جستجوی مقالات مرتبط با کلیدواژه « Regionalization » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Regional Flood Frequency Analysis (RFFA) is an efficient method in flood discharge estimation in ungauged watersheds or with short-term statistics. The purpose of the present study is to evaluate the hybrid method in RFFA and maximum flood discharge estimation in the Namak Lake watershed, Iran. For this purpose, 16 hydrometric stations were selected. The selected hydrometric stations were then divided into five homogeneous areas based on watershed area. Three stations from different homogeneous regions were selected and used for validating the RFFA model, and with remaining 13 stations, the hybrid equations were estimated at 2, 5, 10, 25, 50, and 100-year return periods. The results showed that the accuracy of the RFFA models in the mentioned return periods are acceptable with the agreement index (d) more than 0.8. Root Mean Square Error (RMSE) in high return periods is also more than in low return periods. So that the lowest RMSE in the two-year return period was equal to 1.55 m3 s-1 and the highest in the 100-year return period was estimated at 14.64 m3 s-1.

Daily streamflow information has a major role for solving various hydrologic problems, but the continuous information of daily streamflow is not available in most cases. One of the approaches to estimate daily streamflow at an ungauged location is to transfer rainfall–runoff model parameters calibrated at a gauged (donor) watershed to an ungauged (receiver) watershed of interest. This method is based on selecting a similar gauged watershed to ungauged watershed. The study examines the possibility of simulating time series of daily streamflow for ungauged watersheds based on hydrological similarity. The data of 14 gauged watersheds (with drainage area between 16 to 804 square Kilometers) with 11-year time series of daily streamflow data located in central Alborz region, Iran, are used for this research. The HBV model is applied to simulate daily streamflow with parameters transferred from gauged watersheds counterparts. This model has been used widely in previous similar studies and was selected for this research due to its simplicity, input data availability and adaptability to different climates. Hydrological similarity is defined based on three regionalization methods including drainage area, spatial proximity and physical similarity. The obtained results showed that transferring HBV model parameters based on spatial proximity method produces better runoff simulation compared to the other two methods and physical similarity is preferred to drainage area. Also, the results indicate that the HBV model has a high ability for regionalization of daily streamflow in the study area and the uncertainty related to model parameters could be neglected by using the best calibrated parameters set of model. According to the aforementioned brilliant results of applying HBV model predictions in this study, we suggest this model in future similar study.

landslides situation recognized using interpreting the aerial photos and extensive field measurements. Among total number of 200 identified landslides, %70 (140 landslides) of them have been utilized for model executing and %30 (60 landslides) of them for verification randomly. This research criteria including geomorphological parameters, hydrological parameters , geological parameters and environmental parameters . The Shannon’s entropy model have been used for defining the criteria weight and Area density model for defining classes weight, then the regionalization map obtained by combining the criteria and classes weight in ArcGIS 10.2 software environment and classified to 5 classes very little, little , moderate, high and very high according to natural fractures. The Roc curve have been used for model verification. The clerical accuracy results indicated that the compound model have the high accuracy 0.877 (87.7%) for identifying the regions susceptible to landslide. According to the results, slope length, slope and topography wetness index have had the most effect in occurring the landslide. Among total area of region (168547 hectar), 27.39% (46165.02 hectar) have been placed in high and very high sensitive. The prepared regionalization map can be useful for planning land use and building the infrastructure installations such as road.

A flow-duration curve (FDC) illustrates the relationship between the frequency and magnitude of daily, weekly and monthly stream flow. Applications of FDC are of interest for many hydrological problems related to hydropower generation, river and reservoir sedimentation, water quality assessment, water-use assessment, water allocation and habitat suitability. This study was carried out in 33 selected watersheds for regional analysis of FDC in Namak Lake basin. The FDCs were drawn for 33 selected watersheds, discharges of two to 92 percent were determined and the best probability distribution was recognized among ten probability distributions. The discharges of two to 92 percent with two-year return period were estimated. Using factor analysis selected six factors, includes area, average height, main channel length, drainage density, and percentage of permeable formations among 18 physiographical, meteorological, geological and land use characteristics as independent variables for regional of analysis of FDC. The multiple regression technique and combination of cluster analysis for determination of the homogenous watersheds and multiple regression techniques were carried out regional analysis of FDC. Determination coefficient (R2), root mean square error (RMSE) and efficiency coefficient (CE) statistics are employed to evaluate the performance of the models in all region and homogeneous regions. The results showed that regional analysis with homogeneous regions causes higher efficiency and lower error. According to beta coefficient of the regression equations, the continuity of the main channel flow rate was the most important parameter in the FDC.

Self-Organizing Feature Maps (SOFM) are a variety of artificial neural networks that their applications in the areas of pattern recognition and data clustering makes them noticeable tools to perform regional flood frequency analysis (RFFA). In this study, ability of Self-Organizing Feature Maps for regionalization of Sefidrood watershed in order to perform regional flood frequency analysis using L-moment algorithm is assessed. Results of this study show that SOFMs may be used as an acceptable method for data clustering and regionalization of watersheds. Evaluation of values of cluster validity measures showed that they can’t be a determining factor to identify suitable number of regions for regional flood frequency analysis, but homogeneity of regions is main factor to determine desirable number of regions. According to homogeneity of regions and sizes of formed regions, regionalizations including two regions that formed by Ward’s algorithm and SOFM were chosen as optimum choices to regional flood frequency analysis on Sefidrood watershed. Furthermore, based on results of flood estimation by at-site FFA and two RFFA, regional estimates are very close to each other and their average relative difference is equal to 1% nearly. Also relative difference between regional and at-site estimates doesn’t exceed 17% in any station and its mean value is about 8%.

Cluster analysis methods are one of the most efficient approaches of regionalization of watersheds for Regional Flood Frequency Analysis (RFFA). Fuzzy regionalization is a kind of regionalization in which each site of interest may be assigned to more than one region simultaneously. In order to perform regionalization، a variety of cluster analysis algorithms named fuzzy clustering are used that fuzzy c-means algorithm is most wellknown of them. Also Self-Organizing Feature Maps (SOFM) are a special class of Artificial Neural Networks (ANN) that has found several applications in the areas of pattern recognition. Capability of this class of networks in areas of pattern recognition and data clustering using their attributes has make some hydrologist interested in testing ability of these maps for regionalization of watersheds in order to perform regional flood frequency of analysis. In this study self-organizing feature maps have been used to determine initial centroids of clusters in fuzzy c-means algorithm for regionalization of Sefidrood watershed. Results of this study showed that this approach has an acceptable performance in formation of homogeneous regions and providing suitable estimates in regional flood frequency analysis using L-moment algorithm in interested watershed. Furthermore it is observed that fuzzy clustering may provide longest reliable flood estimates. Also based on fuzzy clustering validity measures it’s seemed that two or three regions is appropriate number of regions for regional flood frequency analysis in this watershed.

One of techniques for estimation of flood quantiles in ungauged watersheds is regional frequency analysis. In the common techniques of regionalization، homogeneous hydrological regions are created based on watershed characteristics such as physiographic characteristics of watershed، geographic location and flood statistics at each station. However، most watersheds only partially resemble other watersheds in a region. Therefore one watershed cannot be completely assigned to one group or another. The fuzzy clustering algorithm allows a watershed to have partial or distributed memberships of all the regions (groups) identified. In this paper، performance of fuzzy clustering algorithm has been evaluated for regionalization of Mazandaran province''s watersheds، and some solutions have been represented to improve homogeneity of the regions that have been clustered by this algorithm and are not statistically homogeneous. In addition، several indices for performance evaluation of fuzzy clustering algorithm have been presented to determine the number of optimum clusters. Then، the Zdist statistic for the region''s stations was calculated by FORTRAN programming language for the 3-parameter distributions and the best distribution was introduced for each region. The results showed that Mazandaran province has been composed of three hydrological homogeneous regions and generalized logistic distribution with different parameters was selected for each of the homogeneous regions.

The regional frequency analysis is one of the methods that estimates maximum daily rainfall in regions where no data is available. Cluster analysis is used to identify hydrologically homogeneous regions in regional studies. In this study، fuzzy cluster analysis was applied to identify hydrologically homogeneous regions at the southern coast of Caspian Sea in the Golestan، Mazandaran and Gilan provinces. The homogeneity of each region identified by cluster analysis was checked by Hosking-Wallis heterogeneity measures. Then، suggestions were proposed for adjusting the homogeneity of the regions. The best regional statistical distribution was selected based on the goodness of fit test. The generalized logistic distribution was chosen as the best regional statistical distribution with different parameters for each homogenous region in this study. Finally، parameters of maximum daily rainfall were estimated by the L-moment method.