Improvement of Huff's Method Considering Severe Rainstorm Events

Choi, S. D.; Joo, Kyungwon; Shin, Hyeonsook; Heo, Jun-Haeng

doi:10.3741/jkwra.2014.47.11.985

Cited by 9 publications

(5 citation statements)

References 8 publications

(3 reference statements)

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“…Still regarding design rainfall, the temporal distribution also aff ects runoff and maximum ow values (Choi et al, 2014;Abreu et al, 2018). Canholi (2005) points out that the temporal distribution of rainfall comprises a major problem for the hydrologist, because, for each temporal distribution, there are diff erent hydrographs.…”

Section: Maximum Observed and Estimated Flow For Diff Erent Return Periodsmentioning

confidence: 99%

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Aguiar

Back²,

Recio

et al. 2021

R. Ci. Amb.

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CN) method is used. To estimate the maximum ow in the watershed, three background moisture scenarios (CNI, CNII and CNIII) and six empirical equations proposed for rural watersheds were adopted to calculate the concentration time. To evaluate the observed upstream ow, a 34-years historical series was used, measured at the São Martinho downstream uviometric station (code 84598002), located in the outlet section of the study watershed.  e results showed that the maximum ows estimated by the SCS-CN method for the conditions of CNII and CNIII had greater diff erences (-198% and -287%) compared to the observed ow.  e smallest diff erences were veri ed for the CNI condition for all the analyzed return periods (2, 5, 10, 15, 20, 25, 50 and 100). Also, it was observed that, the longer is the return period, smaller the diff erence among the maximum observed and estimated ows, and, the smallest was veri ed for the 50year period, indicating that this is the most appropriate for studies of extreme events in the study watershed.

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Section: Maximum Observed and Estimated Flow For Diff Erent Return Periodsmentioning

confidence: 99%

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Aguiar

Back²,

Recio

et al. 2021

R. Ci. Amb.

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show abstract

“…시간분포 모형에 대한 검증은 주로 관측자료와의 비교를 통해 이루어진다 (Guo and Hargadin, 2009;Nguyen et al, 2010;Kimoto et al, 2011). Guo and Hargardin (2009) (Jeong and Yoon, 2007;KWRA, 2009;Choi et al, 2014;Cho and Kim, 2015). Jeong and Yoon (2007)…”

Section: 모형의 검증unclassified

Analysis of a Conventional Huff Model at Seoul Station and Proposal of an Improvisation Method

Yoo¹,

Na²

2019

J. Korean Soc. Hazard Mitig

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The conventional Huff model has a serious limitation with respect to over-smoothing of the rainfall peak because it is derived based on the mean temporal distribution of observed rainfall events. To overcome this limitation of the conventional Huff model, this study proposes a modified Huff model that considers the characteristics of the temporal distribution of observed annual maximum rainfall events. After normalizing and rearranging rainfall events around the storm center, the modified Huff model is formulated to consider their mean characteristics. Compared to the conventional Huff model, the modified Huff model has a unique characteristic of being a centered-type, which is also formulated using the rearranged rainfall events around the storm center. When we evaluated the suitability of the conventional Huff model, we found that the model is not representative of the temporal rainfall distribution of observed storm events. Though the modified Huff model is formulated as a centered-type that is found to perform better than the conventional Huff model formulated for a given quartile, the modified Huff model still has a limitation that the model is derived by considering the mean characteristics of the observed rainfall temporal distribution.

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“…In South Korea, there have been many studies on the distribution of Huff rainfall time; the Huff rainfall distribution is constructed so that the peak of the heavy rain can be placed in the desired time section. This tends to represent the time distribution of heavy rain relatively well; therefore, the Huff method was chosen in this study as the time distribution of rainfall was suitable for the applied rainfall-runoff model [14].…”

Section: Introductionmentioning

confidence: 99%

Case Study of Urban Flood Inundation—Impact of Temporal Variability in Rainfall Events

Lee

Kim

2021

Water

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This study aimed to calculate and analyze total overflows that accumulate in urban manholes in the target drainage basin of Samsung-dong, Seoul in heavy rainfall events with different temporal distribution characteristics, using the EPA’s Storm Water Management Model (EPA-SWMM model). Inundation behaviors were analyzed using the two-dimensional flood model (FLO-2D). The extreme rainfall events were produced using different exceedance probability Huff distributions for different durations and return periods, such as from 1 to 3 h and 10 years, 50 years, 80 years, 100 years, respectively. The inundation model was validated using the actual flood observations on 21 September 2010 in the Samsung-dong drainage basin. The total overflow amount showed considerable differences according to the different time distribution characteristics, such as the temporal location of the storm peak and the concentration level of the storm. Furthermore, the inundation behaviors were also related to the temporal characteristics of storms. The results illustrated that the consideration of the temporal distribution characteristics of extreme rainfall events is essential for an accurate understanding of the rainfall–runoff response and inundation behavior in urban drainage basins.

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Improvement of Huff's Method Considering Severe Rainstorm Events

Cited by 9 publications

References 8 publications

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Maximum Flow Study by the Hydrogram Method for a Watershed in the South of Santa Catarina, Brazil

Analysis of a Conventional Huff Model at Seoul Station and Proposal of an Improvisation Method

Case Study of Urban Flood Inundation—Impact of Temporal Variability in Rainfall Events

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