Global Dataset Research Articles

Long-term (2000-2020) global 0.05° continuous atmospheric carbon dioxide mapping combining OCO-2 observations and model simulations.

We reconstructed a global continuous 8-day XCO2 (column-averaged CO2 dry air mole fraction) product (GCXCO2) at a spatial resolution of 0.05° from 2000 to 2020, combining terrestrial/marine remote sensing data and model simulations based on developed and tested stacking machine learning method. The GCXCO2 product has the similar spatial pattern with OCO-2 satellite observations but with global seamless coverage, showing a higher spatial resolution and accuracy than CarbonTracker and CAMS model simulation data. A novel dynamic normalization strategy was developed to handle the great temporal variation issue and ensure the temporal expansion of the prediction model. The sampled based 10-fold cross-validation shows an overall satisfactory result at a global scale, with R2 = 0.974 and root-mean-square error (RMSE) = 0.551 ppm (parts per million). Further spatial extension and temporal prediction experiments also proved that dependable results could be obtained in the regions and time periods without valid OCO-2 satellite observations (R2 = 0.958 and R2 = 0.886, respectively). Compared with Total Carbon Column Observing Network (TCCON) ground station observations, the GCXCO2 demonstrates a better accuracy and a higher spatial resolution than the model simulation data. Based on the GCXCO2 product, an upward annual trend of approximately 2.105 ppm/year can be found for global XCO2 between 2000 and 2020, with greater seasonal fluctuations in the Northern Hemisphere than in the Southern Hemisphere. This product is one of some remote sensing-based global high-precision long-term XCO2 datasets and an important tool to help advance the understanding of climate change and carbon balance, but also to detect CO2 concentration anomalies. The dataset can be obtained publicly at doi:https://doi.org/10.5281/zenodo.10083102 (Guan and Sun, 2023).

Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ( S r ) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified S r and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale S r with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of S r globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with S r globally, the relation between precipitation and S r varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and S r for precipitation of up to 3 mm d − 1 , above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and S r . Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of S r , which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate S r on a global scale has the potential to be readily and easily integrated into the parameterization of S r in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

Democracy, visa-waivers, and international mobility

In examining visa-waiver agreements, previous studies have primarily focused on economic motivations and effects. In this article, we explore the potential political motivations and consequences. Particularly, we propose that there is a positive relationship between visa-waivers and democracy. We test this using a global dataset that records bilateral travel visa requirements for all countries between 1973 and 2013. We find support for our main hypothesis. We also examine the relationship between democracy, visa waivers and economic development. Contrary to our expectations, we find that democracy continues to exert influence on visa waivers even at high levels of income. Non-democracies fail to attain visa waivers even when they are wealthy. Finally, we explore whether visa waivers could have an impact on individuals’ attitudes towards democracy. Using survey data from 18 Latin American countries, we find suggestive evidence that that visa-waivers can have a positive impact on individuals’ democratic attitudes. Taken together, the findings suggest that democracy and visa-waivers might have a mutually reinforcing relationship.

A dynamic platform for global pullorum disease and fowl typhoid

Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gallinarum, respectively. The prevalence of these diseases varies across regions and is affected by different risk factors that remain insufficiently documented. To fill this knowledge gap, we have compiled a global dataset for its prevalence, drawing upon a collection of literature from the last seven decades obtained from bilingual databases. However, a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures, limit disease transmission, and prevent future outbreaks at the global and local levels. Here, we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum, especially in China, which is categorized by geographical region and temporal span. The platform offers a user-friendly, efficient, and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different regions. Additionally, this platform allows users to understand the influence of various factors, such as breed, farm mode, economic usage and even the sex of the primary host, chickens, on the prevalence of this disease. We further provided a detailed overview of individual province within China. In particular, by selecting two different provinces on the interface, users can quickly visualize and grasp the disparities in disease prevalence between the chosen regions. This interactive toolkit enables a dynamic exploration of the patterns and factors contributing to the prevalence of Salmonella Pullorum and Gallinarum. This interactive platform is freely available open source at http://139.9.85.208/.

A global dataset of salmonid biomass in streams

Salmonid fishes are arguably one of the most studied fish taxa on Earth, but little is known about their biomass range in many parts of the world. We created a dataset of estimated salmonid biomass using published material of over 1000 rivers, covering 27 countries and 11 species. The dataset, spanning 84 years of data, is the largest known compilation of published studies on salmonid biomass in streams, allowing detailed analyses of differences in biomass by species, region, period, and sampling techniques. Production is also recorded for 194 rivers, allowing further analyses and relationships between biomass and production to be explored. There is scope to expand the list of variables in the dataset, which would be useful to the scientific community as it would enable models to be developed to predict salmonid biomass and production, among many other analyses.

International migration and the advent of a new demographic era

AbstractThe paper explores whether international migration is linked to currently decreasing levels of fertility in high birth rates countries, thereby to the advent of a stage of population degrowth at the world's level. Methodology is in two steps. First, a global dataset is assembled comprising 13 variables for each country. For the country itself: emigrant stock, total fertility rate, girls' enrolment rate, women's labour force participation, global gender gap, and income per capita; for a fictitious average region of destination of migrants originating from this specific country, the last six variables. In the second step, links between origin and destination countries in terms of fertility levels and determinants are analysed using bivariate correlations. A remarkable fact emerges, namely national levels of fertility and their determinants vary quasi perfectly parallel at both ends of international migration corridors. Fertility at origin is not correlated to any phenomenon as much as to fertility, girls' school enrolment and gender equity at destination. Three complementary hypotheses explaining this apparently puzzling fact are discussed: transmission of norms by migrants; cultural similarities at both ends of migrant corridors; and congruency between the global diffusion of norms and the global migration of people. The conclusion highlights the original contribution of the paper, both to the demographic discipline (migration should also be dealt with as a remote determinant of fertility) and political debates on migration (erecting barriers to migration works against the preservation of earth).

Analysis of patterns and trends in competition manipulation in sport global data: 2018–2023

ABSTRACT Competition manipulation in sports has evolved from a localized issue to a global concern. Advances in technology and the growth of sports betting markets have attracted criminal networks that manipulate sporting events for financial gain. The aim of this study was to examine patterns and trends in competition manipulation in sport from a global dataset over a 6-year period. Manipulation of sports competition investigations and sanctions were assessed across world regions and sports. The Macolin Convention typology was also employed to categorize manipulation types and instigators. Results showed an increase in investigations and sanctions year-on-year, with Europe consistently having the highest numbers annually. The majority of investigations and sanctions were consistent across the sports of football, cricket, and tennis, and also an emerging trend in sanctions in esports. Investigation types have mostly been due to direct interference of a sports competition or use of external means. The instigator of manipulation trends shifted from opportunistic types to the exploitation of power or influence. The patterns and trends reported can help inform the work of sport management professionals.

Large disagreements in estimates of urban land across scales and their implications

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts.

Support Carbon Neutral Goal with a High-resolution Carbon Monitoring System in Beijing

Abstract China has committed to peak its carbon emissions before 2030 and achieve carbon neutrality before 2060, known as the Dual Carbon Goals (DCG). To support the DCG, monitoring and assessment of progresses in emissions reduction have become a central research focus. Here we describe the Beijing-Tianjin-Hebei (JJJ) Carbon Monitoring System, established over the last six years through a collaborative effort of 16 research institutions and universities. The monitoring capability includes ground-based high-precision reference stations, a dense network of medium-accuracy low-cost sensors, carbon satellites, vertical profile samplers, and mobile platforms. Observations using the network have shown CO2 variations from diurnal, synoptic to seasonal-interannual timescales, as well as dramatic on-road changes during the COVID-19 lockdown. High-resolution modeling and data assimilation at km-scale reveal major deficiencies in global carbon emissions datasets at regional/city scale. The inversion found the total fossil fuel CO2 emissions of the City of Beijing to be 117 megatonne (Mt), but much lower than the national-data-based inventories (152 MtCO2). The results have been used in emissions assessment by national and local environmental protection agencies. Similar systems are now being developed in other Chinese cities.

Wheat genetic progress in biomass allocation and yield components: A global perspective

BackgroundWheat is an essential food source and is subjected to intense breeding efforts for increased grain yield, but stagnation in grain yield improvements has been reported in many regions. The identification of genetically linked factors impeding further progress in wheat grain yield improvement is therefore urgently required. MethodA comparative meta-analysis of data from 66 publicly available field experiments involving multiple wheat genotypes was performed to identify traits altered in breeding programs, their relationship with grain yield, and their past and current impact on grain yield increases. ResultsWheat grain yield can be increased by increasing either the aboveground biomass (ABM) or the harvest index (HI). However, there was no correlation between these traits since a reduction in plant height can occur with increases in the HI and overall grain yield, but with no reduction in the ABM. The combined data from 32 global datasets revealed a substantial increase in wheat grain yield from 1860 to 2017, accompanied by improvement in HI and yield components. When considering only the genotypes introduced from the 1960s to 2017, there was a linear increase observed in both grain yield and HI until the mid-1980s. However, genetic progress in HI and GY has slowed down since then. Before the mid-1980s, there was a decreasing trend observed in plant height which remained relatively static thereafter. While ABM did not exhibit significant increases during this period. After the mid-1980s, significant improvements have been observed in ABM; however, no obvious increase were observed in other yield components. ConclusionsSince the 1980s, there has been an increase in the aboveground biomass of wheat, while grain weigth and grains per m2 increases trending slowly, and both harvest index and grain yield have almost stagnated. Therefore, increasing grains per m2 and/or grain weight should be the major research direction to further improve the wheat harvest index and grain yield in the future. ImplicationsThe systematic study of changes in wheat traits in past breeding efforts for improved grain yields has provided useful indicators for the direction of wheat breeding in the future.

Unravelling the complex interplay between daily and sub-daily rainfall extremes in different climates

Understanding short-duration intense rainfall is crucial for mitigating flash floods, landslides, soil erosion, and pollution incidents. Yet, most observations from rain gauges are only available at the daily resolution. We use the new Global Sub Daily Rainfall dataset to explore extreme rainfall at both daily and sub-daily durations worldwide. Employing Single Gauge Analysis (SGA) and pioneering global-scale Regional Frequency Analysis (RFA), we reveal for the first time how Generalized Extreme Value Distribution (GEV) parameters change across climates and data durations (1h, 3h, 6h, 24h, and daily). This marks the first-ever near-global-scale RFA, made possible by the development of an algorithm that automates RFA on observed rainfall datasets. We compare our results with GEV applied to a gridded rainfall reanalysis (ERA5). Our key findings are that: 1) using ERA5, return levels are significantly underestimated across all climates for 1h rainfall and across all data durations for gauges in the tropical climate region. Even when accounting for differences between point and areal estimates, the median 1h return level estimates are approximately 40% lower compared to RFA. We therefore strongly advise against the use of reanalysis gridded rainfall for studying these extremes. 2) While most gauges show similar return levels with RFA or SGA, some differ significantly, and either method may yield the highest values. Thus, we strongly recommend using both SGA and RFA simultaneously to estimate return levels for a robust risk assessment in flood infrastructure design. 3) The interaction between daily and sub-daily GEV shape parameters varies across climate regions, rendering a universal method for inferring sub-daily rainfall extremes from daily extremes (e.g., using Intensity-Duration-Frequency curves) impractical. Our research provides innovative methodological insights that warrant consideration in future studies on rainfall extremes. Our results not only benefit local stakeholders globally but also serve as a crucial validation tool for the rising number of convection-permitting climate model experiments conducted worldwide.

A global analysis of nearshore and submarine springs: spatial distribution, controlling factors, and probability of presence

Coastal springs act as bi-directionally preferential flow paths between coastal aquifers and oceans. While these springs can supply coastal ecosystems with nutrients, they also present vulnerabilities such as contamination and seawater intrusion. Despite their significance, substantial knowledge gaps exist regarding coastal springs due to their complex hydrogeological nature. This study provides a comprehensive global assessment of coastal springs, focusing on their distribution, controlling factors, and likelihood of occurrence. A global dataset of known coastal springs was compiled and analyzed, revealing 66 % of identified springs in the Mediterranean region, mainly linked to karst systems. In contrast, fewer springs were noted in Africa, South America, and South Asia. Key factors influencing spring occurrence were examined using geostatistical methods and integrated into a multi-criteria decision-making framework to develop a Coastal Spring Probability Index (CSPI) along coastline. High-potential areas for coastal springs were identified in regions characterized by significant carbonate and volcanic rock formations, wetter climates, and active tectonic margins, such as southern Europe, the Caribbean, tropical islands, and eastern Asia. Conversely, regions with dry climates and high water demand, such as North Africa and South America, exhibited a lower likelihood of spring presence. These findings will serve as a baseline for local-scale studies and aimed at improving coastal spring inventories and establishing monitoring networks. It will contribute to vulnerability assessment studies, thereby enhancing the management of coastal groundwater resources. The study emphasizes the importance of multidisciplinary approaches to understand coastal spring dynamics and advocates for a strategic planning in groundwater management and conservation. Ongoing efforts to inventory and monitor coastal springs, coupled with targeted conservation measures, are essential for ensuring the long-term sustainability of coastal water resources and ecosystems.

Recurrent Flooding and Household Food Access in Central Java, Indonesia.

It is unknown how recurring flooding impacts household diet in Central Java. We aimed to assess how recurrent flooding influenced household food access over 22 years in Central Java by linking the Global Surface Water dataset (GSW) to the Indonesian Family Life Survey. We examined linear and nonlinear relationships and joint effects with indicators of adaptive capacity. We measured recurrent flooding as the fraction of district raster cells with episodic flooding from 1984-2015 using GSW. Food access outcomes were household food expenditure share (FES) and dietary diversity score (DDS). We fit generalized linear mixed models and random forest regression models. We detected joint effects with flooding and adaptive capacity. Wealth and access to credit were associated with improved FES and DDS. The effect of wealth on FES was stronger in households in more flood-affected districts, while access to credit was associated with reduced odds of DDS in more flood-affected districts. Flooding had more predictive importance for FES than for DDS. Access to credit, a factor that ordinarily improves food access, may not be effective in flood-prone areas. Wealthier households may be better able to adapt in terms of food access. Future research should incorporate land use data to understand how different locales are affected and further understand the complexity of these relationships.

Unifying Coral Reef States Through Space and Time Reveals a Changing Ecosystem

ABSTRACTAimEcological state shifts that alter the structure and function of entire ecosystems are a concerning consequence of human impact. Yet, when, where and why discrete ecological states emerge remains difficult to predict and monitor, especially in high‐diversity systems. We sought to quantify state shifts and their drivers through space and time in the most ecologically complex marine ecosystem: tropical coral reefs.LocationWorldwide.Time Period1987–2019.Major Taxa StudiedCoral reef communities.MethodsUsing a global dataset of 3375 coral reef surveys, along with 13 time series datasets ranging between 1987 and 2019, we applied a novel double‐dichotomy approach to classify coral reefs into four simplified and discrete states based on the relative contributions of corals versus algae to benthic cover and small‐bodied versus large‐bodied fishes to fish standing stock. We then examined state shifts considering a range of spatial predictors and tested whether states have shifted directionally over time, and the nature of the most common transitions.ResultsWe show that geographic, environmental and anthropogenic context fundamentally shapes coral reef states at the local scale, which explains disparities among case studies, and stakes out critical baseline expectations for regional management efforts. We also reveal clear multi‐decadal state shifts on coral reefs: over time, systems dominated by reef‐building corals and small‐bodied, planktivorous fishes tend to have been replaced with reefs characterised by algae and larger‐bodied fishes.Main ConclusionsOur results suggest a previously unrecognised transition from systems that harness external subsidies through small‐bodied consumers associated with structurally complex live corals, to herbivore‐dominated systems with stronger bottom‐up dynamics. Overall, the partitioning of complex reef ecosystems into a small suite of discrete ecological states suggests that spatial context‐dependency, shifting baselines and changes in reef functioning are crucial considerations for coral reef management in the 21st century.

Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.

Overlooked branch turnover creates a widespread bias in forest carbon accounting

Most measurements and models of forest carbon cycling neglect the carbon flux associated with the turnover of branch biomass, a physiological process quantified for other organs (fine roots, leaves, and stems). Synthesizing data from boreal, temperate, and tropical forests (184,815 trees), we found that including branch turnover increased empirical estimates of aboveground wood production by 16% (equivalent to 1.9 Pg Cy-1 globally), of similar magnitude to the observed global forest carbon sinks. In addition, reallocating carbon to branch turnover in model simulations reduced stem wood biomass, a long-lasting carbon storage, by 7 to 17%. This prevailing neglect of branch turnover suggests widespread biases in carbon flux estimates across global datasets and model simulations. Branch litterfall, sometimes used as a proxy for branch turnover, ignores carbon lost from attached dead branches, underestimating branch C turnover by 38% in a pine forest. Modifications to field measurement protocols and existing models are needed to allow a more realistic partitioning of wood production and forest carbon storage.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

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.

Comparing Refugee Resettlement Services: A New Global Dataset and Typology

Abstract The number of countries participating in resettlement has increased substantially in recent years: since 2010, one-third of resettlement countries officially began offering permanent homes to refugees. The services provided to refugees upon resettlement are surprisingly distinct, even among countries with similar political and economic contexts. Despite the importance of these services, existing scholarship offers surprisingly little comparison of resettlement policies globally. To address this gap, this multi-method project analyses refugee resettlement services in 26 countries that are responsible for 99.9 per cent of all resettled refugees, from 1951 to present. The study introduces an original Global Resettlement Services dataset with 42 indicators that provide a current, reliable, and easily accessible overview of resettlement services in each country. Cluster analysis of these data helped to construct a typology that captures four distinct “types” of refugee resettlement: Limited Provision, Cultural Transition, Core Services, and Flexible Support. The typology provides a framework to help policy-makers, academics, and practitioners increase collaboration, design innovative programmes, and improve existing resettlement services. This study offers a comprehensive analysis of the different approaches to resettling refugees worldwide, with significant implications for advancing theory and improving global refugee resettlement policies.

The Global Dam Watch database of river barrier and reservoir information for large-scale applications.

There are millions of river barriers worldwide, ranging from wooden locks to concrete dams, many of which form associated impoundments to store water in small ponds or large reservoirs. Besides their benefits, there is growing recognition of important environmental and social trade-offs related to these artificial structures. However, global datasets describing their characteristics and geographical distribution are often biased towards particular regions or specific applications, such as hydropower dams affecting fish migration, and are thus not globally consistent. Here, we present a new river barrier and reservoir database developed by the Global Dam Watch (GDW) consortium that integrates, harmonizes, and augments existing global datasets to support large-scale analyses. Data curation involved extensive quality control processes to create a single, globally consistent data repository of instream barriers and reservoirs that are co-registered to a digital river network. Version 1.0 of the GDW database contains 41,145 barrier locations and 35,295 associated reservoir polygons representing a cumulative storage capacity of 7,420 km3 and an artificial terrestrial surface water area of 304,600 km2.