Browsing by Author "Hanson, Paul C."

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    Anoxia decreases the magnitude of the carbon, nitrogen, and phosphorus sink in freshwaters
    Carey, Cayelan C.; Hanson, Paul C.; Thomas, R. Quinn; Gerling, Alexandra B.; Hounshell, Alexandria G.; Lewis, Abigail S.; Lofton, Mary E.; McClure, Ryan P.; Wander, Heather L.; Woelmer, Whitney M.; Niederlehner, B.R.; Schreiber, Madeline E. (Wiley, 2022-05-05)
    Oxygen availability is decreasing in many lakes and reservoirs worldwide, raising the urgency for understanding how anoxia (low oxygen) affects coupled biogeochemical cycling, which has major implications for water quality, food webs, and ecosystem functioning. Although the increasing magnitude and prevalence of anoxia has been documented in freshwaters globally, the challenges of disentangling oxygen and temperature responses have hindered assessment of the effects of anoxia on carbon, nitrogen, and phosphorus concentrations, stoichiometry (chemical ratios), and retention in freshwaters. The consequences of anoxia are likely severe and may be irreversible, necessitating ecosystem-scale experimental investigation of decreasing freshwater oxygen availability. To address this gap, we devised and conducted REDOX (the Reservoir Ecosystem Dynamic Oxygenation eXperiment), an unprecedented, 7-year experiment in which we manipulated and modeled bottom-water (hypolimnetic) oxygen availability at the whole-ecosystem scale in a eutrophic reservoir. Seven years of data reveal that anoxia significantly increased hypolimnetic carbon, nitrogen, and phosphorus concentrations and altered elemental stoichiometry by factors of 2–5× relative to oxic periods. Importantly, prolonged summer anoxia increased nitrogen export from the reservoir by six-fold and changed the reservoir from a net sink to a net source of phosphorus and organic carbon downstream. While low oxygen in freshwaters is thought of as a response to land use and climate change, results from REDOX demonstrate that low oxygen can also be a driver of major changes to freshwater biogeochemical cycling, which may serve as an intensifying feedback that increases anoxia in downstream waterbodies. Consequently, as climate and land use change continue to increase the prevalence of anoxia in lakes and reservoirs globally, it is likely that anoxia will have major effects on freshwater carbon, nitrogen, and phosphorus budgets as well as water quality and ecosystem functioning.
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    Building, applying, and communicating ecosystem understanding via freshwater forecasts over time and space
    Woelmer, Whitney M. (Virginia Tech, 2023-09-05)
    Accelerating rates of change in ecosystems globally heighten the need for improved predictions of future ecological conditions. Freshwater lakes and reservoirs, which provide numerous ecosystem services, are particularly threatened by global change stressors and have already exhibited substantial changes to their physical, chemical, and biological functioning. Thus, to provide useful predictive tools for managing freshwater resources in the face of global change, we must improve our ability to build, apply, and communicate understanding of lake and reservoir ecosystem dynamics. To address this, I first built ecosystem understanding by conducting multiple whole-ecosystem surveys to quantify the spatial and temporal variability of biogeochemistry in two reservoirs over a year. We found that temporal heterogeneity was higher than spatial heterogeneity for most biogeochemical variables, with the stream-reservoir interface as a consistent hotspot of biogeochemical processing. Second, I applied ecosystem understanding by producing ecological forecasts of physical (water temperature), chemical (dissolved oxygen), and biological (chlorophyll-a) variables across three waterbodies using diverse modeling methods. I developed daily, weekly, and fortnightly forecasts of chlorophyll-a at two drinking water reservoirs using a Bayesian linear model, and found process uncertainty dominated total forecast uncertainty. Additionally, I produced forecasts of water temperature and dissolved oxygen in an oligotrophic lake using a hydrodynamic-ecosystem model and found that water temperature was more predictable than oxygen despite variable performance over depth and between years. Across these two forecasting studies, forecast skill relative to a null model varied among water quality metrics: water temperature forecasts outperformed the null model up to 11 days ahead, oxygen forecasts outperformed the null model up to 2 days ahead, and chlorophyll-a forecasts outperformed the null model up to 14 days ahead. Third, to communicate forecasts for decision-making, I developed an educational module for undergraduate ecology students which taught important concepts on visualization and decision science. Following completion of the module, students' ability to identify methods for uncertainty communication increased significantly, as well as their understanding of the benefits of ecological forecasting. Overall, my dissertation provides insight into how reservoirs function in global biogeochemical cycles, the predictability of multiple water quality variables, and deepens our understanding of how to communicate ecosystem science for improved management and protection of ecosystems.
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    Differential Responses of Maximum Versus Median Chlorophyll‐a to Air Temperature and Nutrient Loads in an Oligotrophic Lake Over 31 Years
    Ward, Nicole K.; Steele, Bethel G.; Weathers, Kathleen C.; Cottingham, Kathryn L.; Ewing, Holly A.; Hanson, Paul C.; Carey, Cayelan C. (AGU, 2020-05-28)
    Globally, phytoplankton abundance is increasing in lakes as a result of climate change and land‐use change. The relative importance of climate and land‐use drivers has been examined primarily for mesotrophic and eutrophic lakes. However, oligotrophic lakes show different sensitivity to climate and land‐use drivers than mesotrophic and eutrophic lakes, necessitating further exploration of the relative contribution of the two drivers of change to increased phytoplankton abundance. Here, we investigated how air temperature (a driver related to climate change) and nutrient load (a driver related to land‐use and climate change) interact to alter water quality in oligotrophic Lake Sunapee, New Hampshire, USA. We used long‐term data and the one‐dimensional hydrodynamic General Lake Model (GLM) coupled with Aquatic EcoDyanmics (AED) modules to simulate water quality. Over the 31‐year simulation, summer median chlorophyll‐a concentration was positively associated with summer air temperature, whereas annual maximum chlorophyll‐a concentration was positively associated with the previous 3 years of external phosphorus load. Scenario testing demonstrated a 2°C increase in air temperature significantly increased summer median chlorophyll‐a concentration, but not annual maximum chlorophyll‐a concentration. For both maximum and median chlorophyll‐a concentration, doubling external nutrient loads of total nitrogen and total phosphorus at the same time, or doubling phosphorus alone, resulted in a significant increase. This study highlights the importance of aligning lake measurements with the ecosystem metrics of interest, as maximum chlorophyll‐a concentration may be more uniquely sensitive to nutrient load and that typical summer chlorophyll‐a concentration may increase due to warming alone.
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    The drivers of freshwater reservoir biogeochemical cycling and greenhouse gas emissions in a changing world
    McClure, Ryan Paul (Virginia Tech, 2020-09-29)
    Freshwater reservoirs store, process, and emit to the atmosphere large quantities of carbon (C). Despite the important role of reservoirs in the global carbon cycle, it remains unknown how human activities are altering their carbon cycling. Climate change and land use are resulting in lower dissolved oxygen (DO) concentrations in freshwater ecosystems, yet more frequent, powerful storms are occurring that temporarily increase DO availability. The net effect of these opposing forces results in anoxia (DO < 0.5 mg L-1) punctuated by short-term increases in DO. The availability of DO controls alternate redox reactions in freshwaters, thereby determining the rate and end products of organic C mineralization, which include two greenhouse gases, carbon dioxide (CO2) and methane (CH4). I performed ecosystem-level DO manipulations and evaluated how changing DO conditions affected redox reactions and the production and emission of CO2 and CH4. I also explored how the magnitude and drivers of CH4 emissions changed spatio-temporarily in a eutrophic reservoir using time series models. Finally, I used a coupled data-modeling approach to forecast future emissions of CH4 from the same reservoir. I found that the depletion of DO results in the rapid onset of alternate redox reactions in freshwater reservoirs for organic C mineralization and greater production of CH4. When the anoxia occurred in the water column (vs. at the sediments), diffusive CO2 and CH4 efflux phenology was affected, and resulted in degassing occurring during storms before fall turnover. I observed that the magnitude of CH4 emissions varied along a longitudinal gradient of a small reservoir and that the environmental drivers of ebullition and diffusion can change substantially both over space (within one hundred meters) and time (within a few weeks). Finally, I developed a forecasting workflow that successfully predicted future CH4 ebullition rates during one summer season. My research provides insight to how changing DO conditions will alter redox reactions in the water column and greenhouse gas emissions, as well as provides a new technique for improving future predictions of CH4 emissions from freshwater reservoirs. Althogether, this work improves our understanding of how freshwater lake and reservoir carbon cycling will change in the future.
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    Dynamic modeling of organic carbon fates in lake ecosystems
    McCullough, Ian M.; Dugan, Hilary A.; Farrell, Kaitlin J.; Morales-Williams, Ana M.; Ouyang, Zutao; Roberts, Derek C.; Scordo, Facundo; Bartlett, Sarah L.; Burke, Samantha M.; Doubek, Jonathan P.; Krivak-Tetley, Flora E.; Skaff, Nicholas K.; Summers, Jamie C.; Weathers, Kathleen C.; Hanson, Paul C. (2018-10-24)
    Lakes are active processors of organic carbon (OC) and play important roles in landscape and global carbon cycling. Allochthonous OC loads from the landscape, along with autochthonous OC loads from primary production, are mineralized in lakes, buried in lake sediments, and exported via surface or groundwater outflows. Although these processes provide a basis for a conceptual understanding of lake OC budgets, few studies have integrated these fluxes under a dynamic modeling framework to examine their interactions and relative magnitudes. We developed a simple, dynamic mass balance model for OC, and applied the model to a set of five lakes. We examined the relative magnitudes of OC fluxes and found that long-term (> 10 year) lake OC dynamics were predominantly driven by allochthonous loads in four of the five lakes, underscoring the importance of terrestrially-derived OC in northern lake ecosystems. Our model highlighted seasonal patterns in lake OC budgets, with increasing water temperatures and lake productivity throughout the growing season corresponding to a transition from burial- to respiration-dominated OC fates. Ratios of respiration to burial, however, were also mediated by the source (autochthonous vs. allochthonous) of total OC loads. Autochthonous OC is more readily respired and may therefore proportionally reduce burial under a warming climate, but allochthonous OC may increase burial due to changes in precipitation. The ratios of autochthonous to allochthonous inputs and respiration to burial demonstrate the importance of dynamic models for examining both the seasonal and inter-annual roles of lakes in landscape and global carbon cycling, particularly in a global change context. Finally, we highlighted critical data needs, which include surface water DOC observations in paired tributary and lake systems, measurements of OC burial rates, groundwater input volume and DOC, and budgets of particulate OC.
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    Dynamics of the stream-lake transitional zone affect littoral lake metabolism
    Ward, Nicole K.; Brentrup, Jennifer A.; Richardson, David C.; Weathers, Kathleen C.; Hanson, Paul C.; Hewett, Russell J.; Carey, Cayelan C. (Springer, 2022-07)
    Lake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream-lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake's epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2-2.5 mg O-2 L-1 day(-1) (9-670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream-lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulated littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors.
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    Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward
    Carey, Cayelan C.; Ward, Nicole K.; Farrell, Kaitlin J.; Lofton, Mary E.; Krinos, Arianna, I.; McClure, Ryan P.; Subratie, Kensworth C.; Figueiredo, Renato J.; Doubek, Jonathan P.; Hanson, Paul C.; Papadopoulos, Philip; Arzberger, Peter (Ecological Society of America, 2019-05)
    In the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high-frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long-term and high-frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long-term.
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    From concept to practice to policy: modeling coupled natural and human systems in lake catchments
    Cobourn, Kelly M.; Carey, Cayelan C.; Boyle, Kevin J.; Duffy, Christopher J.; Dugan, Hilary A.; Farrell, Kaitlin J.; Fitchett, Leah Lynn; Hanson, Paul C.; Hart, Julia A.; Henson, Virginia Reilly; Hetherington, Amy L.; Kemanian, Armen R.; Rudstam, Lars G.; Shu, Lele; Soranno, Patricia A.; Sorice, Michael G.; Stachelek, Joseph; Ward, Nicole K.; Weathers, Kathleen C.; Weng, Weizhe; Zhang, Yu (Ecological Society of America, 2018-05-03)
    Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well‐developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited in capturing these feedbacks. This article presents a paired conceptual–empirical methodology for functionally capturing feedbacks between human and natural systems in freshwater lake catchments, from human actions to the ecosystem and from the ecosystem back to human actions. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution, to connect a suite of models. In doing so, we create an integrated, multi‐disciplinary tool that captures diverse processes that operate at multiple scales, including land‐management decision‐making, hydrologic‐solute transport, aquatic nutrient cycling, and civic engagement. In this article, we build on this novel framework to advance cross‐disciplinary dialogue to move CNHS lake‐catchment modeling in a systematic direction and, ultimately, provide a foundation for smart decision‐making and policy.
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    A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)
    Hipsey, Matthew R.; Bruce, Louise C.; Boon, Casper; Busch, Brendan D.; Carey, Cayelan C.; Hamilton, David P.; Hanson, Paul C.; Read, Jordan S.; de Sousa, Eduardo; Weber, Michael; Winslow, Luke A. (Copernicus GmbH, 2019-01-29)
    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.
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    GRAPLEr: A Distributed Collaborative Environment for Lake Ecosystem Modeling that Integrates Overlay Networks, High-throughput Computing, and Web Services
    Subratie, Kensworth C.; Aditya, Saumitra; Figueiredo, Renato J.; Carey, Cayelan C.; Hanson, Paul C. (2015-09-29)
    The GLEON Research And PRAGMA Lake Expedition -- GRAPLE -- is a collaborative effort between computer science and lake ecology researchers. It aims to improve our understanding and predictive capacity of the threats to the water quality of our freshwater resources, including climate change. This paper presents GRAPLEr, a distributed computing system used to address the modeling needs of GRAPLE researchers. GRAPLEr integrates and applies overlay virtual network, high-throughput computing, and Web service technologies in a novel way. First, its user-level IP-over-P2P (IPOP) overlay network allows compute and storage resources distributed across independently-administered institutions (including private and public clouds) to be aggregated into a common virtual network, despite the presence of firewalls and network address translators. Second, resources aggregated by the IPOP virtual network run unmodified high-throughput computing middleware (HTCondor) to enable large numbers of model simulations to be executed concurrently across the distributed computing resources. Third, a Web service interface allows end users to submit job requests to the system using client libraries that integrate with the R statistical computing environment. The paper presents the GRAPLEr architecture, describes its implementation and reports on its performance for batches of General Lake Model (GLM) simulations across three cloud infrastructures (University of Florida, CloudLab, and Microsoft Azure).
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    Integrating fast and slow processes is essential for simulating human-freshwater interactions
    Ward, Nicole K.; Fitchett, Leah Lynn; Hart, Julia A.; Shu, Lele; Stachelek, Joseph; Weng, Weizhe; Zhang, Yu; Dugan, Hilary A.; Hetherington, Amy L.; Boyle, Kevin J.; Carey, Cayelan C.; Cobourn, Kelly M.; Hanson, Paul C.; Kemanian, Armen R.; Sorice, Michael G.; Weathers, Kathleen C. (Springer, 2019-10-01)
    Integrated modeling is a critical tool to evaluate the behavior of coupled human–freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human–freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human–freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.
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    Lake thermal structure drives interannual variability in summer anoxia dynamics in a eutrophic lake over 37 years
    Ladwig, Robert; Hanson, Paul C.; Dugan, Hilary A.; Carey, Cayelan C.; Zhang, Yu; Shu, Lele; Duffy, Christopher J.; Cobourn, Kelly M. (2021-02-25)
    The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal timescales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic-ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37 year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an optimization algorithm to improve the fit between observed and simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored internal heat), identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing. To qualify which external and internal factors were most important in driving the interannual variation in summer anoxia, we applied a random-forest classifier and multiple linear regressions to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production). Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50-60 d. The summer heat budget, the timing of thermal stratification, and the gross primary production in the epilimnion prior to summer stratification were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. Interannual variability in anoxia was largely driven by physical factors: earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. A measured step change upward in summer anoxia in 2010 was unexplained by the GLM-AED2 model. Although the cause remains unknown, possible factors include invasion by the predacious zooplankton Bythotrephes longimanus. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region.
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    Modeling lake ecosystem change within coupled human-natural systems to improve water resources management
    Ward, Nicole Kristine (Virginia Tech, 2021-05-24)
    Lake ecosystems are sentinels of change in a landscape, integrating upstream terrestrial and aquatic effects of climate and land use drivers. Climate and land use change is mediated by socio-cultural and economic processes, resulting in complex responses in lake ecosystems as a part of coupled natural human (CNH) systems. I used multiple approaches within a CNH framework to better understand the effects of climate and land use on freshwater-human interactions. I first conducted a literature synthesis and found that slow processes (e.g., cultural change) are underrepresented in CNH-freshwater models relative to fast processes (e.g., daily decision-making), though both fast and slow processes are key to assessing decadal trajectories of change. I then examined the interaction of fast and slow variables in lakes through two ecosystem modeling assessments. I used a process-based model to assess drivers of annual chlorophyll-a concentration, a metric of phytoplankton biomass, over three decades in a low-nutrient lake and found that increases in summer median versus maximum chlorophyll-a are related to rising air temperatures and external phosphorus load, respectively. I also conducted a single-year study in the same lake to examine variability in site-specific gross primary production (GPP) and respiration (R), two fast-changing variables that serve as robust indicators of slowly-changing trophic state. I found that higher rates of near-shore GPP and R were partially due to stream-related variables, providing insight into how inflowing streams connect to in-lake processes. These two ecosystem assessment studies indicate fast-changing response variables can be indicative of specific slow-changing variables: annual maximum versus median chlorophyll-a can be used to assess differing impacts from climate and land use change, and estimation of GPP and R near inflow streams integrate sub-catchment drivers. Finally, I evaluated the effectiveness of an online model visualization relating current land use decisions, a fast process, to future water quality outcomes, a slow process, and found that the visualization was effective in altering property owner beliefs and intended behavior related to applying lawn fertilizer and installing waterfront buffers. Collectively, this work advances our understanding of how fast and slow variables interact to improve assessments of changes in CNH-lake systems.
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    A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network
    Bruce, Louise C.; Frassl, Marieke A.; Arhonditsis, George B.; Gal, Gideon; Hamilton, David P.; Hanson, Paul C.; Hetherington, Amy L.; Melack, John M.; Read, Jordan S.; Rinke, Karsten; Rigosi, Anna; Trolle, Dennis; Winslow, Luke A.; Adrian, Rita; Ayala, Ana I.; Bocaniov, Serghei A.; Boehrer, Bertram; Boon, Casper; Brookes, Justin D.; Bueche, Thomas; Busch, Brendan D.; Copetti, Diego; Cortes, Alicia; de Eyto, Elvira; Elliott, J. Alex; Gallina, Nicole; Gilboa, Yael; Guyennon, Nicolas; Huang, Lei; Kerimoglu, Onur; Lenters, John D.; MacIntyre, Sally; Makler-Pick, Vardit; McBride, Chris G.; Moreira, Santiago; Oezkundakci, Deniz; Pilotti, Marco; Rueda, Francisco J.; Rusak, James A.; Samal, Nihar R.; Schmid, Martin; Shatwell, Tom; Snorthheim, Craig; Soulignac, Frederic; Valerio, Giulia; van der Linden, Leon; Vetter, Mark; Vincon-Leite, Brigitte; Wang, Junbo; Weber, Michael; Wickramaratne, Chaturangi; Woolway, R. Iestyn; Yao, Huaxia; Hipsey, Matthew R. (2018-04)
    The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required. (c) 2017 Published by Elsevier Ltd.
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    Oxygen dynamics control the burial of organic carbon in a eutrophic reservoir
    Carey, Cayelan C.; Doubek, Jonathan P.; McClure, Ryan P.; Hanson, Paul C. (Wiley-Blackwell, 2017-12-06)
    Organic carbon (OC) mineralization in freshwaters is dependent on oxygen availability near the sediments, which controls whether OC inputs will be buried or respired. However, oxygen dynamics in waterbodies are changing globally due to land use and climate, and the consequences of variable oxygen conditions for OC burial are unknown. We manipulated hypolimnetic oxygen availability in a whole‐reservoir experiment and used a mass balance OC model to quantify rates of OC burial. Throughout summer stratification, we observed that OC burial rates were tightly coupled to sediment oxygen concentrations: oxic conditions promoted the mineralization of “legacy” OC that had accumulated over years of sedimentation, resulting in negative OC burial. Moreover, our study demonstrates that fluctuating oxygen conditions can switch ecosystem‐scale OC burial in a reservoir between positive and negative rates. Consequently, changing oxygen availability in freshwaters globally will likely have large implications for the role of these ecosystems as OC sinks.
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    Oxygen dynamics in the bottom waters of lakes: Understanding the past to predict the future
    Lewis, Abigail Sara Larson (Virginia Tech, 2024-05-20)
    Dissolved oxygen concentrations are declining in the bottom waters of many lakes around the world, posing critical water quality concerns. Throughout my dissertation, I assessed how bottom-water dissolved oxygen may mediate the effects of climate and land use change on water quality in lakes. First, I characterized causes of variation in summer bottom-water temperature and dissolved oxygen. I demonstrated that spring air temperatures may play a greater role than summer air temperatures in shaping summer bottom-water dynamics. I then characterized the effects of declining bottom-water oxygen concentrations across diverse scales of analysis (i.e., using microcosm incubations, whole-ecosystem oxygenation experiments, and data analysis of >600 widespread lakes). I found that low dissolved oxygen concentrations contributed to release of nutrients and organic carbon from lake sediments, potentially altering the role of lakes in global biogeochemical cycles. Importantly, I also found support for a previously-hypothesized Anoxia Begets Anoxia feedback, whereby bottom-water anoxia (i.e., no dissolved oxygen) in a given year promotes increasingly severe occurrences of anoxia in following summers. This finding demonstrates the need for forecasts of future oxygen dynamics in lakes, as management actions to preempt the first occurrence of anoxia will be more effective than actions to restore ecological function after oxygen concentrations have already declined. To build the capacity for such forecasts, I led a systematic review of ecological forecasting literature that characterized the state of the field, emerging best practices, and relative predictability of four ecological variables. Combined, my dissertation provides a mechanistic examination of the effects of climate change on water quality in lakes worldwide, ultimately helping to anticipate, mitigate, and preempt future water quality declines.
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    Predicting lake surface water phosphorus dynamics using process-guided machine learning
    Hanson, Paul C.; Stillman, Aviah B.; Jia, Xiaowei; Karpatne, Anuj; Dugan, Hilary A.; Carey, Cayelan C.; Stachelek, Joseph; Ward, Nicole K.; Zhang, Yu; Read, Jordan S.; Kumar, Vipin (2020-08-15)
    Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 mu g P L-1, 22.7 mu g P L-1, and 20.7 mu g P L-1, respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 mu g P L-1, 10.9 mu g P L-1, and 10.7 mu g P L-1. Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 mu g P L-1 and 28.0 mu g P L-1, respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.
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    Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories
    Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z.; Read, Jordan S.; Ibelings, Bas W.; Valesini, Fiona J.; Brookes, Justin D. (American Geophysical Union, 2015-09-02)
    Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, 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 sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory 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.
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    Process-Guided Deep Learning Predictions of Lake Water Temperature
    Read, Jordan S.; Jia, Xiaowei; Willard, Jared; Appling, Alison P.; Zwart, Jacob A.; Oliver, Samantha K.; Karpatne, Anuj; Hansen, Gretchen J. A.; Hanson, Paul C.; Watkins, William; Steinbach, Michael; Kumar, Vipin (2019-11-08)
    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 degrees C during the test period (DL: 1.78 degrees C, PB: 2.03 degrees 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.
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    ReaLSAT, a global dataset of reservoir and lake surface area variations
    Khandelwal, Ankush; Karpatne, Anuj; Ravirathinam, Praveen; Ghosh, Rahul; Wei, Zhihao; Dugan, Hilary A.; Hanson, Paul C.; Kumar, Vipin (Nature Portfolio, 2022-06-21)
    Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.