17
© The Author(s), under exclusive license to Springer Nature Switzerland
AG 2021
E. Duulatov et al., Current and Future Trends of Rainfall Erosivity and Soil
Erosion in Central Asia, SpringerBriefs in Environmental Science,
https://doi.org/10.1007/978-3-030-63509-1_3
Chapter 3
Data Sources and Methodology
Abstract This chapter presents the data available together with their analysis for
their suitability for the intended research. Projected rainfall data from GSMs
BCCCSM1-1, IPSLCM5ALR, MIROC5, and MPIESMLR (Central Asia); and
GISSE2H, HadGEM2ES, and NorESM1M (Kazakhstan) for the RCP2.6 and
RCP8.5 greenhouse emission scenarios were used. This chapter analyzes a detailed
methodology on the RUSLE model, predicting the impact of climate change on
rainfall erosivity and soil erosion based on geographic information system GIS and
remote sensing (RS) techniques to assess soil erosion. The performance of the rainfall erosivity model was assessed by comparing the rainfall erosivity of observation
data with that of the baseline data using coefficient of determination (R
2
), rootmean- square error (RMSE), and Nash–Sutcliff efficiency (NSE).
Keywords GCMs · RCPs · WorldClim · Rainfall erosivity · Soil erosion · Soil
erodibility · Cover management · Support practice · Slope length and steepness ·
Erosivity density · Central Asia · Climate change · Delta method · RUSLE · GIS ·
RS · Mann-Kendall trend test
3.1 Projected and Observed Rainfall Data
Compared with the Coupled Model Intercomparison Project Phase 3 (CMIP3),
CMIP5 is a remarkable improvement as it uses a new set of emission scenarios
called RCPs (Amanambu et al. 2019; Taylor et al. 2012). The projected rainfall data
obtained from GSMs BCCCSM1-1, IPSLCM5ALR, MIROC5, and MPIESMLR
(Central Asia) and GISSE2H, HadGEM2ES, and NorESM1M (Kazakhstan) for the
RCP2.6 and RCP8.5 greenhouse emission scenarios were used (Table 3.1) (Taylor
et al. 2012).
The GCMs were selected owing to their relative independence and good performance in the precipitation simulation for Central Asia (Luo et al. 2019) and the
© The Author(s), under exclusive license to Springer Nature Switzerland
AG 2021
E. Duulatov et al., Current and Future Trends of Rainfall Erosivity and Soil
Erosion in Central Asia, SpringerBriefs in Environmental Science,
https://doi.org/10.1007/978-3-030-63509-1_3
Chapter 3
Data Sources and Methodology
Abstract This chapter presents the data available together with their analysis for
their suitability for the intended research. Projected rainfall data from GSMs
BCCCSM1-1, IPSLCM5ALR, MIROC5, and MPIESMLR (Central Asia); and
GISSE2H, HadGEM2ES, and NorESM1M (Kazakhstan) for the RCP2.6 and
RCP8.5 greenhouse emission scenarios were used. This chapter analyzes a detailed
methodology on the RUSLE model, predicting the impact of climate change on
rainfall erosivity and soil erosion based on geographic information system GIS and
remote sensing (RS) techniques to assess soil erosion. The performance of the rainfall erosivity model was assessed by comparing the rainfall erosivity of observation
data with that of the baseline data using coefficient of determination (R
2
), rootmean- square error (RMSE), and Nash–Sutcliff efficiency (NSE).
Keywords GCMs · RCPs · WorldClim · Rainfall erosivity · Soil erosion · Soil
erodibility · Cover management · Support practice · Slope length and steepness ·
Erosivity density · Central Asia · Climate change · Delta method · RUSLE · GIS ·
RS · Mann-Kendall trend test
3.1 Projected and Observed Rainfall Data
Compared with the Coupled Model Intercomparison Project Phase 3 (CMIP3),
CMIP5 is a remarkable improvement as it uses a new set of emission scenarios
called RCPs (Amanambu et al. 2019; Taylor et al. 2012). The projected rainfall data
obtained from GSMs BCCCSM1-1, IPSLCM5ALR, MIROC5, and MPIESMLR
(Central Asia) and GISSE2H, HadGEM2ES, and NorESM1M (Kazakhstan) for the
RCP2.6 and RCP8.5 greenhouse emission scenarios were used (Table 3.1) (Taylor
et al. 2012).
The GCMs were selected owing to their relative independence and good performance in the precipitation simulation for Central Asia (Luo et al. 2019) and the