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RISK ASSESSMENT OF SOIL EROSION IN RAWAL WATERSHED USING GEOINFORMATICS TECHNIQUES.

Byline: Bashir, Baig, Ashraf, Anwar, Bhalli and Munawar

Abstract: Soil erosion due to water is becoming a serious problem in rain fed areas of Pothwar region,Pakistan. Rawal lake, a major source of water supply for Islamabad and Rawalpindi has reduced in its storage capacity over the past few decades. This research aims to assess soil erosion risk in Rawal watershed using Coordination of Information on the Environment (CORINE) model which is based on soil, precipitation, temperature, slope and vegetation cover. Soil maps are used to extract soil texture, soil depth and stoniness data which are overlaid to generate soil erodibility map. Precipitation and temperature data of 18 years (1988-2005) is used to compute Modified Fournier Index (MFI) and Bagnouls-Gaussen aridity Index (BGI) that is overlaid to generate erosivity map. Slope is generated from DEM of 20m spatial resolution which is extracted from topographic sheet of scale 1:25,000. Soil erodibility, erosivity and slope maps are overlaid to produce potential soil erosion risk.

Vegetation cover is generated by NDVI using SPOT-5 satellite image which is used to generate actual erosion risk map by overlay analysis with potential erosion risk map. Soil loss computed for both potential (28 tons/ha/yr) and actual erosion risks (24 tons/ha/yr) by CORINE model integrated with GIS and RS techniques proved efficient and inexpensive way to generate soil erosion risk maps.

Keywords: CORINE, Rawal watershed, risk maps, soil erosion, soil loss

1. INTRODUCTION

Soil erosion, which has been occurring throughout the history of agriculture, is intensified in the recent years because of human activities, deforestation, and over- exploitation for fuel wood, overgrazing, agricultural activities and industrialization. Soil erosion rates are estimated to be highest in Asia, Africa and South America averaging 30-40 tons/ha/yr [1]. Pakistan is one of those countries of the world that are severely affected by soil erosion and it is prevailing in most parts of the country including mountainous regions, barani lands and deserts [2]. Out of the total geographical area of Pakistan, almost 15.9 Mha of land (20 per cent of total) is affected by soil erosion and out of this, 11.2 Mha (70 per cent) is affected by water erosion [3]. The sub mountainous region of Pothwar Plateau is a large barani (rain-fed) area comprising of 1.82 Mha, out of which 1.21 Mha is affected by gully erosion and only 0.61Mha is cultivated.

Intensive precipitation, steep slopes and erodible soils without adequate protection have led to extensive soil erosion in the area and the consequences are devastating including loss of fertile soil and vegetation [4]. Rawal watershed is of prime importance in Pothwar region which is subject to serious problem of soil erosion. Soil erosion models are considered as a powerful tool to predict erosion on various scales. CORINE is an erosion prediction methodology based on GIS and RS techniques has been successfully used in various regions of the world to produce erosion risk maps for large watershed that can predict erosion prone areas efficiently and inexpensively. The research has used CORINE model to identify erosion prone areas, calculate soil loss and develop an erosion risk map for Rawal watershed.

1.1. Study Area Description: The study area is Rawal watershed (Fig.1) that is located within an isolation section of Margalla Hills National Park. The drainage area is 268.69 km2 while storage capacity of lake is 58.58 MCM, covers area of 6.35 km2 and is located at an altitude of 675 m from sea level. The area lies in sub-humid transitional to humid sub-tropical continent with highest rainfall in northern part during monsoon season and annual rainfall is 950 - 2000 mm. Temperature ranges between 4.7 degC to 38.9 degC. The soil comprises of sandy loam and sandy clay loam with Haro basin in North, Soan River in South and Jehlum River in East of the study area. [5; 6; 7].

2.1. Datasets Used: CORINE model based on soil, climate, topography (slope) and land cover parameters. Soil data is obtained from Soil Survey Reports by Soil Survey of Pakistan (SSP) of Rawalpindi, Murree-Kahuta and Haro Basin were used. Climate data comprising of monthly, annual rainfall and mean temperature for 18 years (1988-2005) is obtained from Pakistan Meteorological Department (PMD), National Agriculture Research Center (NARC), Satra Meel and Murree weather stations. Topographic sheet (1:25,000) is obtained from Survey of Pakistan (SOP) in order to generate 20 meters digital elevation model (DEM). SPOT-5 multispectral image with 2.5 m spatial resolution is purchased from Space and Upper Atmospheric Research Commission (SUPARCO), Islamabad, to calculate NDVI.

2.2. Methodology: The data sets are used to map actual soil erosion risk according to the flow chart shown in Fig. 2. Soil erodibility is calculated by overlay analysis of soil texture, soil depth and stoniness. Soil texture is classified into three classes ranging from slight (clay, sandy clay, silty clay) to moderate (sandy clay loam, clay loam, silt clay loam, loamy sand) and high (loam, silt loand, sandy loam) erodibility. Soil depth of Rawal watershed ranged from 0- 190 cm and classified into three classes i.e., low (above 75 cm depth), moderate (25-75 cm depth) and high (below 25 cm depth). Stoniness is defined as the percentage surface cover of soil with stones having diameter greater than 20 mm; study area is classified into two classes; fully protected soil (above 10% stoniness) and not fully protected soil (below 10% stoniness). Finally, soil erodibility index is classified into low (0-3 index value), moderate (3-6 index value) and high (above 6 index value) classes.

Two climate indices i.e., Modified Fournier Index (MFI) and Bagnouls-Gaussen aridity Index (BGI) are combined to map erosivity for Rawal watershed.MFI, for each climate station, is calculated by considering total monthly precipitation, Pi (mm) and mean annual precipitation, P and number of years, b using Eq. 1.

Weighted (IDW) algorithm to determine spatial distribution of index for the watershed which is classified into five classes according to CORINE model (Table 1).

Table 1: Classification of MFI for Rawal watershed

Class###MFI (mm)###Description

1###less than 60###Very Low

2###60 - 90###Low

3###90 - 120###Moderate

4###120 - 160###High

5###greater than 160###Very High

BGI is generally defined as dry or arid month in which monthly precipitation, P (mm) is less than twice mean monthly temperature, T (degC) [10]. To evaluate the number of dry months for Rawal watershed, precipitation and temperature data are plotted on the same graph known as ombrothermic graph while doubling the values on precipitation scale. Dry months are with mean temperature curve higher than the precipitation curve [11]. Bagnouls- Gaussen aridity index was then calculated using Eq. 2 where, it is the mean monthly temperature, pi is the average monthly precipitation , ki is a coefficient indicating the number of months, in which 2ti greater than pi or 2ti - pi greater than 0.

(Equations)

Source: [12].

BG aridity index value for each climate station is interpolated using IDW technique in order to generate BGI surface which is classified into four classes as per CORINE requirements (Table 2).

Table 2: Climate classification based on BGI

Class###BGI Value###Climate Type

1###0###Humid

2###0 - 50###Moist

3###50 - 130###Dry

4###greater than 130###Very Dry

Erosivity map is prepared by multiplying MFI and BGI maps using Raster Calculator and classified into low (index less than 4), moderate (4-6 index) and high (index above 6).

DEM of 20 m resolution is used for slope angle generation which is classified into four classes: gentle to flat (slope less than 5%), flat (5%-15%), steep (15%-30%) and very steep (above 30% slope). Finally, for potential soil erosion risk map overlay analysis is performed on erodibility, erosivity and slope which is classified into low (0-5 index), moderate (5-11 index) and high (above 11 index) erosion risk areas. Vegetation cover is calculated by NDVI using SPOT-5 multispectral image and classified in to two classes of fully protected soil and not fully protected soil.

Finally actual soil erosion risk map of study area is computed by overlay analysis of vegetation cover and potential soil erosion risk map and classified into low, moderate and high erosion risk area. CORINE is a qualitative erosion risk model that shows the severity of soil erosion in a specific area by classifying it into low, moderate and high risk. In order to calculate soil loss from CORINE erosion index values, Eq. 3 is used.

(Equation)

3. RESULTS and DISCUSSIONS

Soil erodibility map for Rawal watershed is produced by overlay analysis of soil texture, depth and stoniness. The map (Fig. 3) shows that 45 per cent (121 km2) area is highly erodible soils found in south-west and some patches in the north. The area comprises loam, sandy loam and silty loam soils with soil depth less than 25 cm. The soil is not protected with stones which increases the risk of soil erosion. Moderately erodible soils (23 per cent, 61 km2) have soil texture classified as sandy clay loam, clay loam and silty clay loam. The depth of these soils range between 25-75 cm and are located in a continuous pattern in north and north-east part of study area. Low erodible soils (32 per cent, 88 km2) have clay, sandy clay and silty clay texture with soil depth greater than 75 cm and are located in south- east part. Highly erodible soils are found in Islamabad and some of the Abbottabad district.

Furthermore, moderately erodible soils fall in Abbottabad and Rawalpindi district while low erodible soils fall in Rawalpindi district.

MFI value classified the total study area in high class because total annual rainfall in the study area is greater than 1000 mm. MFI value indicate that high precipitation causes higher runoff which is associated with erosion and classified the climate into humid, moist, dry and very dry (Table 2) by determining dry months of the year with the help of ombrothermic graph. High amount of precipitation and low temperature generate low BGI values and the index value for study area ranged between 0-21, indicating that climate of the area is sub-humid to humid. The humid climate comprises 1 per cent of the study area while sub-humid Erosivity is based on the amount and intensity of rainfall and it is determined by combining two climatic indices i.e., MFI and BGI. Erosivity map is generated by overlay analysis of these climatic indices. The moist climate contributes to erosivity and hence the area is more prone to erosion.

The combined effect of indices indicates that the Rawal watershed lies in moderate erosivity (Fig. 5).28 per cent has steep slope (15-30% gradient) which is located in a zig-zag pattern in north, north-west and south- east part (Figure 6). Flat slope (5-15% gradient) is located in central-southern part making up 19 per cent and gentle slope (less than 5% gradient) is found in south-west part making 30 per cent of area. The study area lies in a mountainous region with varying slope and high elevation range. Very steep and steep slope is distributed in upper and central parts of the study area whereas gentle slope of less than 5% is mainly located in Islamabad district.

Finally, actual erosion risk map (Fig. 9) was generated by overlay analysis of vegetation cover map and potential erosion risk map. The map indicates spatial distribution of high, moderate and low erosion risk areas in Rawal watershed. The red color (Fig. 9) shows high erosion risk that is 26 per cent (71 km2) of the study area. It is distributed in north-west and southern part while some of the area in north and north-east is also under high risk. These areas have highly erodible soil consisting of loam, silt loam and sandy loam. The erosivity index is also moderate with greater than 1000 mm total annual rainfall. The slope in most of the area is very steep with low vegetation cover. Moderate erosion which is 48 per cent (129 km2), indicated by green color, can be found in the south, south-west and north-east part of study area (Figure 9). The area comprises moderately erodible soils with 5-15% slope in central region and a few very steep slopes in the northern region of study area.

Sparse vegetation also exists in this area whereas low erosion area which accounts 26 per cent (69 km2) is observed in the central part of study area. Here exists thick vegetation and the soil is slightly erodible, covered with stones thus protecting it from rain splash. The area consists of flat slopes which are fully protected with the vegetation. The CORINE erosion risk model is an estimation that provides a trend in soil erosion so that high, moderate and low erosion risk areas could be identified.

In order to After computing soil loss for two maps (potential and actual erosion risk), the area underlying various categories of erosion along with annual soil loss is shown in Table 3.

Table 3: CORINE erosion index and soil loss (tons/ha/yr) for the study area

CORINE Erosion###Soil Loss Range###Potential Soil###Actual Soil

Class###(tons/ha/yr)###Erosion Risk###Erosion Risk

###Area###Area###Soil Loss###Area (ha)###Area###Soil Loss (tons)

###(ha)###(%)###(tons)###(%)###

Low###0 - 10###4930###18###24650###6828###25###34140

Moderate###10 - 40###11095###41###277375###12918###48###322950

High###greater than 40###10844###41###444604###7123###27###292043

Total###26869###100###746629###26869###100###649133

Soil Loss###28 tons/ha/yr 24 tons/ha/yr

It clearly indicates that the annual soil loss in potential soil erosion risk (28 tons/ha/yr) is higher than in actual soil erosion risk (24 tons/ha/yr). This is because the potential soil erosion does not consider vegetation cover and the soils in this region consist of loam, silt loam, clay loam or silty clay loam which has low water holding capacity. The slope is more than 30% gradient with high precipitation and low temperature values. Thus, all these factors collectively res 4.in high runoff and soil erosion. From various studies it has been verified that the gentle slope (1-10% gradient) in Pothwar region can cause 17-41 tons/ha/yr of soil loss under fallow condition and steep slopes cause 28 tons/ha/yr of soil loss [14; 3]. The soil loss in actual erosion risk is lower because of vegetation that protects soil from erosion.

However, in moderate erosion index, rate of soil loss has increased because of deforestation, inappropriate agricultural practices and urbanization. [14] estimated soil loss rate in this region to be 9-26 tons/ha/yr which coincides with the soil loss estimated in this research (i.e., 24 tons/ha/yr). Taking average soil loss of 24 tons/ha/yr and assuming that 100% of it is deposited in the Rawal lake, then the gross reservoir capacity of the lake would have been reduced from 58.26 MCM to 45.56 MCM (about 78%) during the last 50 years. Therefore, the watershed management activities should be carried out in the watershed of Rawal Lake to reduce the volume of sediment coming into the lake.

4. CONCLUSIONS

CORINE model integrated with GIS and RS techniques proved efficient and inexpensive in generating soil erosion risk maps for mountainous region of Pothwar region. These maps can be used for determination of discrete degrees of potential and actual risks, as well as of their distribution. The soils in the study area are highly erodible, intense rainfall and very steep slopes cause significant amount of soil loss contributing to high soil erosion risk. Vegetation helps in reducing soil erosion but with increase in population, urbanization and deforestation is a major phenomenon taking place in the vicinity of Rawal watershed. So, with the decrease in vegetation, soil erosion in Rawal watershed has been intensified. The estimated soil loss varies between 24- 28 tons/ha/yr with high erosion risk (26 per cent) at areas having very steep slopes and less vegetation cover. The soil erosion rate in these areas can be minimized by protecting soil with proper land management practices.

5. RECOMMENDATIONS

The CORINE model can be helpful in monitoring soil erosion and sedimentation rate of large dams annually by using satellite and climatic data. Better results can be obtained by having consistent dataset with same period of climatic data and satellite image of high resolution for small watersheds. Close spatial distribution of meteorological stations can be helpful in order to estimate soil erosion rate in mountainous regions. The relation of urban growth and soil erosion should also be incorporated in order to observe the rate of urbanization and its effect on soil erosion. Temporal analysis can also be performed to monitor soil erosion and change detection over past few years.

ACKNOWLEDGMENT: This paper is the part of 1st author MS thesis. I acknowledge IGIS-NUST for providing logistics and resources to carry out this research.

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