Geology topics

Kevin C. Rose

Publications and source records attributed to Kevin C. Rose.

9 recordsLinked to original sources

USGS2024

Controls on lake pelagic primary productivity: Formalizing the nutrient-color paradigm

Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We used data from 58 diverse and globally distributed temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. Across these diverse systems, and given relatively limited inputs, model predictions of primary production were highly correlated with observed values derived from high-frequency sensor data. Our analysis provides a model structure, including calibrated parameter estimates, that may be broadly useful for understanding current and future patterns in lake primary production.

JGR Biogeosciences↗

USGS2024

New York State climate impacts assessment chapter 05: Ecosystems

The people of New York have long benefited from the state's diversity of ecosystems, which range from coastal shorelines and wetlands to extensive forests and mountaintop alpine habitat, and from lakes and rivers to greenspaces in heavily populated urban areas. These ecosystems provide key services such as food, water, forest products, flood prevention, carbon storage, climate moderation, recreational opportunities, and other cultural services. This chapter examines how changes in climatic conditions across the state are affecting different types of ecosystems and the services they provide, and considers likely future impacts of projected climate change. The chapter emphasizes how climate change is increasing the vulnerability of ecosystems to existing stressors, such as habitat fragmentation and invasive species, and highlights opportunities for New Yorkers to adapt and build resilience.

New York↗

USGS2023

Indicators of the effects of climate change on freshwater ecosystems

Freshwater ecosystems, including lakes, streams, and wetlands, are responsive to climate change and other natural and anthropogenic stresses. These ecosystems are frequently hydrologically and ecologically connected with one another and their surrounding landscapes, thereby integrating changes throughout their watersheds. The responses of any given freshwater ecosystem to climate change depend on the magnitude of climate forcing, interactions with other anthropogenic and natural changes, and the characteristics of the ecosystem itself. Therefore, the magnitude and manner in which freshwater ecosystems respond to climate change are difficult to predict a priori. We present a conceptual model to elucidate how freshwater ecosystems are altered by climate change. We identify eleven indicators that describe the response of freshwater ecosystems to climate change, discuss their potential value and limitations, and describe supporting measurements. Indicators are organized in three interrelated categories: hydrologic, water quality, and ecosystem structure and function. The indicators are supported by data sets with a wide range of temporal and spatial coverage, and they inform important scientific and management needs. Together, these indicators improve the understanding and management of the effects of climate change on freshwater ecosystems.

Climate Change↗

USGS2021

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.

Environmental Modelling & Software↗

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↗

USGS2017

Seasonality of change: Summer warming rates do not fully represent effects of climate change on lake temperatures

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 in air temperature trends. Monthly surface-water temperature warming rates varied across the open-water season, ranging from 0.013 in August to 0.073°C yr −1 in September (standard deviation [SD]: 0.025°C yr −1 ). Deep-water trends during summer varied less among months (SD: 0.006°C yr −1 ), but varied broadly among lakes (–0.056°C yr −1 to 0.035°C yr −1 , SD: 0.034°C yr −1 ). Trends in monthly surface-water temperatures were well correlated with air temperature trends, suggesting monthly air temperature trends, for which data exist at broad scales, may be a proxy for seasonal patterns in surface-water temperature trends during the open water season in lakes similar to those studied here. Seasonally variable warming has broad implications for how ecological processes respond to climate change, because phenological events such as fish spawning and phytoplankton succession respond to specific, seasonal temperature cues.

Wisconsin↗

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↗

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

A Global Lake Ecological Observatory Network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate process- based ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest.

Inland Waters↗