Gridded Datasets Research Articles

Risks of high temperature exposure on infectious diseases in Portuguese hospitals

Abstract Background The convergence of climate change and infectious diseases presents a growing public health challenge. As global temperatures continue to climb, the geographical spread of climate-sensitive diseases is shifting, underscoring the necessity for comprehensive early warning systems grounded in a One Health framework. Methods This retrospective observational study analyzed daily maximum temperature from May to September, between 2000 and 2018, in mainland Portugal, at a county level, obtained from a European-level gridded observations dataset. Hospital admissions with a primary diagnosis of infectious and parasitic diseases were sourced from the Hospital Morbidity Database. A distributed lag non-linear model was developed to estimate the immediate and lagged relative risks (RR) of temperature exposure on admissions, focused on a lag of 10 days. Results Our research reveals a 16% (95% CI: 9% - 23%) rise in the RR of hospital admissions due to infectious and parasitic diseases after exposure to extremely high temperatures (39 °C). This elevated risk persists for up to 2 days following exposure, with the second day exhibiting the largest increase at 53% (31% - 80%). Furthermore, our findings indicate a subsequent resurgence in RR, reaching 6% (2% - 11%) 9 days after exposure. Conclusions Our results highlight a short-term effect of extreme temperature exposure on infectious and parasitic disease admissions. This data provides valuable insights to guide policymakers in identifying critical periods of vulnerability and implementing this knowledge into heat early warning systems, contributing to developing effective public health strategies against climate change. Acknowledgments The authors thank Fundação para a Ciência e a Tecnologia (FCT, Portugal) for research grant UIDP/04923/2020 and NOVA National School of Public Health for access to the Hospital Morbidity Database made available for research purposes by Administração Central do Sistema de Saúde, IP (ACSS). Key messages • Exposure to extremely high temperatures increases the risk of admission due to infectious and parasitic diseases by 16%, with a peak occurring two days after exposure. • This data is essential for policymakers to identify and integrate vulnerable periods into early heat warning systems, enhancing effective public health strategies against climate change.

High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Marco Greater Bay Area

ABSTRACT Accurate population mapping is crucial for disaster management, urban planning, etc. However, current methods using nighttime light (NTL) and gridded population datasets are limited by low spatial resolution and insufficient training data for complex models such as deep learning. These models do not adequately utilize spatial information in population mapping. To address these limitations, this study proposes a high-resolution population mapping method using the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) glimmer imager data and deep learning. The method includes a sample generation strategy with multiple regression and multilevel screening to provide sufficient, high-quality samples for deep learning. A Fine Population mapping network (FinePop-net) is also developed to train regression models using image samples, capturing multi-scale features for model training. When applied to the Guangdong-Hong Kong-Macao Greater Bay Area with 40-meter resolution SDGSAT 1 glimmer imagery, the method significantly reduced the average absolute error and root-mean-square error by 9.35% and 11.44%, respectively, compared with those of the pixel-level learning methods. It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.

Evaluation of gridded precipitation datasets in mountainous terrains of Northwestern Mexico

Study regionThe complex mountainous terrains of the Sierra Madre Occidental in northwestern Mexico. Study focusAcquiring high-resolution precipitation data in regions with limited conventional rain gauge coverage poses significant challenges. Gridded precipitation (GP) datasets, including gauge-based, satellite, and reanalysis products, provide a potential solution, but their reliability in areas with complex terrain and intricate precipitation patterns remains uncertain. This study comprehensively evaluates the performance of four GP datasets—AORC, CHIRPS, Daymet, and ERA5—in estimating precipitation. The evaluation was conducted at a daily, monthly, and seasonal timescale, further analyzing extreme precipitation, the influence of elevation, and spatial averaging across hydrologic basins, using as reference the NERN rain gauge data from 2002 to 2004. New hydrological insights for the regionResults indicate that Daymet and AORC are the most accurate GP datasets for daily and monthly timescales, respectively. All datasets improve in accuracy over longer timescales but face challenges during the wet summer monsoon months and extreme events, with Daymet performing relatively better. Terrain elevation had a minimal impact on overall dataset accuracy, though a slight improvement in precipitation detection was noted as elevation increased. This work provides valuable insights into the strengths and limitations of GP datasets in regions with complex terrain and orographically-forced convective precipitation, offering practical outcomes for climate and hydrologic studies in similar regions.

The effective elastic thickness along the Emperor Seamount Chain and its tectonic implications

Summary The Hawaiian-Emperor Seamount Chain, a pre-eminent example of a hotspot-generated intraplate seamount chain, provides key constraints not only on the kinematics of plates but also on their rigidity. Previous studies have shown that the effective elastic thickness, Te, a proxy for the long-term strength of the lithosphere, changes abruptly at the Hawaiian-Emperor ‘bend’ from low values (∼16 km) at the Emperor Seamounts to high values (∼27 km) at the Hawaiian Ridge. To better constrain Te along the poorly explored Emperor Seamounts we have used a free-air gravity anomaly and bathymetry gridded data set, together with fully 3-dimensional elastic plate (flexure) models, to estimate the continuity of Te and volcano load and infill densities along 1000 profiles spaced 2 km apart of the chain. Results show that Te generally decreases northward along the chain. The decrease is most systematic between Ojin and Jimmu guyots where Te depends on the age of the lithosphere at the time of volcano loading and is controlled by the 340°C and 400°C oceanic isotherms. The largest variation from these isotherms occurs at the northern and southern ends of the chain where Te is smaller than expected suggesting the influence of pre-existing, older, loads. We use these results to constrain the subsidence, flexural tilt, rheological properties, and tectonic setting along the seamount chain. We found an excess subsidence in the range 1.2-2.4 km, a tilt as large as 2-3°, oceanic lithosphere that is weaker than it is seaward of the weak zone at subduction zones, and a tectonic setting at Detroit and Koko seamounts that, despite their forming an integral part of the hotspot generated seamount chain, retains a memory of their proximity to earlier loads associated with plume influenced mid-oceanic ridges.

Insights from a population grid of South Africa: An applied spatial satellite data analysis

The present study explores the reliability and accuracy of various spatial mapping methodologies in estimating and presenting the spatial characteristic and dynamics (location, distribution, density, and size) of the population in South Africa. As a basic underlying concept, the study first explores spatial heterogeneity, that is, that every location is related to every other location, and those nearby are related stronger. This study, therefore, illustrates the spatial relationships between locations and the spatial pattern of the population in South Africa. Analyzing the spatial images determines the extent of such influence and the nature of the spatial patterns. To this end, a granular gridded population dataset was derived using satellite image data, and the NASA’s Socioeconomic Data and Applications Center gridded population of the world version 4 population images and datasets were used. Several spatial data models and geostatistical applications were applied to study the spatial characteristics and dynamics of the population of South Africa from 2000 to 2020. Spatial analysis was performed using R-Studio, QGIS, and GeoDa. Among others, the results point to the fact that the South African population is very densely located that population density decreases marginally outward and suggests that the underlying process for the population distribution is stationary. This study proposes that it is indeed possible to reliably and accurately estimate and present gridded population images and datasets using spatial and geostatistical methodologies.

Attributions of Rainfall Anomalies to Weather Systems and Their Spatial and Temporal Variabilities: A Case Study of Victoria in Southeast Australia

ABSTRACTAttributions of rainfall anomalies to weather systems and their spatio‐temporal variability in Victoria, southeast Australia are investigated with a multimethod weather type dataset and two popularly used gridded daily rainfall datasets for the period 1979–2015. The rainfall anomalies before, during and after the Millennium Drought (1997–2009) are compared to quantify the temporal variability of rainfall responses to weather type changes. The results show: (1) Three weather systems (Front, Cyclone and Thunderstorm) and their combinations contribute 89% of total rainfall; (2) Contributions of weather types to rainfall vary from month to month with winter season rainfall coming from more diverse weather types than summer rainfall; (3) The contributions of weather types to rainfall in three periods show temporal variabilities and there is a clear shift of contribution pattern after the Millennium Drought, such as front‐thunderstorm (FT) is now the largest contributor to rainfall compared with cyclone‐frontal‐thunderstorm (CFT) before and during the Millennium Drought; (4) A seasonal shift in the post‐drought period is found with higher rainfall in February and March and lower rainfall in September and October. The increased rainfall in February mainly results from Front–Thunderstorm (FT) and Thunderstorm‐only (TO), while rainfall declines in September from all weather types; (5) Several rainfall characteristics that are important for streamflow generation, such as rainfall intensity, probability of rainfall occurrence, number of rainfall days and the maximum daily rainfall, do depend on the weather types; (6) The results are similar with different rainfall datasets, but differences do exist, especially at the local scale. The conclusions of this study are drawn from an Australian case study but have implications for other regions to investigate the attributions of rainfall characteristic changes to weather systems.

A high temporal resolution global gridded dataset of human thermal stress metrics

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

GHOST: a globally harmonised dataset of surface atmospheric composition measurements

Abstract. GHOST (Globally Harmonised Observations in Space and Time) represents one of the biggest collections of harmonised measurements of atmospheric composition at the surface. In total, 7 275 148 646 measurements from 1970 to 2023, of 227 different components from 38 reporting networks, are compiled, parsed, and standardised. The components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of the information provided by major public reporting networks, which is saved in 21 data variables and 163 metadata variables. Extensive effort in particular is made towards the standardisation of measurement process information and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use) and temporal classifications per measurement (e.g. day or night). A range of standardised network quality assurance flags is associated with each individual measurement. GHOST's own quality assurance is also performed and associated with measurements. Measurements pre-filtered by the default GHOST quality assurance are also provided. In this paper, we outline all steps undertaken to create the GHOST dataset and give insights and recommendations for data providers based on the experiences gleaned through our efforts. The GHOST dataset is made freely available via the following repository: https://doi.org/10.5281/zenodo.10637449 (Bowdalo, 2024a).

Spatiotemporal Dynamics of Hot-Dry Winds in Northern China: A Multidimensional Analysis Using Gridded Datasets

Abstract Hot-dry winds (HDWs), characterized by simultaneous high temperature, low humidity, and strong wind speed, represent prevalent agrometeorological hazards in northern China. To date, our comprehension of HDWs has been constrained to regional scales due to the dependence upon in situ meteorological observations and the lack of a universal definition of HDWs. In this study, we leveraged a gridded dataset to elucidate the spatiotemporal dynamics of HDWs in northern China from 1961 to 2022. We also introduced a standardized HDW index (sHDWI) to investigate HDW severity. The results indicated more HDW occurrences in eastern Northwest China (NW) and western Inner Mongolia (IM). While there were declining trends in HDW frequency, extent, and severity on average in northern China from 1961 to 1993, these trends were reversed by an overall increase afterward. Intriguingly, regions spanning from NW to IM exhibited significant upward trends in HDW severity according to sHDWI. The spatial extents of light, moderate, and heavy HDWs consistently decreased in North China (NC), with notable expansions over IM, NW, and Northeast China (NE) from 2001 to 2022. Importantly, the increased frequency, expanded extent, and intensified severity of HDWs, particularly, the heavy ones, underscored the escalation of HDW events in recent decades over the arid IM and hyperarid NW, known for their fragile ecological environment. The findings from this research carry significant implications for agricultural production and water management in northern China influenced by the changing patterns of HDWs in the context of climate change. Significance Statement This study aims to enhance our understanding of the large-scale spatiotemporal dynamics of hot-dry winds (HDWs) in northern China using a 0.25° × 0.25° gridded dataset spanning the period from 1961 to 2022. The adoption of the gridded dataset enables a multidimensional exploration of HDWs, encompassing their frequency, extent, and severity which cannot be fully captured by in situ observations. Meanwhile, to quantify changes in HDW severity, we introduce a standardized HDW index that surpasses existing HDW definitions relying on absolute thresholds or relative percentiles of maximum air temperature, relative humidity, and wind speed. Our results reveal a consistent reversal in HDW frequency, extent, and severity around 1993 in northern China and highlight the intensification of heavy HDWs, particularly in the arid and hyperarid regions within the study area.

How do gridded meteorological datasets perform in a typical data-scarce cryospheric basin?

Gridded meteorological datasets have been widely used in meteorological and hydrological studies, especially in data-scarce regions. However, assessments of these datasets in cryospheric basins, which are highly sensitive to global warming, remain inadequate. Therefore, three representative gridded meteorological datasets, i.e., the China meteorological forcing dataset (CMFD), CN05.1, and the meteorological forcing dataset for the third-pole region (TPMFD), were systematically evaluated in the Nachitai Basin based on comparisons with limited gauge observations, pairwise intercomparisons, and hydrological simulations. All gridded precipitation and temperature datasets were in good agreement with gauge observations on an intra-annual scale, while substantial differences existed among them in terms of precipitation and temperature on an annual scale. Additionally, pairwise correlation analyses indicated that the temporal consistency between the datasets was higher than their spatial consistency. Furthermore, all three datasets achieved satisfactory and reasonable results in simulating glacier area and streamflow variations when used as inputs for the hydrological−hydrodynamic model, exhibiting the robustness of the model in this data-scarce basin. This study provides a more profound understanding of the significance of gridded datasets in data-scarce areas situated on the Tibetan Plateau.

Fast generation of high-dimensional spatial extremes

Widespread extreme climate events cause many fatalities, economic losses and have a huge impact on critical infrastructure. It is therefore of utmost importance to estimate the frequency and associated consequences of spatially concurrent extremes. Impact studies of climate extremes are severely hampered by the lack of extreme observations, and even large ensembles of climate simulations often do not include enough extreme or record-breaking climate events for robust analysis. On the other hand, weather generators specifically fitted to extreme observations can quickly generate many physically or statistically plausible extreme events, even with intensities that have never been observed before. We propose a Fourier-based algorithm for generating high-resolution synthetic datasets of rare events, using essential concepts of classical modelling of (spatial) extremes. Here, the key feature is that the stochastically generated datasets have the same spatial dependence as the observed extreme events. Using high-resolution gridded precipitation and temperature datasets, we show that the new algorithm produces realistic spatial patterns, and is particularly attractive compared to other existing methods for spatial extremes. It is exceptionally fast, easy to implement, scalable to high dimensions and, in principle, applicable for any spatial resolution. We generated datasets with 10000 gridpoints, a number that can be increased without difficulty. Since current impact models often require high-resolution climate inputs, the new algorithm is particularly useful for improved impact and vulnerability assessment.

Multi-scale assessment of high-resolution reanalysis precipitation fields over Italy

This study focuses on the validation of high-resolution regional reanalyses to understand their effectiveness in reproducing precipitation patterns over Italy, a climate change hotspot characterized by coastal sea-land interaction and complex orography. Nine reanalysis products were evaluated, with the ECMWF global reanalysis ERA5 serving as a benchmark. These included both European (COSMO-REA6, CERRA) and Italy-specific (BOLAM, MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT) datasets, using different models and parametrizations. The inter-comparison involved determining the effective resolution of daily precipitation fields using wavelet techniques and assessing intense precipitation statistics through frequency distributions. In-situ observations and observational gridded datasets were used to independently validate reanalysis precipitation fields. The capability of reanalyses to depict daily precipitation patterns was assessed, highlighting a maximum radius of precipitation misplacement of about 15 km, with notably lower skills during summer. An overall overestimation of precipitation was identified in the reanalysis climatological fields over the Po Valley and the Alps, whereas multiple products showed an underestimation of precipitations across the North-West coast, the Apennines, and Southern Italy. Finally, a comparison with a time-consistent observational dataset (UniMi/ISAC-CNR) revealed a non-stable deviation from observations in the annual precipitation cumulate of the reanalysis products analyzed. This should be taken into account when interpreting precipitation trends over Italy.

Assessing climate trends in the Northwestern Himalayas: a comprehensive analysis of high-resolution gridded and observed datasets

Climate change poses significant challenges to the Himalayas, a region characterised by its fragile ecosystems and vulnerable communities dependent on environmental resources. Accurate climate data are crucial for understanding regional climatic variations and assessing climate change impacts, particularly in areas with limited observational networks. This study represents a pioneering effort in evaluating climatic fluctuations in the Jhelum basin, located in the North Western Himalayas, by utilising a diverse range of gridded meteorological datasets (APHRODITE, CHIRPS, CRU, and IMDAA) alongside observed climate data from the Indian Meteorological Department. The primary goal is to identify the most effective gridded climate data product for regions with limited data and to explore the potential of combining gridded data sets with observed data to understand climatic variability. Findings indicate a consistent upward trend in temperature across all datasets, with varying rates of increase. CRU records a rise of 1 °C in Tmax and 1.6 °C in Tmin, while APHRODITE shows a Tmean increase of approximately 1 °C. IMDAA reports increases in Tmax and Tmin. Observed mean annual Tmax and Tmin show net increases of 1 °C and 0.6 °C, respectively. Regarding precipitation, all datasets except IMDAA exhibit an increasing trend, contrary to observed data, which decreases from 1266 mm to 1068 mm over 40 years. CHIRPS, CRU, and APHRODITE display increasing trends, while IMDAA aligns closely with observed data but tends to overestimate precipitation by about 30%. Our research identifies IMDAA as the most suitable gridded climate data for the Jhelum basin in the North-western Himalayas. Despite some discrepancies in precipitation trends, IMDAA closely aligns with observed data, providing valuable insights for scholars and policymakers navigating climate data uncertainties in complex environments. Our findings contribute to informed decision-making and effective climate change mitigation strategies in the region.

PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China

Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.

Multimodal Fusion for Talking Face Generation Utilizing Speech-Related Facial Action Units

Talking face generation is to synthesize a lip-synchronized talking face video by inputting an arbitrary face image and corresponding audio clips. The current talking face model can be divided into four parts: visual feature extraction, audio feature processing, multimodal feature fusion, and rendering module. For the visual feature extraction part, existing methods face the challenge of complex learning task with noisy features, this article introduces an attention-based disentanglement module to disentangle the face into Audio-face and Identity-face using speech-related facial action unit (AU) information. For the multimodal feature fusion part, existing methods ignore not only the interaction and relationship of cross-modal information but also the local driving information of the mouth muscles. This study proposes a novel generative framework that incorporates a dilated non-causal temporal convolutional self-attention network as a multimodal fusion module to enhance the learning of cross-modal features. The proposed method employs both audio- and speech-related facial AUs as driving information. Speech-related AU information can facilitate more accurate mouth movements. Given the high correlation between speech and speech-related AUs, we propose an audio-to-AU module to predict speech-related AU information. Finally, we present a diffusion model for the synthesis of talking face images. We verify the effectiveness of the proposed model on the GRID and TCD-TIMIT datasets. An ablation study is also conducted to verify the contribution of each component. The results of quantitative and qualitative experiments demonstrate that our method outperforms existing methods in terms of both image quality and lip-sync accuracy. Code is available at https://mftfg-au.github.io/Multimodal_Fusion/.

Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps.

A Statistical Analysis of Drought and Fire Weather Indicators in the Context of Climate Change: The Case of the Attica Region, Greece

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change.

Global population datasets overestimate flood exposure in Sweden

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.

Do Experimental Forests and Ranges of the Southeastern United States Represent the Climate, Ecosystem Structure, and Ecosystem Functions of the Region?

Abstract There are twenty experimental forest and range sites (EFRs) across the southeastern United States that are currently maintained by the USDA Forest Service (Forest Service) to conduct forest ecosystem research for addressing ecosystem management challenges. The overall objective of this study was to use multiple gridded datasets to assess the extent to which the twenty EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests. The EFRs represent the large variability of climate conditions across the region relatively well, but we identified small representation gaps. The representativeness of ecosystem structure by these EFRs can be improved by establishing EFRs in forests with relatively low tree cover, leaf area index, or tree canopy height. The current EFRs also represent the forest ecosystem functions of the region relatively well, although areas with intermediate and low aboveground biomass and water yield are not well represented. The trends in climate, ecosystem structure, and ecosystem functions were generally consistent between the region and the EFRs. Our study indicates that the current EFRs represent the region relatively well, but establishing additional EFRs in specific areas within the region could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices. Study Implications This study across the experimental forests and ranges (EFRs) and the southeastern forest region fills the knowledge gap regarding climate, ecosystem structure, and ecosystem functions of EFRs in the context of the broader southeastern forest region. Understanding ecosystem functions and structures across the EFR network can help the Southern Research Station to address new research questions. Our study indicates that the current EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests well. However, establishing additional EFRs in certain regions could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices.

Maternal exposure to ambient ozone and fetal congenital heart defects: a national multicenter study in China.

Ambient O3 has demonstrated an aggravated increasing trend in the context of global warming. The available evidence of maternal exposure to ambient O3 on fetal congenital heart defects (CHD) is still limited, especially in high polluted areas. To examine associations of maternal exposure to ambient O3 during early pregnancy with fetal CHDs. We conducted a national multicenter study in 1313 hospitals from 26 provinces in China and collected a total of 27,817 participants athigh risk of CHD from 2013 to 2021. Exposure to ambient O3 during the embryonic period, preconception, the first trimester and periconception was assessed by extracting daily concentrations from a validated grid dataset at each subject's residential district. CHDs were diagnosed based on fetal echocardiography. Each 10 µg/m3 increase of exposure to ambient O3 during the embryonic period was approximately linearly associated with a 12.7% (odds ratio [OR]: 1.127, 95% confidence interval [CI]: 1.098, 1.155) increase in odds of pooled CHD (p < 0.001). The associations remain robust after adjusting for ambient PM2.5 and NO2 exposure. The odds of different types of CHD in association with ambient O3 exposure varied greatly. We observed significant association of ambient O3 exposure with ventricular septal defect (VSD), tetralogy of Fallot (TOF); pulmonary stenosis (PS), pulmonary atresia (PA), transposition of great arteries (TGA) and persistent left superior vena cava (PLSVC), with TOF demonstrating the strongest estimates (OR: 1.194, 95% CI:1.107, 1.288). The estimates for preconception, the first trimester and periconception demonstrate consistent findings with the main analyses, indicating stronger associations of ambient O3 exposure during the periconception period. Our study provides evidence that higher ambient O3 during early pregnancy was significantly associated with increased odds of fetal CHD. Our findings suggest that pregnant women, clinical practitioners, and policy makers need to pay more attention to the exposure to higher ambient O3 during early pregnancy to reduce the risk of developing CHD and to improve outcomes across the life span.