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At least 19 recordsLinked to original sources
USGS2016

A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO 2 Atlas (SOCAT) is a synthesis of quality-controlled f CO 2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million f CO 2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million f CO 2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water f CO 2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water f CO 2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014).

Earth System Science Data↗
USGS2020

The Modern Geological Survey; a model for research, innovation, synthjesis: A USGS perspective

Geological Surveys have long filled the role of providing Earth system science data and knowledge. These functions are increasingly complicated by accelerating environmental and societal change. Here we describe the USGS response to these evolving conditions. Underpinning the USGS approach is the recognition that many of the issues facing the U.S. and the world involve the interaction among geologic, hydrologic, and biologic processes, and how these interactions in turn affect society. Therefore, a goal of USGS planning is fostering interdisciplinary science. This focus is occurring in part through implementation of the recommendations of strategic planning teams. The USGS has also put in place groups building a broad information technology infrastructure as well as identifying and disseminating new Earth science research tools. In addition, the USGS has established an analysis and synthesis center that brings together groups of scientists who address interdisciplinary Earth system science issues. The goal is for these building blocks to evolve towards a comprehensive USGS data and knowledge platform; EarthMAP (Earth Monitoring, Assessment, and Projection). We also recognize that the modern geological survey must be a member of a community of geological surveys contributing data to a global database of 3-dimensional biogeophysical observations and interpretations.

Book chapter↗
USGS2014

Land processes distributed active archive center product lifecycle plan

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the National Aeronautics and Space Administration (NASA) Earth Science Data System Program worked together to establish, develop, and operate the Land Processes (LP) Distributed Active Archive Center (DAAC) to provide stewardship for NASA’s land processes science data. These data are critical science assets that serve the land processes science community with potential value beyond any immediate research use, and therefore need to be accounted for and properly managed throughout their lifecycle. A fundamental LP DAAC objective is to enable permanent preservation of these data and information products. The LP DAAC accomplishes this by bridging data producers and permanent archival resources while providing intermediate archive services for data and information products.

Open-File Report↗
USGS2014

Temperature data acquired from the DOI/GTN-P Deep Borehole Array on the Arctic Slope of Alaska, 1973-2013

A homogeneous set of temperature measurements obtained from the DOI/GTN-P Deep Borehole Array between 1973 and 2013 is presented; DOI/GTN-P is the US Department of the Interior contribution to the Global Terrestrial Network for Permafrost (GTN-P). The 23-element array is located on the Arctic Slope of Alaska, a region of cold continuous permafrost. Most of the monitoring wells are situated on the Arctic coastal plain between the Brooks Range and the Arctic Ocean, while others are in the foothills to the south. The data represent the true temperatures in the wellbores and surrounding rocks at the time of the measurements; they have not been corrected to remove the thermal disturbance caused by drilling the wells. With a few exceptions, the drilling disturbance is estimated to have been on the order of 0.1 K or less by 1989. Thus, most of the temperature measurements acquired during the last 25 yr are little affected by the drilling disturbance. The data contribute to ongoing efforts to monitor changes in the thermal state of permafrost in both hemispheres by the Global Terrestrial Network for Permafrost, one of the primary subnetworks of the Global Terrestrial Observing System (GTOS). The data will also be useful for refining our basic understanding of the physical conditions in permafrost in Arctic Alaska, as well as providing important information for validating predictive models used for climate impact assessments. The processed data are available from the Advanced Cooperative Arctic Data and Information Service (ACADIS) repository at doi:10.5065/D6N014HK.

Alaska↗
USGS2026

Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations

In April 2020, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center introduced a Level 2 provisional Aquatic Reflectance (AR) product for the Landsat 8 Operational Land Imager (OLI), marking the initial phase in developing a standardized global product for Landsat-derived surface water measurements. The goal of USGS EROS aquatic product research and development is to prepare for an operational processing architecture for Landsat Collection 3 in the late 2020s that will enable use of quality-controlled data for emerging Landsat aquatic science applications. To achieve this, we released a subset of the Landsat 8/9 provisional AR products (Crawford et al., 2025, https://doi.org/10.5066/P14MBBRM) and examined its general performance through the Science Algorithms to Operations (SATO) framework alongside quantitative assessment using community made inland water data records (GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments, GLORIA) and radiometric coastal validation platforms (NASA’s Ocean Color component of the Aerosol Robotic Network, AERONET-OC). Variability within the validation datasets indicate that the performance of the Landsat 8/9 provisional AR retrieval is highly context-dependent; errors are minimal in optically simple waters (e.g., clear to moderately turbid coastal waters) but increase considerably in optically complex waters where factors such as elevated levels of turbidity, chlorophyll (Chl a ) concentrations, or colored dissolved organic matter (CDOM) dominate the water column. Additionally, this paper examines key algorithmic considerations for atmospheric correction, highlighting factors that influence accuracy, scalability, and computational efficiency necessary for collection processing in the operational Landsat Product Generation System (LPGS). This paper is intended to communicate with aquatic scientists, satellite oceanographers, and the broader Earth observation community on the origins, requirements, challenges, successes, and future objectives for operationalizing global AR data products for Landsat satellite missions.

Earth System Science Data↗
USGS2020

Generalized models to estimate carbon and nitrogen stocks of organic soil horizons in Interior Alaska

Boreal ecosystems comprise one tenth of the world’s land surface and contain over 20 % of the global soil carbon (C) stocks. Boreal soils are unique in that its mineral soil is covered by what can be quite thick layers of organic soil. These organic soil layers, or horizons, can differ in their state of decomposition, source vegetation, and disturbance history. These differences result in varying soil properties (bulk density, C concentration, and nitrogen (N) concentration) among soil horizons. Here we summarize these soil properties, as represented by over 3000 samples from Interior Alaska, and examine how soil drainage and stand age affect these attributes. The summary values presented here can be used to gap-fill large datasets when important soil properties were not measured, provide data to initialize process-based models, and validate model results. These data are available at https://doi.org/10.5066/P960N1F9 (Manies, 2019).

Alaska↗
USGS2023

Inventory of glaciers and perennial snowfields of the conterminous USA

This report summarizes an updated inventory of glaciers and perennial snowfields of the conterminous United States. The inventory is based on interpretation of mostly aerial imagery provided by the National Agricultural I magery Program, US Department of Agriculture, with some satellite imagery in places where aerial imagery was not suitable. The inventory includes all perennial snow and ice features ≥ 0.01 km 2 . Due to aerial survey schedules and seasonal snow cover, imageries acquired over a number of years were required. The earliest date is 2013 and the latest is 2020, but more than 73 % of the outlines were acquired from 2015 imagery. The inventory is compiled as shapefiles within a geographic information system that includes feature classification, area, and location. The inventory identified 1331 (366.52 ± 14.34 km 2 ) glaciers, 1176 (31.01 ± 9.30 km 2 ) perennial snowfields, and 35 (3.57 km 2 ± no uncertainty) buried-ice features. The data including both the shapefiles and tabulated results are publicly available at https://doi.org/10.15760/geology-data.03 (Fountain and Glenn, 2022).

Earth System Science Data↗
USGS2020

A pan-African high-resolution drought index dataset

Droughts in Africa cause severe problems, such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security on Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectional perspective that includes crops, hydrological systems, rangeland and environmental systems. Such assessments are essential for policymakers, their advisors and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high-resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole of Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time Series (TS) datasets, Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project and root zone soil moisture modelled by GLEAM. Agreement found between coarse-resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with an average correlation coefficient ( R ) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information for the study of drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) via the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a).

Earth System Science Data↗
USGS2019

Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories ( https://doi.org/10.6071/M3FH3D ).

California↗
USGS2015

A global satellite assisted precipitation climatology

Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05°) global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, http://dx.doi.org/10.15780/G2159X ), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.

Earth System Science Data↗
USGS2022

A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan

From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56 ∘ N, 30–100 ∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).

Earth System Science Data↗
USGS2022

Multi-year, spatially extensive, watershed-scale synoptic stream chemistry and water quality conditions for six permafrost-underlain Arctic watersheds

Repeated sampling of spatially distributed river chemistry can be used to assess the location, scale, and persistence of carbon and nutrient contributions to watershed exports. Here, we provide a comprehensive set of water chemistry measurements and ecohydrological metrics describing the biogeochemical conditions of permafrost-affected Arctic watersheds. These data were collected in watershed-wide synoptic campaigns in six stream networks across northern Alaska. Three watersheds are associated with the Arctic Long-Term Ecological Research site at Toolik Field Station (TFS), which were sampled seasonally each June and August from 2016 to 2018. Three watersheds were associated with the National Park Service (NPS) of Alaska and the U.S. Geological Survey (USGS) and were sampled annually from 2015 to 2019. Extensive water chemistry characterization included carbon species, dissolved nutrients, and major ions. The objective of the sampling designs and data acquisition was to characterize terrestrial–aquatic linkages and processing of material in stream networks. The data allow estimation of novel ecohydrological metrics that describe the dominant location, scale, and overall persistence of ecosystem processes in continuous permafrost. These metrics are (1) subcatchment leverage, (2) variance collapse, and (3) spatial persistence. Raw data are available at the National Park Service Integrated Resource Management Applications portal (O'Donnell et al., 2021, https://doi.org/10.5066/P9SBK2DZ ) and within the Environmental Data Initiative (Abbott, 2021, https://doi.org/10.6073/pasta/258a44fb9055163dd4dd4371b9dce945 ).

Alaska↗
USGS2013

A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region

High-latitude terrestrial ecosystems are key components in the global carbon cycle. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify stocks of soil organic carbon (SOC) in the northern circumpolar permafrost region (a total area of 18.7 × 10 6 km 2 ). The NCSCD is a geographical information system (GIS) data set that has been constructed using harmonized regional soil classification maps together with pedon data from the northern permafrost region. Previously, the NCSCD has been used to calculate SOC storage to the reference depths 0–30 cm and 0–100 cm (based on 1778 pedons). It has been shown that soils of the northern circumpolar permafrost region also contain significant quantities of SOC in the 100–300 cm depth range, but there has been no circumpolar compilation of pedon data to quantify this deeper SOC pool and there are no spatially distributed estimates of SOC storage below 100 cm depth in this region. Here we describe the synthesis of an updated pedon data set for SOC storage (kg C m -2 ) in deep soils of the northern circumpolar permafrost regions, with separate data sets for the 100–200 cm (524 pedons) and 200–300 cm (356 pedons) depth ranges. These pedons have been grouped into the North American and Eurasian sectors and the mean SOC storage for different soil taxa (subdivided into Gelisols including the sub-orders Histels, Turbels, Orthels, permafrost-free Histosols, and permafrost-free mineral soil orders) has been added to the updated NCSCDv2. The updated version of the data set is freely available online in different file formats and spatial resolutions that enable spatially explicit applications in GIS mapping and terrestrial ecosystem models. While this newly compiled data set adds to our knowledge of SOC in the 100–300 cm depth range, it also reveals that large uncertainties remain. Identified data gaps include spatial coverage of deep (> 100 cm) pedons in many regions as well as the spatial extent of areas with thin soils overlying bedrock and the quantity and distribution of massive ground ice. An open access data-portal for the pedon data set and the GIS-data sets is available online at http://bolin.su.se/data/ncscd/ .

Earth System Science Data↗
USGS2017

Glaciological measurements and mass balances from Sperry Glacier, Montana, USA, years 2005–2015

Glacier mass balance measurements help to provide an understanding of the behavior of glaciers and their response to local and regional climate. In 2005 the United States Geological Survey established a surface mass balance monitoring program on Sperry Glacier, Montana, USA. This project is the first quantitative study of mass changes of a glacier in the US northern Rocky Mountains and continues to the present. The following paper describes the methods used during the first 11 years of measurements and reports the associated results. From 2005 to 2015, Sperry Glacier had a cumulative mean mass balance loss of 4.37 m w.e. (water equivalent). The mean winter, summer, and annual glacier-wide mass balances were 2.92, −3.41, and −0.40 m w.e. yr −1 respectively. We derive these cumulative and mean results from an expansive data set of snow depth, snow density, and ablation measurements taken at selected points on the glacier. These data allow for the determination of mass balance point values and a time series of seasonal and annual glacier-wide mass balances for all 11 measurement years. We also provide measurements of glacier extent and accumulation areas for select years. All data have been submitted to the World Glacier Monitoring Service and are available at doi:10.5904/wgms-fog-2016-08 . This foundational work provides valuable insight about Sperry Glacier and supplies additional data to the worldwide record of glaciers measured using the glaciological method. Future research will focus on the processes that control accumulation and ablation patterns across the glacier. Also we plan to examine the uncertainties related to our methods and eventually quantify a more robust estimate of error associated with our results.

Montana↗
USGS2023

GRiMeDB: The Global River Database Methane Database of concentrations and fluxes

Despite their small spatial extent, fluvial ecosystems play a significant role in processing and transporting carbon in aquatic networks, which results in substantial emission of methane ( CH 4 ) into the atmosphere. For this reason, considerable effort has been put into identifying patterns and drivers of CH 4 concentrations in streams and rivers and estimating fluxes to the atmosphere across broad spatial scales. However, progress toward these ends has been slow because of pronounced spatial and temporal variability of lotic CH 4 concentrations and fluxes and by limited data availability across diverse habitats and physicochemical conditions. To address these challenges, we present a comprehensive database of CH 4 concentrations and fluxes for fluvial ecosystems along with broadly relevant and concurrent physical and chemical data. The Global River Methane Database (GriMeDB; https://doi.org/10.6073/pasta/f48cdb77282598052349e969920356ef , Stanley et al., 2023) includes 24 024 records of CH 4 concentration and 8205 flux measurements from 5029 unique sites derived from publications, reports, data repositories, unpublished data sets, and other outlets that became available between 1973 and 2021. Flux observations are reported as diffusive, ebullitive, and total CH 4 fluxes, and GriMeDB also includes 17 655 and 8409 concurrent measurements of concentrations and 4444 and 1521 fluxes for carbon dioxide ( CO 2 ) and nitrous oxide ( N 2 O ), respectively. Most observations are date-specific (i.e., not site averages), and many are supported by data for 1 or more of 12 physicochemical variables and 6 site variables. Site variables include codes to characterize marginal channel types (e.g., springs, ditches) and/or the presence of human disturbance (e.g., point source inputs, upstream dams). Overall, observations in GRiMeDB encompass the broad range of the climatic, biological, and physical conditions that occur among world river basins, although some geographic gaps remain (arid regions, tropical regions, high-latitude and high-altitude systems). The global median CH 4 concentration (0.20 µmol L −1 ) and diffusive flux (0.44 mmol m -2 d -1 ) in GRiMeDB are lower than estimates from prior site-averaged compilations, although ranges (0 to 456 µmol L −1 and − 136 to 4057 mmol m -2 d -1 ) and standard deviations (10.69 and 86.4) are greater for this larger and more temporally resolved database. Available flux data are dominated by diffusive measurements despite the recognized importance of ebullitive and plant-mediated CH 4 fluxes. Nonetheless, GriMeDB provides a comprehensive and cohesive resource for examining relationships between CH 4 and environmental drivers, estimating the contribution of fluvial ecosystems to CH 4 emissions, and contextualizing site-based investigations.

Earth System Science Data↗
USGS2026

CoralCache: A virtual coral core repository for transparent and reproducible annual growth rate analyses

As science fields enter the Big Data revolution, open-access repositories are essential for addressing larger-scale questions than are possible for single researchers by making data findable, accessible, interoperable, and reusable (FAIR). Furthermore, transparent data and code are increasingly important for reproducible research, especially for data types that inherently require subjective human interpretations. These ideas are applicable to coral sclerochronology, as the field has long been characterized by individual researchers collecting and analyzing coral skeletal cores from their study sites without substantial data sharing or archiving of the core images to meet FAIR principles. Here, we present CoralCache, a virtual coral core repository that not only archives image datasets (i.e., digitized X-rays and computed tomography scans), but also observer interpretations of the density banding patterns. CoralCache is linked to a graphical user interface, CoralCT, which together offer a way forward for coral growth rate analysis that is reproducible and collaborative. The data organization systems presented here could also be readily applied to related archives such as tree rings or bivalve shells.

Earth System Science Data↗
USGS2021

The Boreal-Arctic Wetland and Lake Dataset (BAWLD)

Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

Earth System Science Data↗