Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Daily Precipitation Data
2.3. RE Calculation and Accuracy Evaluation
2.4. Trend Analysis
3. Results
3.1. Spatial Distribution of Average Annual RE for Different Data Sources
3.2. Evaluation of RE Accuracy at Station Scale
3.3. Comparison of RE Trends on the TP
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Zone | Annual Precipitation (mm) | Average Temperature of Warmest Month (°C) | Area (Million km2) | No. Stations (Stations) | Station Density (Unit/10,000 km2) |
---|---|---|---|---|---|
Broad-leaved evergreen forest zone on the southern flank of the Himalayas (VA6) | 1000–4000 | 18–25 | 0.087 | 3 | 0.34 |
Eastern Qinghai–Qilian montane basin coniferous forest steppe zone (IIC1) | 300–600 | 12–18 | 0.171 | 32 | 1.87 |
Eastern Sichuan and Tibet montane coniferous forest (IIAB1) | 500–1000 | 6–18 | 0.424 | 45 | 1.06 |
Golog-Nagqu plateau high–cold shrub–meadow zone (IB1) | 400–700 | 6–12 | 0.255 | 16 | 0.63 |
Qiangtang Plateau internally flowing rivers zone (IC1) | 200–400 | 6–10 | 0.186 | 3 | 0.16 |
Qiangtang Plateau lake basin high–cold steppe zone (IC2) | 100–300 | 6–10 | 0.52 | 4 | 0.08 |
Qaidam basin desert zone (IID1) | 10–200 | 10–18 | 0.257 | 9 | 0.35 |
Southern Tibet montane valley shrub–steppe zone (IIC2) | 200–300 | 10–16 | 0.177 | 16 | 0.90 |
Northern flank of Kunlun Mountains desert zone (IID2) | 70–150 | 12–20 | 0.158 | 1 | 0.06 |
Kunlun montane plateau high–cold desert zone (ID1) | <100 | 3–7 | 0.266 | 0 | 0 |
Ali Mountains desert zone (IID3) | 50–200 | 10–14 | 0.079 | 2 | 0.25 |
Zone | Gauge | IMERG-F | TPHiPr | ||||||
---|---|---|---|---|---|---|---|---|---|
Min | Max | Mean | Min | Max | Mean | Min | Max | Mean | |
TP | 8 | 3534 | 775 | 4 | 3655 | 1028 | 10 | 4280 | 883 |
VA6 | 1186 | 3534 | 2591 | 1837 | 3655 | 2977 | 1307 | 4280 | 2884 |
IIAB1 | 249 | 2065 | 1035 | 602 | 2593 | 1398 | 277 | 2713 | 1182 |
IB1 | 351 | 1254 | 744 | 470 | 1950 | 1068 | 410 | 1517 | 892 |
IIC2 | 320 | 1125 | 643 | 500 | 2477 | 1025 | 286 | 1407 | 715 |
IIC1 | 255 | 1154 | 658 | 213 | 1622 | 700 | 295 | 1154 | 736 |
IC2 | 288 | 553 | 413 | 478 | 1129 | 841 | 243 | 576 | 398 |
IC1 | 288 | 361 | 314 | 360 | 677 | 501 | 412 | 522 | 462 |
IID1 | 8 | 294 | 112 | 5 | 256 | 93 | 10 | 326 | 125 |
IID2 | 42 | 42 | 42 | 4 | 4 | 4 | 43 | 43 | 43 |
IID3 | 53 | 211 | 132 | 50 | 1245 | 647 | 58 | 404 | 231 |
ID1 | / | / | / | / | / | / | / | / | / |
Dataset | Time | R2 | NSE | RMSE (MJ·mm·ha−1·h−1·yr−1) | SD (MJ·mm·ha−1·h−1·yr−1) | RMSE/SD | PBIAS (%) |
---|---|---|---|---|---|---|---|
Gauge | Year | / | / | / | 499.27 | / | / |
TPHiPr | 0.93 | 0.85 | 195.84 | / | 0.39 | 14.03 | |
IMERG-F | 0.61 | 0.07 | 481.19 | / | 0.96 | 32.77 | |
Gauge | Month | / | / | / | 90.09 | / | / |
TPHiPr | 0.92 | 0.87 | 35.89 | / | 0.40 | 14.03 | |
IMERG-F | 0.73 | 0.39 | 77.22 | / | 0.86 | 32.77 |
Significance Trend | Gauge (%) | IMERG-F (%) | TPHiPr (%) |
---|---|---|---|
Significant Decrease | 0.00 | 4.00 | 0.32 |
Nonsignificant Decrease | 49.24 | 22.16 | 16.21 |
Stable | 0.00 | 17.30 | 17.36 |
Nonsignificant Increase | 40.03 | 40.15 | 45.37 |
Significant Increase | 10.73 | 16.39 | 20.74 |
Study Area | Precipitation Data Source | No. Station | Time Resolution | Spatial Resolution | Calculation Method | Spatial Method | Max. RE | Avg. RE | Reference |
---|---|---|---|---|---|---|---|---|---|
TP | Weather stations, ERA5 | 1787 | 1 min, hour | 0.25° | Standard | Proportional IDW interpolation correction | 13,947 | 290 | Chen et al. [27] |
China | Weather stations | 2381 | Hourly | 1 km | Standard | Universal kriging | 3906 | 288 | Yue et al. [18] |
Global | Precipitation stations | 3540 | Hourly and sub-hourly | 30 arc seconds | Standard | Gaussian process regression | 12,347 | 980 | Panagos et al. [17] |
Global | CMORPH | 30 min | 8 km | Standard | Generic correction linear | 91,746 | 440 | Bezak et al. [22] | |
TP | Weather stations | 131 | Daily | 1/30° | Experience | Ordinary kriging | 3521 | 490 | This study |
TP | TPHiPr | Daily | 1/30° | Experience | 30,685 | 806 | This study | ||
TP | IMERG-F | Daily | 0.1° | Experience | 18,203 | 787 | This study |
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Yin, B.; Xie, Y.; Liu, B.; Liu, B. Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole. Remote Sens. 2023, 15, 5267. https://doi.org/10.3390/rs15225267
Yin B, Xie Y, Liu B, Liu B. Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole. Remote Sensing. 2023; 15(22):5267. https://doi.org/10.3390/rs15225267
Chicago/Turabian StyleYin, Bing, Yun Xie, Bing Liu, and Baoyuan Liu. 2023. "Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole" Remote Sensing 15, no. 22: 5267. https://doi.org/10.3390/rs15225267