Showing 220 results for Model
F. Hayati, A. Rajabi, M. Izadbakhsh, . S. Shabanlou,
Volume 25, Issue 1 (5-2021)
Abstract
Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy.
Z. Kolivand, Sh. Ghazimoradi, F. Kilanehei, O. Naeini,
Volume 25, Issue 2 (9-2021)
Abstract
The reuse of treated wastewater in countries such as Iran that suffers from drought is considered an important challenge in water management programs. The application of modern wastewater treatment systems particularly attached growth systems, owing to the short time required for start-up, low land requirements, and the absence of problems associated with sludge handling may be a resolution. The objective of this study is to investigate the performance of the Moving Bed Biofilm Reactor (MBBR) in treating synthetic municipal wastewater and selecting an appropriate model. In this way, a bench-scale reactor possessing an effective volume of 15 liters, and synthetic wastewater with influent COD of 500 mg/l (similar to typical municipal wastewater) has been used and the experiments with media filling percentages of 30%, 50%, and 70% and hydraulic retention times (HRT) of 4, 8, and 12 hours have been carried out. The observed data show that the optimum bulk density and hydraulic retention time are 50% and 4 hours, respectively. Also, the kinetic study of reactor performance indicates that Grau second-order model has better conformation with Moving Bed Biofilm Reactor results. In addition, a regression model for predicting effluent COD based on the filling percentage and retention time is presented.
A. Motamedi, M. Galoie,
Volume 25, Issue 2 (9-2021)
Abstract
The annual soil erosion in different regions of the world has been estimated using various empirical and numerical methods whose accuracy is very dependent on their utilized parameters. One of the most common methods in the evaluation of the mean annual soil erosion especially in sheet and furrow regions is the USLE method. In this relationship, almost all factors that normally affect the soil loss process such as land cover, slope, precipitation, soil type, and support practice parameter of soil have been employed but, in this research, it was shown that the accuracy of this method in mountainous areas covered by rock and snow is somewhat low. To do this, a part of the Tibet plateau in China, where observation soil loss data were available, was selected for investigation. To implement the numerical and analytical analysis, many maps including DEM, NDVI, orientation, soil type, mean monthly and annual precipitation for 30 years were collected. For increasing the accuracy of the model, the cover management parameter was extracted from high accuracy NDVI maps and all USLE parameters were calculated in ArcGIS. The final results were shown that the amount of annual soil loss which was estimated by the USLE method is more than the observed data which were collected by Chinese researchers. This is because the large areas of the study area are covered by lichen and snow where soil loss due to the erosion process is very low but these regions cannot be recognized from NDVI maps. Also, the analysis of the NDVI maps was shown that the relationships of Fu, Patil, and Sharma were not suitable for soil loss estimation in elevated mountainous areas. If the other relationships such as Lin, Zhu, and Durigon are used for the regions with a height of more than 5500 m, a new correction coefficient needs to be used for the C factor which was calculated as 0.2 for the study area.
Prof. J. Abedi-Koupai, S. Rahimi, S. Eslamian,
Volume 25, Issue 3 (12-2021)
Abstract
Changing the date of the first fall frost and the last spring frost is an important phenomenon in agriculture that can be one of the consequences of global warming. Using general circulation models (GCMs) is a way to study future climate. In this study, observations of temperature and precipitation were weighted by using Mean Observed Temperature-Precipitation (MOTP) method. This method considers the ability of each model in simulating the difference between the mean simulated temperature and mean precipitation in each month in the baseline period and the corresponding observed values. The model that had more weight, selected as the optimum model because it is expected that the model will be valid for the future. But, these models are not indicative of stationary climate change due to their low spatial resolution. Therefore, in this research, the outputs of GCM models are based on the three emission scenarios A2 and B1 and A1B, downscaled by LARS-WG for Isfahan station. The data were analyzed by SPSS software at a 95% confidence level (P<0.05). The results indicated that in the Isfahan in the future period 2020-2049 based on the three scenarios, as compared with baseline period 1971-2000, the first fall frost will occur later and the last spring frost will occur earlier. The first fall frost will occur later for 2 days (based on the A1B emission scenario) to 5 days (based on the A2 emission scenario) and the last spring frost will occur earlier for 2 days (based on the and B1 emission scenario) to 4 days (based on the A2 emission scenario). Finally, the best distribution functions for the first fall frost and the last spring frost for the baseline period and under climate change were selected and compared using the EasyFit software.
M.h. Rahimian, J. Abedi Koupaei,
Volume 25, Issue 3 (12-2021)
Abstract
Soil salinization is a phenomenon that threatens agricultural lands and natural areas, leading to reduced productivity, declinations of soil resources and vegetation covers, and finally, the abandonment of these areas. This study has quantified the groundwater Capillary Rise (CR) and actual Evapotranspiration (ETa) and their relationship with the soil salinity of Azadegan plain, west of Khuzestan Province. The study area has an arid climate, characterized by shallow and saline water table and a high potential evaporation rate. For this purpose, field samplings were carried out in four consecutive seasons of the year to measure salinity, soil moisture, and texture, groundwater table, and salinity at 27 scattered representative points of the study area. The CR values were estimated in different seasons of the year using UPFLOW model. Moreover, four representative Landsat satellite images were acquired to map seasonal changes of ETa through the SEBAL algorithm. Then, the effects of ETa on CR and consequent soil salinity build up were quantified in a seasonal time scale. The results showed that the average daily ETa of Azadegan plain varied from 1.55 to 7.96 mm day-1 in different seasons which caused a capillary rise of around 1.2 to 1.5 mm.day-1. This has led to the upward movement of 12 to 18.8 ton ha-1 month-1 of salts from shallow groundwater to the soil surface, which has caused surface soil salinization. Also, there was a close relationship between ETa, CR, and soil salinity parameters, which can provide insight into modeling of spatial and temporal changes of soil salinity and provision of solutions to reduce the accumulation of solutes in the soils of the study area.
M. Sayadi, H. Khosravi, S. Zareh, Kh. Ahmadali, S. Bagheri,
Volume 25, Issue 3 (12-2021)
Abstract
Desertification is a phenomenon that has more destructive effects in arid, semi-arid, and semi-humid regions than in other regions. This paper tries to provide a map of the future of desertification in Tehran Province, for futurism in the face of land degradation and desertification. The IMDPA model was used to evaluate land degradation and desertification. To use this model and evaluate desertification, three criteria of groundwater including groundwater depletion, electrical conductivity, and sodium adsorption ratio indices, climate criterion including precipitation, aridity, and drought indices, and land use criteria were selected as key criteria effected on desertification according to regional conditions. Land use index map with IGBP standard and zoning map of other indicators were prepared by IDW method for 2011 and 2016. The maps of land use index and other indices were predicted using the CA-Markov model in TerrSet software, and using the RBF method in artificial neural network toolbox, respectively. Scoring based on the IMDPA model, the maps of indices and criteria maps were prepared for 2011, 2016, and 2021. Finally, the desertification intensity map was calculated by geometric averaging for all three criteria for all three time periods. The results showed that 59.78% and 40.22% of the area of Tehran Province were in the low and medium classes, respectively. However, in 2016, the area of the medium class has increased to a 44.8%, and it is predicted that this increase will continue until 2021 so that 47.65% of the area of Tehran Province will be in the medium class. In addition, in this year, about 1% of the area of Tehran Province will be allocated to the high class in the western regions, which did not exist in the previous two periods. In general, due to human activities, the intensity of desertification in the western and southern parts of the province is higher than in the eastern and northern regions.
M. Ghodspour, M. Sarai Tabrizi, A. Saremi, H. Kardan Moghadam, M. Akbari,
Volume 25, Issue 3 (12-2021)
Abstract
The application of simulation-optimization models is a valuable tool for selecting the appropriate cropping pattern. The main objective of this research is to develop a two-objective simulation-optimization model to determine the pattern of cultivation and water allocation. The model performs the optimization with the multi-objective metamorphic algorithm (MOALO) after simulating different states of the cultivation pattern. The decision variables including land and water allocated to ten-day periods of plant growth were designed in a way that the minimum utilization of water resources and economic maximization were identified as target functions. The developed model was used to simulate and optimize the cultivation pattern with an area of 5500 hectares and water allocation of Semnan plain with renewable water at the rate of 60.8 million cubic meters. Harvesting scenarios of 80 (GW80) and 100 (GW100) percent of renewable groundwater and scenarios of change in existing cropping pattern of 30 (AC30) and 60 (AC60) percent were considered and each scenario was simulated with the MOALO algorithm. Optimization using the proposed model in four scenarios improved the water and economic objective functions compared to the initial simulation performance. The results showed that the four proposed scenarios were obtained by minimizing the water objective function and maximizing the economic objective function relative to the current situation (simulation). In general, the proposed model had a good performance despite its simplicity, which is a specialized tool to optimize the crop pattern with water allocation.
M.m. Matinzadeh, J. Abedi Koupai, M. Shayannejad, A. Sadeghi-Lari , H. Nozari,
Volume 25, Issue 4 (3-2022)
Abstract
Using water and fertilizer management at the farm level can be increased water use efficiency and reduce the volume of drainage water, fertilizer losses, and other pollutants in farmland with deep underground drains such as Khuzestan agro-industrial Companies. In the present study, a comprehensive simulation model for the water cycle and the nitrogen dynamics modeling was used for water and fertilizer management modeling on farmland of sugarcane in Imam Agro-Industrial Company using a system dynamics approach. To reduce irrigation water consumption and nitrogen fertilizer losses, five different scenarios were considered including four scenarios of water management consist of 5, 10, 15, and 20 percent reduction in the amount of irrigation water (I1, I2, I3, and I4) compared to the current situation of irrigation in Imam agro-industrial Company (I0), and one scenario of integrated water and fertilizer management (20% reduction in the amount of irrigation water and urea fertilizer 210 Kg/ha, I4F). The results of modeling showed that the scenario of I4F caused to reduce 31, 70, 71, 70, and 85 percent of the cumulative volume of drainage water, cumulative nitrate and ammonium losses, total losses of cumulative nitrate, and ammonium by tile-drain and cumulative losses of denitrification process, respectively. Thus, the implementation of this scenario, not only saves water and fertilizer consumption but also reduces environmental pollution effectively. So the scenario of I4F (amount of irrigation water for six months 2656 mm and urea fertilizer 210 Kg/ha) is recommended for sugarcane in the Imam agro-industrial Company.
A. Mehrabi, M. Heidar Pour, H. R. Safavi,
Volume 25, Issue 4 (3-2022)
Abstract
Designing an optimal crop pattern and on-time water allocation of water resources along with deficit irrigation are among the optimal solutions to maximize the water economic efficiency index. In this paper, the simultaneous optimization of crop pattern and water allocation are discussed using the deficit irrigation method. The study area is located west of the Qazvin plain irrigation network. The six different levels of percentage reduction of irrigation rate (0, 0 to 10, 0 to 20, 0 to 30, 0 to 40, and 0 to 50%) in three climatic conditions consist of dry, normal, and wet years were compared. The best irrigation scenario was selected for each year, and the results were compared with the existing crop pattern of the same year. The new crop pattern included the main crops of the region and the addition of rapeseed. The objective was to reach the maximum net benefit per unit volume of water by considering the maximum extraction of monthly and annual surface and groundwater. The results showed that the best scenario in the dry year was maximum deficit irrigation up to 20%, in a normal year full irrigation, and a wet year maximum deficit irrigation up to 10%. The improvement of economic water productivity in a dry year was 52.2%, in a normal year 41.5%, and in a wet year is 19.6% compared to the existing crop pattern. The average percentage of annual irrigation supply increases from 64.3 to 91.7% in a dry year, from 70 to 100% in a normal year, and from 77.5 to 97.1% in a wet year. Also, the relative yield of all crops, especially wheat, alfalfa, and sugar beet significantly increases. Therefore, the gravitational search algorithm as an optimization model can be considered in selecting the suitable crop pattern and allocation of surface and groundwater resources concerning economic benefits in irrigation networks management.
S. Toghiani Khorasgani, S. Eslamian, M.j Zareian,
Volume 25, Issue 4 (3-2022)
Abstract
In recent decades, water scarcity has become a global problem due to the growth of the world's population as well as the increase in per capita water consumption. Therefore, planning and managing water resources to prevent potential risks such as floods and drought in the future is one of the important measures of water resources management. One of the important measures to avoid potential risks and predict the future is rainfall-runoff modeling. The objective of this study was to investigate the efficiency of the WetSpa hydrological model in estimating surface runoff in the Eskandari watershed, which is one of the important sub-basins of the Zayandehrood watershed. In this study, Daran and Fereydunshahr synoptic stations have been used to collect meteorological information in the Eskandari watershed. Also, to study the flow of the Plasjan river, daily data of Eskandari hydrometric station, located at the outlet of the basin, have been used. Climatic data along with digital maps of altitude, soil texture, and land use were entered as input to the WetSpa model. Finally, the ability of the WetSpa model was evaluated in estimating river surface runoff. The observed flow at the basin outlet in the hydrometric station was used to evaluate and calibrate the model. The model was calibrated for the statistical period (1992-2000) and its validation was performed for the statistical period (2001-2004). In the calibration period, the trial and error method were used to calibrate the model parameters. The simulation results showed a good correlation between the simulated flow and the measured flow. In the present study, the Nash Sutcliffe coefficient in the calibration and validation stages was equal to 0.73 and 0.75, respectively which shows the good and acceptable ability of the model in estimating the surface runoff of the study basin.
A. Rezapour, M. Hosseini, A. Izady,
Volume 25, Issue 4 (3-2022)
Abstract
Integrated assessment of the watershed is critical in arid and semi-arid areas due to the severe water stress in these regions. Data and information are an essential part of decision making and water governance to obtain integrated water resources management at the watershed scale. Water accounting is a helpful tool to organize information and present them as the standard indicators to achieve this goal. Therefore, the objective of this study is to implement the Water Accounting Plus framework (WA+) in the Ferizi watershed located in the Khorasan-e Razavi Province. In this study, water accounting indicators of the Ferizi watershed for a period of 28 years (1990-2017) and wet (1990-1997) and dry (1998-2009) periods were calculated using the SWAT model. The calculated indicators showed that the amount of manageable water and usefulness of consumption (transpiration) is low in the watershed and a large part of the share of irrigation in the watershed is provided by groundwater resources. Generally, the results of this study showed that the use of the SWAT model, WA+ framework, and analysis of water accounting indicators play a significant role in assessing the agricultural and hydrological conditions of the watershed. The proposed approach in this study can help managers make enlightened decisions to keep the sustainability of the watershed.
K. Ghaderi, B. Motamedvaziri, M. Vafakhah, A.a. Dehghani,
Volume 25, Issue 4 (3-2022)
Abstract
Proper flood discharge forecasting is significant for the design of hydraulic structures, reducing the risk of failure, and minimizing downstream environmental damage. The objective of this study was to investigate the application of machine learning methods in Regional Flood Frequency Analysis (RFFA). To achieve this goal, 18 physiographic, climatic, lithological, and land use parameters were considered for the upstream basins of the hydrometric stations located in Karkheh and Karun watersheds (46 stations with a statistical length of 21 years). The best Probability Distribution Function (pdf) was then determined using the Kolmogorov-Smirnov test at each station to estimate the flood discharge with a return period of 50-year using maximum likelihood methods and L-moments. Finally, RFFA was performed using a decision tree, Bayesian network, and artificial neural network. The results showed that the log Pearson type 3 distribution in the maximum likelihood method and the generalized normal distribution in the L moment method are the best possible regional pdfs. Based on the gamma test, the parameters of the perimeter, basin length, shape factor, and mainstream length were selected as the best input structure. The results of regional flood frequency analysis showed that the Bayesian model with the L moment method (R2 = 0.7) has the best estimate compared to other methods. Decision tree and artificial neural network were in the following ranks.
N. Pourabdollah, J. Abedi Koupai, M. Heidarpour, M. Akbari,
Volume 25, Issue 4 (3-2022)
Abstract
In this study accuracy of the ANFIS and ANFIS-PSO models to estimate hydraulic jump characteristics including sequence depth ratio, the jump length, the roller length ratio, and relative energy loss was evaluated in stilling basin versus laboratory results. The mentioned characteristics were measured in the stilling basin with a rectangular cross-section with four different adverse slopes, four diameters of bed roughness, four heights of positive step, three Froude numbers, and four discharges. The average statistical parameters of NRMSE, CRM, and R2 for estimating hydraulic jump characteristics with the ANFIS model were 0.059, -0.001, and 0.989, respectively. While, the mean values of these parameters for the ANFIS-PSO model were 0.185, 0.002, and 0.957, respectively. The results indicated that these models were capable of estimating hydraulic jump parameters with high accuracy. However, the ANFIS model was moderately more accurate than the ANFIS-PSO model to estimate the sequence depth ratio, the jump length, the roller length ratio, and relative energy loss.
Sh. Nasiri, Hossein Ansari, A.n. Ziaei,
Volume 26, Issue 1 (5-2022)
Abstract
Hydrological models are useful tools in water resources planning, but some of them do not have satisfactory performance on a regional scale. Hydrological models are appropriate for a specific spatial scale and the lack of input data is a limiting factor in the modeling. One way to overcome this limitation is by using a flexible comprehensive model in different watersheds. Since surface and ground water have dynamic interaction in environmental ecosystems and form a combined water resources system so, the application of two general methods including fully integrated and coupled regions has been evaluated in this research. An investigation of these methods showed that the major focus in most studies is on increasing the accuracy of recharge and evapotranspiration rates in simulation. The results showed that the simultaneous use of SWAT and MODFLOW models to understand the hydrological conditions in a region has been able to cover the defects associated with the semi-distributional and distributive constraints of two models, simulating the surface-groundwater and the interaction between the aquifer and river. This method can provide useful information about the water balance of the basin and help to plan water resources more accurately
S. Ebrahimiyan, M. Nohtani, H. Sadeghi Mazidi, E. Soheili,
Volume 26, Issue 1 (5-2022)
Abstract
The basis of land management is the geomorphological zoning of the land surface, which is determined based on the same geomorphological characteristics of the zoning. Ground zoning detect land features by basic surface features such as height, slope, and slope direction. In this study, quantitative zoning of the land surface with small coefficients to the surface has been used to identify suitable areas for artificial feeding in the mountainous region of Gohar and Dasht-e Gorbayegan in Fars province. Quantitative zoning of the land surface has been performed by Evans-Shri coefficients due to the accurate determination and separation of types, faces, and surface features of the land has an important role in determining the exact land use. In this research basic models included linear, circular, and divergent models. These basic models with the dimensions of the final windows are ranked second in the MATLAB software to the level the ground is fitted to determine the fit of these models, the parameter of total squared difference has been used. In addition, the suitability of the study area for flood distribution in five different classes was determined using fuzzy logic. The most suitable areas for feeding downstream of the cones had five parameters with a maximum score of 20. The inappropriate class related to the lower plains of alluvial fans have a minimum score of five input classes in fuzzy logic, which is equal to zero.
S. Azadi, H. Nozari, S. Marofi, Dr. B. Ghanbarian,
Volume 26, Issue 1 (5-2022)
Abstract
In the present study, a model was developed using a system dynamics approach to simulate and optimize the profitability of crops of the Jofeyr (Isargaran) Irrigation and Drainage Network located in Khuzestan Province. To validate the results, the statistical indicators of root mean square error (RMSE), standard error (SE), mean biased error (MBE), and determination coefficient (R2) were used. To validate the simulation results of the benefit-cost ratio, the values of these indicators were obtained 0.25, 0.19, 0.005, and 0.96, respectively. Then, to determine the optimal cultivated area of the network and increase the profitability, the cropping pattern was determined both non-stepwise and stepwise in 2013 to 2017 cropping years. In the non-stepwise, the cultivated area of each crop changed from zero to 2 times of current situation. In stepwise, due to social and cultural conditions of inhabitants, this change was slow and 10% of the current situation every year. The analysis of the results showed the success of the model in optimizing and achieving the desired goals and the total benefit-cost ratio increased in all years both non-stepwise and stepwise. For example, in 2017 compared to 2016, production costs decreased by 7.1 percent and sales prices increased by 5.8 percent, and increased the benefit-cost in 2017 compared to the previous year. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, its cropping pattern, and defining other scenarios.
M. Zareian,
Volume 26, Issue 2 (9-2022)
Abstract
This study was conducted to investigate the effects of climate change on temperature and precipitation changes in important synoptic weather stations in Yazd province (including Yazd, Bafgh, Marvast, and Robat-e-Poshtebadam). Accordingly, a combination of the outputs of the latest AOGCM models presented in the IPCC sixth assessment report (CMIP6) were used to increase the accuracy of temperature and precipitation forecasts. A weighting method was used based on the Kling-Gupta combined index (KGE) to combine these models. After weighting the models, the monthly temperature and precipitation changes were calculated based on SSP126, SSP245, and SSP585 emission scenarios. Then, daily temperature and precipitation time series were extracted for different weather stations using the LARS-WG downscaling model. The results showed that in all the weather stations, CanESM5 and BCC-CSM2-MR models have the best ability to simulate the temperature and precipitation of the historical period, respectively. Results also showed that in all emission scenarios, the annual temperature will increase and the annual precipitation will decrease. The annual temperature of this region will increase between 0.2 to 0.6 °C, and the annual precipitation will decrease between 2.9 and 13.7% in different weather stations. Also, the maximum temperature increase and precipitation decrease in this region, will occur in spring and autumn, respectively.
T. Tahmasbi, Kh. Abdollahi, M. Pajouhesh,
Volume 26, Issue 2 (9-2022)
Abstract
The runoff curve number method is widely used to predict runoff and exists in many popular software packs for modeling. The curve number is an empirical parameter important but depends largely on the characteristics of soil hydrologic groups. Therefore, efforts to reduce this effect and extract more accurate soil information are necessary. The present study was conducted to integrate fuzzy logic for extraction runoff curve numbers. A new distribution model called CNS2 has been developed. In the first part of this research, the formulation and programming of the CNS2 model were done using the Python programming language environment, then the model was implemented in the Beheshtabad watershed. This model simulates the amount of runoff production in a watershed in the monthly time step with the fuzzy curve number and takes into account the factor of rainy days, the coefficient of management of the RUSLE-3D equation, and the soils theta coefficient. The results indicated that the model with Nash-Sutcliff 0.6 and the R2 coefficient 0.63 in the calibration set and Nash index 0.53 and R2 coefficient 0.56 in the validation set had appropriate efficiency in runoff simulation. The advantage of the model is that distributive and allows for the identification of areas with higher runoff production.
F. Daechini, M. Vafakhah, V. Moosavi, M. Zabihi Silabi,
Volume 26, Issue 2 (9-2022)
Abstract
Surface runoff is one of the most significant components of the water cycle, which increases soil erosion and sediment transportation in rivers and decreases the water quality of rivers. Therefore, accurate prediction of hydrological response of watersheds is one of the important steps in regional planning and management plans. In this regard, the rainfall-runoff modeling helps hydrological researchers, especially in water engineering sciences. The present study was conducted to analyze the rainfall-runoff simulation in the Gorganrood watershed located in northeastern Iran using AWBM, Sacramento, SimHyd, SMAR, and Tank models. Daily rainfall, daily evapotranspiration, and daily runoff of seven hydrometric stations in the period of 1970-2010 and 2011-2015 were used for calibration and validation, respectively. The automated calibration process was performed using genetic evolutionary search algorithms and SCE-UA methods, using Nash Sutcliffe Efficiency (NSE) and root mean of square error (RMSE) evaluation criteria. The results indicated that the SimHyd model with NSE of 0.66, TANK model using Genetic Algorithm and SCE-UA methods with NSE of 0.67 and 0.66, and Sacramento model using genetic algorithm and SCE-UA methods with NSE of 0.52 and 0.55 have the best performance in the validation period.
S. Dehghan Farsi, R. Jafari, A.r. Mousavi,
Volume 26, Issue 2 (9-2022)
Abstract
The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.
