Analysis of Uncertainty in Discharge Data and Assessing its Impact on Model Calibration

In hydrological practice, discharge data, which is considered observed, is not actually observed because continuous measurement of discharge is time consuming, costly and infeasible during high floods. Therefore, most discharge records are developed from converting the water level data to discharge data by using a stage-discharge relationship, which is also called rating curve. The discharge data is subjected to uncertainty due to a number of factors. These factors can be classified into two categories: measurement uncertainty and rating curve uncertainty. In the development of hydrological models, calibration of model parameters is based on the discharge data. Therefore, the quality of discharge data is extremely important for reducing uncertainty in model parameters. In hydrological modeling, the uncertainty in discharge measurement is either ignored or assumed error range is applied to understand the influence of error in discharge data. Therefore, the objective of the study is to find out the ranges of error in discharge data due to measurement error as well as rating curve error and to use this range in Monte Carlo framework to analyze to what extent hydrological model parameters and model performance is affected. The study area for the research is the West Rapti River Basin, which is located in the south west of Nepal. Jalkundi gauging station, the most downstream station is used as an example for the analysis of uncertainty. The discharge is measured by velocity area method and the stage is measured by staff gauge. First, the magnitude of uncertainty in discharge measurement due to the uncertainty in velocity measurement and uncertainty in cross-sectional properties measurement is computed. Then, a rating curve in the form of power equation is developed using least square optimization. The uncertainty due to rating curve is analyzed statistically. Based on the uncertainty range found from the analysis, perturbation to discharge is applied and NAM, a lumped and conceptual hydrological model is calibrated for each realization using SCE-UA method. The result of the study shows that the uncertainty in discharge for the station under study ranges from 10%-16% at 95% confidence interval. The uncertainty in discharge has significant influence on the model parameter and model performance.