Uncertainty analysis of extreme hydrological events in the semi-arid Zayanderood Basin, central Iran

• Main Author: Mirzaei, Majid
• Format: Thesis
• Language: English
• Published: 2012
• Online Access: http://psasir.upm.edu.my/id/eprint/34086/1/FK%202012%209R.pdf; http://psasir.upm.edu.my/id/eprint/34086/
• Institution: Universiti Putra Malaysia

Stream flow, which is a part of the integrated process of atmospheric and topographic processes, is of prime importance to water resources planning. Hydrologic simulation models for stream flow have implicit uncertainty in their handling of processes.Uncertainty may occur during data collection, modeling, and analysis of the engineering system and model predictions. Common methods for stream flow forecasting use historical discharge data series at some reach of the river in the watershed. It is universally believed that climate and landuse change can affect the spatial and temporal distribution of water resources and also change hydrological parameters such as intensities and frequencies of extreme hydrological events. Distributed watershed models are increasingly being used to support decisions about alternative management strategies in the areas of land use change and climate change. In this study, the kinematic runoff and erosion model KINEROS2 which is an event oriented, physically based model was used for rainfall-runoff simulation. The main objective of this study is to investigate the effects of uncertainty in rainfall and models input parameters for extreme events in a semi-arid region and quantifying the uncertainties in frequency analysis of extreme rainfall events which are associated with Depth Duration Frequency (DDF) curves. The calibration scheme is carried out under the Generalized Likelihood Uncertainty Estimation (GLUE) framework to quantify uncertainty in the rainfall-runoff modelling process. These uncertainties are presented in the rainfall-runoff modeling for investigation of uncertainty effects in discharge and volumes of extreme hydrological events and subsequently embedded into guidelines for risk based design and management of urban water infrastructure. The uncertainty in the rainfall input data was studied using the rainfall data of 16 gauging stations in the Zayanderood basin, central Iran. The 36 rainfall series were generated based on rainfall at each of the gauging stations. Statistical evaluations for stream flow prediction indicate that there is good agreement between the measured and simulated flows with Nash Sutcliffe values of efficiency of 0.85 and 0.79 for calibration and validation periods respectively. Uncertainty analysis was carried out on the new distribution of input parameters and various duration and frequency of rainfall for extreme events are considered. The watershed was simulated for each event using Monte Carlo sampling from statistical distribution of input parameters. Uncertainty analysis applied to the hydrologic model indicated that uncertainty in input parameters affects the results significantly. The uncertainty in output is expressed through peak discharge values and stream flow volumes. For peak discharges the amounts of upper uncertainty is reduced with increasing rainfall duration, for all return periods. Maximum reduction was with the return period of 100 years that was reduced from 1327m3/s for 24 hour rainfall duration to 582m3/s for 120 hours rainfall duration. By increasing rainfall duration, the difference between upper limit of uncertainty and predicted value declines. In fact, model accuracy in discharge calculation increases with rainfall duration. However for stream volumes, the variation of upper uncertainty band does not have a recognizable trend with increased rainfall duration for all return periods. The difference between upper limit of uncertainty and predicted values is significant for all return periods and rainfall durations. This difference slightly increases with increase of rainfall duration for all return periods. The validated KINEROS2 model enables predicting streamflow volume for extreme events with reliable accuracy without uncertainty analysis. The findings pointed out that extreme discharges prediction should not be static tools but instead should undergo continuous adaptation with uncertainty analysis, relative to possible changes in watershed hydrology.