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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 (3) Past month (0) Past week (0) Past day (0) Custom range... To   Search Extensions Citation (8) Types Citation (8) Contacts Alison P Appling Jared Willard Gretchen Hansen North Central CASC Vipin Kumar More... Tag Types Theme (1) Science Themes (2) Organization (2) Water, Coasts and Ice (2) Wildlife and Plants (2) Tag Schemes https://www.sciencebase.gov/vocab/category/NCCWSC/ScienceThemes (2) https://www.sciencebase.gov/vocab/category/NCCWSC/Project/OrganizationName (2) https://www.sciencebase.gov/vocab/category/NCCWSC/WaterCoastsandIce (2) https://www.sciencebase.gov/vocab/category/NCCWSC/WildlifeandPlants (2) View Results as: JSON ATOM CSV Browse Folder

The metabolic regimes of flowing waters Abstract The processes and biomass that characterize any ecosystem are fundamentally constrained by the total amount of energy that is either fixed within or delivered across its boundaries. Ultimately, ecosystems may be understood and classified by their rates of total and net productivity and by the seasonal patterns of photosynthesis and respiration. Such understanding is well developed for terrestrial and lentic ecosystems but our understanding of ecosystem phenology has lagged well behind for rivers. The proliferation of reliable and inexpensive sensors for monitoring dissolved oxygen and carbon dioxide is underpinning a revolution in our understanding of the ecosystem energetics of rivers. Here, we synthesize... Categories: Publication; Types: Citation 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 Final Report: Understanding Historical and Predicting Future Lake Temperatures in North and South Dakota Lakes, reservoirs, and ponds are central and integral features of the landscape of the North Central US. These water bodies provide aesthetic, cultural, and ecosystem services to surrounding wildlife and human communities. Lakes are warming, resulting in the loss of many native fish. In order to manage economically valuable fisheries and other ecosystem services provided by lakes, it is important for managers to have access to accurate estimates of water temperature to better understand past change and to plan for potential future further warming. These data are invaluable for making decisions such as whether to continue stocking plans as usual in certain lakes or how to set specific harvest limits. This project... Categories: Publication; Types: Citation Seasonality of change: Summer warming rates do not fully represent effects of climate change on lake temperatures Abstract (from http://onlinelibrary.wiley.com/doi/10.1002/lno.10557/abstract): Responses in lake temperatures to climate warming have primarily been characterized using seasonal metrics of surface-water temperatures such as summertime or stratified period average temperatures. However, climate warming may not affect water temperatures equally across seasons or depths. We analyzed a long-term dataset (1981–2015) of biweekly water temperature data in six temperate lakes in Wisconsin, U.S.A. to understand (1) variability in monthly rates of surface- and deep-water warming, (2) how those rates compared to summertime average trends, and (3) if monthly heterogeneity in water temperature trends can be predicted by heterogeneity... Categories: Publication; Types: Citation; Tags: Fish, Northeast CASC, Rivers, Streams and Lakes, Water, Coasts and Ice, Wildlife and Plants 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 Process‐guided deep learning predictions of lake water temperature Abstract(from:https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024922)The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedbacks (model penalties... Categories: Publication; Types: Citation; Tags: Fish, Northeast CASC, Rivers, Streams and Lakes, Water, Coasts and Ice, Wildlife and Plants, All tags... data science, deep learning, lake modeling, process‐guided deep learning, temperature prediction, theory‐guided data science, Fewer tags Grand challenges for integrated USGS science—A workshop report Executive Summary The U.S. Geological Survey (USGS) has a long history of advancing the traditional Earth science disciplines and identifying opportunities to integrate USGS science across disciplines to address complex societal problems. The USGS science strategy for 2007-2017 laid out key challenges in disciplinary and interdisciplinary arenas, culminating in a call for increased focus on a number of crosscutting science directions. Ten years on, to further the goal of integrated science and at the request of the Executive Leadership Team (ELT), a workshop with three dozen invited scientists spanning different disciplines and career stages in the Bureau convened on February 7-10, 2017, at the USGS John Wesley... Categories: Publication; Types: Citation Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta-Transfer Learning Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach... Categories: Publication; Types: Citation