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Predicted risk of heavy rainfall and flooding in Pakistan's premontane plains
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Ming CHANG1, Kangchi ZHOU1, *, Liang CHEN1, Bo YU1, Xiaoping ZHAO2, Fenghuan SU3, Xisong ZHU1
Science & Technology Review | 2025, 43(15) : 90 - 101
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Science & Technology Review | 2025, 43(15): 90-101
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Predicted risk of heavy rainfall and flooding in Pakistan's premontane plains
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Ming CHANG1, Kangchi ZHOU1, *, Liang CHEN1, Bo YU1, Xiaoping ZHAO2, Fenghuan SU3, Xisong ZHU1
Affiliations
  • 1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China
  • 2. POWERCHINA Chengdu Engineering Co., Ltd., Chengdu 610031, China
  • 3. Institute of Mountain Hazards and Environment, CAS, Chengdu 610213, China
Published: 2025-08-13 doi: 10.3981/j.issn.1000-7857.2024.06.00643
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With the frequent occurrence of extreme rainfall events around the world in recent years, the floods caused by them seriously threaten the safety of human life and property. Due to the abundant rainfall, undulating terrain, and good runoff conditions in mountainous areas. After rain, the flood flow of rivers rises sharply. Serious floods often form in the plains in front of mountains where human activities are frequent. To reduce the impact of floods on the human living environment, it is urgent to research the risk of heavy rainfall floods in the plains in front of mountains. This research will provide scientific guidance for flood prevention and mitigation. In this paper, the Piedmont area in northwestern Balochistan, severely affected by the 2022 floods in Pakistan, is taken as the study area. Two factors were selected as the flood indicators: substrate and climate. The weights of hierarchical analysis and mean squared difference decision method is assigned using the combination of weighting rules. The risk of the study area is calculated by combining the flood hazard resistance of different surface types, infrastructure, and economic development levels. The results show that the flood inundation prediction based on the combined weighting rule is consistent with reality. The predicted inundation area encompasses 90.31% of the actual inundated area. The results indicate that areas of very high risk are primarily located in regions with abundant farmland, low topography, and weak flood resistance. In these areas, floodwaters tend to accumulate.The study demonstrates that this inundation model can accurately predict flood inundation areas. The evaluation results can guide disaster prevention and mitigation efforts in the study area and similar regions along the "Belt and Road" to reduce the risk of flooding.

piedmont  /  Pakistan  /  floods disaster risk  /  degree of inundation  /  risk resilience
Ming CHANG, Kangchi ZHOU, Liang CHEN, Bo YU, Xiaoping ZHAO, Fenghuan SU, Xisong ZHU. Predicted risk of heavy rainfall and flooding in Pakistan's premontane plains[J]. Science & Technology Review, 2025 , 43 (15) : 90 -101 . DOI: 10.3981/j.issn.1000-7857.2024.06.00643
Year 2025 volume 43 Issue 15
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Article Info
doi: 10.3981/j.issn.1000-7857.2024.06.00643
  • Receive Date:2024-06-05
  • Online Date:2025-12-18
  • Published:2025-08-13
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  • Received:2024-06-05
  • Revised:2025-02-24
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Affiliations
    1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China
    2. POWERCHINA Chengdu Engineering Co., Ltd., Chengdu 610031, China
    3. Institute of Mountain Hazards and Environment, CAS, Chengdu 610213, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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