Effects of Non-Stationarity on Flood Frequency Analysis: Case Study of the Cheongmicheon Watershed in South Korea

Kim, Sang Ug; Son, Minwoo; Chung, Eun‐Sung; Yu, Xiao

doi:10.3390/su10051329

Cited by 4 publications

(6 citation statements)

References 43 publications

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“…Rather, an increase in the frequency of floods has been observed (Hirsch and Archfield, 2015). Prediction of the probability of flood events has traditionally been based on flood frequency analysis (Davie and Quinn, 2019) based on the assumption of flow stationarity (Kim et al, 2018;Do et al, 2020), defined by Milly et al (2008) as "natural systems fluctuating within an unchanging envelope of variability." However, climate and landscape change predict non-stationarity in rainfall and flood frequencies and the stationarity assumption of traditional flood frequency may no longer be applicable (Milly et al, 2008;Villarini et al, 2018).…”

Section: Flood Frequency Analysismentioning

confidence: 99%

Hydrological characteristics of extreme floods in the Klaserie River, a headwater stream in southern Africa

Marr

Swemmer

2023

J Limnol

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Climate change models for southern Africa predict less frequent, but more intense, rainfall events, and an increased frequency of tropical cyclones. With their steep topography and small catchments, headwater streams generate large floods following intense rainfall events. Large flooding events in headwater streams are under studied in southern Africa. In this paper, we explore flooding in the upper Klaserie River, Limpopo River System, South Africa to determine the flow distribution and flood frequency for the catchment. In addition, we determine the return level for a large, economically damaging, flood generated following the landfall of a sub-tropical depression in January 2012 and, attempt to identify rainfall patterns that resulted in similar floods. An annual hydrological cycle with summer maxima and winter minima for both rainfall and flow was identified. The flood frequency analysis demonstrated that the January 2012 flood had an estimated return level of 225 years. This flood had a peak flowrate exceeding 1200 m3s-1 in a system with an average daily flowrate of 1 m3s-1. Regression tree analysis showed that a two-day rainfall in excess of 240 was a predictor for four of the five largest floods. A two-day rainfall in excess of 400 mm distinguished the January 2012 flood from other floods. Non-stationarity analyses for the flow and rainfall data and a SWAT hydrological model are recommend for the upper Klaserie River to evaluate climate and land cover changes, and their relationship to the magnitude of the 2012 flood. Our study demonstrates that South African river monitoring data can be used to detect and characterize major floods, despite deficiencies in these data. Continuation of these monitoring programs is vital for river health monitoring and the detection of trends in floods resulting from human activities and climate change.

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Section: Flood Frequency Analysismentioning

confidence: 99%

Hydrological characteristics of extreme floods in the Klaserie River, a headwater stream in southern Africa

Marr

Swemmer

2023

J Limnol

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“…Two meteorological drought indices, Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI) were applied, and a hydrological drought index, Streamflow Drought Index (SDI), was used in determining drought from the simulation results of Soil and Water Assessment Tool (SWAT) using the two GCMs. The study area is the Cheongmicheon watershed, which has suffered from frequent droughts and thus has been a popular subject in Korea [39].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5

et al. 2021

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Due to the recent appearance of shares socioeconomic pathway (SSP) scenarios, there have been many studies that compare the results between Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs). This study attempted to project future drought characteristics in the Cheongmicheon watershed using SSP2-4.5 of Australian Community Climate and Earth System Simulator-coupled model (ACCESS-CM2) in addition to Representative Concentration Pathway (RCP) 4.5 of ACCESS 1-3 of the same institute. The historical precipitation and temperature data of ACCESS-CM2 were generated better than those of ACCESS 1-3. Two meteorological drought indices, namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to project meteorological drought while a hydrological drought index, Standardized Streamflow Index (SDI), was used to project the hydrological drought characteristics. The metrological data of GCMs were bias-corrected using quantile mapping method and the streamflow was obtained using Soil and Water Assessment Tool (SWAT) and bias-corrected meteorological data. As a result, there were large differences of drought occurrences and severities between RCP4.5 and SSP2-4.5 for the values of SPI, SPEI, and SDI. The differences in the minimum values of drought index between near (2021–2060) and far futures (2061–2100) were very small in SSP2-4.5, while those in RCP4.5 were very large. In addition, the longest drought period from SDI was the largest because the variation in precipitation usually affects the streamflow with a lag. Therefore, it was concluded that it is important to consider both CMIP5 and CMIP6 GCMs in establishing the drought countermeasures for the future period.

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“…The measured hydrological data, particularly in developing countries such as Ethiopia can be limited, short, or nonexistent to the extent that they are far from representative of the basin under consideration [1,6]. A promising and elegant way to solve this problem is deriving the flood frequency curve by using the commonly used probability distribution functions, which is the most frequently used statistical method in hydrology all over the world [2,[7][8][9][10][11][12][13][14][15]. Moreover, it is essential to note that the climate and land use changes, as well as human activities, have been known to alter the catchment flood response dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…The samples for flood frequency analysis can be chosen based on the annual maximum flow or the annual peak flows over some defined truncation levels. The annual maximum flow method has been widely used for flood frequency analysis in different regions [2,[7][8][9][10][11][12][13][14][15][25][26][27][28], in which the sample is defined by the maximum flow of each year of the study period. It is essential to note that the main drawbacks of relying on peaks over threshold method are the threshold selection and assuring independence criteria [27].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have been reported to be related to the investigation of the most suitable probability distribution for flood frequency analysis in different regions worldwide [2,[7][8][9][10][11][12][13][14][15][26][27][28][29][30][31][32][33]. Ahilan et al [29] compared the family of extreme value distributions using the data from 172 gauging stations in Ireland; they reported that the Gumbel distribution outperformed the other two types of extreme value distributions (i.e., Frechet, and Weibull).…”

Section: Introductionmentioning

confidence: 99%

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Flood Frequency Analyses over Different Basin Scales in the Blue Nile River Basin, Ethiopia

Tegegne¹,

Melesse²,

Asfaw³

et al. 2020

Hydrology

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The frequency and intensity of flood quantiles and its attendant damage in agricultural establishments have generated a lot of issues in Ethiopia. Moreover, precise estimates of flood quantiles are needed for efficient design of hydraulic structures; however, quantification of these quantiles in data-scarce regions has been a continuing challenge in hydrologic design. Flood frequency analysis is thus essential to reduce possible flood damage by investigating the most suitable flood prediction model. The annual maximum discharges from six representative stations in the Upper Blue Nile River Basin were fitted to the commonly used nine statistical distributions. This study also assessed the performance evolution of the probability distributions with varying spatial scales, such that three different spatial scales of small-, medium-, and large-scale basins in the Blue Nile River Basin were considered. The performances of the candidate probability distributions were assessed using three goodness-of-fit test statistics, root mean square error, and graphical interpretation approaches to investigate the robust probability distribution for flood frequency analysis over different basin spatial scales. Based on the overall analyses, the generalized extreme value distribution was proven to be a robust model for flood frequency analysis in the study region. The generalized extreme value distribution significantly improved the performance of the flood prediction over different spatial scales. The generalized extreme value flood prediction performance improvement measured in root mean square error varied between 5.84 and 67.91% over other commonly used probability distribution models. Thus, the flood frequency analysis using the generalized extreme value distribution could be essential for the efficient planning and design of hydraulic structures in the Blue Nile River Basin. Furthermore, this study suggests that, in the future, significant efforts should be put to conduct similar flood frequency analyses over the other major river basins of Ethiopia.

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Effects of Non-Stationarity on Flood Frequency Analysis: Case Study of the Cheongmicheon Watershed in South Korea

Cited by 4 publications

References 43 publications

Hydrological characteristics of extreme floods in the Klaserie River, a headwater stream in southern Africa

Hydrological characteristics of extreme floods in the Klaserie River, a headwater stream in southern Africa

Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5

Flood Frequency Analyses over Different Basin Scales in the Blue Nile River Basin, Ethiopia

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