مقالات رزومه دکتر مریم حافظ پرست

There are two approaches in dealing with environmental changes. The first approach is based on ignoring these changes and continuing the current situation, which will result in further environmental destruction. The second approach is based on identifying these changes from the past to the present and formulating an environmental management plan to control these changes and plan for improving the environmental situation. Wetlands are one of the most important natural habitats in the world and have many values and benefits, especially in energy circulation in the environment. In the last 20 years, the area of many wetlands has decreased due to the increase in industrial and agricultural development and the lack of water in arid and semi-arid regions (Cui et al., 2009). One of the effective tools in the field of wetland area changes is the use of remote sensing technology and satellite data. The use of satellite images, due to their wide spatial coverage, high resolution, low cost, temporal archive, free access to satellite images, and the availability of practical software and spectral indices, has become very important in estimating the area of aquatic zones. Monitoring and assessing the water level of wetlands using remote sensing to protect this natural heritage has become a focus for many countries in recent years, and this is of particular importance as the first step in identifying problems and attempting to solve them. Heshilan wetland plays a significant role in nourishing and increasing the stability of underground waters, preventing floods, and promoting agriculture in the region, as well as providing a unique habitat for native and migratory birds to easily pass winter. The aim of this research is to monitor changes in the water level of Heshilan wetland over a 22-year period using remote sensing and geographic information systems.

Landsat satellite images were downloaded from the United States Geological Survey website. After downloading the Landsat satellite images, the Heshilan wetland area was delineated, and the images were radiometrically and atmospherically corrected. To enhance the spatial resolution of the study area, the 30-meter pixel size image was converted to a 15-meter pixel size image for better spatial resolution (the 15-meter resolution provides more accurate information than approximate surface area estimates of wetlands), then we normalized the surface reflectance values. The Target Detection Wizard tool in ENVI 5.6 software was used to determine the aquatic zone area of the wetland in Murch of each year during (2000-2022), and the output file was imported into ArcMap 10.8.2 to calculate the aquatic zone area of Heshilan wetland in square kilometers on the date of image acquisition.

The results of interpreting satellite images over the years under study indicate that in 2018, the wetland had its lowest surface area in two decades, with an area of 0.34 square kilometers in that year. Additionally, the highest area of the aquatic zone of the wetland occurred in 2005, with an area of 3.83 square kilometers. The results show that over the past 22 years, the wetland has faced a decrease in water level by 0.79 square kilometers for the specific month.

The research results indicate that analyzing satellite images using remote sensing techniques can effectively show changes in wetland areas over time intervals. The results of interpreting satellite images over the years under study show a reduction in the extent of Heshilan wetland, especially in the western and southwestern regions, which can be attributed to natural phenomena such as drying up some springs due to droughts and periods of low water levels, as well as undesirable effects of human activities such as increased human access to wetlands through access roads in this area, land grabbing by villagers converting them into agricultural lands, deep wells around the wetlands, and utilizing wetland water through agricultural canals without considering proper water inflow. In these conditions, the wetland ecosystem has completely changed, and other aquatic birds no longer live in this area, with many migratory birds moving to tropical regions, depending on their habitats along the migration route. Given the unfavorable trends in Heshilan wetland developments, continuing the current conditions could jeopardize the existence and integrity of Heshilan wetland irreversibly. Ecosystem management of wetlands by developing a management plan to preserve and revitalize the structure and functions of this ecosystem with long-term stability can be a solution.

Simulation of runoff and hydrograph production is widely used in analyzing the behavior of basin against precipitation, calculating the volume and peak of floods, the amount of losses and the possibility of designing the dimensions of structures. HEC-HMS is one of the most common simulator models In this study, with the aim of analyzing the sensitivity of flood hydrograph routing parameters in the Merk River basin, first, the physiographic characteristics of the basin were constructed with the HEC-GeoHMS plugin in the Arc GIS environment and the DEM map of the area and they were called in the HEC-HMS model. then by entering the three flood event data and its corresponding precipitation, computational hydrograph is created.  For calibration, at first by trial and error, the model was placed in a suitable range which can be optimized using differential evolution algorithm. In optimizing the target function to make the average at least of the sum of squares of error, the K and X Muskingum parameters, which are particularly sensitive to hydrograph production, placed the model optimization and its performance in the excellent category so that for the event of November 2015 floods and a stop of 50 iteration (NSE=0.871, PBias=25.52, RMSE=0.4, NRMSE=2.63), for the event of March 2016 floods (NSE=0.731, PBias=28.82, RMSE=0.5, NRMSE=1.01) and in February 2020 (NSE=0.834, PBias=7.96, RMSE=0.4, NRMSE=0.95) which indicate an excellent performance of the model after optimization of the Muskingum coefficients by differential evolution algorithm (DE).

Monitoring the quality parameters of the river is very important in the management of surface water resources. One of the effective methods in this field is entropy theory which has a very good capability in designing quality monitoring network. In this research, discrete entropy theory has been studied and monitored in order to qualitatively evaluate the temporal and spatial frequencies of the network of hydrometric stations in Kermanshah province with 16 stations and a 29 years common statistical period. The amount of data transfer between each station and all other stations is calculated in two to determine the information coverage between the network stations. The results showed that Bavleh station on The Gavehrud River in Kermanshah province is the most efficient network station in terms of potential of data giving by getting the highest amount in border entropy ranking. Pirsalman, Ghorbaghestan, Doab-mereg and Khersabad stations were also in good condition in terms of potential for information capture, but Totshami station is in poor condition. Polchehr, Biar, Sarpol-e Zahab and Kamish Road Bridge stations have poor status in terms of potential of giving information, bridge stations, Quantified road bridges and biyar in terms of quality indicators are not suitable and the information coverage of qualitative variables in almost all stations in the field is weak. Information transfer index is deficient throughout the study area and there is a need to revise the network. . In terms of optimal sampling distances, EC variable can maintain the effective quality of this index, considering that it is in a state of severe lack of information transmission (current network situation) up to 50 km distance.  Esp index is less sensitive to distance than Ec index and according to data transfer index and distance, the variability of this index is less at distances and distance of 55 km is suitable and for Cl the distance of less than 50 km was obtained for sampling. According to the data transfer index for mg quality variable, there is appropriate information coverage in the network at a distance of 55 km.

Groundwater monitoring has long been considered as one of the main sources of agricultural water supply. . In this regard, indiscriminate harvesting of this God-given resource, as well as the construction of unauthorized wells and lack of rainfall have caused the water level to drop in many aquifers of the country. The plains of Kermanshah province with its good climate and fertile soil are the agricultural and horticultural hubs of Iran. Therefore, in this study, piezometric well data in the period 1360-1397 and GRACE satellite data with a resolution of one degree in the period 2002 to 2020 with JPL, GFZ and CSR methods were used to study the changes in groundwater levels of aquifers in Kermanshah province. The amount of soil moisture was extracted from Google Earth Engine cloud computing environment using GLDAS model with a resolution of one degree. Zoning maps of all aquifers in the province for the years 1395 and 1397 were drawn by kriging method in ARC GIS software. The results showed that a number of aquifers, including Hassanabad-Shian, Sanjabi and Dinehvar are in critical condition. Long-term monthly and annual water level data of aquifers for different plains according to their effective areas have also been calculated and displayed by Thissen method. The map of changes in soil moisture, actual evapotranspiration, and cumulative precipitation in Kermanshah province was extracted by TERRA satellite with a resolution of 0.04 degrees equivalent to 4 km by 4 km, which shows the changes in these parameters in December 2019. The values ​​of TWS groundwater level changes were plotted by subtracting soil moisture values ​​against the aquifer observational data and the changes in groundwater level decreasing trend in Kermanshah province indicate a decrease of -1.5 cm and -3.8 cm in the period 2002 to 2016 by GRACE satellite and observational values respectively While from 2016 to 2020, groundwater level changes have been increasing.

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.

Water is a treasure to be passed on to future generations, and it is now clear to the world that human well-being and industrial and agricultural development are all at stake unless the optimal use of water resources as well as integrated environmental planning and management become a key element of plans and investments. Water resources management is a set of various management measures, which is done to optimize the use of water resources and reduce economic, social and environmental damage. Sustainability assessment by indicators is useful for planning, management and educational purposes. Indicators should be easy to use, comprehensive and universal. They must also be accessible, understandable and valuable, revealing and comprehensive. Due to the importance of sustainable development, quantifying this concept of sustainable water resources management indicators is done by considering economic and social ecosystem considerations. The use of multi-criteria decision-making methods in water-related issues has been used by many researchers.

The purpose of this study is to investigate the stability of Gamasiab basin. The study area, Gamasiab catchment is one of the main sub-basins of Karkheh basin. Figure (1) shows the geographical location of the Gamasiab catchment. Figure (2) shows the algorithm of research steps. The studied indicators are as follows: Percentage of normal rainfall index (PNPI): Its basic concept is the ratio of actual rainfall to its normal amount over a period of time, expressed as a percentage and can be calculated on a scale (weekly, monthly, seasonal and annual). Equation (1) shows how to calculate this index. A standard precipitation index (SPI) was provided to monitor and determine droughts. This index is calculated in time scales of 48,24,18,12,9,6,6,3,1 months and one year (Equations 2 and 3). Surface water storage index (SWSI): The purpose of this index is to consider hydrological and meteorological characteristics as a single index (Equations 4 and 5). Standard runoff index (SRI): This index is obtained according to Equation (6). Available water index (WAI) This index includes surface water resources, groundwater and its difference from the needs of all household sectors of the agricultural industry (Equation 7). WEAP model: This model is flexible due to the data structure in a range of highly decomposed to highly aggregated modes. The input to the reservoir can be given to the model both through the hydrological model and as a time series . Integrated management of water resources and consumption was simulated in the WEAP model and its output was used in EXCEL software to calculate ASI, ESI, out-of-reach water supply reliability and fairness in water allocation. Figure (3) shows a schematic of the resources and uses of the Gamasiab catchment area. Reliability Index (REI) Reliability or reliability is the probability with which water allocated to the consumer meets its needs (Equations 8 and 9). Reversibility index (Res) The probability that a system will return to normal after a failure is called reversibility (Equation 10). Vulnerability index (Vul) The most widely used definition is the ratio of the total deficits to the number of steps in which the deficit occurred divided by the total need (Equation 11). Sustainability Index (SI) Sustainability Index Summarizes system performance metrics in an index to facilitate comparison and decision making between different water resources management and planning options (Equation 12). Environmental Indicators: The Agricultural Sustainability Index (ASI) is defined as the sum of the nominal ratio of agricultural demand in the time series. Environmental sustainability (ESI) is defined as the ratio of supply to demand in an environmental measure over time. The Wilcox Index The Wilcox diagram is based on the electrical conductivity (EC) values of water-soluble solutes in different classes. Schuler Index The Schuler Index divides water into six groups: good, acceptable, suitable, bad, drinkable in an emergency, and non-potable. Social indicators: Drinking water supply reliability index in the form of reliability of drinking water transmission links in which the amount of supply to demand is equal to 1. Justice in water allocation index (CV) This index shows the coefficient of variation of supply to demand ratio in all agricultural demands. Economic indicators: Profit to cost ratio (B / C) This index is obtained by dividing the profit on existing costs. The total value of production is obtained by subtracting the total costs of crop production from the gross value (the product of product yield per unit area multiplied by the unit price). Shannon entropy method was used to weight the indices, which can be calculated using relations 13 to 18. TOPSIS, CP and SAW multi-criteria decision-making methods were used to rank the indicators (relationships 19 to 32). Ranking options using different decision-making methods are different. Therefore, the Bordeaux counting method has been used for the final selection of the superior option. In this method, instead of using the evaluation values of each option, only their comparative rank, which is determined by using different methods, is used to minimize uncertainty in them (Equations 33 and 34).

In this study, multi-criteria decision making (MCDM) methods were used to rank the sub-basins of Songor, Sahneh, Kangavar, Harsin-Biston and intermediate in terms of economic, social, environmental and water resources criteria. The results of weighting the indicators by Shannon entropy method showed that the reliability index of drinking supply has the highest weight with 0.29. After that, out of reach water indices and WAI index with weights of 0.188 and 0.186, respectively, have the highest weights. The lowest weights among the indices are related to PNPI index with a value of 0.000053, followed by SWSI index with a value of 0.00018 and then the lowest weight is related to Schuler diagram index with a value of 0.00082. Finally, due to the ranking differences of the above methods, the Bordeaux counting group decision-making method was used for the final ranking. The results of this study using multi-criteria decision making methods including SAW, TOPSIS and CP decision making models showed that the most sustainable sub-basin in terms of economic, social, environmental and water resources for Gamasiab catchment in Kermanshah province is Harsin-Biston catchment (due to the usefulness of the calculated indicators),. The intermediate and Songhar sub-basins, respectively, have the ranks of 2 and 3 in terms of stability. The weakest sub-basins are Sahneh and Kangavar sub-basins in terms of criteria.

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.

Accurate and correct prediction of surface water flow plays an important role in the principled planning and proper management of water resources. To achieve this, various prediction models using mathematical relationships based on hydrological information can be used. In this study, monthly discharge of Polechehr hydrometric station for a 48-year has been used (Sep. 2018-October 1971). Two main scenarios with and without pre-processing (standardization), two time series or non-time series approaches were considered. Also, two cases with and without feature selection have been considered by a random forest algorithm. In all cases, 80% and 20% of the data are intended for model training and testing, respectively. The entire coding process is done in the Python programming platform. Genetic algorithm was used to optimize the parameters of the support vector regression method. The results showed that standardization, non-time series approach, reducing the dimensionality of the model input to select and also using genetic algorithm to optimize the parameters of the support vector regression model have the greatest effect on prediction accuracy, respectively. So that the highest coefficient of explanation for training data is 0.85 and for testing is equal to 0.6.If standardization is not applying on the data, adopting a time series approach and feature selection will lead to better results in predicting the SVR model, and also the use of genetic algorithm optimizer compared to the simple model will have a significant effect on improving results.

Determining water quality is of particular importance in water resources management. Today, due to the existence of numerous polluting sources of water resources, the chemical quality of water resources has been greatly reduced. The purpose of this study is to qualitatively classify the water resources of Gamasiab catchment area of Kermanshah province for drinking and agricultural uses and the quality of surface and groundwater resources. The qualitative data of five stations from 1358-97 and 5 wells were used. Qualitative data studied in this study are 10 parameters including: So4، cl، Hco3، ، ، TDS، EC ، pH and SAR. The results showed that according to the Wilcox diagram, all samples are in the C2S1 class range, which indicates good quality water for irrigation and agriculture. . According to the piper diagram, the facies is calcite water and fresh water type. River water is acceptable in terms of hardness and bicarbonate based on Schuler index in terms of hardness and in good range in terms of other parameters. The results of calculating WQI and GWQI indices show excellent water quality for surface and groundwater of this basin.

Today, the effects of climate change and global warming have been demonstrated by rising greenhouse gases. Occurrence of these conditions affects hydrological processes such as precipitation and river flow, which is one of the main sources of water for the basin. In this study, the monthly values of precipitation, temperature and inflow of Jamishan Dam during the period of 1988-2017 are considered as the basic period. The output of climate models does not have the desired accuracy and spatial and temporal resolution, so it is necessary to downscale the output of CMIP5 models for the study area. In this study, using Change Factor Method (CFM), the data of two FLO_ESM and CNRM_CM5 models were downscaled under the RCP8.5 scenario and the monthly temperature and precipitation parameters of Jamshan dam were produced for the period 2021-2050. To evaluate the effect of climate change on runoff in the region, SVM, GEP and IHACRES models were studied and compared. The results of climate model indicate an increase in temperature between 0.1 to 1.4 degrees of Celsius for both FLO_ESM and CNRM_CM5 models. Also, the results of simulated precipitation in FLO_ESM and CNRM_CM5 models show that the monthly long-term average under the RCP8.5 scenario in the next period decreased 1.1 and 5.8%, respectively, as compared to the baseline period. In general, the results show a reduction in runoff in all three models (SVM, GEP and IHACRES), which the highest reduction (28.9%) is corresponded to SVM in FLO_ESM model and the lowest reduction (14.1%) is corresponded to GEP in CNRM_CM5 model. In this study, GEP and IHACRES models are more accurate than the SVM model.

The Relationship between parameters of Geomorphology and hydrology, provides the possibility to watersheds that there are similarities in terms of geomorphology, by creating relationship between these Parameters, attempting to predict floods in watersheds that have data and its generalization to no data watersheds was similar to each other based on geomorphic parameters. In this context, several methods have been proposed. The aim of this study is to investigate the efficiency of geomorphologic instantaneous unit hydrograph (GIUH) in the preparation of flood hydrograph and compare its performance with Snyder, SCS and Triangular unit hydrograph models, in the Mereg catchment, Kermanshah.  In order to implement these methods, information relating to 12 rainfall –runoff events were collected and the direct hydrograph was obtained. According to statistical indices, Mean Relative Error (MRE), Root Mean Square Error (RMSE), coefficient of bias and Nash Sutcliffe, compare observed and predicted hydrograph showed that the most efficient model in estimating peak discharge is GIUH method and after it Snyder, SCS and triangle methods have the next, respectively. According to the results, Snyder, GIUH, SCS and Triangular methods are respectively estimated as highest performance at the time to peak. Moreover, the results indicate overestimation of GIUH, SCS and triangular methods in estimating peak flows and overestimated and underestimated of Snyder and GIUH methods in estimating of the time to peak. The SCS method also has the best flood volume estimation.

Due to population growth, shortage and severe constraints of water resources in the country, one of the main steps in management and planning of water resources is to optimize reservoirs and change the cultivation pattern. In this study, using 40 years discharge data of Kamish River and WEAP model, Hersin dam reservoir which is uder study, was simulated. Firstly, by considering different cultivated patterns, the reliability of domestic, industrial, agricultural, and environmental requirements were simulated by WEAP model and the proper crop pattern was selected on the basis of greater sustainability of the system. Then, optimization was performed by minimizing the shortages in downstream of the dam using LINGO model. According to the results, the annual average demands of environment, drip-irrigated gardens, sprinkler-irrigated lands and wastewater waterline, were estimated to be 98.98, 87.51, 89.79, and 95.63%, respectively, while these values obtained from the optimization model were 100, 99.99, 99.21, and 99.12%, respectively, which indicates that the percentage of shortcomings is less than 1%. The average overflow volume in simulation and optimization models were 7.31 and 4.09 million m3/year, respectively, which indicates that the optimization model has a lower water lost than the simulation model. Hence, by appropriate planning and management, it is possible to deduct the amount of deficits significantly, especially in the sector of agriculture.

Having predicted river flow, we can predict and control natural disasters such as flood and drought in addition to managing utilization of water resources. New models in this domain can help correct management and planning. In this study, three models are evaluated: Gene Expression Planning (GEP), Bayesian Network (BN), and Support Vector Machine (SVM). The data used for this research is precipitation data and daily flow of Gamasiab River in Nahavand during 10 years period (1381-1391). Results indicated that the relative superiority of the gene expression planning model to other models and better performance of SVM model in comparison with BN in daily river flow modeling. In addition, implementing gene expression planning model was faster than other models and could provide results in a short time. The SVM model is also more fitted to estimate the final minimum values. Finally, GEP model with coefficient of determination of 0.9230 and root mean square of 0.5867 in the training phase and coefficient of determination of 0.9025 and root mean square of 0.4936 in the test phase was selected as the superior model.

Water availability significantly affects development of agriculture, industry and urban services. Thus, the water industry is among the fundamental industries. Yet, most countries, especially developing countries, are suffering from water deficit, water pollution and flood hazards.  These problems are more severe in Iran due to improper management of water resources. Integrated water management is a systematic process for sustainable development and water monitoring which considers socioeconomic and environmental needs. The objective of this study was to evaluate the capability of Harsin dam reservoir on Babazaid River, located 45 km east of Kermanshah, Iran for storage of surface water and groundwater recharge. Water productivity and the results of simulation were analyzed through LINGO and WEAP software using 42 years’ data. Water sustainability index was also calculated via the WEP model and Excel while taking into account the water needs of agriculture, environment and urban supply. The results indicated that the annual water deficit for urban supply, agriculture, environment and industry were 1.74, 14.2, 6.04 and 0.4%, respectively. The water need for agriculture sector in summer was considerable.