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The most important information required to successfully issue a flood warning is the quantitative precipitation forecasts (QPFs). This is important to run subsequent rainfall-runoff simulations. A rainfall-runoff simulation derives its accuracy mainly from the accuracy of the input QPFs. The dynamically based global numerical weather prediction models (NWPMs) are strong candidate sources of QPFs. A main problem is the real-time selection of which NWPM should be used to provide the QPFs for flood warning simulations. This paper develops an automated technique to solve this problem. The technique performs real-time comparisons with measured rainfall fields using a novel 'tolerant' hydrologic approach. The 'tolerant' approach performs the comparison on the basin scale and allows for timing shifts in the forecasts. This is because QPFs can be good but only a few hours early or late. Two events are used for illustration, and the proposed real-time application in flood warning is presented. The developed technique, employing the tolerant approach, could eliminate the effects of the timing shifts and, accordingly, succeeded to select the QPFs to be used. A Python package was developed for automation. The developed technique is expected to also be useful for offline assessments of historical performances of NWPMs.

A 16-month monitoring program was conducted on a prototype sanitary system in a coastal city in China. The groundwater infiltration (GWI) on dry weather days and the rain-derived inflow and infiltration (RDII) on wet weather days were quantified and analyzed. The proportion of monthly averaged GWI to total flow can be as high as 70% during the observation period mainly due to the high groundwater level. The results also show that the ratio of RDII volume to total rainfall volume (defined as R-value) reaches a limited value of approximately 10% for the studied system when the total rainfall depth increases. A reference indicator R lim for the limited R-value was proposed for assessing the conditions of sewer systems in terms of RDII. The R lim value depends on local sewer conditions and in general, a lower R lim value represents a better performance on RDII and vice versa. This study enriches the case studies on the performance of a specific sanitary sewer system on inflow and infiltration in a typical coastal city with exceptionally high groundwater levels, excess rainfall events in the monitoring season and possible typhoon events, which addresses the unique locational and hydrological properties of a representative coastal city.

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe 1-4 . However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales 3,4 . Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN) 5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations 6 . After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged., (© 2023. The Author(s).)

Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (< 10 s) resolution. We further show the potential of this approach to monitor hail with joint analysis of seismic intensity and independent precipitation rate measurements, and advocate for coordinated seismological-meteorological field campaign design., (© 2023. The Author(s).)

Cities are generally warmer than their adjacent rural land, a phenomenon known as the urban heat island (UHI). Often accompanying the UHI effect is another phenomenon called the urban dry island (UDI), whereby the humidity of urban land is lower than that of the surrounding rural land 1-3 . The UHI exacerbates heat stress on urban residents 4,5 , whereas the UDI may instead provide relief because the human body can cope with hot conditions better at lower humidity through perspiration 6,7 . The relative balance between the UHI and the UDI-as measured by changes in the wet-bulb temperature (T w )-is a key yet largely unknown determinant of human heat stress in urban climates. Here we show that T w is reduced in cities in dry and moderately wet climates, where the UDI more than offsets the UHI, but increased in wet climates (summer precipitation of more than 570 millimetres). Our results arise from analysis of urban and rural weather station data across the world and calculations with an urban climate model. In wet climates, the urban daytime T w is 0.17 ± 0.14 degrees Celsius (mean ± 1 standard deviation) higher than rural T w in the summer, primarily because of a weaker dynamic mixing in urban air. This T w increment is small, but because of the high background T w in wet climates, it is enough to cause two to six extra dangerous heat-stress days per summer for urban residents under current climate conditions. The risk of extreme humid heat is projected to increase in the future, and these urban effects may further amplify the risk., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Water quality risks from overflows have attracted significant research attention, and the reliability of urban drainage systems (UDS) is in urgent need of assessment and improvement. The overflow volume and concentration of critical pollutants are generally used as assessment indicators, which is quite time consuming and cumbersome especially under continuous rainfall. Simplifying the water quality risk assessment indicators for the UDS reliability is intractable. For this purpose, this study proposes the detention tank emptying time as a new reliability evaluation indicator, which greatly reduces the calculation burden by converting water quality risk into hydraulic risk. On this basis, the effects of rainfall, dry weather flow (DWF), actuators and their interactions on reliability are quantified by massive scenarios. It shows that the DWF affects the emptying process via weekly and daily seasonality and its interaction with rainfall is mainly responsible for unreliability. Further, the engineering facility linkage controlled by the actuator to cope with the interaction is the key. Particularly, the Prophet algorithm is innovatively applied to mine the patterns and generate the DWF series for the challenge of sparse DWF data. In conclusion, the indicator proposed expands the connotation of UDS reliability assessment, prompting a small investment in replacing actuators with better controllability and greatly improving reliability. It guides the engineering planning and enhancement from a new perspective of whole-chain optimization from the global to the detailed level., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash-Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study utilises ground, satellite and model data to investigate the observed and future precipitation changes in Pakistan. Pakistan Meteorological Department's (PMD) monthly precipitation data set along with the Tropical Rainfall Measuring Mission (TRMM) monthly dataset TRMM&#95;3B43 (0.25° × 0.25° resolution) and European Centre for Medium-Range Weather Forecasts's (ECMWF) monthly reanalysis product ERA5 have been used to evaluate rainfall trends over the climatic zones of Pakistan through Mann-Kendall test, Sen's slope estimator and innovative trend analysis for the time period 1978-2018. Community Climate System Model (CCSM4) projections have been employed to explore the projected changes in precipitation until 2099. Furthermore, TRMM and CCSM4 projections have been correlated and validated using root mean square error (RMSE) and mean bias error (MBE). There is a good correlation between TRMM and PMD ground observation at all stations of the country for all seasons, with correlation coefficient values ranging from 0.89 (November) to 0.97 (July and August). However, ERA5 monthly precipitation tends to overestimate rainfall in the winter months. The study shows a decreasing trend in winter precipitation in all zones of the country with a significant decrease over western mountains, i.e. zone C of the country. During 2008-2018, a sharp decrease in winter precipitation is observed as compared to the baseline value of 1978-2007 in all climatic zones. Rainy days have also shown a decrease in winter and pre-monsoon seasons. There seems to be a shift in precipitation from winter towards pre-monsoon season as pre-monsoon precipitation in the last 11 years increased in all zones except for zone C. Coherently, there is a decrease in an area affected by winter precipitation and an increase in area for pre-monsoon precipitation. Future precipitation estimates from the CCSM4 model for RCP 4.5 and RCP 8.5 overestimate precipitation in most parts of the country for the first 9 observed years (2010-2018) and predict a rise in precipitation by 2099 which is more pronounced in the northern and western Pakistan while a decrease is predicted for the plains of the country, which might have negative consequences for agriculture., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Perception of the spatio-temporal events of extreme precipitation and their variations is essential for diminishing the natural hazards linked with extreme events. In this research, a satellite-based precipitation dataset derived from remotely sensed soil moisture (SM2RAIN-ASCAT, obtained from ASCAT satellite soil moisture data through the Soil Moisture to Rain algorithm) was selected to evaluate the accuracy of daily precipitation and extreme events estimations against a regional gridded weather dataset by employing various performance indicators, and ETCCDI indicators (CDD, and CWD, SDII, R10mm, R20mm, R95p, R99p, Rx1day, and Rx5day). The study area includes entire Poland as well as small parts of Ukraine, Belarus, Slovakia, the Czech Republic, Russia, and Germany. According to PBIAS (~ -3.9 %) and coefficient of correlation (~0.74), SM2RAIN-ASCAT has good accuracy in the study area. Assessments reveal that, in general, over southern, mountainous part SM2RAIN-ASCAT does not have accurate estimations. According to the reference dataset, during the 2007-2019 period, on average, the length of dry days was ~22 days, while SM2RAIN-ASCAT shows ~19.6 consecutive dry days. In contrast, SM2RAIN-ASCAT overestimated (16 days/year) the consecutive wet days compared to the reference dataset (~8.7 days/year). SM2RAIN-ASCAT underestimated the number of heavy precipitation days index (R10mm) over the northern part of the region, close to the Baltic Sea), but the accuracy increased in the southern parts. SM2RAIN-ASCAT underestimated the maximum 1-day rainfall total and relative max 5-day precipitation amount indices. The total precipitation divided by the amount of wet days index shows that SM2RAIN-ASCAT has relatively acceptable accuracy in the center and south of the study area. Our results show that SM2RAIN-ASCAT should be improved for relatively higher extreme indicators., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

A physiographic-based multilinear regression model supported by GIS was developed to estimate spatial rainfall variability in the Southwest Iberian Peninsula. The area study includes a wide diversity of landscape features and comprises four Portuguese regions and one Spanish province (totalizing 28,860 km 2 ). The region suffers a very strong Mediterranean influence, with a major cleavage between winter and summer seasons. Thus, the analysis was carried out separately for the wet (October to March) and dry (April to September) semesters. From an initial set of 10 explanatory physiographic variables, five were selected to be used in the multilinear regression, as they allowed generating models by map algebra that fitted well with the last 40 years of monthly rainfall data records. These records were obtained from 163 weather stations, filtered from an initial set of 230 (142 stations in Portugal and 88 in Spain). The correlation between the physiographic-based multilinear regression model and a model obtained by interpolation from rainfall historical data showed to be good or very good in approximately 75% of the area under study. Results show that physiographic-based models can be effectively used to estimate rainfall where there is a lack of rain gauges, or to densify spatial resolution of rainfall between rain gauges., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Measuring weather data in an urban environment is an important task on the journey towards smart cities. Heavy rain can cause flooding in cities and prevent emergency services from reaching their destination because roads or underpasses are blocked. In order to provide a high-resolution site-specific overview in urban areas during heavy rainfall, a dense measurement network is necessary. To achieve this, a smart low-cost rain gauge is needed. In this paper, the current status of the development of an inductive rain gauge is presented. The sensor is based on the eddy current principle and evaluates the frequency of an electrical resonant circuit. For this purpose, a coil is placed under a metal plate. When raindrops hit the plate, it starts to oscillate, which changes the distance to the coil accordingly and causes changes in the frequency of the resonant circuit. Since the sensor is cost-effective, operates self-sufficiently in terms of energy and transmits data wirelessly via LoRaWAN, it can be used flexibly. This enables dense, area-wide coverage over the urban area of interest. The first experimental investigations show a correlation between the size of the rain droplets and the frequency change. Small droplets cause a shift of about 8 kHz and larger droplets of up to 40 kHz. The results prove that raindrops can be detected and categorized using this measurement principle. These data will be used as a basis for future work on calculating precipitation.

Typhoid fever is a major cause of illness and mortality in low- and middle-income settings. We investigated the association of typhoid fever and rainfall in Blantyre, Malawi, where multi-drug-resistant typhoid has been transmitting since 2011. Peak rainfall preceded the peak in typhoid fever by approximately 15 weeks [95% confidence interval (CI) 13.3, 17.7], indicating no direct biological link. A quasi-Poisson generalised linear modelling framework was used to explore the relationship between rainfall and typhoid incidence at biologically plausible lags of 1-4 weeks. We found a protective effect of rainfall anomalies on typhoid fever, at a two-week lag ( P = 0.006), where a 10 mm lower-than-expected rainfall anomaly was associated with up to a 16% reduction in cases (95% CI 7.6, 26.5). Extreme flooding events may cleanse the environment of S. Typhi, while unusually low rainfall may reduce exposure from sewage overflow. These results add to evidence that rainfall anomalies may play a role in the transmission of enteric pathogens, and can help direct future water and sanitation intervention strategies for the control of typhoid fever.

The research presents the inter-comparison of atmospheric variables measured by 9 automatic weather stations. This set of data was compared with the measurements of other weather stations in order to standardize the values that must be adjusted when taken to different areas. The data of a set of a total of 9 GMX500, which measures conventional meteorological variables, and 10 WS100 sensors, which measures precipitation parameters. The automatic stations were set up at the Huancayo Observatory (Geophysical Institute of Peru) for a period of 5 months. The data set of GMX500 were evaluated comparing with the average of the 9 sensors and the WS100 was compared with an optical disdrometer Parsivel2. The temperature, pressure, relative humidity, wind speed, rainfall rate, and drop size distribution were evaluated. A pair of GMX500 sensors presented high data dispersion; it was found found that the errors came from a bad configuration; once this problem was solved, good agreement was archived, with low RMSE and high correlation. It was found that the WS100 sensors overestimate the precipitation with a percentage bias close to 100% and the differences increase with the greater intensity of rain. The drop size distribution retrieved by WS100 have unrealistic behavior with higher concentrations in diameters of 1 mm and 5 mm, in addition to a flattened curve.

Illicit discharges in urban stormwater drains are a major environmental concern that deteriorate downstream waterway health. Conventional detection methods such as stormwater drain visual inspection and dye testing have fundamental drawbacks and limitations which can prevent easy location and elimination of illegal discharges in a catchment. We deployed 22 novel low-cost level, temperature and conductivity sensors across an urban catchment in Melbourne for a year to monitor the distributed drainage network, thereby detecting likely illicit discharges ranging from a transitory flow with less than 10 minutes to persistent flows lasting longer than 20 hours. We discuss rapid deployment methods, real-time data collection and online processing. The ensemble analysis of all dry weather flow data across all sites indicates that: (i) large uncertainties are associated with discharge frequency, duration, and variation in water quality within industrial and residential land uses; (ii) most dry weather discharges are intermittent and transient flows which are difficult to detect and not simply due to cross-connections with the sewerage network; (iii) detectable diurnal discharge patterns can support mitigation efforts, including policies and regulatory measures (e.g., enforcement or education) to protect receiving waterways; and, (iv) that it is possible to cost effectively isolate sources of dry weather pollution using a distributed sensor network.

This paper is based on the fact that some climatic variables show a preferential directionality and grant a markedly anisotropic character to the weathering system acting on rocks. The aim of this work is to quantify the anisotropic degree of the weathering system and its effects on rock erosion. For this purpose, a new methodology based on the vector analysis of directional and time-dependent parameters is proposed to quantify the annual or seasonal anisotropy of the weathering system. Results show that, on the one hand, wind-driven rain and solar radiation are the most anisotropic variables, being north and east the most intense directions for wind-driven rain and southeast for solar radiation, in the case of the San José Tower, the reference monument of this study. On the other hand, the ranking from the most to the least eroded façades of the tower are: east (maximum recession depth of 26.77 mm) > south (15.53 mm) ≈ west (13.56 mm) > north (6.37 mm). Solar radiation and indirect processes arising therefrom are the most important weathering agents in the semiarid Mediterranean climate, whilst wind-driven rain is the main erosion factor especially due to its torrential character. According to our results, weathering and erosion agents are strongly anisotropic, which emphasizes the importance of integrating the anisotropic character of the weathering system in preventive strategies against surface deterioration of monuments. In this sense, this paper advances the United Nations' 2030 Agenda for Sustainable Development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Under adverse weather conditions, visibility and the available pavement friction are reduced. The improper selection of speed on curved road sections leads to an unreasonable distribution of longitudinal and lateral friction, which is likely to cause rear-end collisions and lateral instability accidents. This study considers the combined braking and turning maneuvers to obtain the permitted vehicle speed under rainy conditions. First, a braking distance computation model was established by simplifying the relationship curve between brake pedal force, vehicle braking deceleration, and braking time. Different from the visibility commonly used in the meteorological field, this paper defines "driver's sight distance based on real road scenarios" as a threshold to measure the longitudinal safety of the vehicle. Furthermore, the lateral friction and rollover margin is defined to characterize the vehicle's lateral stability. The corresponding relationship between rainfall intensity-water film thickness-road friction is established to better predict the safe speed based on the information issued by the weather station. It should be noted that since the road friction factor of the wet pavement not only determined the safe vehicle speed but also be determined by the vehicle speed, so we adopt Ferrari's method to solve the quartic equation about permitted vehicle speed. Finally, the braking and turning maneuvers are considered comprehensively based on the principle of friction ellipse. The results of the TruckSim simulation show that for a single-unit truck, running at the computed permitted speed, both lateral and longitudinal stability meet the requirements. The proposed permitted vehicle speed model on horizontal curves can provide driving guidance for drivers on curves under rainy weather or as a decision-making basis for road managers., Competing Interests: The authors have declared that no competing interests exist. There are no patents, products in development or marketed products association with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Sewers can become hydraulically overburdened during high-intensity precipitation resulting in untreated water entering receiving streams. Combined (CSOs) and sanitary sewer overflows (SSOs) cause adverse public health and environmental impacts as well as management challenges for many wastewater utilities. This novel review presents information regarding wet weather flow regulation, impacts, and current management methods, and offers ideas for future approaches in the United States. Currently, storage followed by conventional municipal water reclamation facility treatment after precipitation events is often employed. Stand-alone alternative technologies include high-rate solids removal, rapid disinfection, filtration, and green infrastructure. However, most current stand-alone approaches do not address soluble BOD 5 or emerging contaminants in stormwater and wastewater. As the needs for wet weather flow management change, future approaches should include a goal of zero overflows and achieve effluent quality as good as or better than conventional treatment. To help achieve zero overflows and complete treatment, the "peaker facility" concept is proposed. The peaker facility often remains idle but treats excess flow when needed. Considering the challenges of remaining idle for long periods, starting up quickly, and handling high flows, chemical oxidation may be an applicable peaker facility component. However, more research and development are needed to determine best practices. PRACTITIONER POINTS: Combined (CSO) and sanitary sewer overflows (SSOs) pose both environmental and public health risks as untreated water is discharged into lakes and rivers during high-intensity rain events. Current stand-alone approaches for managing or treating CSOs focus on particulate BOD/COD and solids removal, and do not typically address soluble BOD or emerging contaminants in stormwater and wastewater (including pathogens). New wet weather policies and regulations encourage more holistic approaches by wastewater utilities, and future approaches should include a zero-overflow goal for all CSOs and SSOs. To help achieve zero overflows, the concept of the "peaker facility" is proposed. Chemical oxidation may be an applicable component of peaker facilities for its short detention time and ability to remove, oxidize, or inactive water impairment-causing contaminants., (© 2021 Water Environment Federation.)

The quality of water in many urban rivers in Latin America is increasingly degrading due to wastewater and runoff discharges from urban sprawl. Due to deficits in sanitary drainage systems, greywater is discharged to the stormwater drainage network generating a continuous dry-weather runoff that reaches rivers without treatment. One of the main challenges in the region is to achieve sustainable management of urban runoff for the recovery of rivers ecosystem integrity. However, retrofitting conventional centralized wastewater drainage networks into the existing urban grid represents important social, economic and technical challenges. This paper presents an alternative adaptive methodology for the design of Nature-based Solutions for decentralized urban runoff treatment. Through this study, technical solutions commonly used for stormwater management were adapted for dry-weather runoff treatment and co-designed for the particular conditions of a representative study area, considering space availability as the main constraining factor for retrofitting in urban areas. The application of a co-design process in a dense neighbourhood of the Great Metropolitan area of Costa Rica brought to light valuable insights about conditions that could be hindering the implementation of NBS infrastructures in Latin America.

This paper presents the first causal estimates of the effect of weather on children's time allocation. It exploits exogenous variations in local weather observed during the random diary dates of two nationally representative cohorts of Australian children whose time-use diaries were surveyed biennially over 10 years. Unfavorable weather conditions, as represented by cold or hot temperature or rain, cause children to switch activities from outdoors to indoors, mainly by reducing the time allocated to active pursuits and travel and increasing the time allocated to media. Furthermore, the effects of bad weather are more pronounced on weekends and for children with asthma. Our results also provide some evidence of adaptation, as temperature tends to have greater impact not only in winter months but also in colder regions. Our findings are robust to a wide range of sensitivity checks, including controlling for individual fixed effects and using alternative model specifications. Overall, the results suggest that extreme weather conditions may diminish children's health, development and long-term achievements through their effects on children's time allocation., (© 2021 John Wiley & Sons Ltd.)

Wet weather sewer overflows pose potential short-term public health risks. With increasing populations, aging infrastructure and climate change, utilities are challenged with managing sewerage infrastructure to provide optimum outcomes. This study compared how modelled public health risk profiles could change under alternative sewer overflow management strategies during 12 and 24-month rainfall-runoff events. Specifically, existing conditions were compared with both a 'business-as-usual' (BAU) sewer upgrade and a more holistic 'effects-based planning' (EBP) approach based on pumped wet weather sewage overflows directed to a local receiving waterway. Options were compared based on their efficacy to reduce manhole overflows, recreational waterway guideline exceedances and downstream recreational waterway health risks estimated through a screening-level Quantitative Microbial Risk Assessment (QMRA). Results indicated that the two management strategies would be equally effective in reducing the frequency, duration and volume of manhole sewer overflows, eliminating them in the 12-month scenarios and reducing them from >5000 m 3 for the 24-month baseline scenario, to 23 and 35 m 3 for BAU and EBP, respectively. Baseline, BAU and EBP scenarios produced similar hours of enterococci guideline exceedances, ranging from 1 to 4 h difference. The QMRA produced similar health risk profiles for downstream recreational waterway users for all design events, suggesting that sewer overflows are not the primary driver of public health risks during and immediately following high rainfall events. As such, QMRA provided evidence that an EBP strategy may be used to manage wet weather sewer overflows in lieu of an expensive BAU upgrade, without exacerbating the public health of downstream waterway users. Further investigation of the broader environmental health impacts of implementing this type of innovative approach is warranted. Nonetheless, this work highlights the value of integrating QMRA with other modelling approaches to guide and inform sewer overflow management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

East Africa is an underrepresented region in respect of monitoring the stable isotopic composition of precipitation (δ 18 O prec and δ 2 H prec ). In 2017, we collected precipitation samples from ten weather stations located along an altitudinal transect ranging from 1304 to 4375 m a.s.l. The δ 18 O prec and δ 2 H prec values varied from -8.7 to +3.7 ‰ and -38 to +29 ‰, respectively. The local meteoric water line is characterised by a lower slope, a higher intercept and more positive d -excess values (δ 2 H = 5.3 ± 0.2 * δ 18 O + 14.9 ± 0.9) compared to the global meteoric water line. Both altitude and amount of precipitation clearly correlate with our isotope data. However, the δ 18 O prec and δ 2 H prec values show at the same time a seasonal pattern reflecting rainy versus dry season. More enriched isotope values prevailed shortly after the end of the dry season; more depleted isotope values coincided with high precipitation amounts recorded in May, August and September. Moreover, HYSPLIT trajectories reveal that during the dry season water vapour originates primarily from the Arabian Sea, whereas during the wet season it originates primarily from the Southern Indian Ocean. These findings challenge the traditional amount effect interpretation of paleoclimate isotope records from East Africa and rather point to a previously underestimated source effect.

Accurate and robust short-term rainfall forecasts (nowcasts) are useful in operational flood forecasting. However, the high temporal and spatial variability of rainfall fields make rainfall nowcasting a challenging endeavour. To cope with this variability, nowcasting techniques based on weather radar imagery have been proposed. Here, we employ radar rainfall nowcasting for discharge predictions in three lowland catchments in the Netherlands, with surface areas ranging from 6.5 to 957 km 2 . Deterministic (Lagrangian persistence) and probabilistic (SBMcast) nowcasting techniques are used to produce short-term rainfall forecasts (up to a few hours ahead), which are used as input for the hydrological model WALRUS. Rainfall forecasts were found to deteriorate with increasing lead time, often due to underestimation. Discharge could be forecasted 25-170 min earlier than without rainfall nowcasting, with the best performance for the largest catchment. When accounting for catchment response time, the best (but most variable) relative performance was found for the smallest catchment. Probabilistic nowcasting effectively accounted for the uncertainty associated with rainfall and discharge forecasts. The uncertainty in rainfall forecasts was found to be largest for the smaller catchments. The uncertainty in how much earlier the discharge could be forecasted (the gain in lead time) ranged from 15 to 50 min., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Thunderstorm ground enhancement (TGE) is a phenomenon that enhances radiation background on the ground related to thunderstorm activity and charge structure of the thundercloud. On the other hand, the rise of gamma background is connected with precipitation by the washout of radon progeny from the atmosphere. In our analysis, we examined known enhancements of gamma background, previously attributed solely to radon progeny, using data from the Czech Radiation Monitoring Network (RMN) to investigate the enhancements with respect to thunderstorms and TGE phenomena. We also used radar precipitation data and data from the lightning location network to analyze their influences on the radiation background enhancement during three thunderstorm events that occurred in summer 2016 over the Czech Republic (Central Europe). We state that the RMN might have detected TGE over the Czech Republic., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Walking is the most accessible form for seniors to engage in daily light or moderate physical activity. Walking activity depends on both individual and environmental factors, the latter including how walkable a given setting is. Recent papers have pointed at the relevance of also considering meteorological conditions in relation to the walking behavior of older adults. This paper explores the combined effect of neighborhood walkability, temperature and rain on daily walking time among seniors residing in Barcelona. Daily walking time was extracted from 7-day GPS (Global Positioning System) devices and accelerometer data of 227 seniors residing in the Barcelona Metropolitan Region (Spain). Temperature and rain data were extracted from official governmental weather stations. Mixed-effects linear regression models were adjusted to test the combined association between weather and walkability on daily walking time. Neighborhood walkability is positively associated with walking time among seniors, while rain generally deters it. Additionally, this study demonstrates that temperature and rain modify the effect of residential walkability on senior walking activity: low temperatures are particularly associated with lower walking activity among those residing in low walkable areas, while the presence of rain presents a negative association with walking time in high walkable environments. The combined effect of walkability and weather should be considered both in design actions that aim at improving walking infrastructure and also in prevention programs aimed at encouraging daily walking among seniors.

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman's correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p -value = <0.001), Mopani (r = 0.53; p -value = <0.001), Waterberg (r = 0.40; p -value =< 0.001), Capricorn (r = 0.37; p -value = <0.001) and lowest in Sekhukhune (r = 0.36; p -value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer-December, January, and February (DJF)-can be explained by spring-September, October, and November (SON)-rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.

This paper studies in depth the first flush concept with the aim of exploiting the potential of this phenomenon for an effective and economical implementation of stormwater quality control practices. A quantitative first flush methodology recently proposed in the scientific literature is applied to discrete water quality data of different pollution parameters from an Italian database. The methodology is rigorous and effective for characterising the dynamics of different pollutant types in wet-weather runoff, allowing an assessment of the first flush strength and the detection of the runoff volume required to reduce concentrations to background levels peculiar of the catchment. A strong reduction in concentration is attained after 3 mm runoff, but the achievement of background levels for all pollutant parameters requires the transit of 6 mm runoff. Sensitivity analysis shows the crucial role of the event selection criteria for enhancing the robustness of the methodology. The advantages of the adopted procedure are also highlighted by comparison with the widely used Mass First Flush Ratio method. The results are also compared with Italian guidelines for the design of stormwater quality control measures, pointing out the fruitfulness and profitability of the methodology for decision making in this context., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In this article, a dynamical downscaling (DD) procedure is proposed to downscale tropical cyclones (TCs) from a general circulation model, with the goal of investigating inland intense precipitation from these storms in the future. This DD procedure is sequential as it is performed from the large scale to the small scale within a one-way nesting modeling framework with the Weather Research and Forecasting (WRF) model. Furthermore, it involves a two-step validation process to ensure that the model produces realistic TCs, both in terms of their general properties and in terms of their intense precipitation statistics. In addition, this procedure makes use of several algorithms such as for the detection and tracking of TCs, with the objective of automatizing the DD process as much as possible so that this approach could be used to downscale massively many climate projections with several sets of model options. The DD approach was applied to the Community Climate System Model (CCSM) version 4 using Representative Concentration Pathway (RCP) 4.5 during the period 2005⁻2100, and the resulting TCs and their intense precipitation were examined.

Background: Rotavirus and norovirus are infectious pathogens primarily affecting children under 5 years old. The impact of rainfall on diarrheal diseases remains inconclusive. This study aimed to evaluate the association between short-term variation in rainfall, temperature and humidity, and rotavirus and norovirus hospitalizations among young children in Hong Kong., Methods: Generalized additive negative binomial regression models with distributed lag non-linear terms, were fit with daily counts of hospital admissions due to rotavirus and norovirus infection as the outcomes and daily total rainfall and other meteorological variables as predictors, adjusting for seasonality and trend., Results: Generally, greater rainfall was associated with fewer rotavirus, but more norovirus hospitalizations. Extreme precipitation (99.5 mm, 99th percentile) was found to be associated with 0.40 (95% confidence interval (CI) 0.20-0.79) and 1.93 (95% CI 1.21-3.09) times the risk of hospitalization due to rotavirus and norovirus infection respectively, relative to trace rainfall. Stronger associations were observed in winter for rotavirus and in summer for norovirus. The duration of association with rotavirus was notably longer than norovirus. Higher temperatures were found to be associated with fewer hospitalizations for both rotavirus and norovirus infection, while higher relative humidity was generally associated with more norovirus, but fewer rotavirus, hospitalizations., Conclusions: Both rotavirus and norovirus hospitalizations were strongly associated with recent precipitation variation but in opposite directions. With the introduction of the rotavirus vaccine norovirus is likely to become a greater threat than rotavirus and thus greater precipitation may become more clearly associated with more childhood diarrhea., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Category 4 landfalling hurricane Harvey poured more than a metre of rainfall across the heavily populated Houston area, leading to unprecedented flooding and damage. Although studies have focused on the contribution of anthropogenic climate change to this extreme rainfall event 1-3 , limited attention has been paid to the potential effects of urbanization on the hydrometeorology associated with hurricane Harvey. Here we find that urbanization exacerbated not only the flood response but also the storm total rainfall. Using the Weather Research and Forecast model-a numerical model for simulating weather and climate at regional scales-and statistical models, we quantify the contribution of urbanization to rainfall and flooding. Overall, we find that the probability of such extreme flood events across the studied basins increased on average by about 21 times in the period 25-30 August 2017 because of urbanization. The effect of urbanization on storm-induced extreme precipitation and flooding should be more explicitly included in global climate models, and this study highlights its importance when assessing the future risk of such extreme events in highly urbanized coastal areas.

Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries., (Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.)

Extreme weather events may be just as important as gradual trends for the long-term trajectories of ecosystems. For alpine lakes, which are exposed to both exacerbated atmospheric warming and intense episodic weather events, future conditions might not be appropriately forecast by only climate change trends, i.e. warming, if extreme events have the potential to deflect their thermal and metabolic states from their seasonal ranges. We used high-frequency monitoring data over three open-water seasons with a one-dimensional hydrodynamic model of the high-altitude Lake Muzelle (France) to show that rainstorms or windstorms, notwithstanding their intensity, did not trigger long-lasting consequences to the lake characteristics when light penetration into the lake was not modified. In contrast, storms associated with high turbidity input from the watershed ("turbid storms") strongly modified the lacustrine hydrodynamics and metabolism for the rest of the open-water season through reduced light penetration. The long-lasting effects of turbid storms were related to the inputs and in-lake persistence of very light glacial suspensoids from the watershed. The occurrence of the observed turbid storms was not related to the wind or rain intensities during the events. Instead, the turbid storms occurred after dry and atypically warm spells, i.e. meteorological conditions expected to be more frequent in this alpine region in the upcoming decades. Consequently, storm events, notwithstanding their intensity, are expected to strongly imprint the future ecological status of alpine lakes under climate warming., (© 2018 John Wiley & Sons Ltd.)

This work proposes a Bayesian non-informative reconstruction of virtual state variables in the representation of stormwater total suspended solids pollutographs by the traditional wash-off models, based on 255 rainfall events measured in a 185 ha French urban catchment. Results from event-based analyses revealed the missing representation of an essential process in the traditional rating curve (RC) model (simplest wash-off model) for 56% of the rainfall events. The unsatisfactory performances of the RC model are found to be not necessarily linked to antecedent dry weather conditions, as assumed by a great number of accumulation/wash-off models. Statistical tests suggest that non-representable rainfall events by the RC model are randomly distributed in time. The proposed Bayesian reconstructions of a potential process missed by the RC model exhibit a suitable identifiability at an intra-event scale. However, these reconstructions are not interpretable from the traditional accumulation/wash-off notions, i.e. in terms of a unique state of virtual available mass over the catchment that is decreasing over time, due to their high unrepeatability regarding their shape and their low prediction capacity for other rainfall events.

Interaction between adverse weather conditions and motor vehicle crashes is an important topic for traffic engineers and hydrometeorologists. With the recent availability of high resolution precipitation products (hourly, 4 × 4 km), it is possible to evaluate crash risk during rainfall events more accurately. Texas, the second largest state in the U.S., with a relatively high population density in its eastern part that receives significant rainfall from tropical events, experiences many hazardous traffic conditions every year. This study investigates temporal and spatial variability of the Relative Accident Risk (RAR) due to rainy conditions across Texas during the year 2015 using a Crash-Based Matched Pairs Analysis (CB-MPA) approach for every 4 × 4 km grid using an hourly time scale. The overall findings show that rainfall increases crash risk across the state by about 57%, while seasonal-based analysis confirms the role of precipitation patterns on crash rates. Although eastern and central counties (wetter and more urbanized) have remarkably higher rates of crash occurrence, the western counties (mainly rural and dry) show higher RAR values. Moreover, higher rainfall intensity can increase RAR up to three-fold while directly having an adverse effect on crash injury type. There is a relatively high correlation between rainfall intensity and RAR values (R 2  = 0.76). The analysis also shows higher RAR values on high-speed interstate highways and tollways compared to urban local streets. RAR values also vary according to the gender and age of drivers. The study findings shed light on future paths toward more detailed applications of high-resolution environmental data in crash risk analysis., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The technical lifetime of urban water infrastructure has a duration where climate change has to be considered when alterations to the system are planned. Also, models for urban water management are reaching a very high complexity level with, for example, decentralized stormwater control measures being included. These systems have to be evaluated under as close-to-real conditions as possible. Long term statistics (LTS) modelling with observational data is the most close-to-real solution for present climate conditions, but for future climate conditions artificial rainfall time series from weather generators (WGs) have to be used. In this study, we ran LTS simulations with four different WG products for both present and future conditions on two different catchments. For the present conditions, all WG products result in realistic catchment responses when it comes to the number of full flowing pipes and the number and volume of combined sewer overflows (CSOs). For future conditions, the differences in the WGs representation of the expectations to climate change is evident. Nonetheless, all future results indicate that the catchments will have to handle more events that utilize the full capacity of the drainage systems. Generally, WG products are relevant to use in planning of future changes to sewer systems.

Despite El Niño events being one of the main forces shaping the coastal desert vegetation in South America, the impacts of the high precipitation typical of this rare but recurrent climatic event remain understudied. Here we monitored the plant community of a coastal lomas, a seasonal desert ecosystem, during 1998 and 2001 to analyse its changes during the 1997-98 El Niño and the following La Niña events. We measured species abundance and vegetation cover in 31 plots, and recorded climate variables in Lomas de Lachay, Peru. We found a significant positive correlation between precipitation and vegetation cover, density, alpha diversity (species diversity at the plot level), total richness and abundance of several key species but no correlation with gamma diversity (species diversity at the whole loma level). During the El Niño event, the seasonality, typical of the lomas ecosystem, disappeared, as evidenced by both the similarity of species composition and mean vegetation cover values between most sampling campaigns of 1998 and 1999. Moreover, total richness was lower during the El Niño event than during the humid season of 2000 and 2001 resulting from the dominance of only a few species, such as Nicotiana paniculata and Loasa urens. Temporal-spatial changes in the abundance of the dominant species caused the differences between alpha and gamma diversity, especially during 1999. Within that year, mean alpha diversity showed similar values whilst gamma diversity values were different. The reestablishment of the seasonality of most plant community characteristics and a clear difference between species composition of the humid and the dry season occurred two years after the El Niño event, suggesting a resilient community. This study provides one of the few quantifications of the Peruvian lomas' response to the 1997-98 El Niño event and the following La Niña, one of the most extreme climatic events in the last century.

Sewage systems are a vital part of the urban infrastructure in most cities. They provide drainage, which protects public health, prevents the flooding of property and protects the water environment around urban areas. On some occasions sewers will overflow into the water environment during heavy rain potentially causing unacceptable impacts from releases of untreated sewage into the environment. In typical Life Cycle Assessment (LCA) studies of urban wastewater systems (UWS), average dry-weather conditions are modelled while wet-weather flows from UWS, presenting a high temporal variability, are not currently accounted for. In this context, the loads from several storm events could be important contributors to the impact categories freshwater eutrophication and ecotoxicity. In this study we investigated the contributions of these wet-weather-induced discharges relative to average dry-weather conditions in the life cycle inventory for UWS. In collaboration with the Paris public sanitation service (SIAAP) and Observatory of Urban Pollutants (OPUR) program researchers, this work aimed at identifying and comparing contributing flows from the UWS in the Paris area by a selection of routine wastewater parameters and priority pollutants. This collected data is organized according to archetypal weather days during a reference year. Then, for each archetypal weather day and its associated flows to the receiving river waters (Seine), the parameters of pollutant loads (statistical distribution of concentrations and volumes) were determined. The resulting inventory flows (i.e. the potential loads from the UWS) were used as LCA input data to assess the associated impacts. This allowed investigating the relative importance of episodic wet-weather versus "continuous" dry-weather loads with a probabilistic approach to account for pollutant variability within the urban flows. The analysis at the scale of one year showed that storm events are significant contributors to the impacts of freshwater eutrophication and ecotoxicity compared to those arising from treated effluents. At the rain event scale the wet-weather contributions to these impacts are even more significant, accounting for example for up to 62% of the total impact on freshwater ecotoxicity. This also allowed investigating and discussing the ecotoxicity contribution of each class of pollutants among the broad range of inventoried substances. Finally, with such significant contributions of pollutant loads and associated impacts from wet-weather events, further research is required to better include temporally-differentiated emissions when evaluating eutrophication and ecotoxicity. This will provide a better understanding of how the performance of an UWS system affects the receiving environment for given local weather conditions., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Malaria cases in South Africa's Northern Province of Limpopo have surpassed known endemic KwaZulu Natal and Mpumalanga Provinces. This paper applies statistical methods: regression analysis and impulse response function to understand the timing of impact and the length that such impacts last. Climate data (rainfall and temperature) are obtained from South African Weather Services (SAWs); global data from the European Centre for Medium-Range Weather Forecasts (ECMWF), while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province). Data collected span from January 1998 to July 2007. Signs of the coefficients are positive for rainfall and temperature and negative for their exponents. Three out of five independent variables consistently maintain a very high statistical level of significance. The coefficients for climate variables describe an inverted u-shape: parameters for the exponents of rainfall (-0.02, -0.01, -0.02, -0.00) and temperature (-46.61, -47.46, -48.14, -36.04) are both negative. A one standard deviation rise in rainfall (rainfall onset) increases malaria cases, and the effects become sustained for at least 3 months and conclude that onset of rainfall therefore triggers a 'malaria season'. Malaria control programme and early warning system should be intensified in the first 3 months following the onset of rainfall.

The literature has documented a positive relationship between walkable built environments and outdoor mobility in older adults. Yet, surprisingly absent is any consideration of how weather conditions modify the impact of neighbourhood walkability. Using archived weather data linked to survey data collected from a sample of older adults in Vancouver, Canada, we found that car-dependent neighbourhoods (featuring longer block lengths, fewer intersections, and greater distance to amenities) became inaccessible in snow. Even older adults who lived in very walkable neighbourhoods walked to 25 per cent fewer destinations in snow. It is crucial to consider the impact of weather in the relationship between neighbourhood walkability and older adult mobility.

The dynamics of a mosquito population depends heavily on climatic variables such as temperature and precipitation. Since climate change models predict that global warming will impact on the frequency and intensity of rainfall, it is important to understand how these variables affect the mosquito populations. We present a model of the dynamics of a Culex quinquefasciatus mosquito population that incorporates the effect of rainfall and use it to study the influence of the number of rainy days and the mean monthly precipitation on the maximum yearly abundance of mosquitoes M max . Additionally, using a fracturing process, we investigate the influence of the variability in daily rainfall on M max . We find that, given a constant value of monthly precipitation, there is an optimum number of rainy days for which M max is a maximum. On the other hand, we show that increasing daily rainfall variability reduces the dependence of M max on the number of rainy days, leading also to a higher abundance of mosquitoes for the case of low mean monthly precipitation. Finally, we explore the effect of the rainfall in the months preceding the wettest season, and we obtain that a regimen with high precipitations throughout the year and a higher variability tends to advance slightly the time at which the peak mosquito abundance occurs, but could significantly change the total mosquito abundance in a year., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

For storm drainages inappropriately connected with sewage, wet weather discharge is a major factor that adversely affects receiving waters. A study of the wet weather influences of rainfall-discharge variables on storm drainages connected with sewage was conducted in the downtown Shanghai area (374 ha). Two indicators, event mean concentration (EMC) and event pollutant load per unit area (EPL), were used to describe the pollution discharge during 20 rain events. The study showed that the total rainfall and discharge volume were important factors that affect the EMCs and EPLs of the chemical oxygen demand, total phosphorus, and especially those of NH 4 + -N. The pollutant concentrations at the beginning of the discharge and the discharge period were also major factors that influence the EMCs of these three pollutants. Regression relationships between the rainfall-discharge variables and discharge volume/ EPLs (R 2 = 0.824-0.981) were stronger than the relationships between the rainfall-discharge variables and EMCs. These regression equations can be considered reliable in the system, with a relative validation error of less than ±10% for the discharge volume, and less than ±20% for the EPLs. The results presented in this paper provide guidance for effectively controlling pollution in similar storm drainages.

Sophisticated strategies are required for flood warning in urban areas regarding convective heavy rainfall events. An approach is presented to improve short-term precipitation forecasts by combining ensembles of radar nowcasts with the high-resolution numerical weather predictions COSMO-DE-EPS of the German Weather Service. The combined ensemble forecasts are evaluated and compared to deterministic precipitation forecasts of COSMO-DE. The results show a significantly improved quality of the short-term precipitation forecasts and great potential to improve flood warnings for urban catchments. The combined ensemble forecasts are produced operationally every 5 min. Applications involve the Flood Warning Service Hamburg (WaBiHa) and real-time hydrological simulations with the model KalypsoHydrology.

Background: To evaluate the 1-year stability of objectively measured physical activity among young adults living in South Brazil, as well as assessing the influences of temperature, humidity and precipitation on physical activity., Methods: A longitudinal study was conducted over 12 consecutive months (October 2012 to September 2013). Sixteen participants (8 men) used GT3X+ accelerometers 1 week per month for the entire year. Climate variables were obtained from an official climate information provider., Results: Physical activity was remarkably stable over the year-the proportion of the day spent in moderate-to-vigorous intensity physical activity (MVPA) was around 5% in every month. Average temperature (ρ = -0.64; P = .007), humidity (ρ = -0.68; P = .004) and rain (ρ = -0.67; P = .004) were inversely correlated to MVPA in the Summer. Rain was also inversely correlated to MVPA in the Spring (ρ = -0.54; P = .03)., Conclusions: Objectively measured physical activity was stable over a 1-year period. Climate variables consistently influenced physical activity practice in the Summer, but not in the other seasons.

Previous researches demonstrated the occurrence of unique in-sewer sediment, the organic layer, on the Marais site in Paris, capable of explaining the entire wet weather sewer production of suspended particles. Other studies on sites having no similar organic sediment, Clichy in Paris and Ecully in Lyon, demonstrated a wet weather sewer deposits contribution (SDC) to effluent pollution comparable to that of the Marais site, casting therefore doubts on the implication of the organic layer to the outlet discharge pollution. So, an in-depth comparative investigation of the different sites' mean SDC was carried out to confirm or refute the major role of this layer vis-à-vis sewer production. The size and characteristics of the events' sample used to calculate the SDC were analyzed to find whether a statistical bias may have masked a difference that would be more coherent with field observations. After homogenizing these elements, the organic layer regained some of its previously alleged participation in sewer contribution (a maximum of 36% of the total SDC) but another unknown source was still dominant. This suggests that sewer sediment production during wet weather is a result of multiple sediment erosion: the organic layer and another major source not yet identified.

Dengue is the major public health burden in Sri Lanka. Kalutara is one of the highly affected districts. Understanding the drivers of dengue is vital in controlling and preventing the disease spread. This study focuses on quantifying the influence of weather variability on dengue incidence over 10 Medical Officer of Health (MOH) divisions of Kalutara district. Weekly weather variables and data on dengue notifications, measured at 10 MOH divisions in Kalutara from 2009 to 2013, were retrieved and analysed. Distributed lag non-linear model and hierarchical-analysis was used to estimate division specific and overall relationships between weather and dengue. We incorporated lag times up to 12 weeks and evaluated models based on the Akaike Information Criterion. Consistent exposure-response patterns between different geographical locations were observed for rainfall, showing increasing relative risk of dengue with increasing rainfall from 50 mm per week. The strongest association with dengue risk centred around 6 to 10 weeks following rainfalls of more than 300 mm per week. With increasing temperature, the overall relative risk of dengue increased steadily starting from a lag of 4 weeks. We found similarly a strong link between the Oceanic Niño Index to weather patterns in the district in Sri Lanka and to dengue at a longer latency time confirming these relationships. Part of the influences of rainfall and temperature can be seen as mediator in the causal pathway of the Ocean Niño Index, which may allow a longer lead time for early warning signals. Our findings describe a strong association between weather, El Niño-Southern Oscillation and dengue in Sri Lanka., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Global precipitation isoscapes based on the Global Network for Isotopes in Precipitation (GNIP) network are an important toolset that aid our understanding of global hydrologic cycles. Although the GNIP database is instrumental in developing global isoscapes, data coverage in some regions of hydrological interest (e.g., drylands) is low or non-existent thus the accuracy and relevance of global isoscapes to these regions is debatable. Capitalizing on existing literature isotope data, we generated rainfall isoscapes for Namibia (dryland) using the cokriging method and compared it to a globally fitted isoscape (GFI) downscaled to country level. Results showed weak correlation between observed and predicted isotope values in the GFI model (r2 < 0.20) while the cokriging isoscape showed stronger correlation (r2 = 0.67). The general trend of the local cokriging isoscape is consistent with synoptic weather systems (i.e., influences from Atlantic Ocean maritime vapour, Indian Ocean maritime vapour, Zaire Air Boundary, the Intertropical Convergence Zone and Tropical Temperate Troughs) and topography affecting the region. However, because we used the unweighted approach in this method, due to data scarcity, the absolute values could be improved in future studies. A comparison of local meteoric water lines (LMWL) constructed from the cokriging and GFI suggested that the GFI model still reflects the global average even when downscaled. The cokriging LMWL was however more consistent with expectations for an arid environment. The results indicate that although not ideal, for data deficient regions such as many drylands, the unweighted cokriging approach using historical local data can be an alternative approach to modelling rainfall isoscapes that are more relevant to the local conditions compared to using downscaled global isoscapes.

The δD and δ¹⁸O values in precpitation have disciplinary variations, and they have close connections with meteorological parameters and moisture sources. Based on the continuously collected precipitation samples in Mengzi from Jan. 2009 to Dec. 2011, the reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR), and the Hybrid Single-Particle Lagrangian Integrated Trajectory for 4.8 (HYSPLIT&#95;4.8) model, we analyzed the variations of δD and δ¹⁸O in precipitation at synoptic scale in Mengzi, and the relations between δ¹⁸O in precipitation and air temperature, δ¹⁸O in precipitation and amount, δ¹⁸O in precipitation and wind speed, δ¹⁸O in precipitation and moisture sources. The results showed that the variations of δD and δ¹⁸O values in precipitation exhibited remarkable seasonal variability. The stable isotopic values in precipitation were higher during dry season than those during wet season. The relations between δ¹⁸O in precipitation and air temperature, δ¹⁸O in precipitation and amount indicated significant negative correlations. However, it exhibited significant positive correlation between precipitation δ¹⁸O and wind speed at different pressure levels (300 hPa, 500 hPa, 700 hPa, and 800 hPa), and this result indicated that the wind speed was an important influencing factor for the variations of precipitation δ¹⁸O. With the increasing rainfall levels, the intercept and slope of meteoric water line also increased, and this phenomenon suggested that there was a secondary evaporation effect under sub-cloud in stable isotopes of precipitation. The backward trajectory model showed that the main moisture sources during dry season came from the westerly and the inland. However, during wet season, the remote ocean vapor was the main moisture source in Mengzi, and the δ¹⁸O values in precipitation were much lower during typhoon period.

Ruminants are considered the main reservoir for transmission of Coxiella burnetii (Cb) to humans. The implementation of effective control measures against Cb in ruminants requires knowledge about potential risk factors. The objectives of this study were (i) to describe the spatial distribution of Q fever-infected dairy cattle herds in Sweden, (ii) to quantify the respective contributions of wind and animal movements on the risk of infection, while accounting for other sources of variation, and (iii) to investigate the possible protective effect of precipitation. A total of 1537 bulk milk samples were collected and tested for presence of Cb antibodies. The prevalence of test-positive herds was higher in the south of Sweden. For herds located in areas with high wind speed, open landscape, high animal densities and high temperature, the risk of being infected reached very high values. Because these factors are difficult to control, vaccination could be an appropriate control measure in these areas. Finally, the cumulated precipitation over 1 year was identified as a protective factor.

Background: Water Sensitive Urban Design (WSUD) systems are frequently used as part of a stormwater harvesting treatment trains (e.g. biofilters (bio-retentions and rain-gardens) and wetlands). However, validation frameworks for such systems do not exist, limiting their adoption for end-uses such as drinking water. The first stage in the validation framework is pre-validation, which prepares information for further validation monitoring., Objectives: A pre-validation roadmap, consisting of five steps, is suggested in this paper. Detailed methods for investigating target micropollutants in stormwater, and determining challenge conditions for biofilters and wetlands, are provided., Methods: A literature review was undertaken to identify and quantify micropollutants in stormwater. MUSIC V5.1 was utilized to simulate the behaviour of the systems based on 30-year rainfall data in three distinct climate zones; outputs were evaluated to identify the threshold of operational variables, including length of dry periods (LDPs) and volume of water treated per event., Results: The paper highlights that a number of micropollutants were found in stormwater at levels above various worldwide drinking water guidelines (eight pesticides, benzene, benzo(a)pyrene, pentachlorophenol, di-(2-ethylhexyl)-phthalate and a total of polychlorinated biphenyls). The 95th percentile LDPs was exponentially related to system design area while the 5th percentile length of dry periods remained within short durations (i.e. 2-8 hours). 95th percentile volume of water treated per event was exponentially related to system design area as a percentage of an impervious catchment area., Conclusions: The out-comings of this study show that pre-validation could be completed through a roadmap consisting of a series of steps; this will help in the validation of stormwater treatment systems.