Population data represent an essential component in studies focusing on human–nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global- and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared, which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.

The accurate estimation of population living in the low-elevation coastal zone (LECZ) – and at heightened risk from sea level rise – is critically important for policymakers and risk managers worldwide. This characterization of potential exposure depends on robust representations not only of coastal elevation and spatial population data but also of settlements along the urban–rural continuum. The empirical basis for LECZ estimation has improved considerably in the 13 years since it was first estimated that 10 % of the world's population – and an even greater share of the urban population – lived in the LECZ (McGranahan et al., 2007a). Those estimates were constrained in several ways, not only most notably by a single 10 m LECZ but also by a dichotomous urban–rural proxy and population from a single source. This paper updates those initial estimates with newer, improved inputs and provides a range of estimates, along with sensitivity analyses that reveal the importance of understanding the strengths and weaknesses of the underlying data. We estimate that between 750 million and nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ, with the variation depending on the elevation and population data sources used. The variations are considerably greater at more disaggregated levels, when finer elevation bands (e.g., the ≤ 5 m LECZ) or differing delineations between urban, quasi-urban and rural populations are considered. Despite these variations, there is general agreement that the LECZ is disproportionately home to urban dwellers and that the urban population in the LECZ has grown more than urban areas outside the LECZ since 1990. We describe the main results across these new elevation, population and urban-proxy data sources in order to guide future research and improvements to characterizing risk in low-elevation coastal zones (https://doi.org/10.7927/d1x1-d702, CIESIN and CIDR, 2021).

Here, we demonstrate that reductions in the depth of inlets or estuary channels can be used to reduce or prevent coastal flooding. A validated hydrodynamic model of Jamaica Bay, New York City (NYC), is used to test nature-based adaptation measures in ameliorating flooding for NYC's two largest historical coastal flood events. In addition to control runs with modern bathymetry, three altered landscape scenarios are tested: (1) increasing the area of wetlands to their 1879 footprint and bathymetry, but leaving deep shipping channels unaltered; (2) shallowing all areas deeper than 2 m in the bay to be 2 m below Mean Low Water; (3) shallowing only the narrowest part of the inlet to the bay. These three scenarios are deliberately extreme and designed to evaluate the leverage each approach exerts on water levels. They result in peak water level reductions of 0.3%, 15%, and 6.8% for Hurricane Sandy, and 2.4%, 46% and 30% for the Category-3 hurricane of 1821, respectively (bay-wide averages). These results suggest that shallowing can provide greater flood protection than wetland restoration, and it is particularly effective at reducing "fast-pulse" storm surges that rise and fall quickly over several hours, like that of the 1821 storm. Nonetheless, the goal of flood mitigation must be weighed against economic, navigation, and ecological needs, and practical concerns such as the availability of sediment.

In recent centuries, human activities have greatly modified the geomorphology of coastal regions. However, studies of historical and possible future changes in coastal flood extremes typically ignore the influence of geomorphic change. Here, we quantify the influence of 20th-century man-made changes to Jamaica Bay, New York City, on present-day storm tides. We develop and validate a hydrodynamic model for the 1870s based on detailed maps of bathymetry, seabed characteristics, topography, and tide observations for use alongside a present-day model. Predominantly through dredging, landfill, and inlet stabilization, the average water depth of the bay increased from 1.7 to 4.5 m, tidal surface area decreased from 92 to 72 km², and the inlet minimum cross-sectional area expanded from 4800 to 8900 m². Total (freshwater plus salt) marsh habitat area has declined from 61 to 15 km² and intertidal unvegetated habitat area from 17 to 4.6 km². A probabilistic flood hazard assessment with simulations of 144 storm events reveals that the landscape changes caused an increase of 0.28 m (12 %) in the 100-year storm tide, even larger than the influence of global sea level rise of about 0.23 m since the 1870s. Specific anthropogenic changes to estuary depth and area as well as inlet depth and width are shown through targeted modeling and dynamics-based considerations to be the most important drivers of increasing storm tides.

The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.

Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.