Rainfall impacts on nonpoint nitrogen and phosphorus dynamics in an agricultural river in subtropical montane reservoir region of southeast China.

The increased frequency and intensity of heavy rainfall events due to climate change could potentially influence the movement of nutrients from land-based regions into recipient rivers. However, little information is available on how the rainfall affect nutrient dynamics in subtropical montane rivers with complex land use. This study conducted high-frequency monitoring to study the effects of rainfall on nutrients dynamics in an agricultural river draining to Lake Qiandaohu, a montane reservoir of southeast China. The results showed that riverine total nitrogen (TN) and total phosphorus (TP) concentrations increased continuously with increasing rainfall intensity, while TN:TP decreased. The heavy rainfall and rainstorm drove more than 30% of the annual N and P loading in only 5.20% of the total rainfall period, indicating that increased storm runoff is likely to exacerbate eutrophication in montane reservoirs. NO3--N is the primary nitrogen form lost, while particulate phosphorus (PP) dominated phosphorus loss. The main source of N is cropland, and the main source of P is residential area. Spatially, forested watersheds have better drainage quality, while it is still a potential source of nonpoint pollution during rainfall events. TN and TP concentrations were significantly higher at sites dominated by cropland and residential area, indicating their substantial contributions to deteriorating river water quality. Temporally, TN and TP concentrations reached high values in May-August when rainfall was most intense, while they were lower in autumn and winter than that in spring and summer under the same rainfall intensities. The results emphasize the influence of rainfall-runoff and land use on dynamics of riverine N and P loads, providing guidance for nutrient load reduction planning for Lake Qiandaohu.

Molecular characteristics of dissolved organic phosphorus in watershed runoff: Coupled influences of land use and precipitation.

Land use and precipitation are two major factors affecting phosphorus (P) pollution of watershed runoff. However, molecular characterization of dissolved organic phosphorus (DOP) in runoff under the joint influences of land use and precipitation remains limited. This study used Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to study the molecular characteristics of DOP in a typical P-polluted watershed with spatially variable land use and precipitation. The results showed that low precipitation and intense human activity, including phosphate mining and associated industries, resulted in the accumulation of aliphatic DOP compounds in the upper reaches, characterized by low aromaticity and low biological stability. Higher precipitation and widespread agriculture in the middle and lower reaches resulted in highly unsaturated DOP compounds with high biological stability constituting a higher proportion, compared to in the upper reaches. While, under similar precipitation, more aliphatic DOP compounds characterized by lower aromaticity and higher saturation were enriched in the lower reaches due to more influence from urban runoff relative to the middle reaches. Photochemical and/or microbial processes did result in changes in the characteristics of DOP compounds during runoff processes due to the prevalence of low molecular weight and low O/C bioavailable aliphatic DOP molecules in the upper reaches, which were increasingly transformed into refractory compounds from the upper to middle reaches. The results of this study can increase the understanding of the joint impacts of land use and precipitation on DOP compounds in watershed runoff.

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.

Potential impact of climatic factors on malaria in Rwanda between 2012 and 2021: a time-series analysis.

BACKGROUND: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. METHODS: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. RESULTS: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. CONCLUSION: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

Determining dengue infection risk in the Colombo district of Sri Lanka by inferencing the genetic parameters of Aedes mosquitoes.

BACKGROUND: For decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes. METHODS: In this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes. RESULTS: Our research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease. CONCLUSION: Our research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors.

Water deficit and storm disturbances co-regulate Amazon rainforest seasonality.

Canopy leaf abundance of Amazon rainforests increases in the dry season but decreases in the wet season, contrary to earlier expectations of water stress adversely affecting plant functions. Drivers of this seasonality, particularly the role of water availability, remain debated. We introduce satellite-based ecophysiological indicators to demonstrate that Amazon rainforests are constrained by water during dry seasons despite light-driven canopy greening. Evidence includes a shifted partitioning of photosynthetically active radiation toward more isoprene emissions and synchronized declines in leaf and xylem water potentials. In addition, we find that convective storms attenuate light-driven ecosystem greening in the late dry season and then reverse to net leaf loss in the wet season, improving rainforest leaf area predictability by 24 to 31%. These findings highlight the susceptibility of Amazon rainforests to increasing risks of drought and windthrow disturbances under warming.

Association between Precipitation Events, Drought, and Animal Operations with Campylobacter Infections in the Southwest United States, 2009-2021.

BACKGROUND: Weather variability is associated with enteric infections in people through a complex interaction of human, animal, and environmental factors. Although Campylobacter infections have been previously associated with precipitation and temperature, the association between precipitation and drought on campylobacteriosis has not been studied. OBJECTIVE: Using data from Arizona, Colorado, New Mexico, and counties in Utah, this ecological study aimed to assess the association between precipitation and the incidence of campylobacteriosis by county from 2009 to 2021 and to determine how this association is modified by prior drought level and animal operations. METHODS: We merged 38,782 cases of campylobacteriosis reported in 127 counties with total precipitation (in inches), temperature (in average degrees Fahrenheit), Palmer Drought Severity Index (PDSI, category), and animal census data (presence, density per square mile) by week from 2009 to 2021. Negative binomial generalized estimating equations adjusted for temperature with a 3-wk lag were used to explore the association between precipitation on campylobacteriosis with resulting incidence rate ratios (IRRs). Stratified analyses explored the association with precipitation following antecedent drought, presence of farm operations, and animal density. RESULTS: A 1-in (25.4 mm) increase in precipitation was associated with a 3% increase in campylobacteriosis reported 3 wks later (IRR=1.03; 95% CI: 1.02, 1.04) after adjusting for average temperature and PDSI. Compared with normal conditions, there were significantly more cases when precipitation followed antecedent extremely wet (IRR=1.15; 95% CI: 1.04, 1.26), very wet (IRR=1.09; 95% CI: 1.01, 1.18), moderately wet (IRR=1.06; 95% CI: 1.01, 1.12), moderate drought (IRR=1.11; 95% CI: 1.07, 1.16), and severe drought (IRR=1.06; 95% CI: 1.02, 1.11) conditions, whereas there were significantly fewer cases (IRR=0.89; 95% CI: 0.85, 0.94) for antecedent extreme drought. Compared to counties with no animal operations, counties with animal operations had significantly more cases following precipitation for every PDSI category except extreme drought. Counties with a higher density of beef cattle, goats for meat, chicken broilers, and chicken layers had significantly higher rates of campylobacteriosis following precipitation than those with no such operations, whereas those with dairy cattle and goats for milk, did not. DISCUSSION: In this majority arid and semiarid environment, precipitation following prior wet conditions and moderate and severe drought were significantly associated with increased rates of campylobacteriosis, and only in prior extreme drought did rates decrease. Where the precipitation fell made a difference; after precipitation, counties with farm operations had significantly more cases compared to counties without farm operations. Further work should assess individual-level risk factors within environmental exposure pathways for Campylobacter. https://doi.org/10.1289/EHP14693.

Bacterial accumulation dynamics in runoff from extreme precipitation.

Extreme precipitation can significantly influence the water quality of surface waters. However, the total amount of bacteria carried by rainfall runoff is poorly understood. Here, thirty rainfall scenarios were simulated by artificial rainfall simulators, with designed rainfall intensity ranging from 19.3 to 250 mm/h. The instantaneous concentration ranges of R2A, nutrient agar (NA) culturable bacteria, and viable bacteria in runoff depended on the types of underlying surfaces. The instantaneous bacterial concentrations in runoff generated by forest lands, grasslands and bare soil were: R2A culturable bacteria = 104.5-6.3, 104.5-6.1, 104.0-5.3 colony-forming units (CFU)/mL, NA culturable bacteria = 104.0-6.0, 103.9-5.8, 103.2-4.9 CFU/mL, and viable bacteria = 106.4-8.0, 107.0-8.9, 106.4-7.6 cells/mL. Based on the measured bacterial instantaneous concentration in runoff, cumulative dynamic models were established, and the maximum amount of culturable bacteria and viable bacteria entering water sources were estimated to be 109.38-11.31 CFU/m2 and 1011.84-13.25 cells/m2, respectively. The model fitting and the bacterial accumulation dynamics were influenced by the rainfall types (p < 0.01). Surface runoff from the underlying surface of forest lands and grasslands had a high microbial risk that persisted even during the "Drought-to-Deluge Transition". Bacterial accumulation models provide valuable insight for predicting microbial risks in catchments during precipitation and can serve as theoretical support for further ensuring the safety of drinking water under the challenge of climate change.

Ecotoxicological assessment of sanitary sewer overflows and rainfall dynamics offers insights into conditions for potential adverse ecological outcomes.

Sewer overflows are an environmental concern due to their potential to introduce contaminants that can adversely affect downstream aquatic ecosystems. As these overflows can occur during rainfall events, the influence of rainwater ingress from inflow and infiltration on raw untreated wastewater (influent) within the sewer is a critical factor influencing the dilution and toxicity of the contaminants. The Vineyard sewer carrier in the greater city of Sydney, Australia, was selected for an ecotoxicological investigation of a sanitary (separate from stormwater) sewerage system and a wet-weather overflow (WWO). Three influent samples were collected representing dry-weather (DW), intermediate wet-weather (IWW) and wet-weather (WW). In addition, a receiving water sample was also collected downstream in Vineyard Creek (WW-DS) coinciding with a WWO. We employed direct toxicity assessment (DTA) and toxicity identification evaluation (TIE) approaches to gain comprehensive insights into the nature and magnitude of the impact on influent from rainwater ingress into the sewer. Three standard ecotoxicological model species, a microalga, Chlorella vulgaris, the water flea, Ceriodaphnia dubia and the midge larva, Chironomus tepperi were used for both acute and chronic tests. The study revealed variable toxicity responses, with the sample of influent collected in wet-weather displaying lower toxicity compared to the dry-weather sample of influent. Ammonia, and metals, were identified in dry weather as contributors to the observed toxicity, however, this risk was alleviated through rainwater ingress in wet-weather with further dilution within the receiving water. Based on toxicity data, dilutions of influent to minimise effects on C. vulgaris and C. dubia ranged from 1 in 12 in DW to 1 in 2.8 in WW, and further diminished in the receiving water to 1 in 1.8. The successful application of ecotoxicological approaches enabled the assessment of cumulative effects of contaminants in influent, offering valuable insights into the sanitary sewer system under rainwater ingress.

The role of plant uptake in total phosphorous and total nitrogen removal in vegetated bioretention cells using vetiver and cattail.

Bioretention cells have emerged as a prominent strategy for mitigating pollutant loads within urban stormwater runoff. This study delves into the role of plant uptake in the simultaneous removal of nitrogen and phosphorus compounds within these systems. Three bioretention cells-CP, P1, and P2-were constructed using local soil, C33 sand, and gravel. CP served as the unvegetated control, while P1 and P2 were vegetated with vetiver and cattail, respectively. The removal efficiencies of NO3⁻-N, NH3⁻-N, NO2⁻-N, TN, TP, and COD from rainwater were evaluated under saturated and unsaturated conditions. The unvegetated control reactor (CP) achieved TN and TP removal rates of 40.44% and 82.52%, respectively. Reactor P1 (vetiver) demonstrated TN and TP removal rates of 62.92% and 97.19%, respectively. Reactor P2 (cattail) showed TN and TP removal rates of 49.71% and 87.78%, respectively. With the introduction of a saturation zone, TN removal efficiencies increased to 51.69%, 89.22%, and 79.91% for CP, P1, and P2, respectively. However, TP removal efficiencies decreased to 74.81%, 95.04%, and 84.58% for CP, P1, and P2, respectively. Plant tissue uptake tests indicated that vetiver could retain 5 times more TN and twice as much TP compared to cattail. This enhanced performance is attributed to vetiver's high photosynthetic potential as a C4 plant, resilience to varying environmental and nutrient conditions, extensive root network, secretion of oil sesquiterpenes from its root cortex, and the presence of arbuscular mycorrhizal fungi, which secrete glomalin, a substance that promotes water retention and nutrient uptake. Findings from this study indicate that the efficacy of traditional bioretention cells can be augmented through the strategic selection and integration of locally adapted plant species, coupled with the incorporation of saturation zones, to enhance pollutant removal capabilities and resilience to drought conditions.

Short Communication: Rotavirus Group A Occurrence in Rural Water Source Samples in a Midwest Region State of Brazil, Comparing Wet and Dry Seasons.

Identified as a potential reference pathogen by the WHO Guidelines for Drinking-Water Quality, Rotavirus (RV) is among the main enteric viruses that cause waterborne diseases. The aim of this study was to identify and correlate the presence of RV in collective and individual water sources of rural communities in the state of Goiás, within the seasons in which the collections were made (rainy and dry seasons). For this, 86 water samples in the dry period and 160 samples in the rainy period were collected. Concentration of water samples, extraction of viral genetic material and molecular tests were performed. When analyzing the presence of RV in the samples, taking into consideration the period studied, RV was found to be more prevalent in the dry season (54.7%) than in the rainy season (20%), showing a strong statistical association with the dry season (p-value < 0.001). The presence of pathogenic microorganisms in water is a public risk issue, enabling the emergence of outbreaks, endemics and epidemics. In the present research, there was an association between the presence of Rotavirus and the dry period of the year when compared to the rainy period.

Deep learning insights into spatial patterns of stable isotopes in Iran's precipitation: a novel approach to climatological mapping.

Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran's precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

How much rainwater contributes to a spring discharge in the Guarani Aquifer System: insights from stable isotopes and a mass balance model.

Outcrops play an important role in groundwater recharge. Understanding groundwater origins, dynamics and its correlation with different water sources is essential for effective water resources management and planning in terms of quantity and quality. In the case of the Guarani Aquifer System (GAS) outcrop areas are particularly vulnerable to groundwater pollution due to direct recharge processes. This study focuses on the Alto Jacaré-Pepira sub-basin, a watershed near Brotas, a city in the central region of the state of São Paulo, Brazil, where groundwater is vital for supporting tourism, agriculture, urban water supply, creeks, river and wetlands. The area has a humid tropical climate with periods of both intense rainfall and drought, and the rivers remain perennial throughout the year. Therefore, the aim of this study is to investigate the interconnections between a spring and its potential sources of contribution, namely rain and groundwater, in order to elucidate the relationships between the different water sources. To achieve this, on-site monitoring of groundwater depth, rainfall amount, and stable isotope ratios (deuterium (2H) and oxygen-18 (18O)) from rain, spring discharge, and a monitoring well was carried out from 2013 to 2021. The results indicate that the mean and standard deviations for δ18O in rainwater exhibit higher variability, resulting in -4.49 ± 3.18 ‰ VSMOW, while δ18O values from the well show minor variations, similar to those of the spring, recording -7.25 ± 0.32 ‰ and -6.94 ± 0.28 ‰ VSMOW, respectively. The mixing model's outcomes reveal seasonal variations in water sources contribution and indicate that groundwater accounts for approximately 80 % of spring discharge throughout the year. Incorporating stable isotopes into hydrological monitoring provides valuable data for complementing watershed analysis. The values obtained support the significance of the aquifer as a primary source, thereby offering critical insights into stream dynamics of the region.

Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally.

Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within-year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within-year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within-year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6-month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation-related shifts, but precipitation-related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no-analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts.

Mechanisms of evolution and pollution source identification in groundwater quality of the Fen River Basin driven by precipitation.

Groundwater pollution has attracted widespread attention as a threat to human health and aquatic ecosystems. However, the mechanisms of pollutant enrichment and migration are unclear, and the spatiotemporal distributions of human health risks are poorly understood, indicating insufficient groundwater management and monitoring. This study assessed groundwater quality, human health risks, and pollutant sources in the Fen River Basin(FRB). Groundwater quality in the FRB is good, with approximately 87 % of groundwater samples rated as "excellent" or "good" in both the dry and rainy seasons. Significant precipitation elevates groundwater levels, making it more susceptible to human activities during the rainy season, slightly deteriorating water quality. Some sampling points in the southern of Taiyuan Basin are severely contaminated by mine drainage, with water quality index values up to 533.80, over twice the limit. Human health risks are mainly from As, F, NO3-, and Cr. Drinking water is the primary pathway of risk. From 2019 to 2020, the average non-carcinogenic risk of As, F, and NO3- increased by approximately 28 %, 170 % and 8.5 %, respectively. The average carcinogenic risk of As and Cr increased by 28 % and 786 %, the overall trend of human health risks is increasing. Source tracing indicates As and F mainly originate from geological factors, while NO3- and Cr are significantly influenced by human activities. Various natural factors, such as hydrogeochemical conditions and aquifer environments, and processes like evaporation, cation exchange, and nitrification/denitrification, affect pollutant concentrations. A multi-tracer approach, integrating hydrochemical and isotopic tracers, was employed to identify the groundwater pollution in the FRB, and the response of groundwater environment to pollutant enrichment. This study provides a scientific basis for the effective control of groundwater pollution at the watershed scale, which is very important in the Loess Plateau.

[Global quantification of the spatial variability and temporal stability of throughfall]. / æž—å† ç©¿é€é›¨ç©ºé—´å¼‚è´¨æ€§åŠå ¶æ—¶é—´ç¨³å®šæ€§ç‰¹å¾çš„å ¨çƒé‡åŒ–.

Spatial variability of throughfall (i.e. the non-uniform characteristics of throughfall at different canopy positions) and its temporal persistence (i.e. time stability) are related to the quantity and efficiency of soil moisture replenishment, and affect plant competition and community succession dynamics by affecting resource availability. We carried out a meta-analysis with 554 papers (from 2000 to 2022) retrieved from Web of Science and China National Knowledge Infrastructure (CNKI) based on keyword search, quantified and compared the amount, spatial heterogeneity, and temporal stability characteristics of penetrating rain in different climate zones and plant functional types. Our results that throughfall proportion was lower in arid regions (72.0%±13.6%) than humid (75.1%±9.3%) and semi-humid areas (79.9%±10.4%). Cold climates had lower values (74.1%±14.6%) than temperate (74.2%±7.5%) and tropical climates (80.9%±14.6%). Shrubs (68.9%±14.9%) generally had lower throughfall proportion than trees (76.7%±9.1%). Broad-leaved trees (75.2%±11.1%) and conifers (75.1%±9.9%) showed similar throughfall proportions, as did evergreen (76.7%±10.0%) and deciduous species (74.7%±11.9%). Additionally, spatial variability (coefficient of variation) did not significantly differ across rainfall zones, temperature zones, or vegetation types. The spatial distribution of throughfall was relatively stable. Canopy structure was the dominant factor affecting temporal stability of throughfall. However, there was a lack of comparison between typical geographic units (i.e. spatial units with basically consistent geographical environmental conditions) at various temporal scales. Future research should expand upwards to the summary of global spatial scale rules and downwards to the analysis of process based temporal scale mechanisms, to depict the dynamic distribution of penetrating rain and unify observation standards to enhance comparability of different studies, in order to efficiently promote research on canopy penetrating rain and provide ecological and hydrological basis for protecting nature, managing artificial activities, and restoring degraded ecosystems.

Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding.

Climate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures. To date, most research on SWF has focused on modelling and prediction, but these models have been limited to 2 m resolution for England to avoid excessive computational burdens. The lead time for predicting convective rainfall responsible for SWF can be as little as 30 minutes for a 1 km x 1 km part of the storm. Therefore, it is useful to identify the locations most vulnerable to SWF based on past rainfall data and microtopography to provide better risk management measures for properties. In this study, we present a framework that uses long-term gridded rainfall data to quantify SWF hazard at the 1 km x 1 km pixel level, thereby identifying localized areas vulnerable to SWF. We also use high-resolution photographic (10 cm) and LiDAR (25 cm) DEMs, as well as a property flood resistance and resilience (PFR) database, to quantify SWF exposure at property level. By adopting this methodology, locations and properties vulnerable to SWF can be identified, and appropriate SWF management strategies can be developed, such as installing PFR features for the properties at highest risk from SWF.

Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters.

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems.

Impacts of climate change on diarrhoeal disease hospitalisations: How does the global warming targets of 1.5-2°C affect Dhaka, Bangladesh?

Dhaka is one of the world's densely populated cities and faces significant public health challenges including high burden of diarrhoeal diseases. Climate change is intensifying existing environmental problems including urban heat island effect and poor water quality. While numerous epidemiological studies have linked meteorological factors to diarrhoeal diseases in Bangladesh, assessment of the impacts of future climate change on diarrhoeal diseases is scarce. We provide the assessment of climate change impacts on diarrhoeal disease in Dhaka and project future health risks under climate change scenarios. About 3 million acute diarrhoea cases presenting to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research Bangladesh (icddr,b) during 1981-2010 were linked to daily temperature, rainfall and humidity and association investigated using time series adapted negative binomial regression models employing constrained distributed lag linear models. The findings were applied to climate projections to estimate future risks of diarrhoea under various global warming scenarios. There was a significantly raised risk of diarrhoea hospitalisation in all ages with daily mean temperature (RR: 3.4, 95% CI: 3.0-3.7) after controlling for the confounding effects of heavy rainfall, humidity, autocorrelations, day of the week effect, long-term time, and seasonal trends. Using the incidence rate ratio (IRR) of 1.034, temperature increases based on the global warming targets of 1.5-2°C could result in an increase of diarrhoea hospitalisations by 4.5-7.4% in all age groups by the 2100s. These effects were more pronounced among <5 children where the predicted temperature increases could raise diarrhoea hospitalisation by 5.7% - 9.4%. Diarrhoea hospitalisation will increase significantly in Dhaka even if the global warming targets adopted by the Paris Agreement is reached. This underscores the importance of preparing the city for management and prevention of diarrhoeal diseases.

Influence of precipitation and temperature on burned areas in the agricultural and cattle ranching frontier of the Brazilian Amazon.

Fire occurrence, intensity, and spread are highly influenced by climatic variables. This study investigates the correlation between burned area, precipitation, and temperature in Rondônia, an agricultural frontier in the southwestern Brazilian Legal Amazon, from 2001 to 2022. The analysis utilized climatological data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and MODIS product MOD11A1.061 for temperature, along with MODIS product MCD64A1 for burned area. The study was conducted on a monthly scale, employing the cross-correlation function to determine the lagged effects of temperature and precipitation on burned areas. Trend analysis was performed using the Mann-Kendall test, with the magnitude of trends estimated by Sen's Slope. Results indicated a significant negative correlation between burned areas and precipitation, with a 2-month lag and an R2 of - 0.51. In contrast, temperature exhibited a significant positive correlation with burned areas, showing a 1-month lag and an R2 of 0.55. Trend analysis revealed a decrease in precipitation by - 0.0542 mm.month-1, temperature increased by 0.006 °C.month-1, while burned areas decreased by - 111.13 km2.month-1. These findings underscore the intricate relationship between climate variables and fire occurrences, highlighting the urgent need for policies addressing climate change and environmental degradation in the Amazon.