The Reliability of W-flow Run-off-Rainfall Model in Predicting Rainfall to the Discharge (original) (raw)
Related papers
Journal of Water and Land Development, 2018
Analysis of rainfall intensity with specific probability is very important to control the negative impact of rainfall occurrence. Rainfall intensity (I), probability (p) and return period (T) are very important variables for the discharge analysis. There are several methods to estimate rainfall intensity, such as Talbot, Sherman, and Ishiguro. The aim of this research is to develop equation model which can predict rainfall intensity with specific duration and probability. The equation model is compared with the other methods. The result of rainfall intensity model with the value of correlation >0.94 and NashβSutcliffe coefficient >99 is quite good enough if compared with the observation result. For specific return period, the modelling result is less accurate which is most likely caused by election of duration. Advanced research in other location indicates that short duration gives the better result for rainfall intensity modelling, which is shown by the decreasing average val...
Journal of Hydrology and Meteorology, 2023
The Bhumibol reservoir in the Ping River basin is the largest reservoir in the Kingdom of Thailand. This reservoir has contributed to economic development of the country by supplying increased electricity and irrigation water demands as well as flood mitigation in riparian areas along the Ping and the Chao Phraya River. The prediction of inflows to the reservoir is crucial for the optimal management of water for irrigation, power generation and flood control. Properly customized rainfall-runoff models of the catchment could provide the basis for predicting the inflows to the reservoir. Hence, five lumped conceptual rainfall-runoff models were developed for the Ping River basin to simulate daily inflows to the Bhumibol reservoir. The rainfall-runoff models are Australian Water Balance Model (AWBM), Sacramento Soil Moisture Accounting Model, Simplified Hydrolog Model (SIMHYD), Soil Moisture Accounting and Routing Model (SMAR) and Tank Model. The evaluation of the performances of these models showed that all models are capable of predicting inflows. However, the SIMHYD, Sacramento, AWBM and Tank models perform better than SMAR model. Hence, these models could be employed for prediction of inflows to the reservoir with acceptable accuracy.
FINAL THESIS RAINFALL RUNOFF MODELING MEKONNEN HAILE..
Mekonnen Haile, 2022
Rainfall-runoff modeling is one of the most complex hydrological models and critical to predicting the general attributes of total surface runoff at the catchment's outflow since it is far a replica of watershed hydrological reaction. The major goal of this investigation was to predict rainfall-runoff modeling using HEC-HMS model using ArcGIS, Google earth, and Erdas 2014 software. The main input data used for modeling are LULC, soil, rainfall, stream flow, and (DEM). An error matrix was used to assess the accuracy of this research with a kappa coefficient of 0.78, and the study's overall categorization accuracy was 84.03% this result in the acceptable range and kappa coefficient is good, the identified image was suitable for research or any other application. Model performance evaluation, NSE,π 2 and percent bias were chosen. The three scenarios outputs are listed below: Transform scenario results during calibration and validation (NSE = 0.83, π 2= 0.89), and (NSE = 0.88, π 2= 0.91) respectively PBIAS for calibration and validation 20.24% and 20.19%, peak flow is 394.4π3/π ππ observed flow was 438.2π3/π ππ, and the peak flow during validation 369.3π3/π ππ, observed flow was 406.5π3/π ππ. Loss scenario results during calibration and validation (NSE = 0.88, π 2= 0.97), and (NSE = 0.96, π 2= 0.97) respectively PBIAS for calibration and validation -30.62% and -8.74%, peak flow is 368.5π3/π ππ observed flow was 438.2π3/π ππ, and the peak flow 478.π3/π ππ And observed flow was 406.5π3/π ππ during validation respectively. In routing scenario during calibration and validation, the model has exhibited genuine overall performance with (NSE = 0.99, π 2= 0.98) for calibration and (NSE = 0.98, π 2= 0.98) for validation, respectively. PBIAS for calibration and validation were tested, and the values of 0.54% and 0.5%, respectively, were within the acceptable range. During calibration, the final peak flow acquired from the model was nearly identical to the computed peak flow, and it was 419.5π3/π ππ while the observed peak flow was 438.2π3/π ππ, but during validation, the final peak flow obtained from the model was 435.5π3/π ππ while the observed peak flow was 406.5π3/π ππ. Therefore, the result of routing scenario is best which, nearly the same outputs of simulated and observed flows during calibration and validation compared from scenario of loss and scenario of transform by using model performance evaluation criteria and the result indicated that the routing scenario model was suitable for hydrological simulation in the Beressa sub-catchment.
Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements
Water Resources Management, 2010
Among several hydrological models developed over the years, the most widely used technique for estimating direct runoff depth from storm rainfall i.e., the United States Department of Agriculture (USDA) Soil Conservation Serviceβs (SCS) Curve Number (CN) method was adopted in the present study. In addition, the Muskingum method, which continues to be popular for routing of runoff in river network, was used in the developed model to route surface runoffs from different subbasin outlet points up to the outlet point of the catchment. SCS CN method in combination with Muskingum routing technique, however, required a detailed knowledge of several important properties of the watershed, namely, soil type, land use, antecedent soil water conditions, and channel information, which may not be readily available. Due to this complexity of semi-distributed conceptual approach (SCS CN method) and non-linearity involved in rainfall-runoff modeling, researchers also attempted another less data requiring approach for runoff prediction, i.e., the neural network approach, which is inherently suited to problems that are mathematically difficult to describe. The purpose of this study was to compare the rainfall-runoff modeling performance of semi-distributed conceptual SCS CN method (in combination with Muskingum routing technique) with that of empirical ANN technique. The models were coded in C language and to make them user friendly, a Graphical User Interface (GUI) was also developed in Visual Basic 6.0. The developed models were tested for Kangsabati catchment, situated in the western part of West Bengal, India. Monsoon data of 1996 to 1999 were used for calibration of the models whereas they were validated for another four years (1987, 1989, 1990, and 1993) monsoon data. Modeling efficiency (ME) and coefficient of residual mass (CRM) were used as performance indicators. Results indicated that for Kangsabati catchment, the empirical runoff prediction approach (ANN technique), in spite of requiring much less data, predicted daily runoff values more accurately than semi-distributed conceptual runoff prediction approach (SCS CN method).
Rainfall-runoff modelling calibration on the watershed with minimum stream gage network data
International Journal of Engineering & Technology
The hydrological model has an important role to present the accurate and reliable information for water resources management. In this research, combination of HEC-GeoHMS and HEC-HMS that adopt the SCS-CN model have been chosen to analyse the hydrological characteristic at Upper Ciliwung Watershed. Ciliwung Watershed is one of 13 watersheds that has big influence to flood management in Jakarta. Flooding is the natural hazard that occurs every year at Jakarta. One of important part of flood early warning system at Jakarta is Katulampa Weir that located at Upper Ciliwung watershed. The area of it watershed is about 150 km2 that only has one stream gauge station at Katulampa. Accurate representation of rainfall runoff modelling at this location is important in order to predict the discharge and water infrastructure design. The objective of this paper is to obtain the parameter combination of Upper Ciliwung Watershed which can produce the discharge close to the discharge observation usin...
Precipitation Run off Simulation Study of Kharkai Basin
Hydrological modeling is a commonly used tool to estimate the basin's hydrological response to precipitation. To compute runoff volume, peak runoff rate, base flow, loss rate, various hydrologic as well as hydrodynamic models are used. Rainfall runoff model is such a model which is used to simulate the rainfall-runoff process in a basin. This model is also very effectively used for flood forecasting and flood plain mapping, which is a natural demolishing phenomenon of high importance. Estimation of rainfall-runoff and flood is a difficult task due to influence of different factors. So far, different models have been proposed and used effectively to analyse such phenomena. In view of the above, the present study has been conducted in the basin of Kharkai River (Eastern India) in Jharkhand and Odisha State using Hydrologic Engineering Centre Hydrologic modeling system (HEC-HMS) model. In this study HEC-HMS hydrological model has been used to simulate the flow in the hydrological units of the area and has helped to compute runoff volume, peak runoff rate, base flow, loss rate of the basin. In the present study for calibration and validation of the model the rainfall data for the basin for the period June 2008 and September 2011 and the observed flow at Adityapur gauging site has been considered. For calibration and validation of HEC-HMS model, the observed flow at Adityapur gauging site has been considered. For calibration of HEC-HMS parameters the guidelines available in the manual has been followed strictly. The calibrated and validated model is tested with Nash Sutcliff efficiency which shows that the model can simulate the rainfall runoff process with an efficiency of 82.3% (tested in validation). The result also shows that the parameter which affects the rainfall-runoff simulation process depends on the intensity of rainfall, land use and land cover of the catchment, catchment characteristics and topography of catchment to a great extent. The results can remarkably contribute to the monitoring system of the flood in both catchment and inundation area of the basin. The result can also effectively used for flood forecasting and flood plain mapping in the deltaic area of the catchment.
2012
Limited hydrological data in Paninggahan sub catchment in Singkarak basin has resulted in inappropriate land management practices for farming system development. Predicting stream flow using an appropriate hydrological model is critical for a catchment with limited data recording. The present study has been conducted from January 2006 to December 2007. The objective of this study is to characterize hydrological condition of the catchment and to predict river flow for supporting design of land and water management options. To some extend, the study is to provide inputs in negotiation of farmers community with other stakeholders in the Singkarak basin. An automatic water level recorder (AWLR) and an automatic weather station (AWS) have been installed in the catchment to record hydro-meteorological data in order to calibrate hydrological model for predicting river flow. An instantaneous discharge model based on Geomorphological Instantaneous Unit Hydrograph (H2U) and a daily discharge ...
Calibrated Win TR-20 model was validated and simulated to predict the peak runoff rates for 2, 5, 10, 25, 50 and 100 years for a small watershed area of 72 km2 in the southern region of Khyber Pakhtunkhwa of Pakistan. The catchment has a gauged spillway outlet at the downstream. The model was initially calibrated on the available known parameters from the grid survey and its derivatives, watershed physical features and other scales endorsed into the reservoir for monitoring. The calibrated model was tested and validated on physical data collected for duration of three months with a coefficient of determination of 98% among the observed and estimated runoff depths and peak runoff. After confirmation log-Pearson type III distribution was fitted to annual one day maximum rainfall upon which one day maximum rainfalls for 2, 5, 10, 25, 50 and 100 years return periods were simulated as 50, 80, 105, 144, 180 and 223 mm respectively. Run off depths for the same one day maximum rainfall of given returns period were further simulated as 10.34, 21.15, 30.3, 39.78, and 84.03 and 53.14 mm. Moreover the peak runoff rates for the return periods of 2, 5, 10, 25, 50 and 100 years were predicted as 11.3, 39.9, 77.7, 147.5, 221.9, and 320 m3s-1. Hence it was concluded that Win TR-20 provided satisfactory simulation of rainfall and their resultant runoffs and peak runoff rates which can be confidently recommended for use in small watersheds in the specific region of Khyber Pakhtunkhwa.