Extreme Daily Rainfall Research Articles

Analysis of Debris Flow Triggering Conditions for Different Rainfall Patterns Based on Satellite Rainfall Products in Hengduan Mountain Region, China

Rainfall is the main factor that induces debris flow. Satellite rainfall products provide a new source of data in terms of debris flow-triggering conditions to overcome the lack of rainfall data coverage from ground-based rainfall gauges in large-scale mountainous regions. In this study, the applicability of four satellite rainfall products (CMORPH, GPM, MSWEP, and PERSIANN) in the Hengduan Mountain region (HMR) was evaluated with reference to ground observation data from 2000 to 2020. The critical rainfall and rainfall thresholds under different rainfall patterns and warning levels that trigger debris flows were analyzed according to the empirical cumulative distribution function (ECDF) and cumulative probability. The results showed that CMORPH (comprehensive indicator score (CI = 0.72) and GPM (CI = 0.70) performed better in the simulation of daily rainfall sequence consistency and extreme rainfall conditions in the study area. CMORPH also had the highest reconstruction rate for correctly capturing rainfall events that triggered debris flows, with a value of 89%. Approximately half of the rainfall patterns that cause debris flows are antecedent-effective-rainfall-dominated. Both intraday-rainfall-dominated and intraday-antecedent-rainfall-balanced patterns were below 30%. There were evident differences in the critical rainfall for different rainfall patterns under the same warning level. By comparing the results of previous studies on rainfall thresholds, it is believed that the results of this study confirm the application of satellite rainfall products; in addition, the calculated rainfall thresholds can provide a reference for the early warning of debris flows in the HMR. In general, this work is of great significance to the prediction and early warning of debris flow hazards.

Statistics of the Performance of Gridded Precipitation Datasets in Indonesia

Gridded precipitation datasets have been used as alternatives to rain gauge observations, but their applicability for a specific region should be thoroughly evaluated. This article aims at finding the most appropriate one for climatological and hydrological applications in Indonesia, by evaluating the statistics of the performance of eight different datasets (research products) having horizontal resolutions between 0.1 and 0.25 and with a time span of data availability from 2003 to 2015. The datasets are compared against the observed daily rainfall at 133 stations using 13 statistical metrics that can be classified into three groups with different characteristics of measurements, namely distribution, time sequence, and extreme value representations. By applying summation of rank (SR), it is found that MSWEP and TMPA 3B42 are the top two datasets that outperformed based on distribution and time sequence performance metric groups. The extreme performances for all datasets are still good in 75th percentiles; however, the performances decrease at more than 75th percentiles indicating still a poorly representation of daily extreme rainfall for all gridded datasets. Results of this study suggest that MSWEP (v2) is presently the best gridded precipitation datasets available for climatological and hydrological applications in Indonesia.

Changes in daily rainfall and temperature extremes of upper Awash Basin, Ethiopia

Climate change has an impact on rainfall and temperature. In this study, changes in rainfall and temperature extremes in Upper Awash Basin (Ethiopia) from 1983 to 2016 were investigated using daily combination of observed and satellite data of ten meteorological stations. To determine the annual trends, two non-parametric methods, namely Mann-Kendall and Sen's slope estimator were utilized. In this study, RClimDex1.1 was utilized to compute the selected temperature and rainfall extreme indices. The Inverse Distance Weighting (IDW) technique was used for spatial interpolation. The findings indicate significant trends in both annual and Kiremt (June – September) season rainfalls in two out of the ten stations, namely, Addis Alem and Koka Dam (p < 0.05). None of the ten stations exhibited significant trends for Belg (February-May) season. Overall, the mean rainfall trends of the basin indicate a considerable decline. However, from the spatial analysis, increasing rainfall and extreme events in the eastern portion of the basin were observed. Moreover, from average anomalies of the rainfall extreme indices of the entire basin showed a tendency of wetness. The maximum and the minimum temperatures displayed increasing trends in most of the weather stations (except in Addis Alem, and Kimoye for maximum temperature and Koka Dam, Hombole, Mojo, and Sendafa (AD) for minimum temperature). The temperature extreme indices showed increasing trends in TXx, TNx, TN90p, TX90p, SU25, and WSDI. The trend of the SU25 index indicated an increasing trend at 90% of the stations (p < 0.05). Overall, average anomalies of all temperature extreme indices of the entire basin (except TXn) revealed warming trends. The possible causes of these extreme events are the results of both local and global processes. Urban expansion and associated land use/cover changes could synergistically contribute to global warming. These findings are useful for the stakeholders in the region.

Influence of changes in extreme daily rainfall distribution on the stability of residual soil slopes

ABSTRACT Many landslides triggered by intense rainfall have occurred in mountainous areas in Thailand, causing major economic losses and infrastructure damage. Extreme daily rainfall is a significant trigger for hillslope instability. Increases in extreme daily rainfall intensity due to climate change may be one of the key factors responsible for the increased landslides. Thus, in this context, changes in the intensity of extreme daily rainfall in Chiang Mai Province in North Thailand and their effects on hillslope stability are analyzed. Extreme rainfall is modeled using a generalized extreme value distribution and estimated for various return periods. A numerical analysis of seepage and an infinite slope stability model are combined to understand the hillslope response under extreme rainfall conditions. The analysis period is divided into two periods of 34 years: 1952 to 1985 and 1986 to 2019. According to the analysis results, the distribution of extreme daily rainfall changes in terms of location. The average annual daily maximum rainfall increased by approximately 11.13%. The maximum decrease in the safety factor is approximately 4.5%; therefore, these changes in extreme daily rainfall should be considered in future landslide prevention policies.

A Framework to Project Future Rainfall Scenarios: An Application to Shallow Landslide-Triggering Summer Rainfall in Wanzhou County China

Fatal landslides are a widespread geohazard that have affected millions of people and have claimed the lives of thousands around the globe. A change in climate has significantly increased the frequency and magnitude of rainfall, which affect the susceptibility of slopes to shallow landslides. This paper presents a methodological framework to assess the future changes in extreme and seasonal rainfall magnitudes with climate model projections. This framework was applied to project summer rainfall over Wanzhou County, China, using an ensemble of four regional climate models (RCMs) from the East Asian domain of the Coordinated Downscaling Experiment (CORDEX) under the Phase 5 Coupled Intercomparison Modeling Project (CMIP5). The results find that extreme daily rainfall was projected to decrease in the mid-21st century, with an uncertainty measured by a coefficient of variation between 5% and 25%. The mean seasonal rainfall is projected to increase in the mid-21st century up to a factor of 1.4, and up to a factor of 1.8 in the late-21st century. The variation in the mid-21st century ranged from 10% to 35%, and from 30% to 50% in the late-21st century. This case study delivered a proof-of-concept for a methodological framework to derive shallow landslide-triggering rainfall scenarios under climate change conditions. The resulting spatially distributed climate change factors (CCFs) can be used to incorporate future rainfall scenarios in slope susceptibility models and climate impact assessments.

Temporal and Spatial Evaluations of Extreme Rainfall Relationship with Daily Surface Air Temperature in Peninsular Malaysia

Extreme rainfall is expected to increase as the climate warms, with the rate of increase at 7% per degree of warming following the Clausius-Clapeyron (CC) relationship. However, studies showed that the rate of increase might not necessarily follow the relationship. This study investigates temporal and spatial variations of daily rainfall relationship with surface air temperature in Peninsular Malaysia using data from the Global Historical Climatology Network (GHCN) using a fixed temperature interval binning method and quantile regression method. Investigation reveals that a negative scaling of daily extreme rainfall was observed for all surface air temperature and percentiles investigated, in contrast with the expectations from the CC relationship. The largest seasonal variation at 99th percentile was observed in December-January (DJF), while the lowest scaling rate was in March-May (MAM). The scaling rate tends to be higher in Subang and Melaka, and lower in Bayan Lepas, Kota Bharu and Kuantan. The scaling rate also tends to be stronger at 99th percentile compared to the lower rainfall events at 95th and 75th percentile and in warmer seasons compared to colder seasons. Further, the results show that the intensity of daily extreme rainfall in Peninsular Malaysia is decreasing with increasing surface air temperature.

Daily rainfall intensity and temporal trends in eastern Paraná state – Brazil

The present study aims to analyze the intensity of daily extreme rainfall and trends for precipitation in Castro, Curitiba and Paranaguá stations, located in the state of Paraná, Brazil. Historical series of rainfall registered by the Brazilian National Institute of Meteorology were used considering the period from 1976 to 2015. The Mann-Kendall and Pettitt statistical tests were applied to identify trends and ruptures in historical rainfall series. Other indicators of trends in rainfall and temperature were calculated using the RClimDex software, developed by the Canadian Meteorological System. The classification of cumulative daily rainfall in 24 h with the application of the quantile technique was defined as a standard for percentiles. The months of January, February and December presented greater occurrence frequency of rainfall equal to or greater than 95% and 99% of the quantiles for all locations. The comparative analysis showed that Paranaguá station has the largest accumulated values of rainfall in 24 h during the rainiest months, between September and April, while Castro and Curitiba stations recorded the highest daily totals between May and August. Increasing daily precipitation totals were identified in all quantile's classes with statistical significance only in Curitiba and Paranaguá station, which is attributed to greater urban densities and its direct impacts over daily rainfall totals. Significant trends for increased rainfall were observed, especially in Curitiba, a condition that is associated with the expressive and also significant increase in climate indicators of air temperature.

Improving daily rainfall extremes simulation using the generalized Pareto distribution: a case study in Western Iran

Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.

The effect of rainfall changes on economic production

Macro-economic assessments of climate impacts lack an analysis of the distribution of daily rainfall, which can resolve both complex societal impact channels and anthropogenically forced changes1-6. Here, using a global panel of subnational economic output for 1,554 regions worldwide over the past 40 years, we show that economic growth rates are reduced by increases in the number of wet days and in extreme daily rainfall, in addition to responding nonlinearly to the total annual and to the standardized monthly deviations of rainfall.Furthermore, high-income nations and the services and manufacturing sectorsaremost strongly hindered by both measures of daily rainfall, complementing previous work that emphasized the beneficial effects of additional total annual rainfall in low-income, agriculturally dependent economies4,7. By assessing the distribution of rainfall at multiple timescales and the effects on different sectors, we uncover channels through which climatic conditions can affect the economy. These results suggest that anthropogenic intensification of daily rainfall extremes8-10 will have negative global economic consequences that require further assessment by those who wish to evaluate the costs of anthropogenic climate change.

A Novel Statistical Modeling Approach to Developing IDF Relations in the Context of Climate Change

Extreme rainfall intensity–duration–frequency (IDF) relations have been commonly used for estimating the design storm for the design of various urban water infrastructures. In recent years, climate change has been recognized as having a profound impact on the hydrologic cycle. Hence, the derivation of IDF relations in the context of a changing climate has been recognized as one of the most challenging tasks in current engineering practice. The main challenge is how to establish the linkages between the climate projections given by climate models at the global or regional scales and the observed extreme rainfalls at a local site of interest. Therefore, our overall objective is to introduce a new statistical modeling approach to linking global or regional climate predictors to the observed daily and sub-daily rainfall extremes at a given location. Illustrative applications using climate simulations from 21 different global climate models and extreme rainfall data available from rain gauge networks located across Canada are presented to indicate the feasibility, accuracy, and robustness of the proposed modeling approach for assessing the climate change impact on IDF relations.

Changes in Extreme Daily Rainfall Characteristics in South Africa: 1921 – 2020

Changes in Extreme Daily Rainfall Characteristics in South Africa: 1921 – 2020

Estimating unprecedented extremes in UK summer daily rainfall

The UNSEEN (unprecedented simulated extremes using ensemble) method involves using a large ensemble of climate model simulations to increase the sample size of rare events. Here we extend UNSEEN to focus on intense summertime daily rainfall, estimating plausible rainfall extremes in the current climate. To address modelling limitations simulations from two climate models were used; an initialised 25 km global model that uses parameterised convection, and a dynamically downscaled 2.2 km model that uses explicit convection. In terms of the statistical characteristics that govern very rare return periods, the models are not significantly different from the observations across much of the UK. Our analysis provides more precise estimates of 1000 year return levels for extreme daily rainfall, reducing sampling uncertainty by 70%–90% compared to using observations alone. This framework enables observed daily storm profiles to be adjusted to more statistically robust estimates of extreme rainfall. For a damaging storm in July 2007 which led to surface water flooding, we estimate physically plausible increases in the total daily rainfall of 50%–100%. For much of the UK the annual chance of record-breaking daily summertime rainfall is estimated to be around 1% per year in the present-day climate. Analysis of the dynamical states in our UNSEEN events indicates that heavy daily rainfall is associated with a southward displaced and meandering North Atlantic jet stream, increasing the advection of warm moist air from across Southern Europe and the Mediterranean, and intensifying extratropical storms. This work represents an advancement in the use of climate modelling for estimating present-day climate hazards and outlines a framework for applying UNSEEN at higher spatial and temporal resolutions.

Impact of climate variability and extreme rainfall events on sugarcane yield gap in a tropical Island

• Sugarcane yield was simulated at the field scale over 20 years in Reunion Island. • Total sugarcane yield gap in Reunion was 96 Mg ha -1 of which 34% was explained throw crop management and hazards. • Yield gap increased with extreme daily rainfall. • Actual yield loss increased by 0.6 Mg ha -1 per daily events with extreme rainfall at regional scale. The increasing incidence and intensity of extreme climate events are a major threat to global crop production. Sugarcane represents the main source of sugar and ethanol in tropical and sub-tropical regions. Here, we assessed the impacts of climatic variability and extreme events on sugarcane yield gaps in Reunion Island which is characterised by extreme spatial contrasts in climate, and is regularly subjected to cyclones or drought. We combined a process-based crop model with spatial databases of soil, climate and management data, to predict the annual sugarcane yield in each field over the whole island from 1998 to 2018. Simulated yields were compared to actual sugarcane yields from sugar producers in five agro-climatic zones. Extreme climate indices were calculated for extreme daily rainfall, temperature and drought, and their influence on yield gap was assessed using a Principal Component Analysis (PCA). The average total yield gap (YG T , i.e. the difference between potential sugarcane yield and actual yield, Mg ha -1 ) over Reunion Island was 96 Mg ha -1 , explained by 34% throw crop management and hazards. YG due to crop management reached 65 Mg ha -1 with increasing rainfall and nitrogen mineralization rate and up to 88 Mg ha -1 with increasing temperature and radiation. Yield gap was correlated with the first axis of the PCA, highlighting how extreme rainfall induced increase in YG T . The actual yield loss was defined as the difference between the actual yield from sugarcane producers in a given year and the highest yield obtained during the whole studied period in the same zone. The annual actual yield loss increased by 0.6 Mg ha -1 per rainfall days higher than 29 mm d -1 along the annual crop cycle. Using this approximation, average actual yield loss due to heavy rain was estimated as 5.5 Mg ha -1 but it was higher than 10 Mg ha -1 for 25% of years in the East coast region. Our results highlight the need for improving sugarcane crop yield response to extreme rain in process-based crop models, in order to better assess climate change impacts in future.

Impacts of the MJO on Rainfall at Different Seasons in Indonesia

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability of rainfall in Indonesia, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. This study aims to investigate the general impacts of MJO on rainfall at different seasons in Indonesia, particularly during boreal summer. Impacts of the MJO on daily rainfall anomaly during the four climatic seasons: DJF, MAM, JJA, and SON in Indonesia have been evaluated using in-situ data from 86 stations during 1981 - 2012 and remote sensing data using GPM IMERGV06 from 2001 - 2019. The greatest impact of the MJO on rainfall over Indonesia occurs during the DJF and MAM seasons (austral summer), with the magnitude varying across regions. Enhanced rainfall generally occurs over the western parts of Indonesia on phases 2 to 4, central parts of Indonesia on phase 4, and eastern parts of Indonesia on phases 4 to 5. Conversely, suppressed rainfall generally occurs over the western parts of Indonesia on phases 5 to 8, central parts of Indonesia on phases 6 to 8, and eastern parts of Indonesia on phases 1 to 2 and 6 to 8. In addition, the MJO influence during the JJA and SON seasons are slightly less, in terms of intensity, than during the DJF and MAM seasons, which is likely due to the northward shift of ITCZ and, hence, the intraseasonal oscillation convective envelope during boreal summer. Generally, enhanced rainfall occurs over the western and northern parts of Indonesia on phases 2 to 3, and suppressed rainfall on phases 6 to 7. The results indicate that convectively active MJO may increase the possibility of daily extreme rainfall in particular regions in Indonesia at different seasons.

Evaluation of Satellite-Derived Products for the Daily Average and Extreme Rainfall in the Mearim River Drainage Basin (Maranhão, Brazil)

Satellite precipitation estimates are used as an alternative or as a supplement to the records of the in situ stations. Although some satellite precipitation products have reasonably consistent time series, they are often limited to specific geographic areas. The main objective of this study was to evaluate CHIRPS version 2, MSWEP version 2, and PERSIANN-CDR, compared to gridBR, as daily mean and extreme inputs represented on a monthly scale and their respective seasonal trends of rainfall in the Mearim River Drainage Basin (MDB), Maranhão state, Brazil. Estimates of errors were calculated (relative error, pbias; root mean square error, RMSE, and Willmott concordance index, d), and the chances of precipitation were estimated by remote sensing (RES). In addition, trends in precipitation were estimated by the two-sample Mann–Kendall test. Given the overall performance, the best products for estimating monthly mean daily rainfall in the MDB are CHIRPS and PERSIANN-CDR, especially for rainy months (December to May). For daily extremes on the monthly scale, the best RES is PERSIANN-CDR. There is no general agreement between gridBR and RES methods for the trend signal, even a nonsignificant one, much less a significant one. The use of MSWEP for the MDB region is discouraged by this study because it overestimates monthly averages and extremes. Finally, studies of this kind in drainage basins are essential to improve the information generated for managing territories and developing regionalized climate and hydrological models.

Linear Rainfall Features and Their Association with Rainfall Extremes near Melbourne, Australia

AbstractLinear precipitation systems are a prominent contributor to rainfall over Melbourne, Australia, and the surrounding region. These systems are often convective in nature, frequently associated with cold fronts, and in some cases can lead to significant rainfall and flash flooding. Various types of linearly organized systems (e.g., squall lines, quasi-linear convective systems) have been the subject of much research in the United States and elsewhere, but thus far relatively little analysis has been done on linear systems in Australia. To begin to understand rainfall extremes and how they may change in this region in the future, it is useful to explore the contribution of these types of systems and the characteristics that define them. To this end, we have examined the recently developed Australian Radar Archive (AURA), identifying objects that meet a specific set of relevant criteria, and used multiple methods to identify heavy and extreme daily rainfall. We found that on average, days with linear systems contribute over half of the total rainfall and 70%–85% of heavy/extreme rainfall in the Melbourne region. The linear systems that occur on heavy rainfall days tend to be larger, slower-moving, and longer-lived, while those on extreme rainfall days also tend to be more intense and have a greater degree of southward propagation than linear systems on other days.

Hierarchical Bayesian Model for Assessing Extreme Daily Rainfall Through Combining Climatic and Topographic Information

Frequency analysis of the annual maximum rainfall time series is essential for designing infrastructures to provide protection against local floods and related events. However, the results of the frequency analysis obtained are ambiguous. In this study, we aimed to develop a spatial hierarchical Bayesian model framework through combining the climatic and topographic information. To confirm the applicability of the proposed method, the results of at-site frequency analysis and regional frequency analysis using the index flood method were compared in the Busan-Ulsan-Gyeongnam region. Furthermore, a hierarchical Bayesian model was developed, in which the parameters of the generalized logistic distribution comprised relatively simple covariate relationships upon considering the possibility of expansion into various probability distributions and more complex covariate structures. The uncertainty of this model was analyzed using the coefficient of variation of rainfall quantile ensemble. The results confirmed that the regional frequency analysis using the hierarchical Bayesian model combined with the climatic and topographic information could provide an accurate estimate of extreme daily rainfall with relatively good agreement with the estimate at a specific site, but is a more reliable approach.

Changes in large-scale circulation over the Indo-Pacific region and its association with 2018 Kerala extreme rainfall event

We investigate the characteristics and causes of extreme rainfall event occurred in Kerala (south western regions of the peninsular India) in August 2018. The changes in large-scale circulations over the Indo-Pacific domain and their association with regional circulation features, which made the extreme rainfall in Kerala are analysed. During this extreme event, Kerala experienced eleven days of incessant rainfall from 8 to 18 August 2018. It is observed that these extreme rainfall days were accompanied with two extreme spells of rainfall during 8–10 and 14–17 August 2018 that made severe floods in Kerala. Kerala experienced rainfall of more than 100 mm day−1, particularly in the south Kerala during these events. The triad (average of three days) analysis of vertical velocity at 500 hPa and moisture convergence at the surface level reveal the dynamics and thermal structure of the regional deep convective activity. A remarkable eastward shift of cross equatorial flow over the Indian Ocean produced an intense offshore vortex, which induced more moisture towards the southern regions especially over Kerala where the extreme event was occurred. The west Pacific subtropical high (WPSH) is one of the large-scale circulations over the Indo-Pacific region, which shifted northward and the western flank of WPSH extended westward resulting a blocking high that was created over East Asia during the period. The eastward shift of cross-equatorial flow over the Bay of Bengal, outflow from the East Asian blocking high were mainly maintaining the low-pressure system over the Bay of Bengal during the extreme event. Therefore, the key cause of the extreme flood over Kerala in August 2018 is the deep moisture convergence through the offshore vortex, intense intrusion from subtropics and out flow from low-pressure system over the Bay of Bengal.

Effect of warming climate on extreme daily rainfall depth using non-stationary Gumbel model with temperature co-variate

Abstract In this study, non-stationary frequency analysis was carried out to apply non-stationarity of extreme rainfall driven by climate change using the scale parameter of two parameters of the Gumbel distribution (GUM) as a co-variate function. The surface air temperature (SAT) or dew-point temperature (DPT) is applied as the co-variate. The optimal model was selected by comparing AICs, and 17 of 60 sites were found to be suitable for the non-stationary GUM model. In addition, SAT was chosen as the more appropriate co-variate among 13 of the 17 sites. As a result of estimating changes in design rainfall depth with future SAT rises at 13 sites, it is likely to increase by 10% in 2040 and 18% in 2070.

Bivariate Modelling of a Teleconnection Index and Extreme Rainfall in a Small North Atlantic Island

This paper explores practical applications of bivariate modelling via copulas of two likely dependent random variables, i.e., of the North Atlantic Oscillation (NAO) coupled with extreme rainfall on the small island of Madeira, Portugal. Madeira, due to its small size (∼740 km2), very pronounced mountain landscape, and location in the North Atlantic, experiences a wide range of rainfall regimes, or microclimates, which hamper the analyses of extreme rainfall. Previous studies showed that the influence of the North Atlantic Oscillation (NAO) on extreme rainfall is at its largest in the North Atlantic sector, with the likelihood of increased rainfall events from December through February, particularly during negative NAO phases. Thus, a copula-based approach was adopted for teleconnection, aiming at assigning return periods of daily values of an NAO index (NAOI) coupled with extreme daily rainfalls—for the period from December 1967 to February 2017—at six representative rain gauges of the island. The results show that (i) bivariate copulas describing the dependence characteristics of the underlying joint distributions may provide useful analytical expressions of the return periods of the coupled previous NAOI and extreme rainfall and (ii) that recent years show signs of increasing climate variability with more anomalous daily negative NAOI along with higher extreme rainfall events. These findings highlight the importance of multivariate modelling for teleconnections of prominent patterns of climate variability, such as the NAO, to extreme rainfall in North Atlantic regions, especially in small islands that are highly vulnerable to the effects of abrupt climate variability.