kamran chapi

Water infiltration into soil profile is one of the most important components of the water cycle, playing an essential role in determining the hydrological behavior of soil, the amount of runoff generation in a watershed, and the amount of soil moisture for the growth of vegetation and crops. Its variability is influenced by many factors such as precipitation characteristics, soil, vegetation type, land use, land slope and season of the year, which must be determined in each region because these characteristics are highly site-specific. Due to the lack of information on the variability of water infiltration into a soil on different parts of a slope, the aim of this research is to investigate the variability of infiltration in different parts of a slope in different seasons of the year.

In this research, a slope with a length of approximately 60 meters was selected inside the University of Kurdistan campus. Three points on this slope, including the upslope point with 0% slope, the middle slope point with 22% slope, and the downslope point with 28% slope, were considered for the establishment of double rings. The soil texture was clay and clay loam and the type of vegetation was sparse raneglands. Infiltration measurement at these three points was repeated three times in the three seasons of Fall, Winter and Spring with time intervals of one month. During the experiment, the initial soil moisture and the saturated moisture contents of the soil were measured by the gravimetric method. The collected data were analyzed in a randomized complete block design.

The results showed that different slopes had no effect on the intensity of initial infiltration, the average intensity of infiltration and the intensity of final infiltration. The results also demonstrated that the initial ifiltration rate and the average infiltration rate in Fall were significantly different from those of Winter and Spring, while the final infiltration rate was the same in all seasons and its value was not affected by seasonal changes. Further investigations indicated that there is no significant relationship between initial infiltration intensity, average infiltration intensity and final infiltration intensity at three points of the slope and in three seasons of the year with initial soil moisture and soil saturation, which implies that the difference of infiltration intensities in different seasons cannot be justified with changes in initial moisture and soil saturation.

It can be suggested that infiltration at any point is completely dependent on the characteristics of that point and it is not possible to generalize a specific behavior pattern of this component to other points. Therefore, it is necessary to measure the infiltration at each point and even during modeling, the natural characteristics of that point must be fully understood.

Determination of the role of each lithological unit in sediment production is an important priority of watershed management. The geochemical method provides the means to determine the origin of sediments in the Gheshlagh Dam, Sanandaj. Various lithological units were studied as sources of sediment deposited behind sediment-retaining structures. A total of 33 samples of sediment and sediment sources were collected and examined in the laboratory after sieving through a 62-micron sieve and identifying by instrument an ICP Initially, 9 sources of potassium, calcium, phosphorus, carbon, magnesium, chromium, sodium, cobalt, and nitrogen were used to determine the optimal composition of the tracers. Using the 2-stage analysis of variance and detection function, 3 elements, namely: cobalt, chromium, and calcium were selected as suitable tracers and entered the equation stepwise to determine the sources of sediment. The results indicated that the andesite lithological units with interlayers of shale (An / Sh) and microfossilized limestone (Lim) with 56.91 and 30.54%, respectively, had the highest share in the sediment production in the study area.

Flooding is one of the most dangerous and common natural disasters, which yearly causes many damages and causalities for communities and the environment. A comprehensive flooding assessment and management is essential to reduce the effects of flooding on people's lives and livelihoods. Due to an increase in flooding in recent years, locating and identifying flood-prone areas is very important to predict this environmental disaster as mis-identification of flood-prone areas in a watershed may have devastating effects. The main purpose of this study was to evaluate the performance of the Bayesian Logistic Regression (BLR) and the weight-of-evidence (WOE) models for preparing flooding susceptibility maps in the Talvar Watershed of Province of Kurdistan. The geographical location of 93 flood prone areas identified in the watershed was randomly divided into one group (70%) for calibration and another (30%) for validation. Both models consider ten effective factors in causing flooding, namely: slope, slope direction, curvature, digital elevation model, distance from stream, topographic wetness index (TWI), stream power index (SPI), rainfall amount, geology, and land use. According to the WOE model, about 35.8% of the area is placed in the medium to very high hazard class, and based on the Bayesian logistic regression model, about 45.08% of the area is placed in the medium to very high hazard class. The relative performance detection curve was used to validate the flooding potential maps. Even though both models offered sufficient accuracy, the higher accuracy was assigned to the BLR model (93.4%). Therefore, the BLR model has better performance than the WOE model in terms of the flooding risk potential.

The importance of snow and its water equivalent in water resources supply has caused many studies and researches to measure snow characteristics and runoff. Conducted in the Saqqez  Watershed, this research attempted to estimate snow–induced runoff in a mountainous area and the SRM Model was selected to simulate daily runoff from snow-melt. Based on the data and variables for four consecutive years of 2006 to 2009 collected and snowmelt runoff was estimated. MODIS satellite images were used to calculate the snow coverage area. After segregating the snow coverage from the images, the daily snow area was calculated using GIS, and along with the other variables, imported into the model. For better evaluation of efficiency of the model, the model was calibrated and validated. The process of calibration was led to the best estimate for each parameter. To evaluate the accuracy of model and comparing results with field data Nash-Sutcliffe coefficient and the percentage difference were used. The results of the Nash-Sutcliffe coefficient were between 0.90 to 0.94 and the differences in the volume were 6.8 to 7.2 percent, which indicates the high-performance of modeling.

The flood is the second natural disaster in terms of the damage that it causes to human societies every year. At the same time, cities and population centers are exposed to the highest risk and potential for physical damage caused by flood events. The growing trend of floods in Iran in recent years indicates that most parts of the country are at risk. According to the conducted studies, about forty large and small floods occur annually in different parts of the country. Since the flood is the most devastating disaster in the world and a serious threat to life, preparation of flood hazard maps is essential in identification of flood-sensitive areas, and is one of the first steps taken to reduce damage. It should be noted that researchers can easily identify areas with high risk using flood hazard maps to prevent damage. In the modeling of research on natural and environmental hazards such as floods, the complexity of natural systems makes it difficult to use physical models, highlighting the use of hybrid models as a suitable alternative. Therefore, it is necessary to provide appropriate methods and suggestions for estimation of runoff and flood in areas with high risk in order to prevent their occurrence. In recent years, a large number of statistical and probabilistic models have been used for flood hazard mapping, and GIS has been used as a basic analysis tool for spatial management and data manipulation due to its capability of managing large amounts of spatial data. Furthermore, it is possible using GIS prediction models to partition urban areas in terms of flood hazard. The obtained hazard maps can be used for identification of areas with high flood hazard. Today, the accuracy of flood susceptibility maps in urban areas can be increased using hybrid GIS models rather than single ones. Therefore, the purpose of this study was to predict the risk of flood in the city of Sanandaj, Kurdistan Province, Iran using hybrid models in the GIS environment. The factors considered in this study as influencing the occurrence of flood included inclination, slope, elevation, distance from the river, river density, flow accumulation, land use, gradient curvature, lithology, distance from the passage, road density, distance from the building, building density, and rainfall. After collecting the required data and layers, we used two algorithms, including the definitive statistical index (SI) and the evidential belief function (EBF), both in isolation and in combination. The locations of flood susceptibility in Sanandaj were predicted. In order to assess the performance of the model, the AUC curve obtained from the ROC curve was used. According to the adopted assessment criteria (ROC) and the validation data, the EBF model (0.840) exhibited better performance than the SI model (0.827) in flood hazard mapping in the area under investigation. The accuracy rate of the hybrid SI-EBF model was 0.849 based on the ROC results, which demonstrated that the hybrid model performed better in prediction of the spatial hazard of flood than the single models. Finally, the results of the study showed that Sanandaj flood spurts resulted from various environmental and human factors, which can be handled using flood hazard maps and appropriate management measures to reduce flood damage.

Varied flows such as flooding resulting from dam break can happen due to earthquakes, seepage, bombing, overtopping, mistake in design and project performance as well as causing financial damages and human losses. Unsteady flow during flooding events has a high impact on flow field pattern, sediment motion, ecology and also, distribution of contamination in the river. In this study, the impact of various slopes, sediment diameters and unsteady flows with different discharges on sediment transport were carried out at the flume with 12 m length, 0.5 m width and height. In this research, two type of uniform sediment particle with 10 and 15 mm in diameter were spread over the bed of above mentioned flume with slope of 0.01 and 0.02. To build a dam physical model, a mechanical lift-gate was used at 2 m from the upstream inlet of the flume. Potential hydrostatic initial conditions of the fluvial experimental model resulted from dam-break was based on using three water depths of 35, 20 and 12 cm at the gate upstream and one water depth of 3cm at the downstream of the gate. By considering the above mentioned procedures, fluvial flows were produced and have been used with discharge between 0.07 to 0.66 m3/s. The result showed that by increasing the sediment particle size 1.35 times the sediment transport have been reduced by 1/4 for 35 cm upstream water depth and also by increasing the slope by 2 times from 0.01 to 0.023 for sediment with 15 mm and with initial upstream water depth condition was 35, 20 and 12 cm the sediment transport have been increased 3.42, 1.83 and 3.38 times respectively. The above mentioned points show the significance of fluvial flows and their effects on sediment transport and therefore the morphological changes of rivers. Result has been shown that high sediment transport rate and bed evolution was resulting from positive wave head and after that, sediment transport rate have been intensity decreased.

Nowadays, water is considered as one of the factors for improvement and economic growth of societies. The hot and cold seasons of the year and its comparison with the non-segregation of the hot and cold seasons are 26 years.

In this study, after studying the accuracy of discharge data, electrical conductivity, soluble matter, chlorine, calcium, sodium, magnesium, acidity and sodium uptake ratio of the two stations, it was tried to find appropriate regression relationships between discharge parameter and qualitative parameters. Provide water.

The results showed that the Chehelgazi sub-watershed in cold months, the liner relationship between discharge with electrical conductivity, dissolved solids, chloride and sodium and also the exponential relationship was suitable between discharging with calcium. In warm months for this sub-watershed, the linear relationship presented the suitable relationship between discharge with dissolved solids, electrical conductivity, magnesium, calcium, sodium and the sodium adsorption. Also, the linear relationship was suitable between discharge with dissolved solids, electrical conductivity, chloride and sodium whereas the exponential relationship was better between the discharging with calcium in Khalifehtarkhan sub-watershed. In this sub-watershed, for warm months, the linear relationship was suitable for the relationship between discharge with dissolved solids, electrical conductivity, chloride, sodium and sodium adsorption. The separation comparison of cold and warm months with non-separation showed that non-separation of cold and warm months is not possible the chloride and magnesium estimation (in Chehelgazi sub-watershed) and calcium estimation (in Khalifetarkhan sub-watershed).

using obtained relationships and seasonal changes of surface waters quality, with discharge rate can create modeling water quality, extraction and recovery of water quality data.

In this study, the seasonal variations of discharge and concentrations of soluble nitrite, nitrate, ammonium, orthophosphate and potassium and also the concentrations of these substances during baseflow and rainfall runoff in the seasonal and perennial rivers entering the Zarivar Lake were investigated. The results showed a great variation of discharge in the spring than the other months because of frequent rainfalls and the maximum discharge in this season. Despite the differences of the stream lake behavior and also the different nature of nutrients, generally, nutrients concentrations have different seasonally significant so that the maximum concentrations and the maximum concentration variations of soluble nitrite, nitrate, orthophosphate and ammonium were seen in summer. The results of the inter and intra storm variability analysis of nutrient concentrations indicated a significant differences between nitrite and nitrate during and between storm events with sig=0 and sig

  Geomorphological hazards such as mass movements are one of the potentially harmful phenomena. Landslides as one of the most traditional earth movements involved all slope failures in which a mass of materials (soil and rock) moves down the slope due to decreasing of safety factor and overcoming of the destructive forces on the resistance forces on a slope.
Kurdistan province has been frequently exposed to mass movement hazards owing to its characteristics of topography, geomorphology, pedology, geology and climatology. Therefore, the objective of this study is 1) to prepare the spatial prediction map of mass movements for the Kurdistan province to manage the areas prone to the hazards and to use the map in the land use planning projects and development of the rural regions 2) to introduce the Bayesian logistic regression (BLR) ensemble and to compare its efficiency with the logistic regression (LR), and 3) to identify the most significant conditioning factors on mass movements occurrence in the Kurdistan province. Based on the literature review, 18 factors affecting landslide susceptibility were selected for modeling including slope degree, slope aspect, elevation above sea, curvature, profile curvature, plan curvature, stream power index, topographic wetness index, length-angle of slope, lithology, rainfall, land use, distance to fault, fault density, distance to stream, stream density, distance to road, and road density. A total of 895 mass movements in the Kurdistan province were divided into 70 % (626 locations) for modelling using training dataset and 30% (269 locations) to evaluate based on the validation dataset. Additionally, 626 locations were randomly selected as areas where mass movements have not occurred and they were classified into 70% and 30% for training and validation in modelling process. Based on the Information Gain Ratio index to modelling process, 7 factors were then applied to the LR method and 11 factors were used in the BLR method. The evaluation of models were performed using Specificity, Sensitivity, Accuracy, the area under the Receiver Operating Characteristic Curve (AUROC), and the Root Mean Square of Errors (RMSE) indices.
Result and According to the results of spatial prediction of mass movements by quantile approach, about 8.06% of the Kurdistan province area is very sensitive to mass movement occurrence using the LR model; while, the BLR model shows a sensitive area of about 12.46%.
The majority of very sensitive and sensitive areas are located to the west of the province. These regions include Oramanat mountainous areas, Kosalan and Chelchama highlands, the Salvatabad Saddle (East of Sanandaj), the Morvarid Saddle (between Sanandaj and Kamyaran), The Khan Saddle (between Saghez and Baneh), and the Arez Saddle (between Sanandaj and Marivan).
In order to assess the spatial map accuracy of mass movement sensitive areas prediction, both training and validating datasets were used. The results showed that the area under the curve of SRC is 0.714 in LR based on training data, indicating that this approach has a potential of 71.4 percent to predict sensitive areas to mass movement. BLR showed a value of 0.672 using the same data which implies a potential of 67.2% for prediction of sensitive areas.
Based on the validating dataset, the area under the curve of PRC was 0.732 and 0.729 for LR and BLR, respectively. These results confirmed the accuracy of the maps prepared by both models; however, it should be noted that LR was slightly providing better results.
To analyze the probability of mass movement occurrence, the potential of the LR and BLR models were evaluated using Friedman test at 5% significant level. The test revealed that there is no significant difference between these two models and both are reasonably able to evaluate spatial prediction of mass movements. The results showed that there is no significant difference (95% level of significance) between the efficiency and the susceptibility maps prepared by the two models for spatial predicting of landslide in the study area; therefore, the BLR model can be applied as an index model for the study area and other similar areas.
Therefore the hybrid model of Bayesian Logistic Regression has a high capability of identifying sensitive areas to mass movement such that it can be compared with previously successfully testes methods such as artificial neural network, fuzzy logic, decision tree algorithms (Naïve, Random Forest, Baes Forest, …)

The present study was conducted to calculate the amount of erosion in monthly, quarterly, six-month and one-year time scales. Three erosion pin networks in three aspects of north, northwest and eastern slopes in control subwatershed of Khamsan representative watershed were selected in order to measure erosion during the period of October 2015 to September 2016. Due to the monthly records of erosion pins, the possibility of erosion analyzing in the intervals of less than one year (monthly, quarterly, and sixth) was used to determine the contribution of these periods in annual soil erosion. Therefore, considering pin records, the average changes in the height of the soil and, finally, the average amount of annual erosion was calculated about 17.22 t ha-1. Also, the participation of monthly, quarterly, and six month intervals in annual erosion was calculated to be 5.81, 7.33 and 9.52 t ha-1, respectively which are about 34, 43 and 55% of annual erosion. Despite significant differences in the average amount of erosion in different time periods and their participation in annual erosion, the results of one-way ANOVA showed that the effect of time scale on soil erosion is not significant.

More than 30 percent of the Earth is covered by seasonal snow and about 10 percent of it by permanent glaciers. Approximately about 5 percent of global precipitation is snow. This amount to about 50 to 95 percent is in polar areas. Spatial-temporal distribution of snow is important. The estimation of snow cover area provides valuable information on snow-melting water in terms of runoff and water supply of the watershed in mountainous regions. Snow is an important geophysical factor for climate through its role in the Earth’s albedo and for hydrology. It has potentials for water storage, agriculture, hydropower generation, and flooding in local scales. Snow creates an insulating to keep plants from the cold weather in winter. Therefore, it is necessary to study the snow cover area, snow depth, and snow water equivalent. The snow cover area is affected by environmental factors, leading to different melting patterns which are important for assigning a deterministic model.
The importance of snow has been recently understood by scientists and watershed managers resulted in different snow studies. Applying remotely sensed data for such studies is cheaper faster and easier than traditional approaches. One can also study larger areas using these data which is more beneficial. Snow cover area is the most accurate factor of snow which can be estimated using remotely sensed data. Different sensors have been applied to study snow areas which have their own advantages and disadvantages. Earth Observing System (EOS)-Terra was launched with 5 mounted sensors on December 18, 1999. One of the 5 sensors in EOS is MODIS. The sensor was embedded on Aqua satellite launched on May 3, 2002. Terra is a sun-synchronous satellite, elevated at 705 Km, having polar orbit. Terra passes occur at roughly 11:00 – 12:00 AM and 10:00 – 11:00 PM local standard time each day. MODIS is the biggest sensor in EOS. Its mission is to measure temperature, ocean color, vegetation and deforestation, clouds, aerosols, and snow covers. Different ground resolution, the capability of distinguishing cloud from snow, and provide complete coverage of the Earth. Therefore, this sensor has very high potentials in snow cover studies. The sensor has a radiometric resolution of 12 bites, spectral resolution of 36 bands from 0.4 till 14.4 µ. It also has a high temporal resolution (Repeating cycle of 1 to 2 days), and moderate spatial resolution (250, 500, and 1000 m).
MODIS satellite images are used for estimating snow cover area in this study. In this research, two common techniques including Normalized Difference Snow Index (NDSI) and Linear Spectral Unmixing (LSU) were used. In order to determine the accurassy of NDSI and LSU approaches (MODIS images), the IRS images were selected since their spatial resolution is very high (24 m). The MODIS pixels were interpreted as snow using Snow map algorithm. A number of 11 similar sites on MODIS and IRS were selected to compare the results. The snow area of MODIS images (NDIS and LSU) were compared with the corresponding value on IRS images using t-student test and regression coefficients. A scatter plot of non-snow against snow was used. A regression model was established for the same purpose.
The scatter plots of snow areas produced by crossing IRS versus snow area estimated by NDSI and LSU approaches were separately studied. The regression model of each scatter plot was then calculated. The results show that both NDSI and LSU methods have high efficiency to compute snow cover area; however, the LSU method shows a little more efficiency than the NDSI method. Another comparative investigation over the NDSI and LSU methods was performed by t-student test with significant level of 5%. The t-student test indicated that the LSU method has a higher potential in estimating snow cover area in the study area than the NDSI method.
The use of remote sensing techniques, satellite images, GIS, and statistical methods for studying and monitoring ground features such as snow is very beneficial due to their lower expenses and ease of use. Among them, high temporal and spatial resolution images are preferred. Due to the importance of snow in the study area, the snow cover area was computed using MODIS and IRS satellite images to determine the best approach. The results showed that to use the methods which apply subpixels to calculate snow cover area is more appropriate. The study reveals that remote sensing techniques can provide reliable information on snow and can overcome the problems stemming from traditional approaches

The accuracy and reliance increase and consequently reduction of uncertainty of spatial prediction maps of environmental hazards including landslides is one of the challenges facing with in such studies Therefore, the objective of this research is to introduce a hybrid model of data mining algorithm named Random Forest (RF)-Random Subspace (RF-RS) in order to enhance the accuracy of spatial prediction map of landslide-prone areas around the city of Bijar, Kurdistan province, Iran. Firstly, 19 affecting factors on shallow landslides in the study area including slope degree, slope aspect, elevation, curvature, profile curvature, plan curvature, solar radiation, stream power index (SPI), topographic wetness index (TWI), length-angle of slope (LS), land use, normalized difference vegetation index (NDVI), litho logy, distance to fault, fault density, rainfall, distance to stream, stream density and distance to road were identified. Then based on Information Gain Ratio (IGR), twelve factors among them were selected to be used in modeling. The elative importance of each factor was assessed by Random Forest (RF) model as well as Random Forest-Random Subspace (RF-RS) hybrid model. Kappa, Precision, Recall, and AUROC indices were used to evaluate the models not only for training dataset but also for testing dataset. Shallow landslide susceptibility maps of the study area were prepared using both models. The results showed that slope aspect in the RF model and slope degree in the RF-RS hybrid model is the most important affecting factor on landslide occurrence in the area. The model evaluation results indicated that both models are reasonable in application for shallow landslide susceptibility mapping. The findings also indicated that the percentage of area under the curve of ROC (AUROC) was 0.729 and 0.784 for training dataset by RF and RF-RS hybrid model, respectively, while these values were 0.717 and 0.771 for testing dataset. In conclusion, it can be claimed that the new technique (RF-RS hybrid model) is able to increase the accuracy of spatial prediction map of shallow landslides in the study area. This accurate map will help decision-makers to protect infrastructures of an area, to develop better land-use planning programs and to more effectively control sediments.

In most regions of Iran the requisite water for populations and industry are supplied by groundwater resources. Drought is an important factor, which effects groundwater quality. Demand for groundwater resources is increasing in Qurveh - Dehgolan plain due to its position and limited access to surface water resources. In this research, the effects of drought on water quality of wells in Qurveh – Dehgolan studied. Water quality data in 38 wells for 21 years (1992 to 2013) used to define dry and wet period the Standardized Precipitation Index (SPI) for 3, 6, 12 and 24 months’ time scales calculated. Using Mann-Kendall tests, the trend of ground water quality parameters investigated and the relation between climatological drought and groundwater quality parameters calculated using correlation coefficients. Study of correlation coefficients between quality parameters and SPI showed in 22.2% of cases, drought effected on groundwater quality (at 95% level). Correlation coefficients were positive in some wells and were negative in other wells. Also in most quality parameters, with increasing of SPI time scale, the rate of correlation was increased. The results of Mann-Kendall test showed the most parameters of water quality had increasing trend and Cl-, HCO3- and Mg2 had negative trend in 60.53%, 60.53%, and 52.63% of wells respectively. In addition, CO32- had not trend in any wells. Zoning map of water quality showed that most reduction of ground water quality was happened in eastern part of plain, because the ground water level downfall in mentioned part was higher than other parts.

Within the climate change debate and its probable impacts on water resources systems, design and operation of management plans based on the assumption of stationary hydrology may cause serious challenge to accurately predict future supplies. Therefore this case study is trying to assess trend in hydroclimatic variables of Karkheh Rivers upstream by applying modified Mann-Kendall trend test on long term daily time series of temperature, precipitation and discharge. Temperature variables are mostly showing meaningful increasing trends but observed changes in assessed stations were not spatially uniform for precipitation. Streamflow variables depict a decreasing trend, though more noticeable in base flows. Decreasing trend is meaningful for annual discharge median in Holailan at 90% confidence level. Total yearly precipitation, number of precipitation days and number of days with precipitation equal to, or greater than, 10 mm/d show the most correlation with stream flow variables. Comparing monthly discharge with temperature and precipitation variables in the studied gages indicates a time-delay in system response to inputs. This may related to snowmelt contributions or contributions of water into streams after passing through different hydrological pathways such as groundwater. Some parts of streamflow changes, especially about base flows, is not completely verified by precipitation changes and can be attributed to changes in temperature or another factors such as groundwater overexploitation.

So far it is well known that the conversion of degraded rangelands to planted forests through afforestation practices affects soil properties. Therefore, we selected one rangeland and two planted forest area to study the physical and chemical properties of soil in different land uses in Hassanabad region of Sanandaj in Kurdistan province. Physical soil characteristics such as the percentage of water content and soil bulk density were sampled in two-week intervals during 2012 to 2013 at three points in each area. In addition, a set of double rings was conducted to measure infiltration rate at three points within each area. The saturated hydraulic conductivity was computed using the experimental infiltration data. The soil samples for measuring chemical properties (e.g. pH, EC, organic carbon content, phosphorus and total nitrogen) were taken from 3 points in each area at the depth of 20 cm with six repeated measurements. The results showed that converting degraded rangelands to tree plantations has resulted in enhancing the quality of soil physical characteristics, whereas only a portion of the chemical characteristics (organic carbon content, phosphorus and total nitrogen) were positively affected.

The behavior of suspended sediment during flood events is not only a function of energy conditions، i. e. sediment is stored at low flow and transported under high flow conditions، but also is related to the variations in sediment supply and sediment depletion. These changes in sediment availability result in so-called hysteresis effects. Therefore، Hysteresis pattern analysis is of great importance in sediment studies in the watersheds. However، their analyses has been rarely considered. In this study، based on the discharge and sediment concentration data collected from 8 storm events occurred during March 2 011 to April 2012، event suspended sediment dynamics of 7 tributaries of the Lake Zarivar watershed was investigated using hysteresis patterns. Based on the fact that all sampling points were not active in all events، about 46 hysteresis patterns were obtained. The analysis of results showed that 16، 13، 11، and 6 events had clockwise، irregular، complex and counterclockwise patterns، respectively. Small tributaries of the Zarivar lake watershed showed the rapid responses to the variation of storm intensity and the most hydrographs of different storms were multi peak discharges and consequently high suspended sediment variations led to different hysteresis patterns. The diversity of patterns suggested that the detailed processes of sediment transport were not only complicated during one event but also varied from event to event. The reasonable and statistically significant relationship (p<0. 05) between suspended sediment yield and peak discharge of each sampling point indicated that the data from all events may be statistically well described by a simple regression equation، regardless of different inter and intra-storm variations of the suspended sediment.

The purpose of this study was to estimate the most important non-market values of the ZW. A survey questionnaire based on Single-bounded dichotomous choice contingent valuation approach was utilized in order to elicit Willingness to Pay (WTP) for the ZW conservation. The study was conducted in Kurdistan province in Iran. Three hundred and thirty respondents were willing to pay for conservation of the ZW. The mean WTP was estimated at RLs 275,010.7 annually per household. Considering the whole number of households in the study area which was 164,483 in 2012, the mean WTP for the study area was calculated to be RLs 45,234,593,192.25 annually. The relationships between various explanatory factors and WTP were discovered by applying the Logit model. ‘Bid’ and ‘age’ variables were statistically significant and had a negative effect on the respondents’ WTP. In addition, ‘income’, ‘membership in NGOs’ and ‘number of household’ variables were statistically significant and had a positive effect on WTP. The results can be used to build local and political support for conservation and sustainable use of the ZW and can convince policy makers and site managers to bring more financial support and better policy program to the ZW preservation.

Although spatial and temporal variability of hydrological parameters are the main inputs for effective soil and water resources management in watershed scale, but this important subject has been scarcely taken into account. Hence, the runoff, suspended sediment and nutrient (Nitrate and Phosphate) yields from main tributaries of the Zarivar Lake were determined and their spatial and temporal variations were also investigated. A number of eight sampling points on the seven lake tributaries were monitored from March 2011 to April 2012 under base flow condition by daily sampling and event bases by hourly sampling intervals. The total yields of runoff, suspended sediment, nitrate and phosphate to the Zarivar lake during the study period were obtained to be 9.7 Mm3, 685.4 t, 25.4 t and 1.15 t, respectively. Almost seven storms were occurred during the same period due to which 1.61 Mm3 runoff, 685.4 t suspended sediments, 4 t nitrate and 211 Kg phosphate were transported. Some 8.1 Mm3 runoff, 718.65 kg suspended sediments, 21.4 t nitrate and 994.35 Kg phosphate were also entered into the Zarivar lake during base flow condition. The results of this study showed that northern parts of the watershed mainly contributed in runoff and nitrate yields. The maximum suspended sediments were also yielded by northern and western parts of the watershed. Whereas, the western parts of the watershed yielded the maximum phosphate to the Zarivar lake. The maximum contribution of runoff, suspended sediments, nitrate and phosphate were respectively happened in spring and winter seasons. About 82 percent of suspended sediments entered to the Zarivar lake during the study period just by one spring event with a duration of some 50 h. It clearly revealed more temporal variability of the suspended sediment and therefore further precaution for better management of soil and water resources of the closed Zarivar lake watershed.