Abstract
The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.
Highlights
Climate changes caused by human activities are affecting the frequency and intensity of extreme weather phenomena and causing rises in temperature, changes in precipitation and precipitation patterns, and rising sea levels
Techniques were applied to the of several rainfall datathe to confirm the uncertainty of Blending rainfall
Blending techniques were applied to results the results of runoffseveral hydrologic to determine single runoff hydrograph
Summary
Climate changes caused by human activities are affecting the frequency and intensity of extreme weather phenomena and causing rises in temperature, changes in precipitation and precipitation patterns, and rising sea levels. Korea has suffered from frequent local storms and typhoons due to climate change [1,2]. It is necessary to predict rainfall and runoff accurately and many methods have been sought to reduce flood damage. It is especially important to minimize rainfall damage by assessing the reliability of rainfall with characteristics of high rainfall intensity in the short term, such as heavy rainfall. It is difficult to quantify and predict the characteristics of rainfall by measuring rainfall from a ground gauge because rainfall shows a wide range of spatiotemporal variability. Radar rainfall data can be used to predict and observe changes in rainfall in real time, but the data are estimates of rainfall based on indirect observations through the reflectivity from airborne bodies.
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