Comparing calculations of sediment yield in Masjed Soleyman dam (SW, Iran) using the MPSIAC and hydrographic models

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1 Journal of Scientific Research and Development 2 (6): , 2015 Available online at ISSN JSRAD Comparing calculations of sediment yield in Masjed Soleyman dam (SW, Iran) using the MPSIAC and hydrographic models Fatemeh Zakeri Hoseini 1, Khalil Rezaei 2,*, Mahmood Shafai Bajestan 3 1Department of Geology, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran 2Assistant Professor, Kharazmi University, Tehran, Iran 3College of Water Science and Engineering, Shahid Chamran University, Ahwaz, Iran وsedimentation Abstract: Numerous methods have been introduced in various countries for estimating erosion and in each of which a number of factors are considered that influence these phenomena. Erosion and soil loss are one of the main factors in environmental pollution, decreased soil fertility, sedimentation in channels, irrigation canals, and rivers, reduced dam capacity and lifespan, and occurrence of floods and roadblocks. Therefore, serious steps must be taken to preserve soil, which is one of the main assets of every country. Moreover, amounts of erosion and sediment yield must be accurately determined to be used as a basis for action that need to be taken for preventing such destructive trends. Use of the geomorphological method based on the MPSIAC model in the GIS environment is one of the methods employed to estimate the amount of erosion and sediment yield. Using this method, erosion and sedimentation in the basin were qualitatively studied and converted quantitatively into the GIS environment. After entering the information layers and determining the scores of each one based on the principles defined in the model and integrating these layers for the basin adjacent to the Masjed Soleyman dam, the erosion class and the amount of erosion were estimated and compared with results obtained from the hydrographic model. Based on results obtained from the model, the sediment yield in the basin adjacent to the Masjed Soleyman Dam was 6.7 tons per hectare, resulting in an annual sediment load of 1.35 million tons. The hydrographic model indicated the annual sediment load in the dam was 1.44 million tons. Therefore, the model could calculate the sediment load with acceptable accuracy. Key words: MPSIAC; GIS; Erosion; Sediment Yield; Masjed Solyman Dam 1. Introduction * Soils are one of the most important natural resources of every country and, in this century, soil erosion among the main factors that destroy the environment. Sediments resulting from soil erosion pollute water; fill dam reservoirs and lower environmental potentials. Therefore, knowledge of the erosion situation and of the total annual sediment production in the watersheds requires more studies and investigation and recognition of effective factors in this complex process. Rapid water flow from the rivers into dam reservoirs and its settlement result in sedimentation along the length of the dam. Sedimentation pattern depends on the length and the shape of the dam and has two important effects on the dam reservoir: (1) Entry of sediments into the reservoir reduces its volume and capacity. (2) Flushing sediments behind dam walls will have undesirable effects on the agriculture, environment, etc., downstream. Erosion and sediment transport in sedimentary basins and sedimentation in dam reservoirs reduce useful lifespan of dams and, hence, factors effective * Corresponding Author. in this sedimentation must be studied (Grams and Schmidt, 2005). On the other hand, very large volumes of sediments move from upstream of basins in various ways and leave them causing very great damages to the environment and people. These sediments may have various origins (Coleman and Scatena, 1986; Renschler and Harbor, 2002). In studies on sedimentary basins, and especially on production and transport of sediments, because of the large volume of data that must be studied, tools are required that make it possible to perform all stages of implementing the models more easily, accurately, and quickly. Amounts of erosion in geological formations and sediment transport by transporting agents to sedimentary basins are among the parameters that determine the geomorphological characteristics and accumulations and relocation of sediment masses (Renschler and Harbor, 2002; Brown et al., 2009; Sui, et al., 2009). One of the basic problems in estimating amount of erosion and sediment yield for planning the utilization of water and soil resources is lack of statistics (especially in small basins), which causes problems for experts and users in the management of watersheds and in the development of protection programs. Empirical relations have been developed for estimating the amount of erosion and sediment 157

2 yield in basins that lack the required data. One of these relations is the MPSIAC model that has applications in many basins of Iran (of course, all stages of employing this model required the help of GIS) (Nikkami et al., 2002; Tangestani, 2006). There are many empirical methods for estimating the amount of erosion and sediment yield, each of which is applied according to the geographical position, weather conditions, and climate of the region. In the past few decades, various methods have been used for estimating the amount of erosion. According to experiences gained, and based on various studies conducted in this regard, the MPAIAC method is very efficient for the weather and climatic conditions of Iran (Tangestani, 2006) the main purpose of this study was to determine the most accurate method of estimating sediment load. To achieve this, the usable factors in MPSIAC were extracted first, the GIS-generated erosion-zoning map was used for estimating sedimentation capability of the study region next, and the results obtained from this model were compared with those found in the last hydrography of the Dam reservoir. 2. Geographical position of the study area The study region, with longitude from 49 26' 30" to 49 44' 48" and latitude from 32 1' 33" to 32 21' 17", is located to the north of Khuzestan Province and includes the watersheds adjacent to the Masjed Soleyman Dam. The Masjed Soleyman Dam, which is considered the outlet of the basin in Khuzestan Province, is located on the Karun River, 160 km northeast of Ahvaz, 25.5 km from Masjed Soleyman, and 26 km downstream from Shahid Abbaspour (Karun-1) Dam. Fig. 1 shows the geographical location of the study basin and its layout in Khuzestan Province and in Iran. The Masjed Soleyman Watershed has an area of 1950 Km2, and the sediments entering the dam reservoir originate from the Shur River, the Marghab River, the Karun-1 Dam, and the small tributaries in the basin between Masjed Soleyman Dam and Karun-1 Dam. This dam is located in a mountainous region in the Zagros mountain range with an average height of 625m. The standard deviation of the elevation model of the region is 531, which shows the height of area where the Dam is built varies greatly. The height parameter increases in the northeast direction. The average annual temperature of the Dam area is about 25.5 oc, the wettest months are December and January, the driest months May, June, and July, and the time of concentration for the Masjed Soleyman Dam is 10 hours. 3. Methodology and results The methodology includes nine sections for model derivation. By reason of the great number of data, activity and changeability of these data in the natural resources, Geographic Information System (GIS), as a useful tool, is carried to solving many problems. Fig. 1: Geographical position of the study area MPSIAC model incorporated nine factors from nine equations are: surface geology, soil, climate, runoff, topography, land use, ground cover, and erosion condition and channel erosion (Table 1). According to the table, the erosion factors maps were encoded and with overlaying these maps in the GIS framework, the sediment yield map in accordance with sediment yield (Qs) equation was obtained. For ease of interpretation, each of the factors is discussed below. Fieldwork was undertaken for 10 days from 5 to 15 January For ease of interpretation, each of the factors is discussed below. 4. Surface geology (y1) To calculate this factor, in the first step, geology map (Fig. 2) was digitized and then based on the stones and sediments sensitivity to erosion, this map was encoded and a new data field in the geology map database (based on X1 factor) was created. The score of each unit of surface geology was determined from the scale between 1 for the most resistant face, to 10 for the most sensitive face to erosion. These scaling factors are based on the local condition of Iran (Shrestha, 2002). 5. Soil (y2) With due attention to the soil studies and soil experiments, the effective factors on the K (k is erodibility factor in the USLE method), namely, silt + very fine sand percent, sand percent, organic matter percent, soil structure and permeability were determined and then by using Wischmeir monograph, K value and at last X2 value was estimated (Fig. 3). 158

3 Table 1: Effective factors on the erosion and calculation method in the MPSIAC model. Description Equation Effective factors No X1 =Stones sensivity to erosion(0-10) Y1=X1 Surface geology 1 K=soil erodibility X2=16.67K Soil 2 P2 =6-hour rainfall with 2-year return period X3=0.2P2 Climate 3 R=runoff height, Qp =1-year specific pick discharge X4=0.006R+10QP Runoff 4 S=slope (%) X5=0.33S Topography 5 Pb=bare ground percent X6=0.2Pb Land cover 6 Pc =crop canopy percent X7=20-0.2Pc Land use 7 SSF=the score of soil surface erosion in the BLM method X8=0.25SSF Surface erosion 8 SSFg=the score of gully erosion in the BLM method X9=1.67SSFg Gully erosion 9 R=sediment yield score R= X K+0.2P R+10QP+0.33S+0.2Pb Pc+ 0.25SSF+ 1.67SSFg QS= sediment yield (ton/hectare/y) = 0.253e 0.036R Fig. 2: Geology map (Right) and geology factor score map of watershed (Left) 6. Climate (y3) In this model, rainfall is considered as the major contributor to soil erosion and sediment movement. Rainfall was estimated based on 6-hours precipitation amount with 2-year return period. In this study, climate factor was based on 32 years ( ) of rainfall record. From the record, the rainfall intensity duration and frequency curve were derived (Table 2). The climate factor was estimated and shown in Fig. 4. Runoff factor was obtained based on analysis of discharge data. In the study areas, runoff largely depends on atmospheric conditions and the surface lithology of the formation permeability. According to the runoff equation (Fig. 5), by calculating the average of runoff height (R) and specific pick discharge (QP). 7. Runoff (y4) Table 2: Location of Rain Station and amount of 6-hour rainfall with 2-year return period 6-hours Rainfall amount with 2-year return period Y X Station name 45.4 ٣٥٨١٣٥٦ ٣١٩٦٢٣ Lali 48.4 ٣٥٤٤٩٩٣ ٣٤٨٥٧٨ Shore Andika 45 ٣٥٥٠٧١٧ ٣٦٥٤١٢ Karun Dam 50.1 ٣٥٢٢٧٧٢ ٣٨٧٢٩٧ Izeh 27.4 ٣٥٤١٩٦٣ ٣٢٢٣١٦ Batvand ٣٥١٤٦٩٢ ٤١٨٢٧٢ Pole Shaloo No ٢ ٣ ٤ ٥ ٨ ٩ 159

4 Fig. 3: Soil factor map of study area. Fig. 4: Climate factor map (Right) and DEM layer (Left) of watershed Fig. 5: Location of Hedromety station (Left) and Runoff factor map (Right) of watershed. 160

5 8. Topography (y5) Topography factor was determined based on average percentage of slope steepness. To providing this layer at first by using DEM layer (Digital Elevation Map) (Fig. 4), slope map was obtained and then this map was multiplied by 0.33, and at last the topographic factor map was obtained. 9. Ground cover (y6) The main characteristics considered as ground cover are vegetation, litter, and rocks. To providing this factor, at first we digitized plant cover map and then in this map based on the bare grounds percent, a new data field was created and classified (Fig. 6). 10. Land use (y7) Land use factor was estimated based on canopy cover. With due attention to the crop canopy percent in each cover type and by using Land sat ETM imagery, topographic maps and field visits, plant cover map based on X7 value was encoded and classified (Fig. 7). 11. Erosion condition (y8) Upland erosion factor was obtained based on Bureau of Land Management (BLM) method (Feiznia, 1995). This method is used from 7 factors: surface erosion, land cover, rill erosion, surface litter, demolition traces on the ground surface, surface flows traces and gully erosion. By field surveying, IRS and Landsat ETM imagery, the score of these factors was determined and then with digitizing geomorphology map, this map was encoded based on X8 factor value and a new data field (based on X8 factor) was created in this map (Fig. 10). 12. Channel erosion (y9) Channel erosion factor was obtained based on the gully erosion factor from the BLM method and by the relationship between yearly rainfall (mm) and gully erosion improvement (Aker, 1971). Based on this factor, geomorphology map was encoded and a new data field in this map was created (Fig. 8). 13. Providing of sediment yield score map and sediment yield map Utilizing GIS, spatial data related to surface geology, soil types, climate, runoff, topography, ground cover, land use, surface erosion and channel erosion were incorporated into MPSIAC model to facilitate the prediction and assessment of sediment yield of the sub-watersheds. Summation of scores in this nine environmental factors is called sediment yield score (R) (Fig. 9). To calculate R in the ArcGIS framework, at first all nine factors rasterized by using raster calculator menu and then combined with each other. In other words, the coordinates of the cells are combined and integrated; for this case, the data should be in raster structure to allow for their integration in ArcGIS framework. Sediment score (R) was employed to estimate sediment yield (QS) according to Johnson & Gebhardt formula (Najm et al., 2013; Najafinejad., 2003)(Fig. 10): Q S =0.253e 0.036R QS= sediment yield (ton/hectare/y) R= sediment yield score Fig. 6: Ground cover factor (up) and classification map (down) of watershed. 161

6 Fig. 7: Land use (up) and classification map (down) of watershed. Fig. 8: River and channel factor (up) and erosion factor map (down). 14. Discussion The MPSIAC model was developed primarily for application in arid and semi-arid areas and is believed to be appropriate for the same environmental conditions in Iran (Meamarian and Esmaeilzadeh, 2003). Utilizing GIS, spatial data related to surface geology, soil types, climate, runoff, topography, ground cover, land use, surface erosion and channel erosion were incorporated into MPSIAC model to facilitate the prediction and assessment of sediment yield of upstream watershed of Abbaspour dam. Accordingly, the sediment yield score map of this sub-basin was prepared (Fig. 10). Fig. 9: Sediment yield score (R) map of study watershed. 162

7 Fig. 10: Sediment yield (Right) and erosion classification map (Left) of study watershed Then based on the table 3, the sediment yield map was classified and the result is shown in Fig. 10. For ease of interpretation and evaluation, the values of R were divided into five classes (Table 3). Majority of the classes are in classes IV and III. The average, minimum and maximum values for R for this study area were calculated 10, 38 and 68, respectively. Also, the amount of sediment yield for upstream watersheds of Abbaspour dam was calculated 7.7 ton/hec that are equal to 2 and 1.35 million ton per year, respectively. The faults and joints are very frequent in this area but MPSIAC model don t consider and input them. Among all parameters, the maximum of changes and fluctuations are related to slope layer. According to average values in calculated layers, maximum to minimum amounts are related to erosion, topography, land use, groundcover, climate, geology, slope and runoff, respectively. Table 3: Sediment yield score and classification in MPSIAC model. Sediment yield scores The amount of sediment yield (m 3 /km 2 /y) Sediment yield intensity Sediment yield class >100 >1429 Very high V Hight IV Medium III Low II <25 <95 Very low I According to the situation of study area and during the study period, it was assumed that the factors of climate, soil and topography are constant and sensitive to sediment yield predicted by the model. But the most sensitive factors to the model are the surface geology, gully erosion and runoff factors. The good correlations between them with sediment yield indicated that changes in natural agents will affect soil erosion and sedimentation. For instance, increase in annual and intensity of rainfall, and increase in runoff could result in an increase in the erosion rate. It is expected that surface erosion, ground cover, and land use were found to be more sensitive to the model results, however in this analysis the results show otherwise. Watershed management goals such as recognition of sensitivity area to erosion, determination of critical area to erosion hazard and ranking of parcels or catchments would be one of the important objectives in watershed study. The nine factors in MPSIAC model almost represent all agents that affected soil erosion and sedimentation either directly or indirectly. Surface erosion and sediment yield are important factors that should be taken into account in planning renewable natural resource projects. Generation of erosion and sediment-yield maps for areas of soil conservation and vegetation improvement is controlled mainly by considering the extensive effects of soil-wasting processes (Najm et al., 2013). Proper environmental planning at different levels has been called for by watershed managers during last years to integrate natural resources limitations and human needs continually and effectively. The watershed optimization for each land use, especially agriculture as one of the significant contributors to the environmental degradation, is, therefore, necessary to achieve sustainable development (Chess and Gibson, 2001; Meamarian and Esmaeilzadeh, 2003). Over the past few decades, the vital resources of almost entire watersheds in Iran have been subject to rapid deterioration resulting from expanding anthropogenic activities such as dams construction. The Iran Forest and Rangeland Nationalization, Act of 56 (fixing and controlling of governmental national land) was effective in reducing land use 163

8 conversion and restoring many of resources. Despite its progress, over-exploitation and mismanagement of watershed resources still remain as major threats to the watersheds in Iran. This is a major challenge because of their complex nature and the existence of diverse and diffuse contributing land uses within the watersheds (Najm et al., 2013). The equilibrium between geological erosion and soil formation is easily disturbed by human activities (Jalalian, et al., 1997). It is estimated that 26.4 million hectares of land in Iran are under the influence of water erosion and 35.4 million hectares are under the influence of wind erosion (Lal, 1999). Iran has more than 10 million hectares of cultivated land under irrigation (Anon, 1974) and more than 8 million hectares of agriculture land under dry farming (Iran Daily, 2000). Overgrazing, deforestation, cultivation, road construction, drought, civil and industrial development are possible causes that tend to accelerate the removal of soil material in excess of which is removed by geological erosion. This type of erosion is known as accelerated erosion. Accelerated erosion takes place when vegetation covers which protect topsoil from erosion agents such as rain and wind is removed. The soil particles movement becomes extensive until the top soil has been completely washed away, leaving the subsoil or the parent rock behind (USDA, 1995). The problem of erosion is further exacerbated by the loss of organic matter in the topsoil that hold the soil particles together due to improper land use activity (Nikkami, 2002). 15. Conclusions With due attention to the studies and surveying of these sub-watersheds, types of erosion that have the largest land areas in the region are rill, stream and gully erosions. Also, most values of erosion are in Shaly, Marly, Gypsum and alluvial deposits parts; which are sensitive and basically covered this region. Thus with applying the reformatory programs and considering these sub-watersheds formations, are often sensitive to erosion, we can decrease erosion. Considering what was said before, we can list the following items as the final and applicable results of our study on the empirical model MPSIAC model in the studied watershed: Based on the conducted studies, we can say that the empirical model MPSIAC is suitable for estimating amounts of erosion and sediment yield under the prevailing conditions in Iran The average values of the computational layers from the highest to the lowest were erosion, topography, land use, coverage, rainfall (weather), geology, slope, and runoff, in that order The estimated sediment yield in the basin adjacent to the Masjed Soleyman Dam was 6.7 t/ha, and was estimated, using the MPSIAC model, to yield annual sediment of 1.35 million tons. The estimated sediment load for the whole watershed adjacent to the Masjed Soleyman Dam showed an error of 7% compared to results of the Dam hydrography (which 164 is the most accurate method of direct observation). This amount of error indicated the suitable efficiency of this method. In the MPSIAC model, percent canopy closure and bare land can be extracted from satellite images and be used for the factors of land cover and land use. These two factors are given scores of zero to 20. Therefore, errors in using satellite images result in high amount of errors in the general estimation made by the model. References Aker, A. (1971) Soil Surface Factors, Determination of erosion condition class. Bureau of Land Management, Department of Interior, USA, form Anon. (1974) Dez River Watershed Resource Management Plan, Iran. New York: Development and Resources. Brown, A.G., Carey, C.,Erkens, G., Fuchs, M., Hoffmann, T., Macaire, J.J., Moldenhauer, K.M., and D.E., Walling, 2009, From sedimentary records to sediment budgets: multiple approaches to cathment sediment flux: Geomorphology, v. 108, (1-2), p Coleman, D.J., and F.N., Scatena, 1986, Identification and evaluation of sediment sources. In: R.F., Hardly, (ED.) Drainage Basin Sediment Delivery: IAHS Publication, V. 173, P Chess, C., and Gibson, G. (2001) Watersheds are not equal: Exploring the feasibility of watershed management, Journal of the American Water Resources Association 37(4): Feiznia,S. (1995) Natural Resources Journal of Tehran University,No.47: Grams, P.E., and J.C., Schmidt, 2005, Equilibrium or indeterminate. Where sediment Budgets fail: sediment mass balance and adjustment of channel form, Green River dowenstream form flaming Gorge Dam, Utah and Colorado: Geomorphology, V. 71(1), p Lal, R. (1999) Erosion impact on soil quality in the topics. In: Lal, R (ed) Soil quality and soil erosion, Soil and Water Conservation Society and CRC Press, Boca Raton, Meamarian, H. and Esmaeilzadeh, H., The Sediment yield potential estimation of Kashmar watershed (Iran) using MPSIAC model in the GIS framework. Najm Z., Keyhani N., Rezaei Kh., Naeimi Nezamabad A., Vaziri S.H.,2013, Sediment Yield and Soil Erosion Assessment by Using an Empirical Model of MPSIAC for Afjeh & Lavarak Sub-Watersheds, Iran, Earth Science. Vol. 2, No. 1, 2013, pp doi: /j.earth

9 Najafinejad, A. (2003) Gully erosion measurement in loess hilly area. Unpublished Ph.D. thesis, Gorgan University, Iran. (In Persian). Nikkami, D., Elektorowicz, M. and Mehuys, G. (2002) Optimizing the management of soil erosion, Water Quality Resource Journal of Canada, 37(3): Iran Daily. (2000) The watershed situation in Iran. Vol.6, No /5/2000. Tangestani, M., Comparison of EPM and PSIAC models in GIS for erosion and sediment yield assessment in a semi-arid environment: Afzar Catchment, Fars Province, Iran. Journal of Asian Earth Sciences, 27, Renschler, C.S., Harbor, J., 2002, Soil erosion assessment tools form point to regional scalesthe role of geomorphologists in land management research and implementation: Geomorphology, v. 47(2-4), P Solamani, K., Modallaldoust, S., Lotfi, S., 2009, Investigation of land use changes on soil erosion process using geographical information system: International Journal of Environmental Science and Technology, V.6(3), P Sui,J., He,Y., liu, C., 2009, Changes in sediment transport in kuye River in Loess Plateau in china: Internatinal Journal of Sediment Research, v.24(2), P Shrestha, M.N. (2002) Assessment of hydrological changes due to landuse modifications, Unpublished Ph.D thesis. Indian Institute of Technology, India. Vent, T.D., Poesen, J., 2005, Predicting soil erosion and sediment Yield at the basin scale: scale issue and semi- quantitative models: Earth- Science Riverws, v. 71, P Jalalian, A., Ghahsareh, A.M. and Karimzadeh, H.R. (1997) Soil erosion estimation for some watershed in Iran. Isfahan: Isfahan University of Technology. United States Department of Agriculture (USDA) (1995) Soil Conservation Service, Engineering Field Handbook, Enhancement Creation, Washington. 165