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.

scholarly journals Evaluation of upper Uruguay river basin (Brazil) operational flood forecasts

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Fernando Mainardi Fan ◽  
Paulo Rógenes Monteiro Pontes ◽  
Diogo Costa Buarque ◽  
Walter Collischonn
Keyword(s):  
River Basin ◽  
Power Plants ◽  
Performance Metrics ◽  
Hydroelectric Power ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Forecasting System ◽  
The Difference ◽  
And Performance ◽  
Uruguay River

ABSTRACT System for hydrological forecasting and alert running in an operational way are important tools for floods impacts reduction. The present study describes the development and results evaluation of an operational discharge forecasting system of the upper Uruguay River basin, sited in Southern Brazil. Developed system was operated every day to provide experimental forecasts with special interest for Barra Grande and Campos Novos hydroelectric power plants reservoirs inflow, with 10 days in advance. We present results of inflow forecasted for floods occurred between July 2013 to July 2016, the period which the system was operated. Forecasts results by visual and performance metrics analysis showed a good fit with observations in most cases, with possibility of floods occurrence being well predicted with antecedence of 2 to 3 days. Comparing the locations, it was noted that the sub-basin of Campos Novos, being slower in rainfall-runoff transformation, is easier forecasted. The difference in predictability between the two basins can be observed by the coefficient of persistence, which is positive from 12h in Barra Grande and from 24h to Campos Novos. These coefficient values also show the value of the rainfall-runoff modeling for forecast horizons of more than one day in the basins.

Rainfall-runoff modeling considering soil moisture accounting algorithm, case study: Karoon III River basin

2016 ◽  
Vol 43 (4) ◽  
pp. 699-710 ◽  
Author(s):  
Homa Razmkhah ◽  
Bahram Saghafian ◽  
Ali-Mohammad Akhound Ali ◽  
Fereydoun Radmanesh
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Rainfall Runoff ◽  
Soil Moisture Accounting ◽  
Runoff Modeling

scholarly journals Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

2012 ◽  
Vol 60 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Hamid Shahraiyni ◽  
Mohammad Ghafouri ◽  
Saeed Shouraki ◽  
Bahram Saghafian ◽  
Mohsen Nasseri
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Support Vector ◽  
Learning Method ◽  
Runoff Simulation ◽  
Karoon River ◽  
Discharge Data ◽  
Daily Streamflow ◽  
Runoff Modeling ◽  
Active Learning Method

Comparison Between Active Learning Method and Support Vector Machine for Runoff ModelingIn this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m3s-1, 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m3s-1, 0.81, 7.3%, and -3.6%, respectively. The results show appropriate and acceptable simulation by ALM and optimized SVM. Optimized SVM is a well-known method for runoff simulation and its capabilities have been demonstrated. Therefore, the similarity between ALM and optimized SVM results imply the ability of ALM for runoff modeling. In addition, ALM training is easier and more straightforward than the training of many other data driven models such as optimized SVM and it is able to identify and rank the effective input variables for the runoff modeling. According to the results of ALM simulation and its abilities and properties, it has merit to be introduced as a new modeling method for the runoff modeling.

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Lawal Billa ◽  
Hamid Assilzadeh ◽  
Shattri Mansor ◽  
Ahmed Mahmud ◽  
Abdul Ghazali
Keyword(s):  
Response Time ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Noaa Avhrr ◽  
Quantitative Precipitation Forecast ◽  
Langat River

AbstractObserved rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3–12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through ‘Nowcasting’, thus increasing the response time for flood early warnings.

scholarly journals IMPLICATION OF SOM-ANN BASED CLUSTERING FOR MULTI-STATION RAINFALL-RUNOFF MODELING

2015 ◽  
pp. 198-210
Author(s):  
Vahid Nourani ◽  
Masoud Mehrvand ◽  
Aida Hosseini Baghanam
Keyword(s):  
Stream Flow ◽  
Precipitation Event ◽  
Time Lags ◽  
Self Organizing Map ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Discharge Data ◽  
Runoff Modeling ◽  
Rainfall Runoff Model ◽  
Runoff Model

In this study the performance of ANN with feed-forward neural network (FFNN) algorithm evaluated rainfall-runoff modeling in five gauging stations in Florida State. In addition, for investigating the performance of ANN in multi-station discharge prediction, self-organizing map (SOM) clustering tool employed in order to cluster the input data with similar patterns, due to the large amount of records in multiple stations. The main aim of study is to investigate capability and accuracy of ANN based methods in multi-station discharge prediction. In order to consider multiple stations effect on watershed outlet discharge, different combinations for precipitation and discharge data of all stations with antecedent values over the watershed have been taken into account. In this way, application of the representatives from each cluster led to significantly reduction in the numbers of the input variables so that the optimal ANN structure could be proposed. Therefore, ANN as a data-driven model was trained to predict daily runoff for the Peace River basin via recorded values from July 1995 to July 2011. Three scenarios conducted the aim of research; first scenario was an integrated ANN model trained by the data of rainfall and runoff at multiple stations. The second scenario was a sequential ANN model processed with upstream discharge records in addition to rainfall data as inputs and downstream discharge values as target. Finally, third scenario was a SOM-ANN model, in which rainfall and runoff data were clustered according the homogeneity of data via (SOM). The center of each cluster as the dominant component of each cluster was imposed to ANN in order to present an optimal rainfall-runoff model over the watershed. In all scenarios, different data sets at various time lags in both rainfall and stream flow data were applied as inputs in ANN-based model to predict stream flow. Results show that ANN model coupled with SOM is useful tools for forecasting multi-station discharge and precipitation event response in the watershed. Furthermore, the comparison of scenarios leads to select the most efficient and optimal inputs to ANN which subsequently, presents the optimal multi-station rainfall-runoff model over the watershed.

scholarly journals Application of Remotely Sensed Rainfall Data in Rainfall-Runoff Modelling. A Case of Pangani River Basin, Tanzania

2014 ◽  
Vol 35 (1) ◽  
pp. 1-14
Author(s):  
Joel Nobert ◽  
Patric Kibasa
Keyword(s):  
River Basin ◽  
Average Method ◽  
River Flow ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Data Set ◽  
Satellite Rainfall ◽  
Areal Rainfall

Rainfall runoff modelling in a river basin is vital for number of hydrologic applicationincluding water resources assessment. However, rainfall data from sparse gauging stationsare usually inadequate for modelling which is a major concern in Tanzania. This studypresents the results of comparison of Tropical Rainfall Measuring Mission (TRMM)satellite rainfall products at daily and monthly time-steps with ground stations rainfalldata; and explores the possibility of using satellite rainfall data for rainfall runoffmodelling in Pangani River Basin, Tanzania. Statistical analysis was carried out to find thecorrelation between the ground stations data and TRMM estimates. It was found thatTRMM estimates at monthly scale compare reasonably well with ground stations data.Time series comparison was also done at daily and annual time scales. Monthly and annualtime series compared well with coefficient of determination of 0.68 and 0.70, respectively.It was also found that areal rainfall comparison in the northern parts of the study area hadpoor results compared to the rest of areas. On the other hand, rainfall runoff modellingwith ground stations data alone and TRMM data set alone was carried out using five Real-Time River Flow Forecasting System models and then outputs combined by Models OutputsCombination Techniques. The results showed that ground stations data performed betterduring calibration period with coefficient of efficiency of 76.7%, 81.7% and 89.1% forSimple Average Method, Weight Average Method and Neural Network Method respectively.Simulation results using TRMM data were 59.8%, 73.5% and 76.8%. It can therefore beconcluded that TRMM data are adequate and promising in hydrological modelling.

scholarly journals Rainfall Runoff Modeling Using HEC-HMS: The Case of Awash Bello Sub-Catchment, Upper Awash Basin, Ethiopia

2020 ◽  
Vol 9 (1) ◽  
pp. 68-86
Author(s):  
Wana Geyisa Namara ◽  
Tamane Adugna Damise ◽  
Fayera Gudu Tufa
Keyword(s):  
Hydrological Modeling ◽  
Meteorological Data ◽  
Base Flow ◽  
Flood Routing ◽  
Coefficient Of Determination ◽  
Physical Parameters ◽  
Hydrological Response ◽  
Rainfall Runoff ◽  
Calibration And Validation ◽  
Runoff Modeling

Rainfall runoff modeling is one of the most complex hydrological modeling due to the involvement of different watershed physical parameters. It is essential for the analysis of watershed hydrological response toward the received precipitation under the influence of watershed parameters. As it is a replica of watershed hydrological response, rainfall runoff modeling is essential to evaluate the general characteristics of total surface runoff at catchment’s outlet.  The main objective of this study was rainfall runoff modeling using HEC-HMS for Awash Bello sub-catchment. Hydro-meteorological data collected from the National Meteorological Agency and Ministry of Water Resource, Irrigation and Electricity were used for model calibration and validation.  SCS-CN, SCS-UH, Muskingum and monthly constant method were used for precipitation loss modeling, transform modeling, flood routing and base flow modeling respectively. Nash Sutcliff Efficiency and coefficient of determination have been selected for model performance evaluation. The model had shown good performance both during calibration and validation with (NSE = 0.855, R2= 0.867) for calibration and (NSE = 0.739, R2 = 0.863) for validation respectively. PBIAS for calibration and validation were checked and they were within the acceptable range with a value of 4.59% and 5.67% respectively. By the successful accomplishing of calibration and validation, the peak flood from the model (573.7m3/s) was compared with direct observed flow (546.4m3/s) and model provided nearly the same result with the direct observed flow.

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