JOURNAL OF WATERSHED MANAGEMENT RESEARCH
Year:2019 | Volume:9 | Issue:18|Papers Count:26

One basis of planning to exploit the country's water resources, is determine the water potentials and assessment the quality of water content. Babaheydar dam is one the most important dam in country which is located in Chaharmahal bakhtiari province and has important role in supplying drinkable water for Farsan County. In this regard, the present study aimed to evaluate the dam’ s water quality by using NSFWQI index. In this indicator 9 chemical and physical parameters used in order to determine water quality. Therefore seven stations for sampling determined and during 2012-13 samples collected and at last by using GIS software, dam condition in according to that index has analyzed. The results show that water of dam overall by earning points within 71-90, set in moderate class and among all stations, station 6 has best condition because only zero coliform was found in this station. In this study, the most important factor which is affected water quality was decreasing the water discharge of dam that means by reducing water rights downstream of the dam, the water discharge is reduced and thus the water quality has also declined.

As one of the most important natural disasters “ drought” is a phenomenon, which has an important impact on water resources, natural ecosystems and human societies economic, social, political and cultural from the point of view. Therefore, it is essential and inevitable to create a proper management system based on the recognition of the drought characteristics including frequency, severity, continuity and spatial extend, and its monitoring to reduce the damages and its consequences. In this study, with the aim of better understanding of droughts in Jazmorian watershed and recognizing dry and wet periods, eight special characteristics of drought were studied using the SPI and CZI Meteorological drought indices and Geographic Information System (GIS). For this the rainfall data of 24 rain gauge stations were used over a 30year period of 1983-2013 and the drought condition were assessed. In order to interpolate and prepare the most suitable zoning map for the studied features, Kriging, Co-kriging and Inverse Distance Weighted (IDW) methods were selected and compared. To select the appropriate model for fitting on the experimental variogram, the ratio of Sill to Nugget was used. To choose the right interpolation method, the mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), and general standard deviation (GSD) were calculated then selected with lower values indicating stronger spatial structures. The results showed that in studying the majority of drought features, the co-kriging method was better than the other two methods, and the SPI index evaluated the drought condition in more detail than the CZI index. On the other hand, the Jazmourian watershed, especially in the eastern part of the province of Sistan-Baluchestan, has a relatively high sensitivity to the drought phenomenon, in which increases the frequency, continuity and severity of droughts to the eastern and southeast regions, and affect the inhabitants of these areas heavily by the consequences of the drought. Therefore, it is suggested that in macro planning and drought management plans for Jazmorian watershed, studying drought in different monthly and annual time scales and providing a suitable strategy for the management of drought in the highlands and plains areas separately.

The biological and mechanical measures adapted to crucial renewable resources (water, vegetation and soil) and sustaining agriculture and soil fertility. However, local inhabitants’ participation is the main challengeable issue, particularly maintaining the constructed watershed management projects. The objectives of this research were to evaluate the watershed management measures which constructed through joint project by Forests and Rangelands Organization (FRM) and Middle East and North Africa Regions Program for Integrated Development (MENARID), conducting in Razin, Kermanshah. Each constructed measure was checked and compared with its designed characteristics. Achieving the goal of joint MENARID project through each measure was strongly considered based on adaptation with climate change condition. The results explored that rare parts of induced measures were applied which included the pit-seeding, rain-fed orchard, concreted-stony dam, gabion and dyke. The pit-seeding contributed to increase in vegetation canopy, but there was subjected to early grazing and thus the young palatable species were found in weak phonological growth. The almond orchard is developed in closed rangeland to villages or rain-fed lands. Seedling was planted in a ditch which was excavated by heavy machine. At least 70% of seedlings were dried and no willing to support by local inhabitants due to supplemental irrigation cost and tangible benefit or incomes. Field survey also did not show any necessary requirements (sever erosion, landslide or flood hazard) for concreted-stony and gabion dams. This dyke evaluated the worst measure because of its effects on heavy soil disturbance. Overall, the evaluated measures were found unsuitable and did not overcome land degradation in Razin catchment. In addition, considering the achievement the goal of MENARID, adversely, the improper agricultural activities such as up-down the slope tillage practice, converting rangeland to rain-fed lands, overgrazing, over agricultural inputs (chemical fertilizers, pests and irrigation water) are going ahead.

Few researches about prioritization of sub-watersheds of watersheds were conducted to watershed management operations in the world. Cause of existing of professional intervention in traditional method of watershed management office, this research was carried out to prioritization of sub-watersheds via multi-criteria decision making techniques (TOPSIS and VIKOR method in 12 different weighting conditions) in Asyabjofeth watershed. Problems was divided to 5 criteria as soil erosion and deposition, drought, flood, aridity, socio-economic problem. According to results, first scenario (equal weight of sub criteria) of TOPSIS and VIKOR methods and traditional method conducted same in recognition of two most and least critical sub catchments and differed together in recognition of sub watersheds with moderate critical.

The global warming due to increased greenhouse gas concentrations, especially carbon dioxide has led to severe climate change over the past decade. This has aggravated the destruction of natural resources, soil and water, especially in arid and semi-arid regions. One of the most important tools for adaptation or correction of the consequences of this crisis is the carbon sequestration using natural ecosystems through efficient management tools such as watershed management. This research was carried out in order to evaluation and quantitative of carbon sequestration by checkdams and banquette in Kurdistan representative site of Gav-dareh. Sampling was done at work units using a systematic random sampling method. And then transferred to the laboratory for analysis. The results of statistical analysis of the data indicate that the amount of carbon dioxide in vegetation cover in treatments has a significant difference at 5% level. Also, the results of total carbon sequestration, including vegetation and soil, indicate that the amount of carbon sequestration has increased due to watershed mechanical management. This increase in banquette and gabion checkdam is significantly higher than the control and in comparison with it, they are in the class of superiority. The mean of total carbon sequestration is 7266 kg/ha which 65 % of that is related to soil sequestration. It is generally concluded that watershed operation, especially banquette as a mechanical operation has high potential in sequestration, and soil is the most important reservoir in this kinds of operations.

One of the most important factors, in a good management in any field, is having a proper perspective of the upcoming events. There is no exception in water resources management and the environment and awareness of the condition of water resources, in an area, plays a decisive role for planning water and agriculture. In this study, the Adaptive Neural Fuzzy Inference System (ANFIS) was used for the monthly forecast of Dalaki Basin groundwater levels in the province Bushehr in a 12-year period (2002-2013). In order to improve the results of the model, the wavelet transform was used and the original signal was decomposed to sub-signals. Then, sub-signals were entered, as input, into ANFIS model to obtain the hybrid model, Wavelet-Adaptive Neural Fuzzy Inference System (WANFIS). To forecast the groundwater level of five observed wells has been used, using groundwater levels, precipitation, evaporation, andtemperature. Results showed that hybrid model, WANFIS, has better performance than ANFIS model. Also, it was showed that hybrid model has better performance in estimate extreme points. So, this method, using wavelet theory, increased the performance by 14%. At the end, groundwater levels were estimated by the best model in a year. The results of thepredicted groundwater levels showed that theincrease of having access to groundwater in the Dalaki area. This problem is noted to authorities of the area regarding the effects on water resources and the environment of the area.

Debris flow, as a severe geological disaster, causes huge damages in the mountainous areas every year. The peak discharge of flood and the hydraulic roughness of flow are affected by sediment concentration of debris flow. Therefore, the estimation of sediment concentration based on physical characteristics of basin, sediment and precipitation are necessary. The aim of this study is proposing an empirical equation for the determination of the sediment concentration of the debris flows in the study area using the rainfall parameters, so that the weakness of applying the fixed value for the debris flow concentration proposed by previous researchers can be removed. For this purpose, the relations between each of the parameters including cumulative rainfall, antecedent rainfall and the total rainfall (sum of cumulative and antecedent rainfalls) parameters with sediment concentration of the debris flows were investigated by using of the rainfall and the debris flow density recorded in the international research station, Jiangjia Gully, for the period of 1999-2004 years. To derive the best equation, cross validation method was used and the relations error were determined by statistical indicators including coefficient of determination, R2, Mean Absolute Relative Error (MARE) and Nash-Sutcliffe Efficiency (NSE) coefficient. Results showed that higher correctness was obtained using the sum of cumulative rainfall and antecedent rainfall parameters (total rainfall) for determining of the sediment concentration of debris flow. The statistical indices of the proposed model (MARE=0. 06, R2=0. 86 and NSE=0. 84) represent the high ability of the proposed equation in the estimation of the sediment concentration of debris flows where the estimation error of the sediment concentration was reduced on average about 80% comparing to other researchers’ equations. The range of the relative density of sediment is between 1. 63-2. 23 gr/cm3. Moreover, the range for the cumulative rainfall and the antecedent rainfall are varied between 3. 36-75. 36 and 0. 772-92. 59 mm, respectively. After calibration of the proposed equation of this research, it can be used for estimation of the sediment concentration of debris flow in the other prone basins, which have the similar storms.

Atmosphere– ocean coupled global climate models (GCMs) are the main source to simulate the climate of the earth climate. The computational grid of the GCMs is coarse and so, they are unable to provide reliable information for hydrological modelling. To eliminate such limitations, the downscaling methods are used. The present study is focused on simulating the impact of climate change on the behavior of precipitation and temperature of Sirjan synoptic station in Kerman Province. At first, the capability of artificial neural network to downscaling of climate variables that predicted by CanESM2 is tested. Then, using the most appropriate models, the mean monthly temperature and precipitation amounts forecast for future periods under RCP 4. 5 scenario. Results of this study for monthly temperature downscaling indicated that the artificial neural network with 2 hidden layer, 8 neurons, with Tangent and Log sigmoid activation function was the best model, so that RMSE, NS and R2 were 0. 387, 0. 973 and 0. 917 respectively. Also, for precipitation variable, the structure with 2 hidden layer feed forward perceptron, 8 neurons, Tangent and Log sigmoid activation function and Levenberg-Marquardt algorithm had better performance, so that RMSE, NS and R2 were 2. 867, 0. 849 and 0. 924, respectively. Results indicate that until 2099, amount of monthly mean temperature under RCP 4. 5 emission scenario will be increased by 3 (˙ C) and the highest increase is predicted for August by 3. 9 (˙ C) and a lower increase in April by 1. 8 (˙ C). The results also showed considerable increase of precipitation for June to November and noticeable decrease for March and May months. However, no change occure in annaul scale (inter-annual).

Simulation of rainfall-runoff process for planning and management of water resources and watersheds requires the use of a conceptual optimized hydrological model. In this study, the hydroPSO package was employed to optimize KINEROS2 (K2) parameters applied in the Tamar watershed, Iran. Four storm events were utilized in hydrograph simulation. Results indicated better efficiency of K2 based on the event #2 with the coefficient of determination and Nash-Sutcliffe Efficiency (NSE) of 0. 9084 and 0. 92, respectively. The events #3 and #4 with NSE of 0. 89 and 0. 86 showed the excellent and very good fitness of simulated flow compared to observed flow, respectively. Sensitivity analysis established that the parameters Ks_p, Ks_c, n_p, n_c, CV_p and Sat were the most effective parameters in K2 calibration, respectively. The posterior distributions of some parameters such as Ks_p and n_c appeared to be more sharply peaked than other parameters which established less uncertainty in hydrological modeling. Visual inspection of boxplots showed that for 6 out of 16 parameters (Ks_c, n_c, G_c, Rock, Dist_c and Smax) the optimum value found during the optimization coincided with the median of all the sampled values confirming that most of the particles converged into a small region of the solution space. Dotty plots showed that the optimum values found for Ks_p, Ks_c, and n_c define a narrow range of the parameter space with high model performance. On the other hand, the model performance was more impacted by the interaction of Ks and n parameters. The parameters CV_p and n_p showed a wider range of the optimized levels. Correlation analysis revealed that the highest linear correlation between the NSE and K2 parameters was obtained for the Ks _p, Ks_c and n_p, followed by CV_p, G_c, Por_p, Dist_p and Smax. Conclusively, this work demonstrated the capability of hydroPSO in optimization of the K2 as an event-based hydrological model.

In recent decades, satellite and model-based precipitation products has attracted attention of the scientists and researchers in hydrology and other disciplines. The purpose of this research is quantitative comparison of MERRA and GPM satellite precipitation products with ground station precipitation values as reference data in Kashafrud basin. The important point about these data is their accuracy and resolution. GPM satellite was launched in February 2014 and up to now there are no studies about these data in Iran and a few studies have been carried out in other countries of the world. This research aims to assess GPM satellite data and MERRA precipitation products in comparison with 34 ground station precipitation data as reference. The assessment carried out in basin and station level and also daily and monthly periods. For validation, the statistical metrics including RMSE, MAE, CC, BIAS, FAR, POD and CSI are used. The results showed that HQ daily precipitation of GPM satellite has higher accuracy than MERRA daily data in this basin and It is in good agreement with ground data and can show the spatial and temporal distribution of rainfall at the basin level. In contrary, MERRA data in monthly temporal scale is superior to GPM data. RMSE values for MERRA in daily basin and station scales are 2. 84 and 3. 6 and for monthly, 2. 8 and 1. 74, respectively. For HQ data, RMSE values for daily basin and station scales are 1. 78 and 1. 72 and for monthly scale, 6. 07 and 6. 46, respectively.

Doroodzan Dam basin is one of the strategic agricultural areas in Fars province. Changes in time series of climatic parameters, especially temperature and precipitation parameters, can have a significant impact on the growth cycle of agricultural products, the status of water resources and the economy of the populations in the area. In the present study, rainfall and temperature trends were investigated in 13 rainfall stations and 4 thermometric stations. Initially, the change point pettitt test was used to assess the homogeneity and change point in the time series. Then, trends in the rainfall, average monthly and annual temperature time series were investigated using Mann-Kendall (MK), modified Mann-Kendall (MMK) and the sen’ s slope methods. The results of the pettitt test showed that there is no significant change point in the monthly and annual rainfall and monthly temperature time series, only in the annual temperature of two stations, there is a significant change point at the 95% confidence level. The results of the trend analysis with all three abovementioned methods showed that the monthly precipitation in all stations and annual precipitation in most stations have a decreasing trend and the average monthly and annual temperature have an increasing trend; however, in none of the time series, the trend was significant in the confidence level of 95%. Based on the results, in the whole area, the mean annual temperature increased by 3. 7 percent and precipitation decreased by 9. 1 percent compared to the mean.

Due to the rapid urbanization in Iran, different issues have emerged in its major cities such as street flooding, dispersal of dangerous chemicals and flood-related risks due to the lack of a proper drainage network and disorders in the flood channels; and, Mashhad is not exempt from these issues. The aim of this study is to determine the most important parameters affecting (area of impermeable surfaces has the highest impact, followed by the Manning roughness coefficient in permeable areas, the equivalent width, slope, height of storage in impermeable areas, and impermeable areas without surface storage) runoff generation in District 9 of Mashhad city and to evaluate the performance of the SWMM model. The SWMM model was used to simulate runoff and identify critical areas. In order to determine the duration of the design storm, time of concentration was measured and used as a surrogate. Sensitivity analysis showed that among the eight considered parameters, area of impermeable surfaces has the highest impact, followed by the Manning roughness coefficient in permeable areas, the equivalent width, slope, height of storage in impermeable areas, and impermeable areas without surface storage. The model calibration results showed a good agreement between the simulations of the four incidents with a NS higher than 0. 5. The obtained NS confirms the SWMM model’ s performance and accuracy. The RMSE was measured 0. 6, 0. 65, 0. 58 and 0. 91 respectively for the incidents dated 2015/1/10, 2015/2/15, 2015/2/21 and 2015/3/6, as well as 0. 61 and 1. 14 for the incidents dated 1394/1/7 and 1394/1/11, which suggest an acceptable model performance. Given the results, there is a good agreement between the simulated and actual runoff volumes which is an indication of the good performance of the SWMM model in predicting urban flood and the model could then be used in urban flood management projects and drainage network planning in Mashhad.

Base-flow can be considered as a part of stream-flow which is connected to discharged flow from aquifers. Due to high variations in rainfall values and other hydrological components in arid and semi-arid areas, watershed scale simulation of the flow is very important. Hydrological models are generally employed as an appropriate tool in this field of study. This work makes uses of use a distributed model called WetSpass-M to investigate water balance and estimate the total flow for Vanak watershed. This model was developed with the aim of simulating water balance in arid and semi-arid regions. Since the model focuses on spatial variations of hydrological components for a desired watershed, all inputs of this model are in the form of raster maps with an ascii format. The temporal resolution for grid inputs was set on a monthly scale except the number of rainy days. The performance of the model for total flow was measured with the Nash-Sutcliff coefficient, the values of 0. 57 and 0. 56 in both calibration and validation periods were obtained respectively. It also shows an efficiency coefficient of 0. 52 for simulated baseflow in both periods. Thus the model result could be regard as an acceptable accuracy for both total flow and base-flow for Vanak watershed. Also a visual comparison of observed and simulated hydrograph demonstrated that model performed better in the case of total flow than filtered base-flow. The other conclusion that we draw from this visualization was low flows simulation were simulated slightly better than high flows. Additionally the results of simulation of water balance components in the study area has demonstrated that the highest portion of annual rainfall in water balance components of the watershed belongs to groundwater recharge.

Evapotranspiration is one of the most important components of the hydrological circle and its proper determination is highly important in most researches such as water hydrological balance, design and management of irrigation systems, simulation of crop production and design and management of water resources. Nonlinear characteristic, uncertainty and needing for different climatological data in simulating evapotranspiration are the reasons that motivate researchers to investigate data mining methods such as M5 model trees and support vector regression. In the present study, the precision of mentioned methods in estimation of reference crop evapotranspiration in comparison with empirical methods such as Hargrivs and Torrent white equations was studied. For that purpose, using meteorological dataset of 1371-1394 years of Tabriz synoptic station, the daily values of reference crop evapotranspiration were computed by FAO-Penman-Monteith method. Then, using these computed values as target outputs, 17 various scenarios combining at last one to up to six meteorological parameters have been considered using mentioned methods. Finally, the capability of support vector regression and M5 model trees for estimation of evapotranspiration was analyzed using test data set. Results of statistical analysis and Taylor diagram showed that support vector regression and M5 model trees in a case of considering all meteorological parameters with root mean square of 0. 398 and 0. 44, respectively, provided precise results comparing with empirical methods such as Hargrivs and Torrent white.

Area-volume-elevation (AVE) curves of dams pose one of the most important tools for water resource planning and reservoir management. Area reduction method is one of the methods which was developed to modify these curves after reservoir sedimentation based on the conditions, statistics and information recorded in reservoirs abroad; however the application of the same methods for domestic dams without optimizing the coefficients is not bug-free, and associates sometimes with a great deal of error. This study aims to calibrate automatically the area reduction method based on its three influencing parameters using two genetic and particle swarm optimization algorithms and to compare the results with reservoir hydrographic studies. So that, predicting the trend of reservoir sedimentation in area reduction method with its hydrographic results at the end of the period has the least difference. Results indicate the excellence of particle swarm algorithm in calibrating the area reduction method. In this algorithm, by selecting the initial population of 50, the time of convergence and the value of the objective function (RMSE of the predicted volume-elevation curve and actual reservoir) in the last iteration was 4. 7 minutes and 7 MCM, which it represents 92. 6% and 48% improvement, respectively compared with the genetic algorithm. Finally, the optimal values of area reduction method parameters were used to match further the estimated and actual volume values of Dez dam reservoir. The results revealed that the predicted error value is less than 1%, which is assessed negligible according to the volume of the reservoir. Accordingly, the developed model can be employed without any change in the optimal parameters of the area reduction method by entering information of reservoir’ s new hydrography to predict the sedimentation trend during the coming years. This will be very helpful in light of the importance of knowing the reservoir’ s useful volume variation in the coming years and its role in future water resource planning.

Selecting appropriate inputs for intelligent models is important due to reduce costs and save time and increase accuracy and efficiency of models. The purpose of this study is using Shannon entropy to select the optimum combination of input variables in time series modeling. Monthly time series of precipitation, temperature and radiation in the period of 1982-2010 was used from Tabriz synoptic station. Precipitation, temperature and radiation parameters with different delays are considered as input to the Shannon entropy. The results showed that time series with three delays provide the better results for the modeling. Applying Bayesian network and multivariate linear regression analysis were performed. Models performance was evaluated using three criteria: coefficient of determination (R2), root mean square error (RMSE), and the dispersion. Index (SI). The results indicated that Bayesian neural network model shows the best performance to simulate time series of precipitation, temperature and radiation in compare to multivariate linear regression analysis. The results showed that Shannon entropy has better performance in selection of the appropriate entry into intelligent models.

Hydrological drought usually have a considerable impact on the quantity and quality of water resources, causing water shortages in consumption sector and its study is important in terms of intensity, frequency and spatial extent. The aim of this study is to determine the periods of hydrological droughts, drought characteristics and amount of flow deficit in a 38-year recorded data over hydrometric stations of the Golestan Province. The weighting factor of drought importance for each month was calculated based on the flow distribution in different months. The subtracted mean discharge of particular month from the long-term average was multiplied by the weighting factor to determine the effective discharge. The difference of mean monthly discharge and effective discharge considered as monthly flow deficit. The onset and end of hydrological drought periods were defined and the cumulated flow deficits were calculated. The results showed that the highest flow deficit amounts were recognized for in Taghiabad, Basirabad, and Hajighoshan stations with 1. 28, 1. 27, and 1. 07 respectively. In general, the flow deficit were observed in June, July and August in almost all river gauge stations of the study area. Moreover, the deficit volume was lower at stations located in upland areas.

Landslide is one of the destructive phenomena of natural resources and threatens the sustainability of forest roads. The purpose of this study is to determine the zoning of land risk for the stability of forest roads using the process of analyzing the hierarchy in the GIS. The final zoning map of landslide hazard based on the combination of different maps and main criteria of topography, vegetation, geology, soil, waterways and roads, in 5 classes of landslide danger (40. 19%), high risk (30. 99%), medium risk (22. 5%), low risk (6. 29%) and very low risk (0. 03%). According to the results, the maximum area of landslide in terms of slope (15-30%), in terms of direction (north direction), in terms of soil depth (100-110 cm depth), soil permeability (with good permeability), in terms of The geology (jmz and ql formations), in terms of distance from the fault (0-200 m), is recorded in terms of distance from the road (0-100 m) in terms of distance from the road (0-100 m). Which is based on the AHP process of accuracy.

Landslide is one of the most common natural disasters that endanger the lives and properties of people in mountainous areas. Therefore, identification of risk exposure areas of landslide is essential to prevent and reduce damages by landslides. The purpose of this study is compared to logistic regression (LR) and generalized additive models (GAM) and the evaluation of their performance for landslide susceptibility mapping in the Chahardangeh Watershed, Mazandaran Province. At the first, landslide locations were identified by Google Earth images and extensive field survey. Then, the landslide inventory map was randomly divided as training data 70% for modeling and the remaining 30% was applied for the model validation. The landslide conditioning factors including topographic, hydrologic, geology and human factors were constructed in GIS. Finally, the receiver operating characteristic (ROC) Curve was used for the model validation. The validation of results showed that the area under the ROC curve for LR and GAM models were 81. 2% and 82. 4%, respectively. So, both of the models are suitable and efficient methods for landslide susceptibility mapping in the study area. Although, the obtained results showed that the GAM model performed is slightly better than the LR model for determining regions of susceptible to occurrence of landslide in the study area.

One of the goals of geomorphologists in working with the models of different landforms is to obtain better relations in realizing the physical realities of environment. In this study, to evaluate the performance of geomorphometric parameters to increase accuracy of zoning landslide susceptibility map has been studied. As the first step by the application of nine initial conditioning factors including slope, aspect, elevation, land use, lithology, distance from roads, rivers and vegetation index (NDVI) the zoning map was provided. In the next step geomorphometric parameters influential on the occurrence of landslide including topographic location index (TPI), surface curvature, curved sections, slope length (LS), Topographic wetness index (TWI), stream flow power (SPI), surface area ration index (SAR), was added to the model and then the zoning map was obtained. In the final step, the zoning maps was evaluated by using ROC curve. To provide zoning maps a new mixed model was applied, so, for determination of criteria weights multivariate regression and to determine weight of the classes' frequency ratio method was utilized. The findings of this research indicated that geomorphometric factors have a considerable influence on the increase of identification of regions that are susceptible to the landslides and enhance the accuracy of zoning maps from 0. 731 to 0. 938. These factors have also increased the resolution of the slip classes. According to the results, topography position index, plan curvature and surface area ratio have the highest influence on the accuracy of zoning maps. Based on superior approach, 8. 68% (6737 ha) of the region are at very high risk and 15. 3% (11906 ha) have been identified as high risk areas. According to the high ability of geomorphologic parameters in the identification of susceptible areas to the landslide, the application of these parameters is recommended in landslide hazard zonation.

Empirical models are one of the appropriate tools to estimate the rate of erosion and sediment yield in ungauged basins. Generally, empirical models might be evaluated before any application in other regions. For assessment of MPSIAC and EPM models in Kerman province, 10 small dams were chosen with rangeland use in their upstream. The age of all selected dams were 12 years without any overflow, and all the sediments had been trapped. The deposited materials were determined using reservoir survey and measurement of sediment depth and density. Observed average annual sediment yield (AASY) for each basin was then obtained by considering the age of dams. Results showed that the minimum, maximum and mean AASY for study catchments were 0. 6, 16. 9 and 6. 3 ton/ha/yr respectively. In the next step, AASYs of those watersheds were estimated by surveying and mapping models' factors. The minimum, maximum and mean AASY for the MPSIAC and EPM models were 3. 7, 5. 7, … and 1. 1, 19. 8, … ton/ha/year correspondingly. Comparison between observed and estimated sediment means were performed by t-student test. The results demonstrated that there were no significant differences between estimated and observed sediment yield at 95% confidence level. However, the MPSIAC is more accurate due to the smaller relative mean square error (0. 09) compared to the EPM (0. 18).

Evaporation is one of the important and effective processes in water cycle that causes loss of large volumes of lake and Abbandan’ s water wich its measurement has great importance in water resources planning. In this research, the amount of evaporation in six months (from April to September in 2016) from the free surface of Dazmirkande Abbandan located at Mazandaran Province has been measured by floating evaporation pan and amount of evaporation has been calculated by Meyer, USBR, Shahthin, Hefner, Penman, Marciano and Ivanov formulas with Dashte-e-Naz station data compared. It was found that by using statistical criteria Ivanov methods, USBR and shahthin provides high precision for determining evaporation from the free surface in this region. Root mean square errors values, deviation and t-test results in the best models are 1/36, 0/18 and 0/95, respectively. Due to the accuracy of Ivanov formula for humid areas, the amounts of daily evaporation have been calculated based on Ivanov formula. For determination of pan coefficient, According to Ivanov formula for this region, amounts of daily evaporation measured with 0/6, 0/7, 0/75, 0/8, 0/85 and 0/9 coefficient. Due to the statistical results, the coefficient of 0/75 with minimum amount of RMSE and MBE and t, has been chosen as the best coefficient for floating evaporation pan. The results indicate a 18 percent of the total volum of Abbandan reservoir evaporation in the study period. Minimizing of evaporation it could be used a wind break, floating objects or proper planning of water resources change to minimize the amount.

Classification of water quality is the most important step for controlling the pollution of water. The aim of this study was classifying the water quality of the Talar River basin by analyzing the existing data of six monitoring stations. Multivariate statistical techniques such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) were used to assessment the spatial variations of water quality in the Talar River. The quality data which were gathered from the 2011 to 2014 were included 14 different chemical parameters. . Quality testing stations were included the Pol Sefid, Pol Shahpor, Kiakola, Kari Kola, Savadkuh, Shirgah and Paland Rodbar. The results of cluster analysis of water quality stations were divided into three groups. The first group includes stations Paland Rodbar, Kari Kola, Savadkuh and Pol Shapur, the second group includes stations Shirgah and Kiakola, and the third group was Pol Sefid station. The results of PC and FA showed that the 80 percent of the variations of water quality were done by three parameters; the first parameters are TDS, EC, Cl, So4, Ca, Mg, Na, K, SAR and TH. The second factors are the Po4 and No3, and the third factor is HCo3-. The results showed that the major factors which are polluted the water quality of the Tlar River are related to inflow of industrial, domestic, hospital and agricultural effluents into the Talar River.

Determining the sediment delivery rate and watershed delineation is among the first stage in environmental research, especially in water erosion estimation. The determining the accurate boundary of watershed is important for the hydrological and morphologic characteristics of watersheds. The study aims to present an automatic extraction model of watershed delineation and calculate sediment delivery rate. The traditional method of determining the watershed delineation, and subsequently, calculating sediment delivery rate is performed manually that using a topographic map, the boundary of the watershed is determined and main flow length is calculated. However, nowadays due to the advancement of digital analytical spatial-based methods in GIS software, automatic delineation of watershed is feasible. For this purpose, the errors of digital elevation model are removed. After calculating the flow direction, and flow accumulation, watershed boundaries can be determined having pourpoints. Then, sediment delivery rate is calculated by local height of watershed, its perimeter, and flow length. In this research, the digital elevation models of ASTER-DEM and SRTM-DEM are utilized to design watershed delineation model and to evaluate the overall accuracy and the correspondence of them, so the border of watershed is delineated using Google map, which is used as a ground truth. The findings of this study show the automatic extraction of watershed boundary and calculation of sediment delivery rate do not have significant differences with traditional mothed. So that, the overall accuracy and Kappa index of the watershed boundary based on ASTER-DEM are 93 percent and 0. 92, respectively, and their values for the watershed boundary based on SRTM-DEM are 94. 3 percent and 0. 94, respectively. The correlation coefficient (r2) of calculated sediment delivery rate based on SRTM-DEM is 0. 98 and its value based on ASTER-DEM is 0. 95.

Climate change is one of the most important challenges that influenced different parts of human life on the earth. Evaluation of climate change phenomenon and its possible outcomes on hydrological processes can decrese the challenges of managers and planners of water resources in the next period. The main aim of this study is to evaluate the effect of climate change on sediment output of Sharekord watershed basin (2046-2065 period). In this research, SWAT distribution model was used to study the impact of climate change on Shahrekord watershed basin hydrology. SWAT was calibrated and accreditated for the base period of 2002 to 2010. In order to evaluate the effects of climate change and global warming on the basin sediment during 2046-2065, HadCM3 climate model data was subscaled under scenario of A1B, A2 and B1 using LARS. In continuation, downscaled climate data were introdued to SWAT model and sediment changes in the next period were estimated. Results show that climate and hydrology variables of Shahrekord basin will have remarkable changes in the next period. Comparison between observed and simulated climate parameters in 2046-2065 period show that the temprature of region will increase in different months between 1. 1 to 2. 6 º C compared to the baseline. Precipitation will have also a change between-30 up to 18 precent, except during the months of low rainfall. It can affect the total quantity of available water, the peak and extreme events. Results of simulations also show the sediment variations between 500 and-90 percent.

The quality of coastal water resources at the downstream of watersheds is decreasing mainly due to different anthropogenic activities. In this research, temporal variations of groundwater quality were investigated in a 25000 ha coastal area in Mazandaran province. Groundwater samples were taken from 37 observation wells in the region before (winter 2009), during (spring 2010) and after (Summer 2010) a rice growing season. The samples were analyzed to determine electrical conductivity, acidity and the concentration of calcium, sodium, magnesium, potassium, bicarbonate, chloride and sulphate. Groundwater quality was assessed for agriculture, industry and drinking uses based on various criteria and standards. The average electrical conductivity of groundwater in winter, spring and summer was 5806, 3524 and 8656 μ s cm-1, respectively, indicating increase in salinization risk of groundwater at the end of rice growing season. From alkalinity viewpoint, approximately 60% of the groundwater samples were in C4-S1, C4-S2, C4-S3 and C4-S4. Chloride concentration in groundwater was more than its permitted limit for industrial use. The groundwater quality of the 2010 summer season was the worst from drinking viewpoint compared with the samples collected in the 2009 winter and 2010 spring. Results showed that rice cultivation had considerable effect on groundwater quality of the study coastal area. Therefore, the proper management of water and nutrient during rice growing season can be effective to protect groundwater quality in the study area.