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© 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_4
Chapter 4
Projected Rainfall Erosivity and Soil
Erosion in Central Asia
Abstract The GCMs (BCCCSM1-1, IPSLCM5BLR, MIROC5, and MPIESMLR)
were statistically downscaled using the delta method under Representative
Concentration Pathways (RCPs) 2.6 and 8.5 for two periods: “Near” and “Far”
future (2030s and 2070s). These GCMs data were used to estimate rainfall erosivity
and its projected changes over Central Asia. WorldClim data were used as the present baseline precipitation scenario for the study area. The rainfall erosivity (R) factor of the Revised Universal Soil Loss Equation (RUSLE) was used to determine
rainfall erosivity. The results show an increase in the future periods of the annual
rainfall erosivity compared to the baseline. For all GCMs (BCCCSM1-1,
IPSLCM5BLR, MIROC5, and MPIESMLR), with an average change in rainfall
erosivity of about 5.6% (424.49 MJ mm ha
−1
h
−1
year
−1
) in 2030s and 9.6%
(440.57 MJ mm ha
−1
h
−1
year
−1
) in 2070s as compared to the baseline of 402 MJ mm
ha
−1
h
−1
year
−1
. The magnitude of the change varies with the GCMs, with the largest
change being 26.6% (508.85 MJ mm ha
−1
h
−1
year
−1
), occurring in the MIROC-5
RCP8.5 scenario in the 2070s. Although annual rainfall erosivity shows a steady
increase, IPSLCM5ALR (both RCPs and periods) shows a decrease in the average
erosivity. Higher rainfall amounts were the prime causes of increasing spatial-temporal rainfall erosivity.
Keywords Rainfall erosivity · Erosivity density · Soil erosion · Central Asia ·
Tien-Shan · Climate change · GCMs · RCPs · RUSLE · GIS · RS · Baseline ·
Future scenarios
4.1 RUSLE Model Factors
RUSLE model was used to calculate the potential annual soil loss in Central Asia.
The input layers were generated using remote sensing data and integrated into GIS
to assess potential soil loss. The potential soil erosion rate for each pixel was com-
© 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_4
Chapter 4
Projected Rainfall Erosivity and Soil
Erosion in Central Asia
Abstract The GCMs (BCCCSM1-1, IPSLCM5BLR, MIROC5, and MPIESMLR)
were statistically downscaled using the delta method under Representative
Concentration Pathways (RCPs) 2.6 and 8.5 for two periods: “Near” and “Far”
future (2030s and 2070s). These GCMs data were used to estimate rainfall erosivity
and its projected changes over Central Asia. WorldClim data were used as the present baseline precipitation scenario for the study area. The rainfall erosivity (R) factor of the Revised Universal Soil Loss Equation (RUSLE) was used to determine
rainfall erosivity. The results show an increase in the future periods of the annual
rainfall erosivity compared to the baseline. For all GCMs (BCCCSM1-1,
IPSLCM5BLR, MIROC5, and MPIESMLR), with an average change in rainfall
erosivity of about 5.6% (424.49 MJ mm ha
−1
h
−1
year
−1
) in 2030s and 9.6%
(440.57 MJ mm ha
−1
h
−1
year
−1
) in 2070s as compared to the baseline of 402 MJ mm
ha
−1
h
−1
year
−1
. The magnitude of the change varies with the GCMs, with the largest
change being 26.6% (508.85 MJ mm ha
−1
h
−1
year
−1
), occurring in the MIROC-5
RCP8.5 scenario in the 2070s. Although annual rainfall erosivity shows a steady
increase, IPSLCM5ALR (both RCPs and periods) shows a decrease in the average
erosivity. Higher rainfall amounts were the prime causes of increasing spatial-temporal rainfall erosivity.
Keywords Rainfall erosivity · Erosivity density · Soil erosion · Central Asia ·
Tien-Shan · Climate change · GCMs · RCPs · RUSLE · GIS · RS · Baseline ·
Future scenarios
4.1 RUSLE Model Factors
RUSLE model was used to calculate the potential annual soil loss in Central Asia.
The input layers were generated using remote sensing data and integrated into GIS
to assess potential soil loss. The potential soil erosion rate for each pixel was com-