Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation simulation (extent and depth of flood) was conducted using hydrodynamic program HEC-RAS for 50-and 100-year interval floods. The validation of the flood hazard prediction map was conducted based on flood extent and depth maps. The results showed that the AHP technique is promising of making accurate and reliable prediction for flood extent. Therefore, the AHP and geographic information system (GIS) techniques are suggested for assessment of the flood hazard potential, specifically in no-data regions.
Snowmelt is of importance for many aspects of hydrology, including water supply, erosion and flood control. In this study, snow accumulation and melt are modeled using a distributed hydrological model with two different snowmelt simulation modules. The model is applied for simulating river discharge in the Latyan dam watershed, in the southern part of central Alborz mountain range, Iran. The data consists of 3 years of observed daily precipitation, air temperature, potential evaporation, windspeed and discharge. The discharge data is used for model calibration. When using the temperature index method for snowmelt three parameters need to be calibrated, while for the energy balance approach all parameters are preset and not optimized. The model performance is satisfactory for both methods with efficiencies of more than 80%. In order to show the performance of the model, two interesting snow accumulation and melt periods are discussed in detail. This study shows that the model has great potentiality to simulate the impact of snow accumulation and melt on the hydrological behavior of a river basin.
Land use classification is often the first step in land use studies and thus forms the basis for many earth science studies. In this paper, we focus on low-cost techniques for combining Landsat images with geographic information system approaches to create a land use map. In the Golestan region of Iran, we show that traditional supervised and unsupervised methods do not result in sufficiently accurate land use maps. Therefore, we evaluated a synthetic approach combining supervised and unsupervised methods with decision rules based on easily accessible ancillary data. For accuracy assessment, confusion matrices and kappa coefficients were calculated for the maps created with the supervised, unsupervised and synthetic approaches. Overall accuracy of the synthetic approach was 98.2 %, which is over the 85 % level that is considered satisfactory for planning and management purposes. This shows that integration of remote sensing data, ancillary data and decision rules provides better classification accuracy than traditional methods, without significant additional use of resources.
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