فهرست مطالب محمد مهدی آرتیمانی

Considering the importance of knowing and awareness of the watersheds water balance status, and analyzing the hydrological behavior of watersheds for planning and implementing water-related projects, the need to use new technologies in predicting water balance components is more evident than ever. Based on this, in the Kashkan basin by utilizing the TOPKAPI-X hydrological model, the water balance components of the basin were simulated according to the cellular network design. Digital maps of the basin, land use, outlet point, soil texture, elevation, and continuous time series of temperature, precipitation, and discharge in the daily time step are the main inputs of the model. The model in each cell network balances the water balance of the entire period. Model calibration was done for the 15 years of the statistical period (1999 to 2014) and model validation for the 6-year period (2014 to 2020). The results showed that 27.02 and 28.43 percent of the total precipitation of the Kashkan basin was discharged from the basin as total runoff (respectively for calibration and validation periods), which is consistent with the observation data at the outlet hydrometry station. Next, to evaluate the efficiency of the model, the simulated values in both statistical periods were compared to the observational discharge. Statistical methods such as the Nash-Sutcliffe evaluation criteria showed that the TOPKAPI-X model predicted the water balance components such as actual and potential evapotranspiration, infiltration, and the amount of runoff, especially the total runoff, in this basin with relatively good accuracy (coefficient above 60%).

Due to the heterogeneity in watersheds and the non-linearity of hydrological behaviors, it is very complicated and difficult to fully understand the relationships within watersheds. Therefore, in evaluating these systems, a modeling process is necessary. Over the last few decades, hydrological/hydraulic models have become essential in hydrology studies due to the development of programming languages and the provision of optimal and efficient algorithms for solving differential problems. The application of rainfall-runoff simulation models for flood events has been extensively studied by researchers in the field of water and soil protection, leading to the development of various models to simulate rainfall-runoff processes. One of the successful models in this field is the TOPKAPI-X model. This model was created in the 1990s at the University of Bologna by Professor Todini as a distributed rainfall-runoff model in watersheds. An important feature of distributed models is their ability to simulate components at any point of the watershed, allowing results to be extracted at any required point. Unlike lumped models that consider the entire watershed as a single unit, distributed models allow spatial distribution at any point in the watershed. Therefore, in this research, after calibrating and validating the TOPKAPI-X physical-distributed model in the studied basin, the model was optimized for flood estimation.

The Gamasiab basin is located in the west of Iran, in the northern region of the Zagros mountain ranges, to the north of the Karkheh dam basin, and primarily within the territories of Hamadan and Kermanshah provinces. The mountainous regions of this basin are mainly concentrated in the northern and southern parts, while its lowlands and plains are mostly located in the middle and southeastern parts of the basin (Ministry of Energy, 2014). In this research, the TOPKAPI-X model was used to simulate floods in the Gamasiab watershed. First, the watershed boundary was delineated using a digital elevation model (DEM) with a resolution of 30 meters. Land use maps, soil texture, watershed network, and climatic components were entered into the TOPKAPI-X model. The outlet location of the basin (hydrometric station) was used to simulate the flow using the TOPKAPI-X distributed hydrological model. Continuous time series data on a daily time step were used in this rainfall-runoff model. Specifically, daily rainfall data from 13 rain gauge stations and temperature data from 4 synoptic stations during the statistical period (1999 to 2020) were used to simulate the flow. After running the model several times, the general parameters were manually adjusted each time until the optimal values of the general parameters were obtained by considering the appropriate values of the evaluation criteria (NS and Bias) for the basin.

This research was conducted to analyze the flood discharge of one of the main sub-basins of the Karkheh dam basin using the TOPKAPI-X model on a daily time scale. In the TOPKAPI-X software environment, simulations were performed during the calibration period using input maps and observational rainfall, temperature, and discharge data. A visual comparison of the observed and simulated hydrographs allows for a general and quick evaluation of the model's accuracy. The graphical results of the comparison between the discharge generated by the TOPKAPI-X model with the calibrated parameters and the measured discharge in the Gamasiab basin were presented. The TOPKAPI-X model has the ability to estimate the maximum daily flow rates of the Gamasiab basin; however, some of the simulated flow rates are higher than the observed flow rates. Four criteria—NSE, R, BIAS, and RMSE—were used to evaluate the model. The evaluation results of the TOPKAPI-X model indicate the accuracy of flow simulation, with a Nash-Sutcliffe criterion of 0.697 during the calibration period (1999-2014) and 0.660 during the validation period (2015-2020) for the Gamasiab basin. Therefore, it can be concluded that this model has good performance for flow simulation.

The importance and usefulness of hydrological models for water resources management, understanding hydrological processes, and conducting impact assessment studies is clear. Hydrological models are crucial tools that enable scientists and policymakers to make informed decisions based on simulations of watershed behavior. Considering the increasing demand for water and the impact of climate change, hydrological simulation will be one of the essential methods for future water management. The results of this study showed that the TOPKAPI-X model has potential in simulating runoff in the selected basin. Due to the capabilities of the TOPKAPI-X distributed hydrological model, this software is recommended as a modeling tool for other basins.

In many regions, ecosystem sustainability and environmental security have become more fragile. Because watersheds are dynamic systems, their hydrological function and health are constantly changing under the influence of land use, climate change, and human interventions. Since the destruction of the ecosystems of a watershed has harmful economic and social consequences, in recent decades there has been a general tendency to evaluate the relative conditions or health of watersheds on a national and local scale. Ecologists have paid special attention to the study of how natural resource ecosystems respond to different types of stress caused by human activities. The watershed sustainability index (WSI) can be considered as an effective tool in watershed management including priorities monitoring changes and influencing factors on ecosystem management. In recent years, various studies and plans have been conducted to preserve natural resources and achieve sustainable development. The sustainability of watersheds includes four important goals of regulating the water flow regime, maintaining and improving water quality, maintaining the ecological quality of plants and animals, and energy resources. In this context, the pressure-state-response (PSR) model has been introduced and used for a comprehensive assessment of the health of an ecosystem. The conceptual model of PSR was developed using a set of criteria expressing environmental performance. This study aimed to evaluate the sustainability level of the Bujin watershed.

One of the methods for evaluating watershed sustainability is the use of the conceptual pressure-state-response model (PSR). Applying the causal-effect PSR model using theWSI criteria in the form of four sub-criteria of hydrology (qualitative and quantitative), environment, life, and policy-making, one can evaluate the sustainability of the watershed numerically. In this method, considering the available data and information to investigate each sub-criteria, the parameter values are determined in three modes of pressure, state, and response, and in the scoring range from zero to one, five categories are converted to quantitative mode. Therefore, the PSR framework has three types of criteria: pressure criteria that evaluate environmental pressure resulting from human activities (waste, sewage), and state criteria that express environmental conditions (water quality). and the response criteria that evaluate the society's reactions (water quality) and the response criteria that evaluate the society's responses (policies, laws, management). The sub-criteria and parameters used in this research were determined based on the index selection criteria published by the HCTF Habitat Protection Fund in 2003. Sub-criteria were investigated based on three conceptual model parameters in 10 years for the Bujin watershed. The WSI criteria were calculated at three low, medium, and high levels to assess the watershed sustainability.

According to the results, the value of the pressure parameter and the quantitative status of the basin's hydrology in terms of available water variable is in class (C), i.e. in the range of 3400 > AW > 1700, which is a poor condition. The average scores were obtained for the water quality part (0.583), which shows the average to low status. The average score for the hydrology sub-criterion was 0.375, which indicates a poor situation in this region. The values of pressure, state, and response parameters for the sub-criterion of life in the Bujin watershed, during the 10 years studied, indicate a change in the state from weak to moderate. The results also showed that the pressure parameter with a score of 0.75 and the response parameter with a score of 0.625 had the highest and lowest scores for evaluating the sustainability of the Bujin watershed, respectively, indicating an appropriate response to reduce the pressure applied to the ecosystems. Sub-hydrology index with a score of 0.16 and environment with a score of 1 had the lowest and highest priority for the management of the basin ecosystem. According to the distribution maps of the criterion for evaluating watershed sustainability in conventional watershed systems during the period (2006-2016), the standard level of watershed sustainability at the beginning of the period was lower than the middle class (score 0.59) and in the middle of the period was in the middle class (score 0.62) and for the end of the period, it was upgraded to the upper than the middle class (score 0.7).

The priorities of achieving sustainable development (the priority in improving the conditions to promote the level of sustainability and achieve sustainable development) are different, and it is important to know which sub-criterion should be improved first and which parameter the decision-makers should pay attention to avoid wasting time, money and energy, and to take faster development steps in an area. Evaluation of relative conditions of watershed sustainability using the PSR model is very useful for providing appropriate management strategies because according to the nature of the conceptual model, a specific dimension of watershed health is explained. Bujin watershed has an unstable condition in the sub-index of hydrology and a good condition in the sub-index of life and human development, although, for this watershed, obtaining a score of 0.7 for WSI criteria in the whole watershed showed that the level of watershed sustainability in the 10 years is in the middle class and it is necessary to pay more attention to improve the level of sustainability and health of the watershed.

The snow melt forecast in mountainous basins plays an important role in the management of water resources. For this reason, snow hydrology in mountainous areas is of great importance. In this study, due to the lack of snow data in the Bujin area, SRM and HBV models were used to simulate snow melting. At first, rainfall data, precipitation data was prepared and verified during the period of Water Year 1387 until 1389. The MODIS sensor data was used in one-day time intervals to monitor the time and location of snow cover surface in the SRM model. ArcGIS software was used to implement the physical properties of the domain. Implementation of SRM and HBV models was performed using snow melt simulation. Then, using the results of the values of the parameters in the calibration step, the validation step was performed. The estimation rate of the R2 parameter obtained by the SRM and HBV model for the Bujin area is about 0.71 and 0.61 for calibration and about 0.72 and 0.69 for validation steps, so that these results, together with the values of the other evaluation criteria such as Nash-Sutcliff (coefficient of 0.71 for SRM model and 0.61 for HBV model at validation stage), showed good accuracy of models in runoff simulation. Also, the SRM model, due to the use of satellite images, showed a more reliable performance in snowmelt runoff simulation than the HBV model in this basin.

Peak flow estimation is one of the major issues in water resources and flood management that have basic role in the design of hydraulic structures and biomechanics activities in basins. So that a proper assessment has a basic role in the success of administrative works. In this paper, using artificial intelligence methods (MLP Neural Network, the mixture of SOFM with MLP, the mixture of FCM with ANFIS) to estimate Yalfan River’s peak discharge in hydrometer local station. For these models, eight variables have been considered as the inputs that includes rainfall amount in the occurrence time of flood, rainfall of five days ago from occurrence of flood, curve number of the basin (CN), basic discharge and finally peak discharge are considered as the output. In the artificial intelligences after preprocessing of the data, the optimal structure of the models are determined with input and output data, evaluation criteria and trial and error. At the end, the MLP model had better performance compared to ANFIS+FCM, MLP+SOFM, GRNN models.

Fast and accurate estimation in Peak flow is one of the major issues in water resources management that have basic role in the design of hydraulic structures and biological activities in drainage basins. So that a proper assessment has a basic role in the success of administrative tasks. In this paper, using artificial intelligence methods (Multi-layer Perceptron Neural Network and the mixture of Multi-layer Perceptron Neural Network with genetic algorithm) to estimate yalfan river, s peak discharge in Yalfan,s sediment and hydrometer local gaging station. For these two models, 8 variables have been considered as the inputs that includes rainfall related to day of peak flow,5 days rainfall that occurs before of the flooding day, curve number of the basin(CN) and base discharge and finally peak discharge is considered as the output. With artificial intelligence after preprocessing of the data, the optimal structure of the model is determined with input and output data by evaluation criteria and trial and error. In the final, with the mixture of artificial network and genetic algorithm model, the optimum neural network model was determined which results were an input to genetic algorithm model, this has been a good performance in runoff forecasting in Yalfan Basin.

For planning and implementing water-related projects, applying new models and methods is necessary to determine water balance components in basins. Therefore, in this research daily runoff of the Gamasiab basin in the south of Hamadan and Kermanshah provinces was simulated based on cellular network using the spatially semi-distributed SWAT hydrological model in a GIS environment. Digital maps of elevation (DEM), land use, and soil texture and continuous time series of temperature, precipitation, evapotranspiration and discharge were the main model inputs. The model simulates all water balance components in each hydrologic response unit (HRU) and sub-basin using rainfall, temperature and evaporation data. Calibration of model parameters was done for the last 20 years of the studied period (1990-2009) and validation of the model was performed for the first 5 years (1984 to 1989). Based on the results of the simulation, 28% of the area’s total rainfall is discharged from the basin as runoff which nearly matches the data observed at the hydrometric outlet station. Subsequently, to evaluate the model’s performance, simulated values of precipitation, evaporation and discharge were compared with observed data. There was a good agreement between observed and simulated data based on the Nash-Sutcliffe efficiency index (0.6). Hence, it can be concluded that, this model is able to simulate surface and subsurface runoff, deep infiltration and evapotranspiration in the Gamasiab basin.

The flood is one of the natural disasters that doesnt create itself but the landuse change will be caused it that makes a lot of human and financial losses every year. In this study in gonbad chi basin to assess the impact of Watershed mechanical operation on flood mapping used fuzzy method and AHP method and composition of slope mapping, landuse, Permeability and flow accumulation that called base mapping. In next setp by determining thelocation ofmechanicaloperations andseparationofthe operationaccordingto dam height and determine the extent of the effect of the mechanical operation using elevation contour lines, the mapping of impact of watershed management operations was prepared. In the end with composition of base mapping with impact of mechanicaloperations, a new map that called flood mapping is created. Although the results have a positiveapproximately9% impact on reducing the risks of flood but these results in a sufficient number of suitable structures would be more effective than showed.