مجله مهندسی و مدیریت آبخیز
سال چهاردهم شماره 3 (پاییز 1401)

Groundwater recharge or deep drainage or deep percolation is a hydrologic process where water moves downward from surface water to groundwater. Recharge is the primary method through which water enters an aquifer. Groundwater recharge depends on several factors such as infiltration capacity, stochastic characteristics of rainfall, and climate factors. Groundwater recharge is of great importance especially in semiarid regions. In arid and semi-arid regions of the world, groundwater serves as an essential alternative to surface water resources for water supply purposes. It plays a significant role in meeting the water demands of man and the ecosystem and is perceived as the panacea to the looming water scarcity scare. Determination of recharge quantity provides worthy help for managers in water resources management. Ground water recharge includes recharge as a natural part of the hydrologic cycle and human-induced recharge, either directly through spreading basins or injection wells, or as a consequence of human activities such as irrigation and waste disposal. The Soil and Water Assessment Tool (SWAT) is a river basin scale model developed to quantify the impact of land management practices in large, complex watersheds. SWAT is a public domain hydrology model with the following components: weather, surface runoff, return flow, percolation, evapotranspiration, transmission losses, pond and reservoir storage, crop growth and irrigation, groundwater flow, reach routing, nutrient and pesticide loading, and water transfer. SWAT is a continuous time model that operates on a daily time step at basin scale. Its objective is to predict the long-term impacts of management and of the timing of agricultural practices within a year (i.e., crop rotations, planting and harvest dates, irrigation, fertilizer, and pesticide application rates and timing). It can be used to simulate at the basin scale water and nutrients cycle in landscapes whose dominant land use is agriculture. It can also help in assessing the environmental efficiency of best management practices and alternative management policies. In this study, the hydrologic process of the Marvast basin was simulated using SWAT Model in order to determine the amount of groundwater recharge in Marvast plain. In this way, firstly, the required maps i.e. slope, soil and land use maps were produced. In order to produce land use maps, panchromatic and multi-spectral imagery were fused to enhance the spectral and spatial resolution of Landsat imagery. In the next step, the fused imagery was used to produce land use maps using pixel based and object oriented image processing techniques. The slope map was produced using digital elevation model. The soil map was also produced using soil profiles in the regions. The requisite climatic data were also imported to the model with a daily scale. According to the importance of irrigation and its effect on evapotranspiration and groundwater recharge, irrigation amounts ​​were also considered importing irrigation plan in SWAT Model. Afterwards, the model was calibrated using SWAT CUP software and the SUFI-2 algorithm. Finally, the verification showed that the model with Nash-Sutcliff of 0.59, coefficient of determination of 0.83 and the root mean square error of 0.05 has a relatively good performance. The results showed that object oriented image processing technique outperformed pixel based technique. It was shown that the amount of groundwater recharge was 27082602 cubic meters and the irrigation water return coefficient is 34%. It was confirmed that SWAT Model has a relatively good performance for groundwater recharge modeling. Improving the cropping pattern, preventing development of unauthorized wells and excessive groundwater withdrawals, as well as proper irrigation systems, can be effective in reducing the groundwater storage deficiency and preventing an increase in water resource crisis. This study showed that this model is not efficient for short term runs, however, its performance is better for long term runs. It is suggested that the SWAT and MODFLOW Model be used together to study both surface and underground currents. Also, lysimeters or SWAP Model can be used to better determine the amount of return flow and groundwater recharge.

Today, land use change is considered as a challenge of environmental issues and known as an ecological problem. Land use changing is one of the most important parameters in planning over time. The purpose of this study is to detect land use changes in the Jiroft Basin in the years 1997, 2008, and 2018 and it’s predicting in 2040. For this, Landsat images were collected and the preprocessing steps, including atmospheric and radiometric corrections, were done by ENVI software. A false-color combination, as well as an NDVI vegetation index map, were prepared for these years. Land use maps were prepared by supervised classification using maximum likelihood algorithm. The land use changes evaluated by Land Change Modeler (LCM) in these periods. Finally, land use map for 2040 was predicted by the Markov chain and IDRISI software. According to the Kappa index, the exported maps showed an acceptable accuracy (>0.76). Land use changes between 2008 and 2018 showed that the urban areas, agricultural lands, gardens, salty lands, and barren area were increased, but rangelands and forests were decreased. In the coming years, it is expected that with the current management method, the urban areas, agriculture and barren area will increase, while forest, gardens, and rangeland areas will decrease. To reduce the land use change effects, it is recommended to act according to sustainable development by notice to the ecological potential of the land.

Hendijan wind erosion center is located in Khuzestan Province and southwest of Iran. In the last decade, with the increase of erosion rate and concentration of important dust centers in this area, the study of heavy metal concentration and pollution in this area has become very important. For this purpose, 33 soil samples of this zone were collected based on land use change and with the aim of uniform distribution and analyzed by ICP-MSS. The obtained data show that the highest average concentrations of metals belong to Cd <As <Cu <Pb <Zn <Ni <Cr, respectively. Enrichment indices, geo-accumulation index and contaminant factor showed that the highest pollution in Hindijan area belongs to nickel, chromium, arsenic, cadmium and plumbum, respectively. On the other hand, ecological risk assessment in the mentioned area has shown that this region has a low ecological risk that among the studied metals, the highest ecological risk belongs to the two metals arsenic and cadmium. The results of PCA test showed that the metals nickel, zinc, copper and lead have both non-anthropogenic and anthropogenic sources and the source of arsenic and cadmium metals is human activities. According to the pattern of metal distribution, it can be stated that the activities related to Bahrkan fishing pier on the one hand and oil rigs off the coast of Hindijan oil field on the other hand have caused the concentration of nickel, lead, zinc and copper in the south of the study area. Agricultural activities have also controlled the concentration of cadmium and arsenic metals in this area, and the source of chromium concentration was determined as agricultural effluents, traffic pollution and residual pollution from the 8-year Iraq-Iran war.

Slope instability and landslides are important hazards to human activities that often result in the loss of economic resources, property damage and facilities. These hazards occur in the natural or man-made slopes. In the current study, the maximum entropy model was used which is one of the progressive data mining models, in order to modelling landslide susceptibility map for Gorganrood watershed. In the first step, the landslide inventory map was prepared consiste of 351 landslides. 18 geo-environmental factors were selected as predictors, such as: Digital elevation model, slope percent, aspect, distance from fault, distance from river, distance from road, rainfall, landuse, drainage density, lithology, soil texture, plan curvature, profil curvature, lithological formation, Topographic wetness index, LS factor, stream power index, Relative Slope Position and Surface roughness index. Three different sample data sets (S1, S2, and S3) including 70% for training and 30% for validation were randomly prepared to evaluate the robustness of the model. The accuracy of the predictive model was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The ME model performed excellently both in the degree of fitting and in predictive performance (AUC values well above 0.8), which resulted in accurate predictions. Furthermore, In this study the importance of variables was evaluated by the model. Dem (digital elevation model) (32.4% importance), lithology (22.9% importance) and distance from fault (14.8% importance) were identified respectively the main controlling factor among all other variables.

Determining precipitation spatial pattern in a catchment is necessary for the calculation of hydrologic quantities such as runoff flow and soil moisture content. Sparse meteorological stations as well as spatial variability of precipitation are major obstacles for accurate spatial estimation of precipitation. The development of remote sensing technology and the possibility of using satellite precipitation products has facilitated attaining spatial precipitation patterns. However, low spatial resolution of satellite precipitation products highlights the need for downscaling methods. Nineteen predictive models were fitted using Regression Learner toolbox in MATLAB software. Annual TRMM precipitation data were downscaled from 2001 to 2017 using Normalized Difference Vegetation Index (NDVI), land surface temperature, land elevation and coordination. Models are divided into five general categories: Linear Regressions, Decision Trees, Support Vector Machines, Ensemble models and Gaussian Process Regression models. Comparing the downscaled TRMM data with gauges data, Boosted Ensemble model had the lowest root mean square error and highest correlation coefficient. On the other hand, two methods of Geographical Distance Adjustment (GDA) and Geographical Ratio Adjustment were compared for calibrating the downscaled precipitation. Smaller errors were obtained using GDA in all models.

In the last decade, the use of remote sensing has played an important role in identifying and assessing natural disasters, especially floods. Among these techniques, the Support Vector Machine algorithm (SVM) and Change Detection technique can be mentioned. The main objective of this study was to evaluate the capability of these techniques in determining the effects of flood in Gori Belmak Wetland and Poldokhtar triple wetlands in the north of Molab and outlet of Poldokhtar watersheds in Lorestan Province, which was faced with flood in April 2019. The land use maps of the region were prepared by applying supervised classification method and the SVM on the Landsat 8 satellite image in the 2013, 2015, 2017 and 2019. Validation of the maps and techniques using indicators of kappa and overall accuracy, showed the high accuracy of maps prepared. The kappa coefficient was calculated to be 0.87, 0.84, 0.83 and 0.87 for the maps of the studied years and the overall accuracy was 90.02, 89.51, 88.11 and 90.32, respectively. By extracting the water class, the changes that occurred on the water body of the wetlands were detected. The results showed that Gori Belmak Wetland, undergo extensive changes due to reasons such as drought in 2015, increase of 112.08 ha of surrounding arable lands between 2013 and 2019, as well as topographic features, especially lower slope than the three wetlands. In 2019, with the storage of flood, this wetland increased to 47.08 ha compared to 2017 and reached an area of 146.15 ha. The similarity of the results obtained in this study with the results of the research conducted in the study area by the Copernicus Emergency Management Service (EMS) and the Geoinformatics Unit research team on the flood of 2019 indicates the high accuracy of the used techniques and results of the present research.

Today, the use of hydrological models is mainly necessary to simulate changes in water source and flow (runoff and evaporation). Proper modeling of hydrological processes requires the determination of model parameters. In calibration processes, the values ​​of the model parameters are estimated so that the model can simulate a natural system well. It is generally impossible to estimate the parameters of such models directly due to the large number of parameters and it is necessary to estimate them with the help of optimization tools (model calibration). In the present study, the parameters of the daily Hymod rainfall-runoff model (a simple conceptual rainfall-runoff model) were calibrated using the Whale algorithm (WOA), which is derived from the way whale food is searched. The evaluation of the mentioned calibration method was performed using daily precipitation, evapotranspiration and transpiration data for 5 years and its validation was performed in 5 years in the Leaf River Basin of the United States. The simulated and observed flow rates were compared using correlation coefficient (R2), root mean square error (RMSE) and Nash-Sutcliffe coefficient (NS). The values ​​of error measurement criteria were 0.91, 1.2 and 0.8 for the calibration period and 0.91, 2.5 and 0.83 for the validation period, respectively. Also, the parameters calculated using the whale algorithm, the maximum moisture storage in the area of 216.95 mm, spatial variation of soil moisture storage 0.38, the distribution factor between the two moisture tanks 0.98, the shelf life in the laminar tank 0.08 days and Shelf life in fast flow tank is 0.47 days. Examination of error values ​​showed that the Whale Optimization Algorithm has high efficiency in calibrating rainfall-runoff models.

Predicting and analysing flow in the vegetated channel and wetland is the main challenging environmental and hydraulic problem. It is due to the high dependency of the hydraulic properties of the flow to the physical structure, density, and distribution of vegetation in wetlands and channels In this study, velocity profiles are investigated in emerge vegetated channels with considering density effect. The experiments were performed in a laboratory flume with vegetation called Eleocharis and three different types of densities. This study aims to provide a comprehensive analysis of velocity profiles in emerged vegetation channels in the presence of real plant species and to provide a more accurate relationship. Therefore, studies were performed in a laboratory flume with a rectangular cross-section 20 meters long and 1.5 meters wide with a fixed floor slope of 0.005, in which a type of vegetation called Eleocharis was used in three types of low, medium, and high density. The results showed that the velocity in the upstream areas is the lowest which on average shows an average velocity in the upstream compared to the downstream for different densities of about 4 to 12 percent. Meanwhile, the average velocity decreases by increasing the plant density from 23 to 42 and from 42 to 72 about 12 and 4.5 percent in the upstream and about 11 and 4 percent in the downstream, respectively. Also, to estimate the average flow velocity in the vegetation area, a modified relationship was presented which has acceptable accuracy. The application and accuracy of this relationship in artificial wetlands and channels are important to eliminate runoff pollution and sedimentation.

The degree of public participation in watershed management programs is a major determinant of success or failure of the programs, but the factors which make people participate still remain unknown. The main objects of current study are comparison evaluation of effective factors on preventing participation of rural societies in watershed management plans based on all people and experts’ viewpoints in Emarat Watershed of Khorasan Razavi Province. In the current study the effective factors on preventing participation of rural societies were classify in four classes include "economic", "educational-extension", "design-executive" and "social" indicators and 14 sub-indicators. Then the indicators and sub-indicators were prioritized using Fuzzy Analytical Hierarchy Process and Friedman Test. Finally, the two-sample Kolmogrov–Smirnov Test was also used to examine the agreement of the two views on the importance of the items. The results showed that, from the perspective of both groups of experts and peoples, economic indicator has a greater role in preventing people participation in relation to other indicators. The results of sub-indicators prioritization based on local people and expert’s viewpoints showed that “ignoring people´s income as a direct economic motivation” and “shortage of education of watershed residents about plants and their purposes”, respectively, were ranked as the most important sub-indices. The most important difference of the sub-indices from the perspective of local people and expert are related to the “ignoring people´s income as a direct economic motivation” and “lack of stakeholder’s consultation in the design and development of projects”. The overall conclusion is that considering the benefits of stakeholders, and multi-purpose projects and factors such as stakeholder consultation, and training of local communities can provide the basis participation of people in the watershed development projects.

Precipitation is of the most important inputs of runoff modeling. The availability of precipitation data with appropriate temporal and spatial accuracy is very important and necessary for watersheds with small and scattered rainfall stations. Nowadays, climatic satellites are practical and widely-used tools in precipitation estimations. In this study, first the efficiency of TRMM satellite precipitation data in the monthly time series of Chehelchai Watershed was evaluated using R2, RMSE, NSE and Bias statistical indices by comparing the precipitation data of rain gauge stations (observed)  and the values of these statistical indices were 0.54, 22.70, 0.44 and -14.86, respectively. Considering the value of the coefficient of determination (R2), it can be concluded that the TRMM satellite was able to estimate the 0.54 of observed precipitation. In the next step, three base data models including MLP, ANFIS and SVR were used to estimate the monthly runoff. Two different input scenarios were selected :1) observed precipitation data in t and t-1 time steps and runoff in t-1 time step and 2) satellite precipitation data in t and t-1 time steps and runoff in t-1 time step. To compare the accuracy and error of the models, R2 and RMSE of the validation stage were used. The ANFIS model with the values of R2 and RMSE were 0.80 and 0.97 for the first type input combination and 0.78 and 1.02 for the second type input combination, respectively, as the suitable single model for estimating runoff in the study area were selected. Then weighted-mean method was used in the data fusion approach to provide a data driven combination model for each combination of inputs into the model in the studied watershed. This data fusion approach data-driven model improved the values (R2=0.81) and (Bias=-4.85) for the first type input combination and also improved the value (R2=0.79) for the second type input combination.