Uncertainty Quantification of Future Design Rainfall Depths in Korea

被引:7
|
作者
Kim, Kyungmin [1 ]
Choi, Jeonghyeon [1 ]
Lee, Okjeong [2 ]
Cha, Dong-Hyun [3 ]
Kim, Sangdan [4 ]
机构
[1] Pukyong Natl Univ, Div Earth Environm Syst Sci, Environm Engn, Busan 48513, South Korea
[2] Pukyong Natl Univ, Inst Environm Res, Busan 48513, South Korea
[3] Ulsan Natl Inst Sci & Technol, Sch Urban & Environm Engn, Ulsan 44919, South Korea
[4] Pukyong Natl Univ, Dept Environm Engn, Busan 48513, South Korea
关键词
climate change; ensemble average; intensity-duration-frequency curves; rainfall extremes; uncertainty; IDF CURVES; CLIMATE-CHANGE; PRECIPITATION EXTREMES; MODEL; INTENSITY; ENSEMBLE; IMPACT; EVENTS;
D O I
10.3390/atmos11010022
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
One of the most common ways to investigate changes in future rainfall extremes is to use future rainfall data simulated by climate models with climate change scenarios. However, the projected future design rainfall intensity varies greatly depending on which climate model is applied. In this study, future rainfall Intensity-Duration-Frequency (IDF) curves are projected using various combinations of climate models. Future Ensemble Average (FEA) is calculated using a total of 16 design rainfall intensity ensembles, and uncertainty of FEA is quantified using the coefficient of variation of ensembles. The FEA and its uncertainty vary widely depending on how the climate model combination is constructed, and the uncertainty of the FEA depends heavily on the inclusion of specific climate model combinations at each site. In other words, we found that unconditionally using many ensemble members did not help to reduce the uncertainty of future IDF curves. Finally, a method for constructing ensemble members that reduces the uncertainty of future IDF curves is proposed, which will contribute to minimizing confusion among policy makers in developing climate change adaptation policies.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Quantification of anticipated future changes in high resolution design rainfall for urban areas
    Onof, C.
    Arnbjerg-Nielsen, K.
    [J]. ATMOSPHERIC RESEARCH, 2009, 92 (03) : 350 - 363
  • [2] Estimating Rainfall Design Values for the City of Oslo, Norway-Comparison of Methods and Quantification of Uncertainty
    Lutz, Julia
    Grinde, Lars
    Dyrrdal, Anita Verpe
    [J]. WATER, 2020, 12 (06)
  • [3] Estimating rainfall design values for the City of Oslo, Norway-comparison of methods and quantification of uncertainty
    Lutz, Julia
    Grinde, Lars
    Dyrrdal, Anita Verpe
    [J]. Water (Switzerland), 2020, 12 (06):
  • [4] Uncertainty quantification for engineering design
    Ghanem, Roger
    Du, Xiaoping
    [J]. AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 2017, 31 (02): : 119 - 119
  • [5] Uncertainty quantification in drug design
    Mervin, Lewis H.
    Johansson, Simon
    Semenova, Elizaveta
    Giblin, Kathryn A.
    Engkvist, Ola
    [J]. DRUG DISCOVERY TODAY, 2021, 26 (02) : 474 - 489
  • [6] Uncertainty quantification for engineering design
    Ghanem, Roger
    Du, Xiaoping
    [J]. AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 2017, 31 (03): : 222 - 222
  • [7] Intercomparison of estimators of EV I for determination of design rainfall depths
    Vivekanandan, N.
    [J]. International Symposium on Rainfall Rate and Radio Wave Propagation (ISRR '07), 2007, 923 : 128 - 137
  • [9] Uncertainty quantification and design under uncertainty of aerospace systems
    Maute, Kurt
    Pettit, Chris L.
    [J]. STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2006, 2 (3-4) : 159 - 159
  • [10] Quantification of Precipitation and Evapotranspiration Uncertainty in Rainfall-Runoff Modeling
    Baig, Faisal
    Sherif, Mohsen
    Faiz, Muhammad Abrar
    [J]. HYDROLOGY, 2022, 9 (03)