Search

Water, food, and energy (WFE) systems are addressed with their complex interactions with each other. Some models are used to simulate WFE concept, but they cannot consider all nexus complexity. Based on author's knowledge, there is still a lack of suitable model that can consider relationships inner each WFE sub-systems and between them in nexus concept. The CWSNeX in this study is specifically tailored for a comprehensive water simulation under the WFE nexus system on a basin scale. It benefits from a modular structure and considers the most important interrelations in water sub-system for addressing the gaps and issues in a holistic WFE nexus simulation. CWSNeX is implemented using the Python programming language and can be utilized both within a WFE nexus platform and as a stand-alone tool with time series data. When integrated into a nexus platform, it interacts with the food and energy sub-systems, exchanging information and outputs in each time step. The CWSNeX consists of quantitative and qualitative parts. In the quantitative part, it simulates evaporation, river routing, groundwater, reservoir operation rule, surface water, and groundwater exchange, withdrawal in demand sites, and in the qualitative part, it simulates Total Dissolved Solid (TDS) that is important for irrigation sites. To evaluate the performance of the CWSNeX model, data from the Sufi Chay basin in Iran is used. The goodness of fit criterion (NS, RMSE, R2, d-factor and p-factor) showed a good performance of each module. [ABSTRACT FROM AUTHOR]

In Streamflow prediction the most important triggering/controlling variables are related to climate, physiography, and landscape patterns. This study investigated the effect of different landscape metrics to relate spatial patterns to surface runoff processes and predict monthly streamflow using climatic and physiographic variables for the 42 sub-basins of the Urmia Lake Basin in Iran. We developed an innovative data-driven framework and considered two different modelling approaches i.e., modelling in homogenous clusters (local approach) and modelling in the entire area as an entity (global approach). The results of basin LULC monitoring from the 20-year experimental period display drastic changes in the land use of the basin such as reduction in lake area (48.3%) due to increasing irrigated areas (22.5%), increasing residential areas (14.2%), and decrease in rangeland (6.0%). Streamflow prediction results in the global experiment showed Group Method of Data Handling (GMDH) and Random Forest (RF) with NSE of 0.76 and NRMSE of 6.44% have similar results and outperformed Partial Least Squares regression (PLS), but in clustering experiment GMDH with NSE of 0.88 and NRMSE of 5% shows the highest accuracy and outperformed both RF and PLS. The results confirmed that modelling in homogenous clusters (local prediction) significantly enhanced the performance of prediction. [ABSTRACT FROM AUTHOR]

The current study introduces a conceptual socio-hydrological-based framework for the water-energy-food (WEF) nexus. The proposed conceptual framework aims to investigate how farmers' dynamic agricultural activities under different socio-economic conditions affect the WEF systems. The WEF nexus model has been integrated with an Agent-Based Model, reflecting the farmers' agricultural activities. Furthermore, the agent-based model benefits from Association Rule Mining to define farmer agents' agricultural decision-making in various conditions. The processes within the WEF nexus are simultaneously physical, socio-economic, ecological, and political. Indeed, there are interrelated interactions among the mentioned processes in ways that have not yet been properly delineated and mapped. Thus, to obtain sustainable outcomes, the current study investigates trade-offs among natural resources and social systems in the WEF nexus approach. The proposed socio-hydrological WEF nexus framework may provide more in-depth future insights for policy-makers through capturing bidirectional feedbacks among farmers and WEF systems. In other words, the proposed framework can help policymakers to capture the dynamic impacts of agricultural activities by farmers on the WEF nexus, which may vary due to different socio-economic conditions. [ABSTRACT FROM AUTHOR]

Water allocation to different users implies a trade-off between the benefits perceived by different sectors and environmental demands. This conflict, considering the income of different sectors and environmental issues, is very crucial, especially in reservoir operation. In this study, a multi-objective optimization algorithm combined with two simulation models (SWAT and WEAP) has been developed to consider the tradeoff between agricultural benefit and environmental water demand through parallel processing. In this model, decision variables are monthly agricultural demands which are considered as inputs into the Water Evaluation And Planning (WEAP) model. Then, the production of different crops and discharge to the downstream lake are obtained based on the allocated water to each sector using the Soil and Water Assessment Tool (SWAT) model. After the simulation, the objective functions are calculated based on agricultural income and environmental flow discharged to the downstream lake. To evaluate the performance of the proposed model, Mahabad reservoir, in Mahabad river basin (located at the south of the Lake Urmia and northwestern of Iran), was studied during a drought period. This reservoir supplies domestic, industry, agriculture water of the Mahabad plain and downstream environmental demand which is discharged to the Lake Urmia. Non-dominated Sorting Differential Evolution (NSDE) algorithm is used to find the optimal water allocation to different sectors and efficient agricultural irrigation patterns of different crops (winter wheat, alfalfa, apple, and sugar beet). Results showed the optimal operation of the reservoir through irrigation management could improve water stress indicator from 80% in traditional irrigation pattern to 60%, only by 6.5% decrease in agricultural income. Also, the inflow to Urmia Lake was increased from 173 MCM to 354 MCM during the drought period (5 years). [ABSTRACT FROM AUTHOR]

The environmental water demand of the Mahabad River in the Urmia Lake basin in Iran was first estimated, using the flow duration curve shifting method (FDC Shifting) in this study. Secondly, the optimal operating model of the reservoir was developed with the goals of decreasing the deficiencies and considering the downstream demands of the reservoir, especially the environmental water demands by employing the simulated annealing (SA) and non-linear programming (NLP) methods. The results of the SA algorithm were compared with those of the NLP model with the indices of reliability, resiliency velocity, vulnerability, and sustainability. Then, the optimum released water values in the current month, the optimum water storage values in the reservoir, reservoir inflows and monthly demands were considered as inputs of the M5 tree model, and the optimum values of released water in the next month were considered as outputs of the M5 model. Finally, the optimum operation rules from the reservoir were developed in the form of if-then linear rules for future uses. One of the main advantages of the M5 tree model is to present two operation rules as if-then rules for all the operating periods with relatively acceptable accuracy. The results showed that the SA-M5 tree model, as a method of data mining, can extract the operation rules with relatively high accuracy. [ABSTRACT FROM AUTHOR]

The water level of Urmia Lake, the largest inland lake in Iran with maximum water surface area of about 6000 km, has been shrinking for the last two decades. Although a number of study have been performed to determine drought condition and coastline changes of Urmia Lake, there has not been a detailed study to distinguish anthropogenic effects from climate impacts on the drying of Urmia Lake. In this study, water budget of Urmia Lake and the intensity of drought in the basin were analyzed in the period from 1985 to 2010 and a new hypothesis is proposed to quantify anthropogenic and climate impacts in reducing the volume of Urmia Lake. The results of this study indicate that human impacts on the Lake and its basin are more important than climate factors. Though previous studies assumed that ground water output from Urmia Lake is negligible, the results of this study show the presence of significant groundwater seepage from Urmia Lake. Major changes in the variables that reduced the water level of Urmia Lake were observed since 1998. Anthropogenic impacts and climate factors have roughly 80% and 20% effects on the drying up of Urmia Lake, respectively. Hence, the first step to recover Urmia Lake could be the revision of management surface water, operation of dams and groundwater resources. The second step could be the review and classification of agricultural products grown in the region in terms of water consumption and teach local people the best practice methods for irrigation. [ABSTRACT FROM AUTHOR]

The present study aims at assessing the hydrochemistry of the groundwater system of the Maragheh-Bonab Plain located in the East Azarbaijan Province, northwest of Iran. The groundwater is used mainly for drinking, agriculture and industry. The study also discusses the issue of the industrial untreated wastewater discharge to the Plain aquifer that is a high Ca-Cl water type with TDS value of about 150 g/L. The hydrogeochemical study is conducted by collecting and analyzing the groundwater samples from July and September of 2013. The studied system contains three major groundwater types, namely Ca-Mg-HCO, Na-Cl, and non-dominant water, based on the analysis of the major ions. The main processes contributing to chemical compositions in the groundwater are the dissolution along the flow path, dedolomitisation, ion exchange reactions, and the mixing with wastewater. According to the computed water quality index (WQI) ranging from 25.45 to 194.35, the groundwater in the plain can be categorized into 'excellent water', 'good water', and 'poor water'. There is a resemblance between the spatial distribution of the WQI and hydrochemical water types in the Piper diagram. The 'excellent' quality water broadly coincides with the Ca-Mg-HCO water type. The 'poor' water matches with the Na-Cl water type, and the 'good' quality water coincides with blended water. The results indicate that this aquifer suffers from intense human activities which are forcing the aquifer into a critical condition. [ABSTRACT FROM AUTHOR]

Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes. [ABSTRACT FROM AUTHOR]

Urmia Lake as a most vital water bodies in Iran, has been shrinking since the late twentieth century and its area has dramatically decreased. To develop and apply any plans to survive the lake, qualitative and quantitative analysis and any modeling, deriving physical information such as volume, area and their changes are very crucial. The objectives of this study were therefore, intended firstly, to study the bathymetry of Urmia Lake with a more satisfactory approach using Landsat- LDCM satellite image and in situ measurement data. The polynomial model was developed to predict the water depth in Urmia Lake area. This model was developed with the input series of reflectance values from blue, green, red and NIR bands in the Landsat- LDCM satellite imagery for Urmia Lake taken on 12 April 2013 of the sampling sites from actual depth measured were taken on the same date. Also, using a large archive of Landsat imagery (TM, ETM+ and LDCM), a counter of equivalent elevation were established for deriving the bathymetry of desiccated areas by mapping the edges of the lake and finally assembled with bathymetry derived from polynomial model. In-situ depth measurements were used to evaluate resultant derived bathymetric map. This comparison shows reasonable agreement between the Landsat-derived depths and those measured in the field with RMSE of 0.27 cm and R = 0.91. The maximum and mean depths measured were 4.9 and 11 m respectively. The maximum depth measured was located at the upper part of the lake. As well as, developed multi-regression equation used for deriving another bathymetry map using Landsat- LDCM satellite image taken on Sep. 2015 for salt deposition monitoring. Results indicates that about 64 cm salt deposition has occurred during the last two years. Secondly, to make stage curves of lake, multi-temporal changes of water body have been derived from Landsat, MODIS and AVHRR satellite images sets since 1972. In this regard, the area of Urmia Lake at different level was estimated base on object oriented and pixel base classification using 78 satellite images. Finally, stage curve (volume- area- level relations) was derived from bathymetry map. [ABSTRACT FROM AUTHOR]

Recently, the Urmia Lake located in northwestern Iran which is the second largest hyper saline in the world suffers from the significant fluctuations of water level and surface area. The current study tries to investigate the spatiotemporal trends of mean (T), maximum (T) and minimum (T) temperatures of monthly, seasonal and annual time-series. To do so, the data of 15 temperature gauge stations within the Urmia Lake basin, for the period 1972-2011 was employed. The pre-whitening approach was applied to remove the effects of serial correlation in the air temperature series based on the Mann-Kendall (MK) test. The results of Ljung-Box test showed positive serial correlation in the T and T series for all of the stations at the 0.05 significance level. In the monthly series, the significant warming trends in the T series were more perceptible than the same ones in T series; however, T trend was found more than T series. The Mann-Whitney (MW) test detected a significance upward shift changes in the annual T, T and T series of about 86, 73 and 80 % of the stations, respectively. The average magnitude of significant warming trends in annual T, T and T series were (+) 0.58 °C, (+) 0.52 °C and (+) 0.69 °C per decade, respectively. Furthermore, the interpolation maps showed that warming trends in the east and west of Urmia Lake were more than southern area. Therefore, the results showed that the basin has suffered from increasing trends in the T, T and T over the recent decades. Finally, significant changes were found in 1980s and 1990s based on the Mann-Kendall ranks and change point tests. In this study, it is interesting that the period of significant changes in warming trends were close to the beginning of decreasing water level of the Lake. [ABSTRACT FROM AUTHOR]

This paper shows the utility of a new interval cooperative game theory as an effective water diplomacy tool to resolve competing and conflicting needs of water users from different sectors including agriculture, domestic, industry and environment. Interval parameter programming is applied in combination with cooperative game theoretic concepts such as Shapley values and the Nucleolus to provide mutually beneficial solutions for water allocation problems under uncertainty. The allocation problem consists of two steps: water resources are initially allocated to water users based on the Nash bargaining model and the achieved nonlinear interval parameter model is solved by transforming it into a problem with a deterministic weighted objective function. Water amounts and net benefits are reallocated to achieve efficient water usage through net benefit transfers. The net benefit reallocation is done by the application of different cooperative game theoretical methods. Then, the optimization problem is solved by linear interval programming and by converting it into a problem with two deterministic objective functions. The suggested model is then applied to the Zarrinehrud sub-basin, within Urmia Lake basin in Northwestern Iran. Findings suggest that a reframing of the problem using cooperative strategies within the context of water diplomacy framework - creating flexibility in water allocation using mutual gains approach - provides better outcomes for all competing users of water. [ABSTRACT FROM AUTHOR]

Undoubtedly, the most significant factor with wise decision making and designing hydrological structures along the lake coasts is an accurate model of lake level changes. This issue becomes more and more important as recent global climate changes have completely reformed the behavior of traditional lake level fluctuations. Subsequently, estimating lake levels becomes more important and at the same time more difficult. This paper deals with modeling lake level changes of Lake Urmia located in north-west of Iran, in terms of both simulator and predictor models. According to this, two traditional simulator models based on water budget are developed which benefit from most effective components on water budget namely precipitation, evaporation, inflow and the lake level antecedents, as model inputs. Most famous linear modeling tools, Autoregressive with exogenous input (ARX) and Box-Jenkins (BJ) models are employed with the same mentioned inputs for prediction purpose. In addition, two other methods that are, Multi-Layer Perceptron (MLP) neural network and also Local Linear Neuro-Fuzzy (LLNF) are applied to investigate capability of intelligent nonlinear methods for lake level changes prediction. All models performances are indicated using both graph and numerical illustrations and results are discussed. Comparative results reveal that the intelligent methods are superior to traditional models for modeling lake level behavior as complex hydrological phenomena. [ABSTRACT FROM AUTHOR]