2075

Advances in River Sediment Research – Fukuoka et al. (eds)© 2013 Taylor & Francis Group, London, ISBN 978-1-138-00062-9

The impacts of climate and land use change on soil erosion risk in the Mae Nam Nan Catchment, Thailand

P. PlangoenDepartment of Civil Engineering, Faculty of Engineering, Siam University, Bangkok, ThailandWater Engineering and Management Program, Asian Institute of Technology, Thailand

M.S. Babel, R.S. Clemente & S. ShresthaWater Engineering and Management Program, School of Engineering and Technology, Asian Institute of Technology, Thailand

ABSTRACT: This study predicts the impacts of climate and land use change on soil erosion in the Mae Nam Nan sub-catchment in Thailand. CA Markov model and HadCM3 were used to characterize future land use and climate changes. Soil Loss Modeling in IDRISI software was employed to estimate potential soil erosion risk and total soil loss generated at a 10 year interval. Results indicate that the percent of the study area in very severe class (erosion rate > 100 Mg/ha/yr) decreased under A2 scenario. While mean soil loss rate in very severe class and the amount of soil loss increased moderately under A2 scenario compared to 2010. The increase in the amount of soil loss was 2.33, 6.05, and 18.19% for A2 scenario from the year 2020, 2030, and 2040, respectively. The high amount of soil loss in 2020, 2030 and 2040 can be attributed to high rainfall erosivity under future climate. Moreover, land use changes have significant effect on soil erosion in the future periods.

(Wischmeier and Smith 1978) and the Revised Universal Soil Loss Equation (RUSLE) (Renard et al. 1997) Both are a set of mathematical equa-tions that estimate average annual soil loss from interrill and rill erosion. Rainfall is the driving forces affecting the energy balance of the soil erosion processes (Sukhanovski et al. 2001) The erosivity of rainfall and its consecutive overland runoff is recognised as a crucial factor for erosion processes (Mannaerts & Gabriels 2000). The rain-fall erosivity is described as the average annual sum of EI30 determined from a rainfall record. Global warming and climate change have impact on soil erosion through many ways, including precipita-tion and rainfall erosivity changes. The erosive power of rainfall is directly impacted on soil loss. Current General Circulation Models (GCMs) (Zhang 2007) cannot furnish detailed precipitation information that enables the computation of the rainfall erosivity directly as a function of rainfall kinetic energy and rainfall intensity. Hence, the Precipitation and Rainfall erosivity (P-R) relation-ships was established on the basis of the GCM output (Renard & Fremund 1994).

A second driver of potentially increased soil ero-sion rates is change in land use. The Land Use Land Cover (LULC) is defined as the observed physical layer including natural and planted vegetation and

1 INTRODUCTION

Soil erosion is considered as one of the major land degradation processes in the Upper Nan Water-shed, Thailand which is also the main source of environmental deterioration. It creates negative impacts on agricultural production, infrastructure and water quality. Soil erosion is also the natural phenomenon such as geological process, climate change which is caused by natural force.

One potential cause of increased soil ero-sion could be increased temperatures, altered precipitation patterns (strength, timing and alti-tude), changes in snow cover and seasonal snow melting caused by climate change (Schertz et al. 1989; Fuhrer et al. 2006). These may have led to increased river runoff, Climate change is expected to affect soil erosion based on a variety of factors (Zhang & Nearing 2005) including precipitation amount and intensities impacts on soil moisture and plant growth, and direct fertilization effects on plants due to greater CO2 concentrations among others. The most direct impact results from the change in the power of rainfall (Nearing 2001). The contribution of the erosive agent water is rep-resented by the rainfall erosivity (R-factor). This factor is may be the most important and dominant factor in the Universal Soil Loss Equation (USLE)

2076

human constructions. The cover and management factor (C factor) in USLE and RUSLE reflection the effect of vegetation on soil erosion by water is one of the important parameters for estimation soil erosion. The C factor reflects the effect of crop-ping and management practices on the soil ero-sion rate and indicates how conservation plans will affect the average annual soil loss (Vander Knijff et al. 2000). Remote Sensing (RS) and Geographic Information System (GIS) are now providing new tools for advanced ecosystem management such to analyze land use change and soil loss. CA_Markov model is a more suitable method than others for the simulation of vegetation cover changes. CA_Markov is a combined Cellular Automata/Markov Chain/Multi-Criteria/Multi-Objective Land Allo-cation (MOLA) land cover prediction procedure that adds an element of spatial contiguity as well as knowledge of the likely spatial distribution of transitions to Markov chain analysis.

The objective of this study is to estimate soil erosion risk under future land use and climate change in the Mae Nam Nan sub-catchment in Thailand.

2 SITE DESCRIPTION

2.1 Study area

The study area, the Mae Nam Nan sub-catchment is located in the northern Thailand (Fig. 1), and the area is about 1532 km2. The topography of study area ranges from flat terrain to mountains, with an elevation of 120 to 1900 meters above mean sea level, and the percentage of covered area is differ-ent slope categories as follow: horizontal (0–2% slope) cover 19.51%; very flat (2–5% slope) cover 24.03%; flat slope (5–10% slope) cover 22.04%; moderate (10–25% slope) cover 25.99% and steep slope (>25% slope) cover 8.43% of area. The land use of the area consists mainly of degraded forest and upland agriculture i.e. upland rice, maize, and vegetables grown in both shifting and permanent cultivation patterns (LDD 2006).

2.2 Climate

The general climate of the study area is tropical monsoon and characterized by winter, summer and rainy season, influenced by the northeast and southwest monsoons. The rainy season brought about by the southwest monsoon originating from the Indian Ocean lasts from mid May until the end of October. July and August are usually the months of intense rainfall. The winter season, during the weather is cold and dry due to north-east monsoons, begins in November and ends in February. From mid February until mid May, the

weather is rather warm. The annual rainfall is about 1,263 mm. More than 80 percent of the rainfall is concentrated in the wet season. Figure 2 shows the mean maximum, minimum, monthly rainfall about 250 mm in August and 6 mm in December and mean monthly temperature during 1961 to 2010.

3 METHODOLOGY

To achieve the objective of the research, the meth-ods consist of three parts, the first part analysis climate change impact on rainfall erosivity, the second part is predicting Land Use Land Cover (LULC) in future to use segmentation classifica-tion, CA_Markov model and the final part use soil erosion modules from IDRISI Taiga software from Clarks Labs (Estmen 2009) to analysis soil erosion risk under climate and land use change in the catchment.

3.1 Rainfall erosivity

Rainfall erosivity (R factor) describes the soil loss potential caused by rainfall. It was calculated from two rainfall characteristics: total kinetic energy of

Figure 1. Location of study area.

Figure 2. Mean monthly rainfall and monthly average temperature between 1961 and 2010.

2077

the storm times its 30 min intensity (EI30). This product reflects the combined potential of rainfall impact and turbulence of runoff to transport dis-lodged soil particles from the field (Wischeimer & Smith, 1978). A direct computation of rainfall ero-sivity requires long-term data for both rainfall amounts and intensities (Renard et al. 1997). However, rainfall intensity is not provided by cur-rent GCMs. Therefore, relationships between rain-fall erosivity and monthly or annual precipitation were developed and used to analyze the poten-tial effects of climate change on rainfall erosivity (Renard & Freimund 1994; Sauerborn et al. 1999; Nearing 2001). In order to analyze the spatial distribution of rainfall erosivity, Plangoen et al. (2012) developed a simple equation for monthly R value and monthly MFI value (Arnoldus 1977) in the Upper Nan Watershed based on seven rain-fall stations, which have long-term daily precipita-tion data. The R values were calculated using the method described by Renard (1994):

R = 85MFI0.70, (1)

and MFI = 0.0009Pm1.96 (2)

where R is rainfall erosivity (MJ mm ha-1 year-1), MFI is modified Fournier index (mm) and Pm is mean monthly precipitation (mm).

3.2 Climate modeling

Estimation of future climate change provided by Global Circulation Models (GCMs) cannot pro-vide the type of detailed rainfall intensity needed to directly compute predicted rainfall erosivity changes. Hence, Statistical relationships between monthly rainfall and the rainfall erosivity must to be used to examine the GCMs output relative to rainfall ersivity changes (Ferro et al. 1999). For the study, we used used HadCM3 to generate the future precipitation scenarios. HadCM3 is a cou-pled atmospheric-ocean GCM developed at the Hadley Centre of the United Kingdom National Meteorological Service that studies climate vari-ability and change. The model includes different land cover classification, soil layers and detail evapotranspiration function (Palmer et al. 2004). The atmospheric component of the model has 19 levels with a horizontal resolution of 2.5° latitude and 3.75° longitude. The ocean component of the model has 20 levels with horizontal resolution 1.25° latitude and 1.25° longitude.

3.3 Statistical Downscaling Model (SDSM)

SDSM developed by Wilby et al. (2002) is hybrid of Multiple Linear Regression (MLR) and Stochastic Weather Generator (SWG). MLR establishes a

statistical/empirical relationship between NCEP, large-scale variables and local scale variables, pro-ducing some regression parameters. These cali-brated parameters, (along with NCEP and GCM predictors) are then used by SWG to simulate up to 100 daily time series in order to create a better correlation with the observed time series (Wilby et al. 2002). The model is calibrated and applied at a daily time series even though the output is at monthly basis. The software manages additional tasks of data quality control and transformation, predicator variable screening, automatic model calibration, statistical analysis and graphing of cli-mate data.

For the this study used SDSM to downscale catchment-scale precipitation from HadCM3. It was selected on the basis of the availability of daily predictors from GCM runs and used NCEP reanalysis data to identify the best variables to predict daily rainfall and to calibrate the models for 7 stations in the Upper Nan Watershed with data from the 15-year period. 1971–1985 period. Twenty separate runs were made for GCM thus providing some element of climate variability for the 1986–2000 baseline period. The empirical pre-dictor predictand relationships for the observed climate were used to downscale ensembles of pre-cipitation for the future climate, using data sup-plied by the HadCM3 driven by both A2 emissions scenario for the full period 1961–2100.

3.4 Downloading the predictors

General Circulation Model (GCM) predictors are freely obtained from the Canadian Climate Impact Scenario Group from web site of: http://www.cics.uvic.ca/scenarios/sdsm/select.cgi.

The predictor variables of HadCM3 are avail-able on a grid box by grid box basis of size 2.5° latitude and 3.75° longitude. The Upper Nan Watershed is locate between between 18° 00′ 45″ and 19° 37′ 53″ latitude north and between 100° 06′ 34″ and 101° 06′ 29″ longitude east. Therefore, the nearest grid box which represents the study is to download the HadCM3 data is 18.5 ºN and 100.5 ºE. The NCEP_1961-2001 data was down-loaded from the specified grid box which repre-sents the Upper Nan Watershed. This data will be used for calibration of the SDSM with the precipitation.

3.5 Model calibration

The model calibration process constructs down-scaling models based on multiple regression equations, give daily weather data (predictand) and regional scale, atmospheric (predictor) vari-ables. The model structure for calibration can be

2078

specified by selection process either unconditional or conditional. The model structure is set to conditional for precipitation. The model type determines whether individual models were cali-brated for each calendar months, climatological season or entire year. The model is structured as a monthly model for precipitation downscaling, in which case, twelve regression equations were derived for twelve months using different regress-ing parameters for each month equation. Finally the data period was set in order to specify the start and end date of the analysis. The calibration was done for a 15 year of period from 1971 to 1985.

3.6 Predicting future land use

This study applied the Geographic Information System (GIS), Remote Sensing (RS) to classify Land Use and Land Cover (LULC) patterns and changes. CA_Markov model (Estmen 2009) was used to perform land use change analysis between 1990 and 2000, 2000 and 2010 land uses. CA_Markov is a combined Cellular Automata/Markov Chain/Multi-Criteria/Multi-Objective Land Allo-cation (MOLA) land cover prediction procedure that adds an element of spatial contiguity as well as knowledge of the likely spatial distribution of transitions to Markov chain analysis. Therefore, the predicting land use/land cover in 2010 (base on land cover data in 1990 and 2000) and 2020, 2030 and 2040 in Figure 3 (base on land cover data in 2000 and 2010).

Land sat 5 TM images in January of the years 1990, 2000 and 2010 were obtained from USGS Global Visualization Viewer. Land sat TM images are available in six reflective bands of 30 m spatial resolution. The module segmentation (Estmen 2009) was used to create the segment images, and the resulting segments are vector files which can be overlaid on the image file for manual classification assignment. All the six bands from a raster group file were given equal weights as they all have the same resolution and parameters. The parameters for three images were window width 3 since they yield optimal results, weight mean factor of 0.5, and weight variance factor of 0.5. The similarity tolerance of all the images was set to 10, 30, 50 for the purpose of classification.

3.7 Predicting future soil erosion risk

The Revised Universal Soil Loss Equation (RUSLE) was used in the soil modeling compo-nent of this study. This study adopts an existing modeling technique, Soil Loss Modeling with RUSLE, embedded in Idrisi Taiga software. The RUSLE equation model is as follows:

A = R * K * L * S * C * P (3)

where: A is the computed soil loss per unit area, (tons/hectare/year), R is the rainfall erosivity fac-tor, K is the soil erodibility factor, is the soil loss rate per erosion index unit for a specified soil as measured on a unit plot, L is the slope-length factor, S is the slope-steepness factor, C is the cover and management factor and P is the support prac-tice factor.

3.7.1 Soil erodibility factor (K-factor)For this research, the digital soil map at 1: 25,000 scale for 2004 originally will obtain from Land Development Department (LDD 2004) is used as base data. K values are based on soil series texture and geological formations pub-lished by LDD at 1:50,000 are used for the study. For slope complex, like forests and high moun-tainous areas k-values set for different soil groups (series) and geological formations are identified and used. For water body, rock land, man-made structures and urban human settlement poly-gons like schools and villages, a value of “0” is assigned as erosion is assumed to be null on these kinds of surfaces.

3.7.2 Slope factor mapThe DEM was imported from ArcGIS to IDRISI software used for topographical aspects (LS factor). The slope map was generated from the DEM using surface analysis tool.Figure 3. Concept framework for future land use.

2079

3.7.3 Crop management and support practice factor

In the study, the land use/land cover map is derived from the satellite images and served as a guiding tool in the allocation of C and P factors for dif-ferent land use classes. C and P in future used the result of CA_Markov model. The C and P factor values set by LDD (2000) for the various vegeta-tion cover types as show in Table 1.

4 RESULTS AND DISCUSSION

4.1 Downscaling the GCM for the baseline period and future scenario

The HadCM3 was downscaled for the baseline period for an emission scenario (A2) and the downscaled data was compared with observed data. The IPCC recommends 1961–1990 as cli-matological baseline period in impact assessment. Thus this period was also used as baseline period for this study. The climate scenario for future period was developed from statistical downscal-ing using the GCM predictor variables based on the HadCM3 A2 and B2 scenarios outputs for 100 years based on the mean of 20 ensembles and the analysis was done based on three 30-year periods centred on the 2020s (2011–2040), 2050s (2041–2070) and 2080s (2071–2099). The regional changes in mean monthly precipitation are shown in the Figure 4. Compared to baseline period, it is clear that the 30-year mean monthly precipita-tion generated by the GCM was greater than the observed 30-year mean monthly precipitation for 1961 to 1990 for the HadCM3 A2 scenario.

4.2 Rainfall erosivity maps under future climate

Based on the historical precipitation data and precipitation output from GCM output, the Modified Fournier Index (MFI) values then was calculated. The relationship between MFI and the

R factor can be used to estimate the annual rainfall erosivity (R factor) values (Pheerawat & Clemente 2012). Therefore, the purpose of this process was to develop mathematical relationship model of MFI and rainfall erosivity (R) values under HadCM3 A2 scenario.

Figure 5 detailed spatial distribution of rainfall erosivity under the A2 scenario in the study area, as is estimated using the annual R factor values. This map was created by using the Inverse Distance Weighted (IDW) interpolation method.

4.3 Predited future land use

4.3.1 Image classification and accuracy assessment

The results of the image classification were obtained using a Segmentation Technique and Maximum Likelihood Classifier (MLC) algorithm

Table 1. Vegetative cover (C factor) and field support practice (P factor).

Land cover class C value P value

Dry evergreen forest 0.019 0.1Mixed deciduous forest 0.048 1.0Forest plantations 0.088 1.0Orchards 0.15 1.0Paddy 0.28 0.1Grassland 0.015 1.0Agricultural (field crops) 0.6 1.0Urban area 0 0Water bodies 0 0 Figure 4. Mean monthly precipitation (mm) downscaled

under A2 scenario.

Figure 5. Potential change in rainfall erosivity under A2 scenario.

2080

for classification. The accuracy of the classified images was assessed with error matrices, percent-age of producer’s and user’s accuracies, and Kappa statistics. The satellite imageries of Landsat 5 (TM) were used to identify land use pattern of study area using Segmentation Classification, IDRISI Taiga software from Clarks Labs. The result of accu-racy of land cover classification will be discussed based on Error/Confusion matrix. Overall accura-cies for the maps of 1990, 2000 and 2010 (Fig. 6) were 85.76%, 81.18% and 82.57% respectively with an overall kappa statistic of 0.83, 0.77, and 0.79, respectively. It is noted that a minimum accuracy value of 80% is required for effective and reliable land cover change analysis and modeling.

The LULC data in 1990 and 2000 were manipu-lated by CA_Markov model in order to identify the probability of changing and transition area. Such probability transition values were applied to predict the LULC for the years 2010 with cellular automata model. The result was the LULC map for the 2010 as shows in the Figure 7. The accu-racy of the simulated LULC maps in the year 2010 was analysed by comparing LULC map from the CA_Markov model with the LULC map in the year 2010 derived from the segmentation classifi-cation technique. The overall accuracy was 67.61% and overall Kappa was 0.55.

4.3.2 The predicted LULC in 2020, 2030 and 2040After defining the parameters used for the calibration and modeling and assessing the

validity, it was interesting to examine the pattern and tendency of change in a long-term simulation. Therefore, LULC for the year 2020, 2030 and 2040 (Fig. 8) were performed in the same way of the Mae Nam Nan catchment. Land use change analysis between 2020 and 2040 illustrates a similar trajectory exemplified by 2030 with the exception of slight difference between spatial gains and losses among land use classes (Table 2).

4.3.3 C factor and P factorThe cover management factor C and conservation practices (P) factor reflect the effect of cropping and management practices on the soil erosion rate (Renard 1997). The crop and management factor

Figure 6. LULC classification maps of the Mae Nam Nan sub-catchment.

Figure 7. Simulated and actual land use/land cover maps for the year 2010.

Figure 8. Simulated LULC maps for the year 2020, 2030 and 2040.

2081

(C-factor) and conservation practices (P) factor corresponding to each land cover condition were estimated from RUSLE guide tables. Table 1 lists the C-factor and P-factor value for the land use categories. These values were used to re-classify the land cover map to obtain the C-factor and P-factor map of the study area.

4.4 Soil loss risk under future climate and land use

The RUSLE module of the IDRISI Taiga was used to calculate the annual average soil loss rate

in (ton/ha/year). In order to predict the annual average soil loss rate in the Mae Nam Nan catch-ment, the DEM, a rainfall erosivity factor (R) image, a soil factor (K) image, a land cover factor (C) image, and a management factor (P) image as show in the Figure 9.

The soil erosion rates for the Mae Nam Nan sub-catchment was determined and compared. Calculation of soil erosion rates was undertaken using the RUSLE module in DIRIS Taiga to gen-erate a composite map of erosion risk under cli-mate and land use change. The erosion risk for 4 scenarios under land use and climate change. The soil erosion risk class (Fig. 10) was based on the erosion classification coding by Morgan (2005) to divided into 6 classes as very slight (<2 ton/ha/yr), slight (2–5 ton/ha/yr), moderate (5–10 ton/ha/yr), high (10–50 ton/ha/yr), severe (50–100 ton/ha/yr) and very server (100 ton/ha/yr>)

The percentage of the erosion risk cate-gory under A2 scenario is shown in Figure 11.

Table 2. Land use area (km2) in 2010 and 2020, 2030 and 2040.

Land use

Area (km2)

2010 2020 2030 2040

Paddy field 119 124 129 132Upland crop 439 458 424 403Orchard 168 143 132 130Forest 724 713 739 751Urban 68 78 89 95Water body 14 16 19 21Total 1532 1532 1532 1532

Figure 9. The RUSLE module to produce the average annual soil loss map in 2020 for A2 scenario.

Figure 10. The spatial distribution of erosion potential risk under A2 scenario.

Figure 11. Percentage of area erosion risk classes between 2010 and 2040 under A2 scenario.

2082

The percentage of area under very severe erosion range between 28–37%, and the moderate erosion risk varies from 16–21%, and severe erosion range between 2–4%. The percentage of area under very severe class (erosion rate > 100 Mg/ha/yr) increases under A2 scenarios. The increase in the amount of soil loss was 2.33%, 6.05%, and 18.19% in 2020, 2030 and 2040 respectively for A2 scenario (Fig. 12),

The expected increase in rainfall erosivity and land use/land cover change may have significant effects on amount of soil loss in the Mae Nam Nan sub-catchment in future. Thus, the increase in the amount of soil loss presumably will make more soil conservation efforts necessary in the region. The relative effect of Land Use/Land Cover (LULC) change on soil erosion is reflected in the cover management factor (C factor) represents effects of LULC. The C factor indicates how conservation plans will affect the amount of soil loss in the Mae Nam Nan sub-catchment and how that soil loss will be distributed in time during crop rotations. LULC protects the soil by dissipating the raindrop energy before reaching the soil surface. Thus, soil erosion can be effectively reduced by LULC.

5 CONCLUSION

The soil erosion risks and amount of soil loss in the Mae Nam Nan sub-catchment were determined and compared based on 4 periods unde A2 scenario. The values of erosion potential were divided into 6 classes as very slight, slight, moderate, high, severe and very severe. A relationship between soil loss and land use and climate change were extended to obtain an esti-mate of changes in rainfall erosivity (R factor) and cover management factor (C factor) due to change in soil loss. The results imply that soil erosion in the Mae Nam Nan sub-catchment may be expected to be more serious in the future because of the increase

in future rainfall erosivity. Based on the RUSLE estimates, a 1% increase in rainfall erosivity will cause a 1% increase in soil loss assuming other fac-tors related to crops, management, and conservation practices remain the same. This is consistent with the relative studies on soil erosion which suggest that increased rainfall amounts and intensities will lead to greater rates of erosion. The expected increase in precipitation may have significant effects on soil loss. However, the quantity and resolution of results of this study should be improved by subsequent inves-tigations or analysis especially on the separate effects of LULC and climate change on erosion.

ACKNOWLEDGEMENT

The authors would like to thank the President of Siam University for financial assistance. We are also thankful to the Landsat TM data from US Geological Survey (USGS), SDSM software pro-vider and Canadian institute for climate studies to provide NCEP, and A2 scenarios of HadCM3.

REFERENCES

Arnoldus H.M.J. Methodology used to determine the max mum potential average annual soil loss due to sheet and rill erosion in Morocco. FAO Soil Bull. 1977, 34, 39–51.

Brown L.C. & Foster G.R. Strom erosivity using idealized intensity distributions. Trans. Am. Soc. Agric. Engrs. 1987, 30(2), 372–386.

Eastman J.R. IDRISI Taiga Guide to GIS and Image Processing, Clark University. Worcester, MA 2009.

Ferro V., Porto P. & Yu B. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia, Hydrological Sciences Journal. 1999, 44, 3–24.

Foster G.R., Mc Cool D.K., Renard K.G. & Moldenhauer W.C. Conversion of the universal soil loss equation to SI metric units. J Soil Water Consev. 1991, 36, 355–359.

Fuhrer J., Beniston M., Fischlin A., Frei C., Goyette S., Jasper K. & Pfister C. Climate risks and their impact on agriculture and forests in Switzerland. Climate Change. 2006, 79, 79–1023.

Grazhdani S. & Shumka S. An approach to mapping soil erosion by water with application to Albania. Desalination. 2007, 213, 263–272.

Land Development Department (LDD), Thailand. Land use map of Nan province. Land Use Planning Division, Land Development Department, Bangkok, Thailand. 2006.

Land Development Department. Soil Erosion in Thailand, 2000. Land Development Department, Ministry of Agriculture and cooperatives.

Land Development Department. Soil Erosion in Thailand, 2004. Land Development Department, Ministry of Agriculture and cooperatives.

Figure 12. Total Soil loss (Mg/yr) between 2010 and 2040 under A2 scenario.

2083

Mannaerts C.M. & Gabriels D. 2000. Rainfall erosivity in Cape Verde. Soil Till. Res. 55, 207–212.

Morgan R.P.C. Soil erosion and conservation. Third Edition. 2005, Oxford: Blackwell Publishing.

Nearing A.M. Potential changes in rainfall erosivity in the US with climate change during the 21st century. J. Soil and Water Conserv. 2001, 56(3), 229–232.

Palmer, Richard N., Erin Clancy, Nathan T. Van Rheenen, Mattew W. Wiley. The impacts of climate change on the Tualatine River basin water supply: An investigation into projected hydrologic and management impact. Department of civil and environmental engineering, University of Washington, 2004, 67pp.

Plangoen P. & Clemente R.S. The impacts of climate change on rainfall erosivity in the Upper Nan Water-shed, Thailand, The 10th International Symposium On Southeast Asian Water Environment, November 8–10, 2012 Hilton Hotel, Hanoi, VIETNAM.

Renard K.G. & Fremund J.R. Using monthly precipita-tion data to estimate the R-factor in the revised USLE. Journal of Hydrology. 1994, 157, 287–306.

Renard K.G. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Washington DC: US Department of Agriculture. 1997.

Renard K.G., Foster G.A., Weesies G.A., McCool D.K. & Yoder D.C. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). USDA Agric. Handbook 703. Agricultural Research Service, Washington, DC. 1997, 404p.

Schertz D.L., Moldenhauer W.C., Livingston S.J., Weesies G.A. & Hintz E.A. Effect of past soil erosion on crop productivity in Indiana. J. Soil and Water Conserv. 1989, 44(6), 604–608.

Sukhanovski Y.P., Ollesch G., Khan K.Y. & Meissner R. A new index for rainfall erosivity on a physical basis. J. Plant Nutr. Soil Sci. 2001, 165, 51–57.

Vander Knijff J.M., Jones R.J.A. & Montanarella L. Soil Erosion risk Assessment in Europe, 2000 Available online:http://eusoils.jrc.ec.europa.eu/ESDB_Archive/pesera/pesera_cd/pdf/ereurnew2.pdf (accessed on 25 February 2012).

Wilby R.L., Dawson C.W. & Barrow E.M. SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Model Software. 2002, 17 (2), 145–157.

Wischmeier W.H. & Smith D.D. Rainfall energy and its relationship to soil loss. Trans Am Geophys Union. 1958, 39(2).

Wischmeier W.H. & Smith D.D. Predicting rainfall erosion losses. USDA Agricultural Handbook, vol. 537. Agricultural Research Service, Washington, DC. 1978, 69 p8.

Zhang X.C. & Nearing M.A. Impact of climate change on soil erosion, runoff and wheat productivity in cen-tral Oklahoma. Catena. 2005, 61, 185–195.

Zhang X.C. A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments. Climate Change. 2007, 84, 337–363.