Spatiotemporal Evaluation and Estimation of Precipitation of Multi-Source Precipitation Products in Arid Areas of Northwest China—A Case Study of Tianshan Mountains
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. Data
2.3. Research Methods
2.3.1. Temporal and Spatial Aggregation of Satellite Precipitation Products
2.3.2. Comparison of Applicability of Precipitation Products
2.3.3. Comparison Method between Satellite Data and Rainfall Station Data
3. Results
3.1. Precipitation Estimation Accuracy at Different Time Scales
3.1.1. Annual Scale Accuracy Assessment
3.1.2. Monthly Scale Accuracy Assessment
3.1.3. Daily Scale Accuracy Assessment
3.1.4. Comparison of Frequency Distribution of Precipitation Intensity
3.1.5. Evaluation of Extreme Precipitation Monitoring Capability
3.2. Precipitation Estimation Accuracy at Spatial Scale
3.2.1. Spatial Distribution Characteristics of Precipitation in the Tianshan Mountains
3.2.2. Spatial Accuracy Assessment of Precipitation in Tianshan Mountains
3.3. Factors Influencing the Accuracy of Satellite Precipitation Products
4. Discussion
4.1. Accuracy Difference between the Three Products
4.2. Precipitation Differences in Different Regions of the Tianshan Mountains
4.3. Application Prospect of Precipitation Products
5. Conclusions
- (1)
- At the annual scale, the three precipitation products showed a strong correlation with the measured precipitation; During the year, the estimation ability of precipitation in the wet season was stronger than that in the dry season. TRMM showed an underestimation of the measured precipitation, GPM improved the underestimation, and MSWEP showed an overestimation.
- (2)
- At the daily scale, TRMM and MSWEP had the best detection rates for light rain events and extreme precipitation events, respectively. The deviation between GPM and daily precipitation is the smallest.
- (3)
- At the spatial scale, the three precipitation products can roughly reflect the distribution characteristics of the measured precipitation, that is, the trend of decreasing from northwest to southeast, and the correlation between GPM and the measured precipitation is the best. In different regions, the detection rate of precipitation in the West region was the highest, and the detection rate of precipitation in the East region was the worst. MSWEP is the closest to the precipitation differentiation pattern in the Tianshan Mountains.
- (4)
- The three precipitation products showed high accuracy in low longitude areas and middle elevation mountain areas; In comparison, MSWEP has the highest applicability in high-altitude mountain areas.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Index Name | Formula | Ideal Value |
---|---|---|
Root Mean Square Error (RMSE) | 0 | |
Mean Error (ME) | 0 | |
Mean Absolute Error (MAE) | 0 | |
Correlation Coefficient (CC) | 1 | |
Frequency Bias (BIAS) | 0 | |
Probability of Detection (POD) | 1 | |
False Alarm Ratio (FAR) | 0 | |
Critical Success Index (CSI) | 1 | |
Standard Deviation Ratio (SDR) | 1 | |
Coefficient of Variation (CV) | or | 100 |
Name | Code | Definition | Unit |
---|---|---|---|
Consecutive Dry Days | CDD | The longest consecutive days with daily precipitation < 1 mm | d |
Consecutive Wet Days | CWD | The longest consecutive days with daily precipitation ≥ 1 mm | d |
Simple Daily Precipitation Intensity Index | SDII | The ratio of the total amount of precipitation ≥ 1 mm to the number of days | mm/d−1 |
Annual Maximum 1-day Precipitation | RX1D | Annual maximum daily precipitation | mm |
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Dataset | Type | Time Resolution | Space Resolution | Time Range | Coverage | Source |
---|---|---|---|---|---|---|
Daily dataset of Surface Climatological Data for China (V3.0) | Observation data from ground stations | 1 d | - | 1951– | China | http://data.cma.cn/data/cdcdetail/dataCode/A.0012.0001.html (accessed on 12 August 2022) |
TRMM3B42 | Combined measurements of satellite estimated precipitation | 3 h | 0.25 × 0.25° | 1998–2019 | Global | https://gpm.nasa.gov/data/directory (accessed on 12 August 2022) |
GPM IMERG(V06) Final Run | Multi-satellite joint retrieval of precipitation data | 0.5 h | 0.1 × 0.1° | 2000– | Global | https://gpm.nasa.gov/data/directory (accessed on 12 August 2022) |
MSWEP (V2.2) | Multi-source fusion of precipitation observation data | 3 h | 0.1 × 0.1° | 1979–2019 | Global | http://www.gloh2o.org (accessed on 12 August 2022) |
Time Scale | Product | BIAS/% | MAE/mm | ||||||
---|---|---|---|---|---|---|---|---|---|
West | East | South | North | West | East | South | North | ||
Wet Season | TRMM | −7.58 | −9.58 | −5.89 | −9.69 | 18.09 | 15.03 | 47.77 | 35.48 |
GPM | −3.27 | −7.87 | −8.90 | −3.36 | 18.48 | 41.37 | 59.84 | 46.84 | |
MSWEP | −9.22 | −5.82 | −4.83 | 2.99 | 20.53 | 41.36 | 66.24 | 66.75 | |
Year | TRMM | −15.08 | −17.41 | −17.31 | −10.88 | 27.88 | 32.03 | 89.67 | 36.71 |
GPM | −5.48 | −10.71 | −9.50 | −5.07 | 36.78 | 80.72 | 67.87 | 52.75 | |
MSWEP | 1.23 | 10.95 | 15.33 | 19.66 | 41.71 | 62.38 | 69.79 | 68.48 | |
Dry Season | TRMM | −18.25 | −20.82 | −19.86 | −15.67 | 68.15 | 128.35 | 107.84 | 92.46 |
GPM | −14.56 | −15.36 | −15.78 | −10.85 | 56.55 | 126.95 | 175.88 | 98.76 | |
MSWEP | 7.54 | 18.82 | 29.32 | 32.85 | 75.90 | 74.76 | 123.05 | 144.76 |
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Li, X.; He, X.; Li, X.; Du, Y.; Yang, G.; Li, D.; Xu, W. Spatiotemporal Evaluation and Estimation of Precipitation of Multi-Source Precipitation Products in Arid Areas of Northwest China—A Case Study of Tianshan Mountains. Water 2022, 14, 2566. https://doi.org/10.3390/w14162566
Li X, He X, Li X, Du Y, Yang G, Li D, Xu W. Spatiotemporal Evaluation and Estimation of Precipitation of Multi-Source Precipitation Products in Arid Areas of Northwest China—A Case Study of Tianshan Mountains. Water. 2022; 14(16):2566. https://doi.org/10.3390/w14162566
Chicago/Turabian StyleLi, Xiaoqian, Xinlin He, Xiaolong Li, Yongjun Du, Guang Yang, Dongbo Li, and Wenhe Xu. 2022. "Spatiotemporal Evaluation and Estimation of Precipitation of Multi-Source Precipitation Products in Arid Areas of Northwest China—A Case Study of Tianshan Mountains" Water 14, no. 16: 2566. https://doi.org/10.3390/w14162566