scholarly journals Rainfall-Runoff Simulation Using HEC-HMS Model in the Benanain Watershed, Timor Island

2021 ◽  
Vol 7 (3) ◽  
pp. 359
Author(s):  
Wilhelmus Bunganaen ◽  
John H. Frans ◽  
Yustinus Akito Seran ◽  
Djoko Legono ◽  
Denik Sri Krisnayanti
Keyword(s):  
Curve Number ◽  
Peak Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Flood Discharge ◽  
Runoff Modeling ◽  
Pearson Type ◽  
Shape Pattern ◽  
Water Recharge ◽  
Parameter Values

Floods in a watershed area are caused by reduced water recharge due to changes in land use, increasing their discharge volume. Benanain watershed is an extensive area with many tributaries. Watershed morphometrics provides initial information about the hydrological behavior and the hydrograph shape of flooding in these areas. Furthermore, rainfall-runoff modeling uses as a unit to approach the hydrological values of the flooding process. This study determines the physical characteristics of the Benanain watershed based on curve number (CN) values, land cover, peak discharge, and peak time. It was conducted on the Benanain watershed with 29 sub-watersheds covering 3,181.521 km2. Data were collected on the rainfall experienced for 13 years from 1996 to 2008 and analyzed using the Log Pearson Type III method, while the HEC HMS model was used for flood discharge analysis. HEC-HMS model must calibrate by adjusting the model parameter values until the model results match historical data such as initial abstraction, lag time, recession, baseflow values, and curve number.  The results show that the curve number values range from 56.55 - 73.90, comprising secondary dryland forest and shrubs. Moreover, the rock lithology in the Benanain watershed is dominated by scaly clay and other rock blocks. This means the area has low to very low permeability, which affects the volume of runoff. The return period of a 1000-year flood discharge obtained a peak of 5,794.50 m3/s, with a peak time of ± 14 hours. Morphometry of the Temef watershed with large catchment, radial shape pattern, an average of steep slope river, and meandering affects the peak of flood discharge hydrograph and the peak time of the flood.  

scholarly journals Rainfall-Runoff Modeling Using NRCS-CN method and GIS approach

2020 ◽  
Author(s):  
Rekha Verma ◽  
Azhar Husain ◽  
Mohammed Sharif
Keyword(s):  
Hydrological Modeling ◽  
Curve Number ◽  
Geographical Information ◽  
Rainfall Runoff ◽  
Moisture Condition ◽  
Natural Resource Conservation ◽  
Antecedent Moisture ◽  
Natural Resource Conservation Service ◽  
Runoff Modeling ◽  
Runoff Depth

Rainfall-Runoff modeling is a hydrological modeling which is extremely important for water resources planning, development, and management. In this paper, Natural Resource Conservation Service-Curve Number (NRCS-CN) method along with Geographical Information System (GIS) approach was used to evaluate the runoff resulting from the rainfall of four stations, namely, Bilodra, Kathlal, Navavas and Rellawada of Sabarmati River basin. The rainfall data were taken for 10 years (2005-2014). The curve number which is the function of land use, soil and antecedent moisture condition (AMC) was generated in GIS platform. The CN value generated for AMC- I, II and III were 57.29, 75.39 and 87.77 respectively. Using NRCS-CN method, runoff depth was calculated for all the four stations. The runoff depth calculated with respect to the rainfall for Bilodra, Kathlal, Navavas and Rellawada shows a good correlation of 0.96. The computed runoff was compared with the observed runoff which depicted a good correlation of 0.73, 0.70, 0.76 and 0.65 for the four stations. This method results in speedy and precise estimation of runoff from a watershed.

scholarly journals Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1269 ◽  
Author(s):  
Yun Choi ◽  
Mun-Ju Shin ◽  
Kyung Kim
Keyword(s):  
Optimal Parameter ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Stream Network ◽  
Time Step ◽  
Simulation Results ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

scholarly journals Investigating the Optimization Strategies on Performance of Rainfall-Runoff Modeling

10.29007/66vq ◽  
2018 ◽  
Author(s):  
Mehdi Sheikh Goodarzi ◽  
Bahman Jabbarian Amiri ◽  
Shabnam Navardi
Keyword(s):  
Model Performance ◽  
Absolute Error ◽  
Model Verification ◽  
Optimization Strategy ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Shuffled Complex Evolution ◽  
Runoff Modeling ◽  
Calibration And Verification

Regarding to importance of modeling calibration, this study will be focused on probabilistic role of different strategies in calibration and verification steps. Tank lumped conceptual model was selected as a hydrological platform to investigate the effects of each optimization strategy on model performance.However, much considerable efforts are required to calibrate a large number of parameters in conceptual models to obtain better results. With development of artificial intelligence, three probabilistic Global Search Algorithms (GSAs) including Shuffled Complex Evolution (SCE), Genetic Algorithm (GA) and Rosenbrock Multi-Start Search (RBN) and also three Objective Functions (OFs) consisted of Nash-Sutcliffe (NSE), Root Mean Square Error (RMSE) and mean absolute error (MAE) were employed for model calibration (comparing the performance of different GSAs versus OFs). The best set of parameters, which is derived from the calibration step, will be used as prediction coefficients for the model verification stage. Performance evaluation of the simulation results was undertaken using Coefficient of Correlation (r) and Descriptive Statistics.Results indicated that all of optimization strategies have a relative ability to retrieve optimal values of eighteen parameters of the Tank model. However, the best GSAs for daily runoff simulation are SCE (0.871) and GA (0.864), respectively, for calibration and verification phases. In case of the OFs result, NSE (0.763) and RMSE (0.834) are more performant for calibration and verification of the model. Finally, the best strategy was selected by combining the results of GSAs and OFs models. Finally, SCE*MAE (0.906) and GA*RMSE (0.868) were selected as a top series.

scholarly journals Data mining based on wavelet and decision tree for rainfall-runoff simulation

2018 ◽  
Vol 50 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Vahid Nourani ◽  
Ali Davanlou Tajbakhsh ◽  
Amir Molajou
Keyword(s):  
Time Series ◽  
Wavelet Transforms ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
M5 Model Tree ◽  
Model Tree ◽  
Four Seasons ◽  
Runoff Modeling ◽  
Training Stage ◽  
M5 Model

Abstract This study introduced a new hybrid model (Wavelet-M5 model) which combines the wavelet transforms and M5 model tree for rainfall-runoff modeling. For this purpose, the main time series were decomposed to several sub-signals by the wavelet transform, at first. Then, the obtained sub-time series were imposed as input data to M5 model tree, and finally, the related linear regressions were presented by M5 model tree. This new technique was applied on the monthly time series of Sardrud catchment and the results were also compared with other models like WANN and sole M5 model tree. The results showed that the accuracy of the proposed model is better than the previous models and also indicated the effect of data pre-processing on the performance of M5 model tree. The determination coefficient of the training stage was 0.80 and improved 31% than the M5 model tree for Sardrud catchment which is recognized as a normal watershed with a regular four seasons' pattern.

scholarly journals Evaluation and Bias Correction of CHIRP Rainfall Estimate for Rainfall-Runoff Simulation over Lake Ziway Watershed, Ethiopia

Hydrology ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 68 ◽  
Author(s):  
Demelash Wondimagegnehu Goshime ◽  
Rafik Absi ◽  
Béatrice Ledésert
Keyword(s):  
Water Balance ◽  
Bias Correction ◽  
Correction Method ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Spatial And Temporal Scales ◽  
Daily Streamflow ◽  
Satellite Rainfall ◽  
Parameter Values

In Lake Ziway watershed in Ethiopia, the contribution of river inflow to the water level has not been quantified due to scarce data for rainfall-runoff modeling. However, satellite rainfall estimates may serve as an alternative data source for model inputs. In this study, we evaluated the performance and the bias correction of Climate Hazards Group InfraRed Precipitation (CHIRP) satellite estimate for rainfall-runoff simulation at Meki and Katar catchments using the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. A non-linear power bias correction method was applied to correct CHIRP bias using rain gauge data as a reference. Results show that CHIRP has biases at various spatial and temporal scales over the study area. The CHIRP bias with percentage relative bias (PBIAS) ranging from −16 to 20% translated into streamflow simulation through the HBV model. However, bias-corrected CHIRP rainfall estimate effectively reduced the bias and resulted in improved streamflow simulations. Results indicated that the use of different rainfall inputs impacts both the calibrated parameters and its performance in simulating daily streamflow of the two catchments. The calibrated model parameter values obtained using gauge and bias-corrected CHIRP rainfall inputs were comparable for both catchments. We obtained a change of up to 63% on the parameters controlling the water balance when uncorrected CHIRP satellite rainfall served as model inputs. The results of this study indicate that the potential of bias-corrected CHIRP rainfall estimate for water balance studies.

scholarly journals A review on the applications of machine learning for runoff modeling

2021 ◽  
Vol 7 (6) ◽  
Author(s):  
Babak Mohammadi
Keyword(s):  
Machine Learning ◽  
Water Cycle ◽  
Rainfall Runoff ◽  
Learning Models ◽  
Runoff Simulation ◽  
Hydrological Simulation ◽  
Theoretical Understanding ◽  
Advantages And Disadvantages ◽  
Runoff Modeling ◽  
Machine Learning Models

AbstractThe growing menace of global warming and restrictions on access to water in each region is a huge threat to global hydrological sustainability. Hence, the perspective at which hydrological studies are currently being carried out across the world to quantify and understand the water cycle modeling requires a further boost. In the past few decades, the theoretical understanding of machine learning (ML) algorithms for solving engineering issues, and the application of this method to practical problems have made very significant progress. In the field of hydrology, ML has been using for a better understanding of hydrological complexities. Then, using ML-based approaches for hydrological simulation have been a popular method for runoff modeling in recent years; it seems necessary to understand the application of ML in runoff modeling fully. Current research seeks to have an overview for rainfall–runoff modeling using ML approaches in recent years, including integrated and ordinary ML techniques (such as ANFIS, ANN, and SVM models). The main hydrological topics in this review study include surface hydrology, streamflow, rainfall–runoff, and flood modeling via ML approaches. Therefore, in this study, the author has critically reviewed the characteristics of machine learning models in runoff simulation, including advantages and disadvantages of three widely used machine learning models.

scholarly journals Rainfall Runoff Modeling Using MIKE 11 Nam Model

2019 ◽  
Vol 14 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Pushpendra Kumar ◽  
A.K. Lohani ◽  
A.K. Nema
Keyword(s):  
River Basin ◽  
Efficiency Index ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Discharge Data ◽  
Mike 11 ◽  
Runoff Modeling ◽  
Basin Scale ◽  
Development And Management

River basin planning and management are primarily based on the accurate assessment and prediction of catchment runoff. A continuous effort has been made by the various researchers to accurately assess the runoff generated from precipitation by developing various models. In this paper conceptual hydrological MIKE 11 NAM approach has been used for developing a runoff simulation model for Arpasub-basin of Seonath river basin in Chhattisgarh, India. NAM model has been calibrated and validated using discharge data at Kota gauging site on Arpa basin. The calibration and validation results show that this model is capable to define the rainfall runoff process of the basin and thus predicting daily runoff. The ability of the NAM model in rainfall runoff modelling of Arpa basin was assessed using Nash–Sutcliffe Efficiency Index (EI), coefficient of determination (R2) and Root Mean Square Error (RMSE). This study demonstrates the usefulness of the developed model for the runoff prediction in the Arpa basin which acts as a useful input for the integrated water resources development and management at the basin scale.

Correction and preparation of continuously measured raingauge data: a standard method in North Rhine-Westphalia

1998 ◽  
Vol 37 (11) ◽  
pp. 155-162 ◽  
Author(s):  
B. Maul-Kötter ◽  
Th. Einfalt
Keyword(s):  
Water Balance ◽  
Standard Method ◽  
The State ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Site Specific ◽  
Free Data ◽  
Important Input ◽  
Raingauge Data ◽  
Environmental Agency

Continuous raingauge measurements are an important input variable for detailed rainfall-runoff simulation. In North Rhine-Westphalia, more than 150 continuous raingauges are used for local hydrological design through the use of site specific rainfall runoff models. Requiring gap-free data, the State Environmental Agency developed methods to use a combination of daily measurements and neighbouring continuous measurements for filling periods of lacking data in a given raindata series. The objective of such a method is to obtain plausible data for water balance simulations. For more than 3500 station years the described methodology has been applied.

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