座建筑物被掩埋,造成27人不幸丧生[5]。而我国,岩体风化强烈、残坡积层覆盖广泛,加之全球气候变化的影响,降雨型群发滑坡将呈现高发态势[6]。例如,在2024年6月,福建省龙岩市降雨触发了超过1.6万处滑坡,导致仅上杭县近10万人受灾,直接经济损失超过20亿元人民币[7]。因此,面向未来气候变化,开展降雨型滑坡灾害的前瞻性研究已成为迫在眉睫的任务[8]。这不仅是全球防灾减灾工作的关键,也是契合我国重大国家需求的必要举措。

    降雨型滑坡灾害具有显著的突发性与强大的破坏性,其相关研究长期以来一直是中国地质灾害防治领域的热点和难点,也是全球地质灾害防治研究的重要挑战之一。Nature期刊报道了2024年7月30日印度喀拉拉邦瓦亚纳德地区发生的降雨型滑坡,已导致多达500人不幸遇难[9]。究其原因,是受灾地区遭遇了超乎预期的高强度、短时降雨,在短短不到一天的时间里降雨量竟达到了其年均降雨量的约7%[9]。这起滑坡灾害事件凸显了气候变化背景下极端降雨事件的不确定性和突发性对滑坡灾害防治带来的严峻挑战。因此,亟需从不确定性的角度出发,来加强与拓展传统的降雨型滑坡灾害风险评估,且进一步凸显了开展气候变化背景下降雨型滑坡风险评估的必要性。

    现有研究在降雨型滑坡灾害的机理[1017]、预警[1826]、评估[2734]、防灾[3538]等方面取得了显著成就。例如,Xu等[3]基于现场调查、室内试验和数值模拟探究了中国东南沿海地区降雨下人工切坡边坡的破坏机制。张明等[17]开展了川东缓倾红层中降雨诱发型滑坡机制的研究。汪丁建等[39]、兰恒星等[40]进行了强降雨作用下滑坡的渗流-稳定性分析。许强等[41]利用灾后光学遥感影像并结合机器学习模型对广东省江湾镇降雨型滑坡进行了快速智能识别与解译。此外,诸多学者也对降雨型滑坡灾害的失稳机理、雨量阈值与物理预警等方面进行了相关综述[4247]。然而,关于降雨型滑坡风险评估,特别是气候变化背景下降雨型滑坡风险评估的现状回顾、挑战分析及未来展望的系统综述仍显不足。

    为此,本文系统回顾了气候变化背景下降雨型滑坡风险评估的研究进展,重点讨论了以下三个关键方面:(1)考虑气候变化的降雨作用下边坡可靠度评估;(2)考虑降雨模式不确定性的边坡易损性评估;(3)基于机器学习方法的降雨型滑坡危险性评估。在此基础上,本文进一步分析了气候变化背景下降雨型滑坡风险评估所面临的多维挑战,包括气候变化带来的不确定性、高时空分辨率地质气象数据缺乏以及模型跨区域的适应性等。最后,本文从精细的地质调查、多因素孕灾机理、基于韧性的风险评估等角度,展望了实现降雨型滑坡灾害韧性防灾的未来研究方向。通过系统性的文献综述与分析,本文旨在为相关领域的研究人员提供全面的知识框架和参考,促进降雨型滑坡灾害风险评估方法的创新与应用。

    滑坡风险评估是地质灾害防治领域的核心课题之一。“风险”作为不确定性结果的一种度量,自20世纪80年代被引入地质灾害研究的范畴以来,逐步发展成为该领域的研究热点[48]。自20世纪90年代起,美国、意大利、澳大利亚、法国等国家积极开展了不同尺度下滑坡灾害的风险评估工作[4951]。相关成果为当地政府部门的土地利用规划提供了坚实的科学支撑,有效减少了滑坡灾害带来的人员伤亡及经济财产损失[50, 52]。自1999年起,我国也有计划、有步骤地推进了大规模的地质灾害调查工作,为地质灾害的防治与管理奠定了坚实的基础[5354]。在此基础上,我国相关研究机构和学者开展了大量地质灾害风险评估的研究,逐步完善了滑坡风险评估的方法与技术[27, 36, 49, 5457]

    滑坡风险指滑坡灾害破坏产生不良后果的可能性(发生破坏的可能性及其所产生的后果),其主要包括危险性评估与脆弱性评估。滑坡危险性评估是通过分析和预测滑坡发生的空间分布和时间变化,评估滑坡发生的可能性以及潜在的影响范围;而滑坡脆弱性评估是分析滑坡灾害对承灾体(如建筑物、基础设施等)的影响及其损失程度及承灾体自身的抗灾能力[5859]。从空间尺度的角度来看,滑坡风险评估研究又可分为区域研究和单体研究两类[50]。区域滑坡灾害风险评估主要基于地理信息系统技术,综合考虑滑坡敏感性和承灾体的易损性,明确风险评估因素,生成风险区划图[6061]。这类研究有助于宏观层面上识别高风险区域,为区域规划和灾害管理提供决策支持。而单体滑坡风险评估则经历了从定性分析到半定量评估再到定量评估的发展过程[58]。具体而言,定量评估的研究主要包括滑坡发生概率及其影响范围的研究(危险性评估),以及滑坡灾害对承灾体(如建筑物、基础设施等)的影响(脆弱性研究)。

    然而,降雨型滑坡灾害作为降雨与地质体相互作用的结果,其风险评估应具备更丰富的科学内涵。研究表明,降雨型滑坡灾害的时空分布特征与降雨事件的时空变化密切相关[62, 18]。因此,降雨型滑坡灾害风险评估必须纳入降雨灾害的全面考虑,尤其在全球气候变化的背景下,极端降雨事件日益频繁,导致降雨型滑坡灾害的发生频率与强度也明显上升[63]。传统的滑坡风险评估方法难以应对降雨模式的不确定性以及气候变化导致的降雨灾害的非平稳性。因此,降雨型滑坡风险评估不仅需要关注降雨灾害的影响,还需要在评估方法上有所拓展,特别是在气候变化日益显著的背景下。

    因此,在气候变化的背景下,本文重点讨论了以下三个关键方面:(1)考虑气候变化的降雨作用下边坡可靠度评估;(2)考虑降雨模式不确定性的边坡易损性评估;(3)基于机器学习方法的降雨型滑坡风险评估。

    随着全球气候变暖,降雨强度和频率呈现出非平稳增长的趋势,加剧了降雨型滑坡灾害的发生及其影响程度[63]。根据第六次国际耦合模式比较计划(Coupled Model Intercomparison Project Phase 6,CMIP6)的预测,在温升1.5 °C和2.0 °C的情景下,全球多年平均降水量预计将比基准时期增加5.4%到9.5%;进而由于降水的增加,降雨型滑坡等地质灾害的发生频率也预计增加7.0%到11.2%[33]。因此,十分有必要在降雨型滑坡灾害风险评估中考虑气候变化的影响。

    目前考虑气候变化的降雨型滑坡灾害风险评估的研究,主要集中在:考虑不同的气候排放情景,基于全球气候模型(global climate model,GCM)获得研究区域预测到的未来降雨数据。进而建立边坡的力学模型或者滑坡统计模型,将获得的降雨数据作为边坡模型的输入,得到边坡的未来水文响应以及滑坡发生的可能性[64]。例如,Sobie[65]的研究表明,在不列颠哥伦比亚省的滑坡易发地区,到21世纪50年代,由于气候变化的影响,滑坡灾害的频率预计将从每年16 d增加到平均每年21 d(增加 约32%)。Lin等[66]评估了中国21世纪降水模式的预计变化,降雨诱发的滑坡事件频率和易发生滑坡的区域预计都会增加。然而,这些研究没有充分考虑气候变化导致的降雨灾害的不确定性与时变性对滑坡灾害的影响。

    尽管Kim等[34]考虑了历史72 h年最大降雨数据中所观测到的降雨强度增长趋势,并采用Gumbel分布预测了未来降雨型滑坡的概率。需要指出的是,气候变化所产生的影响并非局限于降雨强度的改变。事实上,气候变化的导致降雨的非平稳性,也涵盖降雨事件的频率、持时以及两次降雨事件间隔等多方面的变化。因此,在降雨型滑坡灾害风险评估中,如何考虑气候变化导致的非平稳降雨(强度和频率非平稳变化)已成为亟待解决的关键问题之一。

    为此,本小节给出了考虑气候变化的降雨作用下边坡可靠度评估的方法框架(图1),其中考虑了气候变化导致的降雨强度和频率的非平稳变化。该框架主要分为四部分:(I)全球气候模型收集和未来日降雨搜集;(II)考虑气候变化的非平稳降雨模型构建;(III)降雨作用下边坡易损性评估;以及(IV)基于全概率定理的降雨作用下边坡可靠度评估。

    图  1  考虑气候变化的降雨作用下边坡可靠度评估框架(改自He等[33]
    Figure  1.  Framework for reliability assessment of rainfall-triggered landslides considering climate change (adapted from He et al. [33])

    (I)全球气候模型收集和未来日降雨搜集

    从CMIP6中获得全球气候模型,并从中收集预测的日尺度降雨数据。考虑到气候变化的不确定性,可以考虑与共享社会经济路径相关的多种气候变化情景以及CMIP6中多个全球气候模型[33]。由于从CMIP6获得的全球气候模型的分辨率不尽相同,为确保数据一致性和可比性,可将所获得的日尺度降雨数据通过双三次插值法插值到共同的网格分辨率(例如0.5°×0.5°),从而获得统一格式的研究区域内多个全球气候模型的日尺度降雨数据。

    (II)考虑气候变化的非平稳降雨模型构建

    基于交替随机更新过程开展考虑气候变化的非平稳降雨模型构建[33]。交替随机更新过程适用于具有交替阶段或者周期性变化的事件序列,而全寿命周期内的降雨过程恰好是“降雨期”和“无雨期”的交替循环。基于(I)中所获取的考虑气候变化的日降雨数据,量化降雨变量(降雨间隔、降雨持时和降雨强度)的非平稳时变特征。具体而言,降雨变量特定月份跨年的时变统计特征通过移动平均法和标准差法进行计算,并为了简化,可以使用线性模型进行拟合。对于特定年份降雨变量的月度变化特征采用阶跃函数来描述,并将获得的趋势特征转换为日尺度数据,即假设在特定年份的一个月内降雨特征相似。因此,该降雨变量非平稳时变特征的量化,将长期非平稳降雨特征转换为短期日常的平稳特性,使得能够对全寿命周期内的最大降雨强度进行数值评估。关于非平稳降雨的交替随机更新过程模型的具体细节可参见文献[33]

    (III)降雨作用下边坡的易损性评估

    为简化起见,本步骤中的降雨作用下边坡的易损性评估仅考虑给定最大降雨强度作用下边坡的条件失效概率,若想考虑更为复杂的模型可参见下一小节,在此不再赘述。

    (IV)基于全概率定理的降雨下边坡可靠度评估

    基于全概率定理,通过将步骤(II)中考虑气候变化的非平稳降雨灾害曲线和步骤(III)中的降雨作用下边坡的易损性曲线进行卷积,得到可考虑气候变化的降雨作用下边坡可靠度评估结果。

    边坡的易损性是指给定灾害(如地震、降雨等)强度作用下,边坡的条件失效概率[32]。在地震作用下,边坡的易损性评估已取得显著进展,并着重考虑了岩土性质不确定性、地震动随机性等的量化[31, 6768]。然而,目前大量发生的滑坡灾害,其中约90%是由降雨直接诱发或与降雨有关[6970],但对于降雨作用下边坡易损性评估的研究却相对较少。Martinović等[71]提出一种基于蒙特卡洛模拟(Monte Carlo simulation,MCS)的交通网络内降雨边坡易损性曲线的构建方法。Hu等[72]则考虑土壤参数的空间变异性,基于概率密度演化理论给出了降雨作用下边坡的非参数易损性曲线。

    在边坡易损性评估中,不确定性量化起着重要作用,尤其是岩土性质的空间变异性与降雨强度序列的随机性是关键因素。然而,当前的边坡易损性评估研究中主要侧重于岩土性质不确定性的量化,而较少考虑降雨强度时间模式不确定性的量化。在气候变化的影响下,降雨强度时间模式也将发生相应改变。为此,本小节在现有降雨作用下边坡易损性评估文献的基础上,给出了考虑降雨模式不确定性和岩土性质空间变异性的边坡易损性评估方法的通用框架(图1)。

    考虑降雨强度模式不确定性和岩土性质空间变异性的边坡易损性评估方法共分为三部分(图2):(I)研究区域选择与水文地质参数的获取;(II)降雨强度模式不确定性和岩土性质不确定性的量化;(III)考虑双重不确定性的降雨边坡易损性曲面构建。以下分别对每个步骤进行详细介绍:

    图  2  考虑降雨模式及岩土性质不确定性的边坡易损性评估框架示意图 (改自He等[32]
    Figure  2.  Framework for slope fragility assessment considering uncertainties in rainfall patterns and geotechnical properties (adapted from He et al. [32])

    (I)研究区域选择与水文地质参数的获取

    首先,选定易受降雨型滑坡影响的研究区域;随后,对该区域进行水文与地质数据的获取与搜集工作。具体而言,对于水文数据,从雨量站收集小时尺度的降雨数据,为降雨强度模式不确定性的量化奠定数据基础;对于地质数据,获取降雨作用下边坡渗流-稳定性分析所需要的岩土力学参数等,例如,抗剪强度参数、土水特征曲线等。可参见图2中的步骤(I)。

    (II)降雨模式及岩土性质不确定性的量化

    降雨强度模式不确定性,即降雨事件中降雨强度随时间变化表现出一定的不确定性,其对降雨滑坡的触发及演化具有重要影响[7374]。为量化降雨强度模式的不确定性,He等[32]基于高阶距(偏度和峰度)的统计学概念,生成了考虑降雨模式不确定性的随机降雨强度时间序列(图2中的步骤II.1)。偏度可以衡量降雨序列对称性,通过计算小时尺度降雨序列数据的三阶矩来确定。而峰度可以衡量降雨序列尖锐程度的统计指标,通过计算降雨序列数据的四阶矩来确定。具体基于偏度和峰度的降雨模式不确定性量化可参见文献[32]

    此外,Tang等[73]和Ma等[75]采用随机级联模型生成随机降雨模式,探究了不同降雨模式对边坡稳定性的影响。基于随机级联模型的降雨强度模式不确定性的量化也是一种行之有效的方法,具体可参见文献[73][75]

    关于岩土性质不确定性的量化,主要采用随机变量和随机场的方法,其中基于KL展开(Karhunen-Loève expansion)等方法建立空间随机场来描述岩土性质的空间变异性(见图2中的步骤II.2),引起了诸多学者的关注并取得了显著进展,具体可参考文献[7678],本文不再赘述。

    (III)考虑双重不确定性的降雨边坡易损性评估

    降雨作用下边坡的易损性可定义为给定降雨灾害强度(降雨量或降雨持时等)下,边坡发生破坏(失效)的概率。考虑降雨模式及岩土性质双重不确定性的降雨边坡易损性函数可表达如下:

    $$ {P}_{f}=P\left[g\left({\boldsymbol{X}}\right)\leqslant 0|{I}_{A}={i}_{A},T=t\right] $$ (1)

    式中,$ P\left[g\left({\boldsymbol{X}}\right)\leqslant 0|{I}_{A}={i}_{A},T=t\right] $表示给定平均降雨强度IA = iA及降雨到T = t时刻,边坡的失效概率。

    滑坡易发性是指在特定区域内,由于多种致灾环境因子的非线性耦合作用,某一地点发生滑坡的空间概率。传统的基于经验或物理模型的滑坡易发性评估取得了显著进展,但在处理复杂的致灾因子、多源数据融合等方面仍存在局限性。机器学习方法,因其强大的建模能力和适应性,已成为滑坡易发性评估的研究热点[7983]。随着气候变化的影响愈加明显,结合降雨等因素来动态评估滑坡危险性成为了滑坡研究的新方向[84]

    在此背景下,本小节给出了基于机器学习的降雨型滑坡危险性评估方法框架,主要包括以下四部分内容(图3):(I)滑坡致灾环境因子及滑坡-非滑坡样本选取;(II)基于机器学习的滑坡易发性评估;以及(III)降雨诱发滑坡时间概率计算。以下分别对每个步骤进行详细介绍:

    图  3  基于机器学习的降雨型滑坡危险性评估框架示意图 (改自文献[9192]
    Figure  3.  Framework for rainfall-induced landslide hazard assessment based on machine learning (adapted from references [9192])

    (I)滑坡致灾环境因子及滑坡-非滑坡样本选取

    降雨型滑坡的孕灾环境极为复杂,常用的致灾因子总计有40余种,其大致可划分为地形地貌、水文环境、地层岩性和植被覆盖等[85]。然而,传统滑坡易发性评估通常采用递归特征消除、主成分分析等方法仅选择10余种易于获取的因子,导致无法全面反映滑坡发生的多元复杂性,还可能遗漏一些潜在的关键影响因子[8688]。此外,不同区域的滑坡易发性与致灾因子间的关系存在显著差异。因此,如何根据研究区域的具体情况优化致灾因子组合,已成为提升模型精度的关键。

    进一步地,准确选择滑坡-非滑坡样本是保证滑坡易发性评估质量的基础。当前,对于滑坡样本缺失或不均匀的问题,目前主要有两种方法:(a)增强模型的泛化能力:通过优化机器学习算法,选用对样本依赖性较小的模型,以降低滑坡样本缺失的影响。例如随机森林和支持向量机等,能够通过内置的样本权重调整,自动适应不平衡数据[89]。(b)识别潜在滑坡样本:通过多源信息融合(如遥感数据、历史滑坡记录和专家经验等),增强对潜在滑坡区域的识别能力[90]。此外,合理选取高可信度的非滑坡样本将显著提升模型的预测效果,减少误差。

    (II)基于机器学习的滑坡易发性评估

    机器学习算法在滑坡易发性评估中发挥着越来越重要的作用。通过对滑坡和非滑坡样本进行训练,机器学习模型能够识别并建模复杂的非线性关系,从而为滑坡的时空分布提供有效预测。常用的机器学习模型包括决策树、支持向量机、随机森林、神经网络等。然而,机器学习模型的表现依赖于样本的质量、特征选择的合理性以及数据的均衡性。因此,在进行滑坡易发性建模时,确保数据集的多样性和完整性是提高模型准确性的关键。

    (III)降雨诱发滑坡的概率计算

    降雨型滑坡发生的时间概率是实现动态风险评估的核心,其通常通过临界降雨阈值进行计算,主要考虑的参数包括:累计降雨量、降雨强度及降雨历时等。Huang等[84]结合基于逻辑回归、支持向量机和随机森林等机器学习方法的易发性评估与有效降雨强度阈值实现了江西寻乌县降雨型滑坡危险性动态评价。

    然而,随着全球气候变化的加剧,降雨模式及其极端性正发生显著变化,传统的基于历史数据的临界降雨阈值模型面临一定的局限性。气候变化带来的降雨强度增大、降雨持续时间变化以及降雨事件的频率波动,可能导致现有的滑坡危险性评估模型无法准确预测未来的滑坡发生概率。为应对这一问题,未来的研究应通过机器学习和深度学习方法,特别是集成模型来动态调整降雨阈值,从而适应不同气候条件下的降雨变化。通过引入气候变化的情景预测数据、遥感监测数据和实时降雨数据,结合机器学习方法的预测能力,可以提升滑坡发生时间的预测精度。

    尽管目前降雨型滑坡风险评估取得了显著的进展,但仍面临许多挑战和未解决的问题,特别是在全球气候变化的大背景下。以下给出了降雨型滑坡风险评估的挑战与展望。

    在气候变化和城市化加速的背景下,降雨型滑坡灾害的风险评估不仅面临传统方法的技术局限,还受到多维环境和社会因素的复杂影响。这些挑战贯穿降雨特征的动态变化到滑坡机理的多因素耦合等。以下从四个方面详细探讨了降雨型滑坡风险评估面临的具体挑战:

    (1)气候变化的不确定性

    气候变化的不可预测性和其引发的降雨的非平稳性是当前降雨型滑坡风险评估中的最大挑战之一。气候变化的不可预测性受到CO2排放情景、气候系统的自然变率以及不同气候预测模型等的不确定性等的影响。这些因素的变化会导致未来气候的多样性和不确定性,进而影响降雨强度、频率及其时空分布的预测结果。虽然一些学者也作了相关的探索[33],但如何将气候变化的影响纳入降雨型滑坡风险评估中,并量化其不确定性,仍是亟待解决的问题。

    (2)高时空分辨率数据的缺乏

    高时空分辨率的地质气象数据是进行精确降雨型滑坡风险评估的基础。是精确评估降雨型滑坡风险的基础。然而,许多地区特别是地质复杂或气候变化显著的区域,缺乏充足的高精度观测数据。这一数据空缺不仅限于降雨数据的时空不均匀性,还包括地质数据的空间分布不完整、长期历史数据的缺乏,以及地下水位、土壤湿度等动态变化的实时监测不足。此外,现有的气象监测网络在高时空分辨率下的覆盖范围有限,导致滑坡预测模型难以捕捉到降雨与地质变化之间细微的时空变化关系。这些数据缺口限制了传统评估方法和基于模型的预测能力。未来的研究需要通过遥感技术和数据融合方法,提升数据的时空分辨率与空间覆盖度,从而提高风险评估的准确性。

    (3)滑坡机理的多因素耦合

    降雨型滑坡灾害的发生不仅受到气候变化和城市化的影响,还涉及地质构造、地形地貌、植被覆盖等多种因素的耦合作用。而这些因素之间又存在着高度的时空非线性耦合特征。目前的研究更研究倾向于将这些因素分开分析,往往忽视了它们在特定时空条件下的耦合效应。这种分散的分析方法可能导致滑坡风险评估结果的偏差和不准确。因此,如何降雨型滑坡风险评估中,考虑多因素的耦合致灾效应,仍是当前研究中的一大难点。

    (4)模型的跨区域适应性

    降雨型滑坡的发生是区域性强的灾害,其风险评估模型在不同地理区域的适应性面临显著挑战。尤其是在气候变化的背景下,降雨模式和极端事件的空间分布发生了变化,许多现有模型在不同区域的表现差异较大。因此,如何构建具有普适性和适应性的跨区域滑坡风险评估模型,成为提升模型广泛应用性的关键。未来的研究应考虑不同地区的地质特征、气候条件、降雨模式及社会经济环境等因素,开发更为通用的跨区域风险评估方法。此外,跨区域模型的构建还需要兼顾模型的可操作性与实际应用效果,确保评估结果的可靠性和实用性。

    为了应对当前降雨型滑坡灾害风险评估中的多维挑战,未来研究需要在气候变化背景下的韧性动态评估、精细的地质气象监测与多源数据融合、跨区域风险评估模型的构建等方向上不断拓展。以下对未来研究方向展开:

    (1)气候变化背景下的韧性动态评估方法

    随着灾害管理理念的革新,在气候变化的背景下,降雨型滑坡灾害的风险评估需要从传统的危险性和脆弱性评估向韧性评估转变,实现滑坡评估的范式革新。韧性评估不仅关注降雨型滑坡灾害发生的概率和潜在损失,还强调面向极端事件(如气候变化导致的极端降雨)基础设施的恢复能力和适应能力[31, 37, 93]。通过引入多维的韧性指标,可以评估基础设施系统甚至城市系统在气候变化背景下降雨型滑坡灾害情景下的韧性响应,帮助决策者制定更加全面和动态的降雨型滑坡灾害防灾减灾策略。

    (2)精细的地质气象监测与多源数据融合

    高质量的地质数据是滑坡风险评估的核心基础,但当前滑坡灾害评估中仍面临数据覆盖不足、时空分辨率不高等问题。通过引入遥感技术、无人机监测和地球物理探测等手段,可以有效获取高精度的地形、地质结构和降雨分布数据。同时,构建标准化的滑坡灾害数据库,整合历史滑坡记录、边坡地质特性和降雨强度等信息,将为模型验证与应用提供强有力的数据支撑。此外,借助机器学习技术,可以进一步挖掘多源数据中的关键特征,整合历史滑坡记录、地质特性和降雨强度等信息,增强模型的验证和应用能力。

    (3)降雨型滑坡机理多因素耦合模型的开发

    在孕灾机理的探索方面,未来研究需从多维度和多尺度深入挖掘降雨型滑坡的成因机制,尤其是气候变化、地质条件与人类活动的耦合作用过程。构建降雨、土壤力学和地下水动力学的多场耦合模型,将有助于揭示复杂环境下滑坡的致灾机制。同时,研究降雨模式与土体力学性质在气候变化背景下的长期动态演变特征,能够为更准确地动态风险评估提供理论依据。通过局地边坡与区域滑坡集群的多尺度建模和分析,可以量化不同空间尺度上的滑坡灾害风险,为区域性防灾策略的制定提供数据支持和科学依据。

    (4)跨区域风险评估模型的构建

    降雨型滑坡的发生受到区域气候、地质条件、地形地貌和社会经济因素的影响,因此,构建跨区域的风险评估模型至关重要。然而,当前大多数模型局限于单一区域,忽视了不同地区的差异性,导致跨区域应用时存在较大不确定性。构建有效的跨区域模型需要考虑如气候与降雨模式、地形空间异质性等关键因素。此外,跨区域数据的整合与多源数据融合是一个重要挑战,遥感技术和机器学习等手段可以有效提高模型的准确性和可操作性。未来的研究应聚焦于提升跨区域模型的普适性和适应性,以应对全球气候变化背景下的滑坡风险。

    降雨型滑坡风险评估作为地质灾害防治领域的重要研究方向,近年来取得了显著的进展。在气候变化带来的不确定性背景下,本文系统回顾了降雨型滑坡灾害风险评估的研究进展,并重点讨论了以下三个关键方面:(1)考虑气候变化的降雨型滑坡可靠度评估;(2)考虑降雨模式不确定性的边坡易损性评估;(3)基于机器学习方法的降雨型滑坡危险性评估。

    进一步,本文分析了在气候变化背景下,降雨型滑坡灾害风险评估所面临的多维挑战,包括气候变化的不确定性、高时空分辨率数据的缺乏、多因素的耦合致灾效应以及模型的跨区域适应性等。展望未来,降雨型滑坡风险评估将朝着气候变化背景下的韧性动态评估、精细的地质气象监测与多源数据融合、跨区域风险评估模型的构建等方向发展。

    综上所述,降雨型滑坡风险评估在应对气候变化和城市化挑战的过程中,需要不断创新评估方法,完善理论框架,提升技术手段。通过多学科的协作与技术的进步,未来的滑坡风险评估将更加精准、智能和全面,为防灾减灾工作提供更加科学和有效的支持,保障人民生命财产安全,促进社会经济的可持续发展。

  • 图  1   考虑气候变化的降雨作用下边坡可靠度评估框架(改自He等[33]

    Figure  1.   Framework for reliability assessment of rainfall-triggered landslides considering climate change (adapted from He et al. [33])

    图  2   考虑降雨模式及岩土性质不确定性的边坡易损性评估框架示意图 (改自He等[32]

    Figure  2.   Framework for slope fragility assessment considering uncertainties in rainfall patterns and geotechnical properties (adapted from He et al. [32])

    图  3   基于机器学习的降雨型滑坡危险性评估框架示意图 (改自文献[9192]

    Figure  3.   Framework for rainfall-induced landslide hazard assessment based on machine learning (adapted from references [9192])

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出版历程
  • 收稿日期:  2025-01-01
  • 修回日期:  2025-02-16
  • 网络出版日期:  2025-03-16
  • 刊出日期:  2025-04-24

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