6
(McCown et al. 1996) for predicting productivity, WOFOST as a simulation model
of crop production (Van Diepen et al. 1989), and SOVEUR (Batjes 1997) for predicting pollution.
The USLE model was first proposed by Wischmeier and Smith (1965) based on
the concept of separation and transport of particles from precipitates to calculate the
degree of soil erosion in agricultural areas. The equation was improved in 1978.
This is the most widely used and generally accepted empirical model of soil erosion,
which was developed for sheet and rill erosion. USLE has been improved over the
past 40 years by many researchers (Alewell et al. 2019).
Modified Universal Soil Loss Equation (MUSLE) (Williams 1975), Revised
Universal Soil Loss Equation (RUSLE) (Renard et al. 1997), Areal Nonpoint Source
Watershed Environmental Resources Simulation (ANSWERS) (Beasley et al.
1980), and Unit Stream Power-Based Erosion Deposition (USPED) (Mitasova et al.
1996) are based on the USLE and is an improvement of the former. Among these
models, the RUSLE was widely used to assess the long-term rate of soil erosion in
large-scale studies (Amanambu et al. 2019; Naipal et al. 2015; Panagos et al. 2015b;
Teng et al. 2016, 2018; Yang et al. 2003). Soil erosion models facilitate land management and in understanding sediment transport and its impact on the landscape
(Benavidez et al. 2018).
Recently, the application of remote sensing (RS) and geographic information
systems (GIS) with the RUSLE model in the soil erosion study became inevitable
worldwide (Ganasri and Ramesh 2016; Gaubi et al. 2017; Gelagay 2016; Lee et al.
2017; Mukanov et al. 2019; Nasir and Selvakumar 2018; Nyesheja et al. 2018;
Ostovari et al. 2017; Prasannakumar et al. 2012; Sujatha and Sridhar 2018; Thomas
et al. 2018a, b). For example, the RUSLE C factor was estimated using NDVI and
ground data and by calculating the exponential regression for mountain pastures in
the southwest of Kyrgyzstan (Kulikov et al. 2016). Therefore, such methods can
also be successfully applied to Central Asia.
It should be noted that in recent years, the climatic conditions in the Central
Asian countries have changed due to the reduction of glacier areas of most of the
Tien Shan (Aizen et al. 2007; Duishonakunov et al. 2014; Kenzhebaev et al. 2017)
and Pamir-Alay (Chevallier et al. 2014; Hagg et al. 2007) mountain systems from
the south and the drying of the Aral Sea (Issanova et al. 2018; Lioubimtseva and
Henebry 2009) in the north. In this regard, the shortage of water for irrigation, especially of pastures, is felt; natural vegetation cover is degraded, erosion processes and
salinization are increasing, and the productive capacity of irrigated lands is decreasing (Hamidov et al. 2016). Humanity is facing a serious problem – the preservation
of existing natural landscapes, which includes improving and multiplying its types.
Rainfall erosivity is associated with the powerful kinetic energy of raindrops,
which often separate soil elements and transport them along with surface runoff
(Amanambu et al. 2019). To describe the processes of erosion, rainfall erosivity is
the most significant factor and offers conservation actions by the models of soil erosion prediction (Panagos et al. 2017b). Recently, there is an emerging evidence of
the influence of climate change on rainfall erosivity in various parts of the globe
(Almagro et al. 2017; Amanambu et al. 2019; Gu et al. 2018; Gupta and Kumar
1 Introduction and Background of Rainfall Erosivity Processes and Soil Erosion
(McCown et al. 1996) for predicting productivity, WOFOST as a simulation model
of crop production (Van Diepen et al. 1989), and SOVEUR (Batjes 1997) for predicting pollution.
The USLE model was first proposed by Wischmeier and Smith (1965) based on
the concept of separation and transport of particles from precipitates to calculate the
degree of soil erosion in agricultural areas. The equation was improved in 1978.
This is the most widely used and generally accepted empirical model of soil erosion,
which was developed for sheet and rill erosion. USLE has been improved over the
past 40 years by many researchers (Alewell et al. 2019).
Modified Universal Soil Loss Equation (MUSLE) (Williams 1975), Revised
Universal Soil Loss Equation (RUSLE) (Renard et al. 1997), Areal Nonpoint Source
Watershed Environmental Resources Simulation (ANSWERS) (Beasley et al.
1980), and Unit Stream Power-Based Erosion Deposition (USPED) (Mitasova et al.
1996) are based on the USLE and is an improvement of the former. Among these
models, the RUSLE was widely used to assess the long-term rate of soil erosion in
large-scale studies (Amanambu et al. 2019; Naipal et al. 2015; Panagos et al. 2015b;
Teng et al. 2016, 2018; Yang et al. 2003). Soil erosion models facilitate land management and in understanding sediment transport and its impact on the landscape
(Benavidez et al. 2018).
Recently, the application of remote sensing (RS) and geographic information
systems (GIS) with the RUSLE model in the soil erosion study became inevitable
worldwide (Ganasri and Ramesh 2016; Gaubi et al. 2017; Gelagay 2016; Lee et al.
2017; Mukanov et al. 2019; Nasir and Selvakumar 2018; Nyesheja et al. 2018;
Ostovari et al. 2017; Prasannakumar et al. 2012; Sujatha and Sridhar 2018; Thomas
et al. 2018a, b). For example, the RUSLE C factor was estimated using NDVI and
ground data and by calculating the exponential regression for mountain pastures in
the southwest of Kyrgyzstan (Kulikov et al. 2016). Therefore, such methods can
also be successfully applied to Central Asia.
It should be noted that in recent years, the climatic conditions in the Central
Asian countries have changed due to the reduction of glacier areas of most of the
Tien Shan (Aizen et al. 2007; Duishonakunov et al. 2014; Kenzhebaev et al. 2017)
and Pamir-Alay (Chevallier et al. 2014; Hagg et al. 2007) mountain systems from
the south and the drying of the Aral Sea (Issanova et al. 2018; Lioubimtseva and
Henebry 2009) in the north. In this regard, the shortage of water for irrigation, especially of pastures, is felt; natural vegetation cover is degraded, erosion processes and
salinization are increasing, and the productive capacity of irrigated lands is decreasing (Hamidov et al. 2016). Humanity is facing a serious problem – the preservation
of existing natural landscapes, which includes improving and multiplying its types.
Rainfall erosivity is associated with the powerful kinetic energy of raindrops,
which often separate soil elements and transport them along with surface runoff
(Amanambu et al. 2019). To describe the processes of erosion, rainfall erosivity is
the most significant factor and offers conservation actions by the models of soil erosion prediction (Panagos et al. 2017b). Recently, there is an emerging evidence of
the influence of climate change on rainfall erosivity in various parts of the globe
(Almagro et al. 2017; Amanambu et al. 2019; Gu et al. 2018; Gupta and Kumar
1 Introduction and Background of Rainfall Erosivity Processes and Soil Erosion