Future Extreme Rainfall Research Articles

The effects of climate change on historical and future extreme rainfall in Antalya, Turkey

There is increasing concern that flood risk will be exacerbated in Antalya, Turkey as a result of global-warming-induced, more frequent and intensive, heavy rainfalls. In this paper, first, trends in extreme rainfall indices in the Antalya region were analysed using daily rainfall data. All stations in the study area showed statistically significant increasing trends for at least one extreme rainfall index. Extreme rainfall datasets for current (1970–1989) and future periods (2080–2099) were then constructed for frequency analysis using the peaks-over-threshold method. Frequency analysis of extreme rainfall data was performed using generalized Pareto distribution for current and future periods in order to estimate rainfall intensities for various return periods. Rainfall intensities for the future period were found to increase by up to 23% more than the current period. This study contributed to better understanding of climate change effects on extreme rainfalls in Antalya, Turkey.

A Comparative Analysis of Runoff and Soil Loss Characteristics between “Extreme Precipitation Year” and “Normal Precipitation Year” at the Plot Scale: A Case Study in the Loess Plateau in China

The highest annual precipitation in Ansai during the past 60 years was observed in 2013. Studying the effects of such extreme annual precipitation on soil erosion can deepen similar research and help predict environmental damage under future extreme rainfall. We selected the runoff plots (area: 4 m × 10 m) of three land-use types: cultivated land (C), wild grassland (W) and switchgrass (S). We monitored corresponding indices in the “extreme year” (2013) and “normal year” (2010–2012). The results were as follows: (1) The rainfall parameters (rainfall amount, EI30, EI60) in the “extreme year” were all higher than those in the “normal year”, especially in July; (2) In July and August, the runoff and soil loss of cultivated land in the “extreme year” increased by 1492.5% and 538.2%, respectively, compared to those in the “normal year”. The silt content of sediment was the highest generally, but the sand content increased relatively in the “extreme year”; (3) The effect of rainfall on soil erosion was the smallest in W; (4) In the “extreme year”, the same EI30 generated more runoff and soil loss than those in the “normal year”. The high antecedent precipitation which would cause high antecedent soil moisture was one of the reasons. The threshold value (100 MJ·mm·ha−1·h−1) of EI30 related to extreme erosion risk was proposed.

Influence of urban land cover changes and climate change for the exposure of European cities to flooding during high-intensity precipitation

Abstract. The extent and location of impervious surfaces within urban areas due to past and present city development strongly affects the amount and velocity of run-off during high-intensity rainfall and consequently influences the exposure of cities towards flooding. The frequency and intensity of extreme rainfall are expected to increase in many places due to climate change and thus further exacerbate the risk of pluvial flooding. This paper presents a combined hydrological-hydrodynamic modelling and remote sensing approach suitable for examining the susceptibility of European cities to pluvial flooding owing to recent changes in urban land cover, under present and future climatic conditions. Estimated changes in impervious urban surfaces based on Landsat satellite imagery covering the period 1984–2014 are combined with regionally downscaled estimates of current and expected future rainfall extremes to enable 2-D overland flow simulations and flood hazard assessments. The methodology is evaluated for the Danish city of Odense. Results suggest that the past 30 years of urban development alone has increased the city's exposure to pluvial flooding by 6% for 10-year rainfall up to 26% for 100-year rainfall. Corresponding estimates for RCP4.5 and RCP8.5 climate change scenarios (2071–2100) are in the order of 40 and 100%, indicating that land cover changes within cities can play a central role for the cities' exposure to flooding and conversely also for their adaptation to a changed climate.

Understanding the risks from extreme rainfall

Empirical engineering design based on single ‘fail safe’ target return periods is not appropriate in a non-stationary future climate. Extreme responses to rainfall are very likely to increase under climate change, posing increased hazards to critical infrastructure and the most vulnerable sectors of society. Structures with long life spans require reasonable estimates of the hazards that will be faced now and in the future, necessitating some assessment of future rainfall extremes. Such challenges are not new. Although future climate conditions may be unprecedented, techniques to optimise water management and minimise risk in more extreme climate regimes are not. Designs that account for future climatic extremes could benefit from other countries' experiences of highly variable extreme rainfall. This paper synthesises the physical mechanisms leading to extreme rainfall, and their representation in climate models. It then summarises recent observed changes in extreme rainfall and the anticipated future changes in response to changing climate conditions. Finally, it discusses ways in which different measures of extreme rainfall, such as seasonality and intensity, can be used to inform designs for future flood resilience.

Event Based Flood Inundation Mapping Under the Impact of Climate Change: A Case Study in Lower Kelani River Basin, Sri Lanka

The downstream low lying region of the Kelani River including the Colombo suburbs, experience severe inundation due to localized heavy rainfall events and high precipitation in the upper basin. Under the impact of climate change it is very likely that more frequent heavy rainfalls in tropics [1] will occur. Therefore it is extremely important to have a better understanding about future rainfall patterns and intensities in the basin and inundation extents of the low lying regions characterized by high population concentration and economic activities that form the suburbs of the commercial capital. This paper presents the extreme rainfalls occurrence potential and resulting flood inundation along the lower reach of Kelani River. Coarse grid atmospheric parameters provided by GCM models for A2 and B2 scenarios of IPCC [1] are downscaled to catchment scale by the application of Statistical Downscaling Model (SDSM). Flood discharge and inundation along the Kelani River reach below Hanwella was analyzed by the application of two-dimensional flood simulation model (FLO-2D). Inflow to the model at Hanwella, is estimated by the HEC- HMS model under future extreme rainfall events. Areas vulnerable for inundation under the above climate change scenarios are presented.

Extreme Rainfall Event Analysis Using Rain Gauges in a Variety of Geographical Situations

About 30 years of measurements made by the rain gauges located in Piedmont (Italy) have been analyzed. Rain gauges have been divided into 4 datasets considering the complex orography near Turin, namely the flatlands, mountains, hills and urban areas. For each group of gauges, the Generalized Extreme Values (GEV) distributions are estimated considering both the entire dataset of available data and different sets of 3 years of data in running mode. It is shown that the GEV estimated parameters temporal series for the 3 years dataset do not present any specific trend over the entire period. The study presented here is preliminary to a future extreme rainfall event analysis using high temporal and spatial resolution X-band weather radar with a limited temporal availability of radar maps covering the same area.

Effects of climate change on future extreme rainfall indices over Thailand

<div> <p>Extreme rainfall indices were calculated based on daily rainfall data derived from the outputs of MM5-RCM simulations. Projected changes of these indices in the future (2020s) under the IPCC Special Report on Emissions Scenarios (SRES) A1B compared to the reference period (1990s) were analyzed. Before calculating the indices, the raw outputs from the MM5-RCM were adjusted in order to reduce biases as part of the bias-correction process. The validation demonstrates that the average and cycle of annual rainfall in the reference period are reproduced reasonably in the bias-corrected MM5-RCM results. The projected changes of the rainfall indices suggest that in the future most areas of northern, western, and northeastern Thailand will become wetter in the wet season and drier in the dry season. Central and eastern Thailand will become drier in the wet season while in the dry season rainfall will become more intense, and rainy days will become more scattered with a higher number of heavy rainfall days. In the south, the length of the wet spell will be decreased in the Southern Andaman Sea area and increased in East-Southern Region. The dry spell will likely be shorter throughout the southern region for all seasons.  </p> </div> <p> </p>

Slope management criteria for Alishan Highway based on database of heavy rainfall-induced slope failures

In this paper, a study of slope failures along the Alishan Highway (locally, known as “Tai-18”) is carried out. An innovative empirical model is developed based on 15-year records of typhoon rainfall-induced slope failures. This model is intended for assessing the likelihood of slope failure along Tai-18 in the future. The rainfall data considered in the proposed model include the maximum hourly rainfall and the effective cumulative rainfall. The effective cumulative rainfall is defined at the point when the curve of cumulative rainfall goes from steep to flat. Then, a simple criterion is established for assessing the potential of slope failure and issuing warning and/or closure for Tai-18 during a future extreme rainfall. Slope failures during Typhoon Saola in 2012 and those in Japan demonstrate that the new empirical model is effective and applicable to other regions with similar rainfall conditions.

Decision strategies for handling the uncertainty of future extreme rainfall under the influence of climate change

Several extraordinary rainfall events have occurred in Denmark within the last few years. For each event, problems in urban areas occurred as the capacity of the existing drainage systems were exceeded. Adaptation to climate change is necessary but also very challenging as urban drainage systems are characterized by long technical lifetimes and high, unrecoverable construction costs. One of the most important barriers for the initiation and implementation of the adaptation strategies is therefore the uncertainty when predicting the magnitude of the extreme rainfall in the future. This challenge is explored through the application and discussion of three different theoretical decision support strategies: the precautionary principle, the minimax strategy and Bayesian decision support. The reviewed decision support strategies all proved valuable for addressing the identified uncertainties, at best applied together as they all yield information that improved decision making and thus enabled more robust decisions.

Flood Inundation Mapping along the Lower Reach of Kelani River Basin under the Impact of Climatic Change

The downstream low lying region of the Kelani River including the Colombo suburbs, experience severe inundation due to heavy rainfalls in the upper basin of the Kelani River. Occurrence of heavy rainfalls is expected to be more frequent in the tropics with the impact of climatic change (IPCC, 2007). Therefore, understanding future rainfall intensity in the river basin and inundation in the low lying region along the lower reach of the Kelani River is extremely important as this is a region with a high population density and economic activities in the suburbs of the capital. The present study analyses the potential extreme rainfalls and resulting flood inundation along the lower Kelani River. Coarse grid atmospheric parameters provided by Global Climate Model (GCM) models for A2 (high emission scenario) and B2 (low emission scenario) scenarios of Intergovernmental Panel on Climate Change (IPCC, 2007) were downscaled to local scale by applying Statistical Downscaling Model (SDSM). Flood discharge and inundation along the Kelani River reach below Hanwella were analyzed by applying two-dimensional flood simulation model (FLO-2D). Inflow to the model at Hanwella, is estimated by the Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) model under future extreme rainfall events. Areas vulnerable to inundation under the above climatic change scenarios are presented.

Climate Change Impacts on the Extreme Rainfall for Selected Sites in North Western England

Impact and adaptation assessments of climate change often require more detailed information of future extreme rainfall events at higher resolution in space and/or time, which is usually, projected using the Global Climate Model (GCM) for different emissions of greenhouse concentration. In this paper, future rainfall in the North West region of England has been generated from the outputs of the HadCM3 Global Climate Model through downscaling , employing a hybrid Generalised Linear Model (GLM) together with an Artificial Neural Network (ANN). Using two emission scenarios (A1FI and B1), the hybrid downscaling model was proven to have the capability to successfully simulate future rainfall. A combined peaks-over-threshold (POT)-Generalised Pareto Distribution approach was then used to model the extreme rainfall and then assess changes to seasonal trends over the region at a daily scale until the end of the 21st century. In general, extreme rainfall is predicted to be more frequent in winter seasons for both high (A1FI) and low (B1) scenarios, however for summer seasons, the region is predicted to experience some increase in extreme rainfall under the high scenario and a drop under the low scenario. The variation in intensity of extreme rainfall was found to be based on location,season, future period, return period as well as the emission scenario used.

Projections of extreme rainfall in Hong Kong in the 21st century

The possible changes in the frequency of extreme rainfall events in Hong Kong in the 21st century were investigated by statistically downscaling 30 sets of the daily global climate model projections (involving a combination of 12 models and 3 greenhouse gas emission scenarios, namely, A2, A1B, and B1) of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. To cater for the intermittent and skewed character of the daily rainfall, multiple stepwise logistic regression and multiple stepwise linear regression were employed to develop the downscaling models for predicting rainfall occurrence and rainfall amount, respectively. Verification of the simulation of the 1971–2000 climate reveals that the models in general have an acceptable skill in reproducing past statistics of extreme rainfall events in Hong Kong. The projection results suggest that, in the 21st century, the annual number of rain days in Hong Kong is expected to decrease while the daily rainfall intensity will increase, concurrent with the expected increase in annual rainfall. Based on the multi-model scenario ensemble mean, the annual number of rain day is expected to drop from 104 days in 1980–1999 to about 77 days in 2090–2099. For extreme rainfall events, about 90% of the model-scenario combinations indicate an increase in the annual number of days with daily rainfall ≥ 100 mm (R100) towards the end of the 21st century. The mean number of R100 is expected to increase from 3.5 days in 1980–1999 to about 5.3 days in 2090–2099. The projected changes in other extreme rainfall indices also suggest that the rainfall in Hong Kong in the 21st century may also become more extreme with more uneven distributions of wet and dry periods. While most of the model-emission scenarios in general project consistent trends in the change of rainfall extremes in the 21st century, there is a large divergence in the projections among different model/emission scenarios. This reflects that there are still large uncertainties in model simulations of future extreme rainfall events.

Simulation of future changes in extreme rainfall and temperature conditions over the Greek area: A comparison of two statistical downscaling approaches

An estimation of the future changes in the extreme conditions over the Greek area has been attempted, by applying the output data from a GCM to two different statistical downscaling models. The first was a multiple linear regression model based on a circulation type approach (MLRct) and the second one used artificial neural networks (ANNs). Two extreme temperature indices (txq90, tnq10) and two extreme precipitation indices (pq90, pxcdd) were selected for the study and simulated by the statistical models. From the comparison of the models during a validation period, it was found that the MLRct was relatively more efficient and the circulation types were proven to be a more reliable predictor. However, both the models underestimated the natural variability of the observed time-series. Generally, the extreme temperature indices were simulated better than the precipitation ones and winter was the season that showed the highest downscaling skill. Subsequently, the present day and future extreme temperature and rainfall conditions were examined using four ensemble members (control runs) and five scenarios driven by the A2 and B2 emission SRES scenarios, from the atmospheric general circulation model HadAM3P. Concerning extreme temperatures, an increase of their values is expected during the future period 2070–2100, according to the MLRct model. The climate change signal of the ANNs model was not so strong and in some cases it was of opposite direction. The results for the extreme precipitation indices showed a spatial incoherence and more complex structure of change.