Ensemble approach for projections of return periods of extreme water levels in Estonian waters

被引:18
|
作者
Eelsalu, Maris [1 ]
Soomere, Tarmo [1 ]
Pindsoo, Katri [1 ]
Lagemaa, Priidik [2 ]
机构
[1] Tallinn Univ Technol, Inst Cybernet, EE-12618 Tallinn, Estonia
[2] Tallinn Univ Technol, Marine Syst Inst, EE-12618 Tallinn, Estonia
关键词
Water level; Extreme value distributions; Ensemble approach; Baltic Sea; Block maxima method; Wave set-up; SEA-LEVEL; BALTIC SEA; STORM-SURGE; NORTH-SEA; MODEL; COAST; VARIABILITY; TRENDS; GULF; DRIVEN;
D O I
10.1016/j.csr.2014.09.012
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
The contribution of various drivers to the water level in the eastern Baltic Sea and the presence of outliers in the time series of observed and hindcast water level lead to large spreading of projections of future extreme water levels. We explore the options for using an ensemble of projections to more reliably evaluate return periods of extreme water levels. An example of such an ensemble is constructed by means of fitting several sets of block maxima (annual maxima and stormy season maxima) with a Generalised Extreme Value, Gumbel and Weibull distribution. The ensemble involves projections based on two data sets (resolution of 6 h and 1 h) hindcast by the Rossby Centre Ocean model (RCO; Swedish Meteorological and Hydrological Institute) and observed data from four representative sites along the Estonian coast. The observed data are transferred into the grid cells of the RCO model using the HIROMB model and a linear regression. For coastal segments where the observations represent the offshore water level well, the overall appearance of the ensembles signals that the errors of single projections are randomly distributed and that the median of the ensemble provides a sensible projection. For locations where the observed water level involves local effects (e.g. wave set-up) the block maxima are split into clearly separated populations. The resulting ensemble consists of two distinct clusters, the difference between which can be interpreted as a measure of the impact of local features on the water level observations. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:201 / 210
页数:10
相关论文
共 29 条
  • [1] Return periods of extreme water levels estimated for some vulnerable areas of Buenos Aires
    D'Onofrio, EE
    Fiore, MME
    Romero, SI
    CONTINENTAL SHELF RESEARCH, 1999, 19 (13) : 1681 - 1693
  • [2] RETURN PERIODS OF EXTREME SEA LEVELS - THE EXCEEDANCE PROBABILITY METHOD
    HAMON, BV
    MIDDLETON, JF
    INTERNATIONAL HYDROGRAPHIC REVIEW, 1989, 66 (02): : 165 - 177
  • [3] RETURN PERIODS OF EXTREME SEA LEVELS FROM SHORT RECORDS
    MIDDLETON, JF
    THOMPSON, KR
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 1986, 91 (C10): : 1707 - 1716
  • [4] Estimation of return levels with long return periods for extreme sea levels in a time-varying framework
    Jesper Rydén
    Environment Systems and Decisions, 2024, 44 (4) : 1019 - 1028
  • [5] STUDY ON THE EXTREME HIGH WATER LEVELS AND WAVE HEIGHTS OF DIFFERENT RETURN PERIODS IN LAIZHOU BAY, CHINA
    Zhou, Chunyan
    Zheng, Jinhai
    Zhang, Jisheng
    Fu, Xiaoying
    PROCEEDINGS OF THE ASME 36TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2017, VOL 7A, 2017,
  • [6] Estimation of Return Levels with Long Return Periods for Extreme Sea Levels by the Average Conditional Exceedance Rate Method
    Ryden, Jesper
    GEOHAZARDS, 2024, 5 (01): : 166 - 175
  • [7] Multivariate multiparameter extreme value models and return periods: A copula approach
    Salvadori, G.
    De Michele, C.
    WATER RESOURCES RESEARCH, 2010, 46
  • [8] Computing the distribution of return levels of extreme warm temperatures for future climate projections
    M. Pausader
    D. Bernie
    S. Parey
    M. Nogaj
    Climate Dynamics, 2012, 38 : 1003 - 1015
  • [9] Computing the distribution of return levels of extreme warm temperatures for future climate projections
    Pausader, M.
    Bernie, D.
    Parey, S.
    Nogaj, M.
    CLIMATE DYNAMICS, 2012, 38 (5-6) : 1003 - 1015
  • [10] Analysis of return periods and return levels of Yearly July–September extreme droughts in the West African Sahel
    Roméo Chamani
    David Monkam
    Zéphirin Yepdo Djomou
    Climate Dynamics, 2019, 52 : 3421 - 3433