Impact-based probabilistic modeling of hydro-morphological processes in China (1985-2015)

Nan Wang; Weiming Cheng; Hongyan Zhang; Cees van Westen; Junnan Xiong; Changjun Liu; Luigi Lombardo

doi:10.1016/j.jenvman.2023.118463

Impact-based probabilistic modeling of hydro-morphological processes in China (1985-2015)

J Environ Manage. 2023 Oct 15:344:118463. doi: 10.1016/j.jenvman.2023.118463. Epub 2023 Jun 27.

Authors

Nan Wang¹, Weiming Cheng², Hongyan Zhang³, Cees van Westen⁴, Junnan Xiong⁵, Changjun Liu⁶, Luigi Lombardo⁴

Affiliations

¹ Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, 130024, China; State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
² State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, 210023, China; Collaborative Innovation Center of South China Sea Studies, Nanjing, 210093, China.
³ Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, 130024, China. Electronic address: zhy@nenu.edu.cn.
⁴ University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), PO Box 217, Enschede, AE 7500, Netherlands.
⁵ State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu, 610500, China.
⁶ Research Center on Flood and Drought Disaster Reduction of the MWR, Beijing, 100038, China; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.

PMID: 37384982
DOI: 10.1016/j.jenvman.2023.118463

Abstract

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-temporal pattern of precipitation events. The modelling of where HMP-driven hazards may occur can help define the appropriate course of actions before and during a crisis, reducing the potential losses that HMPs cause in their wake. However, the probabilistic information on locations prone to experience a given hazard is not sufficient to depict the risk our society may incur. To cover this aspect, modeling the loss information could open up to better territorial management strategies. In this work, we made use of the HMP catalogue of China from 1985 to 2015. Specifically, we implemented the Light Gradient Boosting (LGB) classifier to model the impact level that locations across China have suffered from HMPs over the thirty-year record. We obtained six impact levels as a combination of financial and life losses, whose classes we used as separate target variables for our LGB. In doing so, we estimated spatial probabilities of certain HMP impact, something that has yet to be tested in the natural hazard community, especially over such a large spatial domain. The results we obtained are encouraging, with each of the six impact categories being separately classified with excellent to outstanding performance (the worst case corresponds to a mean AUC = 0.862, whereas the best case corresponds to a mean AUC of 0.915). The good predictive performance our model produced suggest that the cartographic output could be useful to inform authorities of locations prone to human and infrastructural losses of specific magnitudes.

Keywords: China; Hazard impact; Hydro-morphological processes; Susceptibility modeling.

MeSH terms

China
Floods*
Humans