Extreme rainfall events in the Chi watershed of northeastern 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 watershed 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 2022. The generalized Pareto distribution (GPD) was used to model the extreme rainfall data, with the parameters estimated using maximum likelihood estimation (MLE) and linear moment estimation (L-ME) methods based on specific conditions. The goodness-of-fit tests confirm the suitability of the GPD for the data, with

The distribution of rainfall and atmospheric fluctuations are directly impacted by changes in climate, which have significant implications for water resource management and hydrology. The northeastern region of Thailand is particularly susceptible to frequent flooding, which is often caused by a combination of local conditions, natural variations, and human actions. Unfortunately, this issue shows no signs of abating, and it continues to escalate in severity. In the northeastern region of Thailand, there is an agricultural area of over 63

Over the past three decades, water shortages have affected 57 provinces (or 75 % of the country), 525 districts (or 60 % of the total districts), 3321 sub-districts (or 46 % of the total), and 24 900 villages (or 33 % of the total villages) in Thailand, causing extensive damage. On average, 9.71 million people suffer from drought annually, representing about 15 % of the total population. Additionally, an average of 2.571 million rai of farmland is damaged each year, leading to an average loss of 661 head of livestock. The total cost of damage amounts to THB 656.62 million (or USD 17.57 million) per year. Moreover, the northeastern region has witnessed seven major floods in the years 1983, 1995, 1996, 2002, 2006, 2010, and 2011. These events resulted in substantial harm to both human life and property, posing challenges in accurately evaluating their impact and the overall cost of the incurred damage

Location of all 18 meteorological stations along the Chi watershed in the northeastern region of Thailand.

Descriptive statistics of the maximum cumulative rainfall on consecutive rainy days for 2, 3, 4, 5, 6, and 7 d are provided for selected stations in the northeastern region of Thailand.

According to the Thai Meteorological Department's report in 2006

It is well known that floods occur on average every several years, as supported by numerous studies. In this context,

The current study aimed to fill the research gap by examining the consecutive days of maximum rainfall data for Thailand. This data set was chosen due to the frequent occurrence of flooding caused by continuous heavy rainfall. To the best of our knowledge, no previous studies have been conducted on this specific type of data in Thailand. In this study, we aimed to identify critical areas along the Chi watershed and evaluate their severity for use in planning, resolving flooding, and pre-evaluating damage. To achieve this, we applied the non-stationary (NS) generalized Pareto distribution (NS-GPD) models on the maximum cumulative rainfall data observed for consecutive rainy days (CONS) of 2, 3, 4, 5, 6, and 7 d at 18 stations along the Chi watershed in the northeastern region of Thailand. Section

In this study, we analyzed the maximum cumulative rainfall on CONS data for 2, 3, 4, 5, 6, and 7 d observed by the Thai Meteorological Department (TMD)

Figure

Table

Table

Comparison of the Mann–Kendall test for consecutive rainy days of 2, 3, 4, 5, 6, and 7 d at each station. Mann–Kendall test:

Note: “NT” represents “no trend” in the data and “T” represents the presence of a trend in the data.

Scatter and line plots showing the trends for CONS-2 and CONS-3 (unit: mm) at the Chaiyaphum meteorological station in the Chi watershed, northeastern Thailand.

Panels

The block maxima method is limited for analyzing maximum rainfall data each year. Hence, the peak-over-threshold (POT) method or GPD is commonly employed for this purpose

Functional form of parameters for time dependent non-stationary extreme value models, represented by

The GPD can take on one of three forms depending on the sign of the shape parameter,

Grouping extreme values based on their independence can be achieved by clustering the values that exceed a certain threshold, which makes the GPD a suitable method for analysis

We considered the Mann–Kendall (MK) test of trend, to compare with the NS-GPD model. The MK test is commonly used to detect monotonic trends in time-series data. In the MK test, the null hypothesis is

The selection of an appropriate threshold is a crucial factor in statistical inference of rare events. This study compares three different threshold selection methods and their effectiveness. The first approach involves selecting the threshold based on meteorological conditions, where rainfall greater than 35 mm is considered indicative of heavy rainfall. The second approach uses the 90th percentile of the rainfall data set as the threshold. The third approach involves using the mean residual life (MRL) plot to select a threshold for the GPD or point process models. These approaches are analyzed theoretically and compared with existing procedures through an extensive simulation study, and are then applied to a data set of CONS, where the underlying extreme value index is assumed to vary over time.

The parameters in the GPD are commonly estimated using either the MLE

Assuming observations

The L-ME is widely used in analyzing skewed data, such as extreme rainfall and flood frequency. Although the details of the L-ME are not discussed here, we note that it is considered a standard method in such analyses. To calculate the L-ME of the GPD, we utilize the R package “lmom” developed by

The performance of the marginal probability was evaluated by conducting goodness-of-fit statistical tests. In this study, two tests – the Kolmogorov–Smirnov and Anderson-Darling tests – were used for this purpose. The K–S test is preferred as it does not make any assumptions about the distribution of data

Parameter estimates and standard error (SE) with thresholds

In the case of

Return levels or quantiles are used to interpret extreme values in terms of their probability of return period. Once a suitable model has been defined, return levels can be calculated as follows:

It is a

In this study, the threshold method was employed to select the appropriate threshold

Parameter estimates and standard error (SE) with thresholds

In the case of

Parameter estimation employed both MLE and L-ME methods, depending on the number of exceedances (

The data suitability for the GPD was confirmed via goodness-of-fit tests. Model selection relied on minimizing the Akaike information criterion (AIC) or the Bayesian information criterion (BIC) while ensuring that

The return level estimates for CONS-2 and CONS-7 across various return periods in Tables

Estimated return levels over various return period years, where the values in parentheses are standard error for maximum cumulative rainfall for CONS-2. The bold values present the first three stations which have maximum cumulative rainfall return level.

Estimated return levels over various return period years, where the values in parentheses are standard error for maximum cumulative rainfall for CONS-7. The bold values present the first three stations which have maximum cumulative rainfall return level.

Panels

Since Chaiyaphum Station is the origin station of the Chi watershed and a direct station, we present the quantile and return level plots of this station in Figs.

Panels

To enhance the visualization of the results, return level maps were generated using the Q-Geographic Information System (Q-GIS) program with the Inverse Distance Weighting (IDW) interpolation method. The IDW interpolation method assigns weights to the sample points based on their distance from the unknown point being interpolated. Figures

Estimated return level of maximum cumulative rainfall for 2 consecutive rainy days in the Chi watershed for 2-, 5-, 25-, and 50-year periods.

Estimated return level of maximum cumulative rainfall for seven consecutive rainy days in the Chi watershed for 2-, 5-, 25-, and 50-year periods.

Figures

In addition, it can be observed that there was a significant difference in the return level for the 100-year period as compared with the other return periods in the figures of the maximum cumulative rainfall return level forecast for CONS-7 of rainfall data. The return level increased every year for all stations, indicating the importance of future rainfall management planning. These findings reveal the risk of flooding areas in the Chi watershed, including provinces such as Udon Thani, Chaiyaphum, Khon kaen, Maha Sarakham, Roi Et, and Sisaket. The figures were generated using the Q-GIS program, and they provide valuable insights into the spatial distribution of extreme rainfall events in the study area.

In this study, the generalized Pareto distribution parameters were estimated using both maximum likelihood and L-moment estimation methods. Our decision to use MLE when the number of

We selected a threshold based on meteorological conditions, specifically when rainfall exceeded 35 mm, indicating heavy rainfall

Our analysis pinpointed Udon Thani province as having the highest cumulative rainfall return levels across all return periods, signaling a heightened risk of flooding. This finding holds significant implications for future rainfall management planning, echoing the importance emphasized in prior research advocating for regionally specific flood risk assessment

The utilization of Q-GIS to create return level maps via the inverse distance weight (IDW) interpolation method provides a visually intuitive depiction of flood risk spatial distribution. While common in geographic analysis, this application in mapping extreme rainfall return levels is, to our knowledge, a pioneering instance

Our findings emphasize the necessity for future rainfall management planning specifically within the Chi watershed. This study can be extended beyond the Chi watershed by examining the potential impact of its findings on policy formulation, infrastructure planning, and disaster mitigation strategies in regions confronted with analogous challenges. Broadening the scope, the research probes the implications of its results for the domains of hydrology, climatology, and environmental science.

Numerous organizations, including prominent bodies like the IPCC

Nonetheless, our study acknowledges certain limitations, notably the assumption of stationary rainfall patterns, which may, however, be influenced by climate change. Future research could delve into the impact of changing climate conditions on extreme rainfall events, thereby refining models to accommodate a warming climate.

This study set out to evaluate extreme rainfall events in the Chi watershed in northeastern Thailand with the aim of applying extreme value theory to predict future rainfall patterns. We analyzed maximum cumulative rainfall data from 1984 to 2018 and fitted the generalized Pareto distribution to the data. This model was determined to be appropriate through goodness-of-fit tests, providing a robust method for analyzing extreme rainfall events in the region. Our results reveal that Udon Thani, Chaiyaphum, Maha Sarakham, Tha Phra Agromet., Roi Et, and Sisaket provinces had the highest return levels for CONS-2 and CONS-3, suggesting that these areas are at high risk of flooding.

These findings underscore the importance of forecasting and planning for extreme rainfall events in the Chi watershed. We found that even short periods of continuous rainfall could lead to flash flooding, highlighting the need for effective water management in the region. We also developed 2D maps, which provide a practical tool for visualizing at-risk areas and aiding in the planning of soil and water conservation measures, dam construction, as well as irrigation and drainage work.

The implications of this study extend beyond academia. Our findings provide valuable insights for governmental agencies, private organizations, and individuals alike, empowering them to design more effective flood management strategies, thereby reducing the risk and potential impact of flooding in their communities. In the broader context, managing extreme rainfall events and mitigating flood risks are crucial for safeguarding property, preserving ecosystems, and ultimately saving lives.

Future research should explore spatial analysis to determine interdependencies among different regions and use copula functions for correlation analysis. Such developments could provide a more nuanced understanding of the region's flood risk and further enhance our ability to predict and prepare for extreme rainfall events.

In conclusion, this study underscores the urgency of focusing on extreme rainfall events in our fight against the increasing threat of flooding. With climate change intensifying, the tools and strategies we develop today will be instrumental in managing the water-related challenges of tomorrow.

The code is available at

The data are available at

The supplement related to this article is available online at:

Conceptualization: PB, TP, and JSP; methodology: PB and TP; software: TP, TP, and AA; validation: PC and TP; formal analysis: PB and JSP; investigation: JSP and PB; data curation: PC, TP, and WT; writing – original draft preparation: JSP and PB; writing – review and editing: JSP, PB, and TP; supervision: JSP and TP; project administration: PB; funding acquisition: PB and JSP. All authors read and approved the final version of the paper.

The contact author has declared that none of the authors has any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

We sincerely thank the reviewers and the HESS editor for their invaluable guidance on this paper. This study was supported under that framework of international cooperation program managed by the Mahasarakham University, Thailand. Observational data from Thailand were provided by the Climate Information Services at

This research has been supported by the Mahasarakham University, Thailand. Piyapatr Busababodhin’s work was supported by Mahasarakham University (grant no. 6517004/2565). Additionally, Piyapatr Busababodhin’s work was funded by the Agricultural Research Development Agency (a public organization) of Thailand. Jeong-Soo Park's and Thanawan Prahadchai’s work was supported by the National Research Foundation of Korea (grant no. 2020R1I1A3069260) and the BK21 FOUR (Fostering Outstanding Universities for Research; grant no. 5120200913674) funded by the Ministry of Education and the National Research Foundation of Korea.

This paper was edited by Stefano Galelli and reviewed by AFM Kamal Chowdhury and two anonymous referees.