Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning
<p>Land cover in our study area. The meteorological stations used for validation were also shown.</p> "> Figure 2
<p>Flowchart of the downscaling procedure in this study.</p> "> Figure 3
<p>Density scatter plot between EEAD and predicted SPEI based on (<b>a</b>) DT, (<b>b</b>) RF, (<b>c</b>) GBRT, and (<b>d</b>) ET. Results are from cross-validation.</p> "> Figure 4
<p>Density scatter plot between EEAD and estimated SPEI from Extra Trees (ET) with (<b>a</b>) both gridded climate data and vegetation index (VI) as input, and (<b>b</b>) with only gridded climate data as input. Results are from cross-validation.</p> "> Figure 5
<p>Density scatter plot between estimated and in-situ SPEI.</p> "> Figure 6
<p>Spatial distribution of coefficient of determination (R<sup>2</sup>) between the downscaled SPEI and in-situ SPEI at each meteorological station.</p> "> Figure 7
<p>Boxplot summarizing the sensitivity of predicted SPEI to slope: (<b>a</b>) R<sup>2</sup> and (<b>b</b>) RMSE.</p> "> Figure 8
<p>The temporal evolution of SPEI and interdecadal changes of the frequency, duration and intensity of drought events from 1901 to 2018 and 1980 to 2018: (<b>a</b>) the time series of SPEI; (<b>b</b>) the frequency of drought events from 1901 to 2018; (<b>c</b>) the duration of drought events from 1901 to 2018; (<b>d</b>) the intensity of drought events from 1980 to 2018; (<b>e</b>) the frequency of drought events; (<b>f</b>) the duration of drought events from 1980 to 2018; (<b>g</b>) the intensity of drought events from 1980 to 2018. Black line in b-g is the trend of downscaled SPEI.</p> "> Figure 9
<p>Drought conditions in Southwest China monitored by downscaled SPEI and EEAD SPEI from September 2009 to June 2011. The first and third rows display the downscaled SPEI, and the second and fourth rows show the EEAD SPEI.</p> "> Figure 10
<p>Time series of the in-situ SPEI, downscaled SPEI, and EEAD at the four selected meteorological stations. Panels (<b>a</b>–<b>d</b>) are site 1–4, respectively.</p> "> Figure 11
<p>Dependence of the downscaled SPEI on precipitation and temperature.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
3. Results
3.1. Method Comparison with Cross-Validation
3.2. Influence of Vegetation Indices on Method Performance
3.3. Direct Validation and Sensitivity Analysis
3.4. Derived High-Resolution and Long-Term SPEI Dataset
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type | Name | Resolution | Source |
---|---|---|---|
In-situ climate | Precipitation | station | http://data.tpdc.ac.cn/zh-hans/ (accessed on 12 February 2022) |
Temperature | station | http://data.tpdc.ac.cn/zh-hans/ (accessed on 12 February 2022) | |
Wind | station | http://data.tpdc.ac.cn/zh-hans/ (accessed on 12 February 2022) | |
Sunshine duration | station | http://data.tpdc.ac.cn/zh-hans/ (accessed on 12 February 2022) | |
Gridded climate | Precipitation | 1 km | http://data.tpdc.ac.cn/zh-hans/ (accessed on 12 February 2022) |
Temperature | 1 km | http://data.tpdc.ac.cn/zh-hans/ (accessed on 12 February 2022) | |
Standardized Precipitation Evapotranspiration Index | 0.5° | https://digital.csic.es/handle/10261/202305 (accessed on 12 February 2022) | |
MODIS data | Enhanced Vegetation Index | 500 m | https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 12 February 2022) |
Normalized Difference Vegetation Index | 500 m | https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 12 February 2022) | |
Topographic data | Digital elevation model | 1 km | http://www.resdc.cn/ (accessed on 12 February 2022) |
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Fu, R.; Chen, R.; Wang, C.; Chen, X.; Gu, H.; Wang, C.; Xu, B.; Liu, G.; Yin, G. Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning. Remote Sens. 2022, 14, 1662. https://doi.org/10.3390/rs14071662
Fu R, Chen R, Wang C, Chen X, Gu H, Wang C, Xu B, Liu G, Yin G. Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning. Remote Sensing. 2022; 14(7):1662. https://doi.org/10.3390/rs14071662
Chicago/Turabian StyleFu, Rui, Rui Chen, Changjing Wang, Xiao Chen, Hongfan Gu, Cong Wang, Baodong Xu, Guoxiang Liu, and Gaofei Yin. 2022. "Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning" Remote Sensing 14, no. 7: 1662. https://doi.org/10.3390/rs14071662