Chang Ming, Zhou Kangchi, Dou Xiangyang, Su Fenghuan, Yu Bo
State Key Laboratory of Geohazard Prevention and GeoEnvironment Protection, Chengdu University of Technology, Chengdu, 610059, Sichuan, China.
Faculty of Geo-Information Science and Earth Observation, University of Twente, 7522 NH, Enschede, The Netherlands.
Sci Rep. 2025 Jan 4;15(1):775. doi: 10.1038/s41598-024-84738-w.
Pakistan's geographic location makes it an important land hub between Central Asia, Middle East-North Africa, and China. However, the railways, roads, farmland, riverways, and residential quarters in the Piedmont plains of Baluchistan province in northwestern Pakistan are under serious threat of flooding in the summer of 2022. The urgency and severity of climate change's impact on humanity are underscored by the significant threats posed to human life and property in Piedmont Plains environments through extreme flood events, which has garnered widespread concerns. In flood scenarios, accurately predicting the extent of flooding is crucial for disaster assessment, emergency response, and the efficient allocation of resources. Previous research has primarily predicted flooding likelihood based on topographical factors or integrated annual rainfall data, failing to account for the extent of flooding from short-term rainfall before and after an event. Flood disasters are not caused by a single factor but are influenced by a variety of elements, including terrain and climate. Therefore, current research still lacks a comprehensive consideration of these influencing factors to accurately predict both the range and severity of flood impacts. In this paper, in response to the inability to accurately predict the flood damage in the pre-hill plains region in previous studies, combined with the current Pakistan mega-flood disaster, will couple the impacts of various flood-inducing factors on flooding, construct a prediction model for the degree of inundation of the Pakistani pre-hill plains flood disaster, and combined with the distribution of regional bearers, analyze the risk-resistant capacity of different types of bearers, and draw a comprehensive risk map piece under the flooding disaster. This paper bridges the gap of not integrating various factors in previous studies. Our research results provide strong evidence for flood prediction in Pakistan and similar regions, which is of great significance in reducing the loss of life and property of people around the world.
巴基斯坦的地理位置使其成为中亚、中东 - 北非和中国之间重要的陆上枢纽。然而，巴基斯坦西北部俾路支省山前平原的铁路、公路、农田、河道和居民区在2022年夏季面临严重的洪水威胁。极端洪水事件对山前平原地区人类生命和财产构成重大威胁，凸显了气候变化对人类影响的紧迫性和严重性，这已引起广泛关注。在洪水场景中，准确预测洪水范围对于灾害评估、应急响应和资源的有效分配至关重要。以往的研究主要基于地形因素或综合年降雨数据预测洪水可能性，未考虑事件前后短期降雨造成的洪水范围。洪水灾害并非由单一因素引起，而是受包括地形和气候在内的多种因素影响。因此，当前研究仍缺乏对这些影响因素的全面考虑，难以准确预测洪水影响的范围和严重程度。本文针对以往研究无法准确预测山前平原地区洪水灾害损失的问题，结合当前巴基斯坦特大洪水灾害，将各种诱发洪水因素对洪水的影响进行耦合，构建巴基斯坦山前平原洪水灾害淹没程度预测模型，并结合区域承载物分布，分析不同类型承载物的抗灾能力，绘制洪水灾害下的综合风险图。本文弥补了以往研究未综合考虑各种因素的不足。我们的研究结果为巴基斯坦及类似地区的洪水预测提供了有力证据，对减少全球人民生命和财产损失具有重要意义。
