Why Flash Floods Occur Differently across Regions? A Spatial Analysis of China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Data Processing
2.2.1. Flash Flood Records
2.2.2. Processing of Precipitation Data
2.2.3. Processing of Human Activity Data
2.2.4. Integration of Numerical Factors at Watershed Scale
2.3. Methods
2.3.1. Factor Detector
2.3.2. Interaction Detector
2.3.3. Spatial Zoning Scheme
3. Results
3.1. Spatial Variation in the FFs and Factors
3.2. Driving Factors of Flash Floods Across Different Ecoregions
3.3. Interaction of Influential Factors Driving Flash Floods
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
NWAR | HDM | Inner MP | LP | MLY | North | NER | TP | Ch-Yu | South China | Yun-Gui | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Number of Flash Floods | 148 | 93 | 146 | 1078 | 621 | 231 | 301 | 38 | 1468 | 1688 | 638 | |
P(<10) | Mean | 21.9 | 42.58 | 22.38 | 26.64 | 35.01 | 22.46 | 29.19 | 27.93 | 46.02 | 42.5 | 46.9 |
Min | 7.26 | 28.36 | 14.63 | 17.86 | 28.6 | 18.04 | 17.35 | 9.3 | 25.96 | 29.4 | 34.47 | |
Max | 39.71 | 53.25 | 37.48 | 41.21 | 40.91 | 31.98 | 51.26 | 48.41 | 90.98 | 54.82 | 90.48 | |
P(10–25) | Mean | 10.04 | 25.72 | 12.31 | 15.23 | 19.59 | 12.39 | 15.82 | 16.72 | 25.76 | 23.22 | 25.85 |
Min | 2.92 | 17.11 | 7.03 | 10.65 | 14.82 | 10.64 | 10.81 | 4.11 | 14.18 | 13.59 | 19.13 | |
Max | 22.18 | 35.35 | 17.47 | 25.18 | 22.69 | 17.76 | 20.16 | 31.36 | 39.2 | 30.35 | 39.06 | |
P(25–50) | Mean | 6.99 | 22.85 | 9.62 | 12.51 | 18.03 | 10.23 | 13.19 | 13.91 | 20.81 | 20.36 | 21.22 |
Min | 2.02 | 14.74 | 4.71 | 7.81 | 13.32 | 8.72 | 9.29 | 2.59 | 11.77 | 10 | 14.99 | |
Max | 16.67 | 30.14 | 12.59 | 21.62 | 21.46 | 15.9 | 19.02 | 27.29 | 28.21 | 24.66 | 27.79 | |
P(50–100) | Mean | 5.09 | 22.46 | 8.58 | 11.72 | 19.83 | 10.06 | 12.4 | 13.65 | 19.57 | 21.86 | 21.53 |
Min | 1.49 | 15.47 | 3.94 | 6.98 | 13.83 | 8.88 | 7.59 | 2.09 | 12.13 | 10.18 | 15.71 | |
Max | 12.73 | 29.04 | 11.06 | 20.17 | 24.35 | 16.9 | 18.67 | 26.46 | 27.18 | 25.94 | 31.35 | |
P(100–250) | Mean | 2.95 | 19.19 | 6.98 | 10.36 | 24.01 | 10.34 | 11.63 | 9.81 | 19.27 | 26.62 | 23.62 |
Min | 1.18 | 8.96 | 2.49 | 5.15 | 14.9 | 8.19 | 7.01 | 1.55 | 11.6 | 11.51 | 17.8 | |
Max | 8.29 | 31.04 | 9.2 | 15.07 | 30.35 | 19.32 | 16.95 | 18.85 | 25.51 | 30.88 | 35.63 | |
P(>250) | Mean | 1.2 | 3.88 | 2.4 | 3.85 | 15.23 | 5.87 | 4.82 | 1.66 | 10.09 | 18.08 | 11.28 |
Min | 1 | 1.22 | 1.19 | 1.5 | 7.4 | 2.37 | 2.28 | 1.03 | 2.44 | 10.51 | 5.25 | |
Max | 1.69 | 12.43 | 3.54 | 7.52 | 21 | 12.49 | 11.16 | 2.77 | 15.14 | 26.23 | 18.05 | |
Elevation (m) | Mean | 1764.17 | 3472.59 | 1347.03 | 1325.57 | 167.76 | 410.48 | 385.15 | 4275.7 | 709.48 | 290.53 | 1493.78 |
Min | −26.84 | 765 | 545.19 | 75.11 | 1.46 | 0 | 0 | 1481.37 | 40.23 | −0.65 | 97.13 | |
Max | 5322.5 | 5565 | 15,544.65 | 4245.85 | 1222.22 | 1891.95 | 1640.33 | 6086 | 3832.46 | 1776.13 | 4106.74 | |
Slope (°) | Mean | 13.33 | 28.5 | 7.44 | 18.11 | 14.09 | 11.77 | 8.4 | 18.39 | 21.59 | 17.03 | 21.76 |
Min | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Max | 55.59 | 55.69 | 39.68 | 74.3 | 43.5 | 40.97 | 31.35 | 48.31 | 53.38 | 48.29 | 51.09 | |
land use (%) | field | 19.50% | 2.00% | 36.30% | 43.80% | 39.80% | 51.10% | 34.90% | 3.40% | 43.30% | 25.40% | 18.10% |
forest | 4.90% | 50.40% | 4.10% | 15.40% | 48.10% | 20.60% | 49.80% | 6.30% | 42.70% | 61.60% | 63.60% | |
grass | 75.60% | 47.60% | 59.60% | 40.80% | 12.10% | 28.30% | 15.30% | 90.30% | 14.10% | 13.00% | 18.30% | |
Soil (%) | loam | 78.40% | 78.90% | 85.30% | 83.30% | 51.50% | 86.50% | 72.70% | 87.30% | 65.50% | 50.70% | 38.00% |
clay | 19.00% | 19.70% | 11.60% | 15.90% | 42.80% | 11.00% | 26.60% | 7.70% | 33.80% | 48.00% | 60.90% | |
other | 2.70% | 1.40% | 3.10% | 0.80% | 5.80% | 2.50% | 0.70% | 5.10% | 0.70% | 1.40% | 1.10% | |
Vegetation (%) | natural | 88.00% | 97.20% | 79.80% | 52.70% | 71.60% | 50.00% | 71.10% | 89.80% | 68.20% | 82.90% | 92.40% |
agricultural | 8.60% | 1.00% | 19.60% | 47.30% | 28.20% | 49.60% | 28.90% | 5.20% | 31.60% | 17.10% | 7.50% | |
other | 3.40% | 1.80% | 0.60% | 0.00% | 0.20% | 0.30% | 0.00% | 5.10% | 0.10% | 0.10% | 0.10% | |
Landform (%) | low mountain | 10.60% | 0.10% | 37.00% | 43.30% | 69.80% | 68.40% | 71.50% | 0.00% | 80.20% | 79.60% | 34.70% |
high mountain | 33.00% | 94.90% | 6.30% | 25.50% | 0.00% | 0.40% | 0.40% | 81.10% | 13.70% | 0.10% | 60.60% | |
plain | 56.40% | 5.00% | 56.60% | 31.20% | 30.20% | 31.20% | 28.10% | 18.90% | 6.20% | 20.30% | 4.70% | |
population density (/km²) | Mean | 30 | 17 | 83 | 190 | 322 | 385 | 125 | 16 | 289 | 283 | 137 |
Min | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | |
Max | 2112 | 542 | 2642 | 3398 | 6342 | 12,672 | 4339 | 2413 | 7468 | 3808 | 8191 | |
village density (/100km²) | Mean | 3 | 8 | 15 | 39 | 70 | 44 | 21 | 4 | 51 | 58 | 35 |
Min | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | <1 | |
Max | 62 | 77 | 80 | 1370 | 1497 | 344 | 1045 | 370 | 394 | 261 | 275 |
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Data | Period | Source | Format | Description |
---|---|---|---|---|
Flash floods | 1950–2015 | Investigation Project of Chinese Flash Floods (IPCFF) | Point layer | Location, time, etc. |
Ecoregion | 2012 | Xie et al. | Polygon layer | Classified into 11 Ecoregion |
Precipitation | 1950–2015 | CMA | Table | Daily data of 175 national meteorological station |
DEM | 2003 | NASA | 90 m × 90 m grid | Shuttle Radar Topography Mission (STRM) |
Land use | 2010 | RESDC 1 | Polygon layer | Based on 1:1,000,000 land use dataset |
Soil | 2010 | RESDC | Polygon layer | integrated from 1:1,000,000 soil type maps and the 2nd national soil census data |
Landform | 1994 | RESDC | Polygon layer | 1:4,000,000 geomorphological map of China |
Vegetation | 1996 | RESDC | Polygon layer | 1:4,000,000 geomorphological map of China |
Population | 2000 | RESDC | 1 km × 1 km grid | Grid value is population |
Village | 2000 | RESDC | Point layer | Village layer of national basic geography layer |
Interaction | Description |
---|---|
Weaken, nonlinear | PD(L1∩L2) < Min(PD(L1), PD(L2)) |
Weaken, unilinear | Min(PD(L1), PD(L2)) < PD(L1∩ L2) < Max(PD(L1)), PD(L2)) |
Bilinear enhanced | PD(L∩L2) > Max(PD(L), PD(L2)) |
Independent | PD(L∩L2) = PD(L) + PD(L2) |
Nonlinear enhanced | PD(L∩L2) > PD(L) + PD(L2) |
Ecoregion | NWAR | TP | Inner MP | NER | LP | North China | HDM | Ch-Yu | LMY | Yun-Gui | South China |
---|---|---|---|---|---|---|---|---|---|---|---|
P(<10) | 0.368 | 0.349 | 0.346 | 0.613 | 0.253 | 0.294 | 0.111 | 0.27 | 0.135 | 0.212 | 0.315 |
P(10–25) | 0.567 | 0.348 | 0.216 | 0.466 | 0.297 | 0.176 | 0.276 | 0.336 | 0.176 | 0.353 | 0.205 |
P(25–50) | 0.589 | 0.28 | 0.17 | 0.233 | 0.257 | 0.171 | 0.215 | 0.33 | 0.086 | 0.065 | 0.098 |
P(50–100) | 0.707 | 0.337 | 0.289 | 0.134 | 0.289 | 0.2 | 0.365 | 0.334 | 0.075 | 0.104 | 0.084 |
P(100–250) | 0.756 | 0.104 | 0.434 | 0.162 | 0.422 | 0.065 | 0.255 | 0.392 | 0.059 | 0.062 | 0.126 |
P(>250) | 0.704 | 0.206 | 0.44 | 0.306 | 0.564 | 0.044 | 0.231 | 0.301 | 0.134 | 0.06 | 0.199 |
population density | 0.501 | 0.384 | 0.22 | 0.11 | 0.194 | 0.033 | 0.252 | 0.053 | 0.033 | 0.081 | 0.013 |
village density | 0.597 | 0.321 | 0.236 | 0.273 | 0.155 | 0.446 | 0.194 | 0.031 | 0.087 | 0.018 | 0.011 |
elevation | 0.116 | 0.672 | 0.034 | 0.122 | 0.231 | 0.019 | 0.266 | 0.024 | 0.011 | 0.009 | 0.02 |
Slope | 0.034 | 0.063 | 0.178 | 0.113 | 0.012 | 0.019 | 0.069 | 0.052 | 0.014 | 0.017 | 0.019 |
land use | 0.104 | 0.248 | 0.069 | 0.136 | 0.173 | 0.071 | 0.039 | 0.026 | 0.052 | 0.033 | 0.1 |
vegetation | 0.192 | 0.146 | 0.131 | 0.234 | 0.173 | 0.044 | 0.155 | 0.158 | 0.124 | 0.078 | 0.181 |
soil | 0.285 | 0.343 | 0.093 | 0.149 | 0.067 | 0.125 | 0.149 | 0.031 | 0.11 | 0.146 | 0.08 |
landform | 0.509 | 0.714 | 0.312 | 0.209 | 0.22 | 0.558 | 0.189 | 0.306 | 0.086 | 0.049 | 0.401 |
Ecoregion | Factors with Highest Interactive Power of Determinant (PD) | PD of Interaction Detector | Relationship of the Two Factors |
---|---|---|---|
NWAR | Landform, P(>250) | 0.901 | Bilinear enhanced |
TP | Landform, P(>250) | 0.861 | Bilinear enhanced |
Inner MP | Landform, P(100–250) | 0.737 | Bilinear enhanced |
NER | P(<10), P(>250) | 0.800 | Bilinear enhanced |
LP | P(25–50), P(>250) | 0.819 | Bilinear enhanced |
North China | Landform, P(100–250) | 0.770 | Nonlinear enhanced |
HDM | P(10–25), P(>250) | 0.690 | Nonlinear enhanced |
Ch-Yu | P(50–100), P(>250) | 0.617 | Bilinear enhanced |
LMY | P(<10), P(>250) | 0.478 | Nonlinear enhanced |
Yun-Gui | P(10–25), P(>250) | 0.532 | Nonlinear enhanced |
South China | Landform, P(>250) | 0.641 | Nonlinear enhanced |
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Liu, Y.; Huang, Y. Why Flash Floods Occur Differently across Regions? A Spatial Analysis of China. Water 2020, 12, 3344. https://doi.org/10.3390/w12123344
Liu Y, Huang Y. Why Flash Floods Occur Differently across Regions? A Spatial Analysis of China. Water. 2020; 12(12):3344. https://doi.org/10.3390/w12123344
Chicago/Turabian StyleLiu, Yesen, and Yaohuan Huang. 2020. "Why Flash Floods Occur Differently across Regions? A Spatial Analysis of China" Water 12, no. 12: 3344. https://doi.org/10.3390/w12123344