JWSS - Journal of Water and Soil Science

One of the major challenges in water resources management is the operation of trans boundary watershed. This has been experienced in case of Helmand River between Iran and Afghanistan since the last century. For such a situation, application of a conceptual rainfall-runoff models that can simulate management scenarios is a relevant tool. The SWAT model can be a relevant option in this regard. However, the required hydro-climatic data for them is a serious obstacle. Especially, this problem gets exacerbated in the case of Afghanistan with poor infrastructures. So, application of this type of model would be more problematic. This paper aims to investigate capabilities of SWAT for the simulation of rainfall-runoff processes in such a data-scarce region and the upper catchment of Helmand River is used as the case study. For this purpose, discharge data of Dehraut station from 1969 to 1979 along with some metrological data were prepared and used to calibrate and validate the simulations. The results were acceptable and the coefficients of determinations (R2) during calibration and validation periods were 0.76 and 0.70, respectively. Notably, with respect to snowy condition of the basin, the elevation band option of the snow module of model had a significant effect on the results, especially in the base flows. Moreover, two Landsat satellite images during February 1973 and 1977 when the basin was partly covered with snow was prepared and compared with the SWAT outputs. Similarly, the results showed good performance of the model such that R2 were 0.87 and 0.82, respectively.

Nowadays, due to the high potential of advanced simulation models for groundwater, these models are comprehensively applied in the management and exploitation of groundwater resources. The aim of this study was to investigate and simulate the groundwater resources in Kouhpayeh-Segzi watershed and in particular estimate the hydrodynamic coefficients of unconfined aquifer. After preparation of input layers, efficient parameters in modeling, boundary conditions and aquifer gridding were determined. Then, based on the available data, the model was run and calibrated in a steady state for the water year 2002 and in a transient state for water years between 2002 and 2004. The simulation outputs were confidently verified for the water year 2005. The results indicated that the hydraulic conductivities and storage coefficients were ranged on sub-basin from 15.26 to 19.87 m/day and 0.0107 to 0.0186, respectively. From aquifer's hydrograph for a period from 1995 to 2012, water level dropped about 25 cm. This may be due to two irrigation networks (green area). This leads to rising water level. By ignoring these recharge areas, water level declined up to 80 cm per year. With sensitivity analysis in transient state to evaluate the efficacy of each parameter, the accuracy of the results of calibration model was confirmed. In addition, the hydraulic head values computed by MODFLOW were in good agreement with those that were collected from all piezometers.

The Palmer Drought Severity Index (PDSI), which uses hydrometeorological variables to solve a simple water balance equation in the soil and considers the drought or wet conditions as dynamic phenomena, is used for the assessment of drought conditions in many parts of the world. The main goal of this study was to assess the PDSI based on its original assumptions, its regionalized status, using the outputs of already calibrated and validated SWAT model in central regions of Iran. The PDSI was assessed through five methods: 1) original Palmer Index without calibration in which the climate coefficients and the severity equation were derived for Kansas and central Iowa 2) original Palmer Index in which the coefficients of severity equations were adjusted 3) the Palmer Index with the calibration of equations in central areas of Iran 4) the Palmer Index using the soil moisture and potential evapotranspiration from SWAT model and 5) the Palmer Index using the soil moisture, potential evapotranspiration and runoff from SWAT model. The evaluation was conducted for 17 major basins covering the entire country with a monthly time step for the period 1990-2002. Then, using all five methods, the severity of the drought for 160 sub basins located in central Iran was calculated and evaluated. The results of this study indicated that method 4 provides more acceptable results. Also, the results of this research showed these methods clearly demonstrated (1992) as the wettest year and (2001) as the driest year. The approach used in this study is applicable to regional calibration of Palmer Index and the outputs of other hydrological models.

Rainfall- runoff modeling and river discharge forecasting are an important step toward flood management and control, design of hydraulic structures in basins and drought management. The purpose of this study was simulating the daily flows in the Navrud watershed using WetSpa model. WetSpa is a hydrological- physical model that can predict flood on the watershed scale with different time steps. This model uses topography, land use and soil texture layers and also, the daily meteorological data to predict the flow hydrograph. In this study, the data of 4 stations (Khlyan, Khrjgyl, Gavkhs, Nav) during the water years 2006-2011 were used. 36 months from September 2006 and 36 months from September 2009 to September 2011 were selected for calibration and test of model, respectively. Simulation results of WetSpa model showed that this model simulates river Daily flow using collective measures of 0.63 and 0.61 in calibration and test periods, respectively. According to this result, it can be stated that the model estimates peak discharge and flow volume in both periods very well. Also, this model could simulate well the water balance of Navrud Basin.

Surface runoff is one of the main causes of erosion and loss of soil fertility, sedimentation in reservoirs and reduction of river water quality. Therefore, the accurate prediction of basin response to precipitation events is very important. Hydrological models are simplified views of the actual watershed systems that can help study watershed functions in response to various inputs, and understand hydrological processes better. Due to the variety of Rainfall - Runoff models, choosing a suitable model for the basin is important for water resource planning and management. Thus, the abilities and limitations of basin hydrological models are important to consider in the selection of model. In this study, the performance of IHACRES model in daily runoff simulation of Navroud basin was investigated using evaluation criteria of Nash – Sutcliffe Index (NSH) and the mean total error and the data of Khlyan and Khrjgyl stations during the Water years 2006 - 2011. 36 months from September 2006 and 36 months from September 2009 to September 2011 were selected for calibration and test of model, respectively. Finally, results showed that Nash – Sutcliffe Index and Bias in calibration stage were 0.57 and 8/53, respectively and in verification stage, they were 0.48 and 14/9, respectively. So, the used model has an acceptable accuracy in simulating the studied basin flow.

Salinity and nutrient deficiency particularly nitrogen are two important limiting factors for yield production in arid and semi-arid regions. The objective of this study was to model basil response to combined salinity and nitrogen deficiency. To that end, modified Leibig-Sprengel (LS) and modified Mitcherlich-Baule (MB) and also some newly derived models based on combination of MB with salinity models of Maas and Hoffman (31), van Genuchten and Hoffman (36), Dirksen and Augustijn (17) and Homaee et al., (23) were evaluated. The experiment was conducted under four salinities including 1.175, 3, 5, and 8 dSm^-1 and four nitrogen levels including 100, 75, 50, and 0 percent of fertilizer requirements each with three replicates. Results indicated that from among the evaluated models, the derived models of MB and Maas and Hoffman (MB-MH) (nRMSE=4.9), MB and van Genuchten and Hoffman (MB-VG) (nRMSE=5.4), and also MB and Homaee et al., (MB-H) (nRMSE=7.0) provide best fits to the measured data. Also, the comparison of two modified LS and MB models indicated that the estimated relative yield for irrigation water salinity levels by modified LS model (nRMSE=4.6) provides better results (nRMSE=5.9). However, for soil nitrogen levels and interactive effects of salinity and nitrogen, the modified MB model (nRMSE=10.3) provided better outputs (nRMSE=14.4). Consequently, instead of the modified LS and MB models the proposed models in this research can be recommended for use.

Rainfall-runoff modeling and prediction of river discharge is one important parameter in flood control and management, hydraulic structure design, and drought management. The goal of this study is simulating the daily discharge in Kasilian watershed by using WetSpa model and adaptive neuro-fuzzy inference system (ANFIS). The WetSpa model is a distributed hydrological and physically based model, which is able to predict flood on the watershed scale with various time intervals. The ANFIS is a black box model which has attracted the attention of many researchers. The digital maps of topography, land use, and soil type are 3 base maps used in the model for the prediction of daily discharge while intelligent models use available hydrometric and meteorological stations' data. The results of WetSpa model showed that this model can simulate the river base flow with Nash- Sutcliff criteria of 64 percent in the validation period, but shows less accuracy with flooding discharges. The reason for this result can be the small and short Travel time noted. This model can simulate the water balance in Kasilian watershed as well. The sensitivity analysis showed that groundwater flow recession and rainfall degree-day parameters have the highest and lowest effect on the results, respectively. Also, ANFIS with the inputs of rainfall 1-day lag and evaporation 1-day lag, with Nash-Sutcliff criteria of 80, was superior to WetSpa model with Nash-Sutcliff criteria of 24 percent in the validation period.

Spatial estimation of evapotranspiration (ET) rates is essential for agriculture and water resources management. This study aimed to estimate ET v an ET estimation algorithm called Surface Energy Balance Algorithms for Land (SEBAL) and also by using TM June 2009 satellite data in Damaneh region of Isfahan province. To calculate the ET, all the energy balance components and related parameters including net radiation, surface albedo, incoming and emitting shortwave and longwave radiation, surface emissivity, soil heat flux, sensible heat flux, NDVI vegetation index, Leaf Area Index(LAI),  and surface temperature were extracted from the geometrically and radiometrically corrected TM images. Results showed that ET rate was about 7.2 mm day^-1 in agricultural areas, which was almost equal to 6.99 mm day^-1 extracted from the FAO Penman-Monteith method in the synoptic weather station of Daran. Results here indicate that the extraction of ET rate which is almost equal to plant water requirements from remote sensing data can be used in selecting appropriate plants for agriculture and rehabilitation purposes in extensive arid and semi-arid regions of Isfahan province where severe droughts and water shortage are major problems.

Simulation of water and solute transport in soil is very useful for optimum management of water and fertilizer use. In this study, the HYDRUS-1D model was used to simulate water and nitrate transport in furrow irrigation of sugarcane. For this putpose, a large-scale experiment was performed as a split plot design based on the randomized complete blocks with 3 replications in a 25-hectare piece of land in the Dehkhoda Sugarcane Agro-Industry Company from March 2012 to October 2013. The main factor was split application of fertilizer at three levels: two, three and four splits. The sub-main factor was fertilizer amount, applied at three levels (i.e. 350, 280 and 210 kg urea corresponding to 100%, 80% and 60% fertilizer requirements, respectively). Soil hydraulic parameters were estimated through inverse modeling using moisture data collected during more than 4 months of the sugarcane growing season. Solute transport parameters were then estimated using the hydraulic parameters and nitrate concentration data. In this study, statistical criteria including R², RMSE, ME and SSQ were used to compare the observed and simulated values of moisture content and nitrate concentration. The results indicated that R² for simulated moisture content and nitrate concentration in four splits and 60% fertilizer requirement treatment (i.e. calibrated treatment) were 62.7 and 91.2 percent, respectively. Cumulative infiltration depths were about 46 and 58 mm for calibration and validation treatments, respectively. For these treatments, the cumulative evapotranspiration rates were 50 and 60 mm, respectively. Soil moisture content in the surface layer varied from 21 to 45 and 21 to 42 percent, for calibration and validation treatments, respectively while the changes in the deep layer moisture content were 33 to 38 percent, for both treatments.

Splash erosion is one of the most important water erosion types, causing initiation of other types of water erosion. The objective of this study is to model the splash erosion using fuzzy logic approach in part of northern Karoon basin. The major land usage in the area are irrigated farming, dry land farming, pasture and degraded pasture. For the purposes of this study, soil properties including organic matter; CaCO₃; surface shear strength (SSS); particle size distribution; mean weight diameter (MWD) and soil splash erosion were measured under four different slope conditions (S:%) and rainfall intensity (RI:mm.h-1): 5-50, 5-80, 15-50, 15-80, respectively, using multiple splash sets (MSS) at 80 different locations. Splash erosion was modeled based on combinational rule of inference under five conditions for selection of different operators. The efficiency of the models was evaluated using mean square error (MSE) between observed and estimated values. Results revealed that all models are capable of predicting splash erosion. Also slope, rainfall intensity, MWD, SSS, fine sand and coarse silt attributes were found to be appropriately and precisely using splash erosion.

At the river catchments, different strategies at the whole or different parts of the basin can be applied for water resources management. One of these strategies is optimal water allocation and crop pattern. In this study, an optimization model for water allocation and cropping pattern is presented based on the cooperative game theory. To measure the performance of the developed model, the cultivated area of Ordibehesht Canal in the Doroodzan irrigation network has been studied. First, using a fuzzy model and considering the fuzzy coefficients values in the objective function and constraints, the optimal crop pattern and allocated water has been determined for each crop. Second, benefits of stakeholder’s coalitions have been determined by developing a cooperative game model and based on the structure and properties of the irrigation water distribution network and water rights of each part. Then, the total net benefit has been reallocated to the different stakeholder in a rational and equitable way using Least Core games. The results show that by allocating more water to the sectors with more potential production, more profits are generated and water productivity increases. For example when players cooperate together and form the grand coalition, the net benefit increases from 8.906 billion Tomans to 9.724 billion Tomans that show an increase in the economic productivity of water.

Shortage of rainfall and also relatively high intensity precipitations in short-term are characteristics of arid regions of the world, such as central of Iran. Studies have indicated that massive flooding causes great loss of life and properties every year. Also, Water scarcity in arid and semiarid regions of the world will cause fragile living conditions in these areas. Therefore, it is needed to reduce runoff rates using actions such as dam construction or artificial recharge techniques. In this study, seven factors were applied such as the slope, surface permeability, transmissibility in alluvium, alluvial quality, land use, runoff volume and thickness of the unsaturated layer to determine suitable areas and site selection for flood spreading and artificial recharge in south of Kashan plain. After preparing the digital layers, criteria weights were determined using Fuzzy AHP. The weighted maps were acquired and merged together. Results indicated that land use criterion with the greatest weight (0.22) was determined as the first priority in the site selection for flood speading. The parameters of runoff volume, permeability, slope, depth of the unsaturated layer, alluvial quality, and transfer coefficient were accounted as the second to seventh priorities. 

In this study, two different approaches of infiltration parameters estimation in traditional, variable and fixed alternate furrow irrigation, with and without cutback inflow, were performed and compared. Four usual methods including two-point (Elliott and Walker), Valiantzas one-point, Mailapalli one-point and Rodriguez and Martos optimization methods, as approaches based on advance data, and multilevel optimization method as an approach based on the advance, storage and recession data, were considered. Surface irrigation model: WinSRFR was used to simulate irrigation phases and infiltration value in each method. 13 furrow irrigation field experiments, from two case studies: Karaj and Urmia, were used to perform different methods. Based on the results, the multilevel optimization method predicted the advance and recession phases and runoff-infiltration with high accuracy for traditional, variable and fixed alternate furrow irrigation. The multilevel optimization method for traditional furrow irrigation, showed more accuracy than variable and fixed alternate furrow irrigation in advance and recession phases and the average root mean square error (RMSE) for predicting advance phase for the three furrow irrigation methods was 1.37, 1.8, and 1.57 minutes and for the recession phase was 3.76, 5.0, and 3.03 minutes, respectively. Also the multilevel optimization method for cutback options indicated high performance to advance and recession prediction and the average RMSE of advance and recession prediction were obtained 3.57 and 2.13 minutes for cutback option and 3.8 and 1.3 minutes for no cutback option, respectively. The multilevel optimization method indicated high performance in storage phase, too. The average of relative error (RE) of runoff estimation for traditional, variable and fixed alternate furrow irrigation was calculated 0.5, 0.4 and 0.4 percent, respectively. The runoff average RE of multilevel optimization method with cutback and no cutback option were obtained 1.85 and 0.85 percent, respectively; that showed high performance of this method for no cutback option in comparison with the cutback option. Therefore, the use of data of all irrigation phases to estimate infiltration parameters shows better performance in the prediction of irrigation and water balance components. (run-off and infiltration).

In this research, a comprehensive simulation model for water cycle and the nitrogen dynamics modeling including all the important processes involved in nitrogen transformations such as fertilizer dissolution, nitrification, denitrification, ammonium volatilization, mineralization, immobilization as well as all the important nitrogen transportation processes including nitrogen uptake by the plant, soil particles adsorption, upward flux, surface runoff losses and drain losses, was used for fertilizer management modeling in a sugarcane farmland in Imam Khomeini Agro-Industrial Company using a system dynamics approach. For evaluating the model the data collected from Imam Agro-Industrial Company equipped with a tile drainage system with shallow ground water and located in Khuzestan province, Iran, were used. The statistical analysis of the observed and simulated data showed that the RMSE for determining the accuracy of simulation of the nitrate and ammonium concentration in drainage water is 1.73 mg/L and 0.48 mg/L, respectively. The results indicated that there is good agreement between the observed and the simulated data. Nine scenarios of fertilization at different levels of urea fertilizer were modeled including one scenario of 400 kg/ha, two spilit scenarios of 350 kg/ha, two spilit scenarios of 325 kg/ha, two spilit scenarios of 300 kg/ha, one scenario of 280 kg/ha and one scenario of 210 kg/ha. Results of the modeling showed that the scenario of 210 kg/ha has the highest nitrogen use efficiency (52.3%) and the lowest nitrogen losses consisted of denitrification, ammonium volatilization and drainage losses (17.82, 7.16 and 92.59 kg/ha, respectively). The results revealed that increasing the consumption of urea fertilizer greater than 210 kg/ha increased the overall nitrogen losses and reduced the nitrogen use efficiency. Meanwhile, this model can be used for managing the fertilizer and controlling the nitrate and ammonium concentrations in the drainage water to prevent the environmental pollution. Also, the system dynamics approach was found as an effective technique for simulating the complex water-soil-plant-drainage system.

Accurate design of drip irrigation systems requires sufficient understanding of horizontally and vertically distribution of water flow in soil and modeling the wetting pattern dimensions created under the drip source. Field and laboratory activities are not suitable for this purpose considering their time and financial constraints and it is necessary to apply accurate software for determination of several equations in different situations. This research aimed to present simple models for calculation of wetting pattern dimensions in different discharges and structures in drip irrigation system. For this purpose, HYDRUS-2D model was implemented for four discharges in the same soil texture and different soil textures in the same discharge. The values obtained from running the software such as depth and maximum diameter of wetting pattern have been fitted with time values and corresponding equations were obtained. The results of statistical indices for all obtained equations (R>0.96, RMSE<2.12 and MAD<1.38) represent suitable accuracy of corresponding equations in determination of pattern dimensions under the drip source. The results also showed that Loamy Sand and Silt textures, respectively, have maximum and minimum depth and wetting pattern diameter.

With regard to the confirmation of climate change in most regions of the world and its effects on different parts of the water cycle, knowledge of the status of water resources is necessary for proper management of resources and planning for the future. Hence many studies have been done in different areas with the aim of analyzing the impact of climate change on hydrological processes in the upcoming periods. In present research, the effect of climate change on surface runoff in Bazoft watershed has been studied. Bazoft watershed, located in North-West of Chahar Mahal & Bakhtiari province, has significant contribution in the production of water resources of the region due to its special topographical and geographical status. In this study, climatic model – HadCM3- and A2 and B2 emission scenarios have been used to assess uncertainty in forecasting climate change. For this purpose, a statistical model –SDSM- has been applied to downscale large- scale precipitation and temperature data and hydrological model –WetSpa- has been used to simulate runoff. After calibration of the hydrological model, downscaled precipitation and temperature data in near future (2020-2050) and far future (2070-2100) periods were introduced to WetSpa model and runoff was simulated for mentioned periods. Results of this study represent suitable performance of SDSM model in downscaling climatic data, especially minimum and maximum temperature. Also, performance evaluation of Wetspa model shows proper performance of this model for runoff simulation in Bazoft watershed, so that Nash- Sutcliffe efficiency during calibration and validation was 0.63 and 0.65, respectively. Moreover, assessing the amount of predicted runoff for future periods indicates an increase in annual runoff in the Bazoft watershed under both A2 and B2 scenarios.

This research is aimed to determine the contribution of sub-basins flow to total watershed flood in Khersan river basin located in Kohkilooyeh and Boyer Ahmad province. To do this, the rainfall-runoff model HEC-HMS was used to simulate peak runoff values for 11 sub-basins. HEC-HMS input was constructed using GIS. The results suggest that the change in different return periods is accompanied by small change in prioritization of flood-potential of the sub-basins; so that for return periods of 2, 50 and 100 years, the most contributions came from sub-basins 1 through 11, respectively. With respect to area and flow rate, contribution of sub-basins to watershed total flow was different. The effect of area was between 0.31 to 1.03 percent; namely, sub-basin 6 showed the highest rank and basin 7 showed the lowest one. With respect to peak flow rate, the effect of individual exclusion of sub-basins, resulted in contribution between 51.2 to 1004.2 m³/s, that is, sub-basin 6 showed the lowest effect and the sub basin 11 showed the highest contribution.

Accelerated soil erosion in Iran causes on-site and off-site effects and identifying of sediment sources and determination of their contribution in sediment yield is necessary for effective sediment control strategies in river basin. In spite of increasing sediment fingerprinting studies uncertainty associated with magnetic susceptibility properties has not been fully incorporated in models yet. The objective of this study is determination of the relative contribution of sediment sources using magnetic susceptibility properties (High frequency, Low frequency and Frequency dependence) incorporated in uncertainty mixing model. For this purpose, 25 bed sediment samples were collected from the outlet of drainage basin and outlet of sub-basins and their magnetic susceptibility was measured and calculated. The results of Kruskal–Wallis test and discriminant function analysis showed that magnetic susceptibility properties can be used as optimum set of tracers in the uncertainty mixing model. The results of Bayesian mixing model indicated that mean (uncertainty range) relative contribution of Sparan, Joyband and Boyoukchay are 92 (83.9-94.8), 2.8 (0.2-10.7), 5.7 (0.2- 10.5) percent, respectively. According to these results, the highest amount of sediment yield is related to Sparan sub-basin and these results could be used in soil conservation and management planning.

Landslide susceptibility mapping is essential for land use planning and decision-making especially in the mountainous areas. The main objective of present research is to produce landslide hazard zonation at Sarkhoun basin in Karoon basin using two statistical models such as an index of Shannon’s entropy and weight of evidence and to assess the obtained results. At the first stage, landslide locations were identified in the study area by interpretation of aerial photographs, image sattellites, and from field investigations and then landslide inventory map was created for study area. The landslide conditioning factors such as slope degree, slope aspect, altitude, lithology, land use,distance of road, distance of fault, distance of drainage, topographic wetness index (TWI), Convergence Index, and precipitation were extracted from the spatial database and they were digitized in GIS environment. With integrated variables, landslides were calculated in each variable class and ‎weighted in index of entropy and weight of evidence model. In the last, landslide hazard zonation map ‎were obtained with both of models. The results of landslide susceptibility mapps of both statistical models were indicated more than 70 percent the occurred landslides were located in very high and high zones that about half of the basin area (over 45 percent) constitute. Also, the results of both models together were revealed that land use, has the greatest impact on the occurred landslides. Resolution of the zones, based on the seed cell area index (SCAI) and frequency ratio (FR) were evaluated suitable for both statistical models. Finally, the ROC (receiver operating characteristic) curves for landslide susceptibility maps were drawn and the areas under the curve (AUC) were calculated. The verification results of both models were evaluated very well and showed that the index of entropy model (AUC=89%) performed slightly better than weight of evidence model (AUC=82%).

Soil erosion is undoubtedly one of the most important problems in natural areas of Iran and has destructive effects on different ecosystems. Considering that calculation of the sediment rate in sediment stations and direct measurements of erosion process is costly and difficult, it is critical to find ways to accurately estimate the amount of sediment yield in catchments especially in arid and hyper arid areas because of their high ecological sensitivity. One of the most commonly used methods in these areas is the sediment rating regression method. Therefore, in this study sediment observed data for 48 events (the corresponding discharge and sediment) in a 23-year period from Fkhrabad basin (Mehriz) were compared to the estimated data obtained from Multi-line rating method, extent middle class, middle class rating curve with correction factor QMLE, SMEARING correction coefficient FAO and Artificial Neural networks (ANNs). Finally, the accuracy of these methods were assessed using different evaluation criteria such as Root Mean Square Error (RMSE), coefficient of determination (R2) and the standard Nash (ME). Results showed that ANN outperformed the other methods with the RMSE, R2 and ME of 203.3, 0.86 and 0.66, respectively. The results suggest that these methods should be used cautiously in estimating the suspended sediment load in arid and hyper arid regions due to the nature of the observed data and temporal and seasonal flow systems in these regions. It was also indicated that the artificial neural network models have higher flexibility than other methods which makes them to be useful tools for modeling in poor data conditions.