Showing 8 results for Frequency Analysis
M.a. Izadbakhsh, S.s. Eslamian, S.f. Mosavi,
Volume 5, Issue 2 (7-2001)
Abstract
Flood is one of the catastrophic events that has attracted the hydrologists’ attention. In this research one of the important flood indices, i.e. maximum-daily mean-discharge, was determined for several western Iran watersheds, namely, in the catchments of Gamasiab, Qarasou, Saimare, Kashkan, Sezar and Abshineh. Daily data were prepared from stream-gauging stations and a 30-year concurrent period was selected.
Flood frequency analysis was performed using HYFA and TR computer programs and optimum distributions were chosen by goodness of fit tests. Extreme flow values having different return periods of 2, 5, 10, 25, 50, 100, 500 and 1000 years were calculated. Modeling was done with regional analysis using multiple regression technique between maximum-daily mean-discharge and physiographic characteristics of the basins. The most important parameter for the selection of the model was the adjusted coefficient of determination while significant level, standard error and observed discharger vs. computed discharge plot acted as controlling parameters. Finally, different models with different parameters were selected from power, exponential, linear and logarithmic forms. The results showed the power model to be the best among the four types. The main channel length, drainage density and time of concentration were the most effective parameters on flow. After analyzing the errors, it appeared that increasing the return period would cause an increase in the model error. At 1000-year return period, the error reached 32.2%.
A Sarhadi, S Soltani, R Modaers,
Volume 12, Issue 46 (1-2009)
Abstract
Low flow estimation and its characteristics play an important role in hydrologic studies. However, some low flow events are ignored compared with the lowest annual low flow that may have high risk. These events are taken into consideration by the use of partial duration or peak over threshold models. In this study, a 7-day low flow was applied for frequency distribution and threshold, and the lower events were considered as the number of low flow event ( ) to study seasonal variation of low flows together with two graphical methods. The results showed two major low flow seasons, and for other times of the year, the low flow events are negligible. At last, the region was divided into homogeneous groups based on seasonal variation of low flows.
A. Shirzadi, K. Chapi, P. Fathi,
Volume 15, Issue 58 (3-2012)
Abstract
Estimation of flood hydrograph is of necessities in hydrological studies such as flood mitigation projects. This estimation in un-gauged watersheds is usually taken place using geomorphological characteristics of watersheds. The objective of this research is to estimate synthetic unit hydrograph using regional flood frequency analysis and geomorphological parameters of watersheds. 1-hour and 2-hour hydrographs of two watersheds, Kanisavaran and Maranj Watersheds, were generated using maximum discharge data based on regional flood frequency analysis. Estimated hydrographs were compared with observed data and the efficiency of the model was evaluated using Nash-Sutcliffe coefficient, absolute and bias errors. The results showed that multiple regression models give more acceptable results among others for the computation of synthetic unit hydrograph (higher coefficient of determination). The Nash-Sutcliffe coefficient was 0.98 for 1-hour hydrograph while it was 0.93 for the 2-hour hydrograph. The absolute error in 1-hour hydrograph and 2-hour hydrograph was 0.13 and 1.2, respectively. The bias error was close to zero for both hydrographs, indicating that the proposed model is efficient. The model may be used for estimation of synthetic unit hydrograph in similar un-gauged watersheds.
S. S. Eslamian, M. Ghasemi, S. Soltani Gerdefaramarzi,
Volume 16, Issue 59 (4-2012)
Abstract
In this study, in order to determe low flow conditions in Karkhe watershed, 5 indices of Q7,10, Q7,20, Q30,10, Q4,3 and Q95 were used for analyzing 12 hydrometric station data in the years of 1345-46 to 1380-81. Discharge data homogeneity was performed by Run Test. The Q95 index was determined by flow duration curve (FDC) and other indices were determined using 4, 7 and 30-day low flow frequency analysis. After calculating the indices, periods of low flows were determined. The indices were regionalized by Kriging method. The results showed that for the most stations, low stream flows happened in the years of 1345-46, 1377-78, 1378-79, 1379-80 and 1380-81 and the percentages of stations having low flows in these years were 68, 92, 84, 75 and 59, respectively. According to the regional maps of low flows in Karkhe watershed, maximum low flows are located in central and southern areas and all of the mentioned indices decrease from south to the north of this watershed.
S. Chavoshi,
Volume 22, Issue 4 (3-2019)
Abstract
Regional flood frequency studies are initialized by the delineation of the homogeneous catchments. This study was based on "Region of Influence" concept, aiming to find the similar catchments in the south of Caspian Sea. The methodology utilized the Particle Swarm Optimization Algorithm, PSO, to optimize the fuzzy system over a dataset of catchment properties. The main catchment variables in relation to flood were determined by the principle component analysis method and employed as the inputs in the fuzzy system. Catchments grouping was performed over these fuzzy input variables by the iterative process. The optimum similar groups were obtained by PSO, and the heterogeneous L-moment index was used as the termination criterion for the optimization process. A total of 61 hydrometric stations located in the study area were selected and their relevant catchments' physical, climatic and hydrologic properties in relation to flood were studied. Principle Component Analysis by Variomax Rotation Factor over the catchments datasets tended to four out of 16 physical variables, including area, mean elevation, Gravelious Factor and Form Factor, as the main parameters in terms of homogeneity with 84 percent of accumulative variance. These variables, as well as mean annual rainfall, were used as the input data to define the fuzzy system. PSO algorithm was then employed to optimize the developed fuzzy system. The developed algorithm tended to yield the best result in the 9th iteration with 26 and 22 for the minimum average and the optimum values of cost function, respectively. The topology of the resulting algorithm included inertia weight, local and acceleration rates, the number of generations and population size, with the values of 0.7298, 1.4962, 1.4962, 10 and 5, respectively. This study tended to a total of 61 regions of influence, proportional to the relevant 61 sites. According to the geographical location of the catchments in the region, it could be concluded that the geographical proximity doesn't necessarily involve homogeneity. The obtained results indicated the efficient potential of PSO-FES in the delineation of the homogenous catchments in the study area.
S. Farhadi, M. Galoie, A. Motamedi,
Volume 26, Issue 1 (5-2022)
Abstract
One of the important relationships which are used in the estimation of river discharges and floods is Intensity-Duration-Frequency (IDF). The accuracy of this relation is dependent on the accuracy of its parameters which need to be found based on short-duration rainfall depths (such as 15, 30, 60 minutes, and so on) for a long term (i. e. 30 consecutive years). Unfortunately, only 24-hour rainfall depths are available in many rainfall stations in Iran. Various empirical relations are available to convert 24-hour rainfall depth to sub-daily. One of these methods is IMD and its accuracy in some regions is low. In this research, the IMD method was transformed into a single-parameter equation and then, this parameter is evaluated for some rainfall stations in Iran. To do this, maximum 24, 12, 6, and 3-hour rainfall depths were extracted and their frequencies were calculated using Weibull and Gumbel methods. Regional coefficients in the modified IMD method were estimated using a linear regression method. Although the power of the IMD method is 0.33, results showed that this parameter for the rainfall stations ranged from 0.28 to 0.35. To make more comparison, the IDF relation of Kordan’s watershed was calculated using the short-duration rainfall depth which was estimated using the modified IMD, and then, this IDF was compared to observed data and Ghahraman’s relation which is commonly used in Iran. The comparison showed that the modified IMD relation could estimate the short-duration rainfall data better than Ghahraman’s relation. After calibration of the modified IMD relation for various regions in Iran, the sub-daily rainfall depth can be obtained with high accuracy.
F. Naeimi Hoshmand, F. Ahmadzadeh Kaleybar,
Volume 26, Issue 3 (12-2022)
Abstract
Hydrological models for evaluating and predicting the amount of available water in basins, flood frequency analysis, and developing strategies to deal with destructive floods are expanding daily. In this study, HEC-GeoHMS and Arc Hydro extensions in ArcGIS software and the HEC-HMS model were used to simulate design flood hydrographs in the Aydooghmush basin in the northwest of Iran. SCS-CN, SCS-UH, Maskingham, and monthly fixed methods were used to calculate rainfall losses, rainfall-runoff transformation, flood routing, and base flow, respectively. In model calibration with two real flood events, the average of absolute values of the residuals, the sum of the remaining squares, and the weight of the peak mean the error squares for the flood volume were 2.75, 5.91, and 5.32, respectively and for peak discharge were 8.9, 8.0, and 8.0, respectively. Model validation was evaluated as acceptable with a one percent error rate in the peak of discharge and a 19 percent in the flood volume. For maximum 24-hour precipitation, the log-Pearson type 3 was determined as the most suitable distribution in the SMADA model and design precipitation was extracted in different return periods. Thus, for the return period of 2 to 1000 years, the peak discharge and volume of the design flood were simulated equally to 18.8 to 415.6 m3 s-1 and 5.7 to 87.9 MCM, respectively.
S. Yaghobi, Ch.b. Komaki, M. Hosseinalizadeh, A. Najafinejad, H.r. Pourghasemi, M. Faramarzi,
Volume 27, Issue 1 (5-2023)
Abstract
Frequency analysis of daily rainfall or return period of rainfall and flooding events is very important considering the behavioral complexity in water resources management; because ignoring it can lead to urban destructive floods. In the present research, three distribution functions of Pearson, Beta, and Gamma were compared to investigate and select the most appropriate distribution function for the precipitation data acquired from meteorology stations and CHIRPS satellite in seven stations in the watershed of Bustan Dam. Statistical analyses showed that satellite data were ineffective to estimate daily precipitation due to high errors in RMSE, MAD, and NASH. Meteorological data were used to spot the best distribution. Google Earth Engine and Python programming language were used. Then, the selected distribution function was used to determine the maximum daily rainfall, frequency probability, and return period of 2, 10, 50, 100, and 200 years. The results of the goodness of fit test, Error Sum of Squares, Bayesian Information Criterion, Akaike Information Criteria well as Kullback-Leibler Divergence showed that in five stations of Kalaleh, Qarnaq, Golestan National Park, Golestan Dam, and Glidagh, the Pearson function is the most suitable distribution function. Also, in the other two stations (Gonbad and Tamar), the Beta function was recognized as a suitable function. However, Gamma distribution in the study area is not efficient. So, it can be concluded that heavy and irregular rainfall can be effective in choosing the best distribution function at each station. Therefore, it is recommended to consider the maximum possible rainfall and as a result of the possible occurrence of floods with principled and accurate management to prevent human and financial losses in susceptible areas, especially in the study area.
