Interpreting and modelling the daily extreme sediment events in karst mountain watersheds
Copyright © 2024 Elsevier B.V. All rights reserved..
Increasingly frequent extreme rainfall as a result of climate change is strongly damaging the global soil and water environment. However, few studies have focused on daily extreme sediment events (DESE) in heterogeneous karst watersheds based on long-term in-situ observations. This study quantitatively assessed the time effect of DESE on rainfall response, decoupled the impact of environmental factors on DESE by using structural equation modelling, and finally explored the modelling scheme of DESE based on the hybrid model. The results showed that DESE had the highest frequency of occurrence in May-July, with dispersed distribution in the value domain. Rainfall with a time lag of 1 day and a time accumulation of 2 or 3 days was an important contribution to DESE (P < 0.01, R = 0.47-0.68). Combined effects of environmental factors explained 53.6 %-64.1 % of the variation in DESE. Runoff and vegetation exerted the strongest direct and indirect effects on DESE, respectively (β = 0.66/-0.727). Vegetation was the dominant driver of DESE in Dabanghe and Yejihe (β = -0.725/-0.758), while the dominant driver in Tongzhihe was climate (β = 0.743). In the future, the risk of extreme sediments should be prevented and resolved through the comprehensive regulation of multiple paths, such as runoff and vegetation. Hybrid models significantly improved the modelling performance of machine learning models. Generalized additive model-Extreme gradient boost had the best performance, while Partial least squares regression-Extreme gradient boost was the most valuable when considering performance and input data cost. Two methods can be used as recommended solutions for DESE modelling. This study provides new and in-depth insights into DESE in karst watersheds and helps the region develop forward-looking soil and water management models to cope with future extreme erosion hazards.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:926 |
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| Enthalten in: |
The Science of the total environment - 926(2024) vom: 20. Apr., Seite 171956 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jing, Jun [VerfasserIn] |
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| Links: |
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| Themen: |
Hybrid model |
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| Anmerkungen: |
Date Revised 16.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.scitotenv.2024.171956 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM370374932 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a Increasingly frequent extreme rainfall as a result of climate change is strongly damaging the global soil and water environment. However, few studies have focused on daily extreme sediment events (DESE) in heterogeneous karst watersheds based on long-term in-situ observations. This study quantitatively assessed the time effect of DESE on rainfall response, decoupled the impact of environmental factors on DESE by using structural equation modelling, and finally explored the modelling scheme of DESE based on the hybrid model. The results showed that DESE had the highest frequency of occurrence in May-July, with dispersed distribution in the value domain. Rainfall with a time lag of 1 day and a time accumulation of 2 or 3 days was an important contribution to DESE (P < 0.01, R = 0.47-0.68). Combined effects of environmental factors explained 53.6 %-64.1 % of the variation in DESE. Runoff and vegetation exerted the strongest direct and indirect effects on DESE, respectively (β = 0.66/-0.727). Vegetation was the dominant driver of DESE in Dabanghe and Yejihe (β = -0.725/-0.758), while the dominant driver in Tongzhihe was climate (β = 0.743). In the future, the risk of extreme sediments should be prevented and resolved through the comprehensive regulation of multiple paths, such as runoff and vegetation. Hybrid models significantly improved the modelling performance of machine learning models. Generalized additive model-Extreme gradient boost had the best performance, while Partial least squares regression-Extreme gradient boost was the most valuable when considering performance and input data cost. Two methods can be used as recommended solutions for DESE modelling. This study provides new and in-depth insights into DESE in karst watersheds and helps the region develop forward-looking soil and water management models to cope with future extreme erosion hazards | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Hybrid model | |
| 650 | 4 | |a Impact factors | |
| 650 | 4 | |a Karst system | |
| 650 | 4 | |a Soil erosion | |
| 650 | 4 | |a Time effects | |
| 700 | 1 | |a Li, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Linlv |e verfasserin |4 aut | |
| 700 | 1 | |a Shu, Dongcai |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Pingping |e verfasserin |4 aut | |
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