IRAN-WATER RESOURCES RESEARCH
Year:2016 | Volume:11 | Issue:3 (34)|Papers Count:15

Zarrineh-Rood is one of the most important rivers of Urmia lake basin. Since, agricultural sector plays a key role in this region, and also in recent year the water resources in this basin is faced with a lot of pressure that lead to many environmental problems in urmia lake. so the evaluation of efficient water allocation to the agricultural crops, especially in drought conditions may have a noticeable impact on the water productivity on the basin. In the current study, attempt to determine the best solution for water and land allocation by developing an operational decision support system using prediction of flow during the next year by support vector machine (SVM) and also an agricultural water allocation model that is based on the old and new FAO crop response to water equations. The results show that each of these methods proposes a different composition of optimal allocation and Cultivated area. Also the FAO-2009 method in comparison to FAO-1979, has led to a higher income and productivity at all levels of drought conditions. In addition, SVM prediction results show an over-90% correlation for flow prediction in both supply all of the Urmia lake water requirement and reduction of lake water allocation based on drought condition. So and holistic model with linking prediction model and water allocation model is so suitable for water resources planning and also for applying management scenario in deferent future conditions. This package can be used as an decision support system for basin water resources management.

The optimal design of water conveyance canal sections is one of the guidelines in management of water resources. Design of canal section with minimum loss of water is to consider minimizing seepage and evaporation under uniform flow condition. So far, the optimal design of canal sections was based on inaccurate estimates of seepage rate which needs to be revised. In this study, the seepage from a canal in miscellenouse conditions was modeled using SEEP/W software. The accurate soft model between input and output data was then extracted. Comparing the extracted soft model with other empirical equation, indicate the high accuracy of the selected soft model. This model were then used in optimization process with the aim of minimizing the canal water loss. The results are presented in terms of dimensionless graphs which made a simple design of canal dimensions possible. Comparing the results with other similar studies indicated the significant changes in optimal canal dimension.

Evapotranspiration is the main component of hydrologic cycle and has an important role in crop water requirement estimations, water balances studies, and water resource management. There are a lot of direct and indirect methods to estimate reference crop evapotranspiration, but each has some limitations. For example, limitations that can be mentioned for direct measuring are the insufficient precision in measuring devices and the scale problems. An indirect method like Penman-Monteith on the other hand needs a lot of daily climatic parameters. This research tried to use selforganizing maps as an unsupervised artificial neural network method to predict evapotranspiration by minimum meteorological data input. Based on fuzzy clustering indices, evapotranspiration values in the study area, Mashhad plain, are divided into two clusters with low and high ETo coincided with the climate of the area. Also, in order to validate the model, statistical indices containing root mean square error, determination coefficient, and Nash–Sutcliffe model efficiency coefficient are used and the results are compared with the experimental models output. The results showed that even the simplest SOM model which employs mean temperature and maximum sunshine duration as input have less errors compared to the experimental equations.

In last few decades conflict-resolution models are being increasingly used in water resource management for cases such as the groundwater problems as an appropriate approach to consider the oppositions and trade-offs between the stakeholders involved in the conflict and to reach to an applicable optimal resolution. In this paper, by integrating simulation-optimization models of groundwater exploitation and bargaining methods, the optimal allocation scenarios are derived taking into account the preferences of the stakeholders and social criteria such as justice. Trade-off Pareto front between the rival objectives was computed through linking the NSGA-II multi-objective optimization model and M5P meta model which was trained and validated based on MODFLOW simulation results. Monte-Carlo method was used to develop a database for training and validating meta models for different allocation scenarios.Considering multi-objective nature of the problem, the best solutions on Pareto fronts were selected using fallback bargaining models. The effectiveness of the proposed methodology was verified in a case study performed on Daryan aquifer, Fars province, Iran. Results indicated that the total groundwater withdrawal after applying the optimal scenarios of allocation was reduced approximately 56% which resulted in the mean water level uplift of 4.2 meters in the aquifer.

Due to the drying up issue of Urmia Lake, currently several options are under review. Decreasing water demand in agricultural sector through programs like-change in cropping patterns‖may undoubtedly be considered as a notable option which requires an economic assessment of further implications of it. In the present research, based on Payment for Ecosystem Services (PES) policy tool, we attempted to analyze the results of a technical and economic assessment of likely changes in cropping pattern in a sub-basin of Urmia Lake. For this purpose, first a GIS based land use map was prepared and the suitable areas was identified. Then all information required for assessing the PES scheme were collected according to an Interview-Questionnaire mixed technique. During the field survey phase, 398 questionnaires were completed by farmers in Siminehrud sub-basin. Among various options of PES, payment to farmers to encourage them to change the cropping pattern using three oily plant species (soya, canola and safflower) has been proposed. Then the results have been analyzed in terms of economic impacts.The findings of this study showed that only changing the crop pattern to consider safflower is economically feasible and also technically efficient.

Synthetic data generation models have been recognized as useful tools to predict and generate alternative time series or long-term series throughout the studies conducted in the domain of water resources management. Accordingly, these models have widely been used by different researchers across the world. In the recent decades, these models have been developed to generate annual, monthly, and daily rainfall or river flow data. Among the synthetic data generated, monthly data are of great importance since they are used in the critical and important studies in the field of water resource systems, such as storage tanks and drought monitoring. Accordingly, the utilization of the monthly synthetic data models leads to more detailed analyses about the real performance of such systems. On the other hand, the theoretical basis of different stochastic models is the generation of diverse monthly data and the performance of these models can remarkably be affected by this fact. Therefore, one can argue that selecting an appropriate model is one of the major concerns of water resources experts. As such, this study made use of the Monte Carlo simulation method to compare and assess the performance of four types of non-parametric Bootstrap models as well as parametric models of Valencia-Schaake, Thomas-Fiering, and Fragment in monthly synthetic data generation. To do this, the monthly flow data of Nazluchay, Shaharchay, and Barandozchay rivers, located at the Western Azerbaijan province in the North West of Iran, were analyzed over a 47-year period. Then, 1000 synthetic time series of monthly flows for these rivers were generated and used for each of the desired seven models over a 47-year period thereof. The results indicated that, compared to other models, the Valencia-Schaake distribution model had a very high performance in terms of all well-known assessment statistical indicators.

In recent decades, the application of intelligent evolutionary methods and hybrid models for forecasting groundwater spatiotemporal fluctuations were more focused by researchers. Genetic algorithm and Neuro-Fuzzy are new methods which are applicable in single and hybrid forms for forecasting in complex and nonlinear problems. In this research, aforementioned methods were applied to study the Hadishahr plain aquifer. The Hadishahr plain is located in the north of East Azerbaijan province and it is a part of Julfa–Duzal study area. This aquifer suffers from groundwater level declination due to groundwater withdrawal increase. To achieve practical ways for spatio-temporal groundwater level forecasting, the artificial intelligence methods such as neuro–fuzzy (NF), genetic programming (GP) and combination their best model with geostatistical methods were used.Precipitation and evaporation in t0 time step and groundwater table in t0-1 time step were the inputs to the Neuro-Fuzzy and Genetic Programming. The results showed that the average RMSE of selective piezometers for genetic programming and neuro-fuzzy were 19 and 23 centimeter in the test step, respectively. Then, genetic programming was used to present a hybrid model in combination with the geostatistical model (kriging). Finally, the hybrid model-genetic - kriging‖ were applied to predict the spatiotemporal prediction of the groundwater level. The simulated results were extended to the whole plain and the area with no groundwater level monitoring network.

Urbanisation by decreasing in pervious areas would result in increase in the risk of flood inundation and cause more discharge of pollutants into receiving water bodies. This paper presents the management of urban flood hazard in terms of inundation and pollutants discharge into receiving water bodies using a combination of the conventional and novel techniques. Conventional techniques include increasing the dimension of conduits as well as decreasing their roughness. Novel techniques on the other hand include bioretention systems, pervious pavements, infiltration trenches, and detention ponds in urban drainage networks. In this study the multi-objective optimization model is developed using multi-objective genetic algorithm coupled with a simulation model of urban drainage system using SWMM software. The objectives are to minimize the economic cost, the inundation flood hazard, and the expected pollution reaching the receiving waters. Pollution control consider pollutants of TSS, TN, and TP. The suggested methodology was applied to a case study for the urban drainage system of Golestan city in Tehran Province. Results indicated that applying the optimal methods can considerably decrease the expected flood and pollutants. Results of Pareto front showed that indirect relation exists between the solutions of optimal control of expected inundation and the optimal control of expected pollutants in receiving water bodies.

Present study assess the impact of climate change on the AZAM-HARAT River basin runoff in the 2015-2030 period considering the sources of uncertainty in adjustment of model parameters for two rainfall-runoff models of IHACRES and HEC-HMS, as well as A1B, A2 and B1 greenhouse gasses emission scenarios of AOGCM models, and LARS-WG and SDSM downscaling models. First in calibration and verification of rainfall-runoff models, sensitivity analysis of the model parameters was done. Then the climatic variables of 15 AOGCM models and climatic scenario were downscaled using LARS-WG model and these data were introduced to each of the hydrological models to determine the runoff variation ranges. Results showed that the temperature in the future period will increase about 1.5C and also the amount and distribution of the rainfall will vary greatly. These variations in rainfall will result in changes in the runoff. The results showed that the uncertainty related to hydrological models in some months is higher than AOGCM models and greenhouse gases emission scenarios which is due to the critical parameters in the structure of the hydrological models. To assess downscaling uncertainty, data of HadCM3-A2 model were downscaled using LARS-WG and SDSM models. The results showed that the uncertainty of hydrological models is much greater than that in the downscaling methods. It is also shown that the uncertainties in the AOGCM models are larger than greenhouse gases emissions scenarios.

Accurate estimation of evapotranspiration has a great influence on water resources management and planning, especially in arid and semi-arid areas. Different methods have been presented by researchers for evapotranspiration estimation. These include a variety of empirical equations and data-driven methods. In this study to estimate the daily reference evapotranspiration at eight semi-arid climates in Iran, three methods based on the adaptive network-based fuzzy inference system (ANFIS), support vector machines (SVM), and model tree (M5) as well as five empirical equation were used. Meteorological data including maximum and minimum temperatures, relative humidity, wind speed, and the sunshine hours were used.Eleven different combinations of these variables have been used as input variables in data-driven methods forevapotranspiration modeling for the period of 1980 to 2009.Eighty percent of the data were used for the trainingand twenty percent were used to test the models.The results were compared with those of thestandard Penman-Monteith FAO-56 equation. Performance of the methods was evaluated using statistical indices of mean square of error (RMSE), coefficient of determination (R2), and index of agreement (d).Support vector machines and adaptive networks based on fuzzy inference system methods presented best performance with RMSE between 0.24~1.55 (mm.day-1) in nine combination of meteorological variables. RMSE of empirical equations varied between 0.71~5.96 (mm day-1). Blaney-Criddle and McGunness-Bordne equation presented the highest accuracy in most stations. M5 model has a lower performance compared to ANFIS and SVM methods in the studied climates.

Climate change has significant impacts on living organisms and the environment. Therefore, it is important to predict and assess its impacts in order to reduce vulnerability and also to confront to climate change. Water resources will be the first resources to be affected by climate change and the rivers are considered as vital ecosystems in this situation. So assessing the impacts of the climate change on animal and plant species status in the rivers can provide a projection of the ecosystem.This study attempted to evaluate the effect of climate change on one of the southern Alborz water systems, Kordan River, and to estimate the changes in the aquatic Habitat Suitability Index (HIS) along a two-kilometer reach of the river. In this regard the future climate change in the region was first projected using HadCM3 general circulation model in three 30-year periods of 2011-2040, 2041-2070, and 2071-2099 considering A2 and B1 scenarios. Also the SWAT model was used to simulate effects of climate change on the river flow and the water temperature. Results showed that the changes in temperature and precipitation would have a decreasing effect on the river flow and the water temperature during the future periods; the average flow would decrease from 3.3 cms in the base period to 2.66 and 2.8 cms in A2 and B1 scenarios, respectively. Also it is indicated that the climate change has a significant impact on habitat suitability index for Oxynemacheilus bergianus.Assessing the rational distribution curve would also declare a 20 to 25 decrease in the HSI equaling 0.4 to 0.6 in the period of 2071 to 2099.

Purpose of this study is to used the physicochemical parameters (DO, pH, NO3-, PO4 3-, TDS & TSI), water quality index of IRWQI and statistical methods to assess the pollution and spatial-temporal impact of land use wastewaters on water quality in Choghakhor wetland in Chahar-Mahal Bakhtiari province, Iran. Sampling twelve stations were selected based on systematic non-random for the sampling and measurement. At first, index model of IRWQI was prepared in GIS environment based on average of surface and depth of qualitative parameters by interpolation functions (deterministic and geostatistical methods). Therefore, correlation coefficients of water quality index with stations proximity of land use wastewaters based on buffering method was used for determination of land use efficacy. Results, demonstrated that annual average of wetland IRWQI index (fairly good status of 70) with the pearson correlation coefficient of 0.78 and the parameters of PO4 3-, NO3-and TDS with partial correlation coefficients of -0.82, -0.64 and 0.62 respectively were caused by wastewaters pollution of farmland and residential area in the south and west half of wetland. Also results of spatial-temporal impact of land use wastewaters using statistical and geostatistical methods showed that highest impact on water quality of Choghakhor wetland is created in spring and early autumn with correlation coefficients of 0.70 and 0.59, and lowest impact is reached in summer with correlation coefficient of less than 0.1.Maximum trophic and worst qualitative status is results for water surface temperature of 10.5-13.5oC in first half of autumn and spring with status of moderate to fairly good with values of 50 to 61 for this condition which is due to increased and best quality is in more than 19.5oC and less than 6oC in summer and first half of winter with good status of 70 to 82 for this condition which is due to decreased agricultural activities, floods, wastewaters of seasonal rainfalls and biological and chemical variations caused of temperature.

The risk of flood incorporated with the potential of channel shift across the width of alluvial fans may cause substantial damages. This shift is called avulsion. The earlier methods of determining avulsion on alluvial fans (such as FAN mathematical model) were received negative criticism and did not give accurate results. Given that flow on alluvial fans is two-dimensional, FLO-2D model was used in this research to identify the areas with avulsion potential. The avulsion phenomenon was studied on two alluvial fans in Iran; one in the North-east (Ferizi) and one in the south-east (Sarbaz).Using this model, flow was simulated based on 100 and 500-year discharges. Then areas with avulsion potential were identified by plotting the results of the model on the recent aerial photographs of the alluvial fans. Results showed that massive evulsions had happened in the areas with no expectation of such phenomenon. Thus, using this method can help to identify areas with avulsion risk and reduce the potential damages caused by it.

Recently, new techniques based on geostatistical methods have been used to estimate groundwater nitrate concentrations in unmeasured areas as well as to determine new sampling locations. In this study the Cokriging and Anfis models have been developed in interpolation step for nitrate parameter spatiovariation in Karaj aquifer. Nitrate concentrations have been estimated annually using samples derived from 179 drinking water wells. For this purpose, values of nitrate concentration in 1384 (2005) have been considered as the initial values. Nitrate concentration in 1379 to 1383 (200-2004) have been applied as the covariate for cokriging model and as the input parameters for neuro fuzzy model. The comparison between cokriging and Anfis results showed that in five neuro fuzzy models, the error function are less than the cokrihging model, especially for the data of 2004.