نشریه آب و خاک
سال سی و ششم شماره 4 (پیاپی 84، مهر و آبان 1401)

The world population has grown rapidly over the last 150 years and continues to do so, resulting in impacts on hydrologic resources at both a local and global scale (Yang et al., 2012). The competition for water between humans and ecosystems leads to complex interactions between hydrologic and social systems (liu et al., 2015). From the beginning of human history, it is located in floodplains. Floods can have large societal impacts, such as severe damage to urban areas, which are expected to grow around the world (Alfieriet al., 2018). In traditional hydrology, humans are either conceptualized as an external force to the system under study or taken into account as boundary conditions (Peel and Blöschl, 2011). Sivapalan et al. (2012) proposed a new model for investigating the interactions of the hydrological system and the social system. It explores the procedure coupled human-water system evolves and possible trajectories of its co-evolution, including the possibility of generating emergent, even unexpected, behaviors. Socio-hydrology must strive to be a quantitative science. There are several methods to control and mitigate flood risk, one of these methods is flood zoning (Jha et al., 2012). In last two decates, The Kalat city is flooded almost every year and many houses and historical sites in the city are damaged. Therefore, the main purpose of thisWe paper is to show investigated how changing human behavior with nature can affect the behavior of the natural system.

Method and Materials:

Kalat city located in 59° 43' 23" to 59° 47' 41" northern latitude and 36° 59' 35" to 37° 00' 05" eastern longitude. The city is divided into 11 sub-basins. The city has experienced fast and inappropriate urbanization over the past few years. To collect our data, the annual reports of the Regional Water Organization and the Environment Organization of Khorasan Province were used.SCS method was used to estimate the runoff peak discharge. Precipitation has been estimated for seven return periods: 2, 5, 10, 25, 50, 100, and 200 years. In this study, to analyze the sensitivity of runoff, we considered precipitation and curves number from 20% less to 20% more than the actual values in the study basin (at intervals of 5 %). We used the Cowan method to determine the roughness coefficient in this study. HEC-RAS model has been used for flood zoning. To determine the impact of various factors on the intensification of floods in Kalat city, we obtained questionnaires from relevant authorities. Likert scale was used to measure the results of the questionnaires. We prepared two questionnaires; first one is related to the inner city zone and includes the factors that intensify the occurrence of floods inside the city of Kalat, and it was classified into the following parts: 1) Local community 2) Managerial 3) Physical; and the second one includes the factors that intensify the flood in the upper part of Kalat city. We classified these factors into three parts: 1) Non-local community 2) Managerial 3) Environmental .

Results of sensitivity analyzes demonstrated that land-use and land cover change had a further effect on peak discharge. In sub-basin 1, by 20% increase in the curve number, the level of peak dumping increased by more than 111%, with a return period of 2 year; while a 20% increase in precipitation, in the same return period, rises the peak discharge only 3%. The peak discharge time in some sub-basins was brief due to the presence of impermeable surfaces, so that in sub-basins 4, 6, 7, and 8, the peak discharge time was less than 30 minutes. These results highlight the dangers of these floods and the need for proper flood planning and management in these sub-basins. The results of the Manning coefficient demonstrated that we can reduce flood damage by applying management measures in the future, as well as paying attention to the feedback between urbanization and the flood zone. Roughness control by applying management programs can reduce the area of flood zones to 0.1 square kilometers. In this case, buildings should be removed from the river, and there should be no structure in the path of the river. According to the questionnaires in the inner city part, the most fundamental factor in intensifying the flood damage was related to “activities of local people” with the average of 3.59. In the upper part of the city, the most influential factors were ascribed to “managerial factors” with the average of 3.79.

In a general conclusion, it can be concluded that the role of human factors in the occurrence and intensification of floods was much greater than rainfall. Therefore, in order to manage and control floods, it is necessary to prevent the change of land use and the reduction of permeability. And management programs should be aimed at increasing surface permeability. We suggest that more research be done on the role of economic and social factors in increasing flood risk in other climate zones.

The water quantity and quality has always been one of the main challenges in the issue of allocating water resources for different uses. Water quality management requires the collection and analysis of large amounts of water quality parameters that will be evaluated and concluded. Many tools have been found to simplify the evaluation of water quality data, and the water quality index (WQI) is one of these widely used tools. In summary, the WQI can be defined as a number obtained from the combination of several quality parameters based on standards for its extraction. The aim of this study was to develop and introduce the new Surface water Drinking Water Quality Index (SDWQI) adopt the water quality parameters measured on hydrometric stations of Iran. In developing this index, criteria such as the availability of required parameters in most rivers and simple and accurate methods have been considered. Also, the ability to calculate with the minimum general parameters of water quality, simple calculations and in terms of the international standard WHO for drinking is one of the advantages of the introduced index.

For this purpose, 12 water quality parameters including Total Dissolved Solids (TDS), Electrical Conductivity (EC), Total Hardness (TH), pH, Chloride (Cl-), Sulfate (SO42-), Carbonate (CO32-), Bicarbonate (HCO3-), Magnesium (Mg2+), Sodium (Na+), Calcium (Ca2+) and Potassium (K+) have been used from Rudbar and Astaneh hydrometric stations located on Sefidroud river. Then initial preprocessing on data e.g. correlation analysis, and multivariate statistical methods including cluster analysis (CA) and principal components analysis (PCA) are used to selecting and weighting of water quality parameters using the “clustering” and “factoextra” packages in R 4.1.1. In order to develop the SDWQI were performed four steps including, parameter selection, sub-indexing, weighting and aggregation of the index. Also, in order to evaluate the index of the present research, the results of the SDWQI have been compared with the WHO drinking water quality index and Schoeller drinking water quality classification.

Correlation analysis between water quality parameters shows a significant correlation between TDS, EC and TH parameters and also with Cl-, Ca2+ and Mg2+ parameters at the level of 1% in both Astaneh and Rudbar stations. On the other hand, the lowest values of Pearson correlation coefficient are related to pH and CO32- parameters with other quality parameters. The results of CA indicate that most of the water quality parameters are located in separate clusters. So only the parameters TDS, EC, Cl- and Na+ in both Rudbar and Astaneh stations are in the same cluster. The weights of the parameters showed that TDS and K+ are assigned with the highest and lowest weights equal to 0.163 and 0.031 based on PCA method. Also, PCA results show that first and second principal components covered 59.3% and 67.6% of the total variance of measured water quality parameters in Rudbar and Astaneh stations, respectively. Water quality classification results indicate that (40.5%, 16.4% and 23.7%) and (90.1%, 73.1% and 57.3%) of data in Rudbar and Astaneh stations, respectively, fell into the excellent and good categories for drinking purposes based on Schoeller classification, WHOWQI and SDWQI.

Generally, the comparison of the SDWQI with the WHO index and the Schoeller classification shows the rigidity of the new index in the classification of water quality for drinking purposes. Each water quality index developed in order to evaluate the uncertainty of results, should be tested for data with different characteristics in terms of the range of variation with different limit values​​ (minimum and maximum). The index developed in the present study is no exception to this rule and in order to better evaluate the results, it is suggested that to be evaluated and analyzed with data from other hydrometric stations. Another important points that should be considered in using any water quality index, including the present research index, is to examine the allowable limits of water quality parameters that are not considered in these indicators. The results of the study indicated that, two most important steps in the development of a quality index that have a great impact on its results are sub-indexing and weighting of parameters. According to the results, two ideas recommended for future research. One, choosing an appropriate method such as non-deterministic (fuzzy) and intelligent (machine learning) methods to sub-index the parameters and two, to weigh the parameters more effectively, multivariate statistical methods such as clustering, factor analysis and principal component analysis should be used.

 Farmers and agricultural products face many risks, including adverse weather conditions, pests, diseases, and changes in product prices, laws, and regulations. The first step in managing and minimizing many of these risks is often choosing the right crops for the area under cultivation; Therefore, knowing whether these lands are suitable for a particular crop can determine the success or failure of agricultural strategies. Because farmers are exposed to climate change and the economy, where agricultural frameworks are changing at an unprecedented rate, it is vital for them to be able to adapt to new trends. Increasing the availability of land suitability information for agricultural products will be a valuable aid for farmers and managers in this field to develop new agricultural strategies. At the same time, the growth of computational capabilities and increased access to geographic data has made land suitability assessment faster and easier.

 The study area is located in Abik city, a city located in Qazvin province of Iran, between 50 degrees and 40 minutes to 50 degrees and 41 minutes east longitude and 35 degrees and 52 minutes to 36 degrees and 21 minutes north latitude. The average annual soil temperature at depth of less than 50 cm is 15.8 °C and has thermal heating regime. Furthermore, according to the average rainfall of the region, 222.7 mm, the humidity regime of the region is of Eridic type. Moisture and heat regimes were obtained by Newhall software. According to regional conditions and the size of the area, 60 profiles were drilled for network description and sampling. Field studies including determination, drilling, description of profiles, slope percentage, etc. were determined at the site. Information on soil physical and chemical properties were tested. Parametric, American (USDA) and LSP methods were used to evaluate the land. Necessary climatic characteristics for annual plants include the climatic variables that are necessary to determine the growing season, planting date and type of cultivar. The information of Buin Zahra synoptic station has been used. In this study, CROPWAT software was used to calculate the potential evapotranspiration. Land information such as slope, drainage Condition and flood absorption, as mentioned in the profile description card, was used to assess land suitability. Growth period was also obtained for the region using the area agronomical calendar. To calculate potential of production, the model AEZ which is provided by FAO, is used in this research.

 The decrease in the suitability of the studied lands for the wheat crop is due to the salinity and sodium content of the lands and the presence of surface gravel and shallow soil depth. According to the provided tables and maps, 18% of the study area is unacceptable, 12.5% is average, 12.5% is good, 25% is very good, or very good and 31.25% of the total study area are in the excellent fitness class. The above values have been obtained by considering the rangeland and saline sections as well as the type of product in preparing the fit map. The accuracy of the preferred rational scoring method in land suitability is higher than the parametric method because in this method the land suitability maps of the area are obtained by logical collectors and the output map is the result of all parameters and constraints that the area may have. To have the desired. In the parametric method, this problem is summarized in soil properties and climatic conditions. Due to the lack of direct measurement of product performance, more accurate comparisons were not possible.

 Most of the restrictions were in shallow hilly areas with shallow soils and pebbles, and salinity, alkalinity and gypsum did not impose any restrictions in these areas. Traffic in these areas was difficult and they were mostly in the S3 class by the parametric method and the poor and unacceptable class in the LSP. In land evaluation using LSP method, understanding the relationships of criteria with each other and the amount of impact that each has on the potential of land for different uses is essential. The LSP method is sensitive yet flexible, and may not work well if the data accuracy and number of parameters are low. The application of GIS-based LSP method showed a suitable tool to create accurate, flexible and rationally justifiable criteria in assessing the capability and suitability of land in agriculture. In such studies, by using the Bayer LSP method, prerequisites such as precisely defining the goals of users, managers and agricultural expertise should be considered. This method is a multi-criteria evaluation method that has been improved for measurement among decision makers, land management and other specialties.

The aim of the present study was to investigate the effect of soil conditioners on physiological responses (stomatal resistance, leaf temperature, chlorophyll, percentage of root colonization, carotenoids, proline) of Lycium depressum Stocks to drought stress. The experiments were performed in semi-controlled greenhouse conditions.

The experiment was conducted as a factorial experiment based on a completely randomized design including the main factor, irrigation at 4 levels (100, 75, 50 and 25% of field capacity) and the sub-factor of soil conditioners. In each combined treatment, 5 repetitions of irrigation and soil remediation and a total of 160 pots were used. Subsoil treatments including hydrogel and nitrobacter, mycorrhiza and zeolite were added to each pot. 500 cuttings of the target plant were planted in the greenhouse. The grown cuttings were transferred to the pots where the experiments were carried out. At each irrigation level, 40 pots containing 4 kg of vegetation soil of the target species were considered and the agricultural capacity (FC) of the target soil was determined in the soil laboratory. A total of 160 pots were placed in the greenhouse for testing. The main treatment of the experiment included irrigation levels (100, 75, 50 and 25% of the crop capacity) and sub-treatments of soil conditioners including Stacosorb hydrogel in the amount of 3 grams per kilogram of soil in each pot in the lower part of the plant roots. Zeolite with the industrial name of mineral zeolite (Mineral Zeolite) was added in the amount of 8 grams in each pot in the lower part of the root of the plant. Nitrobacter (a collection of strains of Azotobacter sp, Azospirillum sp and Bacillus sp with the brand name Nitrobacter Diane) was added to the amount of 3 cc in each pot in the upper region of the plant roots. Addition of mycorrhiza (the mycorrhiza used in this experiment was Glomus mosseae and was prepared as soil containing mosseae fungi) in the amount of 10 grams per pot in the lower part of the plant roots. After adding soil conditioners, irrigation was done according to the crop capacity in 4 irrigation levels, in the determined treatments.

Measurement of physiological characteristics showed different responses in each of the variables. Carotenoid changes in 50% irrigation showed the lowest value (p<0.05) and the control treatment without mycorrhiza showed the highest value in the measurement of chlorophyll and carotenoid at 100 and 75% irrigation levels. The results of measuring colonization percentage, stomatal resistance and leaf temperature showed the lowest value in 25% irrigation. In the control treatment, proline parameters and root colonization percentage increased under the influence of drought stress, and stomatal resistance parameters, leaf temperature and chlorophyll decreased under the influence of drought stress. With intensification of drought stress, chlorophyll and carotenoid contents of the plant increased and the amount of proline decreased in Nitrobacter treatment with mycorrhiza, which was significantly different from the control treatment. In the control treatment with mycorrhiza, with increasing drought stress, the leaf temperature increased and the amount of proline decreased, which was different from the control treatment. Aperture resistance decreased from 48 m2 / mol.s 100% irrigation level to 44 m2 / mol.s 25% irrigation, leaf temperature at 100% irrigation level in mycorrhizal-free hydrogel modifier from 26 ° C Decreased to 21.57 ° C in 25% irrigation, at 100% irrigation level in non-mycorrhizal zeolite modifier the amount of chlorophyll b + a from 0.6 mg / g to 1.20 mg / g in 25% irrigation increased. The amount of carotenoids at 100% irrigation level in zeolite modifier with mycorrhiza increased from 0.1 mg / g to 0.2 mg / g in 25% irrigation in control treatment with mycorrhiza at 100% irrigation level compared to the level Irrigation increased by 50% and root colonization by 1.5%. The amount of proline in mycorrhiza-free hydrogel treatment was measured at 100% 2.77 μmol / g irrigation and at 50% irrigation level 2.66 μmol / g. Reduction of proline at 50% irrigation level indicates that the hydrogel modifier has increased the resistance of Lycium depressum Stocks to drought stress.

The results of this study showed that the increase in drought causes changes in the physiological performance of the plant and the use of soil conditioners under drought stress due to the improvement of the physiological parameters, will increase the resistance of the plant by 50%. Nitrobacter treatments without mycorrhiza, hydrogel and zeolite with mycorrhiza and without mycorrhiza, due to further improvement of physiological parameters, is recommended to plants in nature.

 Agriculture is the essential sector for promoting food security. Crop area estimation (CAE) can meet the requirements of the crop monitoring plan. The organizing basis of the cultivation pattern is recognizing the types of crops and examining the condition of their crop area. Shush county in Khuzestan Province has 300,000 hectares of the crop area. It is one of the agricultural hubs of Iran because it has a record annual production of more than two million tons of strategic crops such as wheat, sugar beet, and corn. CAE affects the amount of net production and shortage or surplus of produce for market steadiness. Traditional approaches for CAE are time-consuming and costly and are not widely enforceable. Remote sensing (RS) data provide good information for decision-makers by determining the crop type and the crop area. RS data has made it possible to avoid continuous reference to agricultural lands with less time and cost than another usual method and accurate CAE. Also, the use of multi-time images during the growing season of agricultural products allows the use of spectral curves when related to the crop calendar of each crop. This spectral curve is almost separate for each product and increases the ability to distinguish between products. Therefore, multi-temporal images support segregation based on multispectral images of products. The current study follows a speedy method with appropriate accuracy established on satellite image classification algorithms and spectral indices to identify and separate crops with RS data in Shush County.

 Landsat-8 data with path/row coordinates 166/38 extracted from the USGS website were used to identify and separate the cultivated lands of the region. The reason for choosing Landsat images is the relatively suitable temporal and spatial resolution, availability, and the appropriate time distribution with the product growth period. The Landsat 8 carries 2-sensors, OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor). The OLI sensor with a spatial resolution of 30 meters has 8-bands in the visible spectrum, near-infrared (NIR), short-wavelength infrared (SWIR), and a panchromatic band with a spatial resolution of 15 meters. The TIRS sensor can record thermal infrared radiation with a spatial resolution of 100 meters with the help of 2-bands in atmospheric windows of 10.6 to 11.2 micrometers for band 10 and 11.5 to 12.5 micrometers for band 11. This research used bands 1-7 of the Landsat-8 OLI sensor with a spatial resolution of 30 meters after the initial corrections of satellite images. The spectral similarity between the region's dominant crops has made it impossible to select a single image to differentiate and extract the cultivation pattern. Wheat and barley have a high spectral similarity. The peak of the greenness of these products is in the first four months of the year, which has high NDVI values at this time. Therefore, choosing a good time to separate the crops was feasible by referring to the Khuzestan Organization Agriculture-Jihad (KOAJ) and receiving the regional crops calendar in 2018-19. Then, the low-level cloud cover images on April 24, June 27, and August 30, 2019, were selected for classification based on the crop calendar. Planting, harvesting, maximum greenness, and ripening information of the dominant crops in the area were pivotal in obtaining image dates. In dates selected related to the images were considered planting, harvesting, maximum greenery, and ripening information of the region's dominant crops.

 According to the results, from total crop area in Shush county (163313.7 hectares) is allocated about 103513.2 hectares (63.4% of the county's crop area) to the ANN, about 102875.1 hectares (63.0% of the county's crop area) to the SVM, and about 102,277.3 hectares (62.6% of the county's crop area) to the NDVI, which in comparison with the KOAJ statistics, has an error of 0.11, 6.2 and 1.8%, respectively.
This difference is the similarity of the reflective spectrum in some places, which affects the separability and recognition of phenomena and increases the error in estimating the area under cultivation of different crops. The highest and lowest errors in estimating the area under cultivation in the artificial neural network method were in barley and rice crops, respectively, in the support vector machine method were in wheat and rice crops, respectively, and in NDVI index were in wheat and barley crops, respectively. The difference between the cropped area obtained from classification methods and NDVI index with cropped area statistics of Agricultural-Jihad Organization may be due to the following: First, the cultivation history of different has caused problems such as reflections of diverse agricultural lands in one image. Second, the agricultural lands in this area are small. Most of them are under one hectare. Also, the crops in this area are diverse. Third, the smallest region that the image used in the present study can distinguish is about 900 square meters, which is a large number for the agricultural lands of the study area and causes errors.

 The study results showed that the support vector machine method had the lowest error in CAE than the artificial neural network method, which indicates the higher accuracy of the support vector method in identifying and separating crops in the region. Comparing the area obtained from the NDVI index with the statistics of the Agricultural-Jihad Organization of Khuzestan province and evaluating the accuracy of this method indicated the higher efficiency of spectral indices in CAE for the region compared to classification methods. The NDVI index minimizes the error values of the results due to having a threshold and better identification of vegetation density. Therefore, based on the accuracy assessment results and comparing the cropped area with the KOAJ statistics, the utilization of the NDVI index provides the best CAE in the region.

Increased agricultural activities, the occurrence of successive droughts, and limited freshwater resources, along with increasing population, have made a priority for the importance of protecting water resources in programs of developed and developing countries. Due to the climatic conditions in Iran, which has a wide range of arid and semi-arid characteristics, facing the challenge of water resources crisis, is inevitable. Therefore, the use of wastewater is very important.

This research was conducted in the research farm of Sari University of Agricultural Sciences and Natural Resources (SANRU), which has a silty clay soil texture. The latitude and longitude of the region are 36º 40ʹ N and 53º 04ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Sari city has a temperate humid climate. The long-term average temperature of Sari is 11.18 °C and the total long-term rainfall is 780 mm. In order to evaluate the wastewater effects on soil chemical characteristics, microelements concentrations, heavy metals accumulation and Maize yield (Single Cross 704), an experiment was carried out as factorial based on a completely randomized design with treatments included; Water source factor (wastewater (A1), well water (A2)), Irrigation (subsurface method (I1) and (drip method (I2)) with three replication in 2018-2019 under lycimetric conditions, at the Sari Agriculture and Natural Resources University (SANRU), Iran.

According to this study results, the effect of type of irrigation source on soil electrical conductivity, soil microelements and heavy metals accumulation of the soil was significantly different (P ≤ 0.01). The highest soil electrical conductivity with a value of 1.8 dS.m-1 was observed in the conditions of using treated wastewater. The highest amount of total nitrogen, phosphorus and potassium were related to the source of treated wastewater with values of 0.086, 24.2 and 222.2 mg.kg-1, respectively. The results showed that the accumulation of soil Pb (0.07) and Cd (0.014 mg.kg-1) in irrigation with treated wastewater increased compare to the well water source by 0.05 and 0.010 mg.kg-1, respectively. Also, the effect of irrigation method and the interaction effect of source and method irrigation on soil chemical characteristics, microelements concentration and heavy metals accumulation were not significant. The use of wastewater by increasing soil stability improves soil physical condition, increases soil fertility, increases photosynthetic products, increases the efficiency of plant photosynthetic system and ultimately improves plant growth. The use of subsurface irrigation resulted in a 67% increase in grain yield and 28% increase in biomass productivity compared to the drip method. Adequate nutrients during the reproductive growth stage of the plant play an important role in grain growth. Therefore, it can be said that the nutrients in the wastewater have increased the grain yield compared to using the well water source. Because the wastewater contains nutrients and micronutrients such as; nitrogen, phosphorus, potassium, calcium, zinc and iron were relative to the well water source and increased maize grain yield. The results showed that the use of effluent compared to well water, caused the absorption of more heavy metals lead and cadmium in the grain, leaf and stem of maize. Due to the use of wastewater water source, the amount of Pb uptake among different parts of the maize, with values of 27.2, 22.5 and 20.5 mg / g, respectively, related to the grain, leaf and stem. However, the uptake of Cd in the grains, leaves and stems was 2.32, 1.35 and 2.01 mg / g, respectively. According to the results, the high concentration of heavy metals Pb and Cd due to the use of wastewater in the grain sector directly threatens human health. Also, the concentration of heavy metals Pb and Cd in the leaf and stem parts of corn, by endangering the health of livestock and poultry, indirectly affects human health.

The results showed that irrigation with treated wastewater due to its richness in nutrients and microelements, improves soil fertility and creates favorable conditions by increasing soil organic matter and mineral for plant growth. Also, according to the permissible threshold values of the concentration of heavy metals Pb and Cd in plants, the accumulation of heavy metals Pb and Cd in the grain, stem and leaf of single cross 704 corn, will not be a problem for consumers. Optimal use of wastewater can increase soil fertility and the ability of plants to absorb nutrients from the soil and ultimately increase plant yield.

 Precipitation is one of the most important input parameters of the hydrological models for rainfall-runoff simulation, which due to the lack of proper dispersion of rain gauge stations and the newly established some of these stations in most basins of the country, the use of these precipitation data faces serious challenges. Therefore, the use of remote-sensing methods is one of the ways that can be used for the streamflow simulation using hydrological models. Runoff is also one of the most important hydrological variables and rainfall-runoff modeling is one of the key items in hydrological sciences to estimate runoff characteristics such as volume, peak flow and arrival time to peak flow. In the present study, we used reanalyzed precipitation data and then evaluated the simulated streamflow using this precipitation data in the Zoshk subbasin. The precipitation data was validated with in situ data, of Kashafrood basin.

 The reanalysis precipitation data was selected from the ERA5 precipitation data, and the HEC-HMS was used for the rainfall-runoff simulation. The basin parameters were calculated by the GIS menu. This menu is the newest option in the HEC-HMS software that needs only the DEM basin for calculating the basin parameters. In the present study, we should validate the ERA5 reanalysis precipitation data with in situ data, so we did that in the Kashafrood basin. The number of the rain gauge stations were 34, but some of the stations didn't have complete data and omitted them from the list of the rain gauge stations. For the validation ERA5 reanalysis precipitation data was used from the R, NSE, RMSE, Bias, FAR, POD and TS statistical indicators. These indicators were calculated by programming in EXCEL Visual Basic. The ERA5 precipitation data was evaluated for the Kashfarood basin at daily and monthly time steps. The DEM Zoshk was downloaded with the spatial resolution of 12.5 meters from ALOS-PALSAR satellite and then the basin parameters were calculated by the GIS menu. The SCS curve number was selected as a loss method. In this method, the calculations related to the percentage of impermeability and the average curve number of each sub-basin were obtained through land use and curve number layers, respectively. The SCS unit hydrograph was selected as a transform method. The recession method was selected as a base flow method. NSE and PBias were used for the calibration and validation events in HEC-HMS. In this way, at first the HEC-HMS model was calibrated by tow in situ rainfall-runoff events (91/1/11 and 91/2/6), and then validated by one in situ rainfall-runoff event (99/1/23). For validation streamflow of the ERA5 reanalysis precipitation data, the event on 99/1/23 was used and their streamflow hydrographs were evaluated with each other in Zoshk station.

 The results showed that the reanalysis precipitation data of ERA5 had underestimation in daily and monthly time steps. Also in monthly time step, the accuracy of these precipitation dataset in detecting precipitation events (in terms of FAR, TS, and POD indices) was higher than a daily one. In addition, in monthly time steps it had worse accuracy in summer months than the rest of the year in detecting precipitation events (in terms of FAR, TS, and POD indices). For streamflow evaluation, in the calibration phase both NSE was in very good and good ranges, and PBias was in very good, good and acceptable ranges. In addition, the model underestimated the observational one. Finally the ERA5 reanalysis precipitation data was compared by 99/1/23 hydrograph event. The streamflow hydrograph from the ERA5 reanalysis precipitation data was underestimated due to ERA5 underestimation of the precipitation at the Zoshk rain gauge on the days corresponding to the 23/6/99 incident. The ERA5 reanalyzed precipitation data with NSE and Bias percentage coefficients in unacceptable range (NSE≤0.5 and PBias≤±25), compared to flow hydrograph obtained from Zoshk station precipitation data, the efficiency of this precipitation dataset is low. The range of the streamflow hydrograph from the ERA5 precipitation data was unsatisfactory in compared to the observational hydrograph (NSE = -0.47 and PBias = -55.16).

 In general, the accuracy of the flow hydrograph of this product compared to the flow hydrograph of the precipitation data of Zoshk station (NSE = 0.64 and PBias = -15.82), cannot be a relatively reliable source instead of in situ rainfall data in hydrological simulation. The suggestion for future studies is to evaluate other rainfall data based on remote sensing methods in hydrological modeling.