جستجوی مقالات مرتبط با کلیدواژه « rainfall » در نشریات گروه « مهندسی آب »

In recent years drought and the continuous extraction of water by digging deep wells have led to a drop in groundwater levels. Therefore, in order to manage groundwater consumption for sustainable use of water resources and also to maintain the environmental balance of this important resource, appropriate research and policies are needed. In the present study, in order to make optimal use of groundwater in Qareh Toghan region of Neka, the stochastic multi-criteria decision analysis approach has been used under rainfall uncertainty condition. The required data were collected as a library and GAMS software was used to estimate the results. Based on the results, by increasing the probability of rainfall up to 99%, an increase in gross margin in the region would happen so that in scenarios of 95% and 99% probability of rainfall, the gross margin was increased by about 1.44 and 4.13%, respectively, compared to the condition of 90% rainfall probability. Under these conditions, the amount of groundwater abstraction in various scenarios remained unchanged but the share of groundwater in the total water consumption decreased. In contrast, in the case of reducing the probability of rainfall to 99% compared to the probability of 90%, the gross margin of the proposed cropping pattern decreased by about 33.3%. In this condition, the amount of groundwater abstraction decreased by about 32.8% due to low groundwater level in the region and its unsuitability in terms of salinity. However, the share of groundwater consumption in relation to the existing conditions and in all the mentioned scenarios remained constant. Therefore, due to the high share of groundwater in agriculture in the region, as well as increasing farmers' losses and the possibility of declining groundwater levels in low rainfall years, it is suggested to provide suitable plans for using surface water in the study area for optimal management, and improving the environmental conditions of groundwater in the region while supporting the income status of farmers.

Precipitation forecasting models play important role in the performance of flood and meteorological warning systems. In this research, the efficiency of five numerical weather prediction (NWP) models, which exist in the TIGGE database, are assessed to determine the best temporal resolution of forecasted datasets at distinct climate regions of Iran, during 2014-2018. Findings show that by increasing the lead time the accuracy of all forecasts decreases significantly. Moreover, most of the NWP models, especially the ECMWF and UKMO perform well, based on correlation coefficient (CC) and RMSE metrics, up to lead time of 3 days. Also, results indicate that by removing biases from the raw forecast datasets, the performance of all NWP models in different lead times increases considerably. After bias correction, the RMSE values of ECMWF, JMA, and KMA models in the lead time of 10 days reduces about 70, 65, and 73%, respectively, and, except for JMA, all NWP models perform well in most climate regions. The JMA model in humid climate zones (north and west parts of Iran) has a high level of bias and leads to unreliable forecasts.

Currently, in most of the catchments, the lack of ground-based gauges is one of the most important problems for accurate hydrological modeling. According to the quick developments of satellite-based technologies and the computer’s computational power, several rainfall datasets have been developed with different spatial and temporal resolutions. These datasets usually are based on remote-sensing techniques or the combination of land surface models (LSMs) and general circulation models (GCMs). This research addressed the efficiency of ECMWF reanalysis dataset and PERSIAN for hydrological modeling using VIC-3L large-scale model over the SefidRood catchment. The results of statistical analyses at daily time scale indicated that the correlation coefficient (CC) between ECMWF, PERSIAN, and ground-observed dataset is about 0.83 and 0.48, respectively. In addition, at monthly time scale, the performances of both rainfall datasets approximately are the same and in most parts of the catchment, the value of CC is higher than 0.80. Hydrological analyses by VIC-3L model showed that despite having low efficiency in estimating rainfall, the PERSIAN dataset led to better simulation of runoff when it compared to ECMWF. For example, the Nash-Sutcliffe (NS) coefficient between daily and monthly simulated runoff using PERSIAN and observed runoff at the outlet of SefidRood catchment are about 0.80 and 0.88, respectively, while in the case of ECMWF these coefficients are about 0.67 and 0.72. Moreover, by using the PERSIAN dataset, the performance of the VIC model in simulating daily and monthly peak flows significantly increases.

In the past decade, machine learning for empirical rainfall–runoff modeling is considered to be a promising approach as a useful complement to hydrologic models, particularly in basins where data to support process-based models are limited. In this paper, we used black-box models (i.e. neuro-fuzzy and support vector machine) and gray-box models (i.e. TOPMODEL and HBV) for simulating the transformation of daily rainfall-runoff process in the Nodeh khormaloo watershed located in Gorganrood River Basin and compare their performance in terms of predictive accuracy. For the black-box models, the three input vectors including discharge, temperature and rainfall are selected in nine different scenarios based on the sequential time series data. Our result show that the neuro-fuzzy model which consists of three antecedent values of flow and one antecedent values of temperature outperforms other models when the root mean square error and coefficient of determination are used as quality indicators. In general, the black- box models outperformed the HBV and TOPMODEL simulations for the calibration and validation data sets. A detailed comparison of the overall performance indicated that the neuro-fuzzy and SVM models predicted runoff in warm months were consistently lower than that in the cold months.

The Maroon River, one of the Jarahi River head waters, is located in Maroon Catchment and the upstream of Behbahan City. Maroon Dam with maximum volume of 1193 MCM was built on this river by Khuzestan Water & Power Authority [1]. The dam prevents from seasonal high flood damages and saves large amounts of water for agriculture consumptions. The river has flash floods with high peaks, so it seems that flood control and management are cases of high importance in this catchment and it's necessary to have information about flood volume for flood management, downstream programming and consumption estimation in a water year. Therefore, runoff data were calibrated based on the real precipitation data, using HEC-HMS Rainfall-Runoff Model and Schneider Method. Considering 12 observed floods in the past years, observed and calculated runoff values were compared and the best parameters were calculated for initial & final soil infiltration and time of concentration &Good results from this review were obtained.

The aim of this study is a comparison among two multi-site stochastic weather generators for simulation of winter rainfall occurrence across Iran using data of a selected network consisting of 130 rain gauge stations with a historical data of 21 years. The applied approaches included Hidden Markov Model (HMM) as a parametric approach and K-nearest neighbor (KNN) as non-parametric approach. Six stations namely, Bandar Anzali, Sari, Gharakhil Ghaemshahr, Gorgan, Shiraz and Zahedan were chosen respectively as the representative of different climates including very humid, humid, semi humid, Mediterranean, semi dry and dry climates. In comparison of first and second order momentums, results indicated that HMM performed well in almost every station. Data dispersion was examined using box plot and confidence interval analysis. The results revealed better performance for HMM. Regarding probabilities spaces, HMM showed a better performance in simulation of extreme events and higher percentiles of empirical distribution but KNN approach provided better estimations for middle percentiles values. LEPS Score index was used for comparison of cumulative distribution of observed and simulated series which showed more agreement in case of HMM. The spatial correlation was evaluated using Log-odds ratio index, which indicated that KNN model did better. Both approaches performed well in estimation of duration of wet and dry spells though a tendency to overestimate was observed at HMM and a tendency to underestimate viewed at KNN in simulating of wet spells. In general, HMM has more skill in simulation of daily rainfall series which might be attributed to its complex mathematical structure, however relatively good results of KNN approach showed that it can be recommended for less complicated applications.

Rainfall-runoff relationship is one the most complicated issues in hydrological cycle and its accurate estimation is one of the most important concerns in water resources engineering and management. Nowadays, researchers have focused on the application of intelligent systems for forecasting of river discharge due to their high performance and addition no need for many data and complex relationship. Therefore, the multi-layer perceptron and radial basis function approaches were used in this research for river flow prediction of Kasilian watershed. Some variables such as precipitation, temperature, evapotranspiration, relative humidity and discharge in daily time scale over 42 year period were considered while 40 different scenario structures were assessed for river flow prediction. The results showed that among 5000 available models for estimation of river flow, the multi-layer perceptron including nine input variables and arrangement of 9-5-1-1 has better performance than radial basis function. The MSE, RMSE and MAE of the model in training stage were 0.19, 0.43 while in validation and testing stages was 0.0 2, 0.15 and 0.02 and 0.03, 0.16 , 0.03 respectively.

Runoff hydrograph widths represent the basin's reaction to rainfall. The purpose of this research is evaluating methods of SCS, Snyder and Clark in simulation of flood hydrograph by using HEC-HMS model. For this purpose 0 rainfalls - runoff events were extracted from Tange Chogan and Booshigan hydrometric stations in the Shapoor River Basin. The model parameters were calibrated based on seven observed events. Another three events were used for model verification.Comparing the simulation results showed that the ability of different methods varies in different basins. On the other side a given method estimates flood hydrograph characteristics with different accuracy. Therefore the purpose of simulation should be considered in selecting of model. According to the evaluation indices it can be concluded that in total the Clark method estimated the peak flow, flood volume and time to peak flow more accurate than the other two methods. The mean relative error values of the Clark method were calculated 9.67, .23 and 4.23 percent for peak flow, flood volume, time to peak flow respectively, in the Tange Chogan station. The corresponding values for the Booshigan station were 5.0 , 5.97 and 6.89 percentt.

Rainfall-Runoff is considered one of the most important processes in water resources studies. In this study, to simulate the daily rainfall-runoff process of Balikhluchay Basin, four hybrid models of Support Vector Machine, Artificial Neural Networks, Wavelet-Support Vector Machine, and Wavelet-Neural Networks have been applied and compared. Daily Rainfall-runoff data for the period of 2000 to 2008, have been used for training and testing the models. In general, the results indicated acceptable accuracy of all the models. In terms of priority, the hybrid model of Wavelet-Neural Network with the highest accuracy and lowest errors was in the first rank and the Hybrid models of Wavelet-Support Vector Machines, Artificial Neural Networks and Support Vector Machines, were in next priorities.

The damages of floods can be reduced by evacuation if there is an on-time warning. The Fahlian Basin, in the south of Iran, has steep slopes and flash floods, so the location of rainfall-gauges and their distance from basin’s outlet might have a considerable effect on the estimation of the flood-warning lead time. Thus, the main purpose of this research is to assess the effect of the distribution of rainfall-gauges on flood-warning lead time using WMS/HEC-HMS model in this basin. Therefore, after calibration and validation of the model, double, triple and quadruple combinations of rainfall-gauges were examined. After flood simulation by WMS/HEC-HMS for various return periods, the lead times for the different rainfall combination were determined according tolegalistic river bed. The results showed that the lead time increases by increasing rainfall and decreasing the distance of the rainfall gauges from the basin’s outlet. Finally, a method is proposed to determine the lead time based on multivariate analysis which can be used in flood warning systems.

Collecting and surface water disposal during and after rain is important in flood management. Blockage of water passages, improper steering of surface water flow which contains sand, gravel, mud and debris into the sewage system can cause blocking and much damage to the sewer system. The aim of this study is to evaluate surface water collection system and steer it into the drainage systems and investigate the problems caused by the existence of surface water as a result of heavy rain in University of Shahrekord. After studying the flowing surface water collection and in University of Shahrekord. After studying the collecting systems and surface water disposal, their weaknesses and strengths were investigated using a non-defense approach. Ultimately, some suggestions were proposed to remove the defects. The results were indicative of the ignorance of the principles of non-defense operating in surface water collection in University of Shahrekord.

Helmand River basin upstream Kajakai dam (Afghanistan), has a considerable role in its annual water yield. In order to deal with the ever going water conflict between Iran and Afgh nist n, the knowledge bout this b sin’s long term hydro-meteorological conditions can be a useful measure for exploit tion of the b sin’s w te esou ces. It is obvious th t such an evaluation needs long term data that is a serious obstacle in Afghanistan due to the poor hydro-infrastructures. For such region with serious scarcity of data, application of the global databases and rainfall-runoff models can be a good alternative. The CRU database is one of these databases with relevant temporal and spatial resolution, which contains these climate data since 1901. SWAT model is also a well know rainfall-runoff model that has been applied for a few trans-boundary basins. Nevertheless, any application of CRU data and SWAT needs pre-assessments, which construct the main objectives of this research work. For this, the paper compared CRU data with the observed data of 17 meteorological stations in Afghanistan that resulted 313 mm compared to 323 mm as annual precipitation for the study area (the upper Helmand). The next step relates to calibration and validation of SWAT, which were done by a limited observed discharge data (1969 to 1979). Furthermore, the model was run using measured and CRU climate data, which the latter performed better. The results of SWAT for the entire period of CRU data set (1913-2012) revealed that there is heterogeneity between the time series before and after 1940, such that the annual rainfall decreases and annual temperature increases. Same behavior was seen for the time series of discharges and its annual average of discharges decreases from 8.1 BMC to 6.07 BMS for the same period. One of the considerable points of this research work is its methodology that can be applied for other transboundary river basins.

Due to different reasons like non-normative usage and sequential droughts, water scarcity has highlighted the necessity of water resources management. Studying the processes affecting water resources is necessary for a normative management with sustainable development. The most important hydrologic process is the rainfall-runoff process that influences both ground and surface water resources. The study of this process is closely related to water resources management. In the past two decades, the intelligent models have found more importance because of their high ability in simulation of nonlinear and complex processes like rainfallrunoff process. In this study, the efficiency of common intelligent models in hydrology including Multi-Layer Perceptron artificial neural network (MLP), Radial Basis Function artificial neural network (RBF) and Co-Active Neural Fuzzy Inference System (CANFIS) in rainfall-runoff simulation and estimation of monthly discharge in Balikhluchay watershed was investigated. According to the coefficients of MSE, NMSE, MAE and RMSE (0.0145, 0.276, 0.103 and 0.120 respectively), the MLP artificial neural network which has three hidden layers, three neurons in each layer, a momentum algorithm and hyperbolic tangent transfer which would stimulate the monthly discharge for a 24 month test period with high accuracy was chosen as the most accurate network.

Drought phenomenon as a natural and inevitable disaster has frequently occurred in different countries, especially in hot and arid regions. In this study, monthly rainfall data measured at Bam synoptic station in the period of 1957 through 2010 (54 years) were used for modeling. According to the Akaike criteria (AICC), PARMA (1, 0) model was selected for modeling and forecasting the next 18-years of Bam rainfall series. Correlation coefficient between the modeled and actual data (0.867) confirmed the selected model. Then, a 72-years of rainfall series was studied using PN, DI, CZI, SPI, MCZI and ZSI drought indices. The results showed that seven severe, eight moderate and eight weak drought periods occurred from 1957 to 2010. Moreover, a severe drought period will happen in 2014. Two moderate ones in 2015 and 2023 and a weak one in 2022.

The Instantaneous Unit Hydrograph (IUH) concept is widely applied in the simulation of the rainfall-runoff process. In this paper a new geomorphologic unit hydrograph, based on the concept of cascade linear reservoirs has been developed and analyzed. This method represents the watershed as a cascade of reservoirs across the watershed main channel.The two most important characteristics of the model are: (a) it explicitly includes the watershed morphology in its formulation and (b) it depends on only one uncertain parameter which must be estimated. The result of the model has been compared with Nash’s black box model with one more parameter. The study area was the Ammameh watershed southern of central Alborz mountain chain in Iran. The results showed the efficiency of this model to simulate the rainfall-runoff process using the watershed geomorphologic properties. GIS tools in the current modeling allow the accurate and easy determination of the geomorphologic characteristics of the model.

There were many hydrological researches focused on dynamic and linear modeling of rainfall-runoff process. Conversion of rainfall data to runoff consists of nonlinear complex relationships which are resulted from interactions of different sets of hydrological process. The stochastic modeling is therefore seems to be more sensible in this estimate than deterministic ones. In this research observed runoff is modeled against total rainfall. This will mainly avoid misleading theories in breaking the rainfall and runoff time series into the excess rainfall and the direct runoff time series,. A Transfer Function (TF) model with single input and single output variable (SISO) is used in this research. This function is transferred to the state space equations. The stochastic Data-Based Mechanistic modeling (DBM) method relying upon recursive Kalman filtering algorithm is then used to identify the non-linear relationship between rainfall and runoff. This approach is applied to the Khersan sub basin in the Great Karun catchment south western Iran. The relation between the calibrated parameters and the routine characteristics of the basin flow showed a probable parallel structure of flow routine in this sub basin. Finally the sensitivity analysis is performed using the Monte Carlo Simulation (MCS) in order to quantify the reliability of the model.

Estimation of spatial distribution of rainfall in different time scales is one of the basic steps in water resources studies. Problems in selection of proper methods for spatial variation of rainfall can produce error in input parameters of rainfall-runoff models in design or prediction stages. Also, reducing uncertainty in rainfall distribution is important in water budget studies. There are different methods for estimation of spatial distribution of rainfall based on raingage point data. Geostatistical methods are among such techniques. Geostatistical methods are favored in engineering studies since these methods consider spatial structure and location of data points. In this study, a number of methods were evaluated to estimate spatial distribution of monthly and annual rainfall in southwest of Iran. These methods consist of Thin Plate Smoothing Splines (TPSS), Weighted Moving Average (WMA) and Kriging. Cross validation technique was used for comparison of the methods. Results showed, although the TPSS method with power of 2 was marginally the most accurate method (with minimum MAE) in estimating annual rainfall but co-kriging method showed better consistency with topography of the study area. Analysis of variogram and error variance also demonstrated that generating missing data decreases accuracy of the interpolation.