Integrated in a Geographical Information System (GIS) of the Esil River Basin (ERB), Kazakhstan
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Acta Geophysica https://doi.org/10.1007/s11600-019-00288-0 RESEARCH ARTICLE - HYDROLOGY Estimation of annual average soil loss using the Revised Universal Soil Loss Equation (RUSLE) integrated in a Geographical Information System (GIS) of the Esil River basin (ERB), Kazakhstan Yerbolat Mukanov1,2,3,4 · Yaning Chen1 · Saken Baisholanov5 · Amobichukwu Chukwudi Amanambu1,2,6 · Gulnura Issanova4 · Ainura Abenova3 · Gonghuan Fang1 · Nurlan Abayev3 Received: 21 August 2018 / Accepted: 9 April 2019 © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019 Abstract The Revised Universal Soil Loss Equation (RUSLE) has enormous potential for integrating remote sensing and Geographical Information System (GIS) technologies for producing accurate and inexpensive assessments of soil erosion. In this study, the RUSLE method was applied to the Esil (Ishim) River basin (ERB), which is situated in Northern and Central Kazakhstan. The northern part of the ERB extends through the Tyumen and Omsk regions of the Russian Federation to the confuence of the Irtysh River. This article may be of interest to experts and specialists in the feld of agriculture, as the fndings can assist agricultural producers and government entities in making decisions that prevent soil degradation and promote optimal cropping systems for land and crop cultivation. The objective of this research is to detect, estimate and map areas of land plots most vulnerable to potential soil erosion within the ERB, using the RUSLE model under Arc GIS 10.2. The results reveal that average annual soil loss during the study period ranges from 0 to 32 (t y−1) and that 108,007.5 km2 (48%) of the ERB has no erosion. The remainder of the basin is prone to soil erosion ranging from 1 to 32 t ha−1 y−1, which comprises 117,216.9 km2 (52%), and total soil erosion is 565,368.7 (t y−1). Soil erosion in the ERB is relatively moderate due to low hill steepness and low annual precipitation (198–397 mm). Exceptions occur in plots which feature high slope length steep- ness, which are scattered throughout the region. Keywords Agriculture producers · Cropping system · RUSLE · Soil erosion Introduction * Yaning Chen Soil degradation is a serious problem throughout the world, [email protected] which ultimately afects the reduction in soil fertility— Yerbolat Mukanov reducing productivity in the agricultural sector, creating [email protected] negative impact on the environment and consequently the quality of drinking water with further efects on the quality 1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of life (Ganasri and Ramesh 2016; Issaka and Ashraf 2017; of Sciences, 818 South Beijing Road, Urumqi 830011, China Vaezi and Sadeghi 2011). 2 University of Chinese Academy of Sciences, Beijing 100049, Topsoil is most valuable for agricultural production and China most vulnerable due to natural changes in ecosystems and 3 Regional State Enterprise Kazhydromet, Astana 010000, inappropriate land management systems (Blanco and Lal Kazakhstan 2010). 4 Faculty of Geography and Environmental Sciences, For proper operation and use of land resources, many fac- Al-Farabi Kazakh National University, Almaty 050040, tors need to be taken into account, one of which is the spatial Kazakhstan assessment of soil loss (Prasuhn et al. 2013). 5 Institute of Geography, Astana 010000, Kazakhstan To understand the degree of soil erosion over a large 6 Department of Geography, University of Florida, Gainesville, area, it is necessary to collect soil samples, conduct feld USA experiments and perform required analysis for planning Vol.:(0123456789)1 3 Acta Geophysica and decision making (Alkharabsheh et al. 2013), bear- Also, land degradation by means of vegetation removal is ing in mind that human infuences play a key role in soil one of the phenomena afecting the soil the earth’s surface. degradation. Soil can be detected by electromagnetic spectrum through Anthropogenic factor, especially agriculture, has a strong the brightness of the soil surface, refecting the degree of infuence on the condition of soil. Intensive development of vegetation cover or the intensity of the development of veg- pasture lands under the Agricultural Program of the Virgin etation. Thus, vegetation indices (e.g., NDVI, SAVI, OSAVI and Fallow Lands for the period 1954–1963 in Northern and MSAVI) can be useful for assessing the spatial defnition Kazakhstan (including ERB) led to large-scale soil degra- of land degradation (Wu et al. 2008). Escadafal et al. (1994) dation (Kraemer 2015). Such a transformation of land into and Hill et al. (1995) applied the concept of soil science arable land during the frst year of operation has led to a loss and geomorphology to obtain qualitative indicators on the of 50% of organic matter in the soil. Periodic use of fallows processes of soil degradation and erosion based on spectral in crop rotation also leads to the depletion of organic matter vegetation indices of soil condition indicators. in the soil (Yanai 2005). Spatial and quantitative information on the extent of soil One of the most popular modern models for estimat- erosion in a basin, sub-basin, watershed, sub-watershed can ing soil loss is the Revised Universal Soil Loss Equation make a signifcant contribution in developing measures to (RUSLE) (Renard 1997), which has been widely used for the protect soil from erosion and manage the catchment environ- past several decades. The RUSLE model is a convenient and ment (Van De et al. 2008). practical approach to assessing soil loss and can be applied The present study focuses on the following three aspects wherever soil erosion or the scale of erosion processes might in relation to soil erosion: be of concern, including in river basin watersheds or on individual properties (Chen et al. 2011; Farhan et al. 2013). (i) The application of RS and GIS technologies in com- This model calculates approximate annual soil losses, taking bination with the RUSLE model for exploring the into account factors such as precipitation, soil erodibility, intensity of soil water erosion in the ERB (Esil River relief parameters (slopes), vegetation and land use schemes basin), (Renard 1997; Wischmeier and Smith 1965, 1978). The ero- (ii) the delineation of the plots most vulnerable and sion of the soil characterizes the susceptibility of the soil to prone to soil erosion processes, and its environmental biological, chemical, physical, mineralogical, hydrological impacts; and and other properties (Shabani et al. 2014). (iii) recommendations and possible solutions for prevent- Unfortunately, however, spatial and quantitative assess- ing and mitigating the efect of soil erosion. ment of the extent of soil loss through observation alone does not always provide objective information because of Future researchers could use this work as an information limitations in data reliability (Chen et al. 2011; Prasannaku- source for similar or related research. The results of this mar et al. 2011). Spatial and quantitative assessment of the study will be an asset to policy-makers, land use planners extent of soil loss in sites with observations, unfortunately, and nature resource managers in planning and implementing does not always provide objective information, primarily efective soil conservation strategies to prevent or reduce because of the data limitation and cost. Despite these issues, soil erosion. many scientists still use the RUSLE model (Abu Hammad 2011), even though it is not capable of providing an accurate spatial representation of the scale of the erosion processes Materials and methods due to the lack or absence of data and the complexity of the study area (Prasannakumar et al. 2011). Study area Geographical Information Systems (GIS) and remote sensing technologies, by allowing for spatial analysis of soil The Esil River basin (ERB) is situated in the northern erosion assessment over a large area (Adediji et al. 2010; and central portions of Kazakhstan between 48°39′07″ Farhan et al. 2013), can be used to augment the RUSLE and 56°54′82″ north, and 65°45′62″ and 75°04′29″ east. model. The inclusion of satellite images can provide a more The ERB covers approximately 225,224 km2. Land use comprehensive idea of the state of the vegetation cover in this region is primarily agricultural (38.01%), fol- (C-factor) for the area under observation (Adediji et al. lowed by barren areas (36.26%), urban areas (17.39%), 2010; Alkharabsheh et al. 2013; Chen et al. 2011). Com- shrub (3.12%), water bodies (2.21%), wetlands (1.71%), bining DEM (Digital Elevation Model) with ArcGIS gives grasslands (0.81%) and forest (0.45%). The climate con- ample opportunities to use various methods for calculating ditions of the study area are generally dry (annual pre- the topographic features (LS-factor) of a specifc terrain cipitation 198–397 mm) and windy. The climate steppe (Panagos et al. 2015). and forest steppe zones of Northern Kazakhstan, due to 1 3 Acta Geophysica their remoteness from the moderating efects of seas and Data oceans, quickly become very hot in summer while rapidly losing heat in winter. The region’s cultivated land mostly In our study, we used the DEM 25.43 m resolution, obtained occupies low-lying plains with elevations up to 200 m from the United States Geological Survey (USGS), to cal- as well as slightly undulating plains (250–300 m). Some culate the topographic parameters of the terrain slopes. To agriculture also occurs in the relatively fat upland plains defne the soil structure and physical soil properties, a digital situated at 350–400 m above sea level. Overall, the region soil map obtained from the Food Agriculture Organization is characterized as steppe and forest steppe (Fig. 1). Geonetwork at a scale of 1:5,000,000 was used. As well, the Northern Kazakhstan, with annual precipitation of Normalized Diference Vegetation Index (NDVI) and satel- 300–400 mm, is the wettest region in the ERB.