Geology topics

Luke A. Winslow

Publications and source records attributed to Luke A. Winslow.

At least 19 recordsLinked to original sources

USGS2019

A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.

Geoscientific Model Development↗

USGS2019

Widespread loss of lake ice around the Northern Hemisphere in a warming world

Ice provides a range of ecosystem services—including fish harvest , cultural traditions , transportation , recreation and regulation of the hydrological cycle —to more than half of the world’s 117 million lakes. One of the earliest observed impacts of climatic warming has been the loss of freshwater ice , with corresponding climatic and ecological consequences . However, while trends in ice cover phenology have been widely documented , a comprehensive large-scale assessment of lake ice loss is absent. Here, using observations from 513 lakes around the Northern Hemisphere, we identify lakes vulnerable to ice-free winters. Our analyses reveal the importance of air temperature, lake depth, elevation and shoreline complexity in governing ice cover. We estimate that 14,800 lakes currently experience intermittent winter ice cover, increasing to 35,300 and 230,400 at 2 and 8 °C, respectively, and impacting up to 394 and 656 million people. Our study illustrates that an extensive loss of lake ice will occur within the next generation, stressing the importance of climate mitigation strategies to preserve ecosystem structure and function, as well as local winter cultural heritage.

Nature Climate Change↗

USGS2018

The metabolic regimes of 356 rivers in the United States

A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practical to estimate for many sites due to improved modeling approaches and the availability of requisite model inputs in public datasets. We assembled inputs from the U.S. Geological Survey and National Aeronautics and Space Administration for October 2007 to January 2017. We then ran models to estimate daily GPP, ER, and the gas exchange rate coefficient for 356 streams and rivers across the continental United States. We also gathered potential explanatory variables and spatial information for cross-referencing this dataset with other datasets of watershed characteristics. This dataset offers a first national assessment of many-day time series of metabolic rates for up to 9 years per site, with a total of 490,907 site-days of estimates.

Scientific Data↗

USGS2018

The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: An extension of the Plankton Ecology Group (PEG) model

The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence (ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3 methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3) profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling techniques would have missed SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean water column data. Contrary to the PEG model’s proposed negligible role for physical control of phytoplankton during the growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus, an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.

Inland Waters↗

USGS2018

Effects of water level and climate on the hydrodynamics and water quality of Anvil Lake, Wisconsin, a shallow seepage lake

Interannual differences in the water quality of Anvil Lake, Wisconsin, were examined to determine how water level and climate affect the hydrodynamics and trophic state of shallow lakes, and their importance compared to anthropogenic changes in the watershed. Anvil Lake is a relatively pristine seepage lake with hydrology dominated by precipitation, evaporation, and groundwater exchange enabling the typically subtle effects of water level and climate to be evaluated. Groundwater and hydrodynamic models were used to describe lake water and phosphorus budgets and how its hydrodynamics are affected by water level and air temperature. Decreases in water level are expected to cause Anvil Lake and other shallow lakes to stratify fewer days, and have warmer bottom temperatures and more deep-mixing events. Increasing air temperatures should cause these lakes to have shorter ice cover, longer summer stratification periods, and warmer bottom temperatures. How water level affects water quality depends on how nutrient loading and lake volume vary: during drier, low-water years, lakes with large interannual changes in loading should have better water quality, whereas lakes with small changes in loading should degrade slightly. Anthropogenic changes in Anvil Lake's watershed over the past ∼100 yr were about 1.5 times the effects of changes in water level when levels were low, but the effects were similar when levels were high. Climate warming is expected to increase productivity in shallow lakes because warmer air temperatures will likely increase bottom temperatures increasing sediment phosphorus release and deep-mixing events enabling this phosphorus to reach the epilimnion.

Wisconsin↗

USGS2018

Prediction of lake depth across a 17-state region in the United States

Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Z max ) observations. The model was used to predict depth for all lakes ≥4 ha ( n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Z max . Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance ( R 2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.

Inland Waters↗

USGS2018

Generating community-built tools for data sharing and analysis in environmental networks

Rapid data growth in many environmental sectors has necessitated tools to manage and analyze these data. The development of tools often lags behind the proliferation of data, however, which may slow exploratory opportunities and scientific progress. The Global Lake Ecological Observatory Network (GLEON) collaborative model supports an efficient and comprehensive data–analysis–insight life cycle, including implementations of data quality control checks, statistical calculations/derivations, models, and data visualizations. These tools are community-built and openly shared. We discuss the network structure that enables tool development and a culture of sharing, leading to optimized output from limited resources. Specifically, data sharing and a flat collaborative structure encourage the development of tools that enable scientific insights from these data. Here we provide a cross-section of scientific advances derived from global-scale analyses in GLEON. We document enhancements to science capabilities made possible by the development of analytical tools and highlight opportunities to expand this framework to benefit other environmental networks.

Inland Waters↗

USGS2017

Large-scale modeled contemporary and future water temperature estimates for 10774 Midwestern U.S. Lakes

Climate change has already influenced lake temperatures globally, but understanding future change is challenging. The response of lakes to changing climate drivers is complex due to the nature of lake-atmosphere coupling, ice cover, and stratification. To better understand the diversity of lake responses to climate change and give managers insight on individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota, and Wisconsin for contemporary (1979–2015) and future (2020–2040 and 2080–2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. In addition to lake-specific daily simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We include all supporting lake-specific model parameters, meteorological drivers, and archived code for the model and derived metric calculations. This unique dataset offers landscape-level insight into the impact of climate change on lakes.

Michigan, Minnesota, Wisconsin↗

USGS2016

Community for Data Integration 2015 annual report

The Community for Data Integration (CDI) continued to experience success in fiscal year 2015. The CDI community members have been sharing, learning, and collaborating through monthly forums, workshops, working groups, and funded projects. In fiscal year 2015, CDI coordinated 10 monthly forums with 16 different speakers from the U.S. Geological Survey and external partners; funded 11 collaborative projects; and hosted an in-person, four-day workshop, which attracted 168 (134 in-person and 34 remote) data practitioners, data providers, and data consumers from across the USGS, academia, industry, and other government agencies. The Citizen Science, Connected Devices, Data Management, Semantic Web, and Tech Stack Working Groups continued to accomplish great things in fiscal year 2015. These working groups were major stakeholders in planning the 2015 CDI Workshop; they continued developing solutions to pressing challenges, and they brought in speakers throughout the year for more focused presentations and discussions. Additionally, a new working group formed during the 2015 CDI Workshop—the Earth-Science Themes Working Group.

Open-File Report↗

USGS2016

Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity

Climate change is rapidly warming aquatic ecosystems including lakes and reservoirs. However, variability in lake characteristics can modulate how lakes respond to climate. Water clarity is especially important both because it influences the depth range over which heat is absorbed, and because it is changing in many lakes. Here, we show that simulated long-term water clarity trends influence how both surface and bottom water temperatures of lakes and reservoirs respond to climate change. Clarity changes can either amplify or suppress climate-induced warming, depending on lake depth and the direction of clarity change. Using a process-based model to simulate 1894 north temperate lakes from 1979 to 2012, we show that a scenario of decreasing clarity at a conservative yet widely observed rate of 0.92% yr −1 warmed surface waters and cooled bottom waters at rates comparable in magnitude to climate-induced warming. For lakes deeper than 6.5 m, decreasing clarity was sufficient to fully offset the effects of climate-induced warming on median whole-lake mean temperatures. Conversely, a scenario increasing clarity at the same rate cooled surface waters and warmed bottom waters relative to baseline warming rates. Furthermore, in 43% of lakes, increasing clarity more than doubled baseline bottom temperature warming rates. Long-term empirical observations of water temperature in lakes with and without clarity trends support these simulation results. Together, these results demonstrate that water clarity trends may be as important as rising air temperatures in determining how waterbodies respond to climate change.

Limnology and Oceanography Letters↗

USGS2016

Building the team for team science

The ability to effectively exchange information and develop trusting, collaborative relationships across disciplinary boundaries is essential for 21st century scientists charged with solving complex and large-scale societal and environmental challenges, yet these communication skills are rarely taught. Here, we describe an adaptable training program designed to increase the capacity of scientists to engage in information exchange and relationship development in team science settings. A pilot of the program, developed by a leader in ecological network science, the Global Lake Ecological Observatory Network (GLEON), indicates that the training program resulted in improvement in early career scientists’ confidence in team-based network science collaborations within and outside of the program. Fellows in the program navigated human-network challenges, expanded communication skills, and improved their ability to build professional relationships, all in the context of producing collaborative scientific outcomes. Here, we describe the rationale for key communication training elements and provide evidence that such training is effective in building essential team science skills.

Ecosphere↗

USGS2015

geoknife: Reproducible web-processing of large gridded datasets

Geoprocessing of large gridded data according to overlap with irregular landscape features is common to many large-scale ecological analyses. The geoknife R package was created to facilitate reproducible analyses of gridded datasets found on the U.S. Geological Survey Geo Data Portal web application or elsewhere, using a web-enabled workflow that eliminates the need to download and store large datasets that are reliably hosted on the Internet. The package provides access to several data subset and summarization algorithms that are available on remote web processing servers. Outputs from geoknife include spatial and temporal data subsets, spatially-averaged time series values filtered by user-specified areas of interest, and categorical coverage fractions for various land-use types.

Ecography↗

USGS2015

Automated calculation of surface energy fluxes with high-frequency lake buoy data

Lake Heat Flux Analyzer is a program used for calculating the surface energy fluxes in lakes according to established literature methodologies. The program was developed in MATLAB for the rapid analysis of high-frequency data from instrumented lake buoys in support of the emerging field of aquatic sensor network science. To calculate the surface energy fluxes, the program requires a number of input variables, such as air and water temperature, relative humidity, wind speed, and short-wave radiation. Available outputs for Lake Heat Flux Analyzer include the surface fluxes of momentum, sensible heat and latent heat and their corresponding transfer coefficients, incoming and outgoing long-wave radiation. Lake Heat Flux Analyzer is open source and can be used to process data from multiple lakes rapidly. It provides a means of calculating the surface fluxes using a consistent method, thereby facilitating global comparisons of high-frequency data from lake buoys.

Environmental Modelling and Software↗

USGS2015

Examining the utility of satellite-based wind sheltering estimates for lake hydrodynamic modeling

Satellite-based measurements of vegetation canopy structure have been in common use for the last decade but have never been used to estimate canopy's impact on wind sheltering of individual lakes. Wind sheltering is caused by slower winds in the wake of topography and shoreline obstacles (e.g. forest canopy) and influences heat loss and the flux of wind-driven mixing energy into lakes, which control lake temperatures and indirectly structure lake ecosystem processes, including carbon cycling and thermal habitat partitioning. Lakeshore wind sheltering has often been parameterized by lake surface area but such empirical relationships are only based on forested lakeshores and overlook the contributions of local land cover and terrain to wind sheltering. This study is the first to examine the utility of satellite imagery-derived broad-scale estimates of wind sheltering across a diversity of land covers. Using 30 m spatial resolution ASTER GDEM2 elevation data, the mean sheltering height, h s , being the combination of local topographic rise and canopy height above the lake surface, is calculated within 100 m-wide buffers surrounding 76,000 lakes in the U.S. state of Wisconsin. Uncertainty of GDEM2-derived h s was compared to SRTM-, high-resolution G-LiHT lidar-, and ICESat-derived estimates of h s , respective influences of land cover type and buffer width on h s are examined; and the effect of including satellite-based h s on the accuracy of a statewide lake hydrodynamic model was discussed. Though GDEM2 h s uncertainty was comparable to or better than other satellite-based measures of h s , its higher spatial resolution and broader spatial coverage allowed more lakes to be included in modeling efforts. GDEM2 was shown to offer superior utility for estimating h s compared to other satellite-derived data, but was limited by its consistent underestimation of h s , inability to detect within-buffer h s variability, and differing accuracy across land cover types. Nonetheless, considering a GDEM2 h s -derived wind sheltering potential improved the modeled lake temperature root mean square error for non-forested lakes by 0.72 °C compared to a commonly used wind sheltering model based on lake area alone. While results from this study show promise, the limitations of near-global GDEM2 data in timeliness, temporal and spatial resolution, and vertical accuracy were apparent. As hydrodynamic modeling and high-resolution topographic mapping efforts both expand, future remote sensing-derived vegetation structure data must be improved to meet wind sheltering accuracy requirements to expand our understanding of lake processes.

Remote Sensing of Environment↗

USGS2015

New insight into California’s drought through open data

Historically unprecedented drought in California has brought water issues to the forefront of the nation’s attention. Crucial investigations that concern water policy, management, and research, in turn, require extensive information about the quality and quantity of California’s water. Unfortunately, key sources of pertinent data are unevenly distributed and frequently hard to find. Thankfully, the vital importance of integrating water data across federal, state, and tribal, academic, and private entities, has recently been recognized and addressed through federal initiatives such as the Climate Data Initiative of President Obama’s Climate Action Plan and the Advisory Committee on Water Information’s Open Water Data Initiative . Here, we demonstrate an application of integrated open water data, visualized and made available online using open source software, for the purpose of exploring the impact of the current California drought. Our collaborative approach and technical tools enabled a rapid, distributed development process. Many positive outcomes have resulted: the application received recognition within and outside of the Federal Government, inspired others to visualize open water data, spurred new collaborations for our group, and strengthened the collaborative relationships within the team of developers. In this article, we describe the technical tools and collaborative process that enabled the success of the application.

BayGEO Journal↗

USGS2014

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

Limnology and Oceanography: Methods↗

USGS2014

Simulating 2,368 temperate lakes reveals weak coherence in stratification phenology

Changes in water temperatures resulting from climate warming can alter the structure and function of aquatic ecosystems. Lake-specific physical characteristics may play a role in mediating individual lake responses to climate. Past mechanistic studies of lake-climate interactions have simulated generic lake classes at large spatial scales or performed detailed analyses of small numbers of real lakes. Understanding the diversity of lake responses to climate change across landscapes requires a hybrid approach that couples site-specific lake characteristics with broad-scale environmental drivers. This study provides a substantial advancement in lake ecosystem modeling by combining open-source tools with freely available continental-scale data to mechanistically model daily temperatures for 2,368 Wisconsin lakes over three decades (1979-2011). The model accurately predicted observed surface layer temperatures (RMSE: 1.74°C) and the presence/absence of stratification (81.1% agreement). Among-lake coherence was strong for surface temperatures and weak for the timing of stratification, suggesting individual lake characteristics mediate some - but not all - ecologically relevant lake responses to climate.

Ecological Modelling↗

USGS2014

Does lake size matter? Combining morphology and process modeling to examine the contribution of lake classes to population-scale processes

With lake abundances in the thousands to millions, creating an intuitive understanding of the distribution of morphology and processes in lakes is challenging. To improve researchers’ understanding of large-scale lake processes, we developed a parsimonious mathematical model based on the Pareto distribution to describe the distribution of lake morphology (area, perimeter and volume). While debate continues over which mathematical representation best fits any one distribution of lake morphometric characteristics, we recognize the need for a simple, flexible model to advance understanding of how the interaction between morphometry and function dictates scaling across large populations of lakes. These models make clear the relative contribution of lakes to the total amount of lake surface area, volume, and perimeter. They also highlight the critical thresholds at which total perimeter, area and volume would be evenly distributed across lake size-classes have Pareto slopes of 0.63, 1 and 1.12, respectively. These models of morphology can be used in combination with models of process to create overarching “lake population” level models of process. To illustrate this potential, we combine the model of surface area distribution with a model of carbon mass accumulation rate. We found that even if smaller lakes contribute relatively less to total surface area than larger lakes, the increasing carbon accumulation rate with decreasing lake size is strong enough to bias the distribution of carbon mass accumulation towards smaller lakes. This analytical framework provides a relatively simple approach to upscaling morphology and process that is easily generalizable to other ecosystem processes.

Inland Waters↗