Integrating a Geographic Information System (GIS) with an expert system can overcome the deficiencies of each system. This paper illustrates the integration of a GIS with an expert system in order to find the suitability rating of seven crops (maize, cotton, barley, soybean, tobacco, wheat, and rice) for the Kor and Sivand sub-basin. Although GIS can perform various analyses like overlaying, buffering, and etc., but it does not have the tools to analyze multiple interrelated factors. On the other hand, expert systems can carry out various analyses but they cannot consider georeferenced information. Land evaluation is a vital activity for rural development and planning worldwide. A physical suitability evaluation indicates the degree of suitability for land use, without respect to economic conditions. Since different crops need different physical conditions to grow, the main aim here is to find how much each crop yields given the climatic, edaphic and slope conditions. The model discussed in this paper considers not only the management views (practicality of raising and saving different crops) but also the interaction between different multiple interrelated factors. In addition, the suitability rating discussed in this paper is based on the FAO framework.

The desert areas of the playas are important due to the diversity of geomorphological landforms and the great potential for geo-tourism development. These areas can be provided using statistical and quantitative models to determine the areas with a higher geomorphological diversity value to land planners for the sustainable development. This research was conducted to evaluate the geomorphodiversity in Mesileh playas in Namak Lake and Hoz-e- Sultan region. The geomorphological landforms in this area were extracted using satellite images and field studies, Fragstats software and the eight quantitative indicators of the landscape metrics include Patch Richness Density, Relative Patch Richness, Simpsons Evenness Index, Index Shannon Evenness, Shannons Diversity Index, Simpson's Diversity Index, Modified Simpson's Diversity Index, Modified Sahanon Evenness Index were exerted to evaluate the richness of geomorphological units in Masileh playas. The results indicate that the geomorphological units of alluvial fans, salt flats, salt mud flats and dunes, respectively, have the highest value among the landscape metrics. In addition, from the point of view of the landscape metrics, the northwest regions of Kashan and Abuzeydabad have the most incredible variety of geomorphology. This research showed that geomorphological units could be used as a suitable indicator in evaluating the diversity of the landscape, especially in flat areas with morphometric and geological diversity.

Increasing world population has led to product more foods andcrops, while agricultural lands have been decreased. Then, itis necessary to use the maximum potential of these lands whichproduct maximum yield without any damage. To reach this objective, land suitability evaluation is the most important waythat can reach-this objective. The main objective of this researchwas to compare different irrigation methods based on a parametricevaluation system in an area of 100 ha in the Chikan andMourzian Subbasin of the Fars province, in the south of Iran. After preparing land unit map, 10 points were selected forsampling. Soil properties were evaluated and analyzed. Suitabilitymaps for drop and gravity irrigation were generated using GIStechnique. The result revealed land suitability of 71. 9 ha (71. 9%)of the case study was classified as permanently not suitable(N2) and 28. 1 ha (28. 1%) currently not suitable (N1) for gravityirrigation. On the other hand, land suitability of 47. 3 ha (47. 3%)of the case study was classified as permanently not suitability(N2), 28. 5 ha (28. 5%) currently not suitable (N1) and 24. 3 ha(24. 3%) marginally suitable (S3) for drop irrigation. The limitingfactor for drop irrigation was slope and for gravity irrigationwere slope and drainage.

A major part of hydrological researches focused on complex and non-linear rainfall-runoff process. Mathematical models were presented to describe this process including a wide range from simple black-box representation to complex physically-based models. Considering inherent uncertainty associated with the process as a result of uncertain input variables and uncertain calibrated parameters, stochastic modeling seemed preferable to deterministic approaches. In this study, data-based mechanistic modeling (DBM) was selected to identify non-linearities of the process. The method is categorized as a stochastic approach relying upon recursive parameter estimation using Kalman filtering algorithm in state space system of equations. In addition, it is capable to reflect a physical interpretation of rainfall-runoff conversion to describe the behavior of the system. The later capability differs it from other black-box modeling approaches. In this research, a parallel structure of flow routes was identified in upper-Karoun subbasin of the great Karoun catchment. Sensitivity analysis was also carried out based on Monte Carlo simulation (MCS) method and the reliability of the presented model were quantified.

Predicting of the river discharge is one of the most important issues in the planning and management of water resources in terms of energy generation, water allocation and use of water resources. In this study, the trend of climate parameters such as temperature and precipitation and so discharge as a hydrologic parameter were investigated based on the Mann-Kendall test and then was modeled and predicted the discharge of monthly streamflow Godarkhosh in the period 1992-2012, using linear time series SARIMA. To investigate the static model, tests autocorrelation (ADF) and partial autocorrelation (PACF) were used. The results of the Mann-Kendall test indicated the temperature parameter has an increasing trend and precipitation and discharge parameters showed reduction trend at all stations of the studied area at 99% confidence level. Also, the result of time series model indicated that the time series data of river flow were non-static data that with the differencing method, the non-static data transformed into static data. Then different models were fitted into static series to estimate the river flow and finally, model ARIMA (1, 0, 0) (0, 1, 1) was selected as an appropriate model to predict discharge and the river flow was predicted for 4 years from 2013 to 2016. The results showed that the discharge has increased at the end of the period, in which the model estimated river flow as well based on rainfall data from Meteorological Organization in the 2015-2016 year. Therefore, application of time-series models in water resources management can be useful.

In order to manage the rangeland resources, remote sensing data is able to provide a sensible role of different cases in flora community such as biomass. The study area in SefidAb subbasin of the Lar Dam basin is located in central Alborz, where the climatic condition is semi humid and near to moderate. For the assessment of the sattelite data and their capability in estimation of the range production, Landsat-TM data with different bands was used. In this research, the field data was collected using random-systematic method in 20 sampling units of 200 plots. For geographic coordinates of the sampling units and related pixels in digital data, GPS and also existing benchmark data of the nearest points were used. Then correlation between ground data and vegetation index from different band combination was investigated and the reasonable vegetation indices were obtained. Finally, the best models were extracted for this purpose, which showed sensible relation between the field data and vegetation index. Therefore, it is possible to estimate range production using Landsat TM data related to ground control.

The results of this study indicated that the synoptic patterns that lead to heavy rainfall in 22 March 1996, 24 mar 1995 and 17 November 1994 in the northern portion of FARS province includes: the low pressure that located in eastern part of Mediterranean and Black sea and southern part of Caspian Sea that created by trough that located at the middle level of atmosphere…..

Identification and prioritization of subbasin areas in relation to flood potential is a key task in management of large basins. Golestan Dam basin has recently experienced severe floods so that the basin is considered crucial for flood control planning. In this research, HEC-HMS hydrologic model and geographic information system (GIS) were jointly used to investigate the contribution of subbasins to the flood peak at the basin outlet. Unit flood response method was applied to prioritize subbasins with respect to a flood index. The results showed that subbasin flood index was not solely dependent on area but location, river routing characteristics, and rainfall spatial distribution have major effects. It was evident that the southern Galikesh subbasin ranks first in flood index due to receiving high intensity rainfall, whereas Robat-Gharahbill located near the most upstream part of the basin ranks last.

Surface water collection networks are responsible for collecting, transporting and directing runoff to the nearest acceptable outlet. In this research, simulation and evaluation of Ilam City runoff system with the ASSA mathematical model developed by the Autodesk Inc. was carried out. In the present research, the entire study area, which includes in-city and outsides, was divided into 46 sub-basins. The physical properties of each sub-basin were determined using the ArcGIS software and field investigation. Within the Ilam City there are four main channels that lead the runoff of sub-basins out of the city. The runoff from sub-basins was calculated using three methods of rational, modified rational and SWMM. The results of the study showed that the collecting system only had a flood transfer ability during the two year return period and during the return periods greater than five years the major part of the main channels in the city were waterlogged and damaged.

This investigation was aimed at developing an equation for suspended sediment yield prediction in Uromia Lake basin using regional analysis. Twenty-seven subbasins were selected based on data availability, data quality of sediment concentration and flow, and spatial distribution. From among the 27 subbasins seventeen and ten were respectively used to develop the regression equation and to validate the model. Twenty-two characteristics (explanatory variables) were chosen and assessed for each subbasin. Independent combinations of explanatory variables, arranged according to their correlations, were drawn to develop regression equations. The most suitable equation was selected from among the generated equations with respect to determination coefficient, and standard error of the estimate. Variables of product of mean annual discharge and subbasin area, sum of areas facing north and west (in percent), and the areas covered with Quaternary sediments as well as lithologies susceptible to erosion (included in the equation) explain over 94 percent of variation in suspended sediment yield. The final equation bears a 25 percent mean absolute error and its efficiency coefficient is worked out to be 0.99.