سعید امامی فر

The high share of agricultural water consumption compared to other sectors and the serious crisis of dehydration in recent years reform and offer optimal crop pattern that reduces water consumption and increases the efficiency of its use, the requirements of most forbiddens and critical plains of the country. The purpose of this study was to provide optimal cropping pattern in relation to Allocable Water agriculture communicated by the Ministry of Energy and the long-term cumulative accumulation deficit of aquifers and the emphasis on agricultural sustainability indicators in the twenty-year planning horizon, Qom-Kahak study area. To simulate rainfall component behavior, outcomes of A2 scenario for the HADCM3 climatic model were used and using the continuity equation balance,the volume of water allocated to the crop sector was estimated each year. Then, using fuzzy utopian planning models, sustainable agriculture patterns were presented within the framework of technical constraints. The results showed that the implementation of the proposed agronomic models will in addition to offset the deficit of 536 million cubic meters and aquifer balance in the study area over the planning horizon, It will increase labor force and physical productivity of water, fertilizers and pesticides consumption will increase by 10.7%, 106%, 4.1% and 8.8% respectively, and water resources, fertilizers and chemicals consumption will reduce by 48.2%, 3.9%, 8 respectively.. Therefore, it is suggested to regional authorities to prioritize the implementation of proposed agro-projects, which requires the development of legal requirements, incentive and punitive policies, and conducting training and extension courses.

The main purpose of this study is reform, development and evaluation of dynamic water balance model “DWB” with the approach of the proportional coefficients “To the kind of hydrological reality-based constraints” and acts the withdrawal of groundwater and surface water resources along with calibration and uncertainty analysis its parameter. The results showed that generally cannot obtain a set of optimal parameters for calibration SDDWB and DWB model and often the parameters of the models are low detection capability for model calibration in both study area (Neyshabur and Rokh watershed). The average total amount of surface water balance error DWB model for Neyshabur and Rokh watershed is Respectively -9.74 and -69.63 Cubic meters that is Represented poor performance model. there is a significant difference between Aquifer volume changes observed and simulated by DWB That the main reason is the lack beneficial effects of groundwater withdrawals in this model. Reviews performance results showed that the relative PRMSE index of the SDDWB model for both the study areas less than 10 percent, indicating acceptable accuracy SDDWB model. In general, the results indicate that the overall error rate of surface and groundwater discharge equation components using SDDWB model is much lower than the DWB model, which indicates the positive impact reform and development on the DWB model.

The analysis uncertainty of hydrological models and identify the statistical properties them basis of existing relationships between of the model parameters and inputs is numerous of the most important hydrological modeling. In this context one of the known methods for modeling the water balance at the catchements scale is Dynamic water balance model (DWB) that By Zhang et al. (2008) developed. In this study, the model calibration and uncertainty assessment in the Andrab and Farvbrman catchments from the Kalshvr River branches (Nishapur catchment) respectively at the zarandand and Eyshabad hydrometery stations, located in semi-arid regions by using Generalized likelihood uncertainty estimation (GLUE) method was studied. The results indicated that in terms of the ability to identify, Between four parameters of the model, Retention efficacy parameter (ω_1) and basin water storage capacity (s_max [mm]) in the calibration process was less of the ability to identify (The specific domain ptimum for their can not be found) and uncertainty in the runoff simulated by the model, have a greater role. In this direction, the efficacy parameter evapotranspiration (ω_2) and the gradual flow (d), resistant behavior of the errors in the runoff observation and are so-called conservative. Evaluation of uncertainty runoff simulated showed that in general, GLUE method is well managed that it calibrate amount of runoff from case study, so that more of the runoff data recorded (over 55%) in the range of 95 percent.

One of the crucial information, as input in modeling of hydrological processes, is relevant to soil moisture and especially the retention characteristic information available at catchment scale. This paper presents a method to relative approximation of these quantities shown. Accordingly, at the beginning, Determination and extraction of soil texture class's USDA method by Using soil texture class proposed by the Commission of the European Communities (CEC) (these method «CEC» has the 6 texture classes with codes 0 to 5) Was conducted. In continuation of these texture classes (texture classes USDA method) to estimate and AS a result in mapping the amount of field capacity and wilting point (for the whole country) is used. In these respect, the results showed that in general, the average range of soil moisture variations in texture classes by CEC method, for permanent wilting point is exceeded field capacity So that the changes for permanent wilting point than the average 6. 77 to 10. 9 and for the field capacity of 6. 03 to 9. 3 percent Is has the variations. Also in this study, according to expert opinion and utilizing scientific resources attempted to investigate the interaction between the effects of land use and slope of watershed in order to assess the information and the resulting the depth map of the surface layer of soil moisture retention. In this direction to assess and evaluate the applicability of the approach taken comply with the physics of the catchment, attempted to utilizing it (Effective depth map of the surface layer the soil moisture retention) along with wetting properties Maps of field capacity and permanent wilting point that was achieved the National Soil Map (the first stage of the research), In order to estimate and prepare a map of available moisture in areas of study there, Nishapur, Attaiyeh, Qaleh Meydan and Sabzevar as the regions chosen for study were used. The results indicate that the resulting map is proportional to the physics of the studied areas in order to achieve total available water and the role of the interaction basin slope, land use And well as the impact the moisture field capacity and permanent wilting point that was extracted from the soil map On Total available water is well evident. Accordingly, due to the integrated and comprehensive characteristics of moisture maps (field capacity and permanent wilting) for the whole country, this amount could be used as a primary domain of soil moisture characterized along with the method described in the effective depth of surface soil moisture retention on Hydrological modeling, particularly Understanding the phenomenon of a small amount of influence and power of water retention in the soil, the estimated components of water balance, etc. in order to prevent land degradation and soil and water resources management, to be used.

The use of satellite data in an estimation of air temperature (Ta) near the earth’s surface has turned into an effective way for a large area of high spatial and temporal resolution. Throughout the present study، Artificial Neural Network (ANN) as well as M5 model tree were employed to estimate Ta in Khuzestan Province (South West of Iran)، using satellite remotely sensed land surface temperature (Ts) data acquired through the MODIS-Terra sensor. The input variables for the models consisted of the daytime and nighttime MODIS Ts as well as extraterrestrial solar radiation. A total of 365 images of MOD11A1 Ts product for the year 2007، covering the area under study were collected from the Land Processes Distributed Active Archive Center (LP DAAC). The results indicated that coefficient of determination (R2) for both models exceeded 0. 96. However، ANN model estimations of air temperature were more accurate than RMSE with the respective R2 values of 1. 7 and 0. 97 oC.

Estimation the amount of radiation reaching the Earth's surface (Rs) is an important factor in the energy balance models simulation of plant growth and evapotranspiration estimation. Most Estimation models to radiation reaching the Earth's surface use satellite data and they are based on land surface temperatures. In this study, the Accuracy of solar radiation estimation is investigated Using four different models of neural networks (with the names of ANN1,ANN2, ANN3, ANN4) with the inputs Including products land surface temperature MODIS sensor (models 1 and 2, and models 3 and 4 are based on MOD11A1 MYD11A1 products, respectively), extraterrestrial radiation (Ra) and relative sunshine (n / N). The results show that four neural network models are able to estimate the amount of radiation reaching the Earth's surface with good correlation (R2>. 85). However, models based on MOD11A1 products have a higher accuracy than models based on MYD11A1 products. Neural network model of ANN1 (based on MOD11A1 products, relative sunshine and extraterrestrial radiation (Ra)) with the coefficient of determination (R2) equal to. 9332 and the root mean square error (RMSE) equal to 1.4448 MJ per square meter per day is more accurate on the estimation of solar radiation than other models. The results also showed that the Neural network model ANN2, comparing with Hargreaves and Samani models based on air temperature and extraterrestrial radiation, is More accurate in estimating of solar radiation.

Solar radiation is one of the input parameters needed for simulation of processes associated with climate changes، hydrology and ecology. Proposed models to estimate this parameter because using the weather data from a spatial point، would also provide point estimations. In this study، two experimental models including Hargreaves - Samani and Mahmood - Hubbard that are based on air temperature data، with the input data of daily LST products MOD11A1 and MYD11A1 MODIS instead of air temperature، were used to estimate the solar radiation. The real data were obtained from Ahwaz synoptic station for 2006 and 2007. Validation results indicated that Hargreaves - Samani input MOD11A1 LST product and the model of Mahmood - Hubbard MYD11A1 products with input LST (Land Surface Temperature) have the larger and lowest accuracies in estimating solar radiation. Validation results were further indicated that Hargreaves - Samani input MOD11A1 LST product and the Mahmood - Hubbard input MYD11A1 LST product، respectively، have maximum and minimum accuracy in estimating solar radiation. For these models، R2 and RMSE statistics were، respectively 0. 83، 2. 46 (mj m-2 d-1)، 0. 79 and 4. 09 (mj m-2 d-1). The Hargreaves -Samani model by using M5 model tree models (RS-M5-1 and RS-M5-2) based on LST data of MOD11A1 MODIS products geographic variables were formulated and the outcomes were compared with results of experimental models. The results showed that compares Tree Model RS-M5-1 and experimental models with the same input، The M5 tree model has higher accuracy. In general، tree model RS-M5-2 with LST products MOD11A1 inputs، can estimate the maximum and actual sundial and extraterrestrial radiation more accurate than other models. For this model، the values of R2 and NSE were estimated to be 0. 87 and 0. 86، respectively. The root mean square error for this was 10. 24 percent.