Extreme Rainfall Indices Research Articles

Hydroclimatic teleconnections of large-scale oceanic-atmospheric circulations on hydrometeorological extremes of Tapi Basin, India

Abstract Current study presents an integrated approach to assess the hydroclimatic teleconnections of large-scale coupled oceanic-atmospheric circulations on basin-scale extreme rainfall and streamflow indices for climatologically sensitive Tapi basin, India. The trend analysis of rainfall extremes revealed spatial heterogeneity across the basin. The coastal and mountainous regions of Lower and Upper Tapi basins respectively experienced wet conditions, while the plains of Middle and Upper Tapi (Purna sub-catchment) basins exhibited drier conditions during the analysis. The decreasing trend in high streamflow indices (except for Burhanpur sub-catchment) and significant increasing trend in low flow days (except for Lower Tapi basin) are observed, indicating the dominance of drier and moisture deficient conditions across Tapi basin. The hydroclimatic teleconnections of large-scale oceanic-atmospheric circulations of tropical Pacific and equatorial Indian Ocean regions, i.e., El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) respectively, with hydrometeorological extremes in Tapi basin for the concurrent period are investigated. As Indian summer monsoon rainfall (ISMR) and Tapi basin monsoon rainfall (TBMR) shows explained variance of around 42%, the influences of ENSO and IOD circulations on TBMR may be asymmetric with ISMR. The anomalous warming of the Indian Ocean, i.e., positive IOD (pIOD), and co-occurrence of pIOD and weak El Nino conditions, are associated with higher flood risk across the basin vis-a-vis other ENSO and IOD conditions. On other hand, the effects of ENSO and IOD are not apparent on low flow days, as the anthropogenic changes in the basin including streamflow regulations have considerably influenced the low flow regimes. The time interlocking between ENSO-IOD conditions and streamflow extremes in Tapi basin is further envisaged by performing a lead-lag correlation analysis.

Changes in future rainfall extremes over Northeast Bangladesh: A Bayesian model averaging approach

AbstractIn this paper, we used a Bayesian model averaging (BMA) approach to analyse the changes in rainfall extremes in the periods 2041–2070 and 2071–2099 over northeast Bangladesh as a consequence of climate change. Climate change over this region could potentially impact agricultural production, water resources management, and the overall economy of the country. We used six regional climate models (RCMs) over the Coordinated Regional Downscaling Experiment South Asia domain. We used one medium stabilization scenario (RCP4.5) and one high‐emission scenario (RCP8.5) for projecting the extreme rainfall indices. A multi‐model ensemble mean was generated using the BMA approach. The BMA mean is a weighted average related to each RCM's predictive skill during the training period. Most of the rainfall extremes are expected to increase in both pre‐monsoon (March–May) and monsoon (June–September) seasons in the future compared with baseline (1976–2005). The average pre‐monsoon rainfall of the study area is projected to increase by 12.93 and 18.42% under RCP4.5 and 18.18 and 23.85% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. The average monsoon rainfall of the study area is projected to increase by 4.96 and 2.27% under RCP4.5 and 6.56 and 6.40% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. All the extreme indices except consecutive wet day are expected to change significantly at the 95% confidence level during the pre‐monsoon season. The study area will potentially be subjected to more frequent floods in the future both in pre‐monsoon and monsoon seasons as a consequence of climate change. Notably, the intensity and the magnitude of flash flooding in the pre‐monsoon season are expected to increase more in the future because the increase in extreme indices is more significant during that season.

Study of rainfall variabilities in Southeast Asia using long-term gridded rainfall and its substantiation through global climate indices

This study utilized a long-term (1951–2014) moderate-resolution observed gridded (0.5°×0.5°) rainfall dataset to analyze long-term and short-term rainfall variability and the seasonality in Southeast Asia (SEA). The previous studies for this region revealed significant enhancement in extreme events and seasonal variabilities in rainfall. In this study, the frequency and intensity-based rainfall extremity analysis was carried out by utilizing a number of rainfall extreme indices (e.g. Dry Days, Wet Days, 90P, 95P, and 99P). A seasonal non-parametric Mann-Kendall test was applied to detect significant increase and decrease of rainfall. The possible causes for the acceleration and variabilities of rainfall were investigated by analyzing the impact of large-scale ocean-atmospheric climate interactions through global climate indices such as El Nino (3.4 and 4.0), Southern Oscillation Index (SOI), Madden-Julian Oscillation (MJO), global mean land and ocean temperature index, QBO and NOI. Our results displayed a significant increase of rainfall amount in most of SEA regions, while a minimal decrease was also found over some regions. The rainfall indices displayed substantial intra-decadal variabilities over the SEA region. The intra-decadal percent of change analysis based on rainfall extreme indices revealed significant changes in rainfall extremes over SEA, which also evidenced the computed acceleration in rainfall over the region. The correlation analysis based findings exhibited that a strong correlation existed between the SEA rainfall and the large-scale global climate indices generated based on the ocean-atmosphere climate interactions. After regionalization of the SEA, the global climate indices based results described the spatiotemporal variabilities in rainfall, which were computed over SEA regions. The global climate indices such as El Nino (3.4 & 4.0), SOI, NOI and MJO suggested significant correlations with the seasonal rainfall and the rainfall extreme indices and highlighted that their collinearities vary within regions as per variations in the monsoon.

Development of multi-model ensemble for projection of extreme rainfall events in Peninsular Malaysia

AbstractPossible changes in rainfall extremes in Peninsular Malaysia were assessed in this study using an ensemble of four GCMs of CMIP5. The performance of four bias correction methods was compared, and the most suitable method was used for downscaling of GCM simulated daily rainfall to the spatial resolution (0.25°) of APHRODITE rainfall. The multi-model ensemble (MME) mean of the downscaled rainfall was developed using a random forest regression algorithm. The MME projected rainfall for four RCPs were compared with APHRODITE rainfall for the base year (1961–2005) to assess the annual and seasonal changes in eight extreme rainfall indices. The results showed power transformation as the most suitable bias correction method. The maximum changes in most of the annual and seasonal extreme rainfall indices were observed for RCP8.5 in the last part of this century. The maximum increase was observed for 1-day and 5 consecutive days' rainfall amount for RCP4.5. Spatial distribution of the changes revealed higher increase of the extremes in the northeast region where rainfall extremes are already very high. The increase in rainfall extremes would increase the possibility of frequent hydrological disasters in Peninsular Malaysia.

Projected changes in the seasonal cycle of extreme rainfall events from CORDEX simulations over Central Africa

This study explores the potential response of the seasonal cycle of extreme rainfall indices over Central Africa (CA) to the global warming for both the middle (2029–2058) and late twenty-first century (2069–2098), based on analysis of multi-model ensembles mean of fifteen regional climate models (RCMs) simulations. Although few dry/wet biases are still evident, for the present day climate, the RCMs ensemble mostly outperforms the driving global climate models, with a better representation of the seasonal cycle of various rainfall indices over two key sub-regions of CA chosen according to their particular rainfall patterns. Both middle and late twenty-first century project a non-significant decrease in total wet-day rainfall amount over the two analysed sub-regions, with peaks found during pre-monsoon months. We also found a significant decrease in wet-day frequency which was consistent with decreases in total wet-day rainfall amount, while wet-day intensity is projected to significantly increase. These results suggest that the decrease in total wet-day rainfall amount could be associated with less frequent events and not with their intensity. The results also have shown that dry (wet) spells are projected to significantly increase (decrease) over both sub-regions with shorter (longer) dry (wet) spells projected during pre-monsoon months. Consequently, countries within these two sub-regions could experience a more extended dry season, and therefore would be exposed to high drought risk in the future under global warming. However, changes in maximum 1-day rainfall amount, maximum 5-day rainfall amount, and 95th percentile are projected to significantly increase during monsoon months, with the maximum 1-day rainfall amount recording largest increases. Additionally, the total amount of rainfall events above the 95th percentile projects a significant increase of about 10–45 % during monsoon months, while the total number of occurrence of rainfall events above the 95th percentile projects a slight significant decrease of 4–8 % during pre-monsoon months but more pronounced for the late twenty-first century. This implies that in the future, extremes rainfall events could be more intense both in terms of rainfall amount and intensity during monsoon months. Such changes are likely to amplify the probability of flood risks during monsoon months over CA, particularly the two sub-regions. This study could therefore be an important input for disaster preparedness, adaptation planning, and mitigation strategies for Central African countries.

Análise de Índices de Extremos Climáticos no Nordeste e Amazônia Brasileira para o Período entre 1980 a 2013

In Brazil, climate extreme events in the Amazon (AMZ) and Northeast (NEB) regions, such as heavy rainfall (floods and flash floods) and severe drought, alternate both in space and time. Floods, flash floods and droughts affect water quality and access which may impact public health through the proliferation of diseases, in addition to social and economic aspects. Thus, the objective of this study is to analyze trends in air temperature and rainfall climate extreme indices in the AMZ and the NEB. Meteorological data used in this study were from a joint project between the University of Texas (USA) and the Federal University of Espirito Santo (Brazil), made available on the website: https://utexas.box.com/Xavier-etal-IJOC-DATA and arranged in a 0.25° × 0.25° grid, for the period from 01 January 1980 to 31 October. 11 extreme air temperature indices and 10 extreme rainfall indices obtained from Climdex were used. Results showed possible local climate change in certain areas of the AMZ and the NEB, with the most important changes occurring in the temperature indices. It was observed that 48 mesoregions indicate an increase for the TXx index (maximum monthly maximum daily temperature), followed by the index TX90p (warm days), with 47 mesoregions, TNx (monthly maximum daily minimum temperature) and reduction for TX10p (cold days) with 42 mesoregions. Regarding rainfall indices in the AMZ and the NEB, it is noted that there have been changes in 20 or more mesoregions, especially in the R20mm (annual count of days when rainfall ≥ 20 mm), CDD (dry spell), R95p (wet days) and PRECPTOT (annual total precipitation on wet days) with positivi trend and RX5day (Monthly maximum consecutive 5-day precipitation) indices with positivi trend. Results also showed that from 21 indices selected according to the characteristics of the AMZ and the NEB, the mesoregion with the best evidence of climate change is the Outback of Sergipe with the occurrence of 20 statistically significant indices, followed by the South of Roraima with 16 indexes. However, there is a conjunction of factors such as deforestation, inefficient land use, among others, that should be taken into consideration and may be impacting the local climate of the studied regions.

Statistical Study of Variability in Rainfall and Analysis of Etreme Rainfall Events for Hill Stations of Uttarakhand

Statistical analysis of rainfall pattern and its variability for 20 years (1990-2010) data is performed for two mountainous urban centres of Uttarakhand i.e. Almora (29.60 N, 79.670 E and altitude 1,204m asl) and Nainital (29.40 N, 79.470 E and altitude 2,020m asl). Non Parametric method of Principal Component Analysis (PCA) gives the correlation between different extreme rainfall indices. It is concluded that PCA suggest 90% of the variance in composite matrix of extreme rainfall indices.

Statistical Study of Variability in Rainfall and Analysis of Etreme Rainfall Events for Hill Stations of Uttarakhand

Statistical analysis of rainfall pattern and its variability for 20 years (1990-2010) data is performed for two mountainous urban centres of Uttarakhand i.e. Almora (29.60 N, 79.670 E and altitude 1,204m asl) and Nainital (29.40 N, 79.470 E and altitude 2,020m asl). Non Parametric method of Principal Component Analysis (PCA) gives the correlation between different extreme rainfall indices. It is concluded that PCA suggest 90% of the variance in composite matrix of extreme rainfall indices.

Long-term trends in precipitation indices at eastern districts of Bangladesh

This study analyzed the trends of extreme daily rainfall indices over three meteorological stations located in the eastern region of Bangladesh from 1960 to 2000. The climate change-related indices included frequency-based indices: number of heavy rainfall days and consecutive dry and wet days, and intensity-based indices: annual wet-day rainfall total, daily and consecutive five-day maximum rainfall, very and extremely wet days and simple daily intensity index. The magnitude of trends in extreme rainfall indices time series was determined using the nonparametric Sen’s slope estimator method, and the statistical significance of the trends was analyzed using the Mann–Kendall test. The rainfall trends exhibited regional patterns. Overall, results suggested an increase in annual rainfall over the study area; however, a tendency toward reduction of rainfall in the wet season was observed. Analysis of extreme rainfall indices demonstrated non-significant increase in frequency of heavy rainfall days, decrease in consecutive dry days and increase in consecutive wet days coupled with regional decline in daily and consecutive five-day maximum rainfall in the monsoon.

Analysis and Modelling of Extreme Rainfall: A Case Study for Dodoma, Tanzania

The analysis of climate change, climate variability and their extremes has become more important as they clearly affect the human society and ecology. The impact of climate change is reflected by the change of frequency, duration and intensity of climate extreme events in the environment and on the economic activities. Climate extreme events, such as extreme rainfall threaten to environment, agricultural production and loss of people’s lives. Dodoma daily rainfall data exported from R-Instat software were used after being provided by Tanzania Meteorological Agency. The data were recorded from 1935 to 2011. In this essay, we used climate indices of rainfall to analyse changes in extreme rainfall. We only used 6 rainfall indices related to extremes to describe the change in rainfall extremes. Extreme rainfall indices did not show statistical evidence of a linear trend in Dodoma rainfall extremes for 77 years. Apart from the extreme rainfall indices, this essay utilized two techniques in extreme value theory namely the block maxima approach and peak over threshold approach. The two extreme value approaches were used for univariate sequences of independent identically distributed (iid) random variables. Using Dodomadaily rainfall data, this essay illustrated the power of the extreme value distributions in modelling of extreme rainfall. Annual maxima of Dodoma daily rainfall from 1935 to 2011 were fitted to the Generalized Extreme Value (GEV) model. Gumbel was found to be the best fit of the data after likelihood ratio test of GEV and Gumbel models. The Gumbel model parameters were considered to be stationary and non-stationary in two different models. The stationary Gumbel model was found to be good fit of Dodoma maximum rainfall. Later, the levels at which maximum Dodoma rainfall is expected to exceed once, on average, in a given period of time T = 2, 5, 10, 20, 30, 50 and 100 years, were obtained using stationary Gumbel model. Lastly, the data of exceedances were fitted to the Generalized Pareto (GP) model under stationary climate assumption.

Evaluation of satellite-based products for extreme rainfall estimations in the eastern coastal areas of China

ABSTRACTRemotely sensed rainfall plays an important role in providing efficient approaches for global or regional rainfall analysis. However, the accuracy of satellite-based products is mainly affected by the errors in sensor observation and retrieval algorithms, particularly with respect to extreme rainfall estimates. The objective of this study is to evaluate the accuracy of satellite-based products in capturing rainfall extremes. The eastern coastal areas of China were chosen as the case study area to compare the accuracy of three mainstream satellite-based products with respect to extreme rainfall estimates during 2003–2015 period. This included the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7, the Climate Prediction Centre Morphing technique RAW (CMORPH-RAW), and the CMORPH bias-corrected product (CMORPH-CRT). In general, all satellite-based products demonstrated numerous errors in extreme rainfall estimates. Based on three different indices of extreme rainfall, it was observed that the satellite-based products underestimated the amounts of rainfall extremes and their respective average values. It was noted that CMORPH-RAW demonstrated the largest relative bias (RB) and underestimated the average extreme rainfall by −31% to −35%. Additionally, all satellite-based products exhibited poor capabilities in capturing the variations in hourly extreme rainfall processes. Finally, a simple potential flood index was developed to simulate the potential flood areas in the eastern coastal areas of China. We found that the potential flood areas can be simulated by combining the potential flood index with the amounts of rainfall derived by satellite-based products.

Rainfall variability in Malay Peninsula region of Southeast Asia using gridded data

Southeast Asia is recognized as a climate-change vulnerable region as it has been significantly affected by many extreme events in the past. This study carried out a rainfall analysis over the Malay Peninsula region of Southeast Asia utilizing historical (1981-2007) gridded rainfall datasets (0.5°×0.5°). The rainfall variability was analyzed in an intra-decadal time series duration. The uncertainty involved in all datasets was also checked based on the comparison of multiple global rainfall datasets. Rainfall gap filling analysis was conducted for producing more accurate rainfall time series after testing multiple mathematical functions. Frequency-based rainfall extreme indices such as Dry Days and Wet days are generated to assess the rainfall variability over the study area. Our results revealed a notable variation existed in the rainfalls over Malay Peninsula as per the long historical duration (1981-2007).

Spatio-Temporal Trend Analysis of Rainfall and Temperature Extremes in the Vea Catchment, Ghana

This study examined the trends in annual rainfall and temperature extremes over the Vea catchment for the period 1985–2016, using quality-controlled stations and a high resolution (5 km) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The CHIRPS gridded precipitation data’s ability in reproducing the climatology of the catchment was evaluated. The extreme rainfall and temperature indices were computed using a RClimdex package by considering seventeen (17) climate change indices from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). Trend detection and quantification in the rainfall (frequency and intensity) and temperature extreme indices were analyzed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator. The results show a very high seasonal correlation coefficient (r = 0.99), Nash–Sutcliff efficiency (0.98) and percentage bias (4.4% and −8.1%) between the stations and the gridded data. An investigation of dry and wet years using Standardized Anomaly Index shows 45.5% frequency of drier than normal periods compared to 54.5% wetter than normal periods in the catchment with 1999 and 2003 been extremely wet years while the year 1990 and 2013 were extremely dry. The intensity and magnitude of extreme rainfall indices show a decreasing trend for more than 78% of the rainfall locations while positive trends were observed in the frequency of extreme rainfall indices (R10mm, R20mm, and CDD) with the exception of consecutive wet days (CWD) that shows a decreasing trend. A general warming trend over the catchment was observed through the increase in the annual number of warm days (TX90p), warm nights (TN90p) and warm spells (WSDI). The spatial distribution analysis shows a high frequency and intensity of extremes rainfall indices in the south of the catchment compared to the middle and northern of part of the catchment, while temperature extremes were uniformly distributed over the catchment.

Investigating observed northwest Australian rainfall trends in Coupled Model Intercomparison Project phase 5 detection and attribution experiments

Mean and extreme northwest Australian (NWA) summertime rainfall has increased significantly since 1950. While previous studies have explored a range of possible factors impacting NWA rainfall, the causes of this increase and possible future changes remain uncertain. This study explores the increasing NWA summertime rainfall trends in Coupled Model Intercomparison Project phase 5 (CMIP5) models. By using a suite of models that contributed realizations of the historical period with various forcings, we explore the impact to this region of greenhouse gases and aerosol emissions since 1950 on mean rainfall and three extreme rainfall indices. The observed NWA rainfall trend is better captured in models when all forcings are included compared to simulations with only greenhouse gas forcing or with only natural forcing, although the models have a large spread. We hypothesise that anthropogenic aerosols played a major role in the observed rainfall trends, and the associated increase in monsoonal flow, and hence historicalNat and historicalGHG simulations tend not to capture observed rainfall trend. Throughout the 21st century, CMIP5 models simulate a stronger increase in mean summer precipitation and extreme indices of NWA rainfall in representative concentration pathway (RCP) 8.5 simulations than in RCP2.6. The NWA region shows intensified extreme events with fewer heavy precipitation days, but the reliability of these projections in this region should be further tested with estimates of future anthropogenic aerosol changes.

Trends and projections of climate extremes in the Black Volta River Basin in West Africa

This study used the RClimDex software to examine trends in extreme air temperature and rainfall in the Black Volta River Basin (BVRB) for the present (1981–2010) and future 2051–2080 (late twenty-first century) horizons. The analysis of the future extreme events was conducted using data output of four ensemble models for two IPCC emission scenarios, Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bias correction method, the quantile-quantile (Q-Q) transformation technique, was applied to all the modelled temperature and rainfall data set prior to the index calculation. The results of analysis of the present-day climate indicate warming and wetting of the BVRB. Increasing trends were seen in the extreme warm indices while the extreme cold indices showed mostly decreasing trends. Majority of the trends observed in the indices were statistically significant (95% confidence level). The extremes in rainfall also showed increasing trends in amounts and intensity of rainfall events (majority of increasing trends were statistically insignificant). Projected temperatures for the late twenty-first century showed decreasing and increasing trends in the cold and warm indices respectively, suggesting warming during the period. Trend analysis of future rainfall projections mostly showed a mix of positive and negative trends offering no clear indication of the direction of change in majority of the extreme rainfall indices. An increase in extremely wet day events is however projected for the period. The results from this study could inform climate change adaptation strategies targeted at reducing vulnerability and building resilience to extreme weather events in the BVRB.

Extreme rainfall indices in Distrito Federal, Brazil: Trends and links with El Niño southern oscillation and Madden–Julian oscillation

This study investigates trends and the effects of the interannual and intraseasonal climate variability on the extreme weather of Brazil's capital city Distrito Federal (DF). This area is highly vulnerable to climate variability, having suffered from droughts and floods that affected the drinking water supply and agriculture. We perform trend analysis of 12 rainfall‐related indices from 13 ground observation stations and assess the influence of El Niño southern oscillation (ENSO) and Madden–Julian oscillation (MJO) on rainfall totals and extreme indices. The trend analysis confirms: (a) the increase of the dry spells length and (b) the anticipation of the onset of dry periods at regional level. That is also true when considering dry spells in the rainy season, a hazard known in the agriculture sector as “verânico.” On the other hand, extreme wet conditions became less severe in the last decades. Supressed monthly rainfall conditions, and some wet indices, are associated with La Niña episodes. The MJO's intraseasonal variability seems to play a substantial role in DF's climate. MJO phases 3, 7 and 8 are associated to enhanced rainfall conditions; whereas rainfall is supressed during phase 5. Moreover, dry spells during the rainy season, or “verânico,” often coincide with MJO phase 5. When combined with ENSO, the basic response of rainfall to MJO activity changes substantially showing statistically significant influence of El Niño on phases 2 and 8; while La Niña on phases 4, 6 and 7. The findings contribute to a better understanding of the ongoing changes in extreme climate as well as the influence of natural climate variability on local's climate, information that can be used in the management of water resources and land use of DF.

Trends in extreme rainfall over ecologically sensitive Western Ghats and coastal regions of Karnataka: an observational assessment

Rainfall is one of the pivotal climatic variables, which influence spatio-temporal patterns of water availability. In this study, we have attempted to understand the interannual long-term trend analysis of the daily rainfall events of ≥ 2.5 mm and rainfall events of extreme threshold, over the Western Ghats and coastal region of Karnataka. High spatial resolution (0.25° × 0.25°) daily gridded rainfall data set of Indian Meteorological Department was used for this study. Thirty-eight grid points in the study area was selected to analyze the daily precipitation for 113 years (1901–2013). Grid points were divided into two zones: low land (exposed to the sea and low elevated area/coastal region) and high land (interior from the sea and high elevated area/Western Ghats). The indices were selected from the list of climate change indices recommended by ETCCDI and are based on annual rainfall total (RR), yearly 1-day maximum rainfall, consecutive wet days (≥ 2.5 mm), Simple Daily Intensity Index (SDII), annual frequency of very heavy rainfall (≥ 100 mm), frequency of very heavy rainfall (≥ 65–100 mm), moderate rainfall (≥ 2.5–65 mm), frequency of medium rainfall (≥ 40–65 mm), and frequency of low rainfall (≥ 20–40 mm). Mann-Kendall test was applied to the nine rainfall indices, and Theil-Sen estimator perceived the nature and the magnitude of slope in rainfall indices. The results show contrasting trends in the extreme rainfall indices in low land and high land regions. The changes in daily rainfall events in the low land region primarily indicate statistically significant positive trends in the annual total rainfall, yearly 1-day maximum rainfall, SDII, frequency of very heavy rainfall, and heavy rainfall as well as medium rainfall events. Furthermore, the overall annual rainfall strongly correlated with all the rainfall indices in both regions, especially with indices that represent heavy rainfall events which is responsible for the total increase of rainfall.

Projections of West African summer monsoon rainfall extremes from two CORDEX models

Global warming has a profound impact on the vulnerable environment of West Africa; hence, robust climate projection, especially of rainfall extremes, is quite important. Based on two representative concentration pathway (RCP) scenarios, projected changes in extreme summer rainfall events over West Africa were investigated using data from the Coordinated Regional Climate Downscaling Experiment models. Eight (8) extreme rainfall indices (CDD, CWD, r10mm, r20mm, PRCPTOT, R95pTOT, rx5day, and sdii) defined by the Expert Team on Climate Change Detection and Indices were used in the study. The performance of the regional climate model (RCM) simulations was validated by comparing with GPCP and TRMM observation data sets. Results show that the RCMs reasonably reproduced the observed pattern of extreme rainfall over the region and further added significant value to the driven GCMs over some grids. Compared to the baseline period 1976–2005, future changes (2070–2099) in summer rainfall extremes under the RCP4.5 and RCP8.5 scenarios show statistically significant decreasing total rainfall (PRCPTOT), while consecutive dry days and extreme rainfall events (R95pTOT) are projected to increase significantly. There are obvious indications that simple rainfall intensity (sdii) will increase in the future. This does not amount to an increase in total rainfall but suggests a likelihood of greater intensity of rainfall events. Overall, our results project that West Africa may suffer more natural disasters such as droughts and floods in the future.

Impacts of climate change on the trends of extreme rainfall indices and values of maximum precipitation at Olimpiyat Station, Istanbul, Turkey

In this study, extreme rainfall indices of Olimpiyat Station were determined from reference period (1971–2000) and future period (2070–2099) daily rainfall data projected using the HadGEM2-ES and GFDL-ESM2M global circulation models (GCMs) and downscaled by the RegCM4.3.4 regional model under the Representative Concentration Pathway RCP4.5 and RCP8.5 scenarios. The Mann-Kendall (MK) trend statistics was used to detect trends in the indices of each group, and the nonparametric Wilcoxon signed ranks test was employed to identify the presence of differences among the values of the rainfall indices of the three groups. Moreover, the peaks-over-threshold (POT) method was used to undertake frequency analysis and estimate the maximum 24-h rainfall values of various return periods. The results of the M-K-based trend analyses showed that there are insignificant increasing trends in most of the extreme rainfall indices. However, based on the Wilcoxon signed ranks test, the values of the extreme rainfall indices determined for the future period, particularly under RCP8.5, were found to be significantly different from the corresponding values determined for the reference period. The maximum 24-h rainfall amounts of the 50-year return period of the future period under RCP4.5 of the HadGEM2-ES and GFDL-ESM2M GCMs were found to be larger (by 5.85%) than the corresponding value of the reference period by 5.85 and 21.43%, respectively. The results also showed that the maximum 24-h rainfall amount under RCP8.5 of both the HadGEM2-ES and GFDL-ESM2M GCMs was found to be greater (34.33 and 12.18%, respectively, for the 50-year return period) than the reference period values. This may increase the risk of flooding in Ayamama Watershed, and thus, studying the effects of the predicted amount of rainfall under the RCP8.5 scenario on the flooding risk of Ayamama Watershed and devising management strategies are recommended to enhance the design and implementation of adaptation measures.

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka

Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.