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Water scarcity is regarded as a primary issue in many dry climates, with significant impacts on food security and national developments. Since agricultural irrigation accounts for most of water consumption worldwide, providing a water management system is critical to cope with water stress and its challenges. Agricultural water management is an interdisciplinary task, dealing with meteorological and environmental factors. In this work, we have established a 24/7 operational system to simulate those land surface variables, associated with evapotranspiration, biomass growth, and water deficit, using the Surface Energy Balance Algorithm for Land (SEBAL). SEBAL simulates the energy balance, using satellite data in shortwave and thermal bands, as well as soil and meteorological data (wind speed, humidity, etc).This workflow consists of 3 interconnected units: WRF model, Python implementation of the SEBAL model (pySEBAL), and a web-based management panel for the visualization, reanalysis, and publishing the results. The WRF model is run in a daily basis for 36 hours, starting from 12:00 UTC, to provide the meteorological data for the next day. At the next stage, the simulated WRF data after some required processing (converting formats and units of files andvariables, etc.) will be incorporated as input data into the SEBAL model. The key data for the SEBAL model is the “Visible Infrared Imaging RadiometerSuite” (VIIRS) real-time data over the Suomi satellite, which is received automatically after tracking the satellite and picking the appropriate data files for download. SEBAL outputs include some of the variables with key role in agricultural water management, such as actual and potential evapotranspiration, biomass production and deficit, albedo, NDVI, etc, with a resolution of 375m over Iran.The third section of the operational system is a web-based panel, consisting of an open-source server to share and edit the SEBAL outputs. An open-source database management system for the client- based analysis of the SEBAL outputs, and an open-source JavaScript library for displaying the maps of the SEBAL outputs in web browsers.

Estimating the outflow of basins is a critical step in surface water resources planning and management, especially in basins that lack reliable long-term observed data of streamflow. Hydrological models, which can simulate the process of rainfall-runoff, can be used to obtain reliable estimates of streamflow from precipitation data and the physical characteristics of basins. The focus of the present study was to estimate the outflow of 19 sub-basins located in Guilan Province, northern Iran. To achieve this, hybrid models were developed by integrating the IHACRES (identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow) hydrological model with the intelligent-based GMDH (group method of data handling) model. The IHACRES model was calibrated using monthly ground-based precipitation and temperature data as well as satellite-based precipitation data. The lowest and highest Nash-Sutcliffe coefficient (NS) for the IHACRES models were, respectively, 0.14 and 0.68 in the calibration phase and 0.11 and 0.73 in the validation phase. It was also observed that using satellite-based precipitation data reduces NS by 10–75% in the 19 sub-basins under study. After calibrating and validating the IHACRES models, the hybrid models were developed by integrating IHACRES and GMDH models. The lowest and highest NS for the hybrid models were, respectively, 0.23 and 0.81 in the calibration phase and 0.11 and 0.81 in the validation phase. It was observed that, on average, integrating IHACRES and GMDH increases the NS by 44.1% in the calibration phase and 37.0% in the validation phase in comparison with the IHACRES model. According to the NS, the hybrid model had ‘acceptable’ performance in six sub-basins in which the IHCRES model had ‘unacceptable’ performance. It was observed that integrating the IHACRES model with a data-driven model (the GMDH model) can generally improve the simulation results in all sub-basins under study.

On 2 June 2014, a Haboob dust storm occurred in Tehran, which lasted less than 2 h, caused severe damages to the properties and involved loss of human life. This paper describes the behavior of this Haboob dust storm, by analyzing of metrological satellites data, as well as assessing the mesoscale and microscale patterns that led to the occurrence of the dust. A dust detection scheme was developed to monitor the dust formation using the Infrared (IR) channels of the Spinning-Enhanced Visible and Infrared Imager (SEVIRI), carried on board MSG satellites. Restrictive thresholds were placed on the individual components to maximize detection success, and to minimize the misidentification of dust. The results indicated cold and strong downdrafts along with the cold front activity during the maximum temperature over the central hot desert caused a strengthened and intensified wind velocity that led to a strong Haboob in Tehran province. The synoptic analysis indicated that a low-pressure system stretching from the southeast of Iran to the northwest of Iran and Iraq was the dominant feature, despite the presence of a weak high-pressure in the center of Iran. The satellite analysis indicates an extreme dustiness discernible in the southwest of Tehran following the formation of a convective storm; however, uncertainty in dust identification increases as the IR dust signal weakens because of underlying dust cloud beneath the thick convective clouds. The analysis of could physical properties reveals the formation of a convective storm after dramatically decrease in cloud top temperature (CTT) and cloud top pressure (CTP), and increasing the cloud optical depth (COT) which led to development of an overshooting cold cloud over Tehran province before dust formation.

Due to unavailability of sufficient discharge data for many rivers, an appropriate approach is required to provide accurate data for estimating discharge in ungauged watersheds. In this study, Global Land Data Assimilation System (GLDAS) datasets were integrated with Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to simulate the outlet river discharge in Polroud watershed, located in the North of Iran. Temperature and precipitation products generated by GLDAS were calibrated using regression analysis based on observation data for the period of 2004–2006. Then, river discharge was simulated by using HEC-HMS based on two different datasets (GLDAS meteorological product and gauged data) on the scale of the basin for the same period. The results clearly indicated that the forcing of GLDAS data into HEC-HMS model leads to promising results with acceptable correlation with observed data. Although, in comparison with direct GLDAS runoff products, the proposed approach improved the accuracy of river discharge, the problem of underestimation still reduces the expected accuracy. Because of global accessibility, GLDAS datasets would be a good alternative in ungauged or poorly gauged watersheds.

Improving the accuracy of single crop coefficient (Kc) for major crops at regional scales would significantly result in optimizing the consumption of agriculture water. In this study, a new approach is applied to generate Kc curves from satellite images based on the Priestley-Taylor equation. The equation mainly consists of shortwave radiation and thermal terms. Therefore in order to meet the equation’s requirements, the Landsat optical data was considered as the essential satellite-based input of the approach, yet thermal data was obtained from the MODIS imagery which was primarily downscaled using the TsHARP algorithm to fit the 30 m spatial resolution of Landsat; therefore, in this study the method is referred to as the MultiSensor Data Fusion for potential EvapoTranspiration calculation (MSDF-ET). The Kc trends were first evaluated for annual crops (forage maize, sugar beet, wheat, and barley) in two different regions (the Qazvin and Tehran-Karaj plains) using field observation data of Leaf Area Index (LAI). The method was then applied to the major crops in 31 selected plains with three different climatic conditions in Iran. Climatic conditions were categorized into cold (North West region), arid (Central region), and warm and humid (South West region). Eventually, crop water requirements (CWR) in different regions were compared considering long-term air temperature, wind, and relative humidity (RH) variations. Evaluation of the method in the Qazvin and Tehran-Karaj plains showed promising results. The Kc trend varied according to the LAI and temperature variability. Crop growth duration and especially Kc curves were successfully extracted using remotely sensed data in the studied areas. CWR in the zone with higher values of air temperature, notably in warm seasons, was higher in comparison with other climatic zones. Also lengths of the growing seasons were shorter. In conclusion, surface and groundwater resources management would be improved as the result of taking satellite data into account for Kc and subsequently CWR calculations.

Evapotranspiration (ET) is one of the key parameters in water and energy balance equation. According to FAO 56, crop evapotranspiration (ETc) is calculated from multiplying reference evapotranspiration (ET0) by crop coefficient (Kc). But, due to excessive simplification of Kc curve in the FAO approach, potential evapotranspiration (ETp) would be miscalculated. Therefore, accurate estimates of ETp entail improving Kc estimates. In this study, Kc curves of early- and late-planted winter wheat were obtained based on two main satellite-based methods: (1) ratio approach (2) vegetation indices (VIs) approach. In the ratio approach, basal crop coefficient (Kcb) and single crop coefficient (Kc) was directly calculated from the ratio of potential transpiration (Tp) to ET0 (using SWAP) and ETp to ET0 (using SWAP and the Priestly-Taylor equation), respectively. The VI approach makes use of Landsat 7 (ETM+) and 8 (OLI) and also MODIS imagery in order to extract soil adjusted vegetation index (SAVI). The Kcb curves were evaluated against field measured leaf area index (LAI) in 2014-15 growing season. After each Kc curve was modeled, net irrigation requirement (NIR) was calculated on daily and season basis. Results showed that the SWAP approach was weak in estimating the Kcb and Kc curves especially at the late-season stage. The VI approach could properly detect changes in vegetation cover during an entire growing season. But, when it came to Kc curve modelling, the VI approach was limited to the values given in FAO 56. However, the Priestly-Taylor approach compensated for this limitation therefore yielded more sensible trends in Kc curves. Results indicated that the VI approach reduced estimates of NIR of late-planted winter wheat compared with the FAO-recommended approach by 5.37%. The Priestly-Taylor approach resulted 21.72 and 0.32% lower NIR compared with the FAO-recommended approach respectively for early- and late-planted winter wheat. The decrease in NIR from satellite-based approaches derived from more realistic Kc curves during the entire growing season. Overall, making use of the satellite-based approaches could improve water management on regional scales.

Reference evapotranspiration (ET0) from FAO-Penman-Monteith equation is highly sensitive to the surface incoming solar radiation (SISR) and therefore accurate estimate of this parameter would result in more accurate estimation of ET0. In this study, the accuracy of three main approaches for SISR estimation including empirical models (Angstrom and Hargreaves-Samani), physically-based data assimilation models (Global Land Data Assimilation System-Noah, GLDAS/Noah, and National Centers of Environmental Predictions/National Center for Atmospheric Research, NCEP/NCAR), and a satellite observation model (Satellite Application Facility on Climate Monitoring, CM-SAF) were evaluated using ground-based measurements from 2012 to 2015. Then SISR outputs from introduced approaches were implemented in FAO-Penman-Monteith equation for ET0 estimation on daily and monthly basis. The Angstrom calibrated model was the most accurate model with a coefficient of determination (R2) of 0.9 and standard error of estimate (SEE) of 2.58 MJ. m−2. d−1, and GLDAS/Noah, Hargreaves-Samani, NCEP/NCAR, and CM-SAF, had lower accuracy, respectively. However, the lack of the meteorological data and required empirical coefficients are the main limitations of applying the empirical models, however, satellite-based approaches are more practical for operational purposes. The results indicated that, in spite of slight overestimation in warm months, GLDAS/Noah model had better performance with R2=0.87 and SEE = 3.5 MJ. m−2. d−1 in case of lack of meteorological data. The accuracy of ET0 derived from FAO-Penman-Monteith equation was directly depended on the accuracy of SISR estimation. The ET0 estimation error was related to SISR estimation error with a fourth-degree function and had a linear relationship with SISR error at daily and monthly scales, respectively.

Precise crop yield forecast at regional scales would increase global food security, especially in strategic crops such as wheat and barley. Soil Water Atmosphere Plant (SWAP) is an agro-hydrological model based on a crop growth detailed module that could properly estimate crop yield using satellite observations as input data. In this study, in order to reduce crop yield estimation errors in wheat and barley, MODIS-based leaf area index (LAI) was assimilated using a sequential update algorithm into SWAP, and GLDAS/Noah-derived surface incoming solar radiation (SISR) was used as an alternative to measured SISR. The assimilation of remotely sensed LAI and using SISR as input was examined in nine different cases. Results showed that soil adjusted vegetation index (SAVI) was the best VI for LAI estimation with coefficient of determination (R2) of 0.72 and root mean square error (RMSE) of 0.87 m2 m−2. Also noise equivalent variations indicated an appropriate sensitivity of SAVI along the entire range of LAI variability. GLDAS/Noah-derived SISR showed good agreement with measured SISR; therefore LAI and SISR were jointly used in the model. Simulation results showed that the lowest percent absolute error (PAE) for aboveground dry biomass and grain yield was obtained in case 7 (the assimilation of the peak LAI in addition to ten days after and before the peak LAI is reached) with 1.59% and case 5 (the daily assimilation of LAI until twenty days after the peak LAI is reached) with 6.06%, respectively. Crop yield estimates were improved by 26.25 and 14.4% compared with no LAI assimilation case. Overall, LAI assimilation into SWAP associated with the most efficient cases in this study would result in an accurate crop yield forecast in wheat and barley.

Soil and water salinities are major environmental factors limiting the productivity of agricultural lands especially in arid and semi arid regions. To determine salinity threshold values and slope of the yield decrements for crops of wheat, barley and maize, SWAP model was study for an arid region located in Semnan Province (52◦25' N 35◦11' E), central part of Iran with area of 35000 ha including 94 villages belonged to irrigation network of Garmsar district. The data collected from the above 94 villages for years 1998 to 2007 were used to calibrate and simulate yield of wheat, barley and maize using the SWAP model. The irrigation water salinities of 2, 4, 6 and 8 dS m-1 were used and yield reductions versus soil saturation extracts were evaluated and salinity threshold values and slope of the yield reductions were determined for each of the above crops. The results showed that the SWAP model predict crop yields with good accuracy and the threshold values and slope of the yield reductions are site dependent. These values should be determined for each area in order to be able to plan better irrigation scheduling for arid regions which have soil and irrigation water salinities problems.

The suitability of groundwater for paddy field irrigation in the alluvial plains of Guilan Province, northern Iran, was investigated using ordinary kriging and ordinary cokriging of continuous and indicator quality variables. The cross validation values of error measures showed that ordinary cokriging provides more accurate estimates of the quality variables of interest. Maps showing the spatial variability of electrical conductivity (EC) and sum of major cations and anions (SCA) were generated for the years 2010 through 2014, using ordinary cokriging. Based on the estimated values of EC and SCA, four groundwater salinity classes (excellent, good, risky, and unsuitable) were considered and the proportion of the study area covered by each class was obtained. Results showed that the portion of the study area covered by the risky class, in which the groundwater salinity is expected to reduce the rice yield, is located in the eastern part of the study area and has an average value of 25.4% in the period 2010–2014. The results also showed that the western part of the study area has excellent or good groundwater quality for rice irrigation. The probability maps of EC were also obtained using ordinary cokriging of EC indicator variable. Five probability classes were considered and the proportion of the study area covered by each class was obtained. It was observed that the probability that the rice yield is reduced more than 10% is above 0.4 in 6.2% of the study area. The maps generated in this study can be used to identify the regions in the province where groundwater could be allowed to be extracted and utilized by farmers to reduce the bad effects of the scarcity of surface water. Also, in the regions with a risk of rice yield reduction, conjunctive use of groundwater and surface water could be planned and advised to farmers.

Concentrations of major inorganic ions (NH 4 ,Mg,SO 4 ,NO 3 ,NO 2 ,Na,Ca,HCO 3 ,F,Cl,Br,K), PH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Total Alkalinity and Total Hardness in bulk precipitation samples collected in the Tehran city of I.R. of Iran were analyzed. Results show that concentrations of the found substances were higher over the Mehrabad station. The mean concentration of ions indicated that, HCO3, SO4 and NO3 and were the main anions, while Ca ,Na,NH 4 and Mg were the main cations. Measured PH had shown that autumn and winter precipitation were mostly acidic while the spring and summer were mostly alkaline. The pH ranged from 4.6 to 8.1 with a mean of 6.3. The alkaline cations Ca and Mg and also Bicarbonate anion acted as acid neutralizers and buffered the acidity of rain. The Bicarbonate anion had the highest concentration, among all other ions, over both stations and plays an important role in reduction of the acidity of the rain water. Study upon the potential sources of the ions over Tehran by means of Correlation Analysis, Enrichment Factor and Principle Component Analysis indicate that existence of Qom salt lake and desert in the south of the city were the main source of the Ca,Mg,K and Na . There are anthropogenic sources for SO 4 ,NO 3 , NO 2 ,F, Br ion’s. The other ions HCO 3 and NH 4 were from anthropogenic and dust origin.

Compared to the small-scale situation, some constraints in large-scale rice fields have caused crop growth models to fail to reach an acceptable estimate of yield. This study was conducted to investigate the possibility of enhancing the accuracy of the CERES-Rice model prediction at a large scale through the use of Landsat 5 satellite imagery (termed ‘assimilation’). Firstly, the model was calibrated by data taken from local research. The model accuracy was then evaluated in 110 paddy fields over 26 000 ha (method A). Then the model was recalibrated by paddy yield estimated from Landsat 5 image (method B). The two methods were compared based on their results. The results revealed that RMSEn in simulating grain yield in small-scale field experiments on the Hashemi cultivar for calibration and validation of the model were 9 and 8%, respectively (R2 = 0.7), which indicated the model's high accuracy in yield prediction. While RMSEn in simulating grain yield in large-scale (methods A) was 22% (R2 = 0.54), the use of Landsat images in the assimilation method (method B) increased its accuracy dramatically to RMSEn of 12.7% (R2 = 0.72). Copyright © 2016 John Wiley & Sons, Ltd.

Numerous equations exist for estimating reference evapotranspiration (ETo). Relationships were often subject to rigorous local calibration, hence having limited global validity. The Penman–Monteith (P − M) equation is widely perceived as the best equation for estimating daily and monthly ETo in all climates. The main shortcoming of the P − M equation is that it requires numerous weather data that may not always be available. This study evaluates the methods to estimate missing data in the context of their influence on the performance of the ETo equations. The performance of other ETo equations under missing data are also compared. ETo equations are ranked individually in semi − humid and semi − arid climates based on their accuracy. Results indicate that the P − M equation is more sensitive in semi − arid climate than semi − humid climate under missing data conditions. The accuracy of the P − M equation under these conditions increases remarkably if any available relationships between dew point and minimum temperatures and also long–term average wind speed for each station are exploited. Finally, the minimum data requirements necessary for adequate performance of the P − M equation are air temperature for semi − humid climates, air temperature and wind speed for semi − arid climates, and the availability of a relationship between dew point and minimum temperature, especially for semi − arid climate. In absence of the satisfaction of such minimum requirements, the Hargreaves–Samani equation is preferable for semi − humid climates and the Hargreaves equation modified by Droogers and Allen (2002) for semi − arid climates.

Uncertainty analysis can provide useful insights into the sources and effects of uncertainty for decision makers to achieve the goals of reliability and sustainability in water management. This study presents parameters uncertainty of a physically based soil–water–atmosphere–plant (SWAP) model and its effect on model prediction within the generalized likelihood uncertainty estimation (GLUE) framework for two irrigated agricultural fields in a dry region of Iran. To simulate soil water dynamics of the two fields, the SWAP model is calibrated using soil moisture observation data. The results demonstrate that predictive uncertainty in soil moisture during the growing season in both fields is relatively small and a good model performance is achieved. Parameter uncertainty analysis of soil hydraulic parameters showed that in spite of similarity of soil texture in both the fields, the estimated parameters (i.e. posterior distribution) exhibit different behaviors. This was because of the dynamics of soil structure which varies considerably within cultivated fields during the growing season. Moreover, the simulated water balance fluxes (actual evapotranspiration and deep percolation) indicate that in irrigated agricultural fields in dry regions, the precision of actual evapotranspiration predicted by the SWAP model is high (i.e. a high degree of model reliability is achieved). However, deep percolation fluxes show higher variation (lower precision) and are more sensitive to soil hydraulic conductivity parameterization. Finally, this study reveals the importance of uncertainty analysis to estimate the degree of reliability associated with model predictions as an important first step for providing decision makers with realistic information about the models outputs.

Soil moisture is considered a key index of agricultural drought monitoring systems due to its importance for plant growth and biological interactions. In this research, a Soil Wetness Deficit Index (SWDI) was developed based on a Soil Wetness Index to evaluate soil moisture deviation as an indicator of agricultural drought. The Soil Wetness Index is derived using a triangle space concept between the land surface temperature (LST) and vegetation index (NDVI). To acquire the triangle space concept, 8-day-products of land surface reflectance and LST derived from MODIS satellite data over Isfahan were used. The data was collected in the period of 2000–01 (dry year) and 2004–05 (wet year) on an 8-day time step. The results indicated that the SWDI index has the capability of mapping the spatial distribution of areas affected by drought, as well as the drought intensity. The estimated cumulative number of dry days (with −4

Snow is one of the integral components of hydrological and climatic systems. Needless to say, snow cover areas (SCA) are considered as indispensable input of hydrological and general circulation models. Studying the spatial and temporal variability of SCA is of the paramount importance for tremendous variety of research such as climate change, water supply and properly managing water resources. In this study by means of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product, the variation of snow cover extent (SCE) in Karoun basin located in western part of Iran is evaluated for twelve years' duration; since 2000 to 2012. The results show that the paramount occurrence of SCE is observed during February months of 2003, 2010 and 2011 as well as during December months of 2006 and 2009.The utmost occurrence of SCE is considered during January months of the other remaining years. Annual average shows that SCE varies from 11% in 2011 to 22% in 2006. According to Mann-Kendal trend test, throughout twelve years; 2000 to 2012, a majority of the pixels in the study area have no considerable trend although there is a decreasing trend on a small portion of the pixels located in the eastern part the study domain.

The numerical weather forecast model Weather Research and Forecasting (WRF) has a range of applications because it offers multiple physical options, enabling the users to optimizing WRF for specific scales, geographical locations and applications. Summer rainfall cannot be predicted well in North West of Iran (NWI). Most of them are convective. Sometimes rainfall is heavy, so that it causes flash flood. In this research, some configurations of WRF were tested with four summer rainfall events in NWI to find the best configuration. Five cumulus, four planetary boundary layers (PBL) and two microphysical schemes were combined. Twenty-six different configurations (models) were implemented at two resolutions of 5 and 15 km for duration of 48 h. Four events, with over 20 mm convective daily rainfall total, were selected at NWI during summer season between 2010 and 2015. These events were tested by developing 26 unique models. Results were verified using several methods. The aim was to find the best results during the first 24 h. Although no single configuration can be introduced for all times, thresholds, and atmospheric system to provide reliable and accurate forecast, the best configuration for WRF can be identified. Kain-Fritsch (new Eta), Betts-Miller-Janjic, Modified Kain-Fritsch, Multi-scale Kain-Fritsch and newer Tiedtke cumulus schemes and Mellor-Yamada-Janjic, Shin-Hong ‘scale-aware’, Medium Range Forecast (MRF) and Yonsei University (YSU) Planetary Boundary Layer schemes and Kessler, WRF Single Moment 3 class simple ice (WSM3) microphysics schemes were selected. The result show that Cumulus schemes are the most sensitive and Microphysics schemes are the less sensitive. The comparison of 15 km and 5 km resolution simulations do not show obvious advantages in downscaling the results. Configuration with newer Tiedtke cumulus, Mellor-Yamada-Janjic PBL, WSM3 and Kessler microphysics schemes give the best results for the 5 and 15 km resolutions. The output image of models and statistical methods verification indexes show that WRF could not accurately simulate convective rainfall in the NWI in summer.

Extreme weather, by definition, is any unexpected, unusual, unpredictable, severe or unseasonal weather condition. A rainfall event that is considered normal in one region may be considered a torrent in a dry region and may cause flash flooding. Therefore, appropriate weather warnings need to be issued with respect to areas with different climates. Additionally, these alerts should be easy to understand—by clear classification—in order to apply reinforcements. Early warning levels not only depend on the intensity and duration of rainfall events, but also on the initial water stress conditions, land cover situations and degree of urbanization. This research has focused on defining different warning levels in northwest Iran using long-term precipitation data from 87 weather stations well distributed across the study area. Here, in order to determine alert levels, TOPSIS (The Order of Preference by Similarity to Ideal Solution), as one of the most common methods in multi-criteria decision making, has been used. Results show that five main levels of alerts can be derived, leading to the provision of spatial maps. Further, it can be deduced that these levels are highly associated to the location of a region at different times: months/seasons. It has been observed that the issuance of a warning for precipitation should correspond with the location and time. At one location during different seasons, different alert levels would be raised corresponding to the rainfall. It was also concluded that using of fixed alert levels and extending them to larger areas without considering the seasons could be grossly misleading.

To maximize total net benefits, farmers often tend to increase the area under cash crops production. Such crops may include water-intensive or salt sensitive crops. Restrictions on available water and drain water disposal conditions in the arid and semi-arid region could limit adoption of high value cropping pattern. Production of huge amounts of saline drainage waters threatens environmental sustainability of downstream water resources. A multi-objective model is presented for simultaneous management of crop yield, irrigation and drain water. The proposed model is an environmental economic model, at irrigation district-scale, that integrates agronomic, irrigation, drainage and economic aspects of irrigated agriculture. Source reduction strategies for irrigation water management are considered because they affect management of drainage water, crop yield and net profits. The recent MOPSO technique was applied to solve the multi-objective problem. Distributed soil-water-atmosphere-plant model (SWAP) was set up for regional simulation of soil salinity, crop yield and drain water. The developed model was applied to Voshmgir irrigation and drainage network in north eastern IRAN. It was found that using a multi-objective model makes feasible a flexible balance between environmental and economic benefits of irrigation and drainage scheme management. The proposed model obtained the optimized irrigation water allocation and cropping area to different crops which resulted in the reduction of drainage salt load to downstream while ensuring the net benefit function in the satisfactory level. The model results showed that variety in the physical and water allocation policy within different parts of irrigation and drainage network have a considerable impact on the cropping area and irrigation water of each crop and thus the net benefits as well as production of drain water for each crop and its salt load.

Dez dam is one of the key dams in Iran. It is 203 m high with a reservoir of 2.3 BCM volume. This double arch dam has been constructed in a narrow gorge in 1962, southern west of Iran. It has three irrigation outlets at level of 222.7 m asl, two power intakes at elevation of 270 m asl. No bottom outlet have been designed and installed for Dez dam and consequently the sediment-entering reservoir cannot be evacuated easily. Considering an annual 15.8 million cubic meter sediment inflow into the reservoir, the bed level is increasing about 1.2 meter per year and therefore it is very necessary to take an appropriate measure saving dam reservoir. One of existing method to save dam reservoir is to flush the sediment out through existing bottom outlets. In this study, the feasibility and efficiency of pressure and free flow sediment flushing techniques have been studied and some good points are presented. The results of present study shows that the flushing method cannot resolve the sedimentation problem of Dez dam and it must be applied jointly by other methods to save the reservoir from being decommissioned.

This paper addresses the question of whether data assimilation of remotely sensed leaf area index and/or relative evapotranspiration estimates can be used to forecast total wheat production as an indicator of agricultural drought. A series of low to moderate resolution MODIS satellite data of the Borkhar district, Isfahan (Iran) was converted into both leaf area index and relative evapotranspiration using a land surface energy algorithm for the year 2005. An agrohydrological model was then implemented in a distributed manner using spatial information of soil types, land use, groundwater and irrigation on a raster basis with a grid size of 250 m, i.e. moderate resolution. A constant gain Kalman filter data assimilation algorithm was used for each data series to correct the internal variables of the distributed model whenever remotely sensed data were available. Predictions for 1 month in advance using simulations with assimilation at a regional scale were very promising with respect to the statistical data (bias = ±10%). However, longer-term predictions, i.e. 2 months in advance, resulted in a higher bias between the simulated and statistical data. The introduced methodology can be used as a reliable tool for assessing the impacts of droughts in semi-arid regions.

Borkhar district is located in an and to semi-arid region in Iran and regularly faces widespread drought. Given current water scarcity, the limited available water should be used as efficient and productive as possible. To explore on-farm strategies which result in higher economic gains and water productivity RP), a physically based agrohydrological model, Soil Water Atmosphere Plant (SWAP), was calibrated and validated using intensive measured data at eight selected farmer fields (wheat, fodder maize, sunflower and sugar beet) in the Borkhar district, Iran during the agricultural year 2004 - 2005. The WP values for the main crops were computed using the SWAP simulated water balance components, i.e. transpiration T, evapotranspiration ET, irrigation 1, and the marketable yield Y-M in terms in terms of YMT-1, YMET-1 and YMI-1. The average WP, expressed as $ T-1 (US $ m(-3)) was 0.19 for wheat, 0.5 for fodder maize, 0.06 for sunflower and 0.38 for sugar beet. This indicated that fodder maize provides the highest economic benefit in the Borkhar irrigation district. Soil evaporation caused the average WP values, expressed as Y-M ET-1 (kg m(-3)), to be significantly lower than the average WP, expressed as Y-M T-1, i.e. about 27% for wheat, 11% for fodder maize, 12% for sunflower and 0.18 for sugar beet. Furthermore, due to percolation from root zone and stored moisture content in the root zone, the average WP values, expressed as Y-M I-1 (kg m(-3)), had a 24 - 42% reduction as compared with VIP, expressed as Y-M ET-1. The results indicated that during the limited water supply period, on-farm strategies like deficit irrigation scheduling and reduction of the cultivated area can result in higher economic gains. Improved irrigation practices in terms of irrigation timing and amount, increased WP in terms of Y-M I-1 (kg m(-3)) by a factor of 1.5 for wheat and maize, 1.3 for sunflower and 1.1 for sugar beet. Under water shortage conditions, reduction of the cultivated area yielded higher water productivity values as compared to deficit irrigation. (c) 2007 Elsevier B.V. All rights reserved.

This study introduces an empirical equation for estimation of Aerosol Optical Thickness (AOT) and visibility reduction based on three main dust indices of Normalized Di erence Dust Index (NDDI), Brightness Temperature Di erence (BTD), and Thermal infrared Dust Index (TDI). The implementation of NDDI, BTD, and TDI in dust enhancement over bright to dark background was evaluated. The thresholds of BTD over dust and cloud pixels revealed the capability of Moderate Resolution Imaging Spectroradiometer (MODIS) images in separating dust from underlying bright surfaces and clouds. The results indicated that solar reffective bands were insu cient to precisely separate dust from clouds, but combination of solar reffective bands and thermal infrared bands synergistically improved the accuracy. The evaluation of results revealed the remarkable correlation of AOT with dust enhancement indices over 11 synoptic stations: BTD (R2 = 0:73), NDDI (R2 = 0:67), and TDI (R2 = 0:71). Hence, AOT and visibility reduction were obtained using multi-regression equation using NDDI and BTD as variables. The accuracy assessment indicated good correlation (R2 = 0:74) between both estimated AOT and the AOT reported by Air Quality Control stations. Moreover, the results con rmed the advantage of proposed AOT as a consistent index for dust enhancement over bright surfaces and dust classi cation as well.