Extreme Rainfall Indices Research Articles

Using human observations with instrument-based metrics to understand changing rainfall patterns.

Shifting precipitation regimes are a well-documented and pervasive consequence of climate change. Subsistence-oriented communities worldwide can identify changes in rainfall patterns that most affect their lives. Here we scrutinize the importance of human-based rainfall observations (collated through a literature review spanning from 1994 to 2013) as climate metrics and the relevance of instrument-based precipitation indices to subsistence activities. For comparable time periods (1955-2005), changes observed by humans match well with instrumental records at same locations for well-established indices of rainfall (72% match), drought (76%), and extreme rainfall (81%), demonstrating that we can bring together human and instrumental observations. Many communities (1114 out of 1827) further identify increased variability and unpredictability in the start, end, and continuity of rainy seasons, all of which disrupt the cropping calendar, particularly in the Tropics. These changes in rainfall patterns and predictability are not fully captured by existing indices, and their social-ecological impacts are still understudied.

Trends in extreme rainfall and their relationship to flooding episodes in Vhembe district, South Africa

Extreme weather events, such as heavy rainfall, are being increased by climate change in various regions, and such events often cause floods. This study examined the trends and variability of extreme rainfall indices using daily rainfall data (1981–2023) from three study sites at different socio-economic development spectra in Vhembe District, Limpopo Province, South Africa. The analyses focus on indices such as the annual total rainfall from wet days (PRCPTOT), the maximum number of consecutive dry days (CDD) and wet days (CWD), annual maximum 1-day and 5-day rainfall (RX1 day and RX5 day), the Simple Daily Intensity Index (SDII), the number of days exceeding varying amounts of precipitation (R10, R20, R40) and the annual number of wet days with rainfall greater than the 95th and 99th percentile (R95p and R99p) of the 1981–2023 daily rainfall. We discuss the observed trends in extreme rainfall indices in light of the actual flood occurrences to establish linkages. Several statistically significant and marginal changes in extreme rainfall trends were identified and provided key insights into reported flooding events in the district—flooding episodes were mainly attributed to the significant increases in total precipitation (PRCPTOT) and rainfall exceeding the 99th percentile of daily rainfall (R99p). Other significant contributors were declining CDD and increasing RX1day at Duthuni, increasing R40 at Musina as well as increasing R1 and declining CDD at Sane. However, the low altitude, urbanization, poor waste management and inadequate drainage systems were among the key non-climatic drivers of flood risk across the study sites, but these warrant further investigation. The complex interplay between climatic and non-climatic drivers of flood risk underscores the importance of localized climate studies and the need for adaptive strategies to minimize loss and damage. Overall, this research provides valuable insights into localized extreme rainfall trends, which are essential for developing site-specific flood mitigation and adaptation strategies. However, such initiatives require placing vulnerable communities at the centre in order to develop solutions that are locally led and relevant.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989–2018) and projected future periods (2041–2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Evaluation of GPM IMERG Early, Late, and Final Run in Representing Extreme Rainfall Indices in Southwestern Iran

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia.

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana sub-basin (LTSB) of Ethiopia's upper Blue Nile basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices are evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). The researcher used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% consecutive wet days (CWD), 13.33% (R95p and R99p), and 6.66% highest rainfall amount in a 1-day period (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of number of heavy precipitation days (R10mm), annual total wet day rainfall (PRCPTOT), and consecutive dry days (CDD), with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations location. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

Effect of climate change to characteristic of extreme rainfall over Batanghari watershed

Climate change will cause extreme rainfall, both wet and dry. Extreme rainfall will cause catastrophic floods and droughts. Therefore, it is important to analyse extreme rainfall and flood events in the future. Disasters and environmental issues in the Batanghari watershed include flooding, drought, forest and land fires, sedimentation, and water quality degradation. Therefore, the Batanghari watershed is selected as a study area in this research. Research on climate change in the Batanghari watershed is crucial to understanding the trend of extreme conditions now and in the future. Characteristics of extreme rainfall will be analysed using the climate index that was developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Results show that climate change in the Batanghari watershed is indicated by increasing 5 of 6 extreme rainfall indices: R95p, R99p, Rx1day, Rx5day, and SDII. An increase in extreme rainfall is correlated with an increase in flood events in the Batanghari watershed in similar periods. The extreme rainfall index that most influences flooding in the Batanghari watershed is PRCPTOT and R99p. Flooding is expected to occur more frequently in the future than it does now. The Batanghari Hilir Sub-watershed is more vulnerable to extreme flooding in the future than other sub-watershed.

Spatiotemporal assessment of rainfall variability and its characteristics over Rapti River Basin, India

Abstract The determination of rainfall characteristics of a basin is obligatory for mitigation planning and adaptation. This study presents the spatiotemporal analysis of gridded precipitation to understand the variability and seasonality over the Rapti Basin. This basin is affected by frequent flooding; hence it becomes necessary to understand the variability of rainfall. To carry out this analysis, daily precipitation data is extracted for 22 grids for 30 years (1990–2020) of high resolution. Extreme rainfall indices are computed as per the threshold decided by the Indian Meteorological Department (IMD). To determine the trend of the rainfall, Mann Kendall (MK) test, Modified MK test and Innovative Trend Analysis was used and magnitude of trend was determined using slope. Trends of daily rainfall, annual, and seasonal rainfall were determined. Annual rainfall has shown significant decreasing trend over the basin. Monsoon season has a noticeable tendency, but other seasons have only seen small shifts. From all the rainfall indices, PRCPTOT and SDII showed a decreasing trend with Sen Slope of −2.56 and 3.00 respectively. While examining the variance in precipitation across the basin, this study will be helpful.

Spatiotemporal Variability of Extreme Rainfall Indices over Nicaragua between 2001 and 2016 and Their Relationship with Teleconnection Patterns

Abstract We investigated nine indices of spatiotemporal extreme precipitation events over Nicaragua during 2001–16, from GPCC, Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and IMERG, and their correlation with teleconnection patterns. The main objectives were to evaluate the variability of extreme precipitation events, to know the performance of IMERG and CHIRPS in the characterization of these extreme events, using GPCC and four rain gauges as references, and finally to determine the teleconnection patterns that have the highest correlation with these indices. The spatial coverage of the area with the highest number of consecutive days with daily precipitation less than 1 mm corresponds to the Pacific region, with annual mean values of up to 120 continuous days. Some extreme precipitation event indices (RR, RX1day, and RX5day) show a decreasing trend, suggesting that the study area has been experiencing a reduction of extreme precipitation indices in terms of intensities and duration throughout the study period. In addition, it was observed that CHIRPS shows a better fit when dealing with precipitation events that do not exceed certain thresholds and IMERG improves when describing intense precipitation event patterns. We found that the tropical Pacific SST EOF (EOFPAC), Niño-3.4, Pacific warm pool region (PACWARM), and SOI have a greater influence on extreme precipitation events, these results suggest that they are being controlled by ENSO episodes, providing a better understanding of the climate configuration, as a prediction and forecasting potential, useful for agriculture, land use, and risk management.

Analysis of The Occurrence of Extreme Events in The City of Rio de Janeiro/RJ

Objective: To investigate the trends of extreme rainfall and temperature indices in the city of Rio de Janeiro, as measures to assess climate change. Theoretical benchmark: Extreme climatic events have been observed more frequently than has become a concern all over the world, so the investigation of trends in extreme precipitation and temperature indices makes it necessary to enter how this dynamic in climatic conditions affects the city of Rio de Janeiro. Method: The methodology for assessing climate change will be used from the indicators defined by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) panel, but of the 27 indicators proposed by the panel, 12 indicators were used that were more adapted to the reality of the city of Rio de Janeiro, for the simulation of climate data, data from the stations of the National Institute of Meteorology (INMET) and for the simulation of the indicators in the RClimdex software. Daily data of 53 years of 19 (six) stations obtained from the historical series of the period between 1970 and 2023 were analyzed, for the precipitation indices: PRCPTOT, R10, R20, R95p and R99p, and for the temperature indices: TX90p, TN90p, TMAXmean, TMINmean and DTR. Results and conclusion: The results indicate a trend of decreasing rainfall of low rainfall and a significant increase of extreme rainfall, the data also show a decrease of thermal amplitude, with the increase of minimum temperatures and a moderate decrease of maximum temperatures. Implications of the research: The study in question presents internationally validated indicators that demonstrate how extreme events are intensifying in the city of Rio de Janeiro. Originality/value: The data demonstrated in this research are of extreme importance as a source of research for future works that address this theme, besides serving as a subsidy to decision makers to propose adaptive measures to the territory, in the face of the intensification of extreme events in the city of Rio de Janeiro.

Comprehensive analysis of spatiotemporal variability of rainfall-based extremes and their implications on agriculture in the Upper Ganga Command Area.

The agricultural sector in India primarily relieson monsoon rains, making it necessary for agricultural water managers to study variations in water availability by analyzing rainfall patterns. This information is critical for deciding cropping patterns and irrigation approaches, managing water resources, and improving crop water productivity. The long-term spatiotemporal variability over the command area, India Meteorological Department (IMD) based selection of thresholds for extremes indices, and thorough analysis of the consequences of extreme on agriculture make the study unique. These aspects collectively enhance the perception of the climate-agriculture relationship, which is essential for promoting agriculture resilience under changing rainfall patterns. This type of research is very limited in the literature. Therefore, the present study focuses on comprehensively analyzing the spatiotemporal variability of rainfall and extreme precipitation indices and their implications on agriculture. The study is carried out in the UpperGanga Command Area (UGCA), India's oldest irrigation canal system, by using long-term (1901-2021) high-resolution (0.25 × 0.25°) daily rainfall gridded data obtained from IMD, Pune. Eight different extreme rainfall indices are used to assess spatiotemporal variations. The Mann-Whitney-Pettitt test identified 1970 as a significant change point. Rainfall trends and extreme indices for pre- and post-1970 periods were examined using the non-parametric modified Mann-Kendall test. The results show significant increases in rainfall trends for the annual, monsoon, and kharif seasons from 1901 to 1970, but significant decreases were observed during 1971-2021. This difference resulted in a noticeable decrease in average precipitation in the latter period, approximately 62mm less than in 1901-1970. Similarly, extreme precipitation indices, including the number of rainy days (NxRainy), consecutive dry days (CWD), simple daily intensity index (SDII), and total precipitation (PRCPTOT), exhibited increasing trends in 1901-1970, but they showed significant decreasing trends in 1971-2021. In addition, there is a strong positive correlation between these indices and total precipitation. The study also found that geographical factors influence these trends, with all indices, except SDII, strongly correlated with latitude and elevation, but not with longitude. The implications of these negative rainfall and extreme rainfall trends were further analyzed, and the results indicate a significant impact on the cropping patterns in the study area. The present research findings will be beneficial for regional water resource managers and policymakers in better understanding the existing trends in rainfall distribution over the UGCA.

WITHDRAWN: Trend analysis of the extreme rainfall indices from Lake Tana Sub-Basin of the Upper Blue Nile, Ethiopia

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana Sub-Basin (LTSB) of Ethiopia's Upper Blue Nile Basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices is evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). We used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% (CWD), 13.33% (R95p and R99p), and 6.66% (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of R10mm, PRCPTOT, and CDD, with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations in spatial distribution. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

Rainfall estimation in the West African Sahel: comparison and cross-validation of top-down vs. bottom-up precipitation products in Burkina Faso

This study compares the performance of satellite precipitation products (SPPs) and soil moisture-based rainfall products (SM2RPPs) in capturing rainfall patterns in Burkina Faso, West Africa. The findings indicate that SPPs outperform SM2RPPs at daily to annual timescales. The decomposition of total bias reveals that hit bias are dominant, but are larger in magnitude in SM2RPPs. Systematic errors are the major component of hit biases, suggesting they could be further treated through bias-correction. Wavelet analysis indicates that SPPs and SM2RPPs well capture seasonal and annual rainfall variability. All products struggle to accurately represent the most extreme rainfall indices, with SPPs performing better than SM2RPPs. For meteorological droughts, RFE2 and CHIRPS show the strongest correlations with observed SPI values across 1- to 12-month timescales. Although SM2RPPs perform less than SPPs in Burkina Faso, they appear mature for hydrometeorological applications and could further benefit from bias-correction to attain similar or better performance than SPPs.

Analysis of Extreme Rainfall in the Mt. Merapi Area

The slopes of Mount Merapi (Mt. Merapi) are an area prone to hydrological disasters due to elevation and orography. Hydrological disasters that have the potential to occur include floods, erosion, landslides, and drought which are closely related to extreme rainfall. Spatial and temporal variability of rainfall in mountainous areas requires rainfall data that can represent rainfall events. Therefore, this research aims to obtain the reliability of satellite rainfall data in the extreme rainfall indices. The CHIRPS, GPM-IMERG FINAL (IMERG-F) and GPM-IMERG LATE (IMERG-L) will be used in the reliability analysis of satellite-based rainfall compared to observed rainfall station. To validate satellite rain data, statistical criteria are utilized with parameters such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Relative Bias (RB). Satellite-based rainfall estimates have a weak to moderate correlation (0.19 – 0.55), the RMSE value is relatively good (12.18 – 31.35 mm) and the observed bias tends to underestimate the estimated values. The capabilities of the IMERG-F, IMERG-L and CHIRPS satellites as alternative rainfall data in the Mt. Merapi area are quite good where IMERG-L has the best performance in capturing rainfall above 50 mm (R50mm), Consecutive Dry Days (CDD) indices, max 1–day and 5-day precipitation (Rx1day and Rx5day). The potential for extreme rainfall that is most prone to trigger lava floods occurs in the western region of Mt. Merapi at Ngandong Station (Sta. Ngandong). In this region, there is a high occurrence of extreme rainfall events. For instance, there were 501 instances of R50mm with an intensity of 77 mm day-1, Total Precipitation (PRCPTOT) reaches 3385 mm, Rx5day reaches 393 mm, and Consecutive Wet Days (CWD) lasts for 30 days. The results of this analysis can assist in climate understanding and modeling of extreme rainfall relevant to the region and support water resource management and disaster risk mitigation.

The Atmospheric Dynamics Related to Extreme Rainfall and Flood Events during September-October-November in South Sulawesi

This study was conducted to analyse the occurrence of extreme rainfall and the dynamics of the atmosphere prior to the occurrence of extreme rainfall and flood events in South Sulawesi during September-October-November (South Sulawesi’s dry season). The data used is daily data for the period 2001-2020. Using 50 mm/day and the 90th percentile rainfall threshold of 119 rain stations distributed over 24 regencies, extreme rainfall events in each region were identified. Furthermore, after screening for extreme rainfall events followed by flood events, a composite analysis was carried out to obtain patterns of atmospheric conditions before the extreme rainfall events. The results of the study confirm that spatially, the highest extreme rainfall indices values dominate in the western and northern regions of South Sulawesi, both frequency and intensity indicators. Flood events in South Sulawesi during September-October-November 2001-2020 were recorded as 23 days, of which 19 days were the flood events after extreme rainfall events. The dynamics of the atmosphere before the extreme rainfall event followed by the flood event showed anomalies in precipitable water, 850 mb winds, and 200 mb winds. An increase in the amount of precipitable water and a wind speed of 850 mb, as well as a decrease in wind speed of 250 mb compared to normal in the South Sulawesi region and its surroundings, has resulted in an increase in the formation of rain clouds that have the potential to increase the chance of extreme rainfall.

Analysis of past and projected changes in extreme precipitation indices in some watersheds in Côte d'Ivoire

Abstract The purpose of this study is to analyse trends in annual rainfall extremes over five watersheds within Côte d'Ivoire using observed (1976–2017) and projected (2020–2050) rainfall data from the fourth version of the Rossby Centre regional atmospheric model, RCA4, for the representative concentration pathways RCP 4.5 and RCP 8.5. Four rainfall extreme indices, namely, the consecutive dry days (CDD), maximum annual rainfall (Pmaxan), very wet day (R95p), and maximum 5-day rainfall (Rx5days), were considered for trend analysis by using the non-parametric modified Mann–Kendall test and the distribution mapping bias-correction technique to adjust the simulated regional climate model of the simulated daily precipitation. As a result, it is found that during the period 1976–2017, there was a significant downward trend in the drought-related index (CDD) at the Bagoue, Baya, Agneby, and Lobo watersheds. The Baya and N'zo watersheds also experienced a significant downward trend under the RCP 4.5 and RCP 8.5 scenarios. The flood-related indices (Pmaxan, R95p, and Rx5days) show a clear downward trend in the recorded data for almost all the considered watersheds and generally a significant upward trend for both cases. These findings indicate that the watersheds are vulnerable to climate-induced disasters.

Extreme rainfall indices and their consequences on the local farming crop calendar: An agro-climatic zone based study

<p>The goal of this study is to investigate the extreme rainfall indices and their consequences on the local farming crop calendar among the agro-climatic zones (ACZs) of the Abiya watershed. Climate Hazards Group Infrared Precipitation (CHIRPS) provided long-term (1981–2019) rainfall data for 50 sample grid points with a spatial resolution of 5 × 5 km. Different crops are affected differently by the same extreme rainfall event depending on when it occurred and how extreme it was; this means crop calendars for a specific time may be properly governed by extreme climatic conditions. There hasn’t been a sufficient published study on extreme rainfall indices and their consequences on crop calendars by considering various ACZ in Ethiopia. The study defined local crop calendar timing and the consequences of the extreme rainfall indices through focus group discussions. INSTAT+ software was applied to calculate eight extreme rainfall indices. The indices were evaluated using Mann-Kendall’s (MK) and Sen’s slope techniques to identify trends and determine variations in the magnitude, respectively. Increase and decrease indications for various crop calendars were found in each ACZ. Further, going-up signals were seen in the highlands, midlands, and all ACZs for land preparation time (LPT), sowing and management time (SaMT), and harvesting and threshing time (HaTP), respectively. While HaTT was found to be uniform in all ACZs, some of the declining trends in the indices were detected for LPT and SaMT in the cold-highland and highland zones. The perceived trend in indices across the whole ACZ will have direct and unintended consequences for watershed crop production. The findings imply that to reduce the unfavourable consequences of these extreme rainfall occurrences in the agricultural sector, it is necessary to develop suitable crop varieties and drought-tolerant crops, as well as an effective early warning system.</p>

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.

Spatiotemporal Analysis of Extreme Rainfall and Meteorological Drought Events over the Angat Watershed, Philippines

Understanding the spatiotemporal distribution of extreme rainfall and meteorological drought on a watershed scale could be beneficial for local management of any water resources system that supports dam operation and river conservation. This study considered the watershed of Angat as a case, given its economic importance in the Philippines. A series of homogeneity tests were initially conducted on each rainfall dataset from monitoring stations in and near the watershed, followed by trend analysis to determine the rate and direction of change in the annual and seasonal rainfall extreme indices in terms of intensity, duration, and frequency. Three indices, using the rainfall deviation method (%DEV), percent of normal rainfall index (PNRI), and Standardized Precipitation Index (SPI), were also used to identify meteorological drought events. Generally, rainfall in the watershed has an increasing annual PCPTOT (4–32 mm/year), with increasing frequency and intensity in heavy rainfall and wet days. A significant increasing trend (α = 5%) in the seasonal PCPTOT (7–65 mm/year) and R10mm (1.7–10.0 days/decade) was particularly observed in all stations during the Amihan Monsoon Season (Dec–Feb). The observed increasing rainfall intensity and frequency, if it continues in the future, could have an implication both for the water resources operation to satisfy the multiple objectives of Angat Reservoir and for the flood operation that prevents damage in the downstream areas. The effect of each ENSO (El Niño- Southern Oscillation) phase on the rainfall is unique in magnitude, intensity, and duration. The seasonal reversal of the ENSO in the extreme rainfall and meteorological drought signals in Angat Watershed was also evident. The identified meteorological drought events in the watershed based on SPI-12 persisted up to 12–33 months, could reduce more than 60% (PNRI < 40%) of the normal rainfall. Insights from the study have implications for the hydrology of the watershed that should be considered for the water resources management of the Angat Reservoir.

Assessments of Climate Indices on the Characterization of Extreme Rainfall in Senegal

In recent years, Senegal has experienced a considerable increase in extreme events. The context of climate change, we conducted this study to characterize the extreme rainfall events in Senegal through nine (9) indices of extreme precipitation calculated, using R software (Rclimdex) for the period 1950 to 2010 on several stations. These analyses show that the annual precipitation totals describe a decreasing trend. However, a return of these accumulations have been noted in 1999 in almost all stations of rainfall in Senegal. The extreme rainfall indices show also a linear decreasing trend over the period 1950 to 2010, with the exception of consecutive dry days (CDD). However, non-linear trend analysis (Loess curve) shows increasing trends in rainfall indices over the last decades (from 1991), except for consecutive dry days (CDD) and extremely wet days (R99p) which show a decreasing trend over the last decade.

Monitoring soil erosion in support of achieving SDGs: A special focus on rainfall variation and farming systems vulnerability

The Sustainable Development Goals (SDGs) and soil-related activities are closely intertwined. Healthy soil ecosystems promote the survival of life on Earth and uplift agricultural productivity. One of the issues that is putting healthy soil ecosystems at risk is soil erosion. The Central Highlands of Sri Lanka were chosen because it is a humid, tropical nation that is particularly susceptible to climate threats. Soil erosion was tracked and predicted in connection to rainfall variation in order to reduce soil erosion in farming systems and in supporting the achievement of the SDGs. A revised universal soil loss equation (RUSLE) was executed to determine soil loss. Rainfall variance was calculated using trend analyses of extreme rainfall indices and rainfall erosivity. Soil erosions for the year 2040 under the Representative Concentration Pathways (RCP) 2.6 and 8.5 scenarios were predicted using a variety of machine learning techniques, including artificial neural networks (ANN), support vector machines (SVM), and adaptive network-based fuzzy inference system (ANFIS). Results indicated a positive trend in rainfall erosivity and several other extreme indices. In addition, soil erosion rates increased, with October recording the highest monthly erosion rates. Based on 2020 soil erosion rates, soil erosion model forecasts suggest that erosion may range from 4 % to 22 % in 2040. Under the RCP 2.6 and 8.5 climate scenarios, the rate of soil erosion can be raised from 10.5 t/ha/yr to 12.4 t/ha/yr, respectively. These facts indicate that rainfall-induced soil erosion will increase the vulnerability of the Central Highlands and pose a severe danger to the achievement of the SDGs. In order to ensure the well-beingof the soil ecosystem, this research offered strategies to monitor and anticipate soil erosion in support of achieving the SDGs to protect the healthy soil ecosystem. This methodology can be replicated to minimize soil erosion in farming systems elsewhere.