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Extended Abstract Background: Depending on rainfall systems and conditions influenced by unprincipled human activities, floods cause a lot of damage to natural resources, settlements, and projects every year. Hydrometric stations are often destroyed during floods or small watersheds lack hydrometric stations, which means that the estimation of runoff and maximum flood discharges requires a suitable method to calculate runoff and flood values in these basins. Prevention of this damage is doubly important when the study area overlooks places with a high density of settlements and urban facilities that can threaten the lives of many residents. The amount of runoff in each sub-basin of the watershed overlooking Malayer City was estimated in this study. Methods: The watershed overlooking the city of Malayer with an area of 14,700 hectares is stretched from the north to the northeast of the city. The processing of digital elevation models identified five sub-basins overlooking the city and field visits. The curve number method was used to estimate runoff height and flood volume in each sub-basin. The most intense rainfall event (88 mm per day) with a return period of 30 years was designed as rainfall, considering the amount of previous rainfall 5 days before this event with a cumulative value of 45.2 mm. To calculate the physical parameters of the watershed in this study, topography, geology, vegetation, soil, and land use maps were digitized using a geographic information system. Simultaneous maps of land use and rainfall were provided using Sentinel-2 images, containing 13 spectral bands and a spatial resolution of 10 meters. Utilizing the SCS method, the layers of land use, vegetation, and soil hydrological groups were combined to produce a curve number map. The value of the curve number was corrected for the wet condition based on the rainfall 5 days before and the location of the basin in the previous wet conditions. A weighted curve number approach for each sub-basin was compared with another approach based on the arithmetic mean of the curve number for each sub-basin, as well as a weighted runoff height approach for each curve number. Kirpich's method was used to calculate the concentration time of each sub-basin. In the next step, using the maximum daily rainfall data from the Malayer synoptic station in the statistical period from 1991 to 2021, the rainfall height values were converted to runoff height using the SCS relationship, and then the peak flood discharge was calculated for each sub-basin. Results: The lack of permanent vegetation and the presence of annual grasses are among the reasons for the high potential of the sub-basins in runoff production. As a result, the average curve numbers of arithmetic and weighted average methods are 79.09 and 81.46, respectively, which shows the high capacity of the basin in producing runoff. Converting curve number values to peak flood height and flow using two commonly used methods of curve number calculation and comparing it with the results of the weighted runoff height calculation approach for each curve number in the working units of the study area showed no significant difference. Finally, the runoff height maps and the peak discharge of each sub-basin were drawn. The northern sub-basin of the basin with an average runoff height of 44.32 mm and a peak discharge of 168 m3/s has the highest participation in flood discharge toward the city of Malayer, and sub-basin 4 in the south-eastern part of the basin has the least participation in flood generation. The results of the relationship between the area and runoff height showed that the basins with a larger area did not necessarily contribute the most to the occurrence of floods in all three calculation methods of the curve number and runoff height. Other factors also play a role in these results, such as the extent of rocky outcrops and reduction time of concentration due to the high slope as one of the reasons for this problem. The formation of the longest watercourse in the basin with a length of 16314 meters under Sub-basin 3, which has the highest peak flood flow after Sub-basin 2, is one of the discussed technical points that can handle the rapid discharge of the peak flow of about 100 m3/s. However, the study of its longitudinal slope of about 2% indicates the high risk of flooding caused by the lack of evacuation in rains with a high return period, which in turn is a serious risk for the threats caused by floods in the southeastern part of the city. The intersection of the end part of the main waterway with the exit of other sub-basins and the collection of runoff under the upstream basins are other problems that increase the risk of flooding in the downstream areas. Conclusion: It is suitable to use the curve number method to estimate the amount of runoff produced in the basins overlooking the settlements that do not usually have hydrometric stations. Because of its low-density pastures and rain-fed agriculture, the watershed overlooking Malayer has a high potential for runoff production.

Introduction and GoalDue to the large volume of human and commercial activities, one of the most important goals of infrastructure is to collect and transfer urban runoff, control floods and prevent flooding in cities. To correctly estimate the runoff and characteristics of hydrological units and channels, it is important to use appropriate hydrological and water models. Considering the importance of this topic, this research was conducted with the aim of estimating the urban runoff of the Malayer to determine the potential flooding points using a storm water management model (SWMM).Material and MethodsOn the basis of available technical reports, the area of the sub-watersheds, nodes and main channels of the city were first determined. Flow direction and slope of the area was determined using a digital elevation model and 11 sub-watersheds were identified. In addition, 11 nodes were determined, regardless of the initial water depth and water level. Using the daily rainfall data of the Malayer synoptic station during the 1992–2020 statistical period, a nine-hour rainfall with a return period of 2 years was calculated and entered into the model. The geometrical shape of the channels was an open rectangle and the flow trend in the channels was determined using the kinetic wave method. The size of the maximum flow of water through the channels was calculated using the relationship between the cross-section of the flow and the speed of the flow a 9-hour the rainfall with a return period of 2 years. To calibrate the model, the variables of percentage of impervious areas, pond storage and roughness coefficient of impervious areas were used in the change range of the allowed modification range. In order to evaluate the model, Nash-Sutcliffe efficiency coefficient and the root mean square error were used.Results and DiscussionThe results showed that after optimizing the sizes of the variables, the Nash-Sutcliffe efficiency coefficient and the root mean square error were 0.73 and 0.02, respectively. Of the total rainfall of 14.54 mm, 5.53 mm was related to infiltration losses, 7.55 mm was related to surface runoff, and 1.45 mm was related to pond storage. The results showed that the sub-watersheds that were in the north of the city and overlooking the heights and leading to node number 10 had more volume and drainage and it is necessary to revise the design and expansion of the channels in this area. In addition, the sub-watersheds of the western part of the city (sub-watersheds no. 4) and the southwestern part (sub-watersheds no. 8) had the highest and lowest flood potential of 0.651 and 0.547, respectively.Conclusion and SuggestionIn this research, the results showed that almost half of the city would be affected by flood risks in the event of rains. Therefore, the current drainage network does not have the necessary efficiency to discharge urban runoff in the northern part of the city, and it is necessary to determine the optimal dimensions of the channels. Because the eastern elevations of the studied area are snow-covered and considering the geology of the area, in future research, models capable of calculating the runoff caused by snow melting should be used.

Introduction Urban flooding is caused by the lack of capacity of the harvesting channel network and occurs when the amount of precipitation exceeds the network's capacity. One of the two main factors contributing to the aggravation of damage caused by urban floods is population growth and the expansion of urbanization, and the second factor is heavy rainfall caused by climate change, which plays an essential role in intensifying and accelerating the hydrological cycle and may change the amount and frequency of precipitation. This factor affects the probability of flooding, runoff volume, and peak flow. It is more visible in arid and semi-arid areas where rainfall usually occurs briefly but with high intensity. Urban flooding is a growing threat to urban infrastructure and public health, posing significant challenges to urban resilience and sustainability. One of the urban infrastructures that is of great importance is the runoff collection network. The increase of impervious surfaces wear and tear on the network. changes in the rainfall pattern due to climate change have increased the occurrence of urban floods and raised the importance of network redesign to minimize the system vulnerability. Materials and Methods In this research, the runoff harvesting network of ten districts of Tehran Municipality was redesigned and optimized. This area, with a population of 327,000 people, is located in the relatively dense fabric of the Tehran metropolis, and its area is 807 ha. Simulating the runoff collection network and checking the performance of the network by two indicators of vulnerability and reliability requires an accurate model with great details. For this purpose, in this research, SWMM version 5.1 software was used to simulate the runoff collection network. The study area was divided into 285 sub-basins to simulate the sub-channels in better detail. Then, information such as slope, area, and percentage of impervious space was introduced through ArcMap software version 10.3.1 as information under the watersheds. The sub-watershed width parameter was calculated by dividing the sub-watershed area by its most significant length using Q-GIS software and applied to the sub-basins. The LARS-WG model has also been used for the exponential micro-scale output of climate models. To simulate the network in the current or present situation, the historical precipitation information of the Mehrabad synoptic station was used, and to affect the network in future conditions, the precipitation information of the climate models of the sixth climate change report was used. Among the predictions of climate models, the most incremental changes in threshold precipitation were selected as a pessimistic scenario, and a system redesign was done to reduce vulnerability under this scenario. Results and Discussion This study was conducted to assess the performance of Tehran municipality's runoff collection network under current and future conditions. The SWMM hydraulic model was employed to simulate the network under various rainfall scenarios. Current conditions revealed a total runoff volume of 45.9, 51.14, and 59.7 thousand m3 for return periods of 2, 5, and 10 years, respectively. This runoff volume resulted in a vulnerability increase from 10.4 to 12.2% and a reliability reduction from 97.5 to 95.8%. To evaluate the network's performance under future climate change scenarios, the SWMM model was used with data from the IPCC sixth assessment report. Among the top five climate models, the one with the highest precipitation was selected as the pessimistic scenario. Simulation results under future conditions indicated a significant runoff volume increase, reaching 64.04 and 72.18 thousand m3 in 5- and 10-year return periods, respectively. This increase corresponded to vulnerability indices of 12.7 and 13.9% and reliability indices of 95.3 and 94.3% for the same return periods. To enhance the network's resilience, a genetic algorithm-based optimization approach was employed. Cost, reliability, and vulnerability index were considered optimization objectives with specific weighting functions. The algorithm converged to an optimal design solution in the 168th iteration, resulting in a 7.6% vulnerability reduction and a 98.1% reliability enhancement. Conclusion The vulnerability index in the return periods of 5 and 10 years is equal to 12.7 % and 13.9 %, respectively, and the reliability index is equal to 95.3 % and 94.3 %. After checking the network in its current state and future conditions, an optimal network redesign was done to improve system performance in both current and future conditions. For this purpose, the genetic algorithm was used for optimization, and the objective function consisting of cost, vulnerability index, and reliability index and the importance weights of each, were defined. Then, MATLAB software did the optimal network redesign by connecting the simulator and optimizer model. The results showed that in the 168th iteration, the algorithm reached its final answer of 0.3, which remained constant until the 300th iteration. Also, the optimal redesign has reduced network vulnerability by 7.6% and increased reliability by 98.1%. This research showed that with an optimal redesign and solving the current network problems, the system's ability to face future climate change threats could also be increased. Of course, spending the least money to achieve the best result was one of the main goals of this research. In future studies, it is recommended to use low-impact development tools along with optimal redesign to fix defects and improve the performance of the runoff collection network.

Introduction Rainfall-runoff modeling is one of the most important components of hydrological processes in water resources management and accurate estimation of runoff and river flow in the short and long term can be of great help to various sectors of water engineering. In several types of rainfall-runoff models, the unit hydrograph methods are still a useful tool for flood estimation in many, except non-gauged, basins. The unit hydrograph is the same as the unit pulse response function of a linear hydrological system. The tank model is one of the hydrological models for analyzing river flow characteristics. In hydrological analysis, simulation models are often used to describe and predict basin response to rainfall events based on mathematical and physical knowledge. In this article, using the concept of linear system theory, the pulse response functions of the runoff components (surface runoff and base flow) using the reservoir model for several flood events related to the two Navrood basin in Gillan province and Liqvan basin in the East Azerbaijan province has been extracted. Since Simulink can schematically show the dynamic relationship between hydrological components such as rainfall, runoff, storage, evapotranspiration, and runoff, it can be useful for rainfall-runoff modeling. Therefore, Modeling is done in the Simulink MATLAB environment. The modular design and block library can help users focus on hydrological analysis, including modeling strategy development, parameter estimation, and model application. Materials and Methods In the present study, to evaluate the capability of the tank model in different climates, the model has been implemented for two regions with dry and wet climates to evaluate the effectiveness of the tank model by comparing the results. Therefore, the Navrood representative basin in Gilan province was selected for a wet climate, and the Liqvan representative basin in East Azerbaijan province for a dry and semi-arid climate. The response of the entire basin to the rainfall that fell on its surface has been determined using the conceptual model of the tank. In order to extract the unit pulse response function for the runoff caused by precipitation, the model of three tanks in series with holes on the side and bottom has been used to show the types of currents prevailing in the process of forming runoff. The internal dependence of reservoirs is described using exponential functions of model parameters. Estimating the model parameters was performed using the cluttered evolution optimization method or SCE-UA for short, a conceptual optimization method. In addition, to extract unit pulse response functions and evaluate the model's efficiency in predicting flood events, it was tried to select events that have rained in the entire basin and correspond to flood events in terms of occurrence time. Results and Discussion The results obtained in this research showed that the tank model provides good results in estimating the peak discharges and the time to reach the peak discharges in the two representative basins of Navrood and Liqvan with two different climates. The parameters of the model, which actually reflect the geomorphological characteristics of the basin, are almost constant, and only the changes in soil moisture storage are variable in the runoff calculations. Response functions as exponential functions of model parameters have simulated the different roles of flow components (quick surface, quick subsurface, delayed subsurface, and underground flow) in relation to the precipitation process. As can be deduced from the results obtained for the Navrood basin, the slow runoff accounts for a major part of the total runoff in the falling limb of the hydrograph. The relations extracted for the response functions of the unit pulse for ru as a unit input that happened in the duration of ∆t have been obtained parametrically, therefore, for different combinations of ru and ∆t, several unit pulse response functions can be extracted. Examining the parameters of the model obtained from the SCE-UA algorithm shows that due to the large value of the b2 parameter compared to other parameters, a major part of the volley losses is penetration losses. In fact, the high permeability of the surface layers of the soil justifies the high value of the b1 parameter compared to other parameters. Conclusion The performance results of the tank model showed that the model has a relatively good capability in predicting the runoff affected by the rainstorm. The comparison of the computational and observed hydrographs shows that the mentioned values have a good correlation. According to the results, it can be seen that the fast runoff, which usually appears in the form of surface flow and waterways in the basin, occupies a significant part of the entire flood hydrograph during the flooding process, and in terms of durability has a shorter duration than slow runoff. Slow runoff hydrograph is slowly affected by precipitation during the flooding process, but in terms of durability, it has a longer duration.

Flooding is one of the most important challenges facing human civilization and its impact is expected to increase due to climate change and massive urbanization. The purpose of this research is to zonate flood occurrence using WMS hydrological model with GIS integration in Shahristanak watershed, one of the sub-basins of Karaj dam in Alborz province. In this study, geological maps 1:100000, topography 1:2000 and 1:50000, soil 1:100000, and data from synoptic and rain gauge stations are considered as the most basic data of this research for flood zoning in Shahristanak catchment area. Also, ArcGIS 10.3, WMS software was used to analyze and prepare maps. A modified version of TOPAZ model along with WMS was used to calculate the flow direction from the DEM active area. The results show that in the upstream and downstream of the catchment basin, as we move away from the center of the river, the area of water depth increases. While in some places upstream of the river, the water depth is less. In other words, the lower the water depth, the greater its area, and the greater the water depth, the smaller its area. Most of the area is 4.03 meters deep, which is equal to 44.2 hectares. The area of the depth of 8.06 meters is equal to 14.5 hectares, the area of the depth of 12.09 meters is equal to 4.6 hectares, the area of the depth of 16.12 meters is equal to 1.4 hectares, and the area of the depth of 20.15 meters is equal to 1.1. It is hectares. In general, the WMS model and its integration with GIS is for flood zoning and determining risk ranges in the region. [ABSTRACT FROM AUTHOR]

Introduction: Eskandari sub-watershed is one of the most important water supply sources for agriculture, drinking and industry in Isfahan region and province. Therefore, better understanding of the problems within this watershed is crucial for planning and proper management. Moreover, a concerted effort to engage all stakeholders in the integrated management of this watershed is necessary. This research aims to investigate the reasons for the increase of runoff in the years 2011, 2021, and 2031, providing management solutions with the active participation of stakeholders. Materials and methods: First, the height of the runoff was estimated using the curve number method. In this regard, Landsat 5 and 8 satellite images from 2011 and 2021 were used to prepare a land-use map of the Eskandari sub-watershed. Additionally, the prediction of land-use changes in the future (2031) was done using the Markov model. To prepare the curve number map, the combination of the land-use map and hydrological groups was used, and then the runoff height was estimated in the studied years. Following this, through collaborative workshops, stakeholders were asked to create a problems and objectives tree based on the main reasons for the runoff progress in the watershed, consulting with each other. Subsequently, based on the objective tree, the initial list of proposed solutions for the Eskandari sub-watershed was presented. The proposed solutions were then refined, classified, and prioritized by experts. Continuing with the goals of the current research, the most important indicators affecting the current situation of the Eskandari sub-watershed were selected based on the opinions of experts and stakeholders. To model, the multi-criteria decision-making method was used to select solutions and prioritize them. Therefore, initially, the researcher's questionnaire was distributed among stakeholders for scoring the selected solutions according to the assessment criteria. Finally, the results obtained from experts' scoring were prioritized using the TOPSIS method. Results and Discussion: The results reveal a significant reduction in natural lands in the region. Consequently, by 2031, rangelands with good and mediocre cover have been completely destroyed, while those with poor cover, irrigation, and rainfed agriculture dominate the region. Based on soil science information, 57.18% of the area is related to hydrological group B with a medium runoff production capacity. Hydrological groups C and D, with percentages of 24.98 and 17.82, respectively, are the next priorities. Following this, by combining the hydrological group map of the watershed with the land-use map in GIS environment, the CN map with curve numbers ranging from 61 to 96 was obtained. Finally, the average infiltration in 2011, 2021, and 2031 was 79.15, 62.82, and 55.12 mm, respectively, while the average runoff in those years was 10.99, 13.15, and 13.38 mm, respectively. According to the results, the losses decrease over time, while the runoff increases. Residential lands and rocky outcrops have the highest amount of runoff production, i.e. 34.38 mm, whereas rangeland with good cover (0.33) has the lowest amount of runoff. In subsequent collaborative workshops, based on the opinions and consensus of stakeholders and experts, improper agriculture was identified as one of the main reasons for increase of runoff in the studied area. Due to a lack of water, insufficient income, and a lack of laws and supervision in the region, watershed farmers and residents are changing land use and cultivating low-yielding lands in an unprincipled manner. Also, in line with the research goals, 11 management solutions and 12 effective indicators (economic, social, and environmental sectors) were identified for the current situation of the watershed. The prioritization results based on the TOPSIS method showed that the solution of creating cover farming to increase surface coverage has been assigned the highest priority. Conclusion: In this research, efforts have been made to address and mitigate the runoff in the study area. Management solutions have been presented based on the preferences and participation of stakeholders. Therefore, to prevent further runoff in the region, it is suggested to establish institutions tailored to the watershed's situation, considering patterns of cultivation and conservation agriculture, and integrating the cooperation of subordinate institutions and organizations. [ABSTRACT FROM AUTHOR]

Introduction Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area. Materials and Methods The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40´ to 46° and 25´and latitudes from 37° and 15´ to 37° and 55´ north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination. Calculating average rainfall through the Thiessen Polygons method Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to: (1) Random Forest Algorithm Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks. Support Vector Machine (SVM) algorithm Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression. Evaluation Criteria To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R2) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria: (2) (3) (4) (5) Results and Discussion Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values. As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively. Conclusion The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend.

Introduction and Objective Rivers serve as crucial sources of freshwater on Earth, and precise prediction of river flows plays a vital role in effective water resource management. To address this need, researchers have proposed various models. These models can be categorized into two types: (1) hydrological/hydraulic physically based models and (2) data-driven models. The cited papers provide evidence of the significance of employing data-driven models for hydrological issues, particularly in predicting river discharge.Materials and Methods In this study, the researchers utilized a gene expression programming model (GEP) and support vector machine (SVM) to predict the discharge of the Ferizi River. The dataset used in the study covered a period of ten years (2011-2020) and consisted of daily Ferizi river discharge readings obtained from the Moushang hydrometry station. The study area had an average altitude of 2171 meters above sea level, with a longitude range of 58° 49' 30" to 59° 4' 30" E and a latitude range of 36° 20' 1" to 36° 32' 1". For the prediction models, daily river discharge data from 1 to 5 days ahead were utilized as input variables for both the GEP and SVM models. To ensure data homogeneity and address any deficiencies, the researchers employed the run test and calculated the correlation coefficient between neighboring stations. Subsequently, the dataset was randomly divided into two groups: 80% for model training and 20% for model testing, as well as evaluating the modeling error. The performance of the models was assessed during the training and validation stages using various evaluation metrics, including root mean square error (RMSE), coefficient of correlation (R), bias, Kling Gupta (KGE), and Nash–Sutcliffe model efficiency (NSE). Additionally, in order to estimate the inflow to the Ferizi River using the SVM model, three common types of kernel functions in hydrology were examined, namely linear kernel, polynomial, and radial basis functions.Results and Discussion Among the different functions considered, the radial basis function was selected due to its lower error compared to other functions when applied to the variables. The best input model, Model No. 5, incorporated previous flow variables with a delay of five time steps. In the training phase, both the GEP and SVM models exhibited the highest performance in forecasting the daily flow of Moshang station. The performance of the applied models suggests that SVM (RSME = 1.15, R = 0.985, NSE = 0.85, and KGE = 0.79) demonstrates higher precision in the validation stage for river discharge prediction compared to GEP (RSME = 1.65, R = 0.964, NSE = 0.78, and KGE = 0.69).Conclusion and Suggestions This study has demonstrated that the utilization of soft computing techniques, such as SVM and GEP, is a powerful tool in predicting river flow. These techniques are capable of establishing a favorable relationship between input and output parameters, enabling accurate simulation of average and maximum daily flow.

Land use changes are one of the main factors in the amount of surface runoff changes in watersheds. Therefore, it is necessary to investigate it to reduce the damages (human and financial) caused by floods and to modify watershed management. The watershed of Nahre Azam is located in the north of Shiraz city and a lot of loss of life and money to the residents of Shiraz due to floods has occurred in previous years. The present research was conducted to investigate the relationship between land use change and runoff in the Nahre Azam watershed in Shiraz using the SWAT model in the period of 2004-2020. The model was calibrated using data from 2004 to 2014 and validated for 2015 to 2020. These images were classified into 6 main land uses using the supervised classification method after performing necessary pre-processing, and a land use map was prepared for 2040 using the Markov chain method. Then, the effect of the land use change in 2003 and 2040 on the amount of simulated runoff was evaluated with the recalibrated model. The calibration results of Nahre Azam watershed for the values of statistical parameters in the calibration step for the coefficient of determination, P-Facor and R-Facor are 0.77, 0.72, and 2.43, respectively, and for the validation step we obtained 0.69, 0.65, and 2.3 respectively. The analysis of the land use map showed that the main land use change in the region related to the conversion of pastures to agricultural land and urban land, which caused a decrease in pastures. Also, the results of the model simulation using the land use maps of 2003 and 2040 indicated that the amount of runoff decreased. The results revealed that if all the uncertainties are minimized, the calibrated SWAT model can produce acceptable hydrological simulation results for the user, which is useful for water resource and environmental managers and politicians as well as city managers of Shiraz.

Since the emergence of the concept of urban resilience, this concept has mostly been concerned with urban planning, and resilient urban design approaches have been a less developed field. The current research is of applied type and uses descriptive-analytical methods. This research deals with urban resilience in urban design and focuses on how urban forms affect urban resilience against floods and inundation. In this regard, indicators for evaluating the resilience of urban forms against floods and urban runoff have been explained and these indicators have been measured and evaluated within the scope of the study area of Noor City at the level of neighborhoods and urban blocks to determine the level of resilience of each of them, that should be determined from the research indicators at the level of urban cells. The TOPSIS technique was used for data analysis. The results show that the study area of Noor City is in an unfavorable situation regarding resilience. Based on the evaluation done at the block level, the results indicated that 27.7 hectares of the study area have very low resilience, 62.6 hectares have low resilience, 48.4 hectares have medium resilience, 53 hectares have high resilience, and 16.8 hectares have very resilience. The largest area is related to the low resilience level with 62.6 hectares. In total, 90.3 hectares of the study area have low and very low resilience, which is about 43% of the total studied area. [ABSTRACT FROM AUTHOR]

Introduction Water plays a crucial role in ensuring the sustainable development of any region. Given that our country consists primarily of arid and semi-arid regions, where the majority of rivers are also found, along with the critical state of groundwater extraction and the growing importance of surface water, It is crucial to have a deep understanding of the future condition of water resources within the country's watersheds (Fathollahi et al., 2015). By utilizing intelligent models, it becomes feasible to represent the inherent relationships between data that cannot be solved by conventional mathematical methods. Support vector machine (SVM) and Random Forest algorithms are two types of machine learning methods that utilize essential algorithms for making repeated and accurate predictions (Kisi & Parmarm, 2016). The most recent study conducted by Zarei et al. (2022) evaluated the risk of flooding using data mining models of SVM and RF (case study: Frizi watershed). By analyzing the results, it was found that both the SVM algorithm and the new random forest algorithm showed higher accuracy in predicting flooding risks, both in terms of the educational data and algorithmic performance. The purpose of this study is to simulate the precipitation-runoff process in the hydrometric stations at the end of the Maragheh plain (Khormazard station on the Mahpari chai river and Bonab station on the Sufichai river) in East Azerbaijan province using support vector machine and random forest modeling algorithms. This study has been conducted over a period of 43 years, making it one of the few research cases in this area. Materials and Methods The Maragheh Sufi chai basin is situated in the eastern region of Lake Urmia, within the East Azarbaijan province. It covers an area of 611.89 square kilometers and is located between longitudes 45° and 40' to 46° and 25'and latitudes from 37° and 15' to 37° and 55' north. The average height of the basin is 1767 meters above sea level (Sharmod et al., 2015). Based on the substantial changes observed in the runoff trend in the data since 1994 (without any noticeable change in the precipitation trend), the available data was divided into two distinct periods. The first period spans from 1976 to 1994, and the second period covers the years 1995 to 2019. To simulate rainfall-runoff, first the average rainfall of Maragheh plain was calculated by polygonal method. Subsequently, this data was combined with the discharge output from Bonab and Khormazard stations, with a one-day time lag. These inputs were then utilized in two models, SVM (kernel function) and RF. For this purpose, 70% of the data was used for the training stage and 30% of the data was used for the validation stage. Then, the rainfall and runoff training sets from one day before were chosen as the predictor variables, while the runoff training set was designated as the target variable. Several combinations of runoff and rainfall inputs were evaluated for the purpose of modeling. The inputs consist of the monthly Q and P values that were recorded previously (Pt, Qt-1), while the output represents the current runoff data (Qt), with the subscript t indicating the time step. As a result, two input combinations were constructed from Q and P data (as seen in Table 3) and SVM and RF models were used for rainfall-runoff modeling to determine the optimal input combination. Calculating average rainfall through the Thiessen Polygons method Thiessen polygons, which are Voronoi cells, are used to define rainfall polygons that correspond to the surface area (Ai). These polygons are used to weight the rainfall measured by each rain gauge (ri). Consequently, the area-weighted rainfall is equivalent to: ... Random Forest Algorithm Random forest is a modern type of tree-based methods that includes a multitude of classification and regression trees. This algorithm is one of the most widely used machine learning algorithms due to its simplicity and usability for both classification and regression tasks. Support Vector Machine (SVM) algorithm Support vector machines works like other artificial intelligence methods based on data mining algorithm. The most important functions of the support vector machine model are classification and linearization or data regression. Evaluation Criteria To evaluate the models and compare their effectiveness, this research employs metrics such as the root mean square error (RMSE), correlation coefficient (r), explanation coefficient (R²) and Nash-Sutcliffe efficiency coefficient (NS) are used. Below are the relationships among these criteria: ... Results and Discussion Figure 6 displays the time series data for rainfall and runoff during the two study periods, before and after 1994.The analysis of the figures showed that for Bonab station, during the two study periods, the value of Kendall's statistic for precipitation variable was 0.044 and 0.028, respectively. For Khormazard station, this statistic value for the first and second period was 0.030, and 0.028, respectively. However, these values are not significant at the 95% level. This indicates that the annual rainfall for the two studied stations during these years is not statistically significant. Therefore, it is concluded that the annual rainfall in these stations between the years 1976 to 2019 did not show any significant trend. The variations observed during this period were deemed normal, suggesting that the time series of rainfall displayed fluctuating patterns. However, it should be noted that there were instances of both increasing and decreasing trends in certain years Examining the time series reveals varying trends Initially, the outflow from Bonab station (both a and b) displayed fluctuating patterns, followed by periods of both decreasing and increasing trends. However, in recent years, there has an increase in outflow from this station. The Mann-Kendall test statistic for the two study periods for this station is 0.325 and 0.512, respectively. These values are significantly different at the 95% level, indicating that the increasing trend of discharge for both time periods was statistically significant. The reason for this trend at the Bonab station, compared to other entrance stations to Lake Urmia, is the lower demand for water in the Sofichai basin for agricultural and industrial purposes, in contrast to other rivers. To explore the root cause of this issue, studies should be conducted to examine both underground and surface water sources, as well as the utilization of water in the agricultural and industrial sectors of this region. On the contrary, the trend observed at Khormazard station (c and d) is different. Unlike Bonab station, the discharge from Khormazard station exhibited a complete downward trend. The Mann-Kendall test statistic for the discharge variable during our two research periods were -0.269 and -0.412, respectively. At the 95% level, the decreasing trend of discharge in this station was found to be significant. On the other hand, it is apparent that the volume of discharge in this hydrometric station has decreased drastically since 1976 (d). Apart from 2007, when there was a sudden increase in discharge volume, the water inflow into lake Urmia has remained at its lowest level throughout the years. To analyze the Bonab and Khormazard stations during two distinct periods, rainfall and runoff statistics (average, minimum, maximum) for the first period (1976-1994) and the second period (1995-2019) are presented in Tables 4 and 5. Based on the data presented in both tables, the Bonab station displays the highest average rainfall and runoff values in the total data column, while the Khormazard station has the lowest average rainfall and runoff values. As mentioned, in order to model rainfall-runoff data using SVM and RF models, a portion of the data was used for training purposes, while another portion was used for validation. Tables 5 and 6 present the values of the calculated statistical indicators associated with the results obtained from the training and validation sections for both SVM and RF models. According to the results of Tables 6 and 7, it is clear that in both study periods, the SVM model outperformed the RF model at the Bonab station. The SVM model demonstrated superior accuracy in simulating both flow rate and monthly rainfall. Conversely, at the Kharmazard station during these periods, the RF model displayed better performance compared to the SVM model. The modeling results in the test set for both stations revealed that the mutual correlation values for the first and second study periods at the Bonab station were 0.85 and 0.84, respectively. For the Kharmazard station, these values were 0.79 and 0.75, respectively. Conclusion The results indicate that for both periods at the Bonab station, the SVM model exhibited higher efficiency compared to the RF model. Conversely, at the Khormazard station, the RF model outperformed the SVM model for both periods. Mutual correlation values for the test sets were 0.85 and 0.84 for the first and second study periods at the Bonab station, respectively, for the SVM model test set. For the Khormazard station, these values were 0.79 and 0.75, respectively, for the RF model test set. Other notable findings of this research include the analysis of the time series data for rainfall and runoff over 43 years. Graphs obtained for both stations, along with the Mann-Kendall statistic for precipitation and flow parameters, revealed no discernible trend in precipitation during the two study periods. Instead, precipitation in these areas displayed fluctuating patterns However, the analysis of the time series and statistical values for the discharge of Sofichai and Mahpari chai rivers at the Bonab and Khormazard stations showed different results. In the Bonab station, the discharge exhibited fluctuations, with an increase observed in the second period. Conversely, at the Khormazard station, the discharge trend was downward in both study periods. The volume of Mahpari chai River outflow notably decreased in recent years, as evidenced by the Mann-Kendall statistic showing a decreasing trend. [ABSTRACT FROM AUTHOR]

Land use changes are one of the main factors in the amount of surface runoff changes in watersheds. Therefore, it is necessary to investigate it to reduce the damages (human and financial) caused by floods and to modify watershed management. The watershed of Nahre Azam is located in the north of Shiraz city and a lot of loss of life and money to the residents of Shiraz due to floods has occurred in previous years. The present research was conducted to investigate the relationship between land use change and runoff in the Nahre Azam watershed in Shiraz using the SWAT model in the period of 2004-2020. The model was calibrated using data from 2004 to 2014 and validated for 2015 to 2020. These images were classified into 6 main land uses using the supervised classification method after performing necessary preprocessing, and a land use map was prepared for 2040 using the Markov chain method. Then, the effect of the land use change in 2003 and 2040 on the amount of simulated runoff was evaluated with the recalibrated model. The calibration results of Nahre Azam watershed for the values of statistical parameters in the calibration step for the coefficient of determination, P-Facor and R-Facor are 0.77, 0.72, and 2.43, respectively, and for the validation step we obtained 0.69, 0.65, and 2.3 respectively. The analysis of the land use map showed that the main land use change in the region related to the conversion of pastures to agricultural land and urban land, which caused a decrease in pastures. Also, the results of the model simulation using the land use maps of 2003 and 2040 indicated that the amount of runoff decreased. The results revealed that if all the uncertainties are minimized, the calibrated SWAT model can produce acceptable hydrological simulation results for the user, which is useful for water resource and environmental managers and politicians as well as city managers of Shiraz. [ABSTRACT FROM AUTHOR]

Introduction Rainfall-runoff modeling is one of the most important components of hydrological processes in water resources management and accurate estimation of runoff and river flow in the short and long term can be of great help to various sectors of water engineering. In several types of rainfall-runoff models, the unit hydrograph methods are still a useful tool for flood estimation in many, except non-gauged, basins. The unit hydrograph is the same as the unit pulse response function of a linear hydrological system. The tank model is one of the hydrological models for analyzing river flow characteristics. In hydrological analysis, simulation models are often used to describe and predict basin response to rainfall events based on mathematical and physical knowledge. In this article, using the concept of linear system theory, the pulse response functions of the runoff components (surface runoff and base flow) using the reservoir model for several flood events related to the two Navrood basin in Gillan province and Liqvan basin in the East Azerbaijan province has been extracted. Since Simulink can schematically show the dynamic relationship between hydrological components such as rainfall, runoff, storage, evapotranspiration, and runoff, it can be useful for rainfall-runoff modeling. Therefore, Modeling is done in the Simulink MATLAB environment. The modular design and block library can help users focus on hydrological analysis, including modeling strategy development, parameter estimation, and model application. Materials and Methods In the present study, to evaluate the capability of the tank model in different climates, the model has been implemented for two regions with dry and wet climates to evaluate the effectiveness of the tank model by comparing the results. Therefore, the Navrood representative basin in Gilan province was selected for a wet climate, and the Liqvan representative basin in East Azerbaijan province for a dry and semi-arid climate. The response of the entire basin to the rainfall that fell on its surface has been determined using the conceptual model of the tank. In order to extract the unit pulse response function for the runoff caused by precipitation, the model of three tanks in series with holes on the side and bottom has been used to show the types of currents prevailing in the process of forming runoff. The internal dependence of reservoirs is described using exponential functions of model parameters. Estimating the model parameters was performed using the cluttered evolution optimization method or SCE-UA for short, a conceptual optimization method. In addition, to extract unit pulse response functions and evaluate the model's efficiency in predicting flood events, it was tried to select events that have rained in the entire basin and correspond to flood events in terms of occurrence time. Results and Discussion The results obtained in this research showed that the tank model provides good results in estimating the peak discharges and the time to reach the peak discharges in the two representative basins of Navrood and Liqvan with two different climates. The parameters of the model, which actually reflect the geomorphological characteristics of the basin, are almost constant, and only the changes in soil moisture storage are variable in the runoff calculations. Response functions as exponential functions of model parameters have simulated the different roles of flow components (quick surface, quick subsurface, delayed subsurface, and underground flow) in relation to the precipitation process. As can be deduced from the results obtained for the Navrood basin, the slow runoff accounts for a major part of the total runoff in the falling limb of the hydrograph. The relations extracted for the response functions of the unit pulse for ru as a unit input that happened in the duration of ∆t have been obtained parametrically, therefore, for different combinations of ru and ∆t, several unit pulse response functions can be extracted. Examining the parameters of the model obtained from the SCE-UA algorithm shows that due to the large value of the b2 parameter compared to other parameters, a major part of the volley losses is penetration losses. In fact, the high permeability of the surface layers of the soil justifies the high value of the b1 parameter compared to other parameters. Conclusion The performance results of the tank model showed that the model has a relatively good capability in predicting the runoff affected by the rainstorm. The comparison of the computational and observed hydrographs shows that the mentioned values have a good correlation. According to the results, it can be seen that the fast runoff, which usually appears in the form of surface flow and waterways in the basin, occupies a significant part of the entire flood hydrograph during the flooding process, and in terms of durability has a shorter duration than slow runoff. Slow runoff hydrograph is slowly affected by precipitation during the flooding process, but in terms of durability, it has a longer duration. [ABSTRACT FROM AUTHOR]

Introduction Urban flooding is caused by the lack of capacity of the harvesting channel network and occurs when the amount of precipitation exceeds the network's capacity. One of the two main factors contributing to the aggravation of damage caused by urban floods is population growth and the expansion of urbanization, and the second factor is heavy rainfall caused by climate change, which plays an essential role in intensifying and accelerating the hydrological cycle and may change the amount and frequency of precipitation. This factor affects the probability of flooding, runoff volume, and peak flow. It is more visible in arid and semi-arid areas where rainfall usually occurs briefly but with high intensity. Urban flooding is a growing threat to urban infrastructure and public health, posing significant challenges to urban resilience and sustainability. One of the urban infrastructures that is of great importance is the runoff collection network. The increase of impervious surfaces wear and tear on the network. changes in the rainfall pattern due to climate change have increased the occurrence of urban floods and raised the importance of network redesign to minimize the system vulnerability. Materials and Methods In this research, the runoff harvesting network of ten districts of Tehran Municipality was redesigned and optimized. This area, with a population of 327,000 people, is located in the relatively dense fabric of the Tehran metropolis, and its area is 807 ha. Simulating the runoff collection network and checking the performance of the network by two indicators of vulnerability and reliability requires an accurate model with great details. For this purpose, in this research, SWMM version 5.1 software was used to simulate the runoff collection network. The study area was divided into 285 sub-basins to simulate the sub-channels in better detail. Then, information such as slope, area, and percentage of impervious space was introduced through ArcMap software version 10.3.1 as information under the watersheds. The sub-watershed width parameter was calculated by dividing the sub-watershed area by its most significant length using Q-GIS software and applied to the sub-basins. The LARS-WG model has also been used for the exponential micro-scale output of climate models. To simulate the network in the current or present situation, the historical precipitation information of the Mehrabad synoptic station was used, and to affect the network in future conditions, the precipitation information of the climate models of the sixth climate change report was used. Among the predictions of climate models, the most incremental changes in threshold precipitation were selected as a pessimistic scenario, and a system redesign was done to reduce vulnerability under this scenario. Results and Discussion This study was conducted to assess the performance of Tehran municipality's runoff collection network under current and future conditions. The SWMM hydraulic model was employed to simulate the network under various rainfall scenarios. Current conditions revealed a total runoff volume of 45.9, 51.14, and 59.7 thousand m3 for return periods of 2, 5, and 10 years, respectively. This runoff volume resulted in a vulnerability increase from 10.4 to 12.2% and a reliability reduction from 97.5 to 95.8%. To evaluate the network's performance under future climate change scenarios, the SWMM model was used with data from the IPCC sixth assessment report. Among the top five climate models, the one with the highest precipitation was selected as the pessimistic scenario. Simulation results under future conditions indicated a significant runoff volume increase, reaching 64.04 and 72.18 thousand m3 in 5- and 10-year return periods, respectively. This increase corresponded to vulnerability indices of 12.7 and 13.9% and reliability indices of 95.3 and 94.3% for the same return periods. To enhance the network's resilience, a genetic algorithm-based optimization approach was employed. Cost, reliability, and vulnerability index were considered optimization objectives with specific weighting functions. The algorithm converged to an optimal design solution in the 168th iteration, resulting in a 7.6% vulnerability reduction and a 98.1% reliability enhancement. Conclusion The vulnerability index in the return periods of 5 and 10 years is equal to 12.7 % and 13.9 %, respectively, and the reliability index is equal to 95.3 % and 94.3 %. After checking the network in its current state and future conditions, an optimal network redesign was done to improve system performance in both current and future conditions. For this purpose, the genetic algorithm was used for optimization, and the objective function consisting of cost, vulnerability index, and reliability index and the importance weights of each, were defined. Then, MATLAB software did the optimal network redesign by connecting the simulator and optimizer model. The results showed that in the 168th iteration, the algorithm reached its final answer of 0.3, which remained constant until the 300th iteration. Also, the optimal redesign has reduced network vulnerability by 7.6% and increased reliability by 98.1%. This research showed that with an optimal redesign and solving the current network problems, the system's ability to face future climate change threats could also be increased. Of course, spending the least money to achieve the best result was one of the main goals of this research. In future studies, it is recommended to use low-impact development tools along with optimal redesign to fix defects and improve the performance of the runoff collection network. [ABSTRACT FROM AUTHOR]

Since the emergence of the concept of urban resilience, this concept has mostly been concerned with urban planning, and resilient urban design approaches have been a less developed field. The current research is of applied type and uses descriptive-analytical methods. This research deals with urban resilience in urban design and focuses on how urban forms affect urban resilience against floods and inundation. In this regard, indicators for evaluating the resilience of urban forms against floods and urban runoff have been explained and these indicators have been measured and evaluated within the scope of the study area of Noor City at the level of neighborhoods and urban blocks to determine the level of resilience of each of them, that should be determined from the research indicators at the level of urban cells. The TOPSIS technique was used for data analysis. The results show that the study area of Noor City is in an unfavorable situation regarding resilience. Based on the evaluation done at the block level, the results indicated that 27.7 hectares of the study area have very low resilience, 62.6 hectares have low resilience, 48.4 hectares have medium resilience, 53 hectares have high resilience, and 16.8 hectares have very resilience. The largest area is related to the low resilience level with 62.6 hectares. In total, 90.3 hectares of the study area have low and very low resilience, which is about 43% of the total studied area. [ABSTRACT FROM AUTHOR]

Background: Soil erosion is one of the global problems that threatens water and soil resources. Protecting and recovering bare and uncovered soils, as well as diverting surface runoff from the soil, are an efficient method for reducing soil erosion in sensitive and erosion -prone areas. Among the activities to prevent soil erosion are to maintain the natural vegetation of the area and stabilize bare and uncovered soils with the help of temporary or permanent methods. The use of erosion control blankets is a soil bio -engineering method that can effectively reduce runoff and sediment and increase slope stability. Therefore, the current research aimed to temporarily stabilize soil using protective covering treatments, such as jute, straw, wood chips, Festuca grass cover, and nano zeolite super absorbent, to reduce the amount of runoff and sediment . Methods: The study area is the Darabkola Forest (the educational -research forest of Sari University of Agricultural Sciences and Natural Resources), with an area of about 2612 hectares located in the southeast of Sari City between 52° 14′ E and 36° 28′ N. To measure the amount of runoff and sediment in the Darabkola Forest, plots with dimensions of 70 x 50 cm and a depth of 10 cm were created using galvanized sheets. Then, plots were placed on the ground with a slope of 3%. Erosion control blanket treatments made of jute filled with wood chips, straw, and nanozeolite superabsorbents were prepared and located in the plots, as well as jute cover, jute cover with Festuca grass, and Festuca grass. Rain simulation tests were conducted for each sample (30 minutes with an intensity of 50 mm.h -1) and the amount of runoff from the plots was measured at 5 -minute intervals using a graduated cylinder placed at the bottom of the plots. Sediment concentration was measured by weighing the samples after settling the runoff and passing it through filter paper. Sediment concentration was calculated by dividing the amount of sediment by the volume of runoff. Data were analyzed using one -way ANOVA for seven treatments. Duncan's test was used to compare the means. Pearson's correlation analysis was also used to investigate the relationship between runoff and sediment concentration in runoff . Results: The results of statistical analysis showed that the highest amount of runoff (0.77 mm.h - 1) belonged to the control treatment, and the lowest amount was recorded in the treatment of straw and the Festuca herbaceous species (0.24 and 0.27 mm.h -1, respectively). Overall, the straw treatment reduced the amount of runoff by 69% and the Festuca herbaceous species treatment reduced it by 64% compared to the control plot. The results of sediment concentration revealed 63% and 52% decreases in the amount of sediment in the samples belonging to the treatment of jute cover and jute cover together with Festuca species, respectively, compared to the control plot. The highest sediment concentration was measured in the control sample (7.13 g/ml). The presence of Festuca in the soil reduced the sediment by 2%, which shows that the presence of a herbaceous species such as Festuca does not considerably reduce the sediment concentration compared to the other treatments such as jute. Examining the coefficients obtained from Pearson's correlation analysis between runoff rate and sediment concentration in runoff for different treatments indicated that the stabilizer addition to the soil reduced sediment and reduced surface runoff in some treatments. A positive and significant correlation was found between runoff and sediment in wood chips, straw, Fesuka, and nanozeolite treatments. In contrast, a negative and significant correlation was recorded for Jute + Festuca and the Jute treatment . Conclusion: This research demonstrates that the use of erosion control blankets with combinations of different materials, such as straw and stubble, nanozeolite, and wood chips, along with the Festuca grass cover, can reduce runoff volume and reduce soil erosion and waste, especially in forest road trenches containing soils without vegetation. Moreover, grass cover (such as Festuca) increases water permeability in the soil and prevents soil loss. Therefore, using these erosion blankets can reduce soil erosion around forest roads. [ABSTRACT FROM AUTHOR]

Background : Depending on rainfall systems and conditions influenced by unprincipled human activities, floods cause a lot of damage to natural resources, settlements, and projects every year. Hydrometric stations are often destroyed during floods or small watersheds lack hydrometric stations, which means that the estimation of runoff and maximum flood discharges requires a suitable method to calculate runoff and flood values in these basins. Prevention of this damage is doubly important when the study area overlooks places with a high density of settlements and urban facilities that can threaten the lives of many residents. The amount of runoff in each sub - basin of the watershed overlooking Malayer City was estimated in this study. Methods: The watershed overlooking the city of Malayer with an area of 14,700 hectares is stretched from the north to the northeast of the city. The processing of digital elevation models identified five sub -basins overlooking the city and field visits. The curve number method was used to estimate runoff height and flood volume in each sub -basin. The most intense rainfall event (88 mm per day) with a return period of 30 years was designed as rainfall, considering the amount of previous rainfall 5 days before this event with a cumulative value of 45.2 mm. To calculate the physical parameters of the watershed in this study, topography, geology, vegetation, soil, and land use maps were digitized using a geographic information system. Simultaneous maps of land use and rainfall were provided using Sentinel -2 images, containing 13 spectral bands and a spatial resolution of 10 meters. Utilizing the SCS method, the layers of land use, vegetation, and soil hydrological groups were combined to produce a curve number map. The value of the curve number was corrected for the wet condition based on the rainfall 5 days before and the location of the basin in the previous wet conditions. A weighted curve number approach for each sub - basin was compared with another approach based on the arithmetic mean of the curve number for each sub -basin, as well as a weighted runoff height approach for each curve number. Kirpich's method was used to calculate the concentration time of each sub -basin. In the next step, using the maximum daily rainfall data from the Malayer synoptic station in the statistical period from 1991 to 2021, the rainfall height values were converted to runoff height using the SCS relationship, and then the peak flood discharge was calculated for each sub -basin. Results: The lack of permanent vegetation and the presence of annual grasses are among the reasons for the high potential of the sub -basins in runoff production. As a result, the average curve numbers of arithmetic and weighted average methods are 79.09 and 81.46, respectively, which shows the high capacity of the basin in producing runoff. Converting curve number values to peak flood height and flow using two commonly used methods of curve number calculation and comparing it with the results of the weighted runoff height calculation approach for each curve number in the working units of the study area showed no significant difference. Finally, the runoff height maps and the peak discharge of each sub -basin were drawn. The northern sub -basin of the basin with an average runoff height of 44.32 mm and a peak discharge of 168 m 3 /s has the highest participation in flood discharge toward the city of Malayer, and sub -basin 4 in the south -eastern part of the basin has the least participation in flood generation. The results of the relationship between the area and runoff height showed that the basins with a larger area did not necessarily contribute the most to the occurrence of floods in all three calculation methods of the curve number and runoff height. Other factors also play a role in these results, such as the extent of rocky outcrops and reduction time of concentration due to the high slope as one of the reasons for this problem. The formation of the longest watercourse in the basin with a length of 16314 meters under Sub -basin 3, which has the highest peak flood flow after Sub -basin 2, is one of the discussed technical points that can handle the rapid discharge of the peak flow of about 100 m 3 /s. However, the study of its longitudinal slope of about 2% indicates the high risk of flooding caused by the lack of evacuation in rains with a high return period, which in turn is a serious risk for the threats caused by floods in the southeastern part of the city. The intersection of the end part of the main waterway with the exit of other sub -basins and the collection of runoff under the upstream basins are other problems that increase the risk of flooding in the downstream areas. Conclusion: It is suitable to use the curve number method to estimate the amount of runoff produced in the basins overlooking the settlements that do not usually have hydrometric stations. Because of its low -density pastures and rain -fed agriculture, the watershed overlooking Malayer has a high potential for runoff production. [ABSTRACT FROM AUTHOR]

Background and Objectives The industrialization of societies and increasing greenhouse gases have caused climate change and seriously threatened human life. Change in rainfall is one of the essential effects of climate change. Changes in precipitation have affected surface runoff and underground water sources; water resources management becomes more complex and complicated in such conditions. The most reliable tool for investigating the effects of climate change on different systems is the use of climate variables simulated by coupled atmosphere-ocean general circulation models. These models can simulate climate parameters (temperature, precipitation) for future periods. Nevertheless, the main weakness of these models is their low spatial resolution and the simplifications they consider for climate processes. Microscale exponentials are used to cover the weakness of spatial resolution. In this research, the climate was investigated using the fifth report of the International Panel on Climate Change and the CanESM2 model and the application of the SDSM model. This research aims to investigate the effect of climate change on the temperature and precipitation of the Nahand Basin and its effect on the runoff entering the Nahand dam. Methodology In this research, the daily temperature and precipitation data of the Nahand basin in the period (1981-2005) were used as the base period to generate climate parameters (temperature and precipitation) using the CanESM2 climate model and RCPs release scenarios in future periods. Furthermore, for the exponential microscale, SDSM statistical model was used. Microscale statistical methods are based on creating a link between surface observations and atmospheric variables such as geopotential height, temperature, and humidity in the base period. By establishing a suitable statistical relationship between predicting and predicted variables, it is possible to achieve suitable relationships for predicting climate variables in future periods under the influence of the climate change phenomenon. The SDMS model is a multivariate regression model for climate data generation using statistical microscale methods developed by Wilby and Dawson in 2002. Nahand dam is one of the drinking water sources of Tabriz. Therefore, determining the incoming runoff to the tank can help in the optimal management of the system and decision-making by the relevant managers. To simulate the runoff entering the reservoir in future periods, IHACRES integrated conceptual model was used for rainfallrunoff simulation. The main goal of the IHACRES model is to determine the hydrological behavior of the basin using a small number of parameters. This model includes two parts (modules) interconnected, nonlinear and linear. In the nonlinear part, the observed precipitation is converted into adequate precipitation with the help of the station temperature. In the linear part, adequate precipitation is converted into flow. Based on the results of the temperature assessment in both the near future (2021-2060) and the far future (2061-2100) and under all emission scenarios, it is increasing, and the temperature increase in the far future is more than soon. The lowest and highest temperature increase is related to the RCP 2.6 scenario soon, a period of 0.17°C, and the RCP 8.5 scenario in the far future period to the extent of 1.01°C compared to the base period (1981-2005). By examining the trend of changes in the average precipitation in the coming periods, it can be seen that the precipitation, contrary to the temperature, is decreasing in all scenarios, so that the lowest and the highest decrease, respectively, related to the RCP 2.6 scenario in the future period is close to 7.23 mm and The RCP 4.5 scenario will be 25.77 mm in the far future period compared to the base period. The runoff will decrease in future periods under all scenarios. The lowest and highest decrease in the order of the near future (2021-2060) under the RCP 2.6 scenario is 0.08 m3s-1 (8.51%), and the far future (2061-2100) under the RCP 4.5 scenario is 0.08 m³ s -1 It is 0.18 (19.15%). Conclusion In this research, the climate change of future cycles was evaluated using the CanESM2 climate model and the exponential microscale of the SDSM model. The SDSM model evaluation shows that this model has simulated temperature and precipitation well in the base period. The results indicate an increase in temperature in all scenarios in the future, and the increase in the distant future (2061-2100) will be more significant than soon (2021-2060). Also, precipitation will decrease in both future periods under all scenarios. The results of the study of the effect of climate change on the runoff entering the Nahand dam show that the runoff will decrease under the influence of this phenomenon. [ABSTRACT FROM AUTHOR]

Recently, with the advancement of technology, extensive facilities have been provided for monitoring climate data with different resolution. Therefore, the aim of this research is to investigate the combination of precipitation databases and simultaneous monitoring of meteorological and hydrological droughts on a monthly time scale using the Combined Drought Index (CDI) at sub basins of Iran. To this end, observational data (100 synoptic stations on a daily scale) and global precipitation and runoff databases including ERA5, MRRRA2, GRUN, GLDAS and TERRA (with different spatial resolution on a monthly scale) were collected and extracted during the period of 1987-2019. Then, based on the aridity index, the climatic classification of the stations and sub basins has been done and the entropy weight method (EW) was employed to combine databases and indices. The accuracy of the datasets was assessed based on Kling Gupta (KGE) and Standardized Mean Square Error (NRMSE) metrics. The results showed that the combination of precipitation databases in the Hyper-arid, Arid, Semi-arid and Humid climates reduced the error index by 32, 10, 24 and 26%, respectively, compared to individual databases. In the 3-month scale, the sub basin of Sefidroud, Lake Namak, and Talesh, and in the 12-month scale, the sub basin of Lake Urmia, Lake Namak, Sefid Rud, Hamon Jazmourian, Hamon Mashkel, South Baluchistan, and parts of Aras River have faced with drought with more severity, duration, and peak values of drought. It should be said that, on average, in both scales, the middle part of the country has less drought severity and duration. The extent of drought in the river basins of Hyper-arid, Arid and Semi-arid climates in the scale of 3 and 12 months on average is 45 and 53%, and 70 and 40% in Humid climates. Based on these findings, it can be concluded that the combining precipitation databases enhances the accuracy compared to using global single databases. Additionally, drought in the humid climate shows higher intensity, duration, and extent in the short-term scale compared to the long-term scale. [ABSTRACT FROM AUTHOR]

AbstractIntroduction: Accurate forecasting of runoff and flooding to avoid human and financial losses is one of the most challenging tasks in hydrological studies of a given locale. Therefore, researchers have paid more attention to the development of accurate flood forecasting models, including the use of artificial intelligence methods.Methods: In this investigation, the efficiency of 3 models, ANN, GMDH and ARIMA, has been investigated in order to simulate the flood of a part of Halil river basin in Kerman province. ANN model is a non-linear modeling method that improves its performance over time. The GMDH composed code is an artificial intelligence model with exploratory self-organizing features, at the conclusion of which a complex system with optimal performance is formed. Composed ARIMA code builds a model to describe the structure of the data and then predict the time series. The input data to the above models included discharge, precipitation, temperature, wind and monthly humidity, and the simulated runoff values ​​were compared with the observed values.Findings: In order to evaluate the accuracy of the models in this research, statistical indices were used and the results showed that the ANN model (RMSE=0.042, MSD=0.001, MAE=0.027) had the possibility to estimate the runoff with higher accuracy compared to the GMDH model (RMSE=0.068, MSD=0.005, MAE=0.056) and the ARIMA time series (RMSE=0.096, MSD=0.009, MAE=0.063) in the studied basin. The mean error in runoff estimation with ANN model has been reduced by 38.23% and 56.25%, respectively, compared to the values estimated with GMDH and ARIMA models. According to the results obtained in this study, the artificial neural network model has been able to show a better performance than the other two models in predicting the outputs due to its suitable structural ability to find the nonlinear relationship between the input and output data.

Awareness of water resources status is essential for the proper management of resources and planning for the future due to the occurrence of climate change in most parts of the world and its impact on different parts of the water cycle. Hence, many studies have been carried out in different regions to analyze the effects of climate change on the hydrological process in the coming periods. The present study examined the effects of climate change on surface runoff using the Atmosphere-Ocean General Circulation Model (AOGCM) in Khomeini Shahr City. The maximum and minimum temperatures and precipitation of the upcoming period (2020-2049) were simulated using a weighted average of three models for each of the minimum and maximum temperatures and precipitation parameters based on the scenario A2 and B1 (pessimistic and optimistic states, respectively) of the AOGCM-AR4 models. The LARS-WG model was also used to measure the downscaling. The HEC-HMS was used to predict runoff. The effects of climate change in the coming period (2020-2049) compared with the observation period (1971-2000), in the A2 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.6 Degrees Celsius, respectively, and the precipitation would decrease 17.8 percent. In the B1 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.4 degrees Celsius, respectively, and the precipitation would decrease by 13 percent. The results of runoff were different in the six scenarios in the way the most runoff reduction is related to the scenario of fixed land use and scenario A2 (22.2% reduction), and the most increase is related to the scenario of 45% urban growth and scenario B1 (5.8% increase). So, according to increase urban texture in the future and consequently enhance the volume of runoff, this volume of runoff can be used to feed groundwater, irrigate gardens, and green space in the city.

BACKGROUND AND OBJECTIVES: Vital for the insurer's profitability and solvency, a loss reserve is a prediction of the amount an insurer will need to pay for future claims. Researchers have been exploring methods to incorporate dependencies among multiple loss triangles to improve the accuracy of outstanding claim prediction. This study aims to predict outstanding claims in dependent run-off triangles by considering the dependence among the outstanding claims paid in each run-off triangle. METHODS: The study considers the dependence among corresponding outstanding claims in run-off triangles related to different lines of insurance. It also takes into account the calendar year of payment of claims, in addition to factors such as the year of claim occurrence and the number of years of delay in payment. Two methods are used to model the inter-triangular and intra-triangular dependencies. The first method involves modeling the dependence among triangles by using a multivariate distribution for outstanding claims in the corresponding cells of run-off triangles. The calendar dependence within each runoff triangle is incorporated by adding a calendar year effect factor to the mean of the outstanding claims distribution. The second method uses a multivariate distribution for the outstanding claims of the calendar years corresponding to run-off triangles, capturing both types of dependence. Bayesian approach and Hamiltonian Monte-Carlo sampling methods are employed to estimate model parameters. FINDINGS: The study utilizes data from an Iranian insurance company on outstanding claims in car body insurance and third-party car insurance from 2012 to 2015. The two methods of calendar dependence modeling are compared using a scale mixture multivariate distribution with normal marginal distributions and copula dependence. The mean absolute percentage error is used to measure the accuracy of the prediction. The results show that using a multivariate distribution for calendar dependence modeling leads to a more accurate prediction compared to adding the calendar year effect factor to the mean model. CONCLUSION: Based on the findings, it is concluded that modeling the calendar dependence among outstanding claims in run-off triangles using a multivariate distribution improves the accuracy of reserves prediction compared to using the calendar year effect factor. This approach can enhance the prediction of outstanding claims and contribute to the insurer's profitability and solvency. [ABSTRACT FROM AUTHOR]

The present study analyzed changes in land use and its effects on the runoff of Qara Chai Watershed in Hamadan Province using the SWAT model. To this aim, Landsat OLI-TM satellite images of the years of 2001-2020 were used. First, the relevant images were obtained and the necessary pre-processing steps, including atmospheric corrections, were applied by using the FLAASH method. To increase the classification accuracy, the multispectral images were combined with the panchromatic images and the spatial resolution was enhanced up to 15 m. Then, the classification process was done by using the object-oriented method and the nearest neighbor algorithm. The SWAT model was utilized for hydrological simulation of the basin and SUFI-2 algorithm was applied in SWAT-CUP software for conducting sensitivity analysis, calibration, and validation. Due to the sensitivity of the model to the initial loss parameter, it was recalibrated based on the initial loss values. The values of the coefficients for the calibration period were between 0.72 and 0.90 and the model validation results confirmed the calibration accuracy. Assessment of the efficiency of the model by using Nash Sutcliffe coefficients, p-factor, R, and r-factor indicated its high capability for simulating the related runoff. [ABSTRACT FROM AUTHOR]

Introduction and Objective One of the important and effective measures in controlling soil erosion and runoff production is the use of watershed management methods, which is currently receiving less attention. This natural phenomenon is a global environmental problem that reduces soil fertility and water quality, increases sedimentation and the possibility of floods. Therefore, using erosion plots in natural areas can play an effective role in controlling runoff and sediment production. The present study was prepared with the aim of investigating the effect of precipitation and vegetation on the production of runoff and sediment in the Noorroud watershed of Mazandaran province.Materials and Methods Noorroud watershed is located in Mazandaran province, the political area of Noor city, Beldeh and southwest of Amol, and the most important population center is Beldeh. This basin, with an area of 1299.78 square kilometers, is the largest sub-basin of Haraz River. To calculate the runoff from individual showers on a plot scale and two different land use types of hand-planted forest (Pine, Pinus halepensis) with seven and eight years old and rangeland lands, using test plots with a size of two by ten square meters with two treatments. And three repetitions were used in the months of April, April, June, October, November and December of 2016 and 2017 and Anova statistical analysis was used to check the relationship between the parameters.Results and Discussion The results of precipitation from 2017.04.17 to 2018.06.07 showed that the maximum amount of runoff occurred on May 31, 2018 and caused the production of 0.35 mg of sediment per plot unit. The results of the Kolmogorov-Smirnov test also showed that the eastern domain has a normal distribution at the level of 0.95 for the two dominant types. The results of the correlation between the canopy and litter percentage of each land use showed that the presence of natural factors such as litter and vegetation canopy has a strong relationship with the weight of sediment produced in two treatments. The highest value of the runoff coefficient is for rangeland use (33%) and the lowest for hand-planted forest (31%).Conclusion and Suggestions According to the obtained results, it was found that in general, the amount of runoff and sediment produced in forest areas is more than rangeland. On the other hand, there was less relationship with production sediment in the rangeland areas with the eastern slope. One of the main cases in the destruction of national lands is grazing beyond the capacity of rangelands and forests. This factor causes the destruction of the rangeland, and with not very intense rains, the resulting runoff moves some of the soil. Various factors are involved in the occurrence and aggravation of runoff and sediment, and considering the conditions of each region, one or two factors may play a greater role. The role of its use and vegetation under different rainfall conditions has an important role in the amount of runoff. Therefore, it can be seen that the presence of vegetation has a direct effect on reducing the amount of runoff and sediment. According to the obtained results, in order to improve the results, it is better to use artificial rainmakers in the natural field so that the role of vegetation can be better understood.

Introduction and Objective : In recent years, river flow forecasting is one of the most important issues for water resources management in Iran. This prediction requires statistics and information, unfortunately, most of the basins of the country lack data of the desired quantity and quality. Material and Methods: Therefore, hydrological modelling and the use of artificial intelligence are examples of solutions that are used to solve this challenge in hydrology. The criteria for selecting the appropriate model for this process are to evaluate the performance of the models according to the hydrological conditions of each region. In this research, IHACRES model and Artificial Neural Network (ANN) were used to predict the streamflow in Bakhtiary basin. The data from 1984 to 1994 were used as calibration period and the data from 1995 to 2006 were used for validation. Results: The evaluation results of the hydrological model and the artificial neural network were evaluated using Kling -Gupta, Nash -Sutcliffe indices, coefficient of determination, mean squared error and absolute mean error. Results showed that the artificial neural network had better results in the simulation in all the evaluated evaluation criteria. Conclusion: According to the results of the methods used in the research, the artificial neural network method has a more accurate prediction of the Bakhtiary river flow than the hydrological model. [ABSTRACT FROM AUTHOR]

Awareness of water resources status is essential for the proper management of resources and planning for the future due to the occurrence of climate change in most parts of the world and its impact on different parts of the water cycle. Hence, many studies have been carried out in different regions to analyze the effects of climate change on the hydrological process in the coming periods. The present study examined the effects of climate change on surface runoff using the Atmosphere-Ocean General Circulation Model (AOGCM) in Khomeini Shahr City. The maximum and minimum temperatures and precipitation of the upcoming period (2020-2049) were simulated using a weighted average of three models for each of the minimum and maximum temperatures and precipitation parameters based on the scenario A2 and B1 (pessimistic and optimistic states, respectively) of the AOGCM-AR4 models. The LARS-WG model was also used to measure the downscaling. The HEC-HMS was used to predict runoff. The effects of climate change in the coming period (2020-2049) compared with the observation period (1971-2000), in the A2 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.6 Degrees Celsius, respectively, and the precipitation would decrease 17.8 percent. In the B1 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.4 degrees Celsius, respectively, and the precipitation would decrease by 13 percent. The results of runoff were different in the six scenarios in the way the most runoff reduction is related to the scenario of fixed land use and scenario A2 (22.2% reduction), and the most increase is related to the scenario of 45% urban growth and scenario B1 (5.8% increase). So, according to increase urban texture in the future and consequently enhance the volume of runoff, this volume of runoff can be used to feed groundwater, irrigate gardens, and green space in the city. [ABSTRACT FROM AUTHOR]

Soil erosion by water is a global threat to the environment that negatively affects soil and water quality. Soil erosion modeling is a promising method to simulate soil erosion to identify sediment source areas and to evaluate soil conservation measures. The objective of this study was to use the Soil and Water Assessment Tool (SWAT) to model runoff and sediment in Gawshan dam watershed in west of Iran. Materials and Methods The input data required to implement the SWAT include digital elevation map (DEM), soil map, land use/land cover map and climate data. DEM was used to delineate the watershed boundary, divide it into different sub-basins, and prepare slope and flow network maps. It had a resolution of 30 meters which obtained from the United States Geographic Survey. Soil map was prepared from land survey and Landsat ETM remote sensing data. The soil physical and chemical parameters required to run SWAT were measured in the different soil units, and its spatial distribution was coincident with the soil unit boundaries. The climatic data obtained from a period of 11 years, from January 1, 2005 to the end of December 2015, from the stations of Bavale, Ravansar, Songhor, Qurveh, Sanandaj, Kermanshah, Kangavar and Kamyaran were used. The two different approaches for estimating the model parameters have been used. In the forward deterministic approach model, parameters were independently derived from digital elevation model (DEM), soil and land use maps and climate data, whereas in the inverse stochastic approach model, calibration were performed by sensitive model parameters using Sequential Uncertainty Fitting (SUFI-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP (SWAT Calibration Uncertainty Programs). Model performance was evaluated using the coefficient of determination (R2 ) and Nash-Sutcliff efficiency (NS) in the forward approach and P and R factor in the inverse approach. Results and Discussion Results showed that in the first approach, based on monthly data (2008-2013), the model performance was satisfactory for runoff (R2=0.66; NS=0.63) and fair for sediment (R2=0.42; NS=0.3). However, model prediction improved for both runoff (R2=0.94; NS=0.83) and sediment (R2=0.61; NS=0.53) when they were averaged on the monthly basis. Calibration of the model parameters improved its performance for runoff (P factor>0.6; R factor<1) and sediment (P factor>0.4; R factor <1). One of the reasons of the model weakness in simulating runoff is its uncertainty in simulating snow melting or the lack of information about water withdrawal from aquifers in the study area. Also, the lower efficiency of the model in simulating the sediment compared to the runoff can be attributed to the greater uncertainty of the measured data of the sediment compared to the runoff. Overall, these results demonstrated that the SWAT has the potential as a decisionmaking tool in the Gawshan dam watershed management. [ABSTRACT FROM AUTHOR]

Introduction The sustainable availability of water resources and the qualitative and quantitative status of these resources are threatened by many natural and antropogenic factors, among which climate change plays an important role. Climate change can have profound effects on the hydrological cycle through changes in the amount and intensity of precipitation, evapotranspiration, soil moisture, and increasing temperature. On the other hand, the distribution of rainfall in different parts of the world will be uneven. So that some parts of the world may face a significant decrease in the amount and intensity of precipitation, as well as major changes in the timing of wet and dry seasons. Therefore, sufficient knowledge about the effects of climate change on hydrological processes and water resources will be of particular importance. In this research, as the first comprehensive study, the effect of future climate change on the water resources components of Neyshabur-Rookh watershed was investigated by a set of one hydrological model and six General Circulation Models under the RCP4.5 scenario. Materials and Methods The Neyshabur-Rookh watershed with an area of 9449 square kilometers is a sub-basin of Kavir-e Markazi-e Iran and a part of the Kalshoor Neyshabur watershed, which is located between of 58 degrees and 13 minutes and 59 degrees and 30 minutes and east longitude and 35 degrees and 40 minutes and 36 degrees and 39 minutes north latitude. The study area with an average altitude of 1549.6 m above sea level and average annual precipitation of 246.83 mm, a mean annual temperature of 13.3 Celsius has an arid to semi-arid climate. For hydrological simulation of the watershed using WetSpass-M model, maps of Digital Elevation Model (DEM), land-use, soil texture, slope, and distribution map of groundwater depth, Leaf Area Index (LAI), and climate data (rainfall, mean temperature, potential evapotranspiration, wind speed and the number of rainy days) per month in 1991-2017 period were used. Then the prepared model was calibrated and validated. The climatic data of six General Circulation Models (GCMs) under the RCP4.5 scenario (Representative Concentration Pathways) were downscaled using the Quantile Mapping Bias-Corrected method. The downscaled GCM models were ranked and weighted in each station according to results of the Leave one out cross validation method and utilized as an ensemble for projecting the near-future climatic conditions of the water resources components of the watershed. By importing the monthly maps of precipitation, average temperature and evapotranspiration in the period of 2026-2052 into the calibrated hydrological model, the hydrological response of watershed to near future climate change was determined and evaluated. Results and Discussion WetSpass-M was calibrated by changing the calibration parameters in five hydrometric stations and the compared measured and simulated streamflow. The values of four evaluation criteria NS, R2, MB, and RMSE indicated the good performance of the model during the calibration and validation process. By predicting climatic parameters in near future and preparing and importing maps of monthly precipitation, mean temperature, and evapotranspiration to WetSpass-M, the hydrologic simulation of the watershed was done in the 2026-2052 period. The results indicated that the mean annual temperature and precipitation would be respectively increased by 4.66% and 1.21°C under RCP4.5 in the near-future period compared to the baseline period. The average temperature will increase in all months so that the most changes will occur in September and the least changes will occur in March. The rainfall of the watershed will increase in March, April, May, October, and December and will decrease in the rest of the months. The highest and lowest rainfall changes will happen in April and August, respectively. The analysis of the components of water resources in the near future shows that annual total runoff, groundwater recharge, and actual evapotranspiration will increase by 5.9%, 14.85%, and 1.42% compared to the base period, and annual direct runoff and interception will decrease by 15.15% and 3.54%, respectively. Conclusion Considering the importance and major role of the Neyshabur watershed in the economy of agricultural products of Razavi Khorasan province, the results of this research will be of great help to the managers and policymakers of the country's water resources management in order to make appropriate decisions with the aim of reducing the effects of climate change on the water resources of the Neyshabur-Rookh Basin.

Nowadays, machine learning models are able to make good predictions based on pattern extraction between data. In this study, a neural-fuzzy network (ANFIS) was used to predict the inflow to the reservoirs of a dam namely, the Mahabad dam located in the northwestern part of Iran. A new Harris Hawk (HHO) optimization algorithm was also used to improve the ANFIS (HHO-ANFIS) structure. Monthly precipitation and temperature and inlet flow data to the reservoir one to three months ago were used as input parameters as 6 different input patterns. About 70% of the data was used for training and 30% to test the models. The results showed that the ANFIS model has good accuracy in training data although, for test data, its accuracy was greatly reduced. The development of the HHO-ANFIS model improved the accuracy of the prediction. The patterns with all input parameters had the highest prediction accuracy. In this pattern, values ​​of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash Sutcliffe Efficiency coefficient (NSE) for test data were 3.9 MCM, 2.41 MCM, and 0.86, respectively. Due to the good performance of the model used, it can be recommended for time series predictions.

Changes in community conditions, population growth, inappropriate and unprincipled use of available water resources and climate change are known as the reasons for the decrease in available water resources in recent years. Hence the need for integrated management of existing water resources is quite obvious. One of the important parameters for sustainable planning and management of water resources is the estimation of river flow. In recent decades and with the increasing development of computer technologies, many rainfall-runoff models have been developed for different purposes, each of which has advantages and disadvantages. Simulation is to understand the relationships governing the process of runoff. Simulation is used when the goal is not to involve the main system or the main system is not available. Simulation itself has been a major issue in terms of runoff forecasting and management in hydrological research. One of the most basic topics in hydrological sciences is understanding and recognizing and understanding the processes of production and flow transfer from input to output of the field. It is necessary to generalize through them those votes that do not have a voice, which is not a sign that it's not a sign, and the ideas of the day that this method of influence came into being. The relationship between precipitation and runoff is of great importance in hydrological studies.

Abstract Introduction: Rainfall-runoff modeling is considered one of the important methods in the study of hydrology and environmental management, especially in urban areas, in which sudden floods lead to significant financial and human losses. Thereupon, various models have been developed for urban flood simulation, among which selecting the best, is of significant consideration in the literature. Methods: Runoff peak flow and hydroghs measured in three nodes of an urban drainage network were applied for both spatial and temporal evaluation of the SWMM hydrological model in EPA SWMM, ASSA and Bently SewerGEMS V8i softwares. Other rainfall-runoff models also evaluated were SCS TR-55, SCS TR-20, Rational, Dekalb Rational, Santabarbara UH models (in ASSA software) and SCS UH model (in SewerGEMS V8i software). The models were calibrated considering the measurements during three precipitation events, and then validated by the measured data of three other events. The Nash-Sutcliffe coefficient along with the BIAS coefficient, the Coefficient of Determination and the Root Mean Square Error were used as the efficiency indicators. Findings: The measured runoff peak flow and hydrograph were most compatible with those simulated by SWMM models of EPA SWMM, SewerGEMS V8i and ASSA softwares, respectively. Regarding the results of the partial parameter and the Spearman correlation coefficient methods, model outputs were most sensitive to the percentage of impervious areas, equivalent width, roughness coefficient of impervious areas, the depth of depression of impervious and pervious areas, the percentage of impervious areas without surface storage and the curve number, respectively. The model was not sensitive to the roughness of the permeable areas. The results suggest EPA SWMM as the software with more reliable simulation results for runoff management projects in the study area and urban basins alike.

The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.

The runoff curve number method is widely used to predict runoff and exists in many popular software packs for modeling. The curve number is an empirical parameter important but depends largely on the characteristics of soil hydrologic groups. Therefore, efforts to reduce this effect and extract more accurate soil information are necessary. The present study was conducted to integrate fuzzy logic for extraction runoff curve numbers. A new distribution model called CNS2 has been developed. In the first part of this research, the formulation and programming of the CNS2 model were done using the Python programming language environment, then the model was implemented in the Beheshtabad watershed. This model simulates the amount of runoff production in a watershed in the monthly time step with the fuzzy curve number and takes into account the factor of rainy days, the coefficient of management of the RUSLE-3D equation, and the soils theta coefficient. The results indicated that the model with Nash-Sutcliff 0.6 and the R2 coefficient 0.63 in the calibration set and Nash index 0.53 and R2 coefficient 0.56 in the validation set had appropriate efficiency in runoff simulation. The advantage of the model is that distributive and allows for the identification of areas with higher runoff production.

Background and Objective: Nowadays, increases in the extent of impervious surfaces in urban areas causes contributed to flooding and runoff contamination. Therefore, this study was conducted to assess Fe, Pb and Cd concentrations in surface runoff samples collected from regions with high traffic intensity in city of Malayer, west of Iran. Materials and Methods: In the current study, a total of 15 surface runoff samples were collected from five selected stations. The samples were filtered (0.45 μm), stored in polyethylene bottles and were acidified at a pH lower than 2 by adding concentrated HNO3 in order to avoid metal adsorption onto the inner bottle walls. Element (Fe, Pb and Cd) concentrations were determined using ICP-OES (710-ES, Varian). All statistical analyses were done using SPSS version 20.0 Results: The results showed that the mean concentrations of Fe, Pb and Cd in runoff samples were 0.220, 0.320 and 0.150 µg/l, respectively. Comparing the mean concentrations of analyzed elements with maximum permissible concentration (MPC) established by WHO and Iran DOE indicated that the mean concentrations of Pb and Cd were found to be higher than WHO permissible limits. On the other hand, the mean concentration of Cd was higher than Iran DOE permissible limits. Conclusion: The results indicated that surface runoff from regions with high traffic intensity in city of Malayer were polluted with Pb and Cd, which could be threaten the surface water resources and health of citizens. Therefore, periodic monitoring and consequently treatment of runoff from rainfall before entering to surface water resources is recommended.

Today, the effects of climate change and global warming have been proven due to the increase in greenhouse gases in the world. In this research, the monthly values of temperature, precipitation and discharge of Jamishan Dam in the years 1988-2017 have been considered as the base period. Simulation of resources and uses of the jamishan Dam catchment area was investigated using the WEAP model with different cultivation patterns in the area. To evaluate the effect of climate change on precipitation and temperature parameters in this region, RCP8.5 scenario outputs of HADGEM2_ES and MIROC5 models and a series of CMIP5 reporting models were used and the output of these models was downscaled for the region. In this research, change factor method used for downscaling and the monthly temperature and precipitation parameters of Jamishan Dam were produced for the period 2021-2050. In order to study the runoff of the region due to climate change, the SVR model was studied. The results of the climate model show an average temperature increase of 0.5 to 1 ° C. The simulated rainfall results show that the average monthly rainfall under the RCP8.5 scenario in the future period in HADGEM2_ES model increased by 1.3% and in MIROC5 model decreased by 6% compared to the base period. Examination of the results of discharge forecast in SVR method indicates that runoff is associated with a decrease compared to the base period.Simulation of different cultivation patterns in WEAP shows that the average supply is 70% in the SVR method.

Runoff is carving slope surfaces in first time and intensive conditions its is making disturbance surfaces slope .Investigation on runoff property on slopes and influence factors on its property is make possible protective actions slopes. With using the results can be measure of sediment yield rate and is acting protective slopes .Surfaces of slopes is disturbance by runoff(located on eastern slopes of Sahan mt. and is situated 37 00 to 37 20 N and from 46 45 to 47 15 E) ) channel. Investigation on liner erosion must be make about runoff property .In this article is used experimental equations for study on runoff property(by ,V1,Tc,Tr,...).The results of these study show that runoff have very vary velocity on all parts of catchment .In final step is estimated liner erosion(by CIT index)and then mapping rate of yield sediment .The results is suggested that CIT is over 5 in some parts of catchment . This result show that runoff have power for intensive carving on slopes. The carving power of runoff is high at middle parts of catchment .

Runoff is formed by spending some time after rain and significantly depends on rainfall intensity, soil moisture, and slope. One of the fundamental questions about runoff is the time that it starts to create. In this research, the runoff start time in sandy loam soil was evaluated experimentally under different conditions using a precipitation simulator machine. The rainfall intensity parameters of (60, 80, and 100 millimeters per hour) and the slope of (0 and 5 percent) were investigated. The rainfall was created in the three soil treatment types completely dry (Sdry), the dry soil that had been saturated 24 hours before the test (S24hrlag), and the dry soil that had been saturated 48 hours before the test (S48hrlag). Eighteen tests were conducted on this soil. At the end of each test, the soil moisture was measured. The experimental results were compared with the numerical model of Green-Ampt. According to the Kendall and Spearman correlation test results, as the rainfall be intense, the start time of the runoff is lower. Also, the runoff starts at a faster time in the slope of 5 percent for every three types of soil. Also, the results of starting time of the runoff in the soil with a delay of 48 hours in the rain compared to the soil with a delay of 24 hours in the rain are closer in all of the rainfall intensity and slopes compared to the case of dry soil. Therefore, in the experiments related to a delay of 24 hours, the time of the start of runoff decreases. While in tests with a delay of 48 hours, it was not much different from completely dry soil. Also, the Green-Ampt results are close to the experimental results (R²=0.9775), and the maximum difference between the two mentioned methods is 4.8 minutes. Therefore, it can be used with the Green-Ampt method to calculate the start time of runoff in sandy loam soil in different states of rainfall intensity and bed slope. [ABSTRACT FROM AUTHOR]

Introduction The phenomenon of climate change and global warming is one of the biggest challenges of the present age, which has affected the hydrological cycle on a global and regional scale. The purpose of this study is to investigate the effects of climate change on torrential rains And Temprature, Rainfall, Run off in Tehran province. In this study, changes in torrential rainfall in Tehran and the frequency and intensity of torrential rainfall in the future and climate change and the rate of increase of floods on periodic floods have been investigated.Materials and Methods This research has been done in two study discussions (meteorological, hydrological). First, parameters (temperature and precipitation) for synoptic stations (Abali, Shemiran, Mehrabad) for the period (1988-2020) were received from the Meteorological Organization and rainfall and runoff data from hydrometric stations were received from the Tehran Regional Water Authority. Down Scaling model and methods in this research SDSM-DC, Mann-Kendall, Rclimdex, XLSTAT, to simulate future floods and SMADA, HEC-HMS model to simulate floods in current and future periods have been used.Results and Discussion The results showed that the temperature extreme indices in all stations had an increasing trend and the precipitation indices had an increasing trend only in Abali station. But rainfall in Shemiran and Mehrabad stations had a decreasing trend. Examination of rainfall changes using Mann-Kendall test in most months of the year shows a stable trend and jumps are observed in the rainy months, which can be justified by the increase in rainfall frequency. In the CanESM Model 2 simulation, emphasizing the RCP8.5 release scenario, an increasing temperature trend was observed for the coming decades. The highest temperature increase belongs to Mehrabad station with an average temperature increase of 5.1 percent for the period 2021-2083. The highest rainfall for all three stations is estimated in March next year. Therefore, with the increase of temperature trend, changes in the type of precipitation have occurred, which can be affected by urban microclimate. Simulation of the rainfall-runoff model of the basin with the HEC-HMS model showed that the volume of discharge in the future will be reduced by 5 percent and for return periods, the maximum discharge is estimated at 1501.7 cubic meters.Conclusion Due to Rainfall behavior is expected to increase in the coming decades, while reducing its fluctuations. It is predicted that the climate of Tehran in the future will have more fluctuations in precipitation and warmer than the current situation, and this increase and frequency of precipitation is likely to increase flood frequency and reinforce its occurrence, while rainfall will occur more accidentally but with greater intensity, leading to environmental and urban hazards.

In recent decades, population growth, urban sprawl, urban environmental changes, and related issues are one of the significant issues in proper planning to manage the urban environment. One of the issues in urban development is the occurrence of floods and flooding due to heavy rains. In this research, flood modeling was studied in Mashhad Zarkash watercourses. The amount of rainfall for the return period of 10, 25, 50, 100, and 200 years were extracted by CumFreq software using the maximum 24-hour rainfall statistics of three rain gauge stations closer to the Zarkesh, Jagharq, Sar-e-Asyab, and Torqabeh watercourses basins during the statistical years 1364 to 1390. The peak discharge was calculated using the US Soil Protection Organization (SCS) rainfall-runoff method. Zarkesh watercourse is located on the outskirts of Mashhad. River and flood flow modeling was performed using Arc GIS, HEC-GEORAS, and HEC-RAS software in two conditions including structure (bridge) and no structure. Due to urban marginalization, urban development and land use change have greatly expanded in this region. The results of flood simulation showed that flood levels with a return period of 50 years increased by 50000 m2 equal to 22% in the presence of a structure compared to the state without a structure. The results of this research show that the construction of bridges on the river, the roughness coefficient by land use change, and the number of curves due to land permeability changes are effective in the flood zone.

A lack of sufficient water resources, climate change, and especially global warming, is causing apprehension in societies, particularly in watershed managers. Scientists and engineers are forecasting climatic data to prevent the waste of water resources and to optimize their use in watersheds. The Aran Station in the Kangavar Watershed was chosen to predict the trend of temperature, precipitation, and runoff using the CMIP5 climate model. Benefiting from 33 years of data (1983 to 2015), the Gene Expression Programming method (GEPM) and Decision Tree methods were developed to train, test, and predict the river discharge rate. Different climate models were implemented using the historical data of the study area. The Fgoals-g2 was chosen to predict temperature and precipitation data for the 2020-2052 periods. The RCP2.5 climate scenario was used as an optimistic scenario, and the output of the change factor downscaling method was used as an input for the GEP model and the ORANGE Software to find the best prediction of the discharge parameter in the future. The results indicated that the temperature of the next cycle will increase by 13 degrees and the maximum monthly temperature will reach 31.18 degrees centigrade. The maximum monthly precipitation will increase by 4 percent and reach 169.51mm. The longtime yearly mean precipitation will change from 423.39 mm to 427.15 mm. The correlation coefficient of the test data in the GEPM was 0.70. The maximum monthly discharge will decrease 1.84 percent, from 29.31 to 28.77 cubic meters per second (m3s-1). The mean discharge will decrease 5.71 percent, from 3.33 to 3.14 cubic meters per second. The correlation coefficient of the test data in the decision tree regression method, using the ORANGE software was 0.995. The mean discharge will increase by 10 percent and reach 3.69 m3s-1. The maximum yearly discharge will decrease by 6 percent, from 7.62 to 7.12 m3s-1.

The most important human impact on the hydrological cycle of watershed is land use change. Improper management of land use increases the volume of runoff and the frequency of floods. The main purpose of this study is to investigate the effect of land use change on discharge outflow in Karkhane Watershed using the Parameter Allocation Approach. Then, utilizing the philosophy of a modelling framework proposed by Bahremand (2016), we show how calibration of most model parameters can be avoided by allocating or presetting these parameters utilizing knowledge gained from sensitivity analyses, field observations and a priori specifications as a part of a parameter allocation procedure. Evaluation of the WetSpa model performance using the Allocation Parameter Approach to simulate the daily hydrograph based on the Nash-Sutcliffe and Kling-Gupta showed that the values of these criteria are 60.18 percent and 76 percent respectively. Based on this efficiency criterion, the model in the parameter allocation approach has a good performance and shows consistency in the validation period. The results showed that land use change affects the runoff of the Karkhane watershed so that in the pessimistic scenario 2, the peak discharge has increased by 15.74% and in the optimistic scenario 3; the peak discharge has decreased by 11.33.

In this research, monthly temperature, precipitation and discharge values of Jamishan Dam in 1988-2017 have been considered as the base period. Simulation of resources and uses of Dam-d-Gomishan basin in WEAP model with existing cropping patterns in the region was investigated. To evaluate the effect of climate change on precipitation and temperature parameters in this region, the outputs of the RCP8.5 scenario of the HADGEM2_ES models were used from the cmip5 fifth report series and the output of these models was downscaled for the desired area. In this study, using the method of change factor downscaling the climatic model and monthly temperature and precipitation parameters of Jamishan Dam for the period 2021-2050 were generated. In order to investigate the runoff of the area due to climate change, SVM, GEP and IHACRES models were investigated and compared. The results of the climatic model show an average temperature increase of 1.5 °C and an increase in precipitation of 5%. In general, results of discharge forecasting in all three models indicate a decrease in runoff that the highest runoff reduction was related to SVM with 21.6% and the lowest runoff reduction was related to IHACRES with 4%. In this study, IHACRES and GEP models have more favorable accuracy than SVR method. Simulation of different cropping patterns in WEAP shows that the highest supply is equal to 74.9% in GEP model and the lowest supply is equal to 70% in SVR model which is related to cropping pattern 1 and cropping pattern 3, respectively.

Carbonates contaminate soil and water resources. The use of stabilizers is an effective method to improve soil characteristics. The single and interaction effects of two treatments consisting of sodium-silicate and sulfur at 0, 5, 10, and 15 g/m2 were studied on the lime-covered Soil stabilization and water erosion control in a factorial design, on the 9% slope standard plots uses a rainfall simulator with an intensity and duration of 1.4mm/min and 20 min, respectively. The results indicated that between the two the sodium-silicate and sulfur factors, only the main effect of sodium-silicate had a significant effect on the runoff volume, sediment weight, and concentration as well as the runoff coefficient variables; however, it had no significant effect on the total infiltration rate and the lime percentage variables. Moreover, the sulfur and sodium-silicate factors had significant interaction effects on the sediment weight (Sig.=0.011), sediment concentration (Sig.=0.019), and lime percentage (Sig.=0.000) variables. The interactive effect of the two studied treatments significantly reduced the lime in the soil samples. Therefore, the combination of the two treatments had the highest effect on lime stabilization. Moreover, sodium-silicate had an effective role in soil stabilization. Therefore, a single application of 7.5 g/m2 of sodium-silicate and with its combination with 5 g/m2 sulfur is recommended for erosion control and lime stabilization, respectively.

In the present study, CanESM2 climate change model and stormwater management model (SWMM) were employed to investigate the climate change effects on the quantity and quality of urban runoff in a part of Karaj watershed, Alborz Province. The base period (1985-2005) and future period (2020-2040) are considered for this purpose. Based on the existing main and lateral drainage system and to be more accurate, the watershed was divided into 37 sub-watersheds by ArcGIS software. To simulate rainfall-runoff, the intensity-duration-frequency (IDF) curve has been prepared for a 2-hour duration and 10-year return period, for the base period and RCP2.6 and RCP8.5 climate change scenarios based on the obtained precipitation data from Karaj synoptic station. Results showed that mean 24-hour precipitation values in RCP2.6 and RCP8.5 scenarios will increase by 21% and 11%, respectively, and maximum 24-hour precipitation values will decrease by 17% and 23%, respectively, as compared to the observed values in the base period. Also, based on the results of quantitative and qualitative runoff modeling in the study watershed, and according to the outflow hydrograph in the RCP2.6 and RCP8.5 scenarios, the outlet runoff discharge will decrease by 5.8% and 7.1%, respectively. Also, the flooded areas in the watershed will decrease by 13% and 15.28%, respectively. The concentration of pollutants in the RCP2.6 and RCP8.5 scenarios, compared to the base period, including total suspended solids (TSS), will increase by 7.48% and 9.24%, total nitrogen (TN) will increase by 6.93% and 8.48%, and lead (Pb) will increase by 7.32% and 8.91%, respectively.

The effective use of rainwater is a key issue in agricultural development in arid and semi-arid regions. The tillage system as an important soil management measure can affect the rainwater retention, soil moisture content, and in consequence crop yield in rainfed lands. This study was conducted to evaluate the effects of slope gradient and tillage direction on rainwater use efficiency (RWUE) in rainfed lands in Zanjan Province. The field experiment was performed in five slope gradients (12.6, 15.3, 17, 19.4, and 22%) and two tillage directions (along slope and on contour tillage) at two replications. Mass soil water content was determined at 5-day intervals and runoff was measured after rainfalls. Wheat grain yield was determined for each plot and RWUE was computed using the proportion of wheat grain yield and precipitation. Base on the results, runoff, soil moisture, wheat grain yield, and RWUE were affected by tillage directions, so that runoff in contour line tillage decreased about 6.4 times compared to along slope tillage and in consequence increased soil moisture, wheat grain yield, and RWUE about 8.7, 24.8, and 24.8%, respectively. Increasing runoff production in contour line tillage at steeper slopes was associated with a lower capacity of cultivated furrows that strongly declined soil water retention and negatively affected wheat grain yield and RWUE in the lands. This study revealed that the efficiency of the contour tillage in water retention and RWUE decreases in steeper slopes in rainfed lands.

Introduction: Urbanization is increasing in the world and the world's urban population is becoming denser in cities. One of the effects of urbanization is the increase in the percentage of impervious surfaces in these areas. Today, many important cities in the world pay attention to the concept of sustainable development in order to reduce the effects of their city development on the quality and quantity of runoff and use modern green management technologies, including the best management methods and development methods with minimal side effects. A green roof is a multi-layered system that covers the roof and balcony of a building with vegetation and by absorbing and keeping part of the rain, and by influencing the processes of evaporation and transpiration, purification, the volume and intensity of the peak flow of runoff, the dimensions The drainage system reduces the downstream and improves the quality of air and water, preserves the beauty of the city and prevents the wastage of building energy. Material and methods: This research was conducted as a field experiment in the Faculty of Agriculture of Zanjan University. The test period was from April to August of 2017. In this research, the effect of the use of super absorbent (zeolite) on the amount of water absorption and retention, the maximum and minimum volume of runoff, the volume of runoff, sediment and the start time of the runoff resulting from rainfall in the rain intensity of 35, 45, 55, 65 and 75 mm/h has been investigated on a green roof with a slope of 5%, in a cold dry climate. Results and discussion: Based on this, with the increase in the intensity of rainfall, the volume of runoff also increases, and the volume of runoff in barren soil is more than the rest of the treatments, and its downward trend is soil containing 1% zeolite, soil containing 3% zeolite, and cultivated soil. Be Also, the volume of runoff increased with the increase in rainfall intensity and the highest value of runoff volume belongs to barren soil. The sediment measured in the runoff also increases with the increase in the intensity of precipitation in the treatments, except for the grass treatment. Conclusion: Barren soil has a very high volume of runoff due to the sealing of its surface layers and clogging of pores. Adding zeolite to the soil significantly reduced the volume of runoff and retained more water than barren soil. The rate of erosion in soil with 1% zeolite was high and the rate of erosion was the lowest in grass. In barren soil, because the penetration of water is low, after a short period of time after the rain, the water flows as runoff, but zeolite has the property and characteristic that when added to the soil, the time for the start of runoff is lengthened by 3%. [ABSTRACT FROM AUTHOR]

Introduction In recent decades, accurate modeling of runoff has always been one of the hottest topics for researchers in the field of hydrology, as it plays an important role in water resources management, hydropower development, urban planning, irrigation and other hydrological/meteorological activity programs. Concept-based and physical models require huge amounts of data and environmental calculations. The nonlinear nature of the rainfall-runoff process and the complexity of physical models are some of the reasons why researchers have turned to intelligent models. However, these models may not provide logical results for nonlinear hydrological processes. To overcome this shortcoming, nonlinear artificial neural networks (ANNs) have depicted a real success in predicting hydrological time series.Materials and MethodsThe usefulness of wavelet transforms in noise reduction and multi-resolution analysis along with the ability of ANNs to optimize and predict hydrological processes has recently been introduced as a neural network wavelet hybrid model (WANN) and is widely used by hydrologists not only for rainfall-runoff modeling. It has been used to simulate some other components of the hydrological cycle such as river flow, groundwater, precipitation and sediment. In this study, precipitation and discharge data on a monthly scale have been used. For modeling in this research, ANN and WANN have been used. Data were used for Sufi Chay Basin for period 2001-2019.Results and Discussion In this study, for modeling rainfall-runoff by neural network in MATLAB 2018 Software and investigating the effect of using wavelet transform on the accuracy of rainfall-runoff model has been done. First, the existence of autocorrelation and its significance in runoff data were investigated using the partial autocorrelation function (PACF). In the hybrid model, Daubechies mother wavelets 3 and 4 are used. Six scenarios were examined for modeling. Among the selected scenarios, scenarios 4 and 5 had the best results in comparison between the two models. The poor accuracy of the first three scenarios (Scenarios 1-3) indicates that using rainfall data alone are not sufficient to model runoff, thus employing the last monthly runoff data into the model made a significant change in the results. In the ANN model, scenario 4 has more acceptable results with correlation coefficient (r), square root mean square error (RMSE) and Nash Sutcliffe coefficient (NSE) of 0.889, 51.574 and 0.788, respectively for the training section, and 0.779, 70.625, and 0.595, respectively for the test section. In the hybrid model WANN, scenario 5 has the best results with the values of r, RMSE and NSE of 0.997, 23.99 and 0.954, respectively for the training section, and 0.829, 62.334 and 0.684, respectively for the test section.Conclusion In the present study, the performance of ANN and WANN models for rainfall-runoff modeling in Sufi Chay Basin using different parameters of rainfall and discharge delays during the statistical period (2001-2019) was evaluated. The highest accuracy of the models was provided in combining runoff input with a time delay and monthly precipitation. The purpose of this study is the effect of wavelet on increasing the accuracy of estimation in rainfall-runoff modeling. The results of the present study showed that the hybrid model will increase the accuracy of a model.

Nowadays surface runoff pollution as a crisis of human societies, has forced most countries to seek legal solutions to solve this phenomenon, the most important of which is the establishment of civil liability in this area. Although, according to economic analysis, compensation can not have much effect on optimal deterrence, nevertheless, its effect on reducing water pollution can not be denied. This study, after a comprehensive review of available sources and then analyzing the findings, has concluded that the mere existence of pollution causes harm, so it is sufficient to determine the causality relationship between the pollution and its actor in order to establish civil liability; In addition, the government can be held liable for damages, both in terms of supervision and ownership, and by providing a broad interpretation of the concept of "beneficiary", it can be said that NGOs are considered "beneficiaries" in making such claims and assuming victory in litigation, compensation can be used for sustainable development. Finally, according to the model of sustainable development in the economy, the production of goods is not only a subordinate of labor and capital, but must also be considered in the production process, materials and environmental goods such as water. Therefore, from the perspective of environmental economics, water pollution is considered as a cost of production or consumption, and the higher this cost, the optimal production and economic growth will not be a subordinate of sustainable development. [ABSTRACT FROM AUTHOR]