مجله مدیریت آب و آبیاری
سال سیزدهم شماره 2 (تابستان 1402)

One of the major consumers of water and energy sources is agriculture and evaluating the performance of irrigation systems to determine the efficient use of these two sources is one of the priorities of sustainable agriculture. In this regard, the present study has conducted a spatial analysis of water and energy consumption in Qazvin irrigation network. For this purpose, first, the dominant cropping pattern of the study area was extracted and then the water requirement was estimated for the products in the cropping pattern using CropWat 8.0 software. To estimate energy consumption, energy consumption in the sprinkler and drip irrigation systems, groundwater pumping, diesel fuel, labor, seeds, machinery (tractors and combines), chemical fertilizers, and pesticides for each available secondary field in the irrigation network was calculated. The results showed that the highest energy consumption in all secondary farms indirect energy consumption is related to diesel fuel with 40% of total energy consumption and in indirect energy consumption belongs to nitrogen fertilizer with 20% of total energy consumption. Also, in the energy used to pump water in irrigation systems at different heads required for pumping for crops in the dominant cropping pattern, alfalfa consumed the most energy with a value of 4811.44 Kwh / ha and corn consumed the least energy with a value of 1194.19 Kwh / ha. According to the results, the impact of diesel fuel, chemical fertilizers, and water pumping on energy consumption are high, thus controlling the consumption of these inputs would significantly reduce energy consumption.

The climate change effect on future precipitation (2040-2021) of Iran is investigated in this study. For this purpose, the results of three general circulation models (GCM) named GFDL-ESM2M, HadGEM2-ES and IPSL-CM5A-LR were analyzed for two scenarios of greenhouse gas emissions RCP2.6 and RCP8.5. CCT model and daily precipitation data of the base period (1986-2019) were used to downscale and bias correction of future daily precipitation data. According to the annual results, the weighted average of annual precipitation of rain gauges due to all scenarios except RCP8.5 in the IPSL-CM5A-LR model was increasing. The weighted average of seasonal precipitation in winter increased in all of the studied climate change conditions, but in other seasons the amount of precipitation decreased or increased. The highest increase in the weighted average of seasonal precipitation was in winter due to the RCP2.6 scenario and GFDL-ESM2M model (23 mm). The highest decrease in the weighted average of seasonal precipitation was in autumn due to the RSP8.5 scenario and IPSL-CM5A-LR (10.5 mm). Slight changes in mean precipitation, on the other hand, a sharp decrease in minimum precipitation (446 mm due to G3S4) and a sharp increase in maximum precipitation (233 mm due to G2S1), indicate the occurrence of severe extreme events (drought and flood) in the future.

One of the problems of specialists and designers is the incomplete time series in hydrology studies, which causes errors in the results and complicates the implementation of projects. This issue is more acute in areas where the number of rain gauge stations is limited. Currently, it is common to use statistical methods in order to solve statistical data gaps. The current research aims to evaluate the performance of the method of reconstructing missing values ​​of daily rainfall using the waterData package in R software and the time disaggregation method of reconstructing annual values ​​to daily values ​​in the period from 1990 to 2020 using 43 stations with complete statistics among 87 selected synoptic stations. It was done in Iran. Based on the average values ​​of the evaluation indices for two times disaggregation and reconstruction using the waterData package in R software methods, for the CC index 1 and 0.95 respectively, for the MBE index 0 and -0.01 respectively, for the RMSE index 0.3 and 1.1 respectively, for The NSE index is 0.99 and 0.89, respectively, and the CSI and POD index are 0.94 and 0.63, respectively, which shows the better performance of the time disaggregation method. The average values ​​of Bias and FAR index for two methods are equal to -0.01 and 0, respectively, and indicate the similar performance of the two methods.

Water level control and regulators have a main role in water conveyance and distribution. Despite the simplicity of structure settings in a steady-state condition, applying an appropriate setting in unsteady flow is complicated. Hence, control logic is used to set these structures, usually developed in languages such as MATLAB, Python, and FORTRAN. To use these logics, they must be combined with hydraulic models. In HEC-RAS, there is an elevation controlled water level boundary condition that can be used to control structures. In this research, the evaluation of the performance of this boundary condition was considered to regulate the water level in the E1R1 canal of the Dez network. The results showed that the rate of opening and closing of the gate has a significant impact on the performance, and if they are chosen correctly, the depth changes will be small. The results showed that the IAE indicator is around one percent in all the examined options and except in a few cases where the maximum value of MAE exceeds 10 percent and reaches up to 15 percent, its value is also low. Therefore, it is suggested to use this boundary condition in the control of structures.

This study was aimed to investigate the risk of chemical damages originated from water quality on concrete structures of Voshmgir dam and its irrigation network. In this regard, field survey and water sampling from the dam and its network were carried out in June 2022. In order to determine the intensity of water chemical aggression to concrete, the results of water quality tests were analyzed using soft water aggression indices and well known international standards. Also, the temporal changes of water chemical aggression was investigated using the data received from Golestan water authority. Langelier and Ryznar indices for dam water in June 2021 are -0.6 and 8.6, respectively, and based on these indices, the dam water is corrosive and very corrosive, respectively, and the concrete structure of  spillway is exposed to severe soft water attack. In all studied months, there has been aggression risk of at least one damaging agent to concrete. In December 2021, Ryznar index was 8.49 and the amounts of sulfate and magnesium were 400 and 199 mg/liter, respectively, and there has been a risk of simultaneous aggression by three factors of soft water, sulfate and magnesium. Assessment of water quality of Voshmgir dam in five months showed that the water is corrosive in four months and there is a risk of sulfate and magnesium ions reaction with concrete in two months. So, to protect the spillway’s concrete structure and lining of irrigation canals against leaching by the corrosive dam water and damages originated from reaction of sulfate and magnesium ions with concrete, utilization of epoxy coatings is suggested.

In order to investigate the quality of groundwater in September 2022, 35 samples were collected from various groundwater sources in the Azarshahr plain, and the main elements, nitrates, and trace elements such as chromium, cadmium, nickel, lead, and arsenic, as well as two types of pesticides such as imidacloprid and chlorpyrifos, were analyzed and compared to drinking water standards. The drinking water quality index for the study area was calculated based on the concentration of parameters in the samples, and the health risk of cancerous and non-cancerous diseases for two age groups, children and adults, was calculated using the criteria of the United States Environmental Protection Agency (USEPA). According to the findings of the studies, the region's water quality index is average, and the non-carcinogenic risk index for children and adults is less than one. It is hazardous, but the risk of carcinogenesis in the entire region is greater than 10-4, and its consumption poses health hazards due to skin contact. The health risk of cancer is many times higher in the region's south, in the cities of Azarshahr and Gogan, as well as the adjacent villages. The correlation between the GQI map and the health risk maps revealed that the correlation of carcinogenic risk is greater than the correlation of non-carcinogenic risk, and based on the correlation between the parameters in the region's groundwater, the elements influencing the health risk of carcinogenesis caused by geogenic factors and human activities do not play a significant role.

Food and water demand have increased as a result of population growth, economic development, and dietary changes. As a result, it is imperative to consider how to manage groundwater resources under existing and possible future conditions. Methods to assess groundwater depletion and withdrawal include volume-based methods (based on hydraulic head data, remote sensing with Gravity Recovery and Climate Experiment (GRACE) and (global) groundwater models), water balance methods (based on global hydrological models and remote sensing of fluxes beside the models) and indirect geodetic or geodetic estimates. Different methods of assessing groundwater depletion and withdrawal have advantages and disadvantages. The methods based on hydraulic head data are somewhat old and if there are possible human errors in the measurement system, the uncertainty of these methods increases. On the other hand, indirect methods and the use of global models have evolved, which bring significant improvements and accuracy. However, the use of these methods depends on the existence of observational data. In fact, with the development of data collection facilities and increasing accuracy in simulation and data collection, it is possible to change the method to more accurate methods, but without having partial data, the use of these approaches is not recommended. On the other hand, this study shows that both the estimation of current depletion rates and the future availability of non-renewable groundwater in the future is very uncertain. To reduce the uncertainty in the near future, significant data and research challenges must be resolved so that the sustainability plan of groundwater resources in the country can be formulated in order to improve the sustainability program of groundwater resources considering the hydro-economics of these resources.

Groundwater resources are critical water resources for present and future generations. However, it has not been used sustainably in most of  Iran's plains, and due to the increase in population and food demand caused by it in the last century, it has been withdrawn more than in the past. The issue has been raised as a serious threat to the maintenance of livelihoods, the stability of local communities, and economic investments in the region. To prevent the continuation of this process, it seems necessary to identify, review, and formulate solutions. A variety of methods can be employed for estimating the withdrawal (country reports and hydrological modelling) and depletion (water balance methods, volume-based methods and indirect geodetic estimates or geodetic) amounts according to their limitations and characteristics. Based on literature reviews in two parts of the world and in Iran, the methods of estimation of withdrawal and depletion were evaluated to obtain a general understanding of its situation. Studies show that 800 and 184 billion cubic meters are respectively withdrawn and depleted on a global scale. In addition, there are 71 and 6.9 billion cubic meters (which is 10% and 4% of the global amount) for Iran, and this reflects Iran's dependence on groundwater resources. The dependency leads to the emergence of problems such as salinity and groundwater level depletion, subsidence and related social and economic issues. Moreover, most of the use of groundwater is linked to agriculture, and despite the decrease in its portion of withdrawals, the relationship between depletion and withdrawals has not changed.

It is common to use different data mining methods in drought prediction. However, the selection of the best model is mainly based on the accuracy of the simulation, while most of the studies do not mention the features of the models. In this paper, the performance of the most common data mining models, including Multilayer Perceptron Artificial Neural Network (ANN-MLP), Radial Base Function Neural Network (ANN-RBF), Regression Decision Tree (CART), Model Tree (M5P), and Support Vector Machine (SVM) is evaluated in order to predict monthly one year ahead rainfall at Bandar Abbas synoptic station and then the characteristics of each of them are described. Calibration and validation of the models were done using raw data and a three-year moving average of climatic parameters from 1347 to 1396. The performance of the models has been evaluated using different statistical indices and comparative diagrams. The results showed that the SVM and M5P models have good prediction performance with RMSE of 7.93 and 8.31 mm, the MAE of 3.66 and 4.69 mm, and the CC of 0.83 and 0.82, respectively. Also, with the exception of the CART, the change in the data mining tool makes an eight to 11 percent difference in the accuracy of the estimates. Therefore, the most appropriate model should be selected based on other characteristics of the methods besides their accuracy. In addition, using the three-year moving average of the input parameters has increased the correlation coefficient by about 78 percent and reduced the RMSE by about 63 percent. The analysis of the long-term drought situation showed that with the increase in the period of the standard precipitation index, the separation of wet and dry years becomes more specific.

The demand for freshwater is increasing, while the limited water resources are subject to over-harvesting, pollution, and climate change, which require improving water resource management to distribute it equitably and achieve It highlights the goals of sustainable development. A low-cost option to support better water management strategies is to develop models capable of predicting available water amounts, especially amounts related to precipitation and river flow. Climatic diversity and climate changes are basic assumptions for hydro climatological predictions. One of the remarkable aspects of this issue is the correlation between large-scale atmospheric-oceanic phenomena or Teleconnection patterns with hydrological processes on a local scale, and these patterns can also affect the inflow to the dams. This study uses three machine learning models, an artificial neural network, a Bayesian neural network, and an adaptive neuro-fuzzy inference system to predict dam inflow and evaluate their efficiency. For this purpose, 12 scenarios consisting of rainfall variables, inflow to the dam, and nine climatic indicators with a delay of up to six-time steps were designed to investigate the effect of using long-term models as predictive variables of the flow one month later in Amirkabir Dam. to be placed The analysis of the results of this research showed that the use of the Nino3.4 index with one-time step delay as well as the PDO index with two-time step delays can increase the accuracy of the model compared to the scenarios in which only station variables are used. to be According to the results, the Nino 3.4 index was found to be the most effective index on the inflow to Amirkabir Dam, and the scenario in which the mentioned index along with the rainfall and flow data of one and two months before was used as input, in all three The model recorded the highest accuracy. Also, the performance of the ANFIS model for the mentioned scenario (scenario 9), with RMSE and R2 values, equal to 5.69 and 0.79 cubic meters per second, respectively, was better than the ANN and BNN models, so the value of the R2 index for the best scenario consisting of station variables (scenario 5), it increased by 0.15 and the value of RMSE index decreased by 0.78 cubic meters.

Due to the increase in population and the need for water supply, preservation and protection of surface water and groundwater resources has been considered by governments. One of the pollutant sources in rivers is entering salinity from groundwater into the river, that in this research is considered as distributed (non-point) sources. The goal is to identify the salinity intensity, location and length of sources by measuring the temporal distribution of concentration in one observation point. For this purpose, the inverse solution of advection-dispersion equation in the river was employed using the simulation-optimization approach. MIKE11 numerical model was used to simulate flow and transfer of salinity in the river, and genetic algorithm was employed for optimization. In the proposed model, considering only one observation point with some measured intensity data for recovering several sources, unknown location and length of the sources, in addition to their intensities is the most significant advantage of the present study. The model verified by using hypothetical examples, 40 km section of the Karun River and also by applying five and 15 percent noise to the observation data. The results confirm the ability of the model to recover the specifications of several distributed sources using only one observation point. With five percent of noise in the observation data, all three specifications of sources can be recovered with the desired accuracy. While at 15 percent of noise, the accuracy of the model in recovering the location and length of sources was decreased. Also, to recover the specifications of each source, employing only three points of the measured data in the ascending part are sufficient.

The entanglement of power relations with the interactions of societies and political-spatial units over fresh water resources is in the field of hydro politics. Those rivers that cross international borders or its different parts are located in the territory of several countries, have had more frequency in hydro political researches. The purpose of this article, which is of a practical nature, is to explain the possible scenarios for the future hydro political relations of the border and joint rivers of Iran and Iraq. The research methodology is descriptive- analytical. The data needed for the research has been collected by library and field method (interview and questionnaire) and analyzed using FCOPRAS, FSOARA, SAW models and Micmac and Scenario Wizard software. The results of the research showed that out of 31 possible situations related to seven scenario models with strong and likely compatibility, the situations that describe the future hydro political relations of the border and common rivers of Iran and Iraq as critical, the most possible situations are in they take. Therefore, the future situation of the hydro political relations of the border and joint rivers of Iran and Iraq has become critical.

Rainwater harvesting systems is an effective method for the utilization of surface water resources in rural and agricultural areas especially and can be considered as a factor for the development of these areas. However, since site selection and identifying suitable places to implement this technology in rural areas is one of the most important and basic steps of using these systems, the main purpose of this study was to determine the location of rain water harvesting systems for sustainable rural development in Northern Savadkooh county. This research was carried out using remote sensing technology and using satellite images, meteorological organization data and geographic information system and Analytical Hierarchy Process. For this purpose, data were collected through pair wise comparison questionnaire by 16 samples among water sources and the paired matrix of criteria and the weight of each was estimated. Then the layers of each criterion were combined and the final layer of susceptible areas was determined. Based on the results, this county was classified into four categories: unsuitable, medium talent, good talent and suitable talent. According to the talent of the region, this city is classified into four categories: unfit, medium talent, good and suitable, and the suitable and good areas cover a total of 57.2 percent of the study area. Applying appropriate methods of rainwater extraction can be used to store rainwater and optimal management to prevent wastage of surface water.

Nitrate is one of the pollutants that affects groundwater in most aquifers of the country. In order to investigate the behavior of hydrogeochemical parameters of groundwater in the Ardabil plain aquifer, the results of qualitative data of 139 wells that were measured in both wet and dry seasons during the water year 2012-2011 were used. Using XLSTAT software, contamination was identified and samples were classified using principal factor analysis (PCA) and hierarchical clustering (HCA). Factor analysis method led to the extraction of five factors affecting groundwater quality with a total variance of 68.09 percent. Also, the results of factor analysis showed that the first, fourth and fifth factors are due to geogenic processes and the second and third factors are due to anthropogenic processes. The results of the hierarchical clustering method divided the samples into three groups, each group having two subsets. The set related to cluster one is spread in the northern, eastern, southern and parts of the center, west by mix(agriculture-garden), medium pasture and dry farming as well as urban areas. The set of the second cluster is often spread in the central, western and urban areas of the aquifer, with mix(agriculture-garden) and residential areas. The set of third cluster has been expanded to southwestern and urban areas. Generally, the existence of dissolution and ion exchange processes on the one hand, as well as leaching of fertilizers and incomplete development of the sewage system in the aquifer on the other hand has provided the groundwater pollution.

Water, one of the most important elements in the world, is known to be essential for the survival of living organisms and the health of water resources, especially surface water, is a key factor in determining the health and sustainability of aquatic ecosystems. Rivers are among the most important sources of surface water that have been severely affected by pollution in recent years due to population growth, industrial and agricultural activities, and wastewater discharge. Water Quality Index (WQI) is a widely used tool for assessing the quality of river water and other water resources. In this study, water samples were taken from three stations along Babolroud River during one hydrological year (from September 2019 to August 2020) to evaluate its water quality, and the quality parameters were analyzed using three indices: Iran Surface Water Quality Index (IRWQIsc), National Sanitation Foundation Water Quality Index (NSFWQI), and Canadian Water Quality Index (CCMEWQI). The use of these three indices provides a comprehensive approach to water quality assessment and management, enabling us to better understand the health and sustainability of water resources. According to the results, the IRWQISC index ranges from 50 to 78.9, the NSFWQI index ranges from 57 to 73, and the CCME WQI index ranges from 31 to 38, indicating moderate to poor water quality in the river, especially downstream. The main factors contributing to the low quality of water are the entry of pollutants from residential and agricultural areas, uncontrolled human activities, and land use changes along the river.