Global Dataset Research Articles

Biodiversity management and stock price crash risk

AbstractThis study explores the link between corporate biodiversity management and the risk of sudden declines in future stock prices, an area largely overlooked in empirical research despite the societal significance of biodiversity loss. We posit that robust corporate biodiversity efforts mitigate the suppression of negative information, consequently reducing the risk of abrupt stock price declines. Leveraging a global dataset and innovative biodiversity management metrics from Moody's ESG Solutions (former Vigero Eiris), our multivariate regression analysis demonstrates that companies with stronger biodiversity structures and actions exhibit lower stock price crash risk. In an additional analysis, we focus on environmental inspections as a possible conduit for releasing negative information on biodiversity management. Using a subsample of North American firms that were inspected by the U.S. Environmental Protection Agency (EPA), we find that firms which experience an inspection record an increase in their stock price crash risk.

Widespread increase in plant transpiration driven by global greening

Global plant transpiration (PT) is a crucial component of the Earth's hydrological cycle and plays a significant role in regulating the exchange of water and energy between the land surface and the atmosphere. However, the long-term trend and the underlying driver of global PT remain unclear due to the significant uncertainties in estimating PT on a global scale. This study uses two sub-Mixture Density Networks (MDNc and MDNa) to predict vegetation canopy resistance (rc) and aerodynamic resistance (ra), then the predicted rc and ra are imported into the Penman-Monteith-Leuning (PML) model to simulate PT. The observed PT at 112 SAPFLUXNET sites are used to validate the performance of hybrid MDN-PML model. The verified MDN-PML model is further applied to map the spatial distribution of global PT and reconstruct a long-term (1990–2020) global PT dataset. The results indicate that the long-term average global PT is 397.2 ± 63.1 mm. During the period 1990–2020, the global PT exhibit a significant upward trend (0.79 ± 0.28 mm/year (P < 0.05)), which equates to a 6.0% increase compared with the long-term average global PT. A widespread trend of elevated PT is observed in approximately 70% of the global land surface. The trend attribution analysis results show that the change in leaf area index (LAI) can explain 66.2% of the global PT trend, indicating that elevated LAI due to global greening is the dominant factor contributing to the upward trend in global PT. The elevated LAI can be largely attributed to the CO2 fertilization effect induced by elevated atmospheric CO2 concentration. Additional analysis reveals that the increased global PT is more sensitive to CO2 fertilization effect in high LAI areas than in low LAI areas. Projected climate scenarios indicate that global land surface PT will continue to rise from 2023 to 2100, and the rate of increase in the future will be higher than in historical periods. The rising rates of global PT under the three Representative Concentration Pathway scenarios (RCP) 2.6, RCP4.5, and RCP8.5 climate change scenarios are 0.86 mm/year, 1.16 mm/year, and 1.45 mm/year, respectively, during the period 2023–2100. Our results highlight the impact of global change and vegetation greening on the global PT and hydrological cycle. This study is of great significance for the scientific response to the challenges of climate change for regional water resource management.

A global dataset of the shape of drainage systems

Abstract. Drainage basins delineate Earth's land surface into individual water collection units. Basin shape and river sinuosity determine water and sediment dynamics, affecting landscape evolution and connectivity between ecosystems and freshwater species. However, a high-resolution global dataset for the boundaries and geometry of basins is still missing. Using a 90 m resolution digital elevation model, we measured the areas, lengths, widths, aspect ratios, slopes, and elevations of basins over 50 km2 globally. Additionally, we calculated the lengths and sinuosities of the longest river channels within these 0.67 million basins. We built a new global dataset, Basin90m, to present the basins and rivers, as well as their morphological metrics. To highlight the use cases of Basin90m, we explored the correlations among morphological metrics, such as Hack's law. By comparing with HydroSHEDS, HydroATLAS, and Google Earth images, we demonstrated the high accuracy of Basin90m. Basin90m, available in shapefile format, can be used on various GIS platforms, including QGIS, ArcGIS, and GeoPandas. Basin90m has substantial application prospects in geomorphology, hydrology, and ecology. Basin90m is available at https://doi.org/10.5880/GFZ.4.6.2023.004 (He et al., 2023).

The fallacy of global comparisons based on per capita measures.

Media, social scientists and public health researchers often present comparisons across countries, and policy makers use such comparisons to take evidence-based action. For a meaningful comparison among countries, one often needs to normalize the measure for differences in population size. To address this issue, the first choice is usually to calculate per capita ratios. Such ratios, however, normalize the measure for differences in population size directly only under the highly restrictive assumption of a proportional increase of the measure with population size. Violation of this assumption frequently leads to misleading conclusions. We compare per capita ratios with an approach based on regression, a widely used statistical procedure that eliminates many of the problems with ratios and allows for straightforward data interpretation. It turns out that the per capita measures in three global datasets (gross domestic product, COVID-19-related mortality and CO2 production) systematically overestimate values in countries with small populations, while countries with large populations tend to have misleadingly low per capita ratios owing to the large denominators. Unfortunately, despite their biases, comparisons based on per capita ratios are still ubiquitous, and they are used for influential recommendations by various global institutions. Their continued use can cause significant damage when employed as evidence for policy actions and should therefore be replaced by a more scientifically substantiated and informative method, such as a regression-based approach.

QLB-NET: A Dense Soil Moisture and Freeze–Thaw Monitoring Network in the Qinghai Lake Basin on the Qinghai–Tibetan Plateau

Abstract Soil moisture (SM) and soil freeze–thaw (FT) are two relatively active surface parameters that are significant to the sustainable development of the water–land–air–plant–human nexus. Over time, regional or global SM and FT datasets with different spatial resolutions have been developed. In response to the requirements of multiscale product validation and multisource uncertainty tracking, a soil moisture and soil temperature (ST) monitoring network in the Qinghai Lake Basin (QLB-NET) was established in September 2019. The QLB-NET is characterized by densely distributed in situ sites (82 sites) measuring SM and ST at 5-, 10-, and 30-cm depths, with 60 sites in a large-scale network covering an area of 36 km × 40 km and 22 sites evenly distributed across two small-scale 1 km × 1 km networks. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., 36 km × 36 km, 25 km × 25 km, and 0.25° × 0.25°. When statistics are correspondingly performed over 50 out of 54, 25 out of 29, and 25 out of 28 sites, the results are described as follows: 1) the standard deviation of the mean SM varies between 0.0127 and 0.0196 m3 m−3, with the corresponding difference between the upper and lower quartiles being less than 0.02 m3 m−3; 2) the ground freeze–thaw state can be correctly identified with high probabilities ranging from 85.3% to 100% on two freeze–thaw transitional dates. The QLB-NET observed datasets are distributed online and will be continuously updated through cooperation with the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), facilitating product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model.

Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating Utilization in the Vertical Dimension and Pronounced Building Space Inequality

Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable urban development. Regrettably, there exists a significant gap in detailed and consistent data on 3D building space structures with global coverage due to the challenges inherent in the data collection and model calibration processes. In this study, we constructed a global urban structure dataset (GUS-3D), including building volume, height, and footprint information, at a 500 m spatial resolution using extensive satellite observation products and numerous reference building samples. Our analysis indicated that the total volume of buildings worldwide in 2015 exceeded 1 × 1012 m3. Over the 1985 to 2015 period, we observed a slight increase in the magnitude of 3D building volume growth (i.e., it increased from 166.02 km3 during the 1985–2000 period to 175.08 km3 during the 2000–2015 period), while the expansion magnitudes of the two-dimensional (2D) building footprint (22.51 × 103 km2 vs. 13.29 × 103 km2) and urban extent (157 × 103 km2 vs. 133.8 × 103 km2) notably decreased. This trend highlights the significant increase in intensive vertical utilization of urban land. Furthermore, we identified significant heterogeneity in building space provision and inequality across cities worldwide. This inequality is particularly pronounced in many populous Asian cities, which has been overlooked in previous studies on economic inequality. The GUS-3D dataset shows great potential to deepen our understanding of the urban environment and creates new horizons for numerous 3D urban studies.

Deriving high-accuracy lake water-level changes from multi-source satellite datasets

ABSTRACT Satellite data have exhibited a great potential to track lake water-level changes. By integrating multi-source satellite datasets (i.e., Ice, Cloud and land Elevation Satellite-2 (ICESat-2) lidar datasets, Landsat imagery, and Global Surface Water Dataset (GSWD)), we proposed a method to track annual water-level changes of Lake Mead, USA over 1984–2018. Specifically, we first extracted ICESat-2 profiles as elevation information. Second, we used Landsat imagery to extract annual lake boundaries and calculate lake areas. Third, we constructed the relationship between elevations and inundation frequencies provided by the GSWD for ICESat-2 points, and applied it to extract annual water levels by using inundation frequency of Landsat derived lake boundaries. Finally, we created the relationship between lake water level and lake areas via a cubic polynomial model to refine the annual lake water levels. Our water level results agreed well with the in-situ data (R 2 = 0.99, p < 0.01, RMSE = 0.64 m). Our results showed that the ICESat-2 data performed well in providing accurate elevation information around the shore of lake. Using satellite datasets, our proposed method has the potential to derive water levels for water bodies worldwide in the future.

Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS

AbstractGlobal hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change).

Denitrification dominates dissimilatory nitrate reduction across global natural ecosystems.

Denitrification, anaerobic ammonium oxidation (anammox), and dissimilatory nitrate reduction to ammonium (DNRA) are three competing processes of microbial nitrate reduction that determine the degree of ecosystem nitrogen (N) loss versus recycling. However, the global patterns and drivers of relative contributions of these N cycling processes to soil or sediment nitrate reduction remain unknown, limiting our understanding of the global N balance and management. Here, we compiled a global dataset of 1570 observations from a wide range of terrestrial and aquatic ecosystems. We found that denitrification contributed up to 66.1% of total nitrate reduction globally, being significantly greater in estuarine and coastal ecosystems. Anammox and DNRA could account for 12.7% and 21.2% of total nitrate reduction, respectively. The contribution of denitrification to nitrate reduction increased with longitude, while the contribution of anammox and DNRA decreased. The local environmental factors controlling the relative contributions of the three N cycling processes to nitrate reduction included the concentrations of soil organic carbon, ammonium, nitrate, and ferrous iron. Our results underline the dominant role of denitrification over anammox and DNRA in ecosystem nitrate transformation, which is crucial to improving the current global soil N cycle model and achieving sustainable N management.

The walking dead: Are Zombie firms environmentally and socially responsible? A global perspective

This study examines the association between zombie firms and their environmental and social performance. Using a global dataset of listed firms from 49 countries between 2002 and 2019, we find that zombie firms perform poorly on environmental and social responsibility fronts. This finding supports the argument that zombie firms are characterized by consistent losses and that their existence is risky without external support. Zombie firms, while struggling for survival, may not be able to undertake environmental and social activities that require huge investments, thus falling behind other firms. Further analysis highlights that eco-innovation, the presence of a sustainability committee, and industry nature (i.e., heavily polluting industries) mitigate the negative impact of firms' zombie status on their environmental and social performance. Moreover, a zombie firm's engagement in environmental and social activities improves its financial performance. Our main findings are robust to a battery of estimation techniques, alternative proxies, selection bias, and endogeneity issues.

Geochemical Weathering Variability in High Latitude Watersheds of the Gulf of Alaska

AbstractHigh latitude regions across the globe are undergoing severe modifications due to changing climate. A high latitude region of concern is the Gulf of Alaska (GoA), where these changes in hydroclimate undoubtedly affect the hydrogeochemistry of freshwater discharging to the nearshore ecosystems of the region. To fill the knowledge gap of our understanding of freshwater stream geochemistry with the GoA, we compile stream water chemistry data from 162 stream sites across the region. With an inverse model, we estimate fractional contributions to solute fluxes from weathering of silicate, carbonate, and sulfide minerals, and precipitation. We assess weathering rates across the region and compare them against global river yields. The median fractional contribution of carbonate weathering to total weathering products is 78% across all stream sites; however, there are several streams where silicate weathering is a dominant source of solutes. Weathering by sulfuric acid is elevated in glacierized watersheds. Finally, cation weathering rates are lower in GoA streams compared to the world's largest rivers; however, weathering rates are similar when compared to a global dataset of glacier fed streams. We suggest that hydrologic changes driven by glacier ice loss and increased precipitation will alter river water quality and chemical weathering regimes such that silicate weathering may become a more important source of solutes and sulfide oxidation may decrease. This contribution provides a platform to build from for future investigations into changes to stream water chemistry in the region and other high latitude watersheds.

Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years.

Climate change-induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Lessons Learned from the Updated GEWEX Cloud Assessment Database

AbstractSince the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

A workflow for processing global datasets: application to intercropping

A workflow for processing global datasets: application to intercropping

Investigating twelve mainstream global precipitation datasets: Which one performs better on the Tibetan Plateau?

Acquiring precipitation information is a critical yet challenging task for the Tibetan Plateau (TP) with sparse gauges and high altitudes. Presently, there is a multitude of operational precipitation products merged from different data sources. A major concern within the TP science community is which one of these available precipitation datasets performs best on the TP. To address this concern, we selected and compared twelve widely used global precipitation datasets, including six satellite-based precipitation estimates (GSMaP-Gauge, IMERG-Final, TMPA-v7, CMORPH-BLD, PERSIANN-CDR, CHIRPS), five reanalysis data (ERA5, JRA-55, MERRA2, GLDAS, NCEP2), and one multi-source fusion product (MSWEP). Benchmarked against both automatic weather stations and independent field observations, we comprehensively investigate the spatiotemporal accuracy and regional applicability of these global precipitation products on the TP. The evaluation results indicate that almost all the datasets have a consistent tendency to overestimate lower precipitation rates and underestimate higher ones. However, four out of the twelve studied datasets (i.e., GSMaP-Gauge, IMERG-Final, ERA5, and MSWEP) significantly outperform others. Specifically, the two GPM-based satellite precipitation products, GSMaP-Gauge and IMERG-Final, can better reflect the difference between different rain intensities and perform better at moderate rain rates. In contrast, the reanalysis-based ERA5 exhibits a more clustered data series, with relatively higher accuracy at low precipitation rates. Moreover, ERA5 demonstrates a stable superiority in the mountainous areas characterized by complex local terrains. Furthermore, we find that the applicability of different precipitation datasets has an obvious region-related feature. Thus, a regional distribution map was made to clearly outline the optimal choice of current mainstream global precipitation datasets over different climate zones within the TP. This study can offer valuable insights for TP researchers, especially aiding them to select the appropriate precipitation data for their specific studies.

Global paleobiogeography of Albian–Cenomanian (mid-Cretaceous) marine ostracods

Despite their potential for providing important information for paleobiogeography, studies using ostracod assemblages compiled from global datasets have been scarce so far. In this study, the paleogeographic implications of the relationship between different ostracod assemblages are analyzed and discussed for the Albian and Cenomanian. We compiled an ostracod dataset at the genus level, with 168 genera for the Albian and 174 for the Cenomanian, to which different multivariate statistical methods of grouping were applied. The affinity between different ostracod assemblages from different regions allowed for the delineation of 30 operational geographic units (OGUs) for the Albian, and 32 OGUs for the Cenomanian, grouped in three paleobiogeographic units (PBUs; Megatethys, Persia, and Austral) and one sub-unit (PBSU; Maghreb associated to the Megatethys). The Maghreb PBSU showed minor changes between the Albian and Cenomanian. The relationship among the OGUs grouped in each PBU is related to different factors, such as eustatic sea level events, similar climatic zones, and marine current circulation patterns. A relationship was observed between the Gabon and Nigeria OGUs with the Persia PBU, which suggests a direct east–west connection between them during the Cenomanian. The affinity observed in the Austral PBU between the South American, South African, and Australian regions during the Albian can be explained by their relative geographic isolation due to the Walvis Ridge barrier. The eventual flooding of this barrier led to the relationship observed between the SE Brazilian and Bolivian regions with the Austral PBU. Finally, the data also suggested that the separation of the Indian subcontinent from the Austral PBU took place during the Cenomanian.

Assessing five highly used pesticides leaching risk under multi-layered soils using HYDRUS-1D and global datasets in the Northeast region of Thailand

Assessing five highly used pesticides leaching risk under multi-layered soils using HYDRUS-1D and global datasets in the Northeast region of Thailand

Monitoring Spatial–Temporal Variations in River Width in the Aral Sea Basin with Sentinel-2 Imagery

Rivers in arid regions serve as crucial freshwater resources for local communities and play an essential role in global hydrological and biogeochemical cycles. The Aral Sea Basin (ASB) in Central Asia is characterized by an arid climate and river dynamics that are sensitive to climate change and human activities. Monitoring the spatiotemporal variations in river water extent in the ASB is essential to maintain an ecological balance and ensure water security. In this study, we extracted data regarding monthly river water bodies in the ASB from 2017 to 2022 by synthesizing monthly Sentinel-2 images. The water extents on the Sentinel images were automatically mapped using the Otsu method, and the river widths for all river channels were calculated using the RivWidth algorithm. We investigated the relationships between the river dynamics and the geomorphology, climatic change, human activities, and the annual and interannual variations in the river width in different reaches of the basin. The results show a seasonal variability in the river width, with most rivers reaching the largest width in the warm season and a few rivers in the middle and lower areas reaching the valley value in the warm season. Compared to their tributaries, the mainstem in the middle/lower regions showed less seasonal variability. According to interannual analysis, most of the rivers in the ASB significantly narrowed between 2017 and 2022, a phenomenon which is generally impacted by temperature and evapotranspiration variations. Comparisons show that our results provide improved information about the narrow river reaches and denser river networks compared to the previous global dataset, demonstrating the advantageous properties of high spatial resolution in Sentinel-2 imagery.

A Method for Sound Speed Profile Prediction Based on CNN-BiLSTM-Attention Network

In response to the current challenges in efficiently acquiring sound speed profiles and ensuring their representativeness, considering the need to fully leverage historical sound speed profiles while accounting for their spatiotemporal variability, we introduce a model for sound speed profile prediction based on a CNN-BiLSTM-Attention network, which integrates a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism (AM). The synergy of these components enables the model to extract the spatiotemporal features of sound speed profiles more comprehensively. Utilizing the global ocean Argo grid dataset, the model predicted the sound speed profiles of an experimental zone in the Western Pacific Ocean. In predicting sound speed profiles of a single point, the model achieved a root mean square error (RMSE), relative error (RE), and accuracy (ACC) of 0.72 m/s, 0.029%, and 0.99971, respectively, surpassing comparative models. For regional sound speed profile prediction, the mean RMSE, RE, and ACC of different water layers were 0.919 m/s, −0.016%, and 0.9995, respectively. The experimental outcomes not only confirm the high accuracy of the model, but also highlight its superiority in sound speed profile prediction, particularly as an effective compensatory approach when profile measurements are untimely or contain representational errors.

A diverse portfolio of marine protected areas can better advance global conservation and equity

Marine protected areas (MPAs) are widely used for ocean conservation, yet the relative impacts of various types of MPAs are poorly understood. We estimated impacts on fish biomass from no-take and multiple-use (fished) MPAs, employing a rigorous matched counterfactual design with a global dataset of >14,000 surveys in and around 216 MPAs. Both no-take and multiple-use MPAs generated positive conservation outcomes relative to no protection (58.2% and 12.6% fish biomass increases, respectively), with smaller estimated differences between the two MPA types when controlling for additional confounding factors (8.3% increase). Relative performance depended on context and management: no-take MPAs performed better in areas of high human pressure but similar to multiple-use in remote locations. Multiple-use MPA performance was low in high-pressure areas but improved significantly with better management, producing similar outcomes to no-take MPAs when adequately staffed and appropriate use regulations were applied. For priority conservation areas where no-take restrictions are not possible or ethical, our findings show that a portfolio of well-designed and well-managed multiple-use MPAs represents a viable and potentially equitable pathway to advance local and global conservation.