Quantifying the water soil erosion hazard using RUSLE, GIS, and RS approach: A case study of Al-Qshish River Basin, Lattakia, Syria.

Soil erosion is one of the most prominent geomorphological hazards threatening environmental sustainability in the coastal region of western Syria. The current war conditions in Syria has led to a lack of eld data and measurements related to assessing soil erosion. Mapping the spatial distribution of potential soil erosion is a basic step in implementing soil preservation procedures mainly in the river catchments. The present paper aims to conduct a comprehensive assessment of soil erosion severity using revised universal soil loss equation (RUSLE) and remote sensing (RS) data in geographic information system (GIS) environment across the whole Al-Qshish river basin. Quantitatively, the annual rate of soil erosion in the study basin was 81.13 t ha − 1 year − 1 with a spatial average reaching 55.18 t ha − 1 year − 1 . Spatially, the soil erosion hazard map was produced with classication into ve susceptible-zones: very low (40.99%), low (40.49%), moderate (8.90%), high (5.41%) and very high (4.21%). The current study presented a reliable assessment of soil loss rates and classication of erosion-susceptible areas within the study basin. These outputs can be relied upon to create measures for maintaining areas with high and very high soil erosion susceptibility under the current war conditions.


Introduction
Soil is an important and vital non-renewable resource that gives a broad set of ecological services and goods (Pal, 2016;Brevik et al. 2017; Saleem et al. 2020). However, current scienti c literature indicates that soil erosion is responsible for about 85% of the degradation of the agricultural lands, and hence a decrease in productivity by 17% (Lal, 2001 essential matter in the context of agricultural productivity that in uenced by soil erosion in many ways (FAO, 2019). Moreover, humans gain more than 99.7% of their nourishment from the soil, while 0.3% from the oceans and other water ecosystems (Pimentel, 2006).
In the context, Syrian soils, as part of the Mediterranean basin soils, are prone to a remarkable water soil erosion hazard due to climate, terrains, soil features, vegetation and human activity motivating water erosional cycle, especially in the coast region of Syria (Mohammed et al. 2020a). Moreover, about 18% of the agricultural land in Syria is exposed to soil erosion risk which exceeds 100 t/ha/y in some western mountainous areas (ACSAD, 2007; Husein and Kalkha, 2019). SEW in the coastal region of Syria in general and in Al-Qshish river basin in particular is the fundamental risk that threats agricultural, feed, and secure sustainability. The heavy pattern of rainfall intensities, runoff, ash ood, rugged topography, shallow soil pro les, and degraded vegetation are the main physical factors that cause SEW.
Simultaneously, SEW can be fostered by the expansion of human intensi cations such as deforestation, overcrossing, urbanization, landuse/landcover change, intensive cultivation on steep slopes, excessive soil ploughing, land abandonment, poor maintenance procedures, military infrastructure, and armed Investigating the spatial distribution of SEW rates is a critical stage for the conservation of soil and water resources at the catchments scale. Meanwhile, using the experimental models is the most common methods of SEW modeling, especially in areas with limited data. In details, the integration of remote sensing (RS) data, geographic information system (GIS) environment and RUSLE model is a useful, reliable and accurate manner in generating the spatial distribution of SEW (Phinzi and Ngetar, 2019). The war in Syria has contributed to increasing pressure on natural resources in the coastal region of Syria (Abdo HG, 2018; Ghanem and Rukia, 2020). The lack of relevant data also plays a major role in impeding the creation of post-war environmental rehabilitation measures. For several decades, studies have used USLE/RUSLE, therefore, providing reliable results in assessing soil erosion, especially in light of the lack of relevant data under the conditions of war in Syria. For the prior issue discussed, the present paper will reveal the quantities and spatial distribution of SEW in a Al-Qshish river basin by utilizing the RS data in calculating RUSLE parameters in a GIS environment, and thus the possibility of proposing the better spatial conservation strategies with appropriate implementations.

Study area
Al-Qshish river basin is one of the coastal river basins in Lattakia governorate in the west of Syria (Fig. 1).
The study basin occupies an area of roughly 165 km 2 and lies between the latitudes of 35°57¢and 35°5 5¢ N and longitudes of 35°5 8¢and 35°13¢ E with altitude varying from 0 to 1267 m above sea level. This basin boarded Al-Kabeer alshamali river basin and Wadi-Qandil river basin to the east, the Mediterranean to the west, Wadi-Qandil river basin to the south, Turkey to the north. The lithological formations include the rocks of the Archean (Jurassic and Cretaceous eras), Proterozoic (Pliocene ear) and Phanerozoic (Pleistocene and Holocene eras) eons. The limestone and dolomitic rocks of the second eones are concentrated in some mountains and plateaus in the north and east of the basin. While the rocks of the third eon distributed in the central region. The sandy formations of the fourth eon concentrated along with the stream beds and close to the coastline (Salloum, 2012). The basin primarily subjects to the Mediterranean climate pattern: mild and rainy winter and long, dry, and hot summer (Mohammed and Fallah, 2019). The average annual rainfall in the basin varies from 835 to 906 mm (Fig.   2), and most precipitation is concentrated in the winter months (68%). Additionally, the annual mean summer temperature is 23º and in winter 14º. The warmest month is August and the coldest is January. High relative humidity prevails throughout the year due to the effects of the Mediterranean water mass.
The annual average of humidity is 72%. Agriculture and tourism are the basic economic pivots of the population. Olives, citrus and eld crops are among the most important crops in the basin (Ülker et al. 2018; Abdo HG, 2020). where A is the average annual soil erosion (t/ha/year), R is the rainfall erosivity, K is the soil erodibility, LS is the hill slope length and steepness, C is the vegetation factor, and P is the support practice. In order to standardize pixel resolution DEM and Landsat imageries resolution, all inputs and outputs for the calculation of erosion risks is in 30-pixel resolution for each sub-factors of RUSLE model.

Rainfall erosivity factor (R)
The rainfall erosivity (R) factor re ects the impact of rainfall kinetic energy and produced runoff on the erosion cycle (Wischmeier and Smith, 1978;Xu et al. 2009; Demirci and Karaburun, 2012). R factor, however, is signi cantly in uenced by the pattern, volume, intensity, duration of precipitation events (Farhan et al. 2013). R factor index should be acquired by multiplying the total rainstorm energy (E) and the maximum 30-min rainfall intensity (I30) (Renard et al. 1997). The calculation of the R factor values by the prior approach can implement only in regions that are equipped by recording gauging stations that record the rains instantaneously. To overcome this issue, available monthly rainfall data (1989 -2019) of climatic stations established in and around the study basin the map of R-value was prepared by using the following Eq. (2) developed by Wischmeier and Smith, (1978): where R is a rainfall erositivity factor (MJ mm ha _1 h _1 per year); P i is monthly rainfall (mm); P is an annual rainfall (mm).

Soil erodibility factor (K)
K factor characterizes the strength of topsoil particle against the rainfall storms events, which is commonly obtained by assessing the physical-chemical topsoil attributes of a speci c area (Das et al. 2018). K factor is an estimate of the sensitivity of topsoil to detachment and transport by rainfall and runoff. It represents the soil loss rate motivated by rainfall erosivity factor (R) in each point of study area where OM is the organic matter (%), s is soil structure class, p is permeability class, and M is aggregated variable derived from the granular soil texture: M = (%Msilt) × (%silt +%sand), and the modi ed silt (Msilt) is a percentage of grain size between 0.002 and 0.1 mm. However, this Eq.3 was used based on its compatibility with the topsoil characteristics in the Mediterranean region (Abdo HG, 2018).

Slope length and steepness (LS) factor
Slope Length and Steepness Factors (LS) assess the effect of the topography on the acceleration of soil loss (Lu et al. 2004). LS factor was produced from two sub-parameters: a slope degree parameter (S) and  (Fig. 7). The values of C factor was calculated using the Eq.6 where α and β parameters determine the shape of the NDVI curve. Reasonable results are produced using values of α = 2 and β = 1. The NDVI values were relied upon in calculating the spatial distribution of the C factor due to its accuracy and reliability compared to other methods (Vatandaşlar and Yavuz, 2017).

Conservation support practice factor (P)
Maintenance practice factor (P) is the proportion of soil erosion after a selective support exercise to the corresponding soil loss after up and down farming (Samanta et al. 2016). However, P factor fundamentally affects soil loss by modifying the streaming pattern, degree or orientation of overland ow, and decreasing the runoff potentials (Ozsoy and Aksoy, 2015). For cultivated land, the conservation practices included contouring, terracing, strip cropping, and subsurface drainage (Renard et al. 1997). P factor values range from 0 to 1, the value 0 suggests good conservation support practices and the value 1 suggests poor conservation support practices (Wischmeier and Smith, 1978;Das et al. 2018). Field monitoring indicates the loss of prevention support procedures in the study area. Consequently, P factor value for the entire study basin is 1 as proposed by Wischmeier and Smith, (1978).

Results And Discussion
Based on the data entered into the GIS environment (Fig. 10), four thematic raster maps were accurately produced representing the spatial parameters of RUSLE. R, K, LS, and C raster factors. Figure 3 illustrates the spatial distribution of rainfall erosivity. On value, R factor values ranged from 634.53 to 1512.54 MJ mm ha − 1 h − 1 year − 1 . High values of R factor are concentrated in the northern and eastern regions of the study area. This result can be explained by the topographic elevation which enhances the rainfall intensities, and thus greater rainfall erosivity. In the case of soil erodibility, Fig. 4 indicates the spatial distribution of the K values, which ranged from 0.021 to 0.032 ton. ha. MJ − 1 .mm − 1 . However, It can be seen that the study area is divided into three sectors according to the susceptibility of soil erosion, which was the highest in the far north. Referring to the impact of slope factor, LS values of the study area are in the range of 0-33.9 as Fig. 6 illustrated. The higher values of LS factor were distributed mainly in the northern and eastern regions. These areas show a high erosion susceptibility due to the terrain roughness and the slope steepness. As regards the vegetation in uence, C factor values ranged between 0.02-1 (Fig. 8). The spatial distributions of C factor values showed that the southern and western regions showed high values due to the pressure of human activity, particularly urban sprawl and dense agriculture. Meanwhile, low values of factor C are concentrated in the northern and eastern regions due to the density of vegetation and grass cover, which enhances the biological protection of the soil pro le.
Prior RUSEL factor rasters were spatially multiplied in order to generate the spatial distribution of potential soil erosion per hectare per year at cell level as Fig. 9 represents. In the context of present ndings, the annual rate of soil erosion in the study basin ranged from 0 to 81.13 t ha − 1 year − 1 , with spatial mean reached 55.18 t ha − 1 year − 1 . Using the Natural Breaks method, the resulting soil loss map was categorized into ve risk classes: very low (40.99%), low (40.49%), moderate (8.9%), high (5.41%), and very high (4.21%) as Table. 1 illustrates. Although the current results were not subject to direct eld evaluation, hence some limitations, it constitutes an important step with promising results in clarifying the spatial distributions of soil erosion sensitivity degrees and thus the possibility of applying conservation and maintenance procedures. Meanwhile, resulting soil erosion rate is spatially consistent with the estimates provided by scholars in river basins environment in the eastern Mediterranean as Table. 2  year − 1 is the acceptable soil loss tolerances limits for the purposes of agricultural and economic sustainability in the Mediterranean environment. Moreover, Ibrahiem, (1986) and Kbibo and Nesa , (1997) stated that the tolerable limit of soil loss ranged between 1 to 2.5 t h − 1 y − 1 for the coastal region of Syria, owing to the many geo-factors in uences the soil formation. Consequently, in light of these limits, it can be emphasized that most of the study area lands need integrated spatial management of erosion.
In addition, the spatial distributions of high and very high erosion risk hazards are mainly concentrated in the north, northeast and northwest slopes of the study area. Importantly, these areas are prone to high risk of soil erosion as a result of the spatial integration between the kinetic energy of raindrops, runoff and steep slopes. The eld investigation revealed that areas with high and very high values of soil erosion are sloping areas with dense vegetation cover. Importantly, vegetation did not reduce the soil erosion amounts in these areas according to the produced map. This nding can be explained by the intense spatial competition between slope and vegetation factors in favor of the slope factor. Moreover, the slope degrees in these areas reaches more than 70 degrees as Fig. 5 shown. These results are consistent with many studies that have indicated the effect of slopes on soil stability (Thomas et al. 2018; Kayet et al. 2018). Thus, it can be stated that the slope is a critical factor in the acceleration of soil erosion and sediment yield in the study area.
The results presented in this study, that assessed the spatial distribution of potential soil erosion in study basin, are spatial estimates, and thus are still questionable. In this regard, due to the absence of su cient data, information and eld measurements throughout the basin due to several in uencing factors, especially the consequences of the current war, a RUSLE model provides reliable spatial estimates of soil erosion quantities. These mapped estimates can be useful for decision-makers in creating strategies for conserving soils and mitigating the erosion. The RUSLE application could also be expanded at the regional and national levels as part of the spatial management plans for river basins.

Conclusions
Soil erosion is considered as one of the most geo-environmental challenges threats to agricultural and economic sustainability in the coastal region of Syria. In light of the paucity of spatial data associated with soil erosion, this study has provided an objective assessment of the spatial distribution of soil erosion susceptibility in one of the coastal river basins most vulnerable to soil erosion. The aim of this research was achieved by feeding the GIS environment with multi-source data, especially RS data, in the calculation of RUSLE parameters. In the context of current outcomes, the produced soil erosion map illustrates a maximum rate of erosion 81.13 t h − 1 year − 1 . Further, the soil erosion amount exceeded the tolerable threshold of soil loss for the coastal region of Syria (1 to 2.5 t h − 1 y − 1 ). These estimates are closely related to the estimates computed in Syria and the Mediterranean countries using the RUSLE model. Additionally, it was concluded the high effect of slope factor in accelerating soil erosion, especially in the north, northeast and northwest slopes of study area. The spatial integration process between GIS and RS data provided an objective platform for estimating annual rates of soil erosion in study basin, especially in light of the ongoing war conditions in Syria. Thus, it is possible to build on the present outcomes to provide a solid foundation for starting the creation of conservation and maintenance strategies with related spatial applications.