Trends In Extreme Rainfall Indices Research Articles

Large-scale atmospheric teleconnections and spatiotemporal variability of extreme rainfall indices across India

Identifying trends in hydrometeorological time series during extreme weather events and their interactions with large-scale atmospheric teleconnections is crucial for climate change research. This study evaluates 14 precipitation-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across seven climatic zones of India using gridded daily rainfall data from the India Meteorological Department (IMD) for 120 years (1902–2021) utilised. Trend analysis was carried out using the Mann-Kendall (MK) test, Theil-slope Sen's estimator, Innovative Trend Analysis (ITA), and other statistical tools. Change point detection is established using the Pettitte test and Cumulative Sum algorithm. The relationships between large-scale atmospheric teleconnections and ETCCDI indices are also found, and Multiple Linear Regression (MLR) models are developed between them.The results show significant increasing trends in extreme rainfall indices in India's Ladakh region, located in the arid desert-cold climatic zone. The annual, Southwest Monsoon (SW-Monsoon), Northeast Monsoon (NE-Monsoon), and summer rainfall trends were positive, while winter rainfall had a negative trend across most climatic zones. Significant associations between large-scale atmospheric teleconnections, including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Global Temperature Anomaly (GTA), Southern Oscillation Index (SOI), SST of Niño 3.4 region, Oceanic Niño Index (ONI), and Dipole Mode Index (IOD) and ETCCDI indices were established across multiple climatic zones. Using MLR analysis, this study attempts to establish, for the first time, the relationship between teleconnections and ETCCDI indices across India. Extreme rainfall indices are influenced by climate change during the SW-Monsoon across most of the climatic zones of India. During the previous El Niño event (2014–2016), average annual rainfall decreased by 19.5%, SW-Monsoon rainfall decreased by 25.2%, and NE-Monsoon rainfall decreased by 64.1% in India. The findings may provide valuable insights into mitigation strategies to sustain the adverse effects of extreme weather conditions and enhance climate resilience.

Reliability of GPM IMERG Satellite Precipitation Data for Modelling Flash Flood Events in Selected Watersheds in the UAE

Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG V. 06) since 2001 to highlight recent trends of extreme rainfall indices for three selected watersheds in the UAE. Additionally, to validate the trends, the present study incorporated CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) into the analysis. Furthermore, for the first time, this study assessed the performance of the three products of IMERG in modelling flash flood events in the selected watersheds of UAE. A physical-based, fully distributed model was used to simulate the heaviest storm event. Also, a sensitivity analysis of the model’s output to variations in the input parameters was conducted using the one-factor-at-a-time method. The result of the trend analysis indicated that IMERG and CHIRPS show similar trends in both datasets, indicating agreement and reliability in their observations. However, there are a few instances where IMERG and CHIRPS show slight discrepancies in the nature of the trend. In general, the trend analysis results indicated an increasing trend of total precipitation (mm) and consecutive wet days, which suggests a rise in the risk of flash floods. The simulation of the flash flood event showed that the IMERG final product outperformed the other two products, closely matching the model output of the gauge rainfall data with mean absolute error (MAE) of 1.5, 2.37, and 0.5 for Wadi Ham, Wadi Taween, and Wadi Maidaq, respectively. The model’s performance was positively correlated with the size of the watershed. The sensitivity analysis results demonstrated that the model’s output was most sensitive to infiltration parameters. The study’s outcomes provide a good opportunity to improve near-real-time impact evaluation of flash flood events in the watersheds of the UAE.

Changing Trends in Temperatures and Rainfalls in the Western Pacific: Guam

Pacific islands have always been at the front of the great challenge of climate change. In this study, Mann–Kendall’s tau-based slope estimator was implemented to detect statistical trends in daily maximum and minimum temperatures of 2 stations and daily rainfalls at 14 stations over Guam for the period of 1953–2021, respectively, with 17 climate change detection indices. Mann–Kendall tests were implemented to the detection indices with respect to different time frames (i.e., annual, two-seasonal, and four-seasonal). The p-values from Mann–Kendall tests were used to determine the strength of trends, and Sen’s slopes were applied for the magnitudes of trends. The temperature trend analysis results indicate that Guam’s climate is getting warmer year by year. The increasing magnitudes of a seasonal maximum of daily maximum temperatures during the dry season are 0.036 °C/year for the dry season and 0.025 °C/year for the wet season at Anderson Airforce Base, while 0.031 °C/year and 0.023 °C/year for the dry and wet seasons at Guam International Airport. Trend analyses for temperatures have indicated that temperature during April through June has been increasing rapidly compared to other seasons. Strong trends in seasonal total rainfall amounts and the number of wet days were observed from July through December. The increasing trends in extreme rainfall indices during January–March and July–September periods would aggravate water quality due to the more sediments since important ecological reserve areas and coral reef areas are linked to watersheds in southern Guam.

Characteristics of extreme rainfall in different gridded datasets over India during 1983–2015

Understanding the spatial and temporal occurrences of extreme rainfall events and their changes is crucial for reducing the risk associated with extreme events, especially under anthropogenic warming. This study presents a comparative analysis of twelve gridded rainfall datasets in representing the spatial and temporal variation of extreme rainfall events and trends in the recent past (1983–2015) in India. The selected datasets fall into four categories: gauge-based (APHRODITE, CPC, GPCC, REGEN), satellite-derived (CHIRPS, PERSIANN-CDR), reanalysis (ERA5, MERRA-2, PGF, and JRA-55), and product merged from these three types (WFDEI and MSWEP). For comparison, a gauge-based, high resolution (0.25° × 0.25°) daily gridded rainfall dataset, prepared by the India Meteorological Department (IMD) is used as a reference dataset. Eleven extreme climate indices, defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) and the IMD are used to represent the magnitude, frequency, and duration of extreme rainfall events in India. The relative performance of these datasets in capturing extreme rainfall events is evaluated by computing model evaluation matrices i.e. correlation coefficient (CC) and Percentage Bias (PBIAS). Trends in extreme rainfall indices and their magnitudes are calculated using Mann-Kendell and Sen's slope methods respectively, to compare with the IMD dataset. Our study finds large uncertainties in representing extreme rainfall events where in most cases the datasets underestimate higher extreme events compared to the IMD data. GPCC and MSWEP better capture the magnitude, duration, and frequency of extreme rainfall events in India and are comparable to the pattern of the IMD dataset. CHIRPS and ERA5 perform better than other satellite and reanalysis rainfall datasets. All these selected datasets consistently underestimate extreme rainfall events over the northern Himalayan region. The gridded datasets are unable to capture the spatial pattern of observed trends in extreme rainfall indices in India when compared to the IMD dataset.

Interannual Variability and Trends of Extreme Rainfall Indices over Benin

Observed rainfall data (1961–2016) were used to analyze variability, trends and changes of extreme precipitation indices over Benin. Nine indices out of the ones developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) were used. The results indicate a mix of downward and upward trends for maximum 1-day precipitation (RX1day) and maximum 5-days precipitation (RX5day). Decrease trends are observed for annual total precipitation of wet days (P), while significant increases are found for the simple daily intensity index (SDII). The number of wet days (RR1) and maximum consecutive dry days (CDD) show a mix of increase/decrease trends. However, the number of heavy (R10) and very heavy (R20) wet days and maximum consecutive wet days (CWD) show decreased trends. All wet indices increased over 1991–2010 in relation to 1971–1990. The increase in all wet indices over Benin could explain the intensification of hydrology, and the increase in the frequency and the intensity of floods. It caused damages such as soil erosion, crop destruction, livestock destruction, displacement of populations, proliferation of waterborne diseases and loss of human life. Some adaptive strategies are suggested to mitigate the impacts of changes in extreme rainfall.

Trends of daily extreme and non‐extreme rainfall indices and intercomparison with different gridded data sets over Mexico and the southern United States

AbstractTrends of 15 extreme and non‐extreme daily rainfall indices for Mexico and the southern United States are analyzed for the extended boreal winter (November–March) and summer (June–October) during 1981–2010. The analysis includes gridded observations (CHIRPS and Livneh), reanalyses (ERA5, ERA‐Interim, MERRA2, and CFSR), the regional climate model RegCM4.7 (forced by ERA‐Interim), and four general circulation models (GCMs) from the CMIP5 (HadGEM2‐ES, CNRM‐CM5, MPI‐ESM‐MR, and GFDL‐ESM2M). The domain is divided into 13 regions with similar seasonal rainfall patterns according to the k‐means algorithm applied to CHIRPS monthly precipitation, as a reference. Gridded observations and reanalyses (O&R) show similar regional annual cycles and metrics of precipitation. The two observational data sets show the largest differences in the magnitude of some indices in the wettest region (southeastern Mexico) and in the monsoon. During summer, O&R show positive significant trends in extreme rainfall indices in regions commonly affected by tropical cyclones (southern Baja California Peninsula, northeastern Mexico, and the southeastern United States), while negative significant trends of seasonal accumulation of rainfall are seen in the Mediterranean Californias and the Sonoran Desert region. There is an agreement among most data sets that the frequency and intensity of extreme winter precipitation indices have declined in northwest Mexico and the southwestern United States, but trends are not statistically significant, while southeastern Mexico shows significant increases in 5‐days accumulation (rx5d). ERA5 followed by MERRA2 show the best performance. RegCM4.7 reproduces the annual cycle of precipitation in most of the regions and captures the main trend signals especially in winter, but it has difficulties in reproducing the summer interannual variability of few indices. The GCMs tend to overestimate (underestimate) the annual cycle of precipitation and the median values of extreme indices in the driest (wettest) regions. They are not able to capture the regional trends of the indices.

Analyses of rainfall extremes in East Africa based on observations from rain gauges and climate change simulations by CORDEX RCMs

This study derived twelve Extreme Rainfall Indices (ERIs) such as the Maximum Dry Spell (MDS) and Maximum Wet Spell (MWS) from daily rainfall observed over the period 1961–1990 at nine locations across East Africa. Capacity of six CO-ordinated Regional Climate Downscaling EXperiment (CORDEX) Africa Regional Climate Models (RCMs) driven by twenty six Climate Model Intercomparison Project phase 5 (CMIP5) General Circulation Models (GCMs) to reproduce the observed ERIs with respect to long-term mean and trends was evaluated. Four RCMs and their five driving GCMs were further analyzed with respect to ERIs. Ensemble means of the RCMs' biases in simulating trends in several ERIs were of magnitudes above 50%. On average, biases in reproducing long-term mean were smaller than those for trends in ERIs. The difference between the performances of RCMs and GCMs depended on the selected RCM–GCM pair. The ensemble means of the RCMs reproduced observed ERIs better than the individual RCMs corroborating that the use of multi-model ensembles can boost credibility of climate change simulations and projections. The RCMs performed better than their driving GCMs in reproducing MDS. The biases of both the RCMs and GCMs were smaller in reproducing the MWS than MDS. Nonetheless, in reproducing observed MWS, the ensemble mean of RCMs' biases was slightly larger than that of the driving GCMs indicating possible adding up of the uncertainties from the GCMs and RCMs. Suggested RCMs' improvements regarding aerosol impacts on rainfall include adding missing constituents (like nitrate), and refining the crudely represented components. RCMs also require high resolution description (in both space and time) of land use types, land surface covers and characteristics as well as landscape heterogeneity. The GCMs to be used as the initial and lateral boundary conditions for the RCMs require improvement in their representation of key dynamical and thermodynamical feedbacks in the Tropical Indian Ocean.

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.

Assessing climatic trends of extreme rainfall indices over northeast Bangladesh

This study analyzes the trends of extreme rainfall indices over northeast Bangladesh for the period of 1984 to 2016 for the pre-monsoon and monsoon seasons. The research was framed as part of a project co-producing knowledge of climate variability and impacts through collaboration between scientific and local communities in northeast Bangladesh, which found pre-monsoon and monsoon rainfall to be most important. With access to a greater number of rainfall stations than previous work in northeast Bangladesh, we investigated trends in extreme rainfall events using the Mann–Kendall trend test and Sen’s slope estimator. To appraise the quality of the data, we used the Standard Normal Homogeneity and the Pettitt tests to check its homogeneity. Among the seven stations, only Sunamganj was found inhomogeneous, and was not considered for trend analysis. All indices of rainfall extremes showed a decreasing trend in both seasons, with the most significant decrease during the monsoon. Importantly, we saw a decreasing trend in the seasonal total rainfall and consecutive wet days, whereas there was an increasing trend in consecutive dry days. Moreover, we saw a decreasing trend in 1-day maximum rainfall, 5-day maximum rainfall, the intensity of the daily rainfall over 25 mm during the pre-monsoon and 50 mm during monsoon, which together may indicate a future decrease in the magnitude and intensity of flash floods and monsoon floods. If this trend continues, the northeast Bangladesh may suffer from water stress, which could affect the lives and livelihoods of communities living there.

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.

Extreme rainfall indices and its impact on rice productivity—A case study over sub-humid climatic environment

Abstract Frequent occurrences of extreme rainfall events create severe threat to agricultural production. This is one of the most significant consequences of global warming due to increase in greenhouse gases. A precise understanding of frequency and magnitude of these events and its influence on agricultural productivity can extensively help in policy decisions and planning agricultural as well as water management operations. This study has analyzed observed trends in extreme rainfall indices during monsoon months as well as seasonally at four stations located over different agro-ecological zones of Bihar, namely Samastipur (zone-I), Madhepura (zone-II), Sabour (zone-IIIA) and Patna (zone-IIIB). Mann–Kendall nonparametric test was employed for detection of trends and the slopes of the trend lines were determined using the method of least square linear fitting. Since rice is the important crop in this part of the region, the vulnerability of extreme rainfall indices on productivity also analysed using simple correlations. All the sites show an increasing trend of number of days with rainfall 10.0 cm or more (very heavy precipitation event) during monsoon season. Sabour shows a significant increasing trend of 0.4 and 0.6 day/decade, respectively during monsoon and annually. During September, occurrence of heavy precipitation events over Madhepura recorded a significant positive trend of 0.4 day/decade. Highly significant magnitudes of increasing trends were noticed for Madhepura (46.6 mm/decade) and Sabour (27.5 mm/decade) for occurrence of highest five-day total precipitation during monsoon season. The results show statistically significant positive trends of number of days with rainfall > R2.5 cm for all the study sites during August. All the sites, the magnitude of highest 1-day and 5-days maximum rainfall is showing increasing trend. Increasing trends of fraction of rainfall due to extremely wet days is also showing increasing trend in all the sites. The rice productivity showed 10th degree polynomial technological trend in all the sites and steady increase in all the sites except Samastipur. The correlation between extreme rainfall indices during monsoon season and productivity anomaly index indicate that almost all the extreme rainfall indices contribute positively to rice productivity except P95pTOT and R99pTOT over Sabour and R7.5 cm over Patna. If we assume the observed increasing trends in different extreme rainfall indices will continue, as estimated by the global circulation models, the chances of occurrence of intense rainfall events in near future will also increase proportionally and efforts should be made to prepare detailed site specific—at least at district level—disastrous management plan especially to reduce impact of extreme rainfall event on agricultural production system under changing climatic scenarios.

Trends in extreme daily rainfall across the South Pacific and relationship to the South Pacific Convergence Zone

AbstractDaily rainfall records from 22 high‐quality stations located in the South Pacific were analysed, over the common period 1961–2000, in order to assess whether extreme rainfall events have altered in their frequency or magnitude. A comprehensive spatial coverage across the South Pacific was provided, analysing a range of indices of extreme precipitation, which reflect both high rainfall events and drought. Clear spatial patterns emerged in the trends of extreme rainfall indices, with a major discontinuity across the diagonal section of the South Pacific Convergence Zone (SPCZ).Stations located between 180 and 155°W exhibit a greater number of significant abrupt changes in extreme climate than elsewhere in the South Pacific, and the majority of climatic jumps occur in the 1970s or 1980s (coincident with a displacement northeastward of the diagonal part of the SPCZ and a large local increase in mean annual temperature). Notably, all significant abrupt changes in an extreme rainfall intensity index occurred in the late 1970s or early 1980s, and in every case the index showed an increase in extremity following the change point, regardless of station location. For the stations located south of the SPCZ, this may also be linked to the observed warming since the 1970s. Significant abrupt changes in mean precipitation were also identified around the mid 1940s, for two longer, century‐scale records, which again correspond to a major displacement of the diagonal section of the SPCZ.An indicator of the diagonal SPCZ position is significantly temporally correlated with an extreme rainfall intensity index, at two locations either side of the diagonal section of the SPCZ, at decadal time scales or longer. This suggests that the displacement of the diagonal portion of the SPCZ on decadal time scales influences not only mean precipitation, but also daily rainfall extremes. Copyright © 2003 Royal Meteorological Society

Trends in extreme rainfall indices for an updated high quality data set for Australia, 1910-1998

Daily rainfall was analysed at 91 high quality stations over eastern and southwestern Australia to determine if extreme rainfall had changed between 1910 and 1998. Three indices of extreme rainfall were examined: the number of events above an extreme threshold (extreme frequency); the average intensity of rainfall from extreme events (extreme intensity); and the proportion of total rainfall from extreme events (extreme percent). Several problems are discussed associated with designing such indices under a climate with significant trends in the number of raindays. Three different methods are used for calculating the extreme intensity and extreme percent indices to account for such trends in raindays. A separate analysis was carried out for four separate regions with significant results including a decrease in the extreme frequency and extreme intensity in southwest Western Australia and an increase in the extreme percent in eastern Australia. Trends in the extreme intensity and extreme percent are largely dependent on the method used to calculate the index. Total rainfall is strongly correlated with the extreme frequency and extreme intensity indices, suggesting that extreme events are more frequent and intense during years with high rainfall. Due to an increase in the number of raindays during such years, the proportional contribution from extreme events to the total rainfall depends on the method used to calculate this index. Copyright © 2000 Royal Meteorological Society