Environ Geol (2009) 57:695–705 DOI 10.1007/s00254-008-1348-3
ORIGINAL ARTICLE
Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand K. C. Krishna Bahadur
Received: 14 December 2007 / Accepted: 14 April 2008 / Published online: 15 May 2008 Ó Springer-Verlag 2008
Abstract Land degradation is still a very common problem in the mountains of Asia because of inappropriate land use practice in steep topography. Many studies have been carried out to map shifting cultivation and areas susceptible to soil erosion. Mostly, estimated soil loss is taken as the basis to classify the level of soil loss susceptibility of area. Factors that influence soil erosion are: rainfall erosivity, soil erodibility, slope length and steepness, crop management and conservation practices. Thus the reliability of estimated soil loss is based on how accurately the different factors were estimated or prepared. As each and every small pixel of our earth surface is different from one area to another, the manner in which the study area was discretized into smaller homogenous sizes and how the most accurate and efficient technique were adopted to estimate the soil loss are very important. The purpose of this study is to produce erosion susceptibility maps for an area that has suffered because of shifting cultivation located in the mountainous regions of Northern Thailand. For this purpose, an integrated approach using RS and GIS-based methods is proposed. Data from the Upper Nam Wa Watershed, a mountainous area of the Northern Thailand were used. An Earth Resources Data Analysis System (ERDAS) imagine image processor has been used for the digital analysis of satellite data and topographical analysis of the contour data for deriving the land use/land cover and the
K. C. Krishna Bahadur (&) Department of Agricultural Economics and Related Sciences in the Tropics and Subtropics, University of Hohenheim, Stuttgart, Germany e-mail:
[email protected]
topographical data of the watershed, respectively. ARCInfo and ARCView have been used for carrying out geographical data analysis. The watershed was discretized into hydrologically, topographically, and geographically homogeneous grid cells to capture the watershed heterogeneity. The soil erosion in each cell was calculated using the universal soil loss equation (USLE) by carefully determining its various parameters and classifying the watershed into different levels of soil erosion severity. Results show that during the time of this study most of the areas under shifting cultivation fell in the highest severity class of susceptibility. Keywords Land degradation Soil loss mapping GIS Remote sensing
Introduction Soil erosion is a major problem throughout the world (Rauschkalb 1971; Hitzhusen 1993). As the economies of developing countries are based primarily on agricultural production, the primary concern in leveling off the agricultural growth is soil erosion and land degradation. Erosion and degradation not only decrease land productivity but can also result in major downstream or off-site damage than on-site damage. The fact that ‘‘soil erosion is the foreseeable result of poor or incorrect land use and that it cannot be overcome unless land use and land management are improved’’ has generally not been appreciated (Sanders 1992). Water erosion is the major type of physical land degradation in the global perspective. Asia alone has almost half of the area of the total water-eroded land of the world (Haen 1991). In Asia and in the Pacific region, after India and
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Laos, Thailand has the significant proportion of land area (33.7% of the total country’s area) degraded by water. Thus, soil erosion is one of the most critical environmental hazards of modern times. Vast areas of land now being cultivated may be rendered economically unproductive if the erosion of soil continues unabated. Information on the factors leading to soil erosion can be used as a perspective for the development of appropriate land use plan. Simple methods such as the universal soil loss equation (USLE) (Wischmeier and Smith 1965, 1978), the modified universal soil loss equation (MUSLE) (Williams 1975), or the revised universal soil loss equation (RUSLE) (Renard et al. 1991) are frequently used for the estimation of soil erosion from watershed areas (Ferro and Minacapilli 1995; Ferro 1997; Kothyari and Jain 1997; Ferro et al. 1998). This estimation is found to have large variability because of the spatial variation of rainfall and watershed heterogeneity. Such variability has promoted the use of data-intensive process-based distributed models for the estimation of watershed erosion by discretizing a watershed into subareas, each having approximately homogeneous characteristics and uniform rainfall distribution (Young et al. 1987; Wicks and Bathurst 1996). The use of GIS methodology is well suited for the quantification of heterogeneity in the topographic and drainage features of a watershed (Shamsi 1996; Rodda et al. 1999). The objectives of this research were to map erosion prone areas in the Upper Nam Wa Watershed Thailand by using GIS and RS for the discretization of the watershed into small grid cells and for the computation of physical characteristics of these cells such as slope, land use and soil type, all of which affect the processes of soil erosion in the different subareas of a watershed. Further GIS methods are also used to estimate the soil erosion in individual grid cells.
Study area The study area constitutes a mountainous watershed, named Upper Nam Wa Watershed falling in the northern region in Nan Province of Thailand. The watershed area is 64,628.9 ha, between 18°360 31.1700 and 19°200 48.2600 north latitudes and 101°010 3900 to 101°210 38.8800 east longitudes (Fig. 1). The area is known to have an abundance of some resources, such as forests and cultural attraction but severally lacking others, most notably, fertile soil, investment capital, and so on. The ecological condition of the watershed is at risk because a major portion of the area is under unsustainable land use practices. The average slope of this area exceeds 30%. There are four soil series, present in the study area slope complex, alluvial soil, hang dong and rock land. More that
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Fig. 1 Location of the study area
95% of the area falls under the slope complex. Other three soil series are found in the remaining area. The area has a humid tropical climate with a mean annual rainfall of 1,675 mm. The elevations at the highest and lowest points are 2,065 m and 477 m above mean sea level, respectively. The land use/land cover of the area comprises 54% mixed deciduous forest, 22.7% scrubland, 10.5% evergreen forest, 7.9% paddy fields, and 4.9% under shifting cultivation. The chief characteristics of the watershed area are undulating land, high and low hill slopes and eroded surface, and limestone hills running from north to south separating lowland and upland areas. Most part of the watershed lies in the mountainous region under forest cover. The drainage pattern of the area is dendritic. Limestone, sandstone and granite rock types are found in the study area. Limestone is found in large areas of the watershed. Sand stone is found in the southeast corner of watershed, where as, granite rock is found in the upper west and east corner of watershed.
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Methodology Apart from rainfall and runoff, the rate of soil erosion from an area is also strongly dependent on its soil, vegetation and topographic characteristics. In real situations, these characteristics are found to vary greatly within the various subareas of a watershed. A watershed therefore needs to be discretized into smaller homogeneous units before making computations for soil loss. A grid-based discretization is found to be the most reasonable procedure in both processbased models as well as in other simple models (Beven 1996; Kothyari and Jain 1997). For this study, a grid-based discretization procedure was adopted. The 25 m grid size was adopted for discretization because it should be small enough so that a grid cell encompasses a homogeneous area. Soil loss was computed based on USLE in GIS environment using ERDAS IMAGINE and ARCINFOÒ and ARCVIEWÒ GIS Packages. The entire analytical methodology follows the steps shown in Fig. 2. First, grid cell of rainfall, soil units, combined slope length and steepness and land use and practice management were prepared. Computed values for R, K, LS and CP were
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encoded into the respective units of the respective coverage. This coverage was overlaid and soil loss rate was calculated as per USLE equation. These were further grouped into six major groups to show the erosion severity in relation to the spatial distribution and their aerial extent. USLE method has been found to produce realistic estimates of soil erosion over areas of small size (Wischmeier and Smith 1978). Therefore, soil erosion within a grid cell was estimated via the USLE. The USLE is expressed as. E ¼ RKLSCP
ð1Þ
where E is the amount of soil erosion (tons ha-1 year-1); R is the rainfall erosivity factor; K is the soil erodibility factor; LS is the slope steepness and slope length factor; C is the cover management factor and P is the supporting practice factor. R factor is the principal function of USLE, which is mainly responsible for the amount of soil loss. It can be assumed that if there is no rain, contribution of other factors of USLE will result in a much less amount of soil loss, which perhaps can be attributed to erosion because of wind. The landform and the direction of rainfall are also responsible for
Fig. 2 GIS methodology of estimating soil loss
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the varying amount of soil entrainment. R is expressed in terms of annual erosivity in MJ mm ha-1 year-1. There are established relationships between the rainfall amount and R factor. As there is no meteorological station in Upper Nam Wa Watershed, information on rainfall amount and pattern needs to be assumed based on the neighboring stations. The rainfall information available represents the point data and this has to be extrapolated in terms of spatial distribution using arc view contouring function. In this study, the equation (Eq. 2), developed by Lo (Harper 1987) and used by El-swaify et al. (1985); and Funnpheng et al. (1991) in their study in Phetchabun Province Thailand, was used for computing the R factor. R factor map (Fig. 3) based on 30year average rainfall record was used to delineate the different rainfall regimes in the study area. For the study area, seven zones of different rainfall regimes were identified; the computed R for each rainfall regime was encoded into the respective spatial data in the GIS coverage (Table 1).
topographical factor commonly expressed as LS factor. Many researchers have used these two L and S factors as the combined LS factor. Slope length, defined as ‘‘the distance from the point of origin of overland flow to either the point where the slope decreases to the extent that deposition begins or the point where runoff enters welldefined channels’’ (Wischmeier and Smith 1978), is one of the difficult parameter to compute when estimating soil erosion unless an empirical field study is conducted. S factor termed as ‘slope steepness factor’ is important because it determines the velocity of the sediment runoff through water erosion. S factor is basically the function of the slope gradient.
R ¼ 38:5 þ 0:35 r
ð2Þ
Where R = rainfall erosivity and r = total rainfall amount in mm. L factor, which is the function of ‘slope length’ along with the S factor (slope steepness), represents the Fig. 3 R factor map of the study area
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Table 1 Rainfall regime of watershed studied Average annual rainfall range (mm)
Average of average annual rainfall (mm)
Area (ha)
Computed R
1,450–1,500
1,475
7,515.4
554.75
1,500–1,550
1,525
10,795.4
572.25
1,550–1,600
1,575
9,214.7
589.75
1,600–1,650
1,625
16,587.1
607.25
1,650–1,700
1,675
13,633.8
624.75
1,700–1,750
1,725
6,001.9
642.25
1,750–1,800
1,775
880.6
659.75
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For this study, the combined LS factor was computed by means of arc view gis spatial analyst extension. Twentyfive metre DEM (digital elevation model) of the study area was prepared by 20 m interval digitized contour from 1.50,000 scale topographic map of Royal Thai Survey Department. Topographic analysis was carried out using terrain analysis of ERDAS. Grid theme of elevation and slope prepared by 25 m DEM was used for preparing combined LS factor data of the study area. Combined LS factor was estimated using flow accumulation theme. The flow accumulation, which denotes the accumulated upslope contributing area for a given cell, was calculated by summing the cell area of all upslope cells draining into it. Computation was done from DEM using the watershed delineation tool available in hydrological modeling extension in arc view spatial analyst. The slope units were degrees, and elevation values were in meters. The technique for estimating the USLE LS factor that is used here was proposed by Moore and Burch (1986a, b) (Eq. 3). The combined LS factor for the watershed was calculated and
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its spatial distributions in the different spatial gradients of the watershed were presented (Fig. 4) LS ¼ ðFlow accumulation Cell size=22:13Þ0:4 ðsin slope=0:0896Þ1:3
ð3Þ
where flow accumulation denotes the accumulated upslope contributing are for a given cell, LS = combined slope length and slope steepness factor, cell size = size of grid cell (for this study 25 m) and sin slope = slope degree value in sin. C is the crop management factor and P is the erosion control practice or conservation factor. Most of the researchers have reported the use of these two factors as different factors when computing for USLE. These two factors are also treated together as CP factor. For this study, because of the lack of separate information for these two factors, they were treated as a single combined CP factor. The land cover type of the study area was obtained from the classified satellite data. The study area was covered by the satellites Landsat TM (path 129 and row 47 on 4 March
Fig. 4 LS factor map of the study area
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1999). The areas of interest was first cut from the entire path/row of the Landsat TM scene and was then geo-coded using the method suggested by Sabins (1997) at 30 m and 24 m pixel resolutions, respectively, by means of the ERDAS imagine image processing software (ERDAS 1998). The geo-coded scene was then masked by the boundary of the watershed derived earlier for delineating the areas lying within the watershed. Land cover was then generated using the supervised classification scheme (Sabins 1997) using TM data. In the Upper Nam Wa Watershed, five types of land cover, namely, evergreen forest, mixed deciduous forest, scrub, paddy field and shifting cultivation, were identified and mapped. Land cover (Fig. 6) information was thus available for each cell of the watershed. The base map used had relatively less detail as it was of small scale; the situation could be that some or majority of the details on land use and cropping pattern were missing and consequently treated as the same land use types. Cropping pattern and cultivation practices were not fully considered as the value for CP factor was inferred from the previous research; however, as the output of USLE is expressed on an annual basis, the information would not have been possible to consider. P factor can differ according to the farming practices and the level of conservation practice adopted particularly in the agricultural land. By interviewing the farmers during the field visit, it was found that soil conservation measures are not adopted in the area. Therefore, based on the index value used by Funnpheng et al. (1991), the CP value was adjusted for the study area (Table 2). K factor, termed as ‘soil erodibility’, is the integrated effect of processes that regulate rainfall acceptance and the resistance of the soil to particle detachment and subsequent transport. These processes are influenced by soil properties, such as particle size distribution, structural stability, organic matter content and nature of clay minerals, of which soil texture is an important factor that influences erodibility. Soil information was not available on the soil map of Land Development Department of Thailand. In this study, soil erodibility was estimated by using the K value triangle (Fig. 5) based on the soil texture as suggested by Monchareonm 1982.
Required parameters for computing the K factor were carried out by collecting soil samples. For this study, geology and current land use maps were taken as the basis for selecting the soil sample. Twenty-two locations within the watershed with equal intervals were selected. These twenty-two locations represented one from each rock and land use types. On the basis of the geological map of Royal Thai Survey Department, there are four types of rocks. (1) JPW = White to pinkish, cross-bedded, massive sandstone, and interrelated reddish-brown and grey silt stone. The grey silt stone is found in the northwest corner of the watershed and covers about 3% of the watershed and all the land is under forest cover; one sample was collected from this zone. (2) JSK = Reddish-brown or Grey micaceous sandstone; grey or brown silt stone, conglomerate is found in southwest part of the watershed and covers about 19% of the watershed. About 70% of the area is under forest cover and the remaining area is under shifting cultivation. Five soil samples from this zone, two from shifting cultivation and three from forest area were collected. (3) J = Argillaceous limestone, calcareous shale, and silt stone; this type of rock is found in the center of the watershed stretching from north to south in two homogenous blocks covering about 30% of the area and mostly forested. Most of the shifting cultivation and little paddy fields exist in these zones; three soil samples from shifting cultivation, three from forested area, and one from the paddy field were collected. (4) P2 = Greenish grey, grey and dark grey to black shale; brownish-yellow, greenish-grey and grey sandstone; these rock types contain about 48% of the area. Forest, shifting cultivation and paddy fields are the three major land types in this zone; three from each land use type totaling nine soil samples were collected from this zone.
Table 2 Land covers statistics and adjusted CP factors for the watershed studied Land cover Evergreen forest Mixed deciduous forest Scrub Paddy field Shifting cultivation
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Area (ha)
%
CP factor
6,787.5
10.50
0.003
34,912.9
54.02
0.004
5,105.0
22.74
0.020
14,694.0
7.90
0.001
3,129.5
4.84
0.350
Fig. 5 Triangle used for the estimation of K value
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Fig. 6 Spatial distribution of the CP factor in the study area
Geographical locations of each soil sampled were recorded by means of global positioning system (GPS). Altogether 22 samples were taken from the study area representing at least one from each geology and land use type. In terms of the area of the watershed studied, the sample size is quite small but they are assumed to be representative because of the homogenous patterns of rocks and land use types and financial constraints did not permit more soil sample collection. The grain size analysis of these soil samples was done by performing the hydrometer analysis at the Soil Science Laboratory of the Soil Science Department of Kasetsart University of Thailand. Soils were classified into five categories: clay loam, sandy clay loam, sandy loam, loam and silt loam. Thus, the information on soil type in individual grids of the watershed was known. The K value for the mapped soil categories were then estimated for each of the cells using K value triangle suggested by Monchareonm (1982). The estimated K values for the mapped soil units of the study area are listed in Table 3. Computed K factor for each soil sample unit was encoded into the GIS
Table 3 Soil types and estimated soil erodibility (K) factor for the watershed studied Soil type
Area (ha)
K factor
Clay loam
1,157.7
0.232
Sandy clay loam
5,134.9
0.220
Sandy loam
33,706.5
0.256
Loam
20,833.8
0.310
3,796.0
0.450
Silt loam
coverage by means of inverse distance weighted interpolation taking the watershed boundary as the outer extent. Spatial distribution of K factor is represented in the Fig. 7.
Results and discussion In practice, USLE provides two ways of estimating erosion rates. The fundamental difference is the factors considered for the computation of soil erosion. The first termed as
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Fig. 7 Spatial distribution of the K factor in the study area
‘potential erosion’ is computed based only on four factors, namely, R, K, L and S, whereas the second one termed as ‘actual erosion’ is computed with C and P factors taken into account along with the first four used for potential erosion. The first type implies that even in natural conditions, erosion occurs, as the factors considered are difficult to change because of human interference. But in reality, a majority of the area is heavily subjected to human interference regardless of the form of land use. In such cases, the C factor ‘vegetation cover’ plays an important role in the actual amount of soil loss or the rate of erosion. Similarly, the types of conservation measures (mechanical or biological) further determine the extent of actual erosion. Hence, the latter type of erosion gives a better real-world picture of erosion rates when all the factors R, K, L, S, C, and P are considered. The rate of potential soil erosion ranged as low as 0 to a maximum of more than 800 tons ha-1 year-1. Analysis showed that majority of the area has potential soil erosion rate of more than 800 tons ha-1 year-1, followed by areas with 0–50 tons, 400–800 tons, and 50–400 tons. Considering the C and P factors, the estimated actual erosion rates ranged from 0 to 619.29 tons ha-1 year-1. Maximum proportion (46%) of the total area of the watershed have nil to very extremely slight erosion severity with less than 3 tons/ha soil loss annually. At the same
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time, about 19.5% area of the watershed experience more than 9 tons ha-1 year-1 which shows the critical condition of the watershed requiring urgent, need of sustainable land management. Various researchers have categorized erosion severity with different soil erosion rates depending on the erosion range in a specific locality. In this case, areas with more than 100 tons ha-1 of soil erosion annually were classified as extremely severe erosion severity and such area accounted for 4.6% of the total area. Severe classes collectively comprise about 13.7% of the total area (Table 4). The erosion rates were regrouped into six classes and their spatial distributions in the different spatial gradients of the watershed are presented in Fig. 8. After knowing the soil erosion rating classes, it is very important to know the kind of land uses that are most prone to soil erosion. Once the land use types and associated soil erosion severity are known, such information becomes extremely valuable as these can be used to formulate a plan focusing conservation measures in those areas. Thus, not only the on-site effect but also the downstream effect of the sediment transport can be minimized. In this study, an absolute majority of the total soil loss can be attributed to the shifting cultivation along the steep slope, which constitutes about 70% of the total soil loss. An average rate of soil loss found for different land use types are presented in Fig. 8. The average soil loss from shifting
Environ Geol (2009) 57:695–705 Table 4 Soil erosion classes and rating for the watershed studied
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Area (ha)
Erosion severity
%
Class
Soil loss rating (ton ha-1 year-1)
1
0–1
6,915.3
Nil to very extremely slight
10.7
2
1–3
22,814.0
Extremely slight
35.3
3
3–6
16,092.5
Very slight
24.9
4
6–9
6,204.4
Slight
9.6
5
9–12
3,748.5
Moderate
5.8
6
12–25
1,486.5
Severe
2.3
7
25–50
4,007.0
Moderate severe
6.2
8
50–100
Very severe
0.6
Extremely severe
3.3
9
100–400
10
[400
Total
387.8 2,132.7 840.2 64,628.9
Very extremely severe
1.3 100.0
Fig. 8 Spatial distribution of estimated soil loss in the study area
cultivation area is 307.25 tons ha-1 year-1, 0.67 tons ha-1 year-1 from paddy rice, 3.91 tons ha-1 year-1 from mixed deciduous forest and 3.16 tons ha-1 year-1 from evergreen forest (Table 5). Onchan (1993) cited 0.02– 0.2 tons ha-1 year-1 soil loss from the forested land and
10–100 from the cultivated land; and Suddhapreda et al. (1988) reported 4.5–132 tons ha-1 year-1 of soil loss for the field crops and 2–8 tons for the forestland use in their studies on Phetchabun and Uthai Thani Provinces Thailand.
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Table 5 Average rate of soil loss for different land use types of the watershed studied Land use types
Total area (ha)
Average rate of soil loss (ton ha-1 year-1)
%
Total soil loss (ton year-1)
Contribution to total loss%
Paddy
5,105.0
7.90
0.67
3,409.9
0.25
Shifting
3,129.5
4.84
307.25
961,558.7
69.94
14,694.0
22.74
17.14
251,889.0
18.32
Scrub Mixed deciduous Forest Evergreen forest Total
3,4912.9
54.02
3.91
136,525.9
9.93
6,787.5
10.50
3.16
21,452.4
1.56
6,4628.9
100.00
21.27
1,374,835.9
100.00
The estimated actual soil erosion was classified into different erosion severity classes (Table 4; Fig. 8). In the reclassified erosion map (Fig. 8) and result presented in Table 4 shows that the areas under the shifting cultivation at the time of the study were producing large soil loss amounts which can be identified as more susceptible to soil erosion for the Upper Nam Wa Watershed. It should be emphasized that the areas producing more erosion would need special priority for the implementation of soil erosion control measures.
Conclusions Soil erosion is still a serious problem in the mountainous regions of Southeast Asia, and attempting different methods to evaluate soil loss at the watershed scale is necessary for sustainable land use and comprehensive regional development. USLE is often used to estimate average annual soil loss from an area. USLE model in GIS environment is a relatively simple soil erosion assessment method. To adopt the USLE, large sets of data starting from rainfall, soil, slope, crop, and land management are needed in detail. In developing countries all the necessary data are often not available or require ample time, money, and effort to prepare such data sets. This paper attempts to evaluate soil losses and map the area susceptible to the soil erosion in Upper Nam Wa Watershed by means of satellite images and GIS tools, despite the lack of direct observation data. Indirect ways were used to collate the required data of the watershed; this has been discussed in the methodology section. Estimated erosion rates ranged from 0 to 619.29 tons ha-1 year-1. Maximum proportion (46%) of the total area of the watershed have nil to very extremely slight erosion severity with less than 3 tons/ha soil loss annually. At the same time, about 19.5% area of the watershed experience more than 9 tons ha-1 year-1, which shows the critical condition of the watershed requiring urgent need of sustainable land management.
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An assessment of land use prone to soil erosion shows that an absolute majority of the total soil loss can be attributed to the shifting cultivation along the steep slope, which constitutes about 70% of the total soil loss. The average soil loss from shifting cultivation area is 307.25 tons ha-1 year-1, 0.67 tons ha-1 year-1 from paddy rice, 3.91 tons ha-1 year-1 from mixed deciduous forest, and 3.16 tons ha-1 year-1 from evergreen forest. Through the reclassified erosion map, areas under shifting cultivation have been identified as more susceptible to soil erosion for the Upper Nam Wa Watershed. It should be emphasized that the areas producing more erosion would need special priority for the implementation of soil erosion control measures. The predicted amount of soil loss and its spatial distribution can provide a basis for comprehensive management and sustainable land use for the watershed studied. The ways of evaluating soil losses even with the lack of direct observation data presented in this paper could be useful for the land use decision makers in other part of the world. Acknowledgements I would like to thank Dr. Apisit Eiumnoh and Dr. Rajendra P. Shrestha for the helpful discussions, encouragements and their valuable criticism and constructive comments on the draft paper. The author is grateful to anonymous reviewers whose valuable comments and suggestions helped to consolidate and strengthens this article.
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