Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

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.

Evaluations on Radar QPE using raindrop size distribution in Southern Luzon, Philippines

“…Comparing the mean Dm with other studies, results show that the mean raindrop diameter in the Philippines for the wet season (at 1-min Δt) is the same as the value observed by Tokay and Short (1996) for the tropical ocean (1.41 mm). However, the mean Dm in this study is observed to be lower than the study by Lam et al (2015) in Malaysia (1.74 mm), and from the results of Chen et al (2013) in Eastern China (1.66 mm), but is observably higher than that from the study of Lee et al (2019) in Northern Taiwan (1.16 mm), from the study by Seela et al (2017) in Palau (1.11 mm) and from 1-min DSD results in Taiwan from the same study (1.24 mm). These results imply that raindrops in the Southern Luzon, Philippines is generally smaller than that from Malaysia and Eastern China but are larger than those from Taiwan and Palau.…”

“…However, a reason for such high distribution might be the presence of outlying number of drops on larger diameters that affects the value of µ. Moreover, the average µ value for 1-min data for the wet season (µ = 11.20) is also significantly higher than the µ values of China (µ = 3.51, 1-min Δt), Palau (µ = 8.37, 1-min Δt), Taiwan (µ = 6.72 and 5.60 for 1-min and 10-min Δt, respectively), and Malaysia (µ = 6.76, 1-min Δt) (Chen et al 2013;Lam et al 2015;Lee et al 2019;Seela et al 2017). However, when using longer time-steps (> 1-min), the values of µ appears to be closer to the aforementioned areas of comparison, albeit still a little higher in magnitude.…”

“…This also explains why the R derived from the Parsivel disdrometer exhibited a significant amount of overestimation for Yagi (with multiple rainfall bursts) but only a minimal overestimation for the whole duration of the study (which is characterized by moderate to high rainfall stratiform rainfall). Moreover, it was also observed that there is a wide range of raindrop size diameters that is characteristic of the study area as compared to other nearby countries such as Malaysia (Lam et al 2015) and Taiwan (Lee et al 2019). This presence of larger raindrops can further exacerbate the overestimation of the Parsivel disdrometer.…”

“…As a major driving force of global water cycle, precipitation data is extremely valuable in hydrology, meteorology, agriculture, and weather forecasting (Kidd and Huffman 2011;Trenberth et al 2003) and with the continuous progress of technology, the sheer availability of rainfall data is not enough (Jiang et al 2012). The need for high-quality, comprehensive, and accurate precipitation measurements and estimates is becoming more and more important for hydrological modeling ( Lee et al 2019;Liu et al 2017), numerical weather prediction (Rossa et al 2008;Shrestha et al 2013), and other hydrometeorological applications (Pappenberger et al 2008;Yilmaz et al 2005).…”

Urban Runoff Simulation: How Do Land Use/Cover Change Patterning and Geospatial Data Quality Impact Model Outcome?

“…Using this dataset, computing MAP explicitly considers the spatial variability of rainfall compared with ground-based gauge rainfall [24]. Some previous studies have tested the performance of different NEXRAD precipitation products against the ground-based gauge data in hydrological modeling, and results have shown the superiority of NEXRAD data because of their ability to capture the rainfall spatial variations for better outcomes [25][26][27]. Only a few studies have attempted to couple NEXRAD Level 3 rainfall with the HEC-HMS model for the assessment of LULC change impact on urban flooding.…”