Rainfall Distribution Research Articles

Evaluation of Future Changes in Climate Extremes over Southeast Asia Using Downscaled CMIP6 GCM Projections

This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme precipitation and temperature events throughout SEA for both the near-term (2021–2060) and long-term (2061–2100) future under both SSP245 and SSP585 scenarios, in comparison to the historical period (1950–2014). Moreover, we investigated the seasonal fluctuations in rainfall and temperature distributions, accentuating the occurrence of drier dry seasons and wetter rainy seasons in particular geographic areas. The focused examination of seven prominent cities in SEA underscored the escalating frequency of extreme rainfall events and rising temperatures, heightening the urban vulnerability to urban flooding and heatwaves. This study’s findings enhance our comprehension of potential climate extremes in SEA, providing valuable insights to inform climate adaptation, mitigation strategies, and natural disaster preparedness efforts within the region.

Comparative analysis of heavy rainfall area between landfalling typhoon LUPIT (2109) and typhoon LISA (9610)

Based on the ERA5 reanalysis data and the surface observations from automatic weather stations, a comparative analysis has been conducted to investigate the differences in heavy rainfall distributions caused by two landfalling tropical cyclones (TCs): LUPIT (2109) and LISA (9610). The two TCs have similar tracks, intensity and landing points, but show different asymmetric features in their rainstorm location relative to their tracks. The results indicate that the TC rainfall differences are mainly caused by different rainstorm formation mechanisms. The wind shear contributes most to the rainstorm of LISA, while land-sea contrast and topographical effect are the main factors of LUPIT rainstorm. Under the influence of strong environmental vertical wind shear and the weak cold air invasion from the west, the circulation center of LISA tilts westward with height, which cooperates with the low-level water vapor convergence and vertical ascending movement on the western side of the TC center to jointly cause the heavy rainstorm to the west of LISA center. In contrast, LUPIT has weak environmental vertical wind shear and no obvious structure tilting with height. Topographic effect plays a crucial role in causing the heavy rainstorm on the north of TC center. The southeasterly jet is blocked by the Taimu Mountain in the northeastern Fujian Province, and the strong ascending motion caused by the terrain-induced convergence appears to the north of LUPIT center. In addition, the moisture convergence is more pronounced in the north and weaker in the south. The intrusion of weak cold air from the east to the coastal areas of central-northern Fujian, and the moisture-convergence distribution, jointly cause the heavy rainstorm to the north of LUPIT.

Modeling Spatio-Temporal Rainfall Distribution in Beni–Irumu, Democratic Republic of Congo: Insights from CHIRPS and CMIP6 under the SSP5-8.5 Scenario

In light of the lack of ground-based observations, this study utilizes reanalysis data from the CHIRPS database and CMIP6 models under the SSP5-8.5 scenario to predict future rainfall in the Beni–Irumu region of eastern DR Congo. The use of reanalysis data offers a viable method for understanding historical and future climate trends in regions with limited ground data. Using a spatial resolution of 0.05°, selected general circulation models (GCMs) were downscaled to CHIRPS data. Analysis of historical rainfall data over 32 years reveals spatial disparities, with high-altitude regions like Mount Stanley experiencing higher annual mean rainfall (1767.87 ± 174.41 mm) compared to lower areas like Kasenyi (863.65 ± 81.85 mm), in line with orographic effects. Future projections under the SSP5-8.5 scenario indicate significant decreases in rainfall for areas such as Oicha (−565.55 mm) in the near term, while regions like Kasindi/Yihunga exhibit moderate decrease (−58.5 mm). In the mid-term, some areas show signs of recovery, with Bulongo experiencing a minor decrease (−21.67 mm), and Kasindi/Yihunga (+152.95 mm) and Kyavinyonge (+71.11 mm) showing increases. Long-term projections suggest overall improvements, with most areas experiencing positive rainfall differences; however, persistent challenges remain in Oicha (−313.82 mm). These findings highlight the dynamic impacts of climate change on rainfall distribution in the Beni–Irumu region, underscoring the need for targeted interventions to address the varied impacts, especially in vulnerable regions like Oicha.

Navigating the Nile: Ethiopia's Development Plans and the Future of Water Sharing

This article explores the dynamics of the relationship between Egypt and the countries in the Nile River Basin, with a specific focus on the natural challenges Ethiopia faces in developing water projects. This research employs a descriptive qualitative approach with the aim of identifying Ethiopia's natural conditions and their impact on water infrastructure development. Data sources include secondary literature from official reports, case studies, and statistical data related to Ethiopia's water resources and climate. Data were collected through literature reviews and document analysis. The research findings indicate that Ethiopia faces various challenges such as uneven rainfall distribution, high evaporation rates, difficult topography, and rock types that do not support water storage. Data analysis was conducted using qualitative methods to evaluate the impact of natural conditions on Ethiopia's capacity to develop water projects. In conclusion, Ethiopia, despite being a major contributor to the Nile River's water, faces significant natural barriers that limit its ability to build large and stable water infrastructure.

An Alternative Similar Tropical Cyclone Identification Algorithm for Statistical TC Rainfall Prediction in the Western North Pacific

AbstractImproving tropical cyclone (TC) rainfall prediction is vital as climate change has led to an increase in TC rainfall rates. Enhanced reliability in predicting TC tracks has paved the way for statistical methodologies to make use of them in estimating current TC rainfall, achieved by identifying similar past TC tracks and obtaining their corresponding rainfall data. While the Fuzzy C Means (FCM) clustering algorithm is widely used, it has limitations stemming from its clustering‐centric design, hindering its ability to pinpoint the most appropriate similar TCs. Our study introduces the Sinkhorn distance, a novel similarity metric that measures the cost of transforming one set of data to another, for assessing TC similarity in rainfall prediction. Our findings indicate that utilizing Sinkhorn distance enhances the accuracy of TC rainfall predictions across the Western North Pacific region. When compared to the conventional approach using FCM, our Sinkhorn distance‐based methodology yields slightly better yet statistically significant results. The improvement is due to better identification of similar TCs, characterized by closer proximity of similar TC tracks to the target TC track, facilitated by Sinkhorn distance. This underscores how minor differences in TC track can alter rainfall distribution, emphasizing the critical importance of accurate track prediction in rainfall prediction and the need to reconsider how we categorize TCs together, which can have implications for climate and atmospheric sciences. Collectively, the inclusion of Sinkhorn distance stands as a valuable addition to our toolkit for discerning similar TC tracks, thus elevating the accuracy of TC rainfall predictions.

Rainfall projections under different climate scenarios over the Kaduna River Basin, Nigeria

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.

Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

The response of soil carbon mineralization losses to changes in rainfall frequency is seasonally dependent in an estuarine saltmarsh

Altered rainfall distribution patterns resulting from climate change have substantial effects on soil carbon (C) cycling in terrestrial ecosystems particularly in water-limited regions. However, how rainfall redistribution affects soil C mineralization (CO2 and CH4 fluxes) in humid regions such as of the coastal saltmarshes remain unclear. We conducted mesocosm experiments in an estuarine saltmarsh in the Yellow River Delta of China, where we simulated three rainfall frequency scenarios (high-frequency, medium-frequency and low-frequency) with the same total rainfall amount in the dry and wet seasons, respectively. Soil CO2 and CH4 fluxes were measured before and after rain frequency treatment during a 40-day period for each season. The decrease in rainfall frequency significantly reduced the mean soil CO2 and CH4 fluxes during the dry season, but had no effect on either flux during the wet season. The seasonal variation in the response of soil C mineralization to rainfall frequency changes could be explained by the changes in antecedent soil water and salinity conditions, soil C substrate, microbial activities and diversity. Thus, the effects of changes in rainfall frequency on soil C mineralization are regulated by season, and should be considered when predicting the future C balance of coastal wetland ecosystems. Furthermore, the shift in precipitation frequency distribution towards increasing heavy rainfall events during the dry season in this region will have a great effect on soil C losses, potentially feeding back into the soil C budget and stability in this estuarine saltmarsh.

Characteristics of rainfall distribution induced by tropical cyclones using GSMaP data over the Vietnam region

ABSTRACT Tropical cyclones (TCs) contribute significantly to rainfall along Vietnam's coast, yet their complex precipitation structures remain poorly resolved, hindering forecast skill. This study analyzes TC rainfall distributions over the Vietnam East Sea from 2000 to 2020. The Global Satellite Mapping of Precipitation (GSMaP) product provides precipitation estimates with 0.1° resolution at hourly intervals, enabling detailed structural characterization. Rainfall features are analyzed across TC intensities, motion vectors, landfall locations, and interactions with cold surge (CS) air masses. Results show that total coverage differences are less significant than the intensity variations in narrow inner core rainbands. Asymmetric rainfall distributions concentrate in the front-right quadrant but shift after landfall. Northern Vietnam observes higher TC frequencies, but southern regions experience heavier extreme rains. Additionally, CS intrusions substantially intensify eyewall convection and redirect TC precipitation. These structural sensitivities visible in GSMaP observations elucidate the dynamics modulating TC rainfall. Characterizing multi-scale interactions and precipitation processes aids in forecasting and impact assessment for these high-risk storms with complex regional behavior.

Validation of GPM DPR Rainfall and Drop Size Distributions Using Disdrometer Observations in the Western Mediterranean

Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and drop size distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and Dm, and the highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found.

Enhancing Urban Flood Forecasting: Integrating Weather Forecasts and Hydrological Models

Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations.

Observed Changes in Extreme Precipitation Associated with U.S. Tropical Cyclones

Abstract Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities.

Evaluation of weather generator tools to estimate climate conditions in different agro ecological zones of North Shewa, Ethiopia

The information on climatic condition is difficult to obtain, expensive, and time-consuming so as to make timely decision on agricultural activities. As a scientific effort, this study was conducted to assess the temporal changes and trends of rainfall and temperature, to know the performance of weather generator (WG) tools in capturing the temporal and spatial distribution of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) and to evaluate the performance of WG in simulating the observed rainfall, Tmax and Tmin by using statistical methods. Mann–Kendall's trend analysis revealed that rainfall had non-significant (P < 0.05) decreasing trends, while Tmax and Tmin had an increasing significant (P < 0.05) trends at all stations. NASA POWER data followed by NewlocClim exactly capture the temporal changes of rainfall, Tmax, and Tmin in all stations except Debre Birhan and Mehal Meda. NewlocClim well captures rainfall at Alem ketema, while NASA well simulates rainfall at Debre Birhan and Majete stations. However Had-GEM2-ES, MRI-CGCM3, and CSIRO-Mk3.6.0 were not handling the spatial variability of observed rainfall at all stations. Similarly, some WGs showed moderate to good performance in capturing the spatial distributions of Tmax and Tmin. The smallest RMSE and CV, the highest R and d values were observed in NASA POWER and NewlocClim for rainfall, Tmax and Tmin. Therefore, NASA and NewlocClim are more accurate with goodness of fit to estimate rainfall, Tmax and Tmin in most of the stations to access weather data for ungagged stations for timely and reasonable decision-making on agriculture.

Integrating in-situ data and spatial decision support systems (SDSS) to identify groundwater potential sites in the Esan plateau, Nigeria

Establishing suitable groundwater resource areas is a challenging endeavour across the globe. However, novel spatial technologies have emerged as valuable tools for the effective strategy, management, and assessment of groundwater resources, especially in data-scarce emerging economies. The current study used spatial decision support systems (SDSS) for evaluating and defining groundwater potential sites (GPSs) in Nigeria's Edo central region to promote sustainable governance of groundwater. By merging multiple groundwater contributing theme layers, a leading-edge information-based multiparametric analytical hierarchy process (AHP) was applied to define the groundwater prospective areas. By systematically assigning weights to every subject-specific layer and feature, the subject matter layers of geology, geomorphology, drainage density, slope, soil properties, landuse/landcover, rainfall distribution, hydraulic conductivity, transmissivity, curvature, proximity to surface water bodies, and elevation were generated and used for groundwater potential map generation. Each thematic layer's weights were allocated and adjusted depending on their qualities and relevance to groundwater recharge. The multicollinearity (MC) analysis was used to evaluate the model's predictive capacity. Finally, groundwater potential sites were created by integrating the theme-specific maps with the weighted total overlay computation tool. The study area contained three separate groundwater potential sites: low, moderate, and high. According to the regional geographic distribution, the largest portion of the area (65%) fell within the moderately significant groundwater potential geographical area. The high and low GPSs, which both have a low curvature and a valley plain characteristic, account for 25% and 10%, respectively, of the entire area. The outcomes were contrasted with the yield of groundwater from boreholes gathered in the study region. The validation analysis found an acceptable 88.89% similarity. This highlights the potential for the groundwater map's significant prediction. Therefore, the applied approach is a viable choice for the advancement of groundwater in the central Edo region and with comparable geology all over the globe.

Influence of large scale climate drivers on hydro-climate variability in Dwangwa River Basin, Malawi

Large-scale climate processes such as the Indian Ocean Dipole (IOD) have significant roles in modulating rainfall and hydrological systems. Understanding such processes can inform adaptation measures for climate change and variability, as well as water resource management and planning. This study investigated the impact of the Indian Ocean Dipole (IOD) on rainfall and discharge variability in the Dwangwa River Basin (DRB) in Malawi, a key inflow to Lake Malawi. Specifically, the study analysed annual rainfall variability trends from 1985 to 2015 using the Coefficient of Variation (CV) and the annual Precipitation Concentration Index (PCI). The significance and direction of rainfall and discharge trends were quantified using the Mann–Kendall trend test at α = 0.05 significance level. To evaluate the association between rainfall and IOD, the Pearson product moment used three IOD phases: positive, negative, and neutral. Simple linear regression was utilised to check the response of the river during the concerned IOD phases. The study found CVs between 20 and 30%, typical of climates with moderate monthly rainfall variability. The PCI ranged from 20 to 30%, suggesting a strongly seasonal and highly variable temporal intra-annual rainfall distribution in the DRB. Moreover, the Mann–Kendall test statistics showed insignificant decrease in annual rainfall trends. Further, the findings demonstrated an insignificant negative correlation between rainfall and positive IOD, with rainfall increases associated with negative IOD, whereas positive IOD is associated with decreased river discharge. Consequently, El Niño and a positive IOD could cause DRB to have low water availability. Therefore, the study demonstrates that rainfall is experiencing a decreasing trend, which is driven by large-scale mechanics.

Raindrop size distributions of summer monsoon rainfall observed over Eastern India

The present study aims to quantify the raindrop size distributions (RSDs) of Indian summer monsoon (ISM) rainfall over a tropical eastern Indian station, Rourkela using five years (2018–2021) of Thies disdrometer measurements. The RSD measurements of ISM rainfall at Rourkela are segregated into active and break spells using the standardized rainfall anomalies over the study area. The RSDs characteristics of active and break spells are analyzed and their empirical relations, which are appropriate for precipitation estimation algorithms, are also reported. A significant annual and monthly variations in RSDs is noticed between the active and break spells of the ISM rainfall over the study region. The probability distribution function (PDF) RSDs parameters (rainfall rate, liquid water content, mass-weighted mean diameter, and normalized intercept parameter) demonstrated clear distinctions between active and break spells. The active spells showed higher rainfall amounts, large mass-weighted mean diameter, and smaller normalized intercept parameters over the break spells. A further segregation of active and break spells into stratiform and convective types revealed more large size drops in convective (stratiform) precipitations of active (break) spells. Occurrence of higher rainfall amounts and large size raindrops in the active spells over the break spells can be attributed to the presence of abundant moisture and convective activity in active spells. Furthermore, the radar reflectivity-rainfall rate (Z–R), shape and slope (μ–Λ), and mass-weighted mean diameter-rainfall rate (Dm-R) relations established for the active and break spells could aid in improving the precipitation estimations of over the study region.

Establishing resilience-targeted prediction models of rainfall for transportation infrastructures for three demonstration regions in China

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.

Indian ocean warming, extreme positive Indian Ocean Dipole events, and their impact on monthly Indian Monsoon rainfall from June to November in NMME models

Indian Ocean (IO) warming, the frequency of extreme positive Indian Ocean Dipole (EPIOD) events, and its impact on Indian monsoon rainfall (IMR) are more prominent in the recent era. A proper understanding of the relationship between the EPIOD and IMR is very important because two thirds of the people in India are dependent on agriculture and related economy. The primary focus of this study is on three main aspects: (i) analysing the monthly trend of sea surface temperature (SST) in the IO; (ii) the spatial variability of SST anomalies (SSTA) linked to the EPIOD and its impact on the IMR; and (iii) the factor responsible for the intensification of the EPIOD. These aspects are thoroughly assessed using 12 NMME models, comparing them with observations and reanalysis datasets from 1990 to 2020. The analysis reveals a warming trend in the central IO during June and July, with significant increases observed from August to November. Over the past 31 years, the central IO has experienced an approximate rise of 0.9 to 1 °C from June to November. Among the 12 models scrutinised, GFDL_FLOR_A, FLOR_B, and RSMAS_CCSM4 closely align with observed spatial trends. In contrast, models such as GFDL_SPEAR and NASA_GEOSS tend to overestimate these spatial trends. An increase in easterlies over the equatorial eastern IO was identified as a possible driver for the rising SST in the central IO during October and November. In response to EPIOD on the monthly spatial rainfall distribution over India, all NMME models often underestimate rainfall over the west coast and exhibit wet biases over the leeward side of the Western Ghats. Grid-wise skill scores for accumulated monthly rainfall demonstrated variations among NMME models, with GFDL_SPEAR, RSMAS_CCSM4, and other IC3 models showing higher skill. The interhemispheric pressure gradient (IHPG) has been identified as a precursor to EPIOD events. A positive IHPG from March to June is correlated with EPIOD occurrences from October to December. It is observed that half of the NMME models are capable of simulating a positive relationship between IOD and IHPG, consistent with the observed data.

Spatiotemporal variability identification and analysis for non-stationary climatic trends for a tropical river basin of India

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

Spatiotemporal Epidemiology of Malaria in India from 2007 to 2022.

India is a major contributor to the global burden of malaria, especially Plasmodium vivax infection. Understanding the spatiotemporal trends of malaria across India over the last two decades may assist in targeted intervention. The population-normalized spatiotemporal trends of malaria epidemiology in India from 2007 to 2022 were analyzed using a geographic information system with the publicly available "malaria situation" report of the National Vector Borne Disease Control Program (NVBDCP). The NVBDCP data showed malaria cases to have steeply declined from 1.17 million in 2015 to 0.18 million cases in 2022; this is 10.1 and 18.7 fold lower than the WHO's estimate of 11.93 million and 3.38 million cases in 2015 and 2022, respectively. From 2007 to 2022, Mizoram, Meghalaya, Tripura, Odisha, Chhattisgarh, and Jharkhand consistently reported high caseloads of Plasmodium falciparum. In the same period, the P. vivax caseload was high in Arunachal Pradesh, Mizoram, Nagaland, Jharkhand, Odisha, Chhattisgarh, Goa, Daman and Diu, Dadra and Nagar Haveli, and Andaman and Nicobar Islands. The distribution of forest cover, annual rainfall, and proportion of the Scheduled Tribe population (the most underprivileged in Indian society) spatially correlated with malaria cases and deaths. Mizoram is the only state where cases were higher in 2022 than in 2007. Overall, India has made tremendous progress in controlling malaria and malaria-related deaths in the last decade. The decline could be attributed to the effective vector and parasite control strategies implemented across the country.