مجله سامانه های سطوح آبگیر باران
سال دوازدهم شماره 1 (پیاپی 40، بهار 1403)

This research aims to analyze the spatial water footprint of agricultural products in four categories of blue, green, gray, and white water in Khiavchai sub-watersheds. To this end, the meteorological data (monthly rainfall and temperature) were used as input data for the CROPWAT program. The Google Earth images were used to map the land use areas and estimate the area of agricultural and rangelands. It was found that apples, sour cherries, cherries, walnuts, and grapes are the dominant crops produced in the orchard area. The four types of virtual waters of agricultural products were analyzed in four land uses, i.e., orchards, rainfed agriculture, irrigated agriculture, and rangelands. The total water consumed by the apple product is more than other products in all sub-watersheds and the amount of virtual water of walnut is also high in most sub-watersheds. The total consumption of blue, green, white, and gray water in sour cherry, cherry, and grape was the same and was evaluated as less than the other two products. In addition, the average amount of blue water for rainfed agriculture was estimated as 2788.85 m3 t-1, among which lentils and chickpeas had the higher blue water among the rainfed crops. According to the results, sub-watersheds 7 and 14 located in western parts of the study area had the highest of virtual water. In sub-watershed 7, a large part of its area is used for rangeland and only a small part is related to rainfed agriculture, and all area of sub-watershed 14 is used for rangeland. Therefore, the appropriate cultivation pattern can be determined based on the amount of virtual water and its spatial changes.

Watersheds are socio-ecological systems that can be significantly affected by climate change. Changes in the pattern and amount of precipitation and increase in temperature can increase the vulnerability potential of watersheds. Understanding the impacts of climate change on hydrological processes and other aspects at the watershed level is crucial for developing approaches to mitigate or adapt to these effects. Therefore, this research aims to assess the impact of climate change on the health of the Radkan watershed in Razavi Khorasan province. In this study, the health of the watershed was initially evaluated for the historical period of 1990-2014 using the Pressure-State-Response (PSR) framework. Sixteen natural and human criteria were selected and classified into three indices: pressure, state, and response. After calculating the criteria for the 14 studied sub-watersheds, they were standardized and weighted using the Analytic Hierarchy Process (AHP). Finally, the health of the studied watershed was determined using the PSR framework for the 14 sub-watersheds and classified into five categories: healthy, relatively healthy, moderately healthy, relatively unhealthy, and unhealthy. To investigate the impact of climate change on the health of the watershed, data from the climate model MPI-ESM1.2-HR were used under two scenarios: SSP2-4.5 and SSP5-8.5. The predicted climate data in three future time periods of 2026-2050, 2051-2075, and 2076-2100 were investigated. The results indicated that during the historical period, one sub-watershed was classified as healthy, two sub-watersheds were relatively unhealthy, and the remaining sub-watersheds were moderately healthy. Comparing the health index in the future time periods with the historical period revealed a decrease in health under both scenarios. The average decrease in the time periods of 2026-2050, 2051-2075, and 2076-2100 under the SSP2-4.5 scenario was 8.84, 8.49, and 11.11%, respectively. Under the SSP5-8.5 scenario, the average decrease was 7.33, 13.04, and 9.85% for the same time periods.

Reference evapotranspiration (ETo) is considered one of the important variables in hydrology and agricultural science and is a determining factor in water resources management. This study investigates a hybrid model of an artificial neural network with an artificial rabbit optimization algorithm (ANN-ARO) for daily modeling of ETo with limited meteorological parameters. It compares it with other hybrid methods, i.e. ANN with a particle optimization algorithm (ANN-PSO). ANN with genetic algorithm (ANN-GA) and five different data mining models. These models were evaluated using long-term daily climate data from 2000 to 2023 in two climates. The investigated stations included Birjand (with a desert climate) and Mashhad (with a cold semi-arid climate). The statistical comparison showed that considering all climatic parameters, the hybrid ANN-ARO model in Mashhad city with R2=0.9986 and MSE=0.0001 and in Birjand city with R2=0.9986 and MSE=0.0001 gave better estimates than other methods. In addition, the ANN_ARO optimization algorithm has the best estimation with "temperature" and "relative humidity" by considering the minimum meteorological parameter, and also by considering two and three input parameters, it performs better than other methods. In general, nature-inspired optimization algorithms are powerful tools to enhance the performance of ANN in ETo simulation. According to the results, the ANN-ARO model is highly recommended for estimating ETo in similar climate regions with limited climate data. This study proposes powerful models for accurate estimation of ETo with limited inputs in arid and semi-arid climates, which provide practical implications for the development of precision agriculture.

Climate change is one of the main challenges that has affected human life in recent decades. It plays a significant role in changing runoff due to its effects on temperature and precipitation. Therefore, evaluating this phenomenon is very important for assessing water resources and land management based on sustainable development. This research aimed to investigate the effects of climate change on runoff using the Budyko framework. Four sub-watersheds named Olang Asadi, Ardak Band Sarooj, Zoshk, and Kardeh Dam were selected from the QareQom watershed. Then, using the downscaled temperature and precipitation data of the CORDEX project of the GFDL-ESM2M model, under two emission scenarios (RCP4.5 and RCP8.5), the runoff variation was evaluated for the times period of 2006-2100. The Nash-Sutcliffe statistic for the results produced by the Budyko-Zhang model for the studied areas was between 0.72-0.87, which indicated the appropriate performance of this model in runoff estimation. In addition, the results of the GFDL-ESM2M model indicated an increase in temperature and a decrease in rainfall under both RCP4.5 and RCP8.5 climate scenarios. The temperature forecasts from GCM indicated a warmer climate in the future with a significant upward trend and a significant decrease in precipitation with a significant downward trend, both of which play an important role in reducing future runoff. The results of the Mann-Kendall test showed that the runoff in all the studied areas under both RCP4.5 and RCP8.5 scenarios and during the period of 1989-2100 had a downward trend. It can be concluded that the increase in temperature and also the decrease in precipitation in the future can reduce the runoff.

LAndscape ProcesS modeling atmUlti dimensions and scaleS (LAPSUS) model is one of the models that can calculate the multiple flow direction routing. In this model, according to the topography of the watershed, a constant value between 1 and 10 is defined as the convergence factor for the entire studied watershed. The value of this factor is considered constant through calibration or the values considered in previous studies. In this research, to solve the problem of constant convergence factor value, the flow distribution function was defined for this model. This study was conducted in five sub-catchments in the Kakhk experimental watershed located in the northeast of Iran in Gonabad county. In this research, the flow distribution function was defined instead of the convergence factor for the LAPSUS model. In this function, the slope parameter is used to model the effect of topographic characteristics on the flow distribution. In this way, the flow trending part in the LAPSUS model was developed by changing its programmed code in the C++ environment for the flow distribution function. Based on the calibration results, the best value of the convergence factor can be between 1 and 10. After applying the flow distribution function to the model, the value of the flow distribution function was calculated to be 6.3 for sub-catchment 4 and between 5.7 and 6.5 for other sub-catchments. The results of validation of the model outputs showed that the value of the coefficient of determination is 0.99 for runoff volume and 0.92 for erosion and sediment. Also, the amount of normalized RMSE was 3.5% for runoff and 5.5% for erosion and sediment. Based on the results, the flow distribution function can define the value of the convergence factor based on the topographic slope for the LAPSUS model. The value of the convergence factor can be defined for the model by entering the average slope of the watershed. Therefore, one of the main issues in the multiple flow direction routing was solved.

Recently, nanotechnology has received wide applications in environmental health and sustainable water management. Besides, global drinking water shortages lead to the construction of different water collection systems. Therefore, considering that the corrosion of rebar inside concrete structures, especially in areas with acid rain and water structures, is an essential requirement. To this end, in the current research to investigate the effect of nanoparticles on the strength of the mortar and the corrosion resistance of the rebar inside it, Yttrium oxide (Y2O3) nanoparticles were used. Yttrium oxide (Y2O3) nanoparticles were added to the mortar with one weight percent of the cement weight. The strength of the mortar was investigated by measuring the 7-day and 28-day compressive and bending strengths, and the corrosion resistance of the rebar inside the mortar was checked by measuring the polarization and Electrochemical Impedance Spectroscopy (EIS) tests in a 3.5% salt solution. The results showed that Y2O3 nanoparticles increase the strength of the mortar by increasing the compressive and bending resistances. In addition, the result of the polarization test showed that this nanoparticle increases the corrosion resistance by reducing the corrosion tendency and corrosion rate. By examining the results of the EIS test, the reason for the decrease in the corrosion rate was attributed to the increase in the impedance of the mortar. Finally, conserving the water collection systems or watershed management infrastructures using Y2O3 is the suggestion of this research as a new technological-based solution against corrosion conditions.

The integration of a multi-criteria decision-making system and a geographic information system was utilized to select appropriate locations for the construction of ponds. These ponds were intended to facilitate the storage and supply of water for agricultural purposes, the breeding of warm water fish, and the artificial feeding of underground water. This initiative was implemented in the flatlands to the north of the city of Gorgan. Thus, the Boolean model was used to locate the ponds, and the Hierarchical and Copeland analysis process was used to prioritize them. Google Earth was used to determine seven suitable axes for the construction of ponds in potential areas. The Hierarchy and Copeland analysis process was used to prioritize these places based on their main and sub-criteria. The results indicated that in the comparison of criteria with a consistency coefficient (0.03), the hydrometeorological criterion with weight (0.315) and the weighted rainfall sub-criterion (0.485) is the best criterion of hydrometeorology with the consistency coefficient (0.03) and the weighted sub-criterion of the slope of the area (0.394) is the highest criterion of geomorphology with the consistency coefficient (0.03) and the loan resources sub-criterion with weight (0.410) is the highest economic and social criterion with compatibility coefficient (0.004) and the sub-criterion of plant water requirement volume with weight (0.710) is the highest agricultural criterion with compatibility coefficient (0.004) and the sub-criterion of the cost of making a water seal by weight (0.349) is the highest technical criterion of construction with the compatibility factor (0.007) has been recognized as the superior criterion. The result of this work is that by dividing the layers of information with the composition, suitable places for the construction of dams have been selected. The findings of this research showed that the use of geographic information systems and decision support systems and the use of hierarchical analysis method as a powerful and flexible tool to reduce the complexities in identifying suitable places along with field visits in increasing accuracy, reducing costs and Determination has a role Location They are the place of construction of dams.

Satellite products are the only available data source with adequate spatial coverage, however, their data do not match the observed values ​​and have biases, although this discrepancy cannot be fixed precisely, however, a solution to reduce the bias is data recalibration. Currently, machine learning techniques are used to improve the accuracy of forecasting various types of weather phenomena, so regression solving such problems through methods based on machine learning and deep learning is very efficient. The daily precipitation of 19 rain gauge stations of the Ministry of Energy between 2010 and 2021 was extracted and compared to the average values ​​of their corresponding daily precipitation pixels in the ERA5 database. To measure the data, three algorithms D-Tree, KNN, and MLP were used. The range of changes of correlation coefficient in MLP, D-Tree, and KNN is equal to [0.87, 0.98], [0.75, 0.97], and [0.4, 0.87], respectively. In addition, the range of changes for RMSE in MLP varies from 0.7 to 2.4 mm per day, and these changes for D-Tree and KNN are calculated between 0.8 to 2.2 and 1.2 to 2.5, respectively. In 75% of stations, RMSE in MLP, D-Tree, and KNN algorithms is less than 1.5, 1.9, and 2.2 mm per day, respectively. The range of bias changes in MLP is [0.18, -0.6 mm per day] and this range of changes for D-Tree and KNN is respectively [0.16, 0.5 mm per day] and [0.6, -0.8 mm per day] have been calculated. The bias of corrected data and observed values ​​in MLP, D-Tree, and KNN algorithms for the middle of the stations is -0.09, -0.11, and -0.16 mm per day, respectively. The evaluation of the performance of three machine learning algorithms (MLP, D-Tree, and KNN) in correcting the daily precipitation of the ERA5 database and the comparison of CC, RMSE, and bias statistical indices for the reproduced data compared to ground values ​​showed that in all three statistical indices, the MLP algorithm works better than the others and has good accuracy for correcting the daily precipitation.