حسام سیدکابلی

This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected synoptic stations in climates with different topographic characteristics were received daily from the European Centre for Medium-Range Weather Forecasts (ECMWF) website. Bias correction of these data was performed using 5 quantitative mapping methods based on observational data in R software environment. Two-part evaluation and Taylor diagram were used to compare the performance of different methods. The results showed that the performance of the quantification mapping method depends on the performance functions, set of parameters and climatic conditions. In general, non-parametric methods of multiple mapping have better performance than parametric methods, so that the best performance is related to QUANT and RQUANT methods, among which DIST method has the weakest performance.

Monitoring and conservation of the wetlands are very important due to their role in eco-system protection and providing the socio-economic needs of the local community. Sarp and Ozcelik (2017); Nandi et. al. (2018); Ali et. al. (2019) used NDWI, MNDWI and AWEI indices to monitor waterbodies changes based on Landsat (ETM+, OLI) dataset. In this study, these indices were applied to monitor area changes of Hor-alazim and Shadegan wetlands in Khuzestan province that were selected as case study areas for period 2000-2018. Wetlans area changes were evaluated for three months, include April, September, and January in any year that represent inflow changes. Mann-Kendall statistics was also used to study the changes in a wetlands area. The  MNDWI index in comparison to NDWI and AWEI has better performance and higher accuracy. The results of Man-Kendall statistics showed that wetlands area changes have no significance relation in 95% confident level. Also, the correlation between temperature and wetland area changes showed that on average, the wetlands temperature changes by ±0.4 C for per 1000-hectare area change.

Water consumption management and optimal operation of reservoirs are important challenges for sustainable development in arid and semi-arid regions of the world. Increasing water consumption productivity is recognized as an adaptive strategy for coping with drought conditions and increasing water demand due to population growth. Nonetheless, the impact of the increase in water consumption productivity on the operation of reservoirs is neglected in assessment as a management tool. Therefore, in this research, the impact of the increased efficiency in agriculture water on Jareh-Dam operation as a case study has been assessed. For this purpose, an objective function has been developed based on the coefficient of water efficiency to optimize the water release and water storage in the Jareh-Dam. The operation reservoir is then compromised in both optimal and current conditions with considering the four scenarios of water efficiency. Results show that increasing in the coefficient of water efficiency up to 0.5 will result in an increase of 10 percent in the reliability and a 28 percent decrease in the vulnerability. Also, assuming the reservoir performance is constant compared to the base period, with a 17 percent increase in downstream consumption efficiency, a growth of 46.7 percent in the region's gross income is not far from expectation.

Impacts of climate change on water resources will force decision-makers to adopt climate change adaptation policies in order to reduce social-economic problems and difficulties resulting from it and water resource sustainable development. One of the adaptation methods is to increase water use efficiency in agriculture that will adjust climate change impacts include decreasing runoff and increasing water demands. In this study, the impact of water use efficiency as a climate change adaptation approach is assessed in the optimal operation of JAREH dam. Fifteen climate change scenarios were generated by using downscaling technique on CMIP5 data for the near (2020-2044) and far (2070-2094) future. Based on these scenarios, time series of reservoir inflow and downstream water demand were projected for both future periods. An optimization model is developed considering the water efficiency coefficient parameter in order to define four water use efficiency scenarios (0-S1, 0.1-S2, 0.3-S3, 0.5-S4). Results show that reservoir inflow decreases up to 18.8% and water agriculture demand increases up to 29%. The amount of water allocation would increase up to 18.7% in the future periods than in the baseline period under S1 scenario to supply the increased water demand, which may decrease reliability of reservoir system for water allocation. Increasing water use efficiency coefficient up to 0.5 in the future periods would increase system reliability up to 20% that will reduce social-economic problems caused by climate change impact in this study area.

Satellite-based precipitation dataset has been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these datasets has limited their application in localized regions and watersheds. So, having an accurate estimation of precipitation by satellites along with the adequate spatial scale in hydrologic studies is the main goal of this study. In this research, Geographically weighted regression (GWR) method was investigated to downscale the Tropical Rainfall Measuring Mission (TRMM-3B42 Version 7) over the DEZ river basin in the southwest of IRAN for 2010-2011. Downscaling was performed based on the non-stationary relationships between the TRMM precipitation and the Digital elevation model (DEM) derived products, the Normalized difference vegetation index (NDVI), the Enhanced vegetation index (EVI) and the Land surface temperature (LST). The result shows that the downscale precipitation at 1 km spatial scale had significantly improved spatial resolution, and agreed well with data from the rain gauge stations. For the 16-day precipitation, Mean square root means square error (RMSE) and absolute mean error (MAE) values are 22.7 mm and 7.45 mm, respectively. However, the accuracy of the model varies in a different location and depends on the vegetation condition.

In reservoir of dams, especially near the dam body, the cohesive fine sediments are deposited mainly. The mechanical behavior of these sediments is largely controlled by the interparticle attraction caused by electrostatic and physiochemical forces. these properties cause the stickiness and accumulation of clay particles and formation of dense masses called flocs, which is sometimes referred to as flocculation. flocculation is influenced by several factors including salinity, flow regime, sediment concentration and organic matter. Flocculation is the most important factor that makes the settling, fall velocity and transfer of cohesive sediment considerably more complex and dynamic than non-cohesive sediments. In order to determine the relations governing cohesive sediments, the physical characteristics and behavior of these sediments should be identified. The terminal settling velocity of sedimentary particles in liquids, called particle fall velocity, is one of the most important properties in determining the physical properties of sediments caused transfer, deposition and consolidation. The fall velocity of cohesive sediments is influenced by many factors, including salinity, initial particle size, turbulence, temperature of water, and suspended sediment concentration.
McLaughlin (1959) provided a method for measuring the fall velocity of particle (ω) in stillwater, using a settling cylinder with 10 cm diameter and less than 1 meter height and established a differential formula based on its research as follows:d(wC)/dz琯=0
(1)
Fathi Moghadam et al (2011) studied the settling properties of the cohesive sediments in the Dez Dam reservoir. They concluded that particles for all concentrated samples and in all depths reached to their maximum fall velocity approximately at the same time (15 minutes after starting the test). The lower concentration samples appeared to have higher maximum fall velocities than the higher concentration samples, but for a shorter duration.

Due to absolute dependence of agriculture on water, determine of drought condition in each region is very useful in planning the food sourcing. Unfortunately, there is no same definition about the “drought condition”, so there are some indexes to determine it. Standardized Precipitation Index (SPI) is one the meteorological indexes, which widely used to determine agricultural drought conditions. Evapotranspiration Deficit Index (ETDI) was also designed for this purpose. This index is used to determine agricultural drought conditions in arid and semi-arid region. Although there were most research about other drought indexes such as SPI, but there is few studies in oversea countries about use of ETDI index. Thus in this study tried to determine drought by ETDI and SPI indexes in Neyshabur plain by used of climate change models
This research was conducted to determine drought condition in Neyshabur plain located at longitude between 58˚ 13’-59˚ 30’ N and latitude between 35˚ 40’-36˚ 39’ E, Iran. Evapotranspiration Deficit Index (ETDI) was developed based on weekly evapotranspiration deficit to determine drought condition in this region. In order to comparison of the ETDI results to other drought indices, we used Standardized Precipitation Index (SPI) as one the most common drought index. The data were collected from Neyshabur meteorological station for irrigated farms (wheat in Soleymani and Faroub farms, barley and corn) and rain-fed farms (rain-fed wheat) during 1992-2011. In order to estimate weather data for each index in the irrigated farms during two future periods (2020-2039 and 2080-2099), HADCM3, ECHOAM and CGCM3 T47 models were used based on A2, B1 and A1B scenarios and the climate model that has been used in rain-fed farm is the HADCM3 based on A2 and B1 scenarios. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were used to comparison of the ETDI and SPI results.
Results showed that average ETDI were in initial wet condition for Faroub farm during base period (1992-2011) while it will be in drought condition during future periods (2020-2039 and 2080-2099). ETDI index was in normal condition for Soleymani farm during base period. Average ETDI indexes for these farms were in normal and initial dry condition during 2020-2039 and 2080-2099 periods, respectively. For barley and corn, ETDI indexes were in normal and initial dry condition during base period, respectively. This index was in normal statute for both of them during future periods. The ETDI value for rain-fed wheat was less compared to irrigated wheat during base period, although, this index will be increased during future periods. In most of scenarios, ETDI indexes showed negative values. It means that high drought condition will be happened during future periods due to deficit evapotranspiration. Results according to SPI index revealed that this region was in moderately drought condition and this situation will not change.
High differences were obtained between ETDI and SPI results. Since agricultural drought depends on evapotranspiration deficits, ETDI is better index compared to SPI. The value of RMSE revealed poor adaptation between two indexes during future periods. In addition, ETDI were not correlated with SPI for all the scenarios in all scenarios. These differences are reasonable because SPI index only uses precipitation data and ETDI uses evapotranspiration. According to the results, it seems that SPI cannot be suggested as a good index in agricultural studies.

Reservoir storage is essential for developing dependable water supplies and is a major component of the river system water budget. The storage contents of reservoirs fluctuate greatly over time with variations in water use and hydrologic conditions that range from severe multiple-year droughts to floods. Water surface evaporation typically represents a major component of the reservoir water budget. Estimates of the amount and rate of evaporation from open water surfaces are required in water resource management for a variety of purposes, such as the design of storage reservoirs, catchment water balance studies, municipal and industrial water supply, irrigation of agricultural lands and management of wetlands. The impacts of reservoir evaporation on water management vary greatly based on location with differences in climate, reservoir characteristics, and water management and use practices. Determining evaporation rates is essential for efficient management of reservoirs and water resources, particularly in water-scarce countries such as Iran. It is estimated that open water reservoirs in Iran lose high volume of their total water storage capacity per year by evaporation. For example, this loss is around 13% of the volume of inflow to KHARKHEH Dam per year. While this loss is of significant concern, the threat of a changing climate has been directing greater focus to how much water will be lost from Iran’s reservoirs in the future. As a result of the change in climate, particularly the increase in surface air temperatures, evaporation is also expected to increase throughout Iran. This paper analyses evaporation rates from Dez dam reservoir for the two periods (2021-2050 and 2051-2080) under modeled climate change conditions using five General Climate Models (GCMs) with A2 emission scenario. Global climate models, also known as general circulation models (GCMs), are models which solve the primitive equations of mass, momentum and thermodynamics to generate a description of the state of the atmosphere, and produce most of the meteorological variables, such as wind speed, relative humidity, rainfall, surface air temperature and solar radiation. The GCMs are typically used together, as a multi-model analysis, where long time series into the future and different GHG emission scenarios are used in order to create climatic statistics. Inherent to GCMs, however, is their coarse spatial resolution which amongst all the available models varies from 200 to 500 km. With this resolution, albeit GCMs incorporate the important large-scale atmospheric circulation, they are unable to capture local-scale factors such as the orographic elevation, proximity to water bodies and local winds. Therefore, the outputs of GCMs need to downscale for applying to modeled evaporation. Daily meteorological prepared using K-NN downscaling model to estimate evaporation rate from open surface water using equations derived from penman equation. The K-NN downscaling model is a nonparametric resampling method for generating new data series. This method simulates daily precipitation for a weather station by combining a stochastic weather generator model (based on non-parametric K-Nearest Neighbor approach) with future climate scenarios that are established based on projected monthly. In this study, an equation based on penman equation for situations where no wind data are available is used to estimate evaporation rate. The equation is validated by compared to the penman equation in estimating the evaporation rate in a meteorological station that has the same climate as DEZ dam. Then, evaporated volume, which is estimated using area-capacity relations of Dez dam, were compared with observed meteorological variables for the period of 1982–2011. Area-capacity curves are usually used for reservoir flood routing, reservoir operation, determination of water surface area, and capacity corresponding to each elevation, reservoir classification, and reservoir sediment distribution. In this study, a dimensionless curve is used to estimate the changes of capacity in relation to the changes of evaporation rate. Results of the application of the presented model for downscaling monthly precipitation outputs of GCMs show that the model has a high capability for downscaling precipitation. So, the used equation to estimate the evaporation rate is suitable for applying in DEZ dam station. Also, the results showed that the volume of average annual evaporation from the study reservoir will increase about 7% in 2021-2050 and 10% in 2051-2080 more than base period. The main reason of this increase can be global warming in the future. According to the modelling results, the mean annual air temperature will be increased by 1.3C and 3C in 2021-2050 and 2051-2080 respectively. This will have a significant impact on the evaporation rates, especially in spring and summer which the increases in temperature will be too high.

Groundwater is one of the main sources to supply drinking water, especially in arid and semi-arid areas. The interaction of surface water and groundwater is one of the important issues in groundwater management and modeling, and it is essential to understand. This research aims at investigating river and groundwater interactions in the Dezful-Andimesh plain, which is about 2073 Km2 as the largest agricultural plain in the northern part of Khuzestan province, southwest of Iran. This plain contains five smaller plains: western Dez, eastern Dez, Sabili, Deymche and Lor. The Dez, Karkhe, Balaroud, Kohnak and Shavoor rivers are located in the study area. Thus this area is very important for supplying water needs and requires an efficient water resource management, a correct planning and reconsideration of water policy. Only 4% of the region is classified as the urban area and the remaining are agricultural plains. All the plains have irrigation networks except the Lor plain. Although, the irrigation networks supply a great part of the crops demand, in some regions groundwater use is also significant. Wheat, sugarcane and maize (32%, 21% and 16%) are the most common crops in this region. The cultivation period of wheat is usually from November to May, so the maximum recharge of groundwater by irrigation networks occurs in this period. The study area has a semi-arid climate with the mean monthly temperature of 36.5°C in July and 11.8°C in January. The precipitation varies from 296 mm in eastern Dez to 394 mm in Lor. The Dezful aquifer with an average thickness about 100 m is an unconfined aquifer system. There are over 2700 wells which pump about 500 million m3 per year, in this area. The general direction of groundwater flow in the area is from the north to the south. Recharging the aquifer is via direct rainfall infiltration and return water from irrigation networks. Hydraulic conductivity range is from 14 m/day for clayey sediments in Sabili to 49 m/day for sandy deposits, particularly in Lor. In order to model the groundwater, a MODFLOW programming code was implemented in a GMS environment. MODFLOW is a numerical model that is one of the best methods for assessing the quantity and quality of groundwater. The numerical models are difficult and time-consuming. However, in the recent decade's research using simulation models have been developed due to the improvement of high-speed computers. Information related to steady-state flow along river information was used in the model. Water level and bed river data were measured in river-cross sections and hydrometric stations in different months. The model was configured to a steady-state condition in October (2009) and was calibrated using 62 groundwater wells. Also, the model was calibrated in an unsteady-state condition for five years (October 2006 to September 2011). Then, the model was validated for the period of 2011-2012.
According to the results, the regulated rivers such as Karkhe and Dez were the major source of recharging aquifers; however, there was a decrease in their contribution to aquifers in wet seasons. There was an increase in groundwater level during wet seasons due to the groundwater recharge from rainfall and irrigation systems. However, there were no significant changes in the water level of the regulated rivers, and the minimum water level was observed in November. Therefore, an increase in groundwater level could not be related to rivers when the river levels were constant, during the wet season. The Balaroud non-regulated rivers generally drained the aquifers during dry seasons, but not during wet seasons (February), when recharge occurred from these rivers to aquifers. The rivers such as Shavoor and Kohnak and their tributaries drained aquifers in every month. There were not increasing or decreasing patterns in groundwater discharge when there was a change in both rivers and groundwater levels with different patterns in different months of a year.
According to the simulation results over six years, the most river- groundwater interaction was belong to Dez and Karkhe rivers with the highest amount of recharge to aquifers. The Shavoor and Kohnak rivers and their tributaries generally drained groundwater. However, there was no specific pattern of river-groundwater interaction for the Balaroud seasonal rivers. There was more groundwater recharge through regulated rivers in dry seasons compared to wet seasons which was due to the high amount of rainfall (along with the amount of water from irrigation systems) in wet seasons.

Accurate estimation of evapotranspiration plays an important role in quantification of water balance at awatershed, plain and regional scale. Moreover, it is important in terms ofmanaging water resources such as water allocation, irrigation management, and evaluating the effects of changing land use on water yields. Different methods are available for ET estimation including Bowen ratio energy balance systems, eddy correlation systems, weighing lysimeters.Water balance techniques offer powerful alternatives for measuring ET and other surface energy fluxes. In spite of the elegance, high accuracy and theoretical attractions of these techniques for measuring ET, their practical use over large areas might be limited. They can be very expensive for practical applications at regional scales under heterogeneous terrains composed of different agro-ecosystems. To overcome aforementioned limitations by use of satellite measurements are appropriate approach. The feasibility of using remotely sensed crop parameters in combination of agro-hydrological models has been investigated in recent studies. The aim of the present study was to determine evapotranspiration by two methods, remote sensing and soil, water, atmosphere, and plant (SWAP) model for wheat fields located in Neishabour plain. The output of SWAP has been validated by means of soil water content measurements. Furthermore, the actual evapotranspiration estimated by SWAP has been considered as the “reference” in the comparison between SEBAL energy balance models.
Surface Energy Balance Algorithm for Land (SEBAL) was used to estimate actual ET fluxes from Modis satellite images. SEBAL is a one-layer energy balance model that estimates latent heat flux and other energy balance components without information on soil, crop, and management practices. The near surface energy balance equation can be approximated as: Rn = G H λET
Where Rn: net radiation (Wm2); G: soil heat flux (Wm2); H: sensible heat flux (Wm2); and λET: latent heat flux (Wm2). Simulations were carried out by SWAP model for two different sites in Faroub and Soleimani fields. The SWAP is a physically based one-dimensional model which simulates vertical transport of water flow, solute transport, heat flow and crop growth at the field scale level. The period of simulation covered the whole wheat growing season (from 1st of December2008 to 30th of July2009. 16 MODIS images was used to determine evapotranspiration during wheat growing season. Inverse modeling of evapotranspiration (ET) fluxes was followed to calibrate the soil hydraulic. While SWAP model has the advantage of producing the right amount of irrigation and evapotranspiration at high temporal resolution, SEBAL can estimate crop variables like leaf area index, NDVI index, net radiation, Soil heat flux, Sensible heat flux and evapotranspiration athigh spatial resolution.
Actual and potential evapotranspiration were estimated for SWAP Model during the whole wheat growing season around669.5 and 1259.6 mm for Farub field and 583.7 and 1331.2 mm for Soleimani field, respectively. In contrast with NDVI and net radiation,spatial distribution of SEBAL parameters indicated that soil heat flux, sensible heat flux, and surface temperature of land have the same behavior. At the planting date, evapotranspiration was low and about 1 mm/day, but at the peak of plant growth, it was about 9 mm/day. Moreover, evapotranspiration declined at late growing season to about 3 mm/ day. SWAP model has been calibrated and validated with meteorological data and the data of field measurements of soil moisture. The amount of RMSE of 0.635 and 0.674 (mm/day) and MAE of 0.15 and 0.53 (mm/day) and also coefficient of determination (R2) of 0.915 and 0.964 obtained from comparison of SEBAL algorithm with SWAP model for Farub and Soleimani fields showed that no significant differences was seen between results of two models.
The present study supports the use of SEBAL as the most promising algorithm that requires minimum input data of ground based variables. Results of comparison of SEBAL and SWAP model showed that SEBAL can be a viable tool for generating evapotranspiration maps to assess and quantify spatiotemporal distribution of ET at large scales. Also, it feels that SEBAL and SWAP models can be applied in a wide variety of irrigation conditions without the need for extensive field surveys. This helps significantly in identifying performance indicators and water accounting procedures in irrigated agriculture, and to obtain their likely ranges.

Accurate quantification of ET in irrigated agricultural lands is crucial for planning for water allocation, optimizing crop production irrigation management, evaluating the effects of changing land use on water yields. For this purpose in the research, evapotranspiration calculated by three kind of different methods remote sensing, agro-hydrological model and computational method for maize field located in Neyshabour plain. Methods of evapotranspiration determination consist of Surface Energy Balance Algorithm for Land (SEBAL) algorithm and Modis product satellite in the long of during growth, SWAP agro-hydrological and computational methods of FAO Penman-Monteith and Hargreaves-Samani and FAO Blany Criddle. Correlation coefficient 0.67 to 0.91 between SEBAL algorithm with SWAP model and computational method showed high potential for SEBAL algorithms in the evapotranspiration estimation. In between methods for potential evapotranspiration determination, FAO Blany Criddle method has better result in comparison with Penman-Monteith and Hargreaves-Samani methods. Also, this study found that the SEBAL algorithm could be used to determine evapotranspiration in areas with shortages of data and for evaluation of computational methods and hydrological models.

Global Warning has caused the characteristics of extreme rainfall changed out of range of the internal variability of climate. In this research, an analysis is presented from how variation in intensity, Duration-and Frequency (IDF) of rainfall under 10 climate scenarios derived climate models and A1B emission scenario for 2021-2040 periods in Khorasan-Razavi province. The IDF values for 23 raingage have projected under future climate scenarios in 3 risk levels (25%, 50% and 75%) by a proposed combined approach include: (1) Develop a nonparametric downscaling model to projection of future daily rainfall in arid and semi-arid regions, (2) Establishing frequency-quantile relationships between the t-hour and daily rainfall based annual maximum for estimating the IDF values. Under risk level 2, the results show a wide range of between -23% to .3% in rainfall intensity than baseline (1993-2012). The short-duration rainfalls change in more positive range then long-durations; and for return periods of more than 10-year, the range of changes will be wide in this area. Generally, central and southern regions will be received slight increase than northern regions. Also, the high rainfall regions will be ahead a more severe flooding, while the low rainfall regions will be suffered a more severe decrease. Also, in low risk level, rainfall intensity will be increased in the future period; it can be warning to design hydraulic infrastructures with high emphasis.

One of the most serious consequences of climate change is how it will affect droughts and water resources. This study aims at investigating the impact of climate change on droughts conditions in Dezfool-Andimesh Plain using the Standard Precipitation Index (SPI). To achieve this objective, output of four general circulation models (GCMs) under the A2 scenario were downscaled for two climate stations in the period of 2060-2084 compared to the baseline period (1985-2009). Regarding the dominant crop grown in the study area, a 6-month SPI for the baseline and future periods were estimated under the four GCM-scenarios. Results revealed that there is an increase in the duration of severe and mild droughts while there is a decrease in the duration of moderate droughts. This implies that droughts will be a concern in the future during the growing season (for the dominant crop) which should be considered in water resources management.

Nowadays, increasing of greenhouse gases emissionscussed by human activities is the main factor of climate change. Global warming has also changed the frequency of extreme rainfall events in many areas. This research presents an analysis of how the change in the frequency of maximum daily rainfall under three emission scenarios in 2021-2040 periods in the Khorasan-Razavi province. The maximum daily rainfall series areprojected for the future periods in the 23 weather stations by developing a nonparametric downscaling model for arid and semi-arid regions. The uncertainty of climate change scenarios is quantified using a simple parametric uncertainty estimator in the three risk levels (25%, 50% and 75%) for each of emission scenarios. The frequency analysis of maximum series showsthat the daily rainfall intensitiesin therisk level 2 will be changed between -22.9% to .3% than baseline (1993-2012), that a wider range of these changes is related to thelonger return periods. Generally, central and southern regions will be received slight increase than northern regions. The rainfall intensities in more areas decrease with theincreasein greenhouse gases emissions that this decreasewill be more for the rainfalls with lower return periods. Flooding in the high rainfall regions will be also occurred withseverity while the low rainfall regions get a more decrease. Maximum daily rainfall will be increased in the future periods by reducing the level of risk; it can be warning to design hydraulic infrastructures with high emphasis.

Accurate estimation of evapotranspiration plays an important role in quantification of the water balance at regional scale for better planning and managing water resources. Evapotranspiration can be obtained from either estimation of potential ET using data of meteorological stations or، directly، from field measurements. ET is subject to rapid changes in time and space، attributable to the wide spatial variability of precipitation، hydraulic characteristics of soils، and vegetation types and densities. Therefore، it is nearly impossible to determine its spatial and temporal distributions over large areas only from lysimeter and precise measuring instruments. Thus، researchers have used remote sensing data to estimate areal actual ET. In this study، actual evapotranspiration variations of Neyshabour plain was investigated by algorithm of energy balance of the earth since 2000 to 2013 by using MODIS images and meteorological data. Also، the determined evapotranspiration was compared and evaluated with Penman-Monteith and Hargreaves-Samani models. Low amount of coefficients of the error between Penman-Monteith model and SEBAL algorithm showed the accuracy of SEBAL model in the estimation of evapotranspiration and its parameters. The results derived from comparison of evapotranspiration and NDVI vegetation index indicated a good correlation between vegetation and evapotranspiration (R2=0. 908). Also، the variations of NDVI index، land surface temperature، and evapotranspiration in the studied fields showed that evapotranspiration increased by lower land surface temperature and higher densities of vegetation.

General Circulation Models (GCMs) have been identifiedas asuitable tool for studying climate change. Butthese models simulate climatic parametersinthe large-scale which has poor performance in the simulation of processes such asrain fall-run off. There fore، several of down scaling methods were developed. This researchis presented down scaling model based onk-nearest neighbor (K-NN) non-parametric method. The modelis used to simulate daily precipitation data in Ahvaz station for the next period (2015-2044) under climate change scenarios based on out puts of three General Circulation Models، including HADCM3، NCARPC Mand CSIROMK3. 5. The results indicate that them odelhasa high capacity for down scaling data. It is predicted that the frequency of storm is increased with high intensity on future period in Ahvaz station while dry spells will be prolonged.

The aim of this study, is to evaluate the effect of loss methods application on run-off volume and hydrograph shape used by hydrologic models. In this research, four loss methods including constant loss (φ index), initial loss-loss rate, SCS and constant fraction approach were applied to be evaluated. To do this, Clark Rainfall-Runoff model was applied for 24 storm events from kasilain basin. Then, the characteristics of calculated hydrographs were compared with the same characteristics of the same observed hydrographs. The result of statistical analysis showed that methods of SCS, initial loss-loss rate, constant loss (φ index) and constant fraction loss have relative advantage respectively to be used for the mentioned basin.