Browsing by Author "Read, Jordan S."

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    Ecology under lake ice
    Hampton, Stephanie E.; Galloway, Aaron W. E.; Powers, Stephen M.; Ozersky, Ted; Woo, Kara H.; Batt, Ryan D.; Labou, Stephanie G.; O'Reilly, Catherine M.; Sharma, Sapna; Lottig, Noah R.; Stanley, Emily H.; North, Rebecca L.; Stockwell, Jason D.; Adrian, Rita; Weyhenmeyer, Gesa A.; Arvola, Lauri; Baulch, Helen M.; Bertani, Isabella; Bowman, Larry L., Jr.; Carey, Cayelan C.; Catalan, Jordi; Colom-Montero, William; Domine, Leah M.; Felip, Marisol; Granados, Ignacio; Gries, Corinna; Grossart, Hans-Peter; Haberman, Juta; Haldna, Marina; Hayden, Brian; Higgins, Scott N.; Jolley, Jeff C.; Kahilainen, Kimmo K.; Kaup, Enn; Kehoe, Michael J.; MacIntyre, Sally; Mackay, Anson W.; Mariash, Heather L.; McKay, Robert M.; Nixdorf, Brigitte; Noges, Peeter; Noges, Tiina; Palmer, Michelle; Pierson, Don C.; Post, David M.; Pruett, Matthew J.; Rautio, Milla; Read, Jordan S.; Roberts, Sarah L.; Ruecker, Jacqueline; Sadro, Steven; Silow, Eugene A.; Smith, Derek E.; Sterner, Robert W.; Swann, George E. A.; Timofeyev, Maxim A.; Toro, Manuel; Twiss, Michael R.; Vogt, Richard J.; Watson, Susan B.; Whiteford, Erika J.; Xenopoulos, Marguerite A. (2017-01)
    Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass.
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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. © 2019 Author(s).
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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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    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.