مجله مدیریت آب و آبیاری
سال دوازدهم شماره 2 (تابستان 1401)

One of the approaches to equilibrium assessment is the use of quantitative and qualitative indicators that can be a good tool. In this study, in order to evaluate the equilibrium scenarios in Hashtgerd aquifer, two indicators of intrinsic vulnerability of the aquifer and quantitative stability index of the aquifer were used. Five scenarios were defined based on the groundwater resources balancing scheme in the region and simulated using the MODFLOW numerical model. Equilibrium was calculated by combining three indicators of reliability, vulnerability and desirability of groundwater system stability index in different scenarios. The simulation results and application of different scenarios showed that by reducing the water withdrawal by 15 percent, the highest level of stability is created in the aquifer system and the system stability rate increases from 55 percent to 87 percent. The extent of changes in the aquifer stability index as a distribution in the observation wells also indicates that the middle part of the aquifer has the highest status of improving the stability index. This region, with its high density of exploitation wells, is most affected by the reduction of aquifer withdrawals. The results obtained from the aquifer stability index, considering the spatial distribution, show the feasibility of implementing different scenarios. On the other hand, due to the importance of development and the use of vulnerability indicators, the drastic index was analyzed at the regional level. The results showed that the upstream part of the aquifer has the highest level of vulnerability due to hydrogeological characteristics and the level of vulnerability has decreased in the direction of groundwater flow.

The present study, while introducing the Global Land Data Assimilation System (GLDAS) database, evaluates the performance of this database in estimating two meteorological variables of precipitation and air temperature in two daily and monthly time scales in the Helleh catchment area located in southern Iran. To achieve the objectives of the study, the data of eleven rainfall and temperature gauges in the catchment area was used for fourteen years. In this study, in order to compare the gridbased data and station points, laps rate downscale method of temperature and precipitation and statistical indicators were used. The results show that the performance of GLDAS database in estimating air temperature is much better than precipitation, so that on a daily time scale, the average value of the coefficient of determination in eleven stations for estimating precipitation is 0.329, respectively, while the performance in estimating air temperature with the coefficient of determination of 0.934 is Very suitable. On a monthly scale, the results of this study show that the performance of the GLDAS database is very good in estimating both temperature and precipitation variables, so that on a monthly basis, the coefficient of determination in air temperature and precipitation parameters are 0.984 and 0.857, respectively. Therefore, it can be said that the suitability of performance in a meteorological variable is not a reason for suitability in all parameters. According to the mean error index, the GLDAS database overestimates the temperature data, but underestimates the precipitation data. In Error zoning it can be seen that basin surface characteristics such as altitude can also be effective in evaluating the performance of the base.

Protecting the quantity and quality of water resources has always been of great importance in all human societies, and in order to maintain the quality of these resources, numerous monitoring and remedial measures have been taken in most countries of the world. In this regard, water quality monitoring is considered as one of the essential tools and as an integrated activity to evaluate the physical, chemical and biological factors of water that are related to human health and living organisms. In the present study, a model of combining geostatistical methods (Kriging), clustering and entropy theory has been proposed to review and present the groundwater quality monitoring network. This model is presented under the first and second approaches in Tehran-Karaj study area. The first approach, without using the clustering method, reviews the existing monitoring network without using only entropy theory and Kriging geostatistical method as an estimator. The second approach uses the k-means clustering method, entropy theory and Kriging geostatistical method as an estimator to investigate the effect of combining these three methods on the review of the existing monitoring network and then the results of the first and second approaches are compared. The proposed final monitoring network with 44 wells has an average forecast error rate of 19 and a cost reduction of 34 percent compared to the cost of the current monitoring network. Also, using clustering, the average percentage of estimation error has been reduced by 20 percent compared to the case without clustering.

Gates are Flow control structures used in irrigation canals. In the meantime, Lopac Gates have very efficient capabilities such as controlling and regulation the level of water upstream, automation, lower energy consumption than other types of gates (like sliding gate, radil gate) and the possibility of passing floating objects. Elliptical Lopac gate (ELG) are one of the new types of lopac gates that are intended to increase the discharge coefficient compared to other types of lopac gates. The present study was conducted with the aim of modeling ELG in five different elliptical ratios with sudden transition in the channel using Flow3d software. All models are considered in three discharges of 25, 35 and 45 liters per second and in three openings of 20, 30 and 40 cm and simulation has been done with RNG turbulence model. The final results showed that by increasing the elliptical ratio from 0.7 to 1.28, the energy consumption gates ​​will decrease from eight to 20 percent compared to the initial energy. In the study of floor shear stress, the results show that by increasing the elliptical ratio, the average shear stress decreases by 52 percent. In the section on flow vortices, the results show that in elliptic ratios greater than one, vortex currents and current deviation from the path are reduced and the flow rate decreases from 22 to 54 percent.

There are several methods for monitoring evapotranspiration, which are mainly measured on a point that will be difficult to generalize to the whole area. In recent years, remote sensing-based methods for estimating actual evapotranspiration have been widely considered. In this study, the amount of actual evapotranspiration per day in Qazvin plain was used by SEBS and PYSEBAL algorithms for 15 TM images, 22 ETM + images and 24 MODIS images without clouds and snow during the years 2000 to 2003. The results were compared with a drained lysimeter planted with grass in the Qazvin plain. Comparing the outputs obtained from the two algorithms, it was concluded that the PYSEBAL algorithm, using the latest methods of estimating evapotranspiration, such as the user not selecting two hot and cold pixels and minimal use of ground data, has been able to cover many of the weaknesses of other algorithms, so that the PYSEBAL algorithm in all three MODIS sensors, LANDSAT-ETM+ and LANDSAT-TM respectively with RMSE values (0.45, 0.46 and 2.02 mm/day, and R2 values of 0.96, 0.95 and 0.82) had better performance than SEBS algorithm in the study area. Furthermore, considering that determining the amount of water used for evapotranspiration is one of the most basic factors in planning in order to achieve more product, Kc coefficient can be considered as a suitable and fast guide in irrigation management. Studies on grass plant show the high accuracy of PYSEBAL model in estimating this coefficient. And finally, the use of remote sensing methods can be a good alternative to avoid high costs and improve water management in the region.

In this study, digital photography was used to estimate the amount of sugar beet’s canopy cover. For this purpose, a dataset of visible images of sugar beet crops, during the growing season, in 2018, under drought and nitrogen stress were taken in a greenhouse at the ETH research station for plant sciences in Lindau Eschikon, Switzerland. The treatments of this research included two levels of irrigation stress (low water and sufficient water) and three levels of fertilizer stress (20, 40, and 80 kg/ha nitrogen). Image discrimination and threshold algorithms are applied to perform segmentation on the images in Python. Compound segmentation methods using Excess Green, Excess Green minus Excess Red discrimination vegetation indices (plant from soil and background), and without discrimination index and manual input thresholding and Otsu and Triangle automated algorithms were used. Therefore, nine different compound methods including discrimination and thresholding algorithms used to estimate the canopy cover under different stresses. Results showed that compound methods of Excess Green minus Excess Red vegetation index and manual input thresholding and Excess Green Index and Otsu have the highest accuracy, 94.69 and 87.52 percent, respectively. The method without discrimination index and triangle thresholding which has 53.18 percent accuracy was the least accurate method.

Hydrological models make it possible to simulate the rainfall-runoff process, the amount of runoff from rainfall in areas without statistics or with incomplete statistics. One of the most practical and globally accepted rainfall- runoff model provided by American Soil Conversation Service, (SCS) known as SCS-CN where CN refer to Curve Numbers based on soil hydrological conditions. In this research, Rainfall-Runoff-Retention Model (3RM) was used introduced the new concept for rainfall Interceptions as Antecedent Effective Retention (IER) instead of the Antecedent Moisture Content (AMC) and calculating it by water balance method. SCS-CN model with this new revision were applied in Darjazin (semi-arid climate) and Kassilian (very humid climate) catchments in Iran. The results of the study showed that Darjazin watershed with 29.38 persent rock cover (D) and 3.27 persent hydrologic soil group (A) with a holding potential of 20.66 mm and Kassilian watershed with forest cover 77 persent and rock mass cover 0.0 persent has a lot of retention potential (51.11 mm). The value of α (ratio of initial retention to potential retention) was obtained between 0.05 and 0.13 in different basins. Also, the results of model fitting on rainfall-runoff data showed that the evaluation indices including coefficient of determination R2, RMSE, NRMSE and NSE for predicting runoff in Darjazin catchment (0.998, 0.439, 0.029, and 0.998) respectively, while the same indicators for the Kassilian watershed are (0.867, 0.264, 1.009 and 0.859) respectively. The results show that the model has an acceptable ability to predict runoff and actual retention in all two watersheds.

Groundwater is the most valuable water resources in any region and in many arid and semi-arid regions of the world, such as Iran, is the main source for drinking and agricultural needs. In recent years, with the increase in population and as a result of increasing withdrawals from aquifers and climate change, many of aquifers are in poor condition, and these conditions continue or are deteriorating. In this regard, regular monitoring of aquifers is always very important and by making appropriate management decisions, it is possible to prevent more damage to aquifers and reduce the damage. The purpose of this study is to determine the droughts of the future and to determine its impact on the aquifer of Shahrekord plain. In this study, using the output of CMIP6 models, climatic variables such as rainfall and temperature for the next period are simulated and the rainfall situation in the region until 2100 has been determined. Then, using the ANFIS model, groundwater depth in five selected piezometers in the plain is predicted by 2050. According to the results of this study, the aquifer condiotion of Shahrekord plain has been determined by 2050 and it has been determined that in some parts of Shahrekord plain, the groundwater depth will increase to 26 meters. Due to possible changes in the future in order to prevent the situation from deteriorating and increasing the damage, appropriate management decisions must be made in this regard.

Structures operation in traditional water conveyance and distribution canals is manually done using operators’ experience. Determining operational patterns in these canals is an important issue done in recent studies using artificial intelligence. One of the errors occurring during the settings of the structure is the operators’ error applying some errors as they operate the determined setting. This issue and its effect hasn’t been investigated in the previous research so far. In this research, the reinforcement learning model was used to determine the operational patterns considering the operator errors of five percent and 10 percent applied randomly. A non-linear model of the studied canal that is the E1R1 canal as a part of Dez network located in the north of Khuzestan was employed to simulate. The results showed that reinforcement learning can accurately determine the operational patterns with a maximum iteration of 650 so that the action values are more than 0.9 in most cases.

The design of many infrastructures and construction projects requires the extensive studies in the area's geographical conditions and climatic characteristics. The effectiveness of this research itself depends on the information and data required. In many cases, the project area is in a situation where no climatic information such as rainfall is available. Hence, regional frequency analysis has been received much attention. In this way, having specific conditions and mechanisms, the available information in other sites can be expanded and transferred to the other areas. In this research, clustering is one of the most effective steps that divide the existing  stations into the hydrologically  homogeneous areas. Therefore, in this study, in addition to the common methods in clustering, two new models, neural gas network and growing neural gas network, were used to determine the homogeneous regions in Khuzestan province, Iran. One of the unique features of these algorithms is learning the topology or shape of the distributions that governs the data space. Using the variables of longitude, latitude, altitude, mean annual rainfall, and maximum 24-hour rainfall of the station, the design area was divided into two hydrologically homogeneous areas, and the clustering process was performed. The results show that the neural gas networks has a high efficiency and accuracy in view of clustering. The Mean Percentage Difference and Coefficient of Variation of Root Mean Square Error in neural gas were estimated to be 15.56 and 24.39 percent, respectively, which showed a considerable advantages over the conventional methods.

Hydraulic models are often used as a tool for the prediction of the hydrodynamic behavior of flow. But scale effects in the hydraulic modeling process due to deviations of the results from the prototype. This paper discusses to scale effect in the hydraulic flow model. The goal of the research is to investigate the effect of geometric distortion on the flow characteristics and the degree of deviation of the results of distorted models from the prototype, which is done using the two-dimensional numerical model MIKE21. First, the hydrodynamic conditions of the flow were simulated in four models of straight channel, convergent channel, divergent channel and curved channel with four degrees of distortion one (undistorted), two, five and 10. Then, assuming the similarity of the Froude number, the results of the models were compared with the prototype and the relative error in the result of channels was investigated. The results showed that the difference in depth and average velocity in distorted models with prototype is small, but the difference in transverse velocity profile of sloping model with prototype increases with increasing degree of distortion. So that the relative error in transverse velocity modeling in straight, convergent, divergent and curved channels with a degree of G10 was two, 29, 33 and 39 percent, respectively.

By developing a hydraulic simulator model of flow in irrigation canals, this study proceeded to spatially evaluate the adequacy of irrigation water distribution in the main and secondary canals of the waterfall irrigation network located in Isfahan province. For this purpose, a water distribution simulation between main and secondary canal catchments was performed during an irrigation season, based on spatial analysis of the water distribution performance adequacy index. According to what is done, daily distribution and delivery planning based on the information of water supply, water sales, and specified share for each second and third-degree intake valve, and with a simplified integral-delay mathematical model in MATLAB environment was linked. Five exploitation scenarios, representing normal conditions, waterlogging, and water scarcity, were considered following the prevailing patterns of surface water allocation - based on information collected in the last ten years from the irrigation network operation office. The simulation results in the main and secondary canals indicate a frequently decreasing trend of water delivery adequacy from upstream to downstream catchments, so that the adequacy index under water deficit scenarios of five, 15 and 30 percent in the range of 41-69 percent, respectively, 95-29 percent and 65-19 percent. The required water has reservoirs located downstream of the main canals. In addition to the nature of upstream control of the operation, the justification for this phenomenon is the inefficiency of the traditional manual operation method and the insufficient flexibility of water level adjustment structures and reservoirs in the reliable distribution of irrigation water under water scarcity scenarios. With the decrease in inlet flow due to water shortage, the vulnerability of the water distribution process in the downstream catchments has increased, and the favorable water delivery conditions can be seen only in a limited number of catchments located at the beginning of the main and secondary canals.

In this study, optimal designs with minimum costs are obtained for various storm return periods. Then the risk analysis is used to determine the return period in which the design cost plus the damage risk cost is minimum. SWMM software was used to handle the simulation and the Network optimization was performed by using the genetic algorithm. The non-linear reservoir model to convert the rainfall into runoff and the dynamic wave model to perform the network hydraulic simulation in this software are utilized as a complicated simulation model. The results showed that the 10-year return-period storm in which the summation of the design and the damage risk costs are minimum is the one that should be selected. Also, the rational method of the software was applied as the simplest method of rainfall-runoff and the hydraulic calculations were performed using a Manning equation without considering the flow travel time. The results show that the return period of the risk analysis is the same as the first one whereas the total design costs are greater by 16 percent. Afterward, the classical rational method in which the flow travel time is considered was used to design the same network. The peak flows of the pipes were remarkably reduced, causing the design costs to be only 5 percent greater than the complicated precise method. It can be concluded that the simple classic rational method considering the flow travel time may be used in the design of storm sewer networks due to its acceptable accuracy and costs.

The existence of multiple stakeholders with different interests, norms, goals, and values has made conflict an inseparable part of complex water systems. The destructive effects of conflict and undesired consequences on human-natural water resources systems have highlighted the need to recognize the appropriate approach to deal with water conflicts. Various methods and models have been applied in water conflict studies so far - such as optimization-simulation, game theory, water market, benefit-sharing, and interdependence. Although these models have been applied in several studies addressing subnational and transboundary river basin conflicts, the proposed solutions, strategies, and policies using these models have not led to effective dealing with water conflicts in many cases. Analysis and identification of the causes of inefficiency of conventional models in the water conflict studies led to the formation of the present study. The basic approaches of water conflict studies models are compared with the approaches in peace and conflict studies, and, in this regard, the degree of alignment of conventional methods in dealing with water conflicts with the developments in peace and conflict studies are investigated. The research hypothesis is that the lack of alignment of the principles of water conflict studies with recent developments in peace and conflict studies has led to fundamental weaknesses in conventional methods of dealing with water conflicts and failure to cooperate in river basins. Library resources and comparative analysis methods are used to test the research hypothesis.