Simulation Of Rainfall-runoff Process Research Articles

Simulation of Rainfall-Runoff process using SWAT model in Bouhamdane watershed, Algeria

The current research examines the runoff response in the Bouhamdane watershed in Algeria using the soil and water assessment tool (SWAT). The SWAT model is applied for the Bouhamane watershed, which includes three sub-watersheds and 45 Hydraulic Response Units (HRUs). To assess the ability and effectiveness of the model, one-gauge station in the basin (sabat) was chosen. Monthly discharge flow data are sourced from Algeria's National Water Resources Agency (NWRA). The soil and water assessment tool calibration uncertainty programs (SWAT-CUPs) with the sequential uncertainty fitting (SUFI 2) algorithm were used to calibrate and validate the model. The model was run from 1985 to 2004, with a calibration period between 1985 and 1994 and a validation period between 1995 and 2005. The model's runoff simulation efficiency has been improved by adjusting watershed input parameters. The SWAT model's performance was assessed statistically (coefficient of determination [R2], Nash-Sutcliffe Efficiency Coefficient [NSE], and Percent BIAS [PBIAS]). The monthly calibration R2, NSE, and PBIAS were 0.89, 0.68, and 43, respectively, and the monthly validation R2, NSE, and PBIAS were 0.78, 0.76, and 10.4, respectively. These results support that the SWAT model is an effective tool for simulating the surface runoff of the Bouhamdane watershed.

Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data

Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data

Simulation of Rainfall-Runoff Process in a Catchment with a Check-Dam System Equipped with a Perforated Riser Principal Spillway on the Loess Plateau of China

Check dams are applied worldwide as an effective approach for soil and water conservation. To improve the simulation accuracy of the hydrological processes in a catchment with a check-dam system, this study analyzed the applicability and accuracy of a formula for the drainage process of a perforated riser principal spillway based on observational experiments. The rainfall-runoff processes in a catchment with a check-dam system were also simulated with the recommended formulas for the drainage process of a perforated riser principal spillway. The deviations in the calculated discharge from the observed values of the experiment with the recommended formulas under normal and abnormal working conditions were within ±15% and ±5%, respectively. The hydrologic model used in this study needed only a few parameters to achieve a satisfactory simulation accuracy. The recommended formulas for the drainage process of a perforated riser principal spillway can improve the simulation accuracy of a flood peak by 7.42% and 19.58% compared with the accuracies of the technical code formula scenario and no drainage scenario, respectively. The results of this study are expected to provide a reference for flood warnings and safe operations of check-dam systems.

Simulation of rainfall-runoff process using geomorphology-based adaptive neuro-fuzzy inference system (ANFIS)

This research was conducted to present an integrated rainfall-runoff model based on the physical characteristics of the watershed, and to predict discharge not only in the outlet, but also at any desired point within the basin. To achieve this goal, a matrix of hydro-climatic variables (i.e. daily rainfall and daily discharge) and geomorphologic characteristics such as upstream drainage area (A), mean slope of watershed (S) and curve number (CN) was designed and simulated using artificial intelligence techniques. Integrated Geomorphology-based Artificial Neural Network (IGANN) model with Root Mean Squared Error (RMSE) of 0.02786 m3 s-1 and Nash-Sutcliffe Efficiency (NSE) of 0.9403 and Integrated Geomorphology-based Adaptive Neuro-Fuzzy Inference System (IGANFIS) model with RMSE of 0.02795 m3 s-1 and NSE of 0.94467 were able to predict the discharge values of all hydrometric stations of the Chalus River watershed with a very low error and high accuracy. The results of cross validation stage confirmed the efficiency of models. Hydro-climatic variables and geomorphologic parameters selected in the study were: discharge of one day ago, discharge of two days ago, rainfall of current day and rainfall of one day ago and S, CN and A, respectively. In addition, the IGANN model shows superiority compared with the IGANFIS model.

Simulation of Rainfall Runoff Process Using HEC-HMS Model for Upper Godavari Basin Maharashtra, India

The Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS) is a popularly used watershed model to simulate rainfall- runoff process. Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices. It is evident from the extensive review of the literature that the studies on comparative assessment of watershed models for hydrologic simulations are very much limited in developing countries including India. In this study, modified SCS Curve Number method is applied to determine loss model as a major component in rainfall-runoff modeling. The study of HEC-HMS model is used to simulate rainfallrunoff process in Nashik region (Upper Godavari basin), Maharashtra. To compute runoff volume, peak runoff rate, and flow routing methods SCS curve number, SCS unit hydrograph, Exponential recession and Muskingum routing methods are chosen, respectively. The results of the present study indicate that HEC-HMS tool applied to watershed proved to be useful in achieving the various objectives. The study confirmed a significant increase in runoff as a result of urbanization. It is a powerful tool for flood forecasting Index

Simulation of Rainfall Runoff Process Using HEC-HMS Model for Upper Godavari Basin Maharashtra, India

The Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS) is a popularly used watershed model to simulate rainfall- runoff process. Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices. It is evident from the extensive review of the literature that the studies on comparative assessment of watershed models for hydrologic simulations are very much limited in developing countries including India. In this study, modified SCS Curve Number method is applied to determine loss model as a major component in rainfall-runoff modeling. The study of HEC-HMS model is used to simulate rainfallrunoff process in Nashik region (Upper Godavari basin), Maharashtra. To compute runoff volume, peak runoff rate, and flow routing methods SCS curve number, SCS unit hydrograph, Exponential recession and Muskingum routing methods are chosen, respectively. The results of the present study indicate that HEC-HMS tool applied to watershed proved to be useful in achieving the various objectives. The study confirmed a significant increase in runoff as a result of urbanization. It is a powerful tool for flood forecasting Index

Investigation the Ability of Artificial Neural Network in Simulation of Rainfall-Runoff Process under the Climate Change Conditions (Case Study: Pashakola Babol Dam Basin)

Investigation the Ability of Artificial Neural Network in Simulation of Rainfall-Runoff Process under the Climate Change Conditions (Case Study: Pashakola Babol Dam Basin)

A HIGH-PERFORMANCE METHOD FOR SIMULATING SURFACE RAINFALL-RUNOFF DYNAMICS USING PARTICLE SYSTEM

Abstract. The simulation of rainfall-runoff process is essential for disaster emergency and sustainable development. One common disadvantage of the existing conceptual hydrological models is that they are highly dependent upon specific spatial-temporal contexts. Meanwhile, due to the inter-dependence of adjacent flow paths, it is still difficult for the RS or GIS supported distributed hydrological models to achieve high-performance application in real world applications. As an attempt to improve the performance efficiencies of those models, this study presents a high-performance rainfall-runoff simulating framework based on the flow path network and a separate particle system. The vector-based flow path lines are topologically linked to constrain the movements of independent rain drop particles. A separate particle system, representing surface runoff, is involved to model the precipitation process and simulate surface flow dynamics. The trajectory of each particle is constrained by the flow path network and can be tracked by concurrent processors in a parallel cluster system. The result of speedup experiment shows that the proposed framework can significantly improve the simulating performance just by adding independent processors. By separating the catchment elements and the accumulated water, this study provides an extensible solution for improving the existing distributed hydrological models. Further, a parallel modeling and simulating platform needs to be developed and validate to be applied in monitoring real world hydrologic processes.

A HIGH-PERFORMANCE METHOD FOR SIMULATING SURFACE RAINFALL-RUNOFF DYNAMICS USING PARTICLE SYSTEM

The simulation of rainfall-runoff process is essential for disaster emergency and sustainable development. One common disadvantage of the existing conceptual hydrological models is that they are highly dependent upon specific spatial-temporal contexts. Meanwhile, due to the inter-dependence of adjacent flow paths, it is still difficult for the RS or GIS supported distributed hydrological models to achieve high-performance application in real world applications. As an attempt to improve the performance efficiencies of those models, this study presents a high-performance rainfall-runoff simulating framework based on the flow path network and a separate particle system. The vector-based flow path lines are topologically linked to constrain the movements of independent rain drop particles. A separate particle system, representing surface runoff, is involved to model the precipitation process and simulate surface flow dynamics. The trajectory of each particle is constrained by the flow path network and can be tracked by concurrent processors in a parallel cluster system. The result of speedup experiment shows that the proposed framework can significantly improve the simulating performance just by adding independent processors. By separating the catchment elements and the accumulated water, this study provides an extensible solution for improving the existing distributed hydrological models. Further, a parallel modeling and simulating platform needs to be developed and validate to be applied in monitoring real world hydrologic processes.

ASSESSMENT OF RAINFALL-RUNOFF SIMULATION MODEL BASED ON SATELLITE ALGORITHM

Abstract. Simulation of rainfall-runoff process is one of the most important research fields in hydrology and water resources. Generally, the models used in this section are divided into two conceptual and data-driven categories. In this study, a conceptual model and two data-driven models have been used to simulate rainfall-runoff process in Tamer sub-catchment located in Gorganroud watershed in Iran. The conceptual model used is HEC-HMS, and data-driven models are neural network model of multi-layer Perceptron (MLP) and support vector regression (SVR). In addition to simulation of rainfall-runoff process using the recorded land precipitation, the performance of four satellite algorithms of precipitation, that is, CMORPH, PERSIANN, TRMM 3B42 and TRMM 3B42RT were studied. In simulation of rainfall-runoff process, calibration and accuracy of the models were done based on satellite data. The results of the research based on three criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) showed that in this part the two models of SVR and MLP could perform the simulation of runoff in a relatively appropriate way, but in simulation of the maximum values of the flow, the error of models increased.

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.

The time variability of evapotranspiration and soil water storage in long series of rainfall-runoff process

Natural variability, i. e. climatic oscillation, influences the development of vegetation in the annual cycle. At the same time it creates the conditions for the changes of the vegetation cover even in the scale of centuries. This is the phenomenon, which causes the variation or tendencies in evapotranspiration demands and consequently of water storage regime, and its long scale change is sometimes disregarded. The simulation of rainfall-runoff process has been used for the re-evaluation of the assumed evapotranspiration demand due to the developing vegetation cover and of groundwater storage in the catchments. The simulations provide the results, which illustrate the dominant role of transpiration in comparison with other components of evapotranspiration. The simulations also illustrate the interaction between evapotranspiration and groundwater storage. Additionally, the modelling confirms that it could be useful to compare the parameters for the recession process of simulated sub-surface water storage with the decreases of observed outflow of springs and/or with the course of water levels in the bore holes.