Estimation of soil erosion rates using MPSIAC models (Case Study Gamasiab basin)

Transcription

1 International Journal of Agriculture and Crop Sciences. Available online at IJACS/2012/4-16/ ISSN X 2012 IJACS Journal Estimation of soil rates using MPSIAC models (Case Study Gamasiab basin) Maryam Ilanloo Department of Geography, Mahshahar Branch, Islamic Azad University, Mahshahar, Iran Corresponding Author ABSTRACT: The research studies in Iran have shown that the MPSIAC model for estimating and sediment yield in watershed level is one of the most common empirical models which have been widespread used in comparison to the other models on ungagged watersheds of Iran. A reason for use of this model is only because of the accuracy of the obtained result. In this study, and sediment yield rates was estimated by MPSIAC model using geographic information system (GIS) in the Gamasiab basin. In order to run the model, the watershed area was divided to 123 homogenous units and the nine factors used in model, were assist in each unit. Keywords: Erosion, Soil, Gis, Psiac, Gamasiab basin INTRODUCTION Soil is one of the natural processes that would cause many constrains to the environmental and regional planners. Erosion on farm fields reduces potential crop production, and sediment which leaves the field can result in subsequent sedimentation problems which, in turn, can cause off-site environmental problems. An example is the redistribution of fallout plutonium. Erosion on other upland areas--such as construction sites urban areas, mine developments, or other disturbed areas can also cause on-site and off site problems Channel or deposition processes can cause further problems because the stream channels are components of the watershed system. Although total prevention of this process is not possible at all, but reduction of its volume, extent and speed may be practically possible. Many attempts have been made to use different models to estimate the volume of soils eroded every year. Models such as SEIM, USLE, EPM, PSIAC, MPSIAC and FAO are among the most widely used models. The soil index model (SEIM) was used by Su- Chin Chen 2002 to assess and predict the potential areas of in Taiwan. The SEIM model uses different factors such as rainfall, soil characteristics, slope, and cover above soil surface and land use. PSIAC model with its 9 possible parameters, among other model, is the most suitable model for arid and semi-arid regions. Soil in different parts of Iran, as an arid and semiarid region, has been studied by several researchers. Heydarian 1996 and Tajbakhsh et. Al, 2003 used PASIC and Modified PSIAC (MPSIAC) to estimate yield and intensitymap. In this study, Modified PSIAC (MPSIAC) method which is specially design for arid and semi arid area in the United States was assessed for its applicability to the Iranian watershed environment. The MPSIAC was created in 1982 based on PSIAC which was introduced in 1968 for planning purposes by Pacific Southwest Inter Agency Committee in the United States for watershed basins of larger than 10 square mile Both models used nine factors to describe the surface geology, soil, climate, runoff, topography, ground cover, land use, upland and channel. The difference is that nine equations were used in MPSIAC, whereas in PSIAC method, sediment yield is assumed to be directly proportional to the total numerical values assigned to the nine factors. The study area In this reseach an attempt has been made to study the rate of soil in Gamasiab bisan in Hamedan and Kermanshah province of Iran. The study area is located between the longitude 47 3 to no east and has an area of (Figure 1). Gamasiab is one of the sub-basins of Karkhe River. There is only one gauging (hydrometric) station installed at the Karkhe river, where the data of sediment and discharge were used in the present study. The climate of the area is generally semi-arid with average precipitation of 375 mm per year. The maximum and minimum temperature are 30.7 C, -8.2 C MATERIALS AND METHODS Foll To study the influential factors in the occurrence of mass movements, lithological layers, hillside slope, altitude, distance from river and road, rainfall, snow and frost melting effect and land use are digitized at the environment of geographical information systems. Informative layer of land use by using satellite images

2 Intl J Agri Crop Sci. Vol., 4 (16), , 2012 (TM) of 2002, lithology layers by using geology map at scale 1:250000,slope layers, hillside direction and altitude by using topography map at scale 1:50000,rainfall layer through climatic information of the area s existing stations are prepared. Erosion distribution map was prepared by new and old aerial photos of the region at 1:5000 scales. Figure 1. Geographical location of the Gamasiab Bsian in Iran In this regard topographic maps of 1: scales weree diced and then road networks, rivers and residential areas were extracted and saved as shape files. Followings are the list of data and materials used in this research work: 1. Digital topographic maps of Hamedan and Kermanshah province in the scale of 1: Hardcopy topographic maps of Hamedan and Kermanshah province in the scale of 1: Landsat digital data pertaining to the study area. 4. Rainfall dataa for 5 stations. 5. Hardcopy soil map of the study area in the scale of 1: Hardcopy Geology map of the study area in the scale of 1: The general steps followed in this study are as follows. A) Preliminary studies to define the objectives and methodology B) Preparation of different materials and the selection of software package C) Registration of all the available maps on each other D) Production of digital maps from different data E) Assignment of weights to different factors of MPSIAC model F) Calculation of R (sum of the effective factors) and Q values for each location G) Categorizing different levels 4. MPSIAC Model This model was created to estimate the soil according to nine factors consisting of, geological characteristics, soil, climate, runoff, topography, vegetation cover, land use and present soil (PSIAC, 1968). Johnson and Gembhart (1982) improved the original model to have a more accurate estimate of the sedimentation (equation, 1) ). Qs= 18/6e R (1) Were: Qs= sedimentation (m 3/km 2/year), R= sedimentation rate, e = The above model was applied to estimate the soil sedimentation in the study area in this research. The relationship between soil and sedimentation in MPSIAC model and the specific in MPSIAC model is calculated by SDR Coefficient (Sediment Delivery Ratio) by the following equation (2): Log (SDR) = log (10A) (2) Were: A= the area of watershed (mail 2) A Surface geology (Y1) A 1: scale geological map by Iran Geographic Institute (1970) was used as the base map to interpret the geological sensitivity to. Factors on surface geology (Y1) were evaluated based on stone sensitivity to (X1). The score of each unit of surface geology was determined from the scale between

3 for the most resistant face, to 10 for the most sensitive face to. These scaling factors are based on the local condition of Iran (Feiznia,1995). The scaling factor is then used as attributes to the geological map. Table 1. Sediment evaluation and classification in MPSIAC model.application of soil model by GIS t/ Sedimentation yield m3/ Summation of nine effecting factors (R) Quality class < 100< Very high V High IV medium III Low II 200> 95> 0-25 Very low I Erosion class Soil (Y2) Soil factor (Y2) was determined based on soil erodibility factor (K). Soil sampling from six soil components in the study area were undertaken. Financial contraints limit the sampling to three samples from each soil components. The particle distribution of the soil, soil structure and permeability parameters were analysed to obtain the soil erodibility factor. K factor was determined by using Wischmeir nomograph (Renard et al. 1997). Soil factor (Y2) was calculated based on K in each type of soil (equation 2 in Table 2). The Y2 value was then used as attributes in the 1:50,000 scaled digitised soil map published by Iran Soil Institute. Climate (Y3) In this model, rainfall is considered to be the major contributor to soil and sediment movement. Rainfall was estimated based on 6-hours precipitation amount with 2-year return period. In this study, climate factor (Y3) was based on 20 years ( ) of rainfall record. From the record, the rainfall intensity duration and frequency curve were derived. The climate factor was estimated by equation 3 (Table 2). Table 2. Effective factors on the for the MPSIAC model Effective factors Description Equation Surface X1=Stones sensitivity to geology (0-10) Y1=X1 Equation 1 Soil K=soil erodibility Y2=16.67K Equation 2 Climate I6,2=6-hour rainfall with Y3=0.2X3 Equation 3 2-year return period X3=I6,2 Equation 4 Ro=runoff height Y4=0.2 X4 Equation 5 Runoff PSF=yearly peak stream X4=A + B Equation 6 flow A=R.O X Equation 7 Topography S=slope (%) Y5=0.33S Equation 8 Land cover Final land cover Surface Gully Pc= percentage crop canopy cover SSF=the score of soil surface factors from the BLM method SSFg=the score of gully from the BLMmethod Y7=20-0.2Pc Equation 9 Landcover (100%) = canopy cover (%) +bare ground (%) + stone or rock (%) + surface litter(%) Equation 10 Y8=0.25SSF Equation11 Y9=1.67SSFg Equation 12 Runoff (Y4) Runoff factor (Y4) was obtained based on analysis of discharge data. Total average runoff (mm) and peak discharge (m3/sec.km2) for 20 years period ( ) were calculated as in equation 5 (Table 2). Topography (Y5) Topography factor (Y5) was determined based on average percentage of slope steepness. The average slope steepness was generated from digital elevation model derived from topographic map published by Iran Geographic Institute in 1970 using GIS. The topography factor was obtained by using equation 10 (Table 2). Ground cover (Y6) The main characteristics considered as ground cover are vegetation, litter and rocks. Ground cover factor (Y6) for the watershed from were evaluated based on four sub-periods as followed; based on aerial photo interpretation of based on landuse map of

4 based on on landuse map of based on land use map of The groundcover (Y6) factor was then obtained by using equation 11(Table 2). Land use (Y7) Land use factor (Y7) was estimated based on canopy cover using the equation 12 (Table 3) The land use factor is divided into four sub period as in Y6 because there is no other landuse being practised on the watershed. The equation 13 is used for the study area to describe the landuse in the study area Upland (Y8) Upland (Y8) factor was obtained based on Bureau of Land Management method (Aker, 1971). Two sub-periods were evaluated. For the first ten years ( ), six factors in the soil surface factors (SSF1 to SSF6) were obtained from aerial photo interpretation of It was assumed that there are no significant changes in soil surface factors in this period. The seventh factor, SSF7 was estimated from the relation between precipitation and gully formation. The total score for SSF was derived, and then, based on weighted average areas for every kind of landuse, and the upland factor (Y8) was estimated by using equation 14 (Table 2). For the second ten years ( ), SSF1 to SSF6 was obtained from the Statistical Centre of Iran and SSF7 was calculated by relationship between precipitation and gullies formation and land use map of Channel (Y9) Channel factor (X9) was obtained based on gully factor from the BLM method and by the relationship between yearly rainfall (mm) ( ) and gully improvement (Najafinejad 2003). Each factor for every type of landuse was estimated using equation 15 (Table 2). Predicted sediment yield The nine factors were employed to estimate sediment yield (Qs) according to: =38.77 Equation 16 where: : Sediment yield (m³/km²/y) R, sediment score = The sediment yield unit is ton/km²/y if the sediment density is assumed to be equal to 1.2 g/cm³. RESULTS AND DISCUSSION After the preparation and assignment of relative importance weights of all effective factors in MPSIAC model, based on the following equations and using modeling language of Geomatica software package, the final map was produced (Fig 4). R=X X X X X X6+ [20-0.2X7] X X9 X1 through X9 are geology, soil, climate, runoff, topology, land cover land use surface and gully factors respectively.through this procedure, the final map that shows different level of in this area was produced figure 4 and Table 3. Table 3. Estimation of soil rates using MPSIAC models in Gamasiab basin Production Sedimentary K / / Intensity Estimation of soil rates using PSIAC models Estetion low Moderaiate low Very High High Moderaiate Abnahvand Malaier Khoramrod Toiserkan Dinor Gamasiab Az it is clear from the Fig 4, in this area the process is in its high rate in the north. That is because of the geology and steep slopes and less vegetation cover of that area. In eastern and southeastern 1157

5 part of this area, due to the factors like slope, less dept soils, feeding chattels, cultivation in the slopes and the presence of marl formation, the rate is high. In south due to agricultural activities and fewer slopes the rates are very less. Due to high sediment rates in this part of Iran, watershed management plans should be actively defined and implemented. Results of this study show that remote sensing data in conjunction with image processing software packages could be useful to estimate rate in watershed sub-basin area.mpsiac model has shown to be a useful way of estimation total sediment yield for arid and semi-arid regions and powerful image processing software packages like Geomatica could be efficiently used for such multi-layers modeling problems. In the cost and time point of view, doing such tasks is less time consuming and cost effective in comparison with traditional method of soil studies. The importance of this research work is that instead of using GIS software, all the spatial analyses were carried out using image processing software package. CONCLUSIONS Considering the Table 3 revelant sedimentartion intensits causes in P.S.I.A.C model. In Table 3 each map in the sense at sloeing has been divided in to 5 lanks to canfer with similar ranks in P.S.I.A.C model and after combining these maps, and integrated was resnlted qualified with scores revelant integrated unit. In general, the most prominent influential factors on soil consist of: 1-weak plant coverage in that area 2-the flood resulted from rainstorm and weak plant coverage 3-sharp slop in some mountainous area 4-the lack of existing deep soil as well as the penetrating feature of existent soil 5-the structure of Litology.in that area. A vast part of the area is covered with sediment stones, which is characterized less resistant than other structures is included in the above class and is differentiated based on the type of its Limestone 6-human: the effect of human activities on the land is extremely obvious. It may be demonstrated by using up grass by cattle and collecting to be used as fuel. REFERENCES Daly C, Neilson RP, Phillips DL A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33, Amiri F, Chaichi MR, Tabatabai T Soil and sedimentation modeling by MPSIAC model and GIS application (case study: Ghareh Aghach watershed, Iran). Bagherzadeh M A Study on the Efficiency of Erosion Potential and Sediment Yield Models Using Remote Sensing and Geographic Information Systems. Unpublished MSc thesis, Natural Resources. Clark KB An Estimate of Sediment Yield for Two Small Sub- Catchment in a Geographic Information System. Ph. D. thesis, University of New Mexico. Bissonnais YL, Montier C, Jamagne M, Daroussin J, King D College, University of Tarbiat Modares, Tehran, Iran. Cook DJ, Dickinson WT, Rudra RP GAMES Guelph Model for Evaluating the Effects of Agricultural Management Systems on Erosion and Sedimentation. University of Guelph, School of Engineering, Guelph. 75pp. Eastman RJ.1997.IDRISI for Windows: User s Guide, Version 2.0, Clark Labs for Cartographic Technology and Geographical Analysis, Worcester, USA. Erskine WD, Mahmoudzadeh A, Myers C Land use effects on sediment yields and soil loss rates in small basins of Triassic sandstone near Sydney, Australia. Catena 49, Feyznia S.1995.Rocks strength against factors in different climates of Iran. Journal of the Natural Resources of Iran 47, theses/clark.html. Campbell JB Introduction to Remote Sensing. Taylor and Francis, London Mapping risk for cultivated soil in France. Catena 46, Safamanesh R, Sul A, Wan Nor A, Firuz Ramli M Erosion Risk Assessment using an Empirical Model of Pacific South West Inter Agency Committee Method for Zargeh Watershed, Iran. Journal of Spatial Hydrology Vol.6, No.2, Fall. Tangestani M H Comparison of EPM and PSIAC models in GIS for and sediment yi eld assessment in a semi-arid environment: Afzar Catchment, Fars Province, Iran. Journal of Asian Earth Sciences 27, Zeaiean Firouzabadi P, Davoodi A. Study on Soil and sedimentation in Aladhtar Watershed using image orocessing software. 1158