Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty

Kim, Hanbeen; Kim, Taereem; Shin, Ju Young; Heo, Jun-Haeng

doi:10.3390/w14030478

Cited by 7 publications

(5 citation statements)

References 77 publications

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“…However, the non-stationarity of the long-term evolution (in our case the SST warming) breaks this hypothesis, invalidating its use. To treat the non-stationarity with such approaches, continuous time evolving baselines have been introduced in quite recent studies in other areas and contexts (Scannell et al, 2016;Sigauke and Bere, 2017;Tebaldi et al, 2021;Kim et al, 2022). The purpose of detrending is thus analogous to the shifting baseline approach (Oliver et al, 2021;Amaya et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Evolution of marine heatwaves in warming seas: the Mediterranean Sea case study

et al. 2023

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Anomalous warming of the upper ocean is increasingly being observed in the Mediterranean Sea. Extreme events, known as marine heatwaves (MHWs), can have a profound impact on marine ecosystems, and their correct detection and characterization are crucial to define future impact scenarios. Here, we analyze MHWs observed over the last 41 years (1982–2022) in the Mediterranean sea surface temperatures (SSTs). We show that the intensification in frequency, intensity, and duration of Mediterranean MHWs in recent years is mainly due to a shift in SST mean that occurred in the last two decades and largely reduced when analyzing detrended SST data. Detrending thus allows the use of a fixed climatology without overestimating MHW properties over time and distinguishes long-term warming (i.e., trend) from transient and abrupt SST changes. Analogous results are also found over a shorter temporal period, by analyzing 13 years (2007–2020) of in situ data collected at different depths (5 to 40 m) at Columbretes Islands. Additionally, the in situ analysis reveals that atmospheric summer heatwaves could affect a layer of 10 m in depth. Lastly, a catalogue of the major Mediterranean MHWs that have occurred since 1982 is presented. This catalogue evidences an exceptionally long-lasting and intense MHW, starting in May 2022 and persisting, at least, until the end of the year, resulting in the event with the highest cumulative intensity just after the well-known 2003 MHW event.

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Section: Discussionmentioning

confidence: 99%

Evolution of marine heatwaves in warming seas: the Mediterranean Sea case study

et al. 2023

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“…One popular approach is to apply different non-stationary models to non-stationary data and select an appropriate model based on model diagnostics. Due to its adaptability to changes in the data structure, maximum-likelihood estimation of non-stationary model parameters is usually used for this purpose [50,51,53,54]. To date, this approach has been widely studied and can be described as a 'user-friendly' method.…”

Section: Fitting the Gev Distributionmentioning

confidence: 99%

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

TWARÓG

2024

Preprint

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In this paper, the Shannon entropy measure was used to assess changes in precipitation and temperature conditions. Due to the short, low-volume sequences of precipitation and temperature data analysed, a bootstrap method was used in the procedure for calculating Shannon entropy. The analysis used minimum and maximum values of monthly precipitation totals and monthly mean temperatures for 377 catchments distributed across the globe. A 110-year data series from 1901 to 2010 was analysed. Entropy values for the estimated parameters of the generalised extreme value distribution (GEV) were calculated for the adopted data. Entropy value calculations were performed for the left-hand constraint, based on minimum values, and for the right-hand constraint, based on maximum values. The applicability of Shannon's entropy measure in the analysis of climate change was demonstrated by allowing the degree of disorder and complexity of the distributions describing climate variables in the form of precipitation and temperature to be measured. This made it possible to obtain information on the directions of changes occurring with regard to minimum and maximum values in the field of monthly precipitation and mean temperatures in the analysed catchments. The study demonstrated the existence of Shannon entropy trends. The evaluation of entropy trends for precipitation and temperature sequences was performed using non-parametric tests. Mann -Kendall tests at the 5% significance level were used for trend analyses. The Pettitt test was performed to determine the point of change in trend for the rainfall and temperature data. The performed analysis was supported by graphical presentations.

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“…Prahadchai et al (2022) (5) built sixteen non-stationary models for time-dependent functions of the location and scale parameters of the GEV to the annual maximum (AM) daily and 2-day precipitation data observed from Thailand. Kim et al (2022) (6) present a new method for modeling extreme rainfall values in South Korea, this procedure identifies significant seasonal climate indices (SCIs) that influence the longterm trend of AM daily rainfall using statistical techniques such as ensemble empirical mode decomposition and then selects an appropriate GEV distribution among stationary and nonstationary GEVs using time and SCIs as covariates. Wang et al (2022) (7) developed a new non-stationary standardized runoff index by combining climate indices and modified reservoir index as explanatory variables using the generalized additive model for location, scale, and shape for the hydrological drought inspection of the Hanjiang River basin in China.…”

Section: Introductionmentioning

confidence: 99%

Modeling Extreme Values of Non-Stationary Precipitation Data with Effects of Covariates

Sakthivel,

Nandhini

2024

IJST

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Background/Objectives: Climate change is one of the most challenging problems of recent decades as it is highly volatile and needs a very effective scientific approach to find a solution. Further, the changes in the climate especially extreme cases have a more negative influence on day to day affairs of society. Hence, developed countries pay much attention to climate change and make policies at a global level. To find the scientific solution for these kinds of climate change, Extreme value theory offers effective methods for estimating and quantifying these types of natural hazards associated with climate. Methods: Block (Annual) maxima and peak over threshold are two strategies employed in this theory. The data observed on precipitation are mostly having non-stationary characteristics along with covariates. The generalized extreme value distribution and generalized Pareto distribution are used to model this type of non-stationary stochastic process. Findings: This study proposes a pragmatic automated dual-phase threshold selection technique that employs the entropy method to combine the results from various goodness of fit tests into a single unified measure known as the evaluation indicator, resulting in an efficient threshold for capturing extreme values. This allows for a more comprehensive examination of various thresholds using evaluation indicators and avoids assessing each test criterion individually. Novelty: In contrast to the subjective results of threshold stability plots, the dual-phase technique is based on numerical computations, which reduce bias and improve decision-making objectivity. We illustrate the applicability of the proposed technique by analyzing a precipitation dataset that includes time and wind speed as covariates. The results of the comparative analysis reveal that the proposed automated dual-phase threshold approach outperforms the peaks over threshold and annual maxima methods. Keywords: Non-stationary, Extreme values, Annual maxima, Threshold selection, Evaluation indicator

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Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty

Cited by 7 publications

References 77 publications

Evolution of marine heatwaves in warming seas: the Mediterranean Sea case study

Evolution of marine heatwaves in warming seas: the Mediterranean Sea case study

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

Modeling Extreme Values of Non-Stationary Precipitation Data with Effects of Covariates

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