Annual Maximum Rainfall Research Articles

Classification Importance of Seed Morphology and Insights on Large-Scale Climate-Driven Strophiole Size Changes in the Iberian Endemic Chasmophytic Genus Petrocoptis (Caryophyllaceae).

Recruitment poses significant challenges for narrow endemic plant species inhabiting extreme environments like vertical cliffs. Investigating seed traits in these plants is crucial for understanding the adaptive properties of chasmophytes. Focusing on the Iberian endemic genus Petrocoptis A. Braun ex Endl., a strophiole-bearing Caryophyllaceae, this study explored the relationships between seed traits and climatic variables, aiming to shed light on the strophiole's biological role and assess its classificatory power. We analysed 2773 seeds (557 individuals) from 84 populations spanning the genus' entire distribution range. Employing cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability. Linear mixed-effects models were utilized to investigate the relationship between climate predictors and strophiole area, seed area and the ratio between both. The combination of seed morphometric traits allows the division of the genus into three well-defined morphogroups. The subsequent validation of the algorithm allowed 87% of the seeds to be correctly classified. Part of the intra- and interpopulation variability found in strophiole raw and relative size could be explained by average annual rainfall and average annual maximum temperature. Strophiole size in Petrocoptis could have been potentially driven by adaptation to local climates through the investment of more resources in the production of bigger strophioles to increase the hydration ability of the seed in dry and warm climates. This reinforces the idea of the strophiole being involved in seed water uptake and germination regulation in Petrocoptis. Similar relationships have not been previously reported for strophioles or other analogous structures in Angiosperms.

Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method

Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration.

Evaluating Rainfall Intensity-Duration-Frequency Curves: A Comparison of Extreme Value Type I and Log-transformed Pearson Type III Distribution Methods in Bangladesh's Northern Region

In semiarid regions, the Intensity-Duration-Frequency (IDF) curves for rainfall are critical for managing water resources, as water scarcity is often the primary limiting factor. This study takes a comprehensive approach to generalize the relationship between daily rainfall and shorter-duration rainfall across varying return periods. Urban drainage infrastructures—like storm sewers, culverts, and other hydraulic designs—must be developed to address both drought and flooding issues. Daily rainfall data are typically sufficient for hydrologic and floodplain analyses related to designing and evaluating water control mechanisms, including polders, flood barriers, irrigation, and drainage systems. Small hydraulic systems such as culverts, storm drains, and drainage systems in urban areas or airports are often designed based on extreme rainfall data. This study utilized 44 years (1980–2023) of annual maximum rainfall data from the Bangladesh Meteorological Department (BMD). The Indian Meteorological Department's (IMD) empirical reduction formula was applied to estimate short-duration rainfall intensities using this data. IDF curves and equations were developed using the Log Transformed Pearson Type III (LPT III) and Extreme Value Extreme-Value Distribution methods. The results obtained using the Extreme Value method was slightly higher compared to the LPT III distribution results. The estimated rainfall intensities agreed with previous studies in certain areas of the study region. IDF equation parameters and correlation coefficients for various return periods (2, 5, 10, 25, 50, and 100 years) were determined using the least squares method. The results demonstrated high correlation coefficients, indicating the reliability of the equations in estimating IDF curves for the studied region. The study also found that rainfall intensity decreases as the duration increases, and those storms of any duration become more intense over extended return periods.

Utilizing statistical distribution tests to develop rainfall intensity–duration–frequency curves for enhanced hydrological analysis in Kirkuk city, Iraq

ABSTRACT Rainfall intensity is considered one of the basic factors in designing hydrological models based on rainstorm data. The objective of this research is to employ novel intensity–duration–frequency curves and develop empirical equations for rainfall intensity in the city of Kirkuk. The reduction formula adopted by the Indian Meteorological Department was used to divide the maximum daily rainfall for short periods of 0.5, 1, 2, 4, 6, 12, and 24 h. Three key methods of frequency analysis (lognormal distribution, log Pearson type III distribution, and Gumbel extreme value distribution) were utilized to formulate a statistical relationship based on rainfall intensity data from between the years 1981 and 2023 for a gauging station upstream of the Kirkuk city basin to provide the best data set for all periods of 100, 50, 25, 10, 5, and 2 years. Research has shown that the logarithmic distribution is the best fit for modelling the relationship between the annual maximum rainfall at Kirkuk station and duration of the rainfall. The goodness-of-fit results indicate that the lognormal distribution statistically outperforms other distribution models. Hence, the generated rainfall intensity, duration, and frequency curves that were developed led to estimating the intensity of precipitation to build forecasting and hydrological behaviour of the Kirkuk city basin.

A Comprehensive Evaluation of Mean and Extreme Climate for the Conformal Cubic Atmospheric Model (CCAM)

Abstract This study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) reanalysis data from 1985 to 2014, following a 5-yr spinup period. It focuses on daily maximum and minimum temperatures and daily precipitation, comparing CCAM to ERA5 and the Australian Gridded Climate Data (AGCD). The CCAM effectively reduces warm biases in daily minimum temperatures but struggles with cold biases in daily maximum temperatures, particularly in northern Australia during the wet season, possibly due to high-level cloud overestimation. Precipitation tends to be overestimated, especially in extreme rainfall events, though offset by an underestimation of low rainfall. The study showcases improvements in the annual minimum of daily minimum temperatures across most of Australia, while identifying challenges in forecasting cooler extreme temperatures. It adds value to annual maximum daily maximum temperatures in southern Australia but less so in the north. The analysis of the 5% annual exceedance probability (AEP5%) yields mixed results influenced by location and potential ocean temperature changes. Some coastal areas exhibit lost value, possibly linked to ocean temperature shifts. Furthermore, CCAM’s representation of maximum annual daily and 5-day rainfall reveals lost value, particularly in eastern Australia due to an overestimate of extreme rainfall. Despite the challenges of comparing a dynamical downscaling model like CCAM to ERA5, this study highlights its benefits in reducing biases, especially in temperature representation. Given the larger biases in phase 6 of Coupled Model Intercomparison Project (CMIP6) global climate models, CCAM appears suitable for dynamic downscaling in climate projections, emphasizing the need for ongoing model enhancements, including addressing biases related to ephemeral water bodies and extreme rainfall. Significance Statement This study critically assesses the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling ERA5 reanalysis data from 1985 to 2014, offering valuable insights into climate modeling. Focusing on temperature and precipitation, CCAM proves effective in mitigating warm biases in daily minimum temperatures but encounters challenges with cold biases in daily maximum temperatures, particularly in northern Australia. The analysis reveals the overestimation of precipitation, especially in extreme events, yet identifies improvements in annual minimum daily minimum temperatures across Australia. The study underscores CCAM’s potential in reducing biases compared to CMIP6 global climate models, making it a promising tool for dynamic downscaling in climate projections. It emphasizes the necessity for ongoing model enhancements, particularly addressing biases related to ephemeral water bodies and extreme rainfall.

Characteristics of Overburden Damage and Rainfall-Induced Disaster Mechanisms in Shallowly Buried Coal Seam Mining: A Case Study in a Gully Region

Shallow coal mining in gully regions has resulted in significant subsidence hazards and increased the risk of surface water inflow into mining panels, compromising the sustainability of surface water management and underground resource exploitation. In this study, the chain disaster process caused by shallow coal seam mining and heavy rainfall is quantitatively analyzed. The findings reveal that shallow coal seam mining leads to the formation of caved and fractured zones in the vertical direction of the overlying rock. The fractured zone can be further classified into a compression subsidence zone and a shear subsidence zone in the horizontal direction. The shear subsidence zone is responsible for generating compression and shear deformations, intercepting rainfall runoff, and potentially triggering landslides, necessitating crack landfill treatments, which are critical for promoting sustainable mining practices. The HEC-RAS program was utilized to integrate annual maximum daily rainfall data across different frequencies, enabling the establishment of a dynamic risk assessment model for barrier lakes. Numerical simulations based on unsaturated seepage theory provide insights into the infiltration and seepage behavior of rainfall in the study area, indicating a significant increase in saturation within lower gully terrain. Precipitation infiltration was found to enhance the saturation of the shallow rock mass, reducing matric suction in unsaturated areas. Finally, the disaster chain is discussed, and recommendations for managing different stages of risk are proposed. This study offers a valuable reference for the prevention and control of surface water damage under coal mining conditions in gully regions.

Generalized fiducial inference for the GEV change-point model

Generalized extreme value (GEV) distribution is used to analyse the maximum from a block of data. It is very useful to describe the unusual event rather than the usual event. In this paper, we propose a change-point detection procedure for GEV distribution based on generalized fiducial inference. The fiducial distribution of the change-point location is constructed. Meanwhile, Markov Chain Monte Carlo method combined with Gibbs sampling and the Metropolis–Hastings algorithm is utilised to estimate the location of the change point and its confidence interval. In addition, the generalized fiducial factor is used to test whether there is a change point. Simulation results show that the proposed generalized fiducial method performs better in accuracy, robustness and the length of confidence intervals. Finally, we apply the proposed method to the annual maximum rainfall data in Beijing.

Assessing the current and future trends of climate extremes at Zarima subbasin North Western Ethiopia

Climate extreme events in Ethiopia have increased, impacting the country’s agriculture dependent economy. Localized studies on rainfall extremes provides valuable insights to develop mitigation measures. This study focuses on the analysis of rainfall extremes for the observed (1984–2018) and the future mid time period (2031–2065) and end time period (2066–2100) projected under RCP4.5: moderate emissions reduction scenario aiming to stabilize greenhouse gas concentrations by the mid-21st century and RCP8.5: high emissions scenario with continued increase in greenhouse gas emissions throughout the twenty-first century. Trend analysis was done using R statistical software modified Man Kendell package. The result revealed that the observed total rainfall in wet days (PRCPTOT) showed insignificant declining trend across the subbasin. Under the RCP4.5 scenario, mid time period projections indicated increasing trends in PRCPTOT at Maytsebri, Adiremet and Debarik stations while decreasing trends expected at Ketema Niguse and Zarima stations. In the RCP4.5 end time period, Debarik and Zarima stations showed increasing trends while other stations exhibited decreasing trends. Under the RCP8.5 scenario, PRCPTOT showed insignificant decreasing trend except Ketema Niguse station which showed significant increasing trend during the mid time period. The observed annual maximum 1-day rainfall (RX1day) and annual RX5day rainfall had both positive and negative trends over subbasin. The RCP4.5 scenario projected declining trends in annual RX1day rainfall, while the RCP8.5 scenario projected increasing trends, except the end time projection of Debarik station. Consecutive dry days (CDD) increased and Consecutive wet days (CWD) decreased insignificantly in majority the subbasin. Projections indicated higher CDD and decreased CWD are expected in majority of the subbasin area. These finding implied that further investigation is required on the impact of climate extremes.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy

Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.

Trend and Variability Analysis of Annual Maximum Rainfall Using Observed and Remotely Sensed Data in the Tropical Climate Zones of Uganda

Understanding rainfall variability and trends is crucial for effective water resource management and disaster preparedness, particularly in tropical regions like Uganda. This study analyzes the trends and variability of the Annual Maximum Series (AMS) and seasonal rainfall data across four rainfall stations in Uganda, comparing observed data with various Remotely Sensed Rainfall (RSR) products. The key methods used in this study include the Mann–Kendall test and Sen’s slope estimator for trend analysis, AMS rainfall variability analysis using statistical performance metrics such as the Nash–Sutcliffe Coefficient of Efficiency (NSE) and Percent Bias (PBIAS), and data distribution comparisons based on goodness-of-fit evaluation using the Kolmogorov–Smirnov (KS) test. The results indicate that most trends in the seasonal rainfall and AMS data are statistically insignificant. However, the September to November (SON) observed rainfall at the Gulu station shows a statistically significant increasing trend of 7.68 mm/year (p-value = 0.03). Based on the PBIAS metric, GPCC and NOAA_CPC products outperform other RSR data products. At the Jinja station, NOAA_CPC has a PBIAS value of −12.93% and GPCC, −14.64%; at Soroti, GPCC has −9.66% and NOAA_CPC, −14.79%; at Mbarara, GPCC has −5.93% and NOAA_CPC, −11.63%; and at Gulu, GPCC has −3.05% and NOAA_CPC, −19.23%. The KS test results show significant differences in the distribution of RSR data and observed rainfall data, though GPCC shows significant agreement at the Gulu (p-value = 0.60) and Mbarara (p-value = 0.14) stations. Additionally, NOAA_CPC outperforms other RSR data products at the Mbarara station, with a KS p-value of 0.24. This study highlights the limitations of current RSR datasets in replicating observed AMS rainfall data. Based on KS test results, GPCC is identified as a better product for hydrological applications at the Gulu, Jinja, and Soroti station areas compared to other RSR products. For the Mbarara station, NOAA_CPC outperforms other RSR products.

Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia

Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.

Statistical-based spatial analysis on urban water management under changing environments: a case study of Hawassa, Ethiopia

Hawassa characterizes a typical developing city in Ethiopia, owning to rapid urban growth and demographic trends. The combined effect of climate change and urban expansion is increasing the challenge to the environment and the services it provides. Relating changing environments with urban water management (UWM) is required to build resilience in the urban environment. This research analyzed local climate change and urban growth and linked it to UWM. The historical period 1990–2021 of daily rainfall, temperature variables, four satellite imageries, and DEM were analyzed. Changes in rainfall (annual and daily maximum) and temperature (maximum and minimum) trends are detected and projected to 2051 using a statistical-based model. With geospatial techniques sub-watersheds are delineated, and the urban cover change is quantified. The trend detection result implies an upward trend of annual and daily maximum rainfalls however a significance is insufficient (p > 0.05) to associate it with climate change during the study period. Maximum and minimum temperatures change indicate a positive and significant trend. The forecasting result suggests an increment of both temperatures (0.5 °C–1.5 °C) to the projected period compared to historical scenario. The land cover analysis results show the built-up area changed from 11.6 km2 (7.2%) to 42.5 km2 (26.5%) during the historical period, where the rate varies spatially. The surface runoff increased by 30.7% in the urban watersheds. With a growth rate of 8.9% built-up, the urban area will cover 73.6 km2 (45.9%) for the predicted period. The research finding justifies the potential to reorganize the relationship between the spatial effect of climate change and urban growth on UWM. Considering distinct characteristics of urban watershed, exposure to flooding risk, access to water demand and resilient to climate change have spatial variation. Thus, a local-specific planning approach will support effective UWM and climate adaptation for sustainable city development.

Updated Observations Provide Stronger Evidence for Increases in Subhourly to Hourly Extreme Rainfall in Canada

Abstract Global warming is expected to lead to increases in atmospheric moisture and intensify subhourly to hourly rainfall extremes. However, signal-to-noise ratios are low, especially at the local scale, making detection of changes in the observational record difficult. For Canada, previous studies based on short data records from 1965 to 2005 did not show conclusive evidence of increases in short-duration extreme rainfall. This study updates single-site and regional trend analyses of 5-min–24-h annual maximum rainfall in Canada using data from 1950 to 2021. Estimates of temporal trends are extended to also consider the association between rainfall intensity and dewpoint temperature, a measure of moisture availability. With longer records, evidence for increases in extreme rainfall at individual sites is stronger. Field significant increasing trends are found for the majority of durations, whereas before results were mixed and typically not statistically significant. Intensification is even more pronounced in single-site scaling of rainfall intensity with summer mean dewpoint temperature. Field significant positive scaling rates are detected for all durations. When data are pooled in space—irrespective of choice of regionalization—the results are even more clear. Notably, the strongest and most spatially homogeneous intensification of short-duration extreme rainfall is detected in subhourly to 2-h durations. When data are pooled across Canadian climate regions, field significant positive scaling is found in 72.7%–81.8% of regions for 5-min–2-h durations, with median scaling rates ranging from 5.3% to 9.4% °C−1. For durations ≥ 6 h, this falls to 27.3%–53% of regions, with scaling rates less than 4% °C−1.

Evaluation of the relationships between atmospheric indices and rainfall intensities: Black Sea region Türkiye

Recent studies show that global atmospheric oscillations are effective on climate parameters. In this study, the relationship between the North Atlantic Oscillation and the Arctic Oscillation with the annual maximum rainfall intensity series throughout the Black Sea region of Türkiye was investigated. In this context, the correlation coefficients between atmospheric oscillations and rainfall intensities were calculated. The correlations obtained were evaluated at the significance levels of α = 0.01, α = 0.05 and α = 0.10 according to Student t test. As a result of these evaluations; It was determined that the Arctic Oscillation was effective on rainfall intensities in Çorum, Artvin, Rize and Bartın. In particular, significant correlations were found in all standard times in Artvin. Significant correlations were found between the North Atlantic Oscillation and the standard rainfall intensity series in Çorum, Sinop, Artvin, Rize and Bayburt.

Influence of global teleconnections on long-term variability in flood seasonality across peninsular India

Climate change and natural climate variability impact not only the frequency and magnitude of floods but also flood seasonality. However, limited to no study has investigated the seasonality in flood occurrence in peninsular Indian rivers. The Central Water Commission opening its long-term records of river stage and discharge gathered from many monitoring stations offers a unique opportunity to analyse flood seasonality. The primary aims of this study are to identify the time of the year when floods are most likely, investigate whether the occurrence of floods has changed over time due to the influence of climate change and natural climate variability, and determine the most significant large-scale and local climate drivers of flood seasonality. Stage and discharge data from 281 gauging stations across ten peninsular river basins are considered to identify the annual maximum gauge level for a gauging point while retaining the corresponding Julian day (also known as the date of occurrence or DO) for further analysis. Several attributes related to circular statistics are calculated from the DO series to find that 68% of the gauging stations experience floods during August, indicating the dominance of the monsoon system on DO. Preliminary analysis confirms that while most stations experience a non-stationarity in the DO series, a few stations exhibit a non-linear trend. Furthermore, our study develops a machine learning model with DO as predictand and 36 local and large-scale predictors to identify the dominant predictors of DO. The predictor importance metric shows that the Pacific Decadal Oscillation (PDO) and the El Niño and Southern Oscillation (ENSO) explain DO variability better than other drivers. Moreover, since ENSO and PDO are teleconnected with the onset of monsoon and annual maximum rainfall (Pradhan et al 2017 Sci. Rep. 7 14229; Choudhury et al 2021a Q. J. R. Meteorol. Soc. 147 3111–23), flood seasonality demonstrates a connection with both PDO and ENSO.

Establishing rainfall intensity-duration-frequency curves for Baghdad, Iraq using satellite data

Abstract Rainfall intensity-duration-frequency (IDF) curves were generated for Baghdad by utilising three satellite precipitation datasets: Global Satellite Mapping of Precipitation Near Real-Time (GSMaP NRT), gauge-corrected (GSMaP GC) and Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG). Maximum annual rainfall data was fitted using several probability distribution methods. The calculated coefficients from the best-fit distribution were used as fitting parameters to generate IDF curves for return periods of 2, 5, 10, 25, 50 and 100 years using the Sherman equation. To address discrepancies between the satellite-derived IDF curves and observed data, bias correction was performed based on the differences. The analysis revealed that the Generalized Extreme Value Distribution model accurately described the hourly rainfall distribution. GSMaP GC exhibited the highest correlation with the observed data, making it the preferred option for generating IDF curves. The study highlighted the importance of gauge correction for satellite rainfall data to minimise the underestimation or overestimation of rainfall. GSMaP GC demonstrated reasonable accuracy in estimating rainfall in Iraq’s mainly arid climate area. By assisting in the creation of efficient methods for dealing with rainstorm events, the created IDF curves are a major step towards advancing sustainable urban stormwater management in the country.

A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

APPLICATION OF GENERALIZED EXTREME VALUE DISTRIBUTION TO ANNUAL MAXIMUM RAINFALL

Generalized Extreme Value Distributions was used to model annual maximum rainfall data in Akure, Ondo State, Nigeria from 1981-2019. The parameters of the distribution are estimated using the maximum likelihood estimation method. The model fit indicated that the shape parameter is negative this suggests that Fréchet distribution is the appropriate model for describing annual maximum rainfall in Ondo-state Nigeria. The estimated return levels for different return periods revealed an increase in the value over the years.

Development of intensity–duration–frequency relationships in Khon Kaen City, Thailand under changing climate using GCMs and a simple scaling method

Abstract This study analyses the annual maximum (AM) rainfall series (1991–2022) in Khon Kaen City, Thailand. The AM rainfall series ranging from 3 to 24 h was best fitted to the Log-Pearson Type-III distribution. Notably, our findings reveal linear relationships between the moments of rainfall intensities and durations establishing the practicality of the simple scaling method for disaggregating 24-h AM rainfall data. Additionally, the results of this method are influenced by factors such as sample size, rainfall durations and the chosen probability distribution. Comparisons between intensity–duration–frequency (IDF) curves obtained through the simple scaling method and those derived from traditional frequency analysis provide valuable insights. Furthermore, this method was applied to bias-corrected rainfall data of 15 global climate models facilitating the generation of future IDF curves under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. Our results highlight that rainfall events in the SSP5-8.5 scenario are projected to exhibit higher intensities emphasizing the need to understand and prepare for increased rainfall extremes in the context of climate change. This research contributes valuable insights into rainfall analysis and prediction techniques, which are crucial for effective water resource management and climate adaptation strategies in the Khon Kaen region.

Climate change effects on rainfall extreme value distribution: the role of skewness

Quantifying the potential effects of Climate Change (CC) on hydrological scales is a topic with a more and more increasing interest for the scientific community, given the CC impact on agriculture, industry, economy, human health, ecosystems, among the others. In this context, the paper focuses on the sub-daily Annual Maxima (AM) of rainfall height time series, and specifically on the crucial role played by initial skewness, i.e. the skewness of the observed rainfall series used to evaluate potential parametric trends. Here a quick and user-friendly methodology is proposed, that is aimed at quantifying plausible future changes in terms of probability distributions assumed at rain gauge scale, from projections of any climatic model. In detail, the Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distributions are adopted as probability functions suitable for modelling observed rainfall AM series, which could increase their frequency and magnitude into future horizons under CC. EURO-CORDEX projections for Europe are considered, under the hypothesis that the values of the change factor, i.e. the ratio between the values of a specific quantile at two specific time horizons, are invariant when moving from an areally-averaged scale (typical for any climate model) to a point rain gauge scale, which induces that future changes are provided (in terms of frequency and magnitude of extreme events) without the need of any spatial downscaling from the assumed projections. The proposed methodology can contribute to hazard quantification associated to potential climate changes, and thus it can play a crucial role in the assessment of hydraulic structures resilience; the obtained results, specific for the study area of Italy, but easily extendable on a global scale, showed that larger increases in frequency of future heavy events are expected for time series with “EV1 alike” values of initial skewness.