Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Assessment Framework
2.3. Models
2.3.1. Debris Flow Susceptibility
2.3.2. Vulnerability
2.3.3. Debris Flow Risk Assessment
2.4. Data
2.4.1. Daily Precipitation Data
2.4.2. Environment Data
3. Results
3.1. Projection of Extreme Rainfall
3.2. The Distribution of Debris Flow Susceptibility
3.3. Vulnerability and Debris Flow Risk Assessment.
4. Discussion
4.1. Debris Flow Process Models Enhance Susceptibility Evaluation
4.2. Debris Flow Risk Reduction Management in the Future
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variables | Description | Methods and Source |
---|---|---|
Population density | Dpop = P/S | |
Non-agricultural work force | The number of people engage in work other than agriculture, i.e., industrial and service employees | Census data (2014) |
GDP | Gross domestic product | Census data (2014) |
Town-constructed area | Area of regions with complete infrastructure facility and public utilities in town | Census data (2014) |
Industrial production density | Dip = V/S | |
Workforce quantity | Census data (2014) | |
population proportion | The proportion of population of a certain town to that of the superior county | B = P/P0 |
Population concentration | The relative importance of population distribution for a certain town | R = B/(S/S0) |
Traffic net density | Dti = Lti/S | |
Traffic trunk influence | Influence of different types of traffic trunk lines | f(xi) = C11 + C12 + … + Cim i ∈ (1, 2, 3, …, n) m ∈ (1, 2, 3, …, M) |
Primacy index | Primacy index means the transportation radiation force of a primate city and represents the social and economic conditions of towns been affected by the primate cities. | lef (x) = ∑(n → u = 1) lefu fef (x) = min (lef (x)) = min (∑(n → u = 1) lefu) e = (1, 2, 3, …, n) fe (x) = mine (fef (x)) = mine (min (∑(n → u = 1) lefu) e) e = (1, 2, 3, …, n) He ∈ {Hf} He {0,1} |
parameters | ||
Dpop—density of population, P—the permanent population of a town, P0—the permanent population of a county | ||
Dip—density of industrial production, V—annual industrial production, S—Town area, S0—county area | ||
Dti—density of traffic network, Lti—line length or traffic nodes of traffic network | ||
Cim—influence of m traffic trunk lines, I—traffic type (Roads, railways, airports, etc.), m—traffic trunk level | ||
u—minimum line length from e to f. If lef ≤ 100 km, traffic type is set to roads, otherwise railways. Then, the shortest path function, fe (x), is determined by comparing the minimum value of the path from the primate city to region e. According to the function, an important node, f, is selected as a junction node for region e. Hi is a Boolean value. When the fe (x) function is true, Hi is 1 or vice versa. Using the function in a GIS software, the hinterland range of the important node, f, Hm (i) is defined through search and comparison. |
PC1 | PC2 | PC3 | |
---|---|---|---|
Population density | 0.901 | −0.333 | 0.191 |
Town population proportion | 0.901 | −0.332 | 0.192 |
Non-agricultural work force | 0.884 | −0.37 | 0.138 |
Population concentration | 0.864 | −0.033 | −0.001 |
Primacy index | 0.39 | 0.578 | 0.444 |
Traffic trunk influence | 0.468 | 0.533 | 0.436 |
Industrial production density | 0.591 | 0.304 | −0.691 |
GDP | 0.715 | 0.05 | −0.419 |
Traffic net density | 0.604 | 0.368 | 0.004 |
workforce | 0.819 | 0.153 | −0.232 |
Extraction methods: Principal component analysis. | |||
a. Three components extracted |
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Li, Q.; Lu, Y.; Wang, Y.; Xu, P. Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China. Sustainability 2019, 11, 3199. https://doi.org/10.3390/su11113199
Li Q, Lu Y, Wang Y, Xu P. Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China. Sustainability. 2019; 11(11):3199. https://doi.org/10.3390/su11113199
Chicago/Turabian StyleLi, Qinwen, Yafeng Lu, Yukuan Wang, and Pei Xu. 2019. "Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China" Sustainability 11, no. 11: 3199. https://doi.org/10.3390/su11113199