1.中国科学院、水利部 成都山地灾害与环境研究所 山地自然灾害与工程安全;重点实验室, 四川 成都 610299
2.中国科学院大学, 北京 100049
3.中国-巴基斯坦地球科学;研究中心, 巴基斯坦 伊斯兰堡 45320
4.西南石油大学 土木工程与测绘学院, 四川 成都 610500
信珂莹(2002—),女(汉族),河南省许昌市人,硕士研究生,研究方向为自然灾害分布与危险性分析。Email:xinkeying23@mails.ucas.ac.cn。
苏凤环(1977—),女(汉族),山东省阳谷县人,博士,副研究员,主要从事灾害遥感与山地灾害风险评估研究。Email:fhsu@imde.ac.cn。
收稿:2025-06-09,
修回:2025-07-05,
纸质出版:2025-10-10
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信珂莹, 苏凤环, 郭晓军, 等.巴基斯坦洪涝灾害时空分布特征及其影响因素[J].水土保持通报,2025,45(5):316-325.
Xin Keying, Su Fenghuan, Guo Xiaojun, et al. Spatiotemporal distribution characteristics and influencing factors of flood disasters in Pakistan [J]. Bulletin of Soil and Water Conservation,2025,45(5):316-325.
信珂莹, 苏凤环, 郭晓军, 等.巴基斯坦洪涝灾害时空分布特征及其影响因素[J].水土保持通报,2025,45(5):316-325. DOI: 10.13961/j.cnki.stbctb.2025.05.039. CSTR: 32312.14.stbctb.2025.05.039..
Xin Keying, Su Fenghuan, Guo Xiaojun, et al. Spatiotemporal distribution characteristics and influencing factors of flood disasters in Pakistan [J]. Bulletin of Soil and Water Conservation,2025,45(5):316-325. DOI: 10.13961/j.cnki.stbctb.2025.05.039. CSTR: 32312.14.stbctb.2025.05.039..
目的
2
分析巴基斯坦洪涝灾害的时空演化特征及影响因素,为洪灾风险分析与管理提供科学参考依据。
方法
2
基于达特茅斯全球洪水数据库(DFO)记录的1985—2021年143次典型洪涝灾害事件,结合ERA5-Land降水数据、DEM、土地利用、人口密度与GDP等多源空间数据,采用距平分析、重力模型与标准差椭圆等方法分析洪灾的时间变化特征、空间分布特征、重心迁移及分布方向演化,基于最优参数地理探测器模型(OPGD)定量识别洪涝灾害时空分布的主要影响因子及其交互作用。
结果
2
巴基斯坦洪涝灾害在2005—2010年进入高发期,2007年频数达历史记录峰值。灾害在空间上表现出由广泛分布向印度河中上游高密度、高严重程度区域集聚的演化特征,灾害重心呈北移趋势,主分布方向趋于南北,空间格局趋于集中。地理探测结果显示:降雨类因子(Rx
5d
,
R
20 mm
,Rx
1d
)解释力显著,多数因子组合存在显著的交互增强效应。
结论
2
巴基斯坦洪涝灾害风险持续上升,灾害重心和分布方向发生迁移,空间格局日趋集中。强降雨事件是洪涝灾害发生的关键诱因,而地形因子与降水相关因子之间的相互作用决定了洪水发生的空间异质性。
Objective
2
The spatiotemporal evolution characteristics and influencing factors of flood disasters in Pakistan were analyzed, in order to provide scientific references for flood risk analysis and management.
Methods
2
Based on 143 typical flood events recorded by the Dartmouth Flood Observatory (DFO) from 1985 to 2021, this study integrated multi-source spatial datasets, including ERA5-Land precipitation, digital elevation model (DEM), land use, population density, and gross domestic product (GDP). The temporal variation, spatial distribution, gravity center migration, and directional evolution of flood disasters were analyzed using methods such as anomaly analysis, the gravity center model, and the standard deviational ellipse. The optimal parameter-based geodetector (OPGD) model was employed to quantitatively identify the dominant influencing factors and their interactive effects on the spatiotemporal distribution of flood disasters.
Results
2
Flood disasters in Pakistan entered a period of high occurrence from 2005 to 2010, with the frequency reaching a historical record high in 2007. The disasters exhibited spatial evolutionary characteristics, shifting from widespread distribution to high-density and high-severity concentration in the middle and upper reaches of the Indus River. The disaster centroid migrated northward, the primary distribution axis aligned in a north-south direction, and the spatial pattern became increasingly clustered. The geodetector analysis revealed that precipitation-related factors (Rx
5d
,
R
20mm
, and Rx
1d
) possessed strong explanatory power, with most combinations of factors exhibiting significant synergistic effects.
Conclusion
2
Flood disaster risks in Pakistan steadily increased, characterized by shifting disaster centroid and distribution orientation, while the spatial pattern becomes increasingly concentrated. Extreme precipitation events serve as the key trigger of flood disasters, while the interaction between topographic factors and precipitation-related factors critically shapes the spatial heterogeneity of flood occurrence.
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