Uncertainties In Future Projections Of Extreme Rainfall: The Role Of Climate Model, Emission Scenario And Randomness

Climate change has resulted in substantial variations in annual extreme rainfall quantiles in different durations and return periods. Predicting the future changes in extreme rainfall quantiles is essential for various water resources design, assessment, and decision making purposes. Current Predictions of future rainfall extremes, however, exhibit large uncertainties. According to extreme value theory, rainfall extremes are rather random variables, with changing distributions around different return periods; therefore there are uncertainties even under current climate conditions. Regarding future condition, our large-scale knowledge is obtained using global climate models, forced with certain emission scenarios. There are widely known deficiencies with climate models, particularly with respect to precipitation projections. There is also recognition of the limitations of emission scenarios in representing the future global change. Apart from these large-scale uncertainties, the downscaling methods also add uncertainty into estimates of future extreme rainfall when they convert the larger-scale projections into local scale. The aim of this research is to address these uncertainties in future projections of extreme rainfall of different durations and return periods. We plugged 3 emission scenarios with 2 global climate models and used LARS-WG, a well-known weather generator, to stochastically downscale daily climate models’ projections for the city of Saskatoon, Canada, by 2100. The downscaled projections were further disaggregated into hourly resolution using our new stochastic and non-parametric rainfall disaggregator. The extreme rainfall quantiles can be consequently identified for different durations (1-hour, 2-hour, 4-hour, 6-hour, 12-hour, 18-hour and 24-hour) and return periods (2-year, 10-year, 25-year, 50-year, 100-year) using Generalized Extreme Value (GEV) distribution. By providing multiple realizations of future rainfall, we attempt to measure the extent of total predictive uncertainty, which is contributed by climate models, emission scenarios, and downscaling/disaggregation procedures. The results show different proportions of these contributors in different durations and return periods.

A Study On The Flow Characteristics Influenced By Hydraulic Structure At A Channel Junction

Natural riversare consisting of various networks as junction andstreams. And sediment and erosion are occurred by specific stream condition. When flood season,large discharge flew in the river and river bed changed by high flow velocity. Especially junction area’s flow characteristics are very complex. The purpose of this study is to analyze the flow characteristics in channel junction, which are most influenced by large discharge like flooding and input water from tributary. We investigate the flow characteristics by using hydrodynamics and transport module in MIKE 3 FM. MIKE 3 FM model was helpful tool to analysis 3D hydrodynamics, erosion and sediment effect from channel bed. We analyze flow characteristics at channel junction. Also we consider hydraulic structures like a bridge pier which is influencing flow characteristics like a flow velocity, water level, erosion and scour depth in channel bed. In the model, we controlled discharge condition according to Froude Number and reflect various grain diameter size and flow ratio change in main stream and tributary. In the result, flow velocity, water level, erosion and sediment depth are analyzed. Additionally, we suggest a these result relationship with equations. This study will help the understand flow characteristics and influence of hydraulic structure in channel junction. Acknowledgments This research was supported by a grant (12-TI-C01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.