Spatial Quantification of Soil Erosion Using Rusle Approach: A Study of Eastern Hindu Kush, Pakistan

Abstract

Globally, soil erosion is a severe environmental issue, particularly in mountainous regions, leading to substantial declines in soil productivity. This study aims to quantify soil loss in Eastern Hindu Kush region using Revised Universal Soil Erosion Loss Equation (RUSLE) approach integrated with Remote Sensing and Geographic Information System (GIS). The study considers various factors including rainfall, soil erodibility, topography, slope, and land use to model annual soil loss rates. Rainfall erosivity (R), slope length and steepness (LS), soil erodibility (K), cover management (C), and conservation practice (P) were utilized as input parameters. These parameters are integrated to estimate soil erosion risk zones through raster-based GIS analysis, categorizing soil loss severity into five classes. The results show soil loss rates ranging from > 50 to over 276 tons/ha/year, indicating varying levels of severity. The distribution of soil loss severity is as follows: 37% of the area falls under insignificant, 16% under slight, 22% under moderate, 11% under severe, 6% under very severe, and 8% under catastrophic severity zones. Notably, valley areas with steep slopes and significant relief display higher erosion rates. The intricate and challenging terrain of the Eastern Hindu Kush makes it particularly susceptible to soil erosion risks.

1. Introduction

Soil degradation is one of the most leading environmental challenges all around the world. It decreases land for agricultural activities and ultimately lead to less agricultural production. It also leads in the removal of top soil which reduces the fertility rate of that land [1, 2, 3]. Soil is a medium which has been threatened by several factors like soil erosion, decrease in organic matter, contamination. The regions having arid and semi-arid climatic conditions are more prone towards soil erosion [4]. It has influence on land degradation, water quality, sedimentation of rivers, infrastructural damages and on agricultural productivity [5, 6]. The causes for erosion include, agricultural activities, urbanization, population explosion, climate change, infrastructure, mining activities and many other [7].

Short-term environmental and climatic change is resisted by most soils, but may undergo irreversible change such as large-scale erosion. Erosion of the soil is normally used to depict the adverse effects of man’s utilization of soil resource, with soil being a valuable natural resource which is renewed really slowly. Soil erosion is induced by anthropogenic impact on land surface, whether in terms of deforestation, extreme cultivation or the misuse of the land. Rill and sheet erosion proves to be very dangerous form of soil erosion, resulting in an almost imperceptible but constant degradation of land under cultivation. Soil erosion proves to be disastrous natural phenomenon that threatens soil stability [8].

The identification and estimation of erosion risk zones is an important element in preventing land degradation. Among those elements the most important factors are the observation of soil forming factors of that particular region. In Pakistan, the phenomenon of soil degradation is also of utmost importance and is one of the major environmental challenges. Soil erosion has many impacts such as low agricultural productivity and sedimentation. The quantities of soil erosion depend upon the topography, vegetation, soil type, and climatic conditions. Pakistan is a dry land and lies in arid and semi-arid region. Eighty percent of the land in Pakistan is arid or semi-arid, about 12% is dry sub-humid and the remaining 8% is humid. About two-third population in Pakistan are depended on this dry land for their livelihood. Dry lands of Pakistan are drastically affected by degradation of land and desertification because of poor and mismanaged land practices [9].

Soil erosion assessment, zonation and prediction are highly important in order to lessen soil loss [10]. Several soil erosion modeling approaches have been introduced to predict soil erosion in highland regions and to evaluate the transportation and deposition of sediments. Among these models, majority of the models were first introduced in United States based entirely on different equations. Later on, these equations were improvised and many new variables and factors were added. Some of the soil erosion models are, Modified Universal Soil Loss Equation (MUSLE) [11], Universal Soil Loss Equation (USLE), the Unit Stream Power – based Erosion Deposition (USPED) [9, 12], and the Revised Universal Soil Loss Equation (RUSLE) [13].

The RUSLE is a computer-based version of USLE. It has been modified by adding several new factors which include new set of rules and algorithms to compute the cover factor, for slope length and steepness factors. This study aims to spatially quantify the soil loss in Eastern Hindu Kush Region using RUSLE approach integrated with Remote Sensing and Geographic Information System (GIS).

2. The study region

Geographically, Eastern Hindu Kush in Pakistan extends between 34° 34̍11̋ to 36°54̍30̋ North Latitude and 71°11̍56̋ to 73°52̍5̋ East Longitude. Hindu Kush region lies in the west of Himalayas. Its western section falls in Afghanistan, however the eastern section located in Pakistan. The drainage basins of river Swat and Chitral are covering the eastern section of Hindu Kush mountain system. Swat River originates from two major glaciers of Ushu and Gabral, whereas river Chitral originates from Chiantar glacier. The study region is famous for its beautiful and fertile river valleys, which support large population (Figure 1) [14].

Figure 1.

The study area.

Eastern Hindu Kush stretches from Karambar Pass in East to the Dorah Pas not far from Mount Tirch Mir. The Central part continues to the Shebar Pass to the northwest of Kabul. The Western part of Hindu Kush descends to the Kermu Pass. In the extreme eastern region between the Karambar and Baroghil is dominated by very high peaks. Administratively, the Eastern Hindu Kush consists of the following districts, District of Chitral, Upper Dir, Lower Dir and Swat Scott [15]. The region is prone to heavy rainfall, surface runoff and flash floods which further intensity soil loss [16, 17].

3. Spatial quantification of soil loss

The extent of soil erosion estimation is a complex interactions between Geology, topography, climate, soil, land use and land cover. RUSLE approach was selected to predict protracted average annual soil loss rates in an area (Figures 2 and 3). In this model, five major parameters were utilized to quantify soil erosion loss in Eastern Hindu Kush region. The mathematical expression of RUSLE model is given in the following equation:

Figure 2.

RUSLE approach.

Figure 3.

Research work flow.

$$\mathrm{A}=\mathrm{R}\times \mathrm{K}\times \mathrm{L}\times \mathrm{S}\times \mathrm{C}\times \mathrm{P}$$E1

Where,

A = Soil loss per unit area (tons/ha/yr.).

R = Rainfall-runoff erosivity factor (index) (MJ/hectare mm/yr.).

K = Soil erodibility factor (tons/ha/yr).

LS = Slope factor (unit less).

C = Cover management factor (unit less).

P = Conservation practice factor (unit less).

3.1 Rainfall-runoff Erosivity factor (R)

Rainfall-Runoff erosivity (R) quantifies the impact of raindrop on the surface and the rate of runoff likely to take place after a rain event. This factor is well-defined as the mean annual sum of individual or specific storm event energy (E), and also the maximum 30 min rainfall intensity for a specific storm event as described by [13, 18]. In order to estimate the accurate R factor, it is recommended to observe at least 20 to 30 years of rainfall data to accommodate climatic variation. The R factor determines the erosivity by rainfall at a specific region based on the intensity and amount of rainfall. It basically represents the impact of rainfall intensity on soil erosion. The rainfall-runoff erosivity was estimated by the following equation used by many researchers on areas where similar topographic and atmospheric conditions prevail [19, 20].

$$\mathrm{R}=0.05\times \mathrm{P}.$$E2

Where,

R = Rainfall Erosivity Factor, P = Mean Annual Rainfall in (mm).

3.2 Soil Erodibility factor (K)

The K factor is a quantitative measurement of the erodibility of a particular type of soil. It can be also described as a measure of the susceptibility of soil particles towards detachment and transportation by rainfall intensity and runoff. Soil texture, soil structure, soil permeability and the organic matter are the main soil properties influencing K factor. The soil erodibility factor for every particular soil is defined as the rate of erosion per unit erosion index from a standard unit plot of 22.13 m long slope length having 9% of slope gradient [21]. It represents the rate of soil loss per rainfall erosivity index (R).

On the basis of data availability, following equation was used to estimate the soil erodibility of soil given by Wischmeier and Smith [18].

$$\mathrm{K}={\text{Fcsand}}^{\ast }\mathrm{Fsi}-{\mathrm{cl}}^{\ast }{\text{Forgc}}^{\ast }{\text{Fhisand}}^{\ast }0.1317.$$E3

Where,

$$\text{Fcsand}=\left[0.2+0.3exp\left(-0.0256\mathrm{SAN}\left(1-\frac{\mathit{SIL}}{100}\right)\right)\right].$$E4

$$\mathit{Fsi}-\mathit{cl}=\left(\frac{\mathrm{SIL}}{\mathrm{CLA}+\mathrm{SIL}}\right)0.3.$$E5

$$\text{Forgc}=\left(1.0-\frac{0.25C}{C+exp\left\{3.72-2.95C\right\}}\right).$$E6

$$\text{Fhisand}=\left(1.0-\frac{0.70\mathit{SNI}}{\mathit{SNI}+exp\left\{5.51+22.95\mathit{SNI}\right\}}\right).$$E7

Where, C is the organic carbon content, SIL, CLA and SAN are % silt, clay and sand, respectively, SN1 is sand content which is obtained by subtracting it from 1 and dividing by 100, Fcsand = gives a low soil erodibility factor for soil with coarse sand and a high value for soil with little sand content, Fsi-cl gives a low soil erodibility factor with high clay to silt ration, Forgc is the factor that reduces soil erodibility for soil with high organic content, Fhisand is the factor that reduces soil erodibility for soil with extremely high sand content.

3.3 Slope length and slope steepness factor (LS)

The Slope length or Steepness factor (LS) is the output of two individual factors combined together i.e. Slope length factor (L) and a Slope gradient factor (S), both of these factors are delineated from the ALOS PALSAR DEM. The LS factor proves to be an important parameter in the modeling of soil erosion.

The L factor depicts impact of slope on soil erosion. When the length of slope increases, erosion of soil will also increase. Whereas, the S factor represents impact of slope gradient on erosion. The rate of soil loss increases with increasing slope steepness more than it does with length of slope. The LS factor depicts erodibility because of slope steepness and length. It signifies the influence of topography, specifically slope features, on soil erosion. Hence, proving it to be directly proportional to the soil erosion e.g., an increase in slope steepness and length marks an increase in the LS factor.

The LS factor was calculated from after generating the flow direction and flow accumulation grids in ArcMap 10.5 by using Arc Hydro toolset.

$$L={\left(\frac{\mathrm{\lambda }}{22.13}\right)}^{\mathrm{m}}.$$E8

Where, L = Slope length factor, $\mathrm{\lambda }$ = Slope length (m), m = Slope-length exponent

$$m=\frac{F}{1+F^{\prime }}.$$E9

$$\frac{\mathit{sin}\mathrm{\beta }/0.0896}{3\left(\mathit{sin}\mathrm{\beta }\right)0.8+0.56}.$$E10

Where, F = Ratio of rill erosion to inter-rill erosion, $\beta$ = Slope angle (°).

In ArcMap, L was calculated by the following equation,

$$\mathrm{L}=\frac{{\left({\mathit{\text{flow}}}_{\mathit{acc}}+625\right)}^{\left(m+1\right)}-{{\mathit{\text{flow}}}_{\mathit{acc}}}^{\left(m+1\right)}}{{25}^{\left(m+2\right)}\ast {22.13}^m}$$(11).E11

$$S=\begin{array}{l}\mathit{Con}(\left(\mathit{Tan}\left(\text{slope}\ast 0.01745\right)<0.09\right),\left(10.8\ast \mathrm{Sin}\left(\text{slop}\ast 0.01745\right)+0.03\right),\\ \left(16.8\ast \mathrm{Sin}\left(\text{slop}\ast 0.01745\right)-0.5\right))\end{array}$$

For Slope gradient factor,

$$S=\begin{array}{l}\mathit{Con}(\left(\mathit{Tan}\left(\text{slope}\ast 0.01745\right)<0.09\right),\left(10.8\ast \mathrm{Sin}\left(\text{slop}\ast 0.01745\right)+0.03\right),\\ \left(16.8\ast \mathrm{Sin}\left(\text{slop}\ast 0.01745\right)-0.5\right))\end{array}$$(12).E12

Final LS Factor,

$$\mathrm{LS}={\mathrm{L}}^{\ast }\mathrm{S}.$$E13

3.4 Cover management factor (C)

Cover management factor (C) is used for estimation cropping impact and other managing practices on soil erosion. After topography, vegetation is considered the 2nd most vital aspect that helps in minimizing the risk of soil erosion. Different types of land use and land cover intercepts precipitation and increasing infiltration rates and also helps in the reduction of rainfall impact on ground by reducing its energy before hitting the ground.

In the study area, Global land cover data was used to generate a C-factor map. It was generated by modifying the dataset in a raster-based GIS environment. The shape file was then modified in ArcGIS by merging all the attributes of same grid codes of land cover type. The C values were assigned by reviewing the literature of comparable model usage in the areas having similar prevailing climatic conditions as my study area.

3.5 Erosion control practice management factor (P)

Erosion support practice factor (P) indicates the rate of soil loss according to different land cover management practices. This factor accounts for the control practices which reduces the rate of erosion caused by runoff and their influence on runoff concentration, runoff velocity, drainage patterns. P factor also accounts for the hydraulic forces exerted on soil by runoff. Land treatment in the form of contouring, strip cropping and terracing are the precautionary measures taken to prevent erosion. The precautionary measures or any control practices that are being used to minimize the impact of various factors on erosion contributes in the calculation of P factor.

The extent of soil erosion can be predicted by estimating the complex interactions between Geology, topography, climate, soil, land use and land cover. This empirical based technique is used globally to predict protracted average annual soil loss rates in an area. In this model, five major parameters are calculated to measure the soil erosion rates in a specific region (Figure 3). The work flow the Study is given below.

4. Spatial estimation of soil erosion

4.1 The R factor

R factor determines erosivity by rainfall at a specific region, which is usually based on the intensity and amount of precipitation. It mainly represents the impact caused by rainfall intensity on soil erosion. In this study area, the monthly precipitation data for six weather stations situated in the study area were obtained from the World Bank website. It was validated with the satellite based data to integrate the results. Rainfall map was prepared which represents the spatial distribution of rain in the Eastern Hindukush. This map was utilized to estimate R factor map by calculating the Rainfall erosivity for a time period of about 1991 to 2019 in study area. Rainfall-runoff erosivity was calculated by the following equation used by many researchers on areas where similar topographic and atmospheric conditions prevail (Figure 4) [19, 22].

Figure 4.

Rainfall-Erosivity factor (R) of eastern Hindukush, Pakistan.

$$\mathrm{R}=0.05\times \mathrm{P}.$$E14

Where,

R = Rainfall Erosivity Factor, P = Mean Annual Rainfall in (mm).

4.2 Soil Erodibility factor (K)

K factor is the quantitative depiction of soil erodibility analyzed for a particular soil type. It basically is measure of soil particles susceptibility towards the impact of rainfall and runoff. The erodibility factor is mainly influenced by the texture of soil, soil structure, soil permeability and organic matter. For an individual soil type, K factor is particularly defined as the rate of soil erosion per unit erosion index measured by average unit plot of 22.13 m long. It chiefly determines the rate at which soil loss takes place per rainfall erosivity index (Figures 5 and 6).

Figure 5.

Soil content.

Figure 6.

Soil Erodibility factor (K) of eastern Hindukush, Pakistan.

4.3 Slope length and slope steepness factor (LS)

Slope length or Steepness factor, The LS factor is the output of 2 individual factors combined together i.e. Slope length factor (L) and a Slope steepness factor (S), both of these factors were delineated using the ALOS PALSAR DEM. The LS factor proves to be an important parameter in the modeling of soil erosion risk in eastern Hindukush region.

L factor depicts the abrupt impact of slope on soil erosion. The soil loss increases per unit area as the slope length increase. Whereas, the S factor represents the impact of slope steepness on erosion. The rate of soil loss intensifies with increasing steepness of slope more than it does with length of slope. LS factor depicts erodibility because of slope steepness and length. It signifies the influence of topography, specifically slope features, on soil erosion. Hence, proving it to be directly proportional to the soil erosion for example, an increase in slope steepness and length marks an immense increase in the LS factor.

Slope length and steepness for the study area was calculated by utilizing an elevation model. The DEM was filled to fill all the depressions to get accuracy in the imminent analysis. When all the depressions or sinks formed because of erroneous data are filled, by assigning them the values of neighboring cells (Figure 7) [23].

Figure 7.

Slope length and slope steepness (LS) of eastern Hindukush, Pakistan.

4.4 Cover management factor (C)

The Cover management usually known as the C factor is used to estimate the impact of several management and cropping practices on soil erosion. After topography, vegetation is considered as the 2^ndmostsignificant factor that helps in minimizing the risk of soil erosion. Different types of LULC intercepts precipitation and results in increasing infiltration rates and also helps in the reduction of rainfall impact on ground by reducing its energy before hitting the ground (Figure 8).

Figure 8.

Cover management (C) of eastern Hindukush, Pakistan.

In the study area, Global land cover data was used to calculate and analyze a C-factor map. It was generated by converting the dataset into a polygon .shp. The shape file was then modified in ArcGIS by merging all the attributes of same grid codes of land cover type. Now, the C values were assigned by reviewing the literature of comparable model usage in the areas having similar prevailing climatic conditions (Table 1).

Sr. no.	Land Cover	C Factor
1	Grass Land	0.059
2	Built-up Areas	0
3	Barren Land	1
4	Shrub Land	0.69
5	Water Bodies	0
6	Cultivated Land	0.28
7	Forest	0.004
8	Wetland	0
9	Snow Covered	0

Table 1.

C-factor in the eastern Hindukush.

4.5 Erosion control practice management factor (P)

The P factor or erosion support practice factor indicates the rate of soil loss as per different land cover management practices. This factor particularly accounts for the control practices which reduces the rate of erosion caused by runoff, and also focusses their influence on drainage patterns, runoff concentration and runoff velocity. P factor also accounts for the hydraulic forces exerted on soil by runoff. Land treatment in the form of contouring, strip cropping and terracing are the precautionary measures taken to prevent erosion (Figure 9).

Figure 9.

Erosion control practice management (P) of eastern Hindukush, Pakistan.

The precautionary measures or any control practices that are being used to minimize the impact of various elements on erosion contributes in the generation of P factor (Table 2).

Slope %	Contouring
0–7	0.55
7–11.3	0.6
11.3–17.6	0.8
17.6–26.8	0.95
> 26.8	1

Table 2.

P factor values for slope as per agricultural practice.

This issue has never been addressed in the study region. Accordingly, no resistance or management strategies and techniques have been used. Consequently, the value of ‘1’ was generally set for the generalization of the P factor in the eastern Hindukush, Pakistan.

5. Annual soil loss

The final soil loss magnitude was obtained by analyzing five Geo-environmental factors, Rainfall-Runoff Erosivity factor (R), Soil-Erodibility factor (K), Slope length (L) and Slope Steepness (S), Cover management factor (C) and the Erosion Practice Control management factor (P).

The estimated annual average soil loss in the Eastern Hindukush is ranging from 50 to more than 276 Tons/ha/year. The spatial distribution of soil loss severity is represented in Figure 10. The study area is delineated into five different zones showing the severity of soil erosion. Maximum erosion is observed in northern parts of the study area. Bare areas and highlands with steep slopes are more susceptible to soil loss. Bare areas and highlands with steep slopes are more vulnerable to soil loss as shown in the map.

Figure 10.

Average annual soil loss rates (T/H/year) of the eastern Hindukush region, Pakistan.

6. Discussion

Eastern Hindukush is mostly fed by glaciated water. When glaciers melt, it causes rapid erosion. This increased rate of erosion can affect the productivity of Dam which is located on Chitral River, leading to reduced power generation as well as loss of agriculture land. Initial output of the dam was about 250,00 kilowatts but with the passage of time, it reduced to 64,000–20,000 kilowatts.

RUSLE modeling approach comprises of following parameters; The Rainfall-Runoff Erosivity (R), Soil Erodibility (K), Slope Length and Slope Steepness (LS), Cover Management (C) and Support Practice (P) Factor. For the calculation of annual average soil erosion, the above five factors were estimated. Input layers of these factors were in ArcGIS individually. The high R factor values shows that the northern part of District Chitral receive more rainfall, as well as areas of Shoghar, Kalash and the south eastern part of the study area as depicted by the results. The high elevation and topographical features of this region is the cause of excessive rain apart from other regions. The K factor of the study area ranges between −1 to 3.0. Soil erodibility map was generated using the ISRIC Soil Data of organic carbon, Clay, silt an = d sand content found in the region. Lower values of K factor show the soils having low permeability and lower moisture content etc. The slope length and slope steepness (LS) factor has a range of 0 to 28,048. Flow accumulation and accelerated slope is represented by higher values of LS factor. Cover management, the C factor value falls in the range of 0 to 1.1 depicts the maximum land cover, whereas 0 represents the areas having minimum or very little land cover. The value of the support practice (P) factor is generated as 0.2 to 27.8. The region is mostly suffering from the ignorance of authorities on conservation, calculating this factor was a challenge because of the deficiency of data on conservation practices being carried out in the region. The annual average rate of soil loss of the study area is about 276 tons/ha/year, and the overall annual soil loss from this region is nearly 31 million tons/year. The results concluded by the severity classification of soil erosion estimated that % area of study area falls in the very low zone, indicating soil erosion of <10 tons/ha/year, which is contradictory to the bearable limit of <2 tons/ha/year [24]. It clearly shows that this region needs to be addressed properly and timely in this regard. Moreover, almost 30% of the area falls in the low-erosion zone, 22% of the area in the region lies in the moderate zone, 16% lies in high-erosion zones, whereas 8% of the total study area lies under catastrophic erosion zone. The areas having minimum vegetation and steep slopes are more vulnerable to high soil erosion. Several land use activities, such as urbanization, overgrazing and deforestation, considerably increase erosion rates, and make it critical in areas having steep slopes and high elevation.

RUSLE has proved to be efficient and suitable tool for soil loss estimation and is globally acknowledged because of its accurate and reliable calculation of annual soil loss rates [20]. On the other hand, practical validation was not achieved because the resources were insufficient. The final outcome of this study is comparable to formerly conducted studies in the neighboring regions at watershed level. The result of a study proposed in the watershed of Fateh Jang showed that soil loss was 17–41 tons/ha/year for 1–10% slopes in uncultivated land, while the rate was relatively lower (9–26 tons’ ha/year) for vegetative land [20]. Another proposed research for the soil loss appraisal in plain areas using RUSLE showed almost 8 tons/ha/year. Results of the study conducted above showed that the Eastern Hindukush is at more risk to soil erosion than the Potohar region, Pakistan.

This study shows that soil erosion is a severe hazard and is in dire need to be addressed. This study will help in identification and understanding risks associated with soil erosion. It does not only identify the risk but also listed the contributory factors towards the erosion. The management and strategic planning to sustain natural resources and policymaking can analyze the findings of this study to minimizes oil loss.

7. Conclusion

This research provides the quantitative aspect of soil erosion. The research utilized RUSLE with GIS to map the soil erosion severity in the eastern Hindu Kush, Pakistan. The five parameters involved in the RUSLE model are rainfall–runoff erosivity (R), soil erodibility (K)) factor, slope length and steepness (LS) factor, land cover management (C) factor, and support practice (P) factor. These factors are used to estimate the annual average soil loss rate. The maps of these factors were generated separately, and value ranges were obtained from generated maps. The high value of rainfall–runoff erosivity factor (R) indicates that the northern part of the study area receives more rainfall, including the areas of Shoghar, Chitral, and Kalash, as indicated in the results. The topography and high elevation of this region is the major reason for the excessive rains. The soil erodibility (K) factor ranges between −1 to 3. The soil erodibility map was generated by using the raster soil content layers of the respective soil types in the soil map. The slope length (LS) factor has a range of 0 to 28,048. The higher value represents flow accumulation and an increase of slope. Land Cover management (C) factor lies in the range of 0 and 1. Maximum land cover is indicated by assigning it the value of 1, whereas the least land cover is represented by 0. The value of the support practice (P) factor is taken as 1.0 because of the ignorance of authorities on conservation and the deficiency of data on conservation practices.

The study concludes that it is quite significance to recognize and to have a complete understanding of risks related to the erosion of soil in the study area. The current study has proved Eastern Hindu Kush to be under severe threat of soil erosion. The highest rates of soil erosion are found along the path of river. River channel and some areas in Eastern Hindukush are highly vulnerable to soil erosion. Barren land and highlands with steep slopes are more vulnerable to soil loss. The lowermost portion of the Eastern Hindukush is prone to very high soil erosion rates. The magnitude of soil erosion was estimated by calculating the annual average soil loss rates in the Eastern Hindukush, which ranged between >50 to more than 276 Tons/ha/year presented by low and high values. Severity of the soil loss is represented by five different classes. Maximum erosion is observed in northern parts of the study area. Bare areas and highlands with steep slopes are more susceptible to Average annual soil loss. The percentages of the area lie under soil loss rates, concerning its severity, are 37% in insignificant, 16% in slight, 22% in moderate, 11% in severe 6% in very severe, and 8% in the catastrophic severity zone of the study area. Final output of the study was to calculate the soil erosion risk in the Eastern Hindu Kush region, Pakistan. The low and very low class represents areas having no or minimum risk towards soil erosion as compared to other classes. The areas bearing the moderate class are prone to soil erosion but the damages may not be catastrophic. High and very high classes represents the areas having maximum risk of erosion that can be catastrophic in nature if triggered in the near future.

This result of the study shows that the Eastern Hindukush is greatly prone to soil erosion, mainly the southern part of the region including the areas of Ispheru, Arkari, kalash and Harchin and many more. If the phenomenon of erosion expands with the same pace it would cause more land degradation. The results in this study comprise soil erosion severity classes and erosion intensity. The estimated soil loss in the present study was 276 tons/ha/year. The percentage of erosion which is about 8 percent shows that this region is very prone to erosion. The rate of soil erosion is increasing day by day; it will cause serious damage to the living conditions of eastern Hindukush. Thus, livelihoods of many families will be suffered if actions and precautionary measure are not taken in time. There are no precautionary measures in high elevated steep slopes to ensure the mitigation of soil erosion risk. Lack of knowledge and mismanaged agricultural practices in this region is also a major cause of soil erosion. This study will assist the policymakers and planners who can utilize these results to generate a mitigation strategy and can future planning as well.

8. Recommendations

Plantation in the upper parts of the area would be help in sustaining soil and it also will be helpful in stabilizing the climate. Moreover, plantation along the river channels will be helpful in protecting the agricultural land from flash floods resulting in erosion control. The construction of embankments along the river and adjacent to the river will prevent the land from erosion caused by flooding. Hydro-meteorological stations play an important role in collecting hydrological and meteorological observations, the modeling of hydrological and meteorological phenomenon, forecasting weather and warn about the extreme events. Government along with the local administration should create a think tank in order to improve land management.