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Location Folder Show Tree ROOT _ScienceBase Catalog 3DSC Alaska Science Center (ASC) Alkalic Igneous Centers of the Great Plains and Rocky Mountains: Data in Support of Critical Mineral Resource Studies AMMonitor: Remote wildlife monitoring for adaptive management Amphibian Research and Monitoring Initiative More... Filters Date Range Past year (1) Past month (0) Past week (0) Past day (0) Custom range... To   Search Extensions Citation (2) Types Citation (2) Contacts Jared Willard Simon N Topp Vipin Kumar Integrated Information Dissemination Division U.S. Geological Survey More... Categories Publication (2) Data (1) Tag Types Place (1) Theme (1) USGS Scientific Topic Keyword (1) Harvest Set (1) Tag Schemes U.S. Department of Commerce, 1987, Codes for the identification of the States, the District of Columbia and the outlying areas of the United States, and associated areas (Federal Information Processing Standard 5-2): Washington, D. C., NIST (1) none (1) Department of Commerce, 1995, Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions, Federal Information Processing Standard (FIPS) 10-4, Washington, D.C., National Institute of Standards and Technology (1) ISO 19115 Topic Category (1) USGS Thesaurus (1) View Results as: JSON ATOM CSV Browse Folder

Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020) Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes. Categories: Data; Tags: AL, AR, AZ, Alabama, Aquatic Biology, All tags... Arizona, Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, Ecology, FL, Florida, GA, Georgia, Hydrology, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, LA, Limnology, Louisiana, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, USGS Science Data Catalog (SDC), UT, United States, Utah, VA, VT, Vermont, Virginia, WA, WI, WV, WY, Washington, Water Quality, Water Resources, West Virginia, Wisconsin, Wyoming, climate change, deep learning, environment, inlandWaters, machine learning, modeling, reservoirs, surface temperature, temperate lakes, temperature, water, water resources, Fewer tags Comparing models using air and water temperature to forecast an aquatic invasive species response to climate change Abstract (from Ecosphere): Understanding invasive species spread and projecting how distributions will respond to climate change is a central task for ecologists. Typically, current and projected air temperatures are used to forecast future distributions of invasive species based on climate matching in an ecological niche modeling approach. While this approach was originally developed for terrestrial species, it has also been widely applied to aquatic species even though aquatic species do not experience air temperatures directly. In the case of lakes, species respond to lake thermal regimes, which reflect the interaction of climate and lake attributes such as depth, size, and clarity. The result is that adjacent... Categories: Publication; Types: Citation Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020) The dataset described here includes estimates of historical (1980–2020) daily surface water temperature, lake metadata, and daily weather conditions for lakes bigger than 4 ha in the conterminous United States (n = 185,549), and also in situ temperature observations for a subset of lakes (n = 12,227). Estimates were generated using a long short-term memory deep learning model and compared to existing process-based and linear regression models. Model training was optimized for prediction on unmonitored lakes through cross-validation that held out lakes to assess generalizability and estimate error. On the held-out lakes with in situ observations, median lake-specific error was 1.24°C, and the overall root mean squared... Categories: Publication; Types: Citation