جستجوی مقالات مرتبط با کلیدواژه « Model Efficiency » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Water storage dams are considered as strategic constructions of every country. The issue of soil erosion in the watershed of the mentioned dams and the transfer of produced sediment to the reservoir of the dams is one of the issues that is always raised regarding these types of dams. Cheshme Ashegh dam in Fars province is one of the mentioned dams. In the present study, erosion and sediment studies and estimation has been carried out for the first time in this watershed by RUSLE and MPSIAC models as well as the regional analysis method for the aim of design and implementation of suitable conservation measures as well as making policy and plan for soil conservation in the watershed of the dam and preventing the production and transfer of sediment to its reservoir. The results of this research showed that, based on the MPSIAC and RUSLE models, the amount of special sediment is estimated by 1.58 and 3.17 ton/ha/y, respectively. Considering that the result obtained from the regional analysis method based on Gozoon station, the amount of special sediment is estimated by 3.5 ton/ha/y, it can be said that the result of the estimation obtained from the RUSLE model is closer to the amount calculated from the regional analysis method. Therefore, it can be said that the results obtained from the RUSLE model are more efficient than the MPSIAC model for estimating the amount of sediment and future soil conservation measures in the Cheshme Ashagh Dam watershed.

Soil erosion is one of the most important environmental problems in the world, which has created many problems for human societies. Therefore, accurate evaluation of soil erosion in erosion and sedimentation studies is necessary to design and implement appropriate, targeted, and cost-effective conservation measures due to the severity of the problem and socio-economic effects. Therefore, it is necessary to select a suitable and appropriate model for estimating erosion and sedimentation according to the available data, different topographic and climatic conditions of the country and considering the information needs and complexity of the models. Accordingly, in the present study, the strengths and weaknesses of four experimental models including EPM, MPSIAC, RUSLE and IntErO were analyzed qualitatively. The results showed the acceptable results of MPSIAC model in arid regions, the RUSLE model in Mediterranean mountainous areas, high slope forest areas and agricultural areas and EPM and IntErO models in mountainous area and also for watershed management and water resources management. Therefore, it is recommended to select erosion and sediment estimation models in accordance with management goals, available data, topographic and climatic conditions. As a result, it is expected that having suitable selection of a soil erosion and sediment yield model will lead to save time, designing and implementing appropriate, selective and cost-effective conservation measures.

The lack of complete coverage of hydrological data forces hydrologists to use the homogenization methods in regional analysis. In this research, in order to choose the best Hierarchical clustering method for regional analysis, base flow and related index were extracted from daily stream flow data using two parameter recursive digital filters in 43 hydrometric stations of the Kerman province. Physiographic, climatic and hydrological parameters were calculated. Using factor analysis of 14 parameters, six factors including: hydrograph recession constant, mean annual precipitation, actual evapotranspiration, permeability, topography (slope and height) and curve number were chosen as the most important independent factors. Hierarchical clustering of catchments using independent factors in different methods including: Nearest neighbor, Furthest neighbor, Centroid clustering and Ward were down. Then the regional equations using linear regression at 1% significant level for each homogeny region were determined. To compare and evaluate the accuracy and efficiency of the models, Independence errors, Colinerity and normal distribution of errors were tested. The results showed that “Centroid clustering method " because of low relative error with the amount of 27.137% for zone A and 36.89% for zone B of homogeneous region, minimum standard error of the estimate and the high correlation coefficient, diagnosed as the best homogeneity method to estimate regional base flow index in the study area.

Clarification of suspended load (SL) supply variations at different temporal scales is essential to provide a logical management program. In this study, the SL transport variations were investigated at different time scales in the Marboreh River, Darreh-Takht, Lorestan Province, Iran. The four seasonal, seasonal threshold, annual and land use period approaches were analyzed using statistical methods based on the collected data from 1971 to 2016. The seasonal threshold runoff was determined using the best statistical distribution for the return period of 2 years, and the SL concentration-runoff relationships were prepared for large and small events. The land use map was prepared using remote sensing data for the years 1977 and 2016. According to the results, supply and SL transport capacity were high in the large events of spring, when the slope of the sediment rating curve is 1.469. The supply and SL capacity for large events of the autumn was higher than that of the winter. In the study period, although the land use change from rangeland to dry-farming was observed, the impact of other human activities including dam construction on the tributaries has caused a decrease in the mean SL transport, corresponding with the daily runoff in autumn, winter and spring.

Suspended sediment load is the biggest non-point pollution source and a major factor of degradation of surface water quality. Because of hydraulic models of sediment transport can not predict the suspended sediment load, sediment rating curves as usual hydrological methods are utilized spread for this goal. Cause of regression equations of rating curve have a lot of bias due to logarithmic convert, correction factors in optimization of sediment rating curve were used for eliminating of logarithmic conversion effect and bias of extrapolation in 20 hydrometric stations in up streams and major rivers of Sefidrood watershed. Comparing of 9 rating curve methods as one-linear, one-linear with correction factors as CF1, CF2, FAO, two-linear, mean loads within discharge classes, mean loads within discharge classes with correction factors as CF1, CF2 and
FAO was conducted by RMSE and NASH criteria. Results showed that mean loads within discharge classes, mean loads within discharge classes with CF1 and CF2 correction factors have the most fitting to Sefidrood watershed stations. Our findings illustrated that CF1 and CF2 correction factors in majority of stations have compensated underestimation of rating curves and increased efficiency of models. Power of equation between sediment load and area was more than of one. According to results 30 million ton suspended sediment load enter to reservior of Sefidrood dam annually.

Estimates of soil loss are important to issues of land and water management. Much of the soil loss information in erosion control is based on the use of the Revised Universal Soil Loss Equation (RUSLE). This study assessed use of RUSLE as a soil loss predictor on small drainage areas using sediment data. Toward this, twenty drainage areas covering the first order waterways were selected in the semi-arid Tahamchai Watershed, NW Zanjan, Iran. Sediment yield was measured using sediment volume deposited behind the check dam at the outlet of each drainage area for a 16-year study period (1995-2011). Soil erosion factors including rainfall erosivity, soil erodibility, slope, vegetation cover and conservation practices were determined for each drainage area. Soil loss was estimated using the RUSLE model integrated with RS and GIS techniques based on the maps of rainfall erosivity, soil erodibility, slope, cover crop, and conservation practices factors. Based on the results, mean annual estimated soil loss was 43.68 Mg ha-1 year-1 which was 6.58 times bigger than the mean annual sediment yield (8.67 Mg.ha-1.year-1) in the area. The model efficiency was -6.83, indicating the inability of the model to correctly predict net soil loss in the small drainage areas. The estimation error was directly related to overestimation of soil erosion factors in the area. It seems soil erodibility, slope and conservation factors to have the exaggerated estimates in the area on one hand, and some other variables such as slope shape and waterway characteristics to be also effective in controlling the soil loss in the area on the other hand. This study is an important step forward in conducting more accurate erosion evaluation of the drainage areas using the RUSLE.

The WEPP physically model is able to dynamically simulate runoff and soil erosion using physical concepts of erosion and hydraulic of overland flow science considering plant growth، residue decomposition، winter process. Determination of the capability of this model in runoff and erosion estimation in different range type with investigation the effects of management were the main objectives of this research. This research was conducted in the Kabode placed across the Gharasoo watershed by installing erosion plot with dimension of 10×3 meter and three replications in three range types at the slopes of 25، 35 and 45%. Climate data including rainfall and air temperature was recorded in the site and additional data including wind velocity and direction، solar radiation and dew point temperature was prepared from Kermanshah synoptic climate station. Event based erosion and runoff was simulated in each plot by the v2008. 9 version of the model. Results from model assessment for prediction of runoff indicated that the maximum model accuracy was in 45% slope. Also، mode relative error in runoff in 25 and 35% slope was about 0. 61 liter. Overall، model performance in runoff estimation was sought in all three slopes، so that Nash-Sutcliff index was 0. 96 to 0. 73. Minimum and maximum estimation error in erosion prediction was occurred in 35 and 45% slope، respectively. Model prediction results indicated under-estimation in all the slopes. Negative amounts of Nash-Sutcliff index indicated confirms the low efficiency model especially in conditions prevailing in two slopes of 25 and 35%.