气候变化领域集成服务门户(CCPortal): Projected seasonal flooding in Canada under climate change with statistical and machine learning

CCPortal

DOI	10.1016/j.ejrh.2024.101754
	Projected seasonal flooding in Canada under climate change with statistical and machine learning
	Grenier, Manuel; Boudreault, Jeremie; Raymond, Sebastien; Boudreault, Mathieu
发表日期	2024
EISSN	2214-5818
起始页码	53
卷号	53
英文摘要	Study region: Canada Study focus: Floods are among the costliest and deadliest natural hazards in the world. To date, little is known about future seasonal flooding across all Canada. In this paper, data-driven models for flood occurrence (i.e., happening of a flood) and impact (i.e., displaced population) were calibrated for spring and summer seasons in 14,000 watersheds across Canada. Generalized Additive Models (GAM), Random Forests (RF) and Gradient Boosting Machines (GBM) were considered to model seasonal floods. The best -performing flood models were then used with regional climate models to assess the effect of climate change on flooding for three time horizons: historical, medium-term (similar to 2050) and long-term (similar to 2080). New hydrological insights for the region: GAM offered the best out-of-sample performance trade-off in both seasons for predicting flooding in Canada. Projections with GAM showed a general increase in summer flooding occurrence and impact in 2050 and 2080, mainly in the Yukon, western British Columbia, southern Prairies, Ontario and Quebec and some Atlantic provinces. Results for spring flooding were more mixed, but there seemed to be a slight decrease in the impact of spring flooding in the southern Prairies, particularly in 2080. The combination of statistical/machine learning and climate models have provided a more detailed and contrasted picture of the projected seasonal flooding situation over Canada that will help authorities better mitigate future flood risks.
英文关键词	Spring flooding; Summer flooding; Generalized additive model; Random forest; Gradient boosting; Regional climate models
语种	英语
WOS研究方向	Water Resources
WOS类目	Water Resources
WOS记录号	WOS:001215141800001
来源期刊	JOURNAL OF HYDROLOGY-REGIONAL STUDIES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288871
作者单位	University of Quebec; University of Quebec Montreal; University of Quebec; Institut national de la recherche scientifique (INRS)
推荐引用方式 GB/T 7714	Grenier, Manuel,Boudreault, Jeremie,Raymond, Sebastien,et al. Projected seasonal flooding in Canada under climate change with statistical and machine learning[J],2024,53.
APA	Grenier, Manuel,Boudreault, Jeremie,Raymond, Sebastien,&Boudreault, Mathieu.(2024).Projected seasonal flooding in Canada under climate change with statistical and machine learning.JOURNAL OF HYDROLOGY-REGIONAL STUDIES,53.
MLA	Grenier, Manuel,et al."Projected seasonal flooding in Canada under climate change with statistical and machine learning".JOURNAL OF HYDROLOGY-REGIONAL STUDIES 53(2024).