Analysis of maximum precipitation in Thailand using non‐stationary extreme value models

Prahadchai, Thanawan; Shin, Yonggwan; Busababodhin, Piyapatr; Park, Jeong‐Soo

doi:10.1002/asl.1145

Cited by 5 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, where Z is a normalized MK test statistic calculated from data (Naghettini, 2017) , and z α/2 is 100 × (1 − z α/2) percentile of the stan dard normal distribution. A R package "trend" was used to execute the MK test (Prahadchai et al, 2022).…”

Section: Mann-kendall Test For Trendmentioning

confidence: 99%

Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management

Phoophiwfa,

Chomphuwiset,

Prahadchai

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Extreme rainfall events in the Chi watershed of Thailand have significant implications for the safe and economic design of engineered structures and effective reservoir management. This study investigates the characteristics of extreme rainfall events in the Chi watershed, Northeast Thailand, and their implications for flood risk management. We apply extreme value theory to historical maximum cumulative rainfall data for consecutive rainy days from 1984 to 2018. The Generalized Pareto Distribution (GPD) was used to model the extreme rainfall data, with the parameters estimated using Maximum Likelihood Estimator (MLE) and Linear Moment Estimator (L-ME) methods based on specific conditions. The goodness-of-fit tests confirm the suitability of the GPD for the data, with p-values exceeding 0.05. Our findings reveal that certain regions, notably Udon Thani, Chaiyaphum, Maha Sarakham, Tha Phra Agromet, Roi Et, and Sisaket provinces, show the highest return levels for consecutive 2-day (CONS-2) and 3-day (CONS-3) rainfall. These results underscore the heightened risk of flash flooding in these regions, even with short periods of continuous rainfall. Based on our findings, we developed 2D return level maps using the Q-geographic information system (Q-GIS) program, providing a visual tool to assist with flood risk management. The study offers valuable insights for designing effective flood management strategies and highlights the need for considering extreme rainfall events in water management and planning. Future research could extend our findings through spatial correlation analysis and the use of copula functions. Overall, this study emphasizes the importance of preparing for extreme rainfall events, particularly in the era of climate change, to mitigate potential flood-related damage.

show abstract

Section: Mann-kendall Test For Trendmentioning

confidence: 99%

Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management

Phoophiwfa,

Chomphuwiset,

Prahadchai

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Rohmer et al (2021) (4) discussed that non-stationarity in heavy rainfall time series is often apparent in the form of trends because of long-term climate changes. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Sakthivel,

Nandhini

2024

IJST

View full text Add to dashboard Cite

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

show abstract

“…In 2011, Gale and Saunders [3] presented the cause of the 2011 major floods in Thailand and future flood forecasts, which showed that more flooding could occur within the next two to three decades unless flood defenses and flood management practices are improved. The study discovered that such locations were frequently damaged by flooding and the Chi River Basin area had a flood every year, according to the flood situation report [4][5][6]. In addition, the Chi Watershed has been experiencing flooding in many forms, including flooding in Roi Et, Kalasin, and Khon Kaen provinces, water overflowing the bank and wild water flows and mudslide in Kalasin and Chaiyaphum.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach

et al. 2023

Self Cite

View full text Add to dashboard Cite

Parameter estimation strategies have long been a focal point in research due to their significant implications for understanding data behavior, including the dynamics of big data. This study offers an advancement in these strategies by proposing an adaptive parameter estimation approach for the Generalized Extreme Value distribution (GEVD) using an artificial neural network (ANN). Through the proposed adaptive parameter estimation approach, based on ANNs, this study addresses the parameter estimation challenges associated with the GEVD. By harnessing the power of ANNs, the proposed methodology provides an innovative and effective solution for estimating the parameters of the GEVD, enhancing our understanding of extreme value analysis. To predict the flood risk areas in the Chi river watershed in Thailand, we first determine the variables that are significant in estimation of the three GEVD parameters μ,σ, and ξ by considering the respective correlation coefficient and then estimating these parameters. The data were compiled from satellite and meteorological data in the Chi watershed gathered from the Meteorological Department and 92 meteorological stations from 2010 to 2021, and consist of such variables as the Normalized Difference Vegetation Index (NDVI), climate, rainfall, runoff, and so on. The parameter estimation focuses on the GEVD. Taking into consideration that the processes could be stationary (parameters are constant over time, S) or non-stationary (parameters change over time, NS), maximum likelihood estimation and ANN approaches are applied, respectively. Both cases are modeled with the GEVD for the monthly maximum rainfall. The Nash-Sutcliffe coefficient (NSE), is used to compare the performance and accuracy of the models. The results illustrate that the non-stationary model was suitable for 82 stations, while the stationary model was suitable for only 10 stations. The NSE values in each model range from 0.6 to 0.9. This indicated that all 92 models were highly accurate. Furthermore, it is found that meteorological variables, geographical coordinates, and NDVI, that are correlated with the shape parameter in the ANN model, are more significant than others. Finally, two-dimensional maps of the return levels in the 2, 5, 10, 20, 50, and 100-year return periods are presented for further application. Overall, this study contributes to the advancement of parameter estimation strategies in the context of extreme value analysis and offers practical implications for water resource management and flood risk mitigation.

show abstract

Analysis of maximum precipitation in Thailand using non‐stationary extreme value models

Cited by 5 publications

References 21 publications

Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management

Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management

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

Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach

Contact Info

Product

Resources

About