Geology topics

Paul C. Hanson

Publications and source records attributed to Paul C. Hanson.

18 recordsLinked to original sources

USGS2023

Modular compositional learning improves 1D hydrodynamic lake model performance by merging process-based modeling with deep learning

Hybrid Knowledge-Guided Machine Learning (KGML) models, which are deep learning models that utilize scientific theory and process-based model simulations, have shown improved performance over their process-based counterparts for the simulation of water temperature and hydrodynamics. We highlight the modular compositional learning (MCL) methodology as a novel design choice for the development of hybrid KGML models in which the model is decomposed into modular sub-components that can be process-based models and/or deep learning models. We develop a hybrid MCL model that integrates a deep learning model into a modularized, process-based model. To achieve this, we first train individual deep learning models with the output of the process-based models. In a second step, we fine-tune one deep learning model with observed field data. In this study, we replaced process-based calculations of vertical diffusive transport with deep learning. Finally, this fine-tuned deep learning model is integrated into the process-based model, creating the hybrid MCL model with improved overall projections for water temperature dynamics compared to the original process-based model. We further compare the performance of the hybrid MCL model with the process-based model and two alternative deep learning models and highlight how the hybrid MCL model has the best performance for projecting water temperature, Schmidt stability, buoyancy frequency, and depths of different isotherms. Modular compositional learning can be applied to existing modularized, process-based model structures to make the projections more robust and improve model performance by letting deep learning estimate uncertain process calculations.

Journal of Advances in Modeling Earth Systems↗

USGS2022

Long-term change in metabolism phenology in north temperate lakes

The phenology of dissolved oxygen (DO) dynamics and metabolism in north temperate lakes offers a basis for comparing metabolic cycles over multi-year time scales. Although proximal control over lake DO can be attributed to metabolism and physical processes, how those processes evolve over decades largely remains unexplored. Metabolism phenology may reveal the importance of coherence among lakes and facilitate general conclusions about the controls on lake metabolism at regional scales. We developed a Bayesian modeling framework to estimate DO concentrations and metabolism in eight lakes in contrasting landscapes in Wisconsin, USA. We identify the DO and metabolism phenologies for each lake, and use those to compare how decadal patterns relate to trophic state and landscape setting. We show that lakes can be categorized by their hypolimnetic oxygen consumption dynamics, with oligotrophic lakes having a diverse set of patterns and eutrophic lakes having uniform trends of increased oxygen consumption over the last decade. Metabolism phenology is likewise diverse for oligotrophic lakes, whereas eutrophic lakes in southern Wisconsin share consistent long-term patterns of metabolic trends and seasonal DO consumption highlighting the importance of trophic state driving metabolism. Eutrophic lakes had higher magnitudes and more seasonal variation in net ecosystem production in contrast to oligotrophic lakes. Generally, long-term metabolic trends of north temperate lakes suggest a limited influence of climate on lake metabolism and that temporal coherence of long-term metabolism change is driven primarily by the landscape setting.

Wisconsin↗

USGS2021

Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches

Historically, estimates of pelagic primary production in lake ecosystems were made by measuring the uptake of carbon-14 ( 14 C)-labeled inorganic carbon in samples incubated under laboratory or in situ conditions. However, incubation approaches are increasingly being replaced by methods that analyze diel changes in high-frequency in situ data such as free-water dissolved oxygen (O 2 ). While there is a rich literature on the comparison of approaches for estimating primary production using incubations (e.g., 14 C and O 2 bottle experiments), as well for approaches using high-frequency data (e.g., diel O 2 and CO 2 metabolism models), there are few direct comparisons of 14 C incubations and free-water O 2 approaches for estimating primary production. We used 20 lake-years of concurrent measurements of primary production quantified from high-frequency free-water O 2 data and 14 C incubations in four different lakes (4–7 years per lake) to compare these different approaches. Across all lakes, 61% of the 14 C production estimates were within the 95% credible intervals of the free-water O 2 production estimates. Error-in-variable regressions support the assumption that 14 C methods estimate a production value between gross primary production and net primary production and the bottle effect is constant across the entire range of production values considered here. There was little evidence that daily pelagic, epilimnetic estimates of primary production differed substantially based on the selection of free-water O 2 or 14 C approaches in these lakes during summer stratified conditions.

Limnology and Oceanography: Methods↗

USGS2019

Process-guided deep learning predictions of lake water temperature

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 feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.

Minnesota, Wisconsin↗

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↗

USGS2018

LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface ( k ). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.

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

Water quality data for national-scale aquatic research: The Water Quality Portal

Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third-party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.

Water Resources Research↗

USGS2017

Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake

Oxygen concentration is both an indicator and driver of water quality in lakes. Decreases in oxygen concentration leads to altered ecosystem function as well as harmful consequences for aquatic biota, such as fishes. The responses of oxygen dynamics in lakes to climate-related drivers, such as temperature and wind speed, are well documented for lake surface waters. However, much less is known about how the oxic environment of bottom waters, especially the timing and magnitude of anoxia in eutrophic lakes, responds to changes in climate drivers. Understanding how important ecosystem states, such as hypolimnetic anoxia, may respond to differing climate scenarios requires a model that couples physical-biological conditions and sufficiently captures the density stratification that leads to strong oxygen gradients. Here, we analyzed the effects of changes in three important meteorological drivers (air temperature, wind speed, and relative humidity) on hypolimnetic anoxia in a eutrophic, north temperate lake using the anoxic factor as an index that captures both the temporal and spatial extent of anoxia. Air temperature and relative humidity were found to have a positive correlation with anoxic factor, while wind speed had a negative correlation. Air temperature was found to have the greatest potential impact of the three drivers on the anoxic factor, followed by wind speed and then relative humidity. Across the scenarios of climate variability, variation in the simulated anoxic factor was primarily due to changes in the timing of onset and decay of stratification. Given the potential for future changes in climate, especially increases in air temperature, this study provides important insight into how these changes will alter lake water quality.

Ecological Modelling↗

USGS2015

Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories

Maintaining the health of aquatic systems is an essential component of sustainable catchmentmanagement, however, degradation of water quality and aquatic habitat continues to challenge scientistsand policy-makers. To support management and restoration efforts aquatic system models are requiredthat are able to capture the often complex trajectories that these systems display in response to multiplestressors. This paper explores the abilities and limitations of current model approaches in meeting this chal-lenge, and outlines a strategy based on integration of ﬂexible model libraries and data from observationnetworks, within a learning framework, as a means to improve the accuracy and scope of model predictions.The framework is comprised of a data assimilation component that utilizes diverse data streams from sensornetworks, and a second component whereby model structural evolution can occur once the model isassessed against theoretically relevant metrics of system function. Given the scale and transdisciplinarynature of the prediction challenge, network science initiatives are identiﬁed as a means to develop and inte-grate diverse model libraries and workﬂows, and to obtain consensus on diagnostic approaches to modelassessment that can guide model adaptation. We outline how such a framework can help us explore thetheory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry,and, in doing so, also advance the role of prediction in aquatic ecosystem management.

Water Resources Research↗

USGS2015

Supporting open collaboration in science through explicit and linked semantic description of processes

The Web was originally developed to support collaboration in science. Although scientists benefit from many forms of collaboration on the Web (e.g., blogs, wikis, forums, code sharing, etc.), most collaborative projects are coordinated over email, phone calls, and in-person meetings. Our goal is to develop a collaborative infrastructure for scientists to work on complex science questions that require multi-disciplinary contributions to gather and analyze data, that cannot occur without significant coordination to synthesize findings, and that grow organically to accommodate new contributors as needed as the work evolves over time. Our approach is to develop an organic data science framework based on a task-centered organization of the collaboration, includes principles from social sciences for successful on-line communities, and exposes an open science process. Our approach is implemented as an extension of a semantic wiki platform, and captures formal representations of task decomposition structures, relations between tasks and users, and other properties of tasks, data, and other relevant science objects. All these entities are captured through the semantic wiki user interface, represented as semantic web objects, and exported as linked data.

Conference Paper↗

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

CO 2 and CH 4 emissions from streams in a lake-rich landscape: Patterns, controls, and regional significance

Aquatic ecosystems are important components of landscape carbon budgets. In lake-rich landscapes, both lakes and streams may be important sources of carbon gases (CO 2 and CH 4 ) to the atmosphere, but the processes that control gas concentrations and emissions in these interconnected landscapes have not been adequately addressed. We use multiple data sets that vary in their spatial and temporal extent during 2001–2012 to investigate the carbon gas source strength of streams in a lake-rich landscape and to determine the contribution of lakes, metabolism, and groundwater to stream CO 2 and CH 4 . We show that streams emit roughly the same mass of CO 2 (23.4 Gg C yr −1 ; 0.49 mol CO 2 m −2 d −1 ) as lakes at a regional scale (27 Gg C yr −1 ) and that stream CH 4 emissions (189 Mg C yr −1 ; 8.46 mmol CH 4 m −2 d −1 ) are an important component of the regional greenhouse gas balance. Gas transfer velocity variability (range = 0.34 to 13.5 m d −1 ) contributed to the variability of gas flux in this landscape. Groundwater inputs and in-stream metabolism control stream gas supersaturation at the landscape scale, while carbon cycling in lakes and deep groundwaters does not control downstream gas emissions. Our results indicate the need to consider connectivity of all aquatic ecosystems (lakes, streams, wetlands, and groundwater) in lake-rich landscapes and their connections with the terrestrial environment in order to understand the full nature of the carbon cycle.

Global Biogeochemical Cycles↗

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

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↗

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↗

USGS2013

Lake shoreline in the contiguous United States: Quantity, distribution and sensitivity to observation resolution

1. Quantifying lake biogeochemical processing at broad spatial scales requires that we scale processes along with physical metrics. Past work has primarily scaled lentic processes using estimates of lake surface area. However, many processes important to lakes, such as material, energy and biological ﬂuxes and biogeochemical cycling, scale with lake perimeter. 2. We estimate the total lake perimeter for the contiguous United States (U.S.) and examine the sensitivity of this estimate to measurement resolution. At the original mapping resolution, lakes in the contiguous U.S. have a total perimeter of over 1.8 million km. 3. The change in measured perimeter versus measurement resolution for the contiguous U.S. had a log-log slope (also known as the fractal dimension) of 0.21, generally less than previously reported estimates. With changing observation resolution, total measured perimeter was most sensitive to the inclusion or exclusion of small lakes, not shoreline complexity. 4. The total aquatic–terrestrial interface in lakes is less than one-tenth that of streams and rivers, which collectively account for over 21 million km of shoreline in the contiguous U.S. This study further describes the distribution of lake perimeter and proposes a technique that can contribute to understanding continental-scale processes.

Freshwater Biology↗

USGS2013

Potential effects of climate change on inland glacial lakes and implications for lake-dependent biota in Wisconsin: final report April 2013

The economic vitality and quality of life of many northern Wisconsin communities is closely associated with the ecological condition of the abundant water resources in the region. Climate change models predict warmer temperatures, changes to precipitation patterns, and increased evapotranspiration in the Great Lakes region. Recently (1950-2006), many regions of Wisconsin have experienced warming, and precipitation has generally increased except in far northern Wisconsin. Modeling conducted by the University of Wisconsin Nelson Environmental Institute Center for Climate Research predicts an increase in annual temperature by the middle of the 21st century of approximately 6° F statewide, and an increase in precipitation of 1”–2”. However, summer precipitation in the northern part of the state is expected to be less and winter precipitation will be greater. By the end of the 21st century, the magnitude of changes in temperature and precipitation are expected to intensify. Such climatic changes have altered, and would further alter hydrological, chemical, and physical properties of inland lakes. Lake-dependent wildlife sensitive to changes in water quality, are particularly susceptible to lake quality-associated habitat changes and are likely to suffer restrictions to current breeding distributions under some climate change scenarios. We have selected the common loon (Gavia immer) to serve as a sentinel lake-dependent piscivorous species to be used in the development of a template for linking primary lake-dependent biota endpoints (e.g., decline in productivity and/or breeding range contraction) to important lake quality indicators. In the current project, we evaluate how changes in freshwater habitat quality (specifically lake clarity) may impact common loon lake occupancy in Wisconsin under detailed climate-change scenarios. In addition, we employ simple land-use/land cover and habitat scenarios to illustrate the potential interaction of climate and land-use/land cover effects. The methods employed here provide a template for studies where integration of physical and biotic models is used to project future conditions under various climate and land use change scenarios. Findings presented here project the future conditions of lakes and loons within an important watershed in northern Wisconsin – of importance to water resource managers and state citizens alike.

Wisconsin↗