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At least 181 records · Page 10Linked to original sources
USGS2024

National-scale remotely sensed lake trophic state from 1984 through 2020

Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

Scientific Data↗
USGS2023

A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments

The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

Scientific Data↗
USGS2023

A global long-term daily reanalysis of reference evapotranspiration for drought and food-security monitoring

NOAA has developed a global reference evapotranspiration (ET 0 ) reanalysis using the UN Food and Agriculture Organization formulation (FAO-56) of the Penman-Monteith equation forced by MERRA phase 2 (MERRA2) meteorological and radiative drivers. The NOAA ET 0 reanalysis is provided daily from January 1, 1980 to the near-present at a resolution of 0.5° latitude × 0.625° longitude. The reanalysis is verified against station data across southern Africa, a region presenting both significant challenges regarding hydroclimatic variability and observational quantity and quality and significant potential benefits to food-insecure populations. These data are generated from observations from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) network. We further verified globally against spatially distributed ET 0 derived from two reanalyses–the Global Data Assimilation System (GDAS) and Princeton Global Forcing (PGF)–and these verifications produced similar results, yet demonstrated wide regional and seasonal differences. We also present cases that verify the operational applicability of the reanalysis in long-established drought, famine, crop- and pastoral-stress metrics, and in predictability assessments of drought forecasts.

Nature Scientific Data↗
USGS2024

Metabolism regimes in regulated rivers of the Illinois River basin, USA

Metabolism estimates organic carbon accumulation by primary productivity and removal by respiration. In rivers it is relevant to assessing trophic status and threats to river health such as hypoxia as well as greenhouse gas fluxes. We estimated metabolism in 17 rivers of the Illinois River basin (IRB) for a total of 15,176 days, or an average of 2.5 years per site. Daily estimates of gross primary productivity (GPP), ecosystem respiration (ER), net ecosystem productivity (NEP), and the air-water gas exchange rate constant (K 600 ) are reported, along with ancillary data such as river temperature and saturated dissolved oxygen concentration, barometric pressure, and river depth and discharge. Workflows for metabolism estimation and quality assurance are described including a new method for estimating river depth. IRB rivers are dominantly heterotrophic; however, autotrophy was common in river locations coinciding with reported harmful algal blooms (HABs) events. Metabolism of these regulated Midwestern U.S. rivers can help assess the causes and consequences of excessive algal blooms in rivers and their role in river ecological health.

Illinois, Ohio↗
USGS2022

Characterizing storm-induced coastal change hazards along the United States West Coast

Traditional methods to assess the probability of storm-induced erosion and flooding from extreme water levels have limited use along the U.S. West Coast where swell dominates erosion and storm surge is limited. This effort presents methodology to assess the probability of erosion and flooding for the U.S. West Coast from extreme total water levels (TWLs), but the approach is applicable to coastal settings worldwide. TWLs were derived from 61 years of wave and water level data at shore-perpendicular transects every 100-m along open coast shorelines. At each location, wave data from the Global Ocean Waves model were downscaled to the nearshore and used to empirically calculate wave run-up. Tides were simulated using the Oregon State University’s tidal data inversion model and non-tidal residuals were calculated from sea-surface temperature and pressure anomalies. Wave run-up was combined with still water levels to generate hourly TWL estimates and extreme TWLs for multiple return periods. Extremes were compared to onshore morphology to determine erosion hazards and define the probability of collision, overwash, and inundation.

California, Oregon, Washington↗
USGS2025

Sentinel-2 based estimates of rangeland fractional cover and canopy gap class for the western United States

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75 × 75 km tiles covering the 17 western states of the United States.

western United States↗
USGS2013

Partnering for science: proceedings of the USGS Workshop on Citizen Science

What U.S. Geological Survey (USGS) programs use citizen science? How can projects be best designed while meeting policy requirements? What are the most effective volunteer recruitment methods? What data should be collected to ensure validation and how should data be stored? What standard protocols are most easily used by volunteers? Can data from multiple projects be integrated to support new research or existing science questions? To help answer these and other questions, the USGS Community of Data Integration (CDI) supported the development of the Citizen Science Working Group (CSWG) in August 2011 and funded the working group’s proposal to hold a USGS Citizen Science Workshop in fiscal year 2012. The stated goals for our workshop were: raise awareness of programs and projects in the USGS that incorporate citizen science, create a community of practice for the sharing of knowledge and experiences, provide a forum to discuss the challenges of—and opportunities for—incorporating citizen science into USGS projects, and educate and support scientists and managers whose projects may benefit from public participation in science.To meet these goals, the workshop brought together 50 attendees (see appendix A for participant details) representing the USGS, partners, and external citizen science practitioners from diverse backgrounds (including scientists, managers, project coordinators, and technical developers, for example) to discuss these topics at the Denver Federal Center in Colorado on September 11–12, 2012. Over two and a half days, attendees participated in four major plenary sessions (Citizen Science Policy and Challenges, Engaging the Public in Scientific Research, Data Collection and Management, and Technology and Tools) comprised of 25 invited presentations and followed by structured discussions for each session designed to address both prepared and ad hoc "big questions." A number of important community support and infrastructure needs were identified from the sessions and discussions, and a subteam was formed to draft a strategic vision statement to guide and prioritize future USGS efforts to support the citizen science community. Attendees also brainstormed proposal ideas for the fiscal year 2013 CDI request for proposals: one possible venue to support the execution of the vision.

Open-File Report↗
USGS2025

Twenty years (2000-2020) of butterfly monitoring data across the contiguous United States

We present the most comprehensive, integrated, butterfly monitoring dataset ever assembled for the United States. It contains over 1.2 million count records, from 65,000 surveys, representing over 12.6 million individual butterflies. To compile this dataset, we integrated data and harmonized taxonomy across 19 butterfly monitoring programs in the United States – one national, 13 statewide, and 5 local (e.g. individual county or National Park) in scale. In addition to the data, we also provide the taxonomic dictionary used to crosswalk butterfly taxonomy across programs, and the code used to assemble the integrated dataset. The publication of this dataset will inspire new analyses of butterfly population trends and drivers that help to identify solutions to the biodiversity crisis.

contiguous United States↗
USGS2026

Two hundred years of historical spawning and nursery data for coregonine fishes in the Laurentian Great Lakes

Historical data can provide critical ecological information for species across the globe, many of which are facing unprecedented rates of ecosystem change. Yet, historical information related to freshwater species, especially fishes, remains scattered, often in original formats, and underutilized for informing conservation and restoration activities. Here, we present a Data Descriptor called Coregonine Spawning History (CORHIST), a database designed to house diverse data related to past spawning and nursery areas for fishes in the family Salmonidae, subfamily Coregoninae (ciscoes and whitefishes), in the Laurentian Great Lakes and their tributaries. Data for 11 species of coregonines historically occurring in the Great Lakes are included in CORHIST. Over 3,400 occurrence records at the coordinate scale have been entered, over 2,200 of which are for Cisco ( Coregonus artedi ) and Lake Whitefish ( C. clupeaformis )—two focal species for which there is either multinational conservation interest or restoration efforts underway in the Laurentian Great Lakes. CORHIST is already proving useful for several studies developing habitat suitability models and delineating spatial units for conservation or restoration planning.

Laurentian Great Lakes↗
USGS2015

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

Scientific Data↗
USGS2018

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km 2 ) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.

Alaska↗
USGS2021

A global dataset of inland fisheries expert knowledge

Inland fisheries and their freshwater habitats face intensifying effects from multiple natural and anthropogenic pressures. Fish harvest and biodiversity data remain largely disparate and severely deficient in many areas, which makes assessing and managing inland fisheries difficult. Expert knowledge is increasingly used to improve and inform biological or vulnerability assessments, especially in data-poor areas. Integrating expert knowledge on the distribution, intensity, and relative influence of human activities can guide natural resource management strategies and institutional resource allocation and prioritization. This paper introduces a dataset summarizing the expert-perceived state of inland fisheries at the basin (fishery) level. An electronic survey distributed to professional networks (June-September 2020) captured expert perceptions (n = 536) of threats, successes, and adaptive capacity to fisheries across 93 hydrological basins, 79 countries, and all major freshwater habitat types. This dataset can be used to address research questions with conservation relevance, including: demographic influences on perceptions of threat, adaptive capacities for climate change, external factors driving multi-stressor interactions, and geospatial threat assessments.

Scientific Data↗
USGS2023

Watershed carbon yield derived from gauge observations and river network connectivity in the United States

River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. The new riverine carbon load and watershed net gain/loss represent a unique contribution to support future studies for better understanding and quantification of riverine carbon cycles.

Scientific Data↗
USGS2026

Depth-resolved carbon dioxide and methane concentrations in 522 lakes, ponds, and reservoirs worldwide

Lakes, ponds, and reservoirs (hereafter: “lakes”) are important sources of the greenhouse gases carbon dioxide (CO 2 ) and methane (CH 4 ). Emissions of CO 2 and CH 4 from lakes are regulated in part by in-lake processes, including the production and storage of gases in the lower parts of the water column (bottom waters). However, while substantial efforts have been made to improve estimates of greenhouse gas emissions from lakes, limited data on gas concentrations along depth profiles have prevented the incorporation of bottom-water processes in global emission estimates. Here, we present GHG-depths: the largest existing dataset of depth-profile CO 2 and CH 4 measurements worldwide, including 522 lakes across 38 countries and all seven continents. These data include contributions from 45 research teams and 56 published studies, totaling 2558 discrete sampling events. As global change continues to alter biogeochemical cycling in lakes, these data can help improve mechanistic models to better predict greenhouse gas production and emission from lakes worldwide.

Scientific Data↗
USGS2025

HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa

Sub-Saharan Africa (SSA) faces severe agricultural data scarcity amidst high food insecurity and a large agricultural yield gap, making crop production data crucial for understanding and enhancing food systems. To address this gap, HarvestStat Africa presents the largest compilation of open-access subnational crop statistics and time-series across SSA. Based on agricultural statistics collated by USAID’s Famine Early Warning Systems Network, the subnational crop statistics are standardized and calibrated across changing administrative units to produce consistent and continuous time-series. The dataset includes 546,605 records, primarily spanning from 1980 to 2022, detailing crop production, harvested areas, and yields for 33 countries and 90 crop types, including key cereals in SSA such as wheat, maize, rice, sorghum, barley, millet, and fonio. This new dataset enhances our understanding of how climate variability and change influence agricultural production, supports subnational food system analysis, and aids in operational yield forecasting. As an open-source resource, it sets an important precedent for sharing subnational crop statistics to inform decision-making and modeling efforts.

Scientific Data - Nature↗
USGS2022

Global dataset of species-specific inland recreational fisheries harvest for consumption

Inland recreational fisheries, found in lakes, rivers, and other landlocked waters, are important to livelihoods, nutrition, leisure, and other societal ecosystem services worldwide. Although recreationally-caught fish are frequently harvested and consumed by fishers, their contribution to food and nutrition has not been adequately quantified due to lack of data, poor monitoring, and under-reporting, especially in developing countries. Beyond limited global harvest estimates, few have explored species-specific harvest patterns, although this variability has implications for fisheries management and food security. Given the continued growth of the recreational fishery sector, understanding inland recreational fish harvest and consumption rates represents a critical knowledge gap. Based on a comprehensive literature search and expert knowledge review, we quantified multiple aspects of global inland recreational fisheries for 81 countries spanning ~192 species. For each country, we assembled recreational fishing participation rate and estimated species-specific harvest and consumption rate. This dataset provides a foundation for future assessments, including understanding nutritional and economic contributions of inland recreational fisheries.

Scientific Data↗
USGS2024

Advancing water security in Africa with new high-resolution discharge data

VegDischarge v1 is a comprehensive river discharge across Africa (2000–2021), produced by coupling the agro-hydrologic VegET model and the mizuRoute routing framework. Using remote sensing data and hydrological modeling, the 1-km runoff field simulated by VegET, and routed with mizuRoute, covers over 64,000 river segments in Africa. The VegET model simulates runoff based on vegetation and soil moisture dynamics, while mizuRoute processes this runoff through a detailed river network. Performance metrics show strong model reliability, with R² ranging from 0.5 to 0.9, NSE between 0.6 and 0.9, and KGE from 0.5 to 0.8. The total annual average discharge for Africa is quantified at 3238.1 km³ . year-1, with contributions to various oceanic basins: 989.9 km³ . year-1 to the North Atlantic, primarily from West African rivers like the Senegal, Gambia, Volta, and Niger; 1313.7 km³ . year-1 to the South Atlantic, largely from the Congo River; 212.5 km³ . year-1 to the Mediterranean Sea, predominantly from the Nile River; and 722.0 km³ . year-1 to the Indian Ocean, with substantial inputs from rivers such as the Zambezi. This VegDischarge v1 is valuable for policymakers, stakeholders, and researchers to better understand water availability, its temporal and spatial variations, that impact water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience mitigation strategies.

Scientific Data↗
USGS2026

The FunAqua dataset of global fungal biodiversity in aquatic ecosystems

Advances in high-throughput DNA sequencing technologies have accelerated research on aquatic microorganisms, their diversity, and ecological functions. This has also significantly advanced research on fungi that shape aquatic ecosystems and food webs. Here, we present the FunAqua dataset, which describes global fungal biodiversity in aquatic ecosystems based on eukaryotic long-read metabarcoding data from 1,076 sediment and 1,299 filtered water samples collected across 1,452 sampling sites in 87 countries. We targeted the internal transcribed spacer (ITS) region. In total, 50,152 fungal OTUs were recorded. The sequencing data is accompanied by comprehensive sample metadata, including physicochemical data of water and sediments. The FunAqua dataset provides a new resource for analysing and better understanding the biodiversity of fungi in different types of aquatic habitats across biogeographical regions. The dataset described in this data paper will be periodically updated with additional metabarcoding data.

Scientific Data↗