Frequency Of Extreme Rainfall Research Articles

A synoptic assessment of the summer extreme rainfall over the middle reaches of Yangtze River in CMIP5 models

The summer mean and extreme rainfall over the middle reaches of Yangtze River (MRYR) are underestimated in many models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our earlier work has identified three synoptic-scale circulation patterns associated with extreme rainfall events over the MRYR during early summer. It is verified here that the presence of these patterns indeed increases the likelihood of the occurrence of extreme rainfall over the MRYR. This relationship between the synoptic-scale circulation pattern and extreme rainfall is reproduced by only a subset of CMIP5 models, where the underestimated frequency of the corresponding synoptic-scale circulation patterns partly explains the underestimated frequency of extreme rainfall thus the summer total over the MRYR. Our analysis also reveals that a few models could “accidentally” simulate a realistic rainfall total and probability distribution of daily rain rate over the MRYR region during summer by generating their own model-dependent synoptic-scale circulation patterns that are not typically seen in observations. These findings suggest that a projection of future changes in extreme rainfall over the MRYR will be better constrained dynamically if we use a subset of models that can reproduce the “rainfall-circulation” relationship, given the diverse response of different circulation patterns to radiative forcing changes in the atmosphere. The results presented here also demonstrate the importance of tracking biases of synoptic-scale circulations in understanding model deficiencies in precipitation simulation, in addition to investigating problems in model physics such as cumulus schemes and microphysics parameterization. Nevertheless, these results are based on the analyses of extreme rainfall in one region and one season, further research covering other regions/seasons is needed.

Inundation Risk Assessment of Underground Space Using Consequence-Probability Matrix

Increases in the frequency and severity of extreme rainfall might cause catastrophic submergence of underground spaces. Therefore, it is essential to predict the flood risk for proactive design. This study presents the methodology of acquiring risk level considering both flood intensity and evacuation difficulty. The flood momentum was computed by a 2D hydraulic flow model, and the flood intensity (FI) was employed to evaluate the consequence of flooding. To investigate the level of evacuation difficulty, the spatial layout of rooms, together with walking speed, were considered in the risk analysis process. If stormwater runoff enters an underground space, zones far away from the inlet usually have low risk levels. However, when the level of evacuation difficulty was considered, the risk level was dependent on the evacuation distance and location of the inlet and exit. If people are in zones with a risk level of 4 or 5, a rapid evacuation is necessary for preventing human casualties. The proposed methodology incorporated with the inundation model can be applied to any underground space regardless of the location of stairs, the number of exits, shape of rooms, or layout of the floor. Consequently, it will contribute to mitigating flood damage in an underground space.

High-resolution flood hazard mapping based on nonstationary frequency analysis: case study of Ho Chi Minh City, Vietnam

ABSTRACT Flood modelling inputs used to create flood hazard maps are normally based on the assumption of data stationarity for flood frequency analysis. However, changes in the behaviour of climate systems can lead to nonstationarity in flood series. Here, we develop flood hazard maps for Ho Chi Minh City, Vietnam, under nonstationary conditions using extreme value analysis, a coupled 1D–2D model and high-resolution topographical data derived from LiDAR (Light Detection and Ranging) data. Our findings indicate that ENSO (El Niño Southern Oscillation) and PDO (Pacific Decadal Oscillation) influence the magnitude and frequency of extreme rainfall, while global sea-level rise causes nonstationarity in local sea levels, having an impact on flood risk. The detailed flood hazard maps show that areas of high flood potential are located along river banks, with 0.60 km2 of the study area being unsafe for people, vehicles and buildings (H5 zone) under a 100-year return period scenario.

Analysis of climate extreme indices over the Komadugu-Yobe basin, Lake Chad region: Past and future occurrences

This study investigates trends of climate extreme indices in the Komadugu-Yobe Basin (KYB) based on observed data of the period 1971–2017 as well as regional climate model (RCM) simulations for the historical period (1979–2005), the near future (2020–2050), and the far future (2060–2090). In order to correct change points in the time historical series, the Adapted Caussinus Mestre Algorithm for homogenising Networks of Temperature series homogeneity test is used. The magnitude of the linear trends is estimated using the Sen's slope estimator and Mann-Kendall's test is performed to check the statistical significance of the trends. Future trends are assessed using the ensemble mean of eight regional climate model data under two emission scenarios, provided by the Coordinated Regional Climate Downscaling Experiment (CORDEX). Therefore, the projected rainfall and temperature have been corrected for biases by using empirical Quantile Mapping. In the observations, warm spell duration, warm day-, and warm night frequencies exhibit statistically significant positive trends. Although there is a positive trend in the annual total rainfall, the number of consecutive wet (dry) days decreases (increases). The future climate also shows a continuing positive trend in the temperature extreme indices as well as more frequent extreme rainfall events. Therefore, it is pertinent for decision-makers to develop suitable adaptation and mitigating measures to combat climate change in the Basin.

Engaging the Private Homeowner: Linking Climate Change and Green Stormwater Infrastructure

Current and projected climate change in the Minnesota Lake Superior Coastal Area indicates an increase in frequency and intensity of extreme rainfall. One key outcome of this change is a subsequent potential increase in stormwater runoff, a concern exacerbated by the region’s shallow, often clay soils and exposed bedrock, along with highly impervious urban surfaces. This situation, coupled with public perception of climate change that is increasingly inclusive of severe weather, highlights an opportunity to apply green infrastructure to the challenge of stormwater management, referred to as green stormwater infrastructure. In addition to coordinated public action at local, state, and national levels, there is a role for the private landowner to participate in this form of climate adaptation. Private citizens have an opportunity to both protect their home and property while contributing to overall stormwater management for the community in which they live. Focus group research was conducted to better understand outreach and involve local residents in the creation of a tool to assist private green stormwater infrastructure efforts. Results of the focus group sessions were analyzed, and key themes emerged from the data to guide this process and support private home/landowner action. It is recommended that a fifth domain be added to the typology for public and private roles in climate adaptation, i.e. private adaptation for public and private benefit.

Trends in flood events and their relationship to extreme rainfall in an urban area of Sahelian West Africa: The case study of Ouagadougou, Burkina Faso

Urban areas in Sahelian West Africa are highly vulnerable to extreme hydro‐meteorological events. In recent years, Burkina Faso has experienced several natural disasters with floods being the most frequent. This study investigates flood trends in Ouagadougou and their relationship to extreme rainfall events. Fourteen rainfall indices were analysed to characterise the frequency and intensity of extreme rainfall. A frequency analysis of annual maximum daily rainfall series was performed using three statistical distributions. The results showed that few of the rainfall indices have significant trends at 5% level over the period 1961–2015. The generalised extreme value distribution satisfactorily fit the time‐series of annual maximum daily rainfall. An analysis of media flood reports revealed that Burkina Faso experienced approximately three flood events per year throughout the period 1986–2016. In the 2000s, the number of flood events increased to five per year. Most flood events are caused by rainfall events with return periods of less than or equal to 5 years. The results indicated that the significant increase in flood risk in Ouagadougou is not only related to heavy rainfall but also due to human and environmental factors.

Frequency analysis of precipitation extremes under climate change

Frequency analysis of precipitation extremes is significant for the selection of design rainfalls, which are essential inputs for the design of water infrastructure projects, especially when the climate has changed. Therefore, the objective of this study was to propose a framework for more reasonably analysing the frequency of extreme rainfalls. The proposed framework consists of a maximum likelihood estimate (MLE) method for analysing the parameter trends, a hydrological variation diagnosis system to determine abrupt change times, generalized extreme value (GEV) and generalized Pareto distribution (GPD) models for frequency analysis of precipitation extremes, and an ensemble‐methods approach for choosing the most appropriate distributions. The methodology was successfully implemented using a 52‐year time series (1963–2014) of rainfall data recorded by eight rain gauges in Chengbi River basin (south China). The results show that the rainfall series mutated in 1993 and that the entire data set could be divided into two slices (1963–1992 and 1993–2014). Climate change was found to have some impacts on the precipitation extremes: the extreme rainfall value and the parameters of GEV and GPD were variable in the context of climate change. Furthermore, the GPD distribution model outperformed the GEV distribution model.

Flash Flood Verification: Pondering Precipitation Proxies

Abstract Quantitative precipitation estimate (QPE) exceedances of numerous different heavy precipitation thresholds—including spatially varying average recurrence interval (ARI) and flash flood guidance (FFG) thresholds—are compared among each other and against reported and warned flash floods to quantify existing deficiencies with QPEs and to identify best practices for using QPE for flash flood forecasting and analysis. QPEs from three different sources—NCEP Stage IV Precipitation Analysis (ST4), Climatology Calibrated Precipitation Analysis (CCPA), and Multi-Radar Multi-Sensor (MRMS) QPE—are evaluated across the United States from January 2015 to June 2017. In addition to evaluating different QPE sources, threshold types, and magnitudes, QPE accumulation interval lengths from hourly to daily are considered. Systematic errors with QPE sources are identified, including a radar distance dependence on extreme rainfall frequency in MRMS, spurious occurrences of locally extreme precipitation in the complex terrain of the West in ST4, and insufficient QPEs for many legitimate heavy precipitation events in CCPA. Overall, flash flood warnings and reports corresponded to each other far more than any QPE exceedances. Correspondence between all sources was at a maximum in the East and worst in the West, with ST4, CCPA, and MRMS QPE exceedances locally yielding maximal correspondence in the East, Plains, and West, respectively. Surprisingly, using a fixed 2.5 in. (24 h)−1 proxy outperformed shorter accumulation exceedances and the use of ARIs and FFGs. On regional scales, different ARI exceedances achieved superior performance to the selection of any fixed threshold; FFG exceedances were consistently too rare to achieve optimal correspondence with observed flash flooding.

Selecting Time Series Length to Moderate the Impact of Nonstationarity in Extreme Rainfall Analyses

AbstractObserved and projected increases in the frequency of extreme rainfall complicate the extreme value analyses of precipitation that are used to guide engineering design specifications, because conventional methods assume stationarity. Uncertainty in the magnitude of the trend in future years precludes directly accounting for the trend in these analyses. While previous extreme value analyses have sought to use as long a record as possible, it is shown using stochastically generated time series that this practice exacerbates the potential error introduced by long-term trends. For extreme precipitation series characterized by a trend in the location parameter exceeding approximately 0.005% yr−1, limiting the record length to fewer than 70 years is recommended. The use of longer time periods results in partial-duration series that are significantly different from their stationary counterparts and a greater percentage of rainfall extremes that exceed the 90% confidence interval corresponding to a stationary distribution. The effect is most pronounced on the shortest (i.e., 2 yr) recurrence intervals and generally becomes undetectable for recurrence intervals of more than 25 years. The analyses also indicate that the practice of including stations with records of limited length that end several decades prior to the present should be avoided. Distributions having a stationary location parameter but trended scale parameter do not exhibit this behavior.

Contribution of Moisture from Mediterranean Sea to Extreme Precipitation Events over Danube River Basin

In the most recent decades, central Europe and the Danube River Basin area have been affected by an increase in the frequency and intensity of extreme daily rainfall, which has resulted in the more frequent occurrence of significant flood events. This study characterised the link between moisture from the Mediterranean Sea and extreme precipitation events, with varying lengths that were recorded over the Danube River basin between 1981 and 2015, and ranked the events with respect to the different time scales. The contribution of the Mediterranean Sea to the detected extreme precipitation events was then estimated using the Lagrangian FLEXPART dispersion model. Experiments were modelled in its forward mode, and particles leaving the Mediterranean Sea were tracked for a period of time determined with respect to the length of the extreme event. The top 100 extreme events in the ranking with durations of 1, 3, 5, 7, and 10 days were analysed, and it was revealed that most of these events occurred in the winter. For extreme precipitation, positive anomalies of moisture support from the Mediterranean were found to be in the order of 80% or more, but this support reached 100% in summer and spring. The results show that extreme precipitation events with longer durations are more influenced by the extreme Mediterranean anomalous moisture supply than those with shorter lengths. However, it is during shorter events when the Mediterranean Sea contributes higher amounts of moisture compared with its climatological mean values; for longer events, this contribution decreases progressively (but still doubles the climatological moisture contribution from the Mediterranean Sea). Finally, this analysis provides evidence that the optimum time period for accumulated moisture to be modelled by the Lagrangian model is that for which the extreme event is estimated. In future studies, this fine characterisation could assist in modelling moisture contributions from sources in relation to individual extreme events.

Interactive effects of forest die-off and drying-rewetting cycles on C and N mineralization

Mediterranean forests will experience more frequent and intense drought periods and extreme rainfall events in the coming decades. Concomitantly, drought-induced forest die-off is likely to increase. Changes in rainfall patterns and forest die-off directly influence soil microbial communities and activity and, consequently, carbon (C) and nitrogen (N) turnover, but their interactive effects have not yet been explored. We investigated the short-, and the long-term interactive effects of forest die-off and drying-rewetting cycles on soil C and N mineralization rates of a Mediterranean woodland. Soil samples collected under and out of the influence of holm oak (Quercus ilex) trees with different defoliation degrees (six healthy, six affected and six dead) were incubated under two contrasting water regimes (i.e. drying-rewetting cycles vs. constant soil moisture). Potential soil C and N mineralization responded differently to water regimes, with an overall 55% increase in C mineralization and a 22% decrease in N mineralization in the drying-rewetting cycle treatment compared to the constant moisture treatment. Holm oak decline decreased the response of C mineralization while increased the response of N mineralization to the drying-rewetting cycles at both the short- and the long-term. Moreover, N turnover showed a higher sensitivity to these environmental changes than that of C during most of the year. Our study provides solid evidence that an intensification of the drying-rewetting regimes can result in a decoupling of soil C and N cycles in Mediterranean forests and that forest die-off might enhance this decoupling at both the short- and the long-term, with important implications for the ecosystem functioning.

Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes

The goal of quantile regression is to estimate conditional quantiles for specified values of quantile probability using linear or nonlinear regression equations. These estimates are prone to “quantile crossing”, where regression predictions for different quantile probabilities do not increase as probability increases. In the context of the environmental sciences, this could, for example, lead to estimates of the magnitude of a 10-year return period rainstorm that exceed the 20-year storm, or similar nonphysical results. This problem, as well as the potential for overfitting, is exacerbated for small to moderate sample sizes and for nonlinear quantile regression models. As a remedy, this study introduces a novel nonlinear quantile regression model, the monotone composite quantile regression neural network (MCQRNN), that (1) simultaneously estimates multiple non-crossing, nonlinear conditional quantile functions; (2) allows for optional monotonicity, positivity/non-negativity, and generalized additive model constraints; and (3) can be adapted to estimate standard least-squares regression and non-crossing expectile regression functions. First, the MCQRNN model is evaluated on synthetic data from multiple functions and error distributions using Monte Carlo simulations. MCQRNN outperforms the benchmark models, especially for non-normal error distributions. Next, the MCQRNN model is applied to real-world climate data by estimating rainfall Intensity–Duration–Frequency (IDF) curves at locations in Canada. IDF curves summarize the relationship between the intensity and occurrence frequency of extreme rainfall over storm durations ranging from minutes to a day. Because annual maximum rainfall intensity is a non-negative quantity that should increase monotonically as the occurrence frequency and storm duration decrease, monotonicity and non-negativity constraints are key constraints in IDF curve estimation. In comparison to standard QRNN models, the ability of the MCQRNN model to incorporate these constraints, in addition to non-crossing, leads to more robust and realistic estimates of extreme rainfall.

ANALYSIS OF THE EXTREME RAINFALL FREQUENCY IN BANGKA BELITUNG ISLANDS BASED ON CLIMATE HAZARDS GROUP INFRA-RED PRECIPITATION WITH STATIONS (CHIRPS) DATA

This study aimed to know the distribution of extreme rainfall frequency in Babel using CHIRPS data for 36 years from 1981-2016. The determination of extreme threshold value uses four methods, those are BMKG heavy rainfall threshold, Percentile-99, Quartile (Q3 + 3IQR) and Peak Over Threshold (POT). The results of frequency calculations on each grid are then mapped with Inverse Distance Weight (IDW) method in the ArcGIS application. However, there are five areas with different patterns of consistency. Northern Bangka especially in West Bangka regency is indicated as consistent high frequency area, consistent low frequency region is east coast of Bangka, while non-consistent area is west coast of Central Bangka to South Bangka regency. Two patterns in Belitung consistently show high frequency in the west and low in the east. The findings of these patterns are similar to the three zones of the season (ZOM) in Bangka established by BMKG. It can be a consideration to make ZOM in Belitung.

Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns

Abstract. The effects of climate change are causing more frequent extreme rainfall events and an increased risk of flooding in developed areas. Quantifying this increased risk is of critical importance for the protection of life and property as well as for infrastructure planning and design. The updated National Oceanic and Atmospheric Administration (NOAA) Atlas 14 intensity–duration–frequency (IDF) relationships and temporal patterns are widely used in hydrologic and hydraulic modeling for design and planning in the United States. Current literature shows that rising temperatures as a result of climate change will result in an intensification of rainfall. These impacts are not explicitly included in the NOAA temporal patterns, which can have consequences on the design and planning of adaptation and flood mitigation measures. In addition there is a lack of detailed hydraulic modeling when assessing climate change impacts on flooding. The study presented in this paper uses a comprehensive hydrologic and hydraulic model of a fully developed urban/suburban catchment to explore two primary questions related to climate change impacts on flood risk. (1) How do climate change effects on storm temporal patterns and rainfall volumes impact flooding in a developed complex watershed? (2) Is the storm temporal pattern as critical as the total volume of rainfall when evaluating urban flood risk? We use the NOAA Atlas 14 temporal patterns, along with the expected increase in temperature for the RCP8.5 scenario for 2081–2100, to project temporal patterns and rainfall volumes to reflect future climatic change. The model results show that different rainfall patterns cause variability in flood depths during a storm event. The changes in the projected temporal patterns alone increase the risk of flood magnitude up to 35 %, with the cumulative impacts of temperature rise on temporal patterns and the storm volume increasing flood risk from 10 to 170 %. The results also show that regional storage facilities are sensitive to rainfall patterns that are loaded in the latter part of the storm duration, while extremely intense short-duration storms will cause flooding at all locations. This study shows that changes in temporal patterns will have a significant impact on urban/suburban flooding and need to be carefully considered and adjusted to account for climate change when used for the design and planning of future storm water systems.

Investigation of inducements and defenses of flash floods and urban waterlogging in Fuzhou, China, from 1950 to 2010

In recent years, flash floods and urban waterlogging have become a widespread phenomenon in Fuzhou, which pose a serious threat to people’s lives and property. The primary disaster-causing factors include the intensity and duration of rainfall. Therefore, this article analyzes the characteristics, causes of rainfall, and the existing problems of the two disasters in Fuzhou. The main conclusions are as follows: (1) The rainfall in Fuzhou is concentrated in March to September, with high rainfall intensity and rainfall amounts, and frequent extreme rainfall events combined with high rainfall intensity in flash flood-prone areas are higher than that in the plains area. (2) Precipitation, geographical conditions, and urban construction mainly caused the two major disasters and are weak in technology and management. Therefore, it is necessary to adhere to both the structural measures and non-structural measures to coordinate the relationship between people and floods, to strengthen the research on the mechanisms of precipitation, and to forecast and provide early warning of flash floods and urban waterlogging, all of which can provide reference for the defensive disasters in mountainous coastal cities.

Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

AbstractThis study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions.

Spatiotemporal trends in extreme rainfall and temperature indices over Upper Tapi Basin, India

The flood risk across the globe is intensified due to global warming and subsequent increase in extreme temperature and precipitation. The long-term trends in extreme rainfall (1944–2013) and temperature (1969–2012) indices have been investigated at annual, seasonal, and monthly time scales using nonparametric Mann-Kendall (MK), modified Mann-Kendall (MMK), and Sen’s slope estimator tests. The extreme rainfall and temperature indices, recommended by the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI), have been analyzed at finer spatial scales for trend detection. The results of trend analyses indicate decreasing trend in annual total rainfall, significant decreasing trend in rainy days, and increasing trend in rainfall intensity over the basin. The seasonal rainfall has been found to decrease for all the seasons except postmonsoon, which could affect the rain-fed agriculture in the basin. The 1- and 5-day annual maximum rainfalls exhibit mixed trends, wherein part of the basin experiences increasing trend, while other parts experience a decreasing trend. The increase in dry spells and concurrent decrease in wet spells are also observed over the basin. The extreme temperature indices revealed increasing trends in hottest and coldest days, while decreasing trends in coldest night are found over most parts of the basin. Further, the diurnal temperature range is also found to increase due to warming tendency in maximum temperature (T max) at a faster rate compared to the minimum temperature (T min). The increase in frequency and magnitude of extreme rainfall in the basin has been attributed to the increasing trend in maximum and minimum temperatures, reducing forest cover, rapid pace of urbanization, increase in human population, and thereby increase in the aerosol content in the atmosphere. The findings of the present study would significantly help in sustainable water resource planning, better decision-making for policy framework, and setting up infrastructure against flood disasters in Upper Tapi Basin, India.

Impacts of warming and nitrogen addition on soil autotrophic and heterotrophic respiration in a semi-arid environment

Evaluating the responses of soil respiration and its components to global environmental change is crucial for predicting future terrestrial carbon cycle. However, the effects of warming and nitrogen (N) addition on them remain unclear. A field manipulative experiment was conducted in a semi-arid grassland on the Loess Plateau to evaluate the responses of soil total respiration (Rt), autotrophic respiration (Ra) and heterotrophic respiration (Rh) to warming and N addition from April 2015 to December 2016. Open-top chambers were used to elevate temperature and N was added as NH4NO3 at a rate of 4.42gNm−2yr−1. Warming significantly decreased Rt and Rh by 7.4% and 9.5%, respectively, but had no significant effect on Ra. N addition significantly stimulated Ra by 34%, whereas it inhibited Rh by 11% and had no significant effect on Rt. The maximum N-inducing stimulation of Rt and Ra were observed in the month with the most rainfall events in 2015. N addition significantly increased the contribution of Ra to Rt by 10%. Warming decreased the Q10 values of Rt and Rh but had no significant effect on Q10 values of Ra. N addition significantly increased the Q10 values of Rt and Rh, whereas it decreased the Q10 values of Ra. The combination of warming and N addition had a synergistic effect on the cumulant of Rh, whereas it had an antagonistic effect on Ra. No interactive effect between warming and N addition was observed on Rt. Our results emphasized that Ra and Rh responded differently to warming and/or N addition, and the extreme rainfall frequency influenced the responses of Rt and its components to N addition. Our findings suggested that Rt has the potential to resist climate warming and increasing N deposition by differentiating the responses of its inherent components.

Future extreme rainfall change projections in the north of Iran

ABSTRACTFuture changes in extreme rainfall arising from climate change may have a significant influence on flood and water erosion control and management strategies to a great extent. The maximum daily rainfall time series were projected for 2020–2049 using six general climate models and two scenarios through artificial neural networks for 22 stations across the north of Iran. The results indicate a reduction of between −3.0 and −0.2% in maximum rainfall for the selected stations and five out of six of the general climate models. The changes in the frequency and magnitude of extreme rainfall were then investigated by fitting a generalized extreme value distribution to the historical (from 1981 to 2010) and projected maximum rainfall. The location parameter of the generalized extreme value distribution fitted to the projected maximum rainfall does not show a significant change while the scale and shape parameters exhibit significant changes compared to the historical period. Estimating the 2, 50 and 100 year return periods showed that the maximum rainfall will have a reduction in the probability of large amounts across the region compared with the base period while the number of extraordinary extreme events may show growth. As a region vulnerable to flash floods and water erosion due to rainfall characteristics and land use change from forest to agriculture, the results may send an alarm to define long term and effective strategies for future flood control management in the region.

The role of climate variability in extreme floods in Europe

Climate variability is shown to be an important driver of spatial and temporal changes in hydrometereological variables in Europe. However, the influence of climate variability on flood damage has received little attention. We investigated the El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the East Atlantic pattern (EA) during their neutral, positive, and negative phases, to understand their relationships with four flood indicators: Occurrence of Extreme Rainfall, Intensity of Extreme Rainfall, Flood Occurrence, and Flood Damage. We found that positive and negative phases of NAO and EA are associated with more (or less) frequent and intense seasonal extreme rainfall over large areas of Europe. The relationship between ENSO and the Occurrence of Extreme Rainfall and Intensity of Extreme Rainfall in Europe is much smaller than the relationship with NAO or EA, but still significant in some regions. We show that Flood Damage and Flood Occurrence have strong links with climate variability, especially in southern and eastern Europe. Therefore, when investigating flooding across Europe, all three indices of climate variability should be considered. Future research should focus on their joint influence on flood risk. The potential inclusion of seasonal forecasts of indices of climate variability could be effective in forecasting flood damage.