Progress and opportunities in advancing near-term forecasting of freshwater quality

A typology of reviews: an analysis of 14 review types and associated methodologies

Grant, M. J., & Booth, A. (2009) · Health Information & Libraries Journal · doi:10.1111/j.1471-1842.2009.00848.x · Cited by 10,817 · see citing works

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217 · see citing works

Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

Chai, T., & Draxler, R. R. (2014) · Geoscientific Model Development · doi:10.5194/gmd-7-1247-2014 · Cited by 7,178 · see citing works

A note on a general definition of the coefficient of determination

NAGELKERKE, N. J. D. (1991) · Biometrika · doi:10.1093/biomet/78.3.691 · Cited by 5,927 · see citing works

Strictly Proper Scoring Rules, Prediction, and Estimation

Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793 · see citing works

The Ensemble Kalman Filter: theoretical formulation and practical implementation

Evensen, G. (2003) · Ocean Dynamics · doi:10.1007/s10236-003-0036-9 · Cited by 4,612 · see citing works

The quiet revolution of numerical weather prediction

Bauer, P., Thorpe, A., & Brunet, G. (2015) · Nature · doi:10.1038/nature14956 · Cited by 2,729 · see citing works

Probabilistic Forecasts, Calibration and Sharpness

Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00587.x · Cited by 1,783 · see citing works

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi, A., Ozturk, P., & Kwok-wing, C. (2018) · Water · doi:10.3390/w10111536 · Cited by 1,731 · see citing works

Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems

Hersbach, H. (2000) · Weather and Forecasting · doi:10.1175/1520-0434(2000)015 · Cited by 1,533 · see citing works

Widespread global increase in intense lake phytoplankton blooms since the 1980s

Ho, J. C., Michalak, A. M., & Pahlevan, N. (2019) · Nature · doi:10.1038/s41586-019-1648-7 · Cited by 1,431 · see citing works

Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman, T. (2005) · Monthly Weather Review · doi:10.1175/mwr2904.1 · Cited by 1,142 · see citing works

Scoring Rules for Continuous Probability Distributions

Matheson, J. E., & Winkler, R. L. (1976) · Management Science · doi:10.1287/mnsc.22.10.1087 · Cited by 957 · see citing works

Scientists’ warning to humanity on the freshwater biodiversity crisis

Albert, J. S., Destouni, G., Duke-Sylvester, S. M., Magurran, A. E., Oberdorff, T., Reis, R. E., Winemiller, K. O., & Ripple, W. J. (2021) · Ambio · doi:10.1007/s13280-020-01318-8 · Cited by 943 · see citing works

Widespread deoxygenation of temperate lakes

Jane, S. F., Hansen, G. J. A., Kraemer, B. M., Leavitt, P. R., Mincer, J. L., North, R. L., Pilla, R. M., Stetler, J. T., Williamson, C. E., Woolway, R. I., et al. (2021) · Nature · doi:10.1038/s41586-021-03550-y · Cited by 716 · see citing works

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Dietze, M. C., Fox, A. M., Beck‐Johnson, L. M., Betancourt, J. L., Hooten, M. B., Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C., Larsen, L. G., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710231115 · Cited by 592 · see citing works

A practical guide to structured expert elicitation using the IDEA protocol

Hemming, V., Burgman, M. A., Hanea, A. M., McBride, M. F., & Wintle, B. C. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12857 · Cited by 533 · see citing works

Invasive species triggers a massive loss of ecosystem services through a trophic cascade

Walsh, J. R., Carpenter, S. R., & Zanden, M. J. V. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600366113 · Cited by 526 · see citing works

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi, A. H., Öztürk, P., & Chau, K. (2018) · QUT ePrints (Queensland University of Technology) · Cited by 521 · see citing works

Process‐Guided Deep Learning Predictions of Lake Water Temperature

Read, J. S., Jia, X., Willard, J., Appling, A., Zwart, J. A., Oliver, S. K., Karpatne, A., Hansen, G. J. A., Hanson, P. C., Watkins, W., et al. (2019) · Water Resources Research · doi:10.1029/2019wr024922 · Cited by 456 · see citing works

The ecological forecast horizon, and examples of its uses and determinants

Petchey, O. L., Pontarp, M., Massie, T. M., Kéfi, S., Özgül, A., Weilenmann, M., Palamara, G. M., Altermatt, F., Matthews, B., Levine, J. M., et al. (2015) · Ecology Letters · doi:10.1111/ele.12443 · Cited by 346 · see citing works

Weather Forecasts are for Wimps: Why Water Resource Managers Do Not Use Climate Forecasts

Rayner, S., Lach, D. H., & Ingram, H. (2005) · Climatic Change · doi:10.1007/s10584-005-3148-z · Cited by 346 · see citing works

Increasing the Reliability of Reliability Diagrams

Bröcker, J., & Smith, L. A. (2007) · Weather and Forecasting · doi:10.1175/waf993.1 · Cited by 316 · see citing works

Contribution of historical precipitation change to US flood damages

Davenport, F. V., Burke, M., & Diffenbaugh, N. S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017524118 · Cited by 308 · see citing works

A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions

Yip, S., Ferro, C. A. T., Stephenson, D. B., & Hawkins, E. (2011) · Journal of Climate · doi:10.1175/2011jcli4085.1 · Cited by 306 · see citing works

A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

Rousso, B. Z., Bertone, E., Stewart, R. A., & Hamilton, D. P. (2020) · Water Research · doi:10.1016/j.watres.2020.115959 · Cited by 289 · see citing works

Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission

Hestir, E. L., Brando, V., Bresciani, M., Giardino, C., Matta, E., Villa, P., & Dekker, A. G. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.05.023 · Cited by 283 · see citing works

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

Hipsey, M. R., Bruce, L. C., Boon, C., Busch, B., Carey, C. C., Hamilton, D. P., Hanson, P. C., Read, J. S., de Sousa, E., Weber, M., et al. (2019) · Geoscientific model development · doi:10.5194/gmd-12-473-2019 · Cited by 275 · see citing works

A Brief review of flood forecasting techniques and their applications

Jain, S. K., Mani, P., Jain, S. K., Prakash, P., Singh, V. P., Tullos, D., Kumar, S., Agarwal, S. P., & Dimri, A. P. (2017) · International Journal of River Basin Management · doi:10.1080/15715124.2017.1411920 · Cited by 269 · see citing works

A global agenda for advancing freshwater biodiversity research

Maasri, A., Jähnig, S. C., Adamescu, M. C., Adrian, R., Baigun, C., Baird, D. J., Batista‐Morales, A., Bonada, N., Brown, L. E., Cai, Q., et al. (2022) · Ecology Letters · doi:10.1111/ele.13931 · Cited by 227 · see citing works

HEPEX: The Hydrological Ensemble Prediction Experiment

Schaake, J. C., Hamill, T. M., Buizza, R., & Clark, M. (2007) · Bulletin of the American Meteorological Society · doi:10.1175/bams-88-10-1541 · Cited by 226 · see citing works

Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years

Troin, M., Arsenault, R., Wood, A. W., Brissette, F. P., & Martel, J. (2021) · Water Resources Research · doi:10.1029/2020wr028392 · Cited by 221 · see citing works

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

Pappenberger, F., Ramos, M., Cloke, H., Wetterhall, F., Alfieri, L., Bogner, K., Müeller, A., & Salamon, P. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.01.024 · Cited by 212 · see citing works

Data assimilation: making sense of Earth Observation

Lahoz, W., & Schneider, P. (2014) · Frontiers in Environmental Science · doi:10.3389/fenvs.2014.00016 · Cited by 201 · see citing works

Forecasting annual cyanobacterial bloom biomass to inform management decisions in Lake Erie

Stumpf, R. P., Johnson, L. T., Wynne, T. T., & Baker, D. B. (2016) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2016.08.006 · Cited by 196 · see citing works

Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case

Nativi, S., Mazzetti, P., & Craglia, M. (2021) · Remote Sensing · doi:10.3390/rs13112119 · Cited by 196 · see citing works

THE ROLE OF CLIMATE CHANGE IN INTERPRETING HISTORICAL VARIABILITY

Millar, C. I., & Woolfenden, W. B. (1999) · Ecological Applications · doi:10.1890/1051-0761(1999)009[1207:trocci]2.0.co;2 · Cited by 194 · see citing works

Automatic High Frequency Monitoring for Improved Lake and Reservoir Management

Marcé, R., George, G., Buscarinu, P., Deidda, M., Dunalska, J., de Eyto, E., Flaim, G. M., Grossart, H., Istvánovics, V., Lenhardt, M. B., et al. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.6b01604 · Cited by 187 · see citing works

IoT based smart water management systems: A systematic review

Singh, M., & Ahmed, S. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.08.588 · Cited by 174 · see citing works

Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data

Perretti, C. T., Munch, S. B., & Sugihara, G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216076110 · Cited by 173 · see citing works

Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling

Daw, A., Thomas, R. Q., Carey, C. C., Read, J. S., Appling, A., & Karpatne, A. (2020) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976236.60 · Cited by 172 · see citing works

Superforecasting : the art and science of prediction

Tetlock, P. E. 1., & Gardner, D. 1. (2015) · Cited by 158 · see citing works

Bayesian flood forecasting methods: A review

Han, S., & Coulibaly, P. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.06.004 · Cited by 155 · see citing works

Forecast verification a practitioner's guide in atmospheric science

Jolliffe, I. T., & Stephenson, D. B. (2003) · Cited by 151 · see citing works

Applications of deep learning in water quality management: A state-of-the-art review

Wai, K. P., Chia, M. Y., Koo, C. H., Huang, Y. F., & Chong, W. C. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128332 · Cited by 147 · see citing works

Ecological Forecasting

Dietze, M. C. (2017) · Princeton University Press eBooks · doi:10.1515/9781400885459 · Cited by 145 · see citing works

Are invasives worse in freshwater than terrestrial ecosystems?

Moorhouse, T., & Macdonald, D. (2015) · Wiley Interdisciplinary Reviews Water · doi:10.1002/wat2.1059 · Cited by 143 · see citing works

An Empirical Analysis of the Docker Container Ecosystem on GitHub

Cito, J., Schermann, G., Wittern, E., Leitner, P., Zumberi, S., & Gall, H. C. (2017) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2017.67 · Cited by 137 · see citing works

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Turner, S. W. D., Bennett, J., Robertson, D., & Galelli, S. (2017) · Hydrology and earth system sciences · doi:10.5194/hess-21-4841-2017 · Cited by 130 · see citing works

The intrinsic predictability of ecological time series and its potential to guide forecasting

Pennekamp, F., Iles, A. C., Garland, J., Brennan, G. L., Brose, U., Gaedke, U., Jacob, U., Kratina, P., Matthews, B., Munch, S. B., et al. (2019) · Ecological Monographs · doi:10.1002/ecm.1359 · Cited by 129 · see citing works

Regime shifts, trends, and variability of lake productivity at a global scale

Gilarranz, L. J., Narwani, A., Odermatt, D., Siber, R., & Dakos, V. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116413119 · Cited by 129 · see citing works

Complexity is costly: a meta‐analysis of parametric and non‐parametric methods for short‐term population forecasting

Ward, E. J., Holmes, E. E., Thorson, J. T., & Collen, B. (2014) · Oikos · doi:10.1111/j.1600-0706.2014.00916.x · Cited by 118 · see citing works

The rise of research on futures in ecology: rebalancing scenarios and predictions

Coreau, A., Pinay, G., Thompson, J. D., Cheptou, P., & Mermet, L. (2009) · Ecology Letters · doi:10.1111/j.1461-0248.2009.01392.x · Cited by 112 · see citing works

Developing a global operational seasonal hydro-meteorological forecasting system: GloFAS-Seasonal v1.0

Emerton, R., Zsótér, E., Arnal, L., Cloke, H., Muraro, D., Prudhomme, C., Stephens, E., Salamon, P., & Pappenberger, F. (2018) · Geoscientific model development · doi:10.5194/gmd-11-3327-2018 · Cited by 104 · see citing works

Superforecasting: the art and science of prediction

Rötheli, T. F. (2017) · foresight · doi:10.1108/fs-12-2016-0061 · Cited by 103 · see citing works

Anticipatory natural resource science and management for a changing future

Bradford, J. B., Betancourt, J. L., Butterfield, B. J., Munson, S. M., & Wood, T. E. (2018) · Frontiers in Ecology and the Environment · doi:10.1002/fee.1806 · Cited by 102 · see citing works

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Turner, S. W. D., Xu, W., & Voisin, N. (2020) · Hydrology and earth system sciences · doi:10.5194/hess-24-1275-2020 · Cited by 98 · see citing works

Model-based approach for cyber-physical attack detection in water distribution systems

Housh, M., & Ohar, Z. (2018) · Water Research · doi:10.1016/j.watres.2018.03.039 · Cited by 91 · see citing works

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

Fer, I., Gardella, A. K., Shiklomanov, A., Campbell, E. E., Cowdery, E. M., Kauwe, M. G. D., Desai, A. R., Duveneck, M. J., Fisher, J. B., Haynes, K., et al. (2020) · Global Change Biology · doi:10.1111/gcb.15409 · Cited by 90 · see citing works

A data-driven model for real-time water quality prediction and early warning by an integration method

Jin, T., Cai, S., Jiang, D., & Liu, J. (2019) · Environmental Science and Pollution Research · doi:10.1007/s11356-019-06049-2 · Cited by 88 · see citing works

Extreme events, water quality and health: A participatory Bayesian risk assessment tool for managers of reservoirs

Bertone, E., Sahin, O., Richards, R., & Roiko, A. H. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.06.158 · Cited by 87 · see citing works

River/stream water temperature forecasting using artificial intelligence models: a systematic review

Zhu, S., & Piotrowski, A. (2020) · Acta Geophysica · doi:10.1007/s11600-020-00480-7 · Cited by 86 · see citing works

Data assimilation in surface water quality modeling: A review

Cho, K. H., Pachepsky, Y. A., Ligaray, M. V., Kwon, Y., & Kim, K. H. (2020) · Water Research · doi:10.1016/j.watres.2020.116307 · Cited by 86 · see citing works

Meteolakes: An operational online three-dimensional forecasting platform for lake hydrodynamics

Baracchini, T., Wüest, A., & Bouffard, D. (2020) · Water Research · doi:10.1016/j.watres.2020.115529 · Cited by 83 · see citing works

Who decides? Forecasts and responsibilities in the 1997 Red River flood

Pielke, R. A. (1999) · Applied Behavioral Science Review · doi:10.1016/s1068-8595(00)80012-4 · Cited by 82 · see citing works

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Varadharajan, C., Appling, A., Arora, B., Christianson, D., Hendrix, V., Kumar, V., Junior, A. R. L., Müller, J., Oliver, S. K., Ombadi, M., et al. (2022) · Hydrological Processes · doi:10.1002/hyp.14565 · Cited by 82 · see citing works

Multi‐Task Deep Learning of Daily Streamflow and Water Temperature

Sadler, J. M., Appling, A., Read, J. S., Oliver, S. K., Jia, X., Zwart, J. A., & Kumar, V. (2022) · Water Resources Research · doi:10.1029/2021wr030138 · Cited by 82 · see citing works

THE GLOBAL LAKE ECOLOGICAL OBSERVATORY NETWORK (GLEON): THE EVOLUTION OF GRASSROOTS NETWORK SCIENCE

Weathers, K. C., Hanson, P. C., ARZBERGER, P. W., Brentrup, J. A., Brookes, J. D., Carey, C. C., Gaiser, E. E., Gaiser, E. E., Hamilton, D. P., Hong, G. S., et al. (2013) · Limnology and Oceanography Bulletin · doi:10.1002/lob.201322371 · Cited by 80 · see citing works

Developing an automated iterative near‐term forecasting system for an ecological study

White, E. P., Yenni, G. M., Taylor, S. D., Christensen, E. M., Bledsoe, E. K., Simonis, J. L., & Ernest, S. K. M. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13104 · Cited by 77 · see citing works

Building Software, Building Community: Lessons from the rOpenSci Project

Boettiger, C., Chamberlain, S., Hart, E., & Ram, K. (2015) · Journal of Open Research Software · doi:10.5334/jors.bu · Cited by 74 · see citing works

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

Carey, C. C., Woelmer, W. M., Lofton, M. E., Figueiredo, R., Bookout, B. J., Corrigan, R. S., Daneshmand, V., Hounshell, A. G., Howard, D. W., Lewis, A. S. L., et al. (2021) · Inland Waters · doi:10.1080/20442041.2020.1816421 · Cited by 74 · see citing works

Forecasting biodiversity in breeding birds using best practices

Harris, D. J., Taylor, S. D., & White, E. P. (2018) · PeerJ · doi:10.7717/peerj.4278 · Cited by 70 · see citing works

Artificial intelligence application in drought assessment, monitoring and forecasting: a review

Kikon, A., & Deka, P. C. (2021) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-021-02129-3 · Cited by 68 · see citing works

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

Thomas, R. Q., Figueiredo, R., Daneshmand, V., Bookout, B. J., Puckett, L. K., & Carey, C. C. (2020) · Water Resources Research · doi:10.1029/2019wr026138 · Cited by 68 · see citing works

Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

Lewis, A. S. L., Woelmer, W. M., Wander, H. L., Howard, D. W., Smith, J. W., McClure, R. P., Lofton, M. E., Hammond, N. W., Corrigan, R. S., Thomas, R. Q., et al. (2021) · Ecological Applications · doi:10.1002/eap.2500 · Cited by 67 · see citing works

Escape from model-land

Thompson, E., & Smith, L. A. (2019) · Economics · doi:10.5018/economics-ejournal.ja.2019-40 · Cited by 66 · see citing works

From skill to value: isolating the influence of end user behavior on seasonal forecast assessment

Giuliani, M., Crochemore, L., Pechlivanidis, I. G., & Castelletti, A. F. (2020) · Hydrology and earth system sciences · doi:10.5194/hess-24-5891-2020 · Cited by 66 · see citing works

Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty

Heilman, K. A., Dietze, M. C., Arizpe, A., Aragon, J., Gray, A. N., Shaw, J. D., Finley, A. O., Klesse, S., DeRose, R. J., & Evans, M. E. K. (2022) · Global Change Biology · doi:10.1111/gcb.16038 · Cited by 64 · see citing works

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

Mercado‐Bettín, D., Clayer, F., Shikhani, M., Moore, T. N., Frías, M. D., Jackson‐Blake, L., Sample, J. E., Iturbide, M., Herrera, S., French, A. S., et al. (2021) · Water Research · doi:10.1016/j.watres.2021.117286 · Cited by 62 · see citing works

Adaptive forecasting of phytoplankton communities

Page, T., Smith, P. J., Beven, K. J., Jones, I. D., Elliott, J. A., MABERLY, S. C., Mackay, E. B., Ville, M. M. D., & Feuchtmayr, H. (2018) · Water Research · doi:10.1016/j.watres.2018.01.046 · Cited by 59 · see citing works

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Moore, T. N., Mesman, J. P., Ladwig, R., Feldbauer, J., Olsson, F., Pilla, R. M., Shatwell, T., Venkiteswaran, J. J., Delany, A. D., Dugan, H. A., et al. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105101 · Cited by 58 · see citing works

Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources

Hobday, A. J., Hartog, J. R., Manderson, J. P., Mills, K. E., Oliver, M. J., Pershing, A. J., & Siedlecki, S. A. (2018) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsy210 · Cited by 55 · see citing works

Opportunities for seasonal forecasting to support water management outside the tropics

Jackson‐Blake, L., Clayer, F., de Eyto, E., French, A. S., Frías, M. D., Mercado‐Bettín, D., Moore, T. N., Puértolas, L., Poole, R., Rinke, K., et al. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-26-1389-2022 · Cited by 48 · see citing works

Matching the forecast horizon with the relevant spatial and temporal processes and data sources

Adler, P. B., White, E. P., & Cortez, M. H. (2020) · Ecography · doi:10.1111/ecog.05271 · Cited by 46 · see citing works

Mobile weather apps or the illusion of certainty

Zabini, F. (2016) · Meteorological Applications · doi:10.1002/met.1589 · Cited by 41 · see citing works

Near‐term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

Woelmer, W. M., Thomas, R. Q., Lofton, M. E., McClure, R. P., Wander, H. L., & Carey, C. C. (2022) · Ecological Applications · doi:10.1002/eap.2642 · Cited by 40 · see citing works

Individual‐based models forecast the spread and inform the management of an emerging riverine invader

Messager, M., & Olden, J. D. (2018) · Diversity and Distributions · doi:10.1111/ddi.12829 · Cited by 39 · see citing works

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

Carey, C. C., Ward, N. K., Farrell, K. J., Lofton, M. E., Krinos, A. I., McClure, R. P., Subratie, K. C., Figueiredo, R., Doubek, J. P., Hanson, P. C., et al. (2019) · Ecosphere · doi:10.1002/ecs2.2753 · Cited by 39 · see citing works

Prediction of algal bloom occurrence based on the naive Bayesian model considering satellite image pixel differences

Mu, M., Li, Y., Bi, S., Lyu, H., Xu, J., Lei, S., Song, M., Zeng, S., Zheng, Z., & Du, C. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107416 · Cited by 37 · see citing works

Making Sense of Uncertainty in the Science Classroom

Rosenberg, J. M., Kubsch, M., Wagenmakers, E., & Dogucu, M. (2022) · Science & Education · doi:10.1007/s11191-022-00341-3 · Cited by 35 · see citing works

Elements of a decision support system for real-time management of dissolved oxygen in the \nSan Joaquin River deep water ship channel

Quinn, N. W. T., Jacobs, K., Chen, C. W., & Stringfellow, W. T. (2004) · eScholarship (California Digital Library) · Cited by 35 · see citing works

Operational Seasonal Water Supply and Water Level Forecasting for the Laurentian Great Lakes

Fry, L. M., Apps, D., & Gronewold, A. D. (2020) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001214 · Cited by 35 · see citing works

A novel multi-source data fusion method based on Bayesian inference for accurate estimation of chlorophyll-a concentration over eutrophic lakes

Chen, C., Chen, Q., Li, G., He, M., Dong, J., Yan, H., Wang, Z., & Duan, Z. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105057 · Cited by 35 · see citing works

Persistent Forecasting of Disruptive Technologies

National Research Council (2009) · National Academies Press eBooks · doi:10.17226/12557 · Cited by 35 · see citing works

Freshwater Resources

Change, I. P. O. C. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107415379.008 · Cited by 34 · see citing works

Forecasting a bright future for ecology

Dietze, M. C., & Lynch, H. J. (2019) · Frontiers in Ecology and the Environment · doi:10.1002/fee.1994 · Cited by 30 · see citing works

Calibration and quantifying uncertainty of daily water quality forecasts for large lakes with a Bayesian joint probability modelling approach

Peng, Z., Hu, Y., Liu, G., Hu, W., Zhang, H., & Gao, R. (2020) · Water Research · doi:10.1016/j.watres.2020.116162 · Cited by 30 · see citing works

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Khudhair, Z. S., Zubaidi, S. L., Ortega‐Martorell, S., Al-Ansari, N. A., Ethaib, S., & Hashim, K. (2022) · Environments · doi:10.3390/environments9070085 · Cited by 30 · see citing works

The forecast trap

Boettiger, C. (2022) · Ecology Letters · doi:10.1111/ele.14024 · Cited by 29 · see citing works

Towards improving the framework for probabilistic forecast evaluation

Smith, L. A., Suckling, E. B., Thompson, E., Maynard, T., & Du, H. (2015) · Climatic Change · doi:10.1007/s10584-015-1430-2 · Cited by 29 · see citing works

Probabilistic forecast of microcystin toxin using satellite remote sensing, in situ observations and numerical modeling

Liu, Q., Rowe, M. D., Anderson, E. J., Stow, C. A., Stumpf, R. P., & Johengen, T. H. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104705 · Cited by 29 · see citing works

Using Visualization Science to Improve Expert and Public Understanding of Probabilistic Temperature and Precipitation Outlooks

Gerst, M. D., Kenney, M. A., Baer, A. E., Speciale, A., Wolfinger, J. F., Gottschalck, J., Handel, S., Rosencrans, M., & DeWitt, D. G. (2019) · Weather Climate and Society · doi:10.1175/wcas-d-18-0094.1 · Cited by 28 · see citing works

Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models

Huang, Y., Stacy, M., Jiang, J., Sundi, N., Ma, S., Saruta, V., Jung, C. G., Shi, Z., Xia, J., Hanson, P. J., et al. (2019) · Geoscientific model development · doi:10.5194/gmd-12-1119-2019 · Cited by 27 · see citing works

Water Temperature Ensemble Forecasts: Implementation Using the CEQUEAU Model on Two Contrasted River Systems

Ouellet‐Proulx, S., St‐Hilaire, A., & Boucher, M. (2017) · Water · doi:10.3390/w9070457 · Cited by 23 · see citing works

A Forecasting and Prediction Methodology for Improving the Blue Economy Resilience to Climate Change in the Romanian Lower Danube Euroregion

Petrea, Ș. M., Zamfir, C. G., Simionov, I.-A., Mogodan, A., Nuţă, F. M., Rahoveanu, A. T., Nancu, D., Cristea, D. S., & Buhociu, F. M. (2021) · Sustainability · doi:10.3390/su132111563 · Cited by 23 · see citing works

A High-Resolution Earth Observations and Machine Learning-Based Approach to Forecast Waterborne Disease Risk in Post-Disaster Settings

Nusrat, F., Haque, M. A., Rollend, D., Christie, G. A., & Akanda, A. S. (2022) · Climate · doi:10.3390/cli10040048 · Cited by 23 · see citing works

Ecological forecasting—21st century science for 21st century management

Bradford, J. B., Weltzin, J. F., McCormick, M. L., Baron, J. S., Bowen, Z., Bristol, R. S., Carlisle, D. M., Crimmins, T. M., Cross, P. C., DeVivo, J. C., et al. (2020) · Antarctica A Keystone in a Changing World · doi:10.3133/ofr20201073 · Cited by 23 · see citing works

Projecting the effects of agricultural conservation practices on stream fish communities in a changing climate

Fraker, M. E., Keitzer, S. C., Sinclair, J. S., Aloysius, N. R., Dippold, D. A., Yen, H., Arnold, J. G., Daggupati, P., Johnson, M. V., Martin, J. F., et al. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141112 · Cited by 20 · see citing works

Forecasting water quality using seasonal ARIMA model by integrating in-situ measurements and remote sensing techniques in Krishnagiri reservoir, India

Wahid, A. A., & Arunbabu, E. (2022) · Water Practice & Technology · doi:10.2166/wpt.2022.046 · Cited by 20 · see citing works

Better Subseasonal-to-Seasonal Forecasts for Water Management

DeFlorio, M. J., Ralph, F. M., Waliser, D. E., Jones, J., & Anderson, M. L. (2021) · Eos · doi:10.1029/2021eo159749 · Cited by 20 · see citing works

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

Moore, T. N., Thomas, R. Q., Woelmer, W. M., & Carey, C. C. (2022) · Forecasting · doi:10.3390/forecast4030033 · Cited by 19 · see citing works

Assessing and Predicting the Water Resources Vulnerability under Various Climate-Change Scenarios: A Case Study of Huang-Huai-Hai River Basin, China

Chen, Y., Feng, Y., Zhang, F., Yang, F., & Wang, L. (2020) · Entropy · doi:10.3390/e22030333 · Cited by 18 · see citing works

Assimilation of water temperature and discharge data for ensemble water temperature forecasting

Ouellet‐Proulx, S., Chiadjeu, O. C., Boucher, M., & St‐Hilaire, A. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.09.027 · Cited by 18 · see citing works

Using near‐term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Lofton, M. E., Brentrup, J. A., Beck, W. S., Zwart, J. A., Bhattacharya, R., Brighenti, L. S., Burnet, S. H., McCullough, I. M., Steele, B. G., Carey, C. C., et al. (2022) · Ecological Applications · doi:10.1002/eap.2590 · Cited by 16 · see citing works

Ten simple rules for training yourself in an emerging field

Woelmer, W. M., Bradley, L. M., Haber, L. T., Klinges, D. H., Lewis, A. S. L., Mohr, E. J., Torrens, C. L., Wheeler, K. I., & Willson, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009440 · Cited by 15 · see citing works

Reproducible and Portable Workflows for Scientific Computing and HPC in the Cloud

Vaillancourt, P., Wineholt, B., Barker, B., Deliyannis, P., Zheng, J., Suresh, A., Brazier, A., Knepper, R., & Wolski, R. (2020) · Practice and Experience in Advanced Research Computing · doi:10.1145/3311790.3396659 · Cited by 14 · see citing works

OpenForecast v2: Development and Benchmarking of the First National-Scale Operational Runoff Forecasting System in Russia

Ayzel, G. (2021) · Hydrology · doi:10.3390/hydrology8010003 · Cited by 13 · see citing works

The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research

Reich, N. G., Cornell, M., Ray, E. L., House, K., & Le, K. (2021) · Scientific Data · doi:10.1038/s41597-021-00839-5 · Cited by 12 · see citing works

Impact of different types of meteorological data inputs on predicted hydrological and erosive responses to projected land use changes

Bhattacharyya, S., & Sanyal, J. (2019) · Journal of Earth System Science · doi:10.1007/s12040-019-1076-y · Cited by 12 · see citing works

Uncertainty in Environmental Water Quality Modelling: Where Do We Stand?

Jakeman, A. J., Croke, B., & Fu, B. (2018) · Green energy and technology · doi:10.1007/978-3-319-99867-1_96 · Cited by 11 · see citing works

The influence of graphical format on judgmental forecasting accuracy: Lines versus points

Theocharis, Z., Smith, L. A., & Harvey, N. (2018) · Futures & Foresight Science · doi:10.1002/ffo2.7 · Cited by 11 · see citing works

Edge-to-cloud Virtualized Cyberinfrastructure for Near Real-time Water Quality Forecasting in Lakes and Reservoirs

Daneshmand, V., Breef‐Pilz, A., Carey, C. C., Jin, Y., Ku, Y.-J., Subratie, K. C., Thomas, R. Q., & Figueiredo, R. (2021) · IEEE International Conference on eScience · doi:10.1109/escience51609.2021.00024 · Cited by 11 · see citing works

PREMHYCE: An operational tool for low-flow forecasting

Nicolle, P., Besson, F., Delaigue, O., Etchevers, P., François, D., Lay, M. L., Perrin, C., Rousset, F., THIERY, D. P., Tilmant, F., et al. (2020) · Proceedings of the International Association of Hydrological Sciences · doi:10.5194/piahs-383-381-2020 · Cited by 11 · see citing works

The NETLAKE Metadatabase-A Tool to Support Automatic Monitoring on Lakes in Europe and Beyond

Jennings, E., de Eyto, E., Laas, A., Pierson, D. C., Mirceva, G., Naumoski, A., Clarke, A., Healy, M., Šumberová, K., & Langenhaun, D. (2017) · Limnology and Oceanography Bulletin · doi:10.1002/lob.10210 · Cited by 10 · see citing works

Facing the Forecaster’s Dilemma: Reflexivity in Ocean System Forecasting

Record, N. R., & Pershing, A. J. (2021) · Oceans · doi:10.3390/oceans2040042 · Cited by 9 · see citing works

Operational forecast uncertainty assessment for better information to stakeholders and crisis managers

Berthet, L., Piotte, O., Gaumé, É., Marty, R., & Ardilouze, C. (2016) · E3S Web of Conferences · doi:10.1051/e3sconf/20160718005 · Cited by 9 · see citing works

Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates

McClure, R. P., Thomas, R. Q., Lofton, M. E., Woelmer, W. M., & Carey, C. C. (2021) · Frontiers in Environmental Science · doi:10.3389/fenvs.2021.756603 · Cited by 9 · see citing works

Forecasting suppression of invasive sea lamprey in Lake Superior

Lewandoski, S. A., & Brenden, T. O. (2022) · Journal of Applied Ecology · doi:10.1111/1365-2664.14203 · Cited by 8 · see citing works

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real timePreprint

Thomas, R. Q., Figueiredo, R., Daneshmand, V., Bookout, B. J., Puckett, L. K., & Carey, C. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.22.915538 · Cited by 8 · see citing works

A Community Convention for Ecological Forecasting: Output Files and MetadataPreprint

Dietze, M. C., Thomas, R., Peters, J. A., Boettiger, C., Shiklomanov, A., & Ashander, J. (2021) · doi:10.32942/osf.io/9dgtq · Cited by 8 · see citing works

When Weather Forecasters Are Wrong: Image Repair and Public Rhetoric After Severe Weather

Compton, J. (2018) · Science Communication · doi:10.1177/1075547018799101 · Cited by 7 · see citing works

The WATERMAN system for daily beach water quality forecasting: a ten-year retrospective

Choi, K. W., Chan, S. N., & Lee, J. H. W. (2022) · Environmental Fluid Mechanics · doi:10.1007/s10652-022-09839-4 · Cited by 7 · see citing works

Towards Informed Water Resources Planning and Management

Reggiani, P., Talbi, A., & Todini, E. (2022) · Hydrology · doi:10.3390/hydrology9080136 · Cited by 7 · see citing works

The USGS at Embudo, New Mexico: 125 years of systematic streamgaging in the United States

Gunn, M. A., Matherne, A. M., & Mason, R. R. (2014) · Fact sheet · doi:10.3133/fs20143034 · Cited by 6 · see citing works

Modelling past migrations to determine efficient management rules favouring silver eel escapement from a large regulated Floodplain Lake

Bourgeaux, J., Teichert, N., Gillier, J.-M., Danet, V., Feunteun, É., Acou, A., Charrier, F., Mazel, V., Carpentier, A., & Trancart, T. (2022) · Journal for Nature Conservation · doi:10.1016/j.jnc.2022.126192 · Cited by 6 · see citing works

The NEON Ecological Forecasting ChallengePreprint

Thomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., McLACHLAN, J. S., Peters, J. A., Sokol, E. R., Weltzin, J. F., et al. (2022) · doi:10.22541/essoar.167079499.99891914/v1 · Cited by 6 · see citing works

Improving estimates and forecasts of lake carbon dynamics using data assimilation

Zwart, J. A., Hararuk, O., Prairie, Y. T., Jones, S. E., & Solomon, C. T. (2019) · Limnology and Oceanography Methods · doi:10.1002/lom3.10302 · Cited by 5 · see citing works

Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S.Preprint

Thomas, R. Q., McClure, R. P., Moore, T. N., Woelmer, W. M., Boettiger, C., Figueiredo, R., Hensley, R. T., & Carey, C. C. (2022) · doi:10.1002/essoar.10510642.1 · Cited by 5 · see citing works

Ecological Forecasting Initiative: NEON Ecological Forecasting Challenge documentation V1.0

Thomas, R. Q., Boettiger, C., Carey, C., Dietze, M. C., Fox, A. M., Kenney, M. A., Laney, C., McLACHLAN, J. S., Peters, J. A., Weltzin, J. F., et al. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4780154 · Cited by 5 · see citing works

Building software, building community: lessons from the ROpenSci project

Boettiger, C., Hart, E., Chamberlain, S., & Ram, K. (2014) · Figshare · doi:10.6084/m9.figshare.1112581 · Cited by 3 · see citing works

Upgrade of NOS Lake Superior Operational Forecast System to FVCOM: model development and hindcast skill assessment

Kelley, J. G. W. (2022) · NOAA Institutional Repository · doi:10.25923/nbf7-r211 · Cited by 3 · see citing works

Scaling-up Stakeholder Engagement Efforts to Inform Better Communication & Uptake of NOAA Great Lakes Ice Forecast Information

Fujisaki‐Manome, A., Gill, D., Channell, K., Graves, V., Jagannathan, K., Anderson, E. J., & Lemos, M. C. (2022) · Deep Blue (University of Michigan) · doi:10.7302/4389 · Cited by 3 · see citing works

The AIHABs Project: Towards an Artificial Intelligence-Powered Forecast for Harmful Algal Blooms

Cobo, F., Vieira‐Lanero, R., Barca, S., Cobo, M. C., Quesada, A., Nasr, A. E., Bedri, Z., Cid, M. X. A., Saberioon, M., Brom, J., et al. (2022) · Iberian Congress on Cyanotoxins / Iberoamerican Congress on Cyanotoxins · doi:10.3390/blsf2022014013 · Cited by 3 · see citing works

Applying real-time advanced urban management to ensure bathing water quality in Barcelona

Soler, B. A., Gomez, J. G., Torret, X., Medina, V., Varela, J., Chesa, M. J., Llopart-Mascaró, A., Garcia, J. A., Erill, D., Batlle, M., et al. (2022) · Proceedings of the IAHR World Congress · doi:10.3850/iahr-39wc2521711920221658 · Cited by 2 · see citing works

Open-access, online resources for ecological forecasting

Willson, A. (2022) · Figshare · doi:10.6084/m9.figshare.19765834 · Cited by 1 · see citing works

Code repository: Progress and opportunities in advancing near-term forecasting of freshwater quality

Lofton, M. E., Howard, D., Thomas, Q., & Carey, C. C. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7083845 · Cited by 1 · see citing works

State-of-the-art review of near-term freshwater forecasting literature published between 2017 and 2022

Lofton, M. E., Howard, D. W., Thomas, R. Q., & Carey, C. C. (2023) · Environmental Data Initiative · doi:10.6073/pasta/949164a64de132ff3bbb7b92d2e1c729 · Cited by 1 · see citing works

VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY

BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078

Untitled work

Rajagopalan (2002) · doi:10.1175/1520-0493(2002)130

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

Amador-Castro, F., González‐López, M. E., López‐González, G., García, A., Díaz-Torres, O., Carbajal-Espinosa, O. E., & Hernandez, M. S. G. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.119959 · Cited by 37 · see citing works

The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions

Caballero, C. B., Martins, V. S., Paulino, R. S., Butler, E., Sparks, E., de Lima, T. M. A., & Novo, E. M. L. D. M. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113244 · Cited by 37 · see citing works

Causes and consequences of changing oxygen availability in lakes

Carey, C. C. (2023) · Inland Waters · doi:10.1080/20442041.2023.2239110 · Cited by 30 · see citing works

Near-term ecological forecasting for climate change action

Dietze, M. C., White, E. P., Abeyta, A., Boettiger, C., Watts, N. B., Carey, C. C., Chaplin‐Kramer, R., Emanuel, R. E., Ernest, S. K. M., Figueiredo, R., et al. (2024) · Nature Climate Change · doi:10.1038/s41558-024-02182-0 · Cited by 24 · see citing works

Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management

Huang, Y., Chen, S., Tang, X., Sun, C., Zhang, Z., & Huang, J. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.122911 · Cited by 21 · see citing works

A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts

Olsson, F., Moore, T. N., Carey, C. C., Breef‐Pilz, A., & Thomas, R. Q. (2024) · Water Resources Research · doi:10.1029/2023wr035901 · Cited by 19 · see citing works

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Wander, H. L., Thomas, R. Q., Moore, T. N., Lofton, M. E., Breef‐Pilz, A., & Carey, C. C. (2024) · Ecosphere · doi:10.1002/ecs2.4752 · Cited by 18 · see citing works

Disinfection by-product formation potential in response to variability in dissolved organic matter and nutrient inputs: Insights from a mesocosm study

Pedregal-Montes, A., Jennings, E., Kothawala, D. N., Jones, K., Sjöstedt, J., Langenheder, S., Marcé, R., & Farré, M. J. (2024) · Water Research · doi:10.1016/j.watres.2024.121791 · Cited by 18 · see citing works

Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth

Woelmer, W. M., Thomas, R. Q., Olsson, F., Steele, B. G., Weathers, K. C., & Carey, C. C. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102825 · Cited by 18 · see citing works

A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models

Robles, K. P. V., Solmerin, J. G., Pugat, G. C. E., & Monjardin, C. E. F. (2026) · Water · doi:10.3390/w18010119 · Cited by 14 · see citing works

What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge

Olsson, F., Carey, C. C., Boettiger, C., Harrison, G., Ladwig, R., Lapeyrolerie, M., Lewis, A. S. L., Lofton, M. E., Montealegre‐Mora, F., Rabaey, J. S., et al. (2025) · Ecological Applications · doi:10.1002/eap.70004 · Cited by 11 · see citing works

Combining a Multi‐Lake Model Ensemble and a Multi‐Domain CORDEX Climate Data Ensemble for Assessing Climate Change Impacts on Lake Sevan

Shikhani, M., Feldbauer, J., Ladwig, R., Mercado‐Bettín, D., Moore, T. N., Gevorgyan, A., Misakyan, A., Mi, C., Schultze, M., Boehrer, B., et al. (2024) · Water Resources Research · doi:10.1029/2023wr036511 · Cited by 11 · see citing works

Preparation of C-CuxO composite from biosorption product of lactic acid bacteria and its application in solar evaporator for desalination

Cao, H., Zhang, T., Cheng, X., Tian, Y., Yao, P., & Guo, C. (2023) · Separation and Purification Technology · doi:10.1016/j.seppur.2023.125223 · Cited by 11 · see citing works

A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change

Carey, C. C., Calder, R. S. D., Figueiredo, R., Gramacy, R. B., Lofton, M. E., Schreiber, M. E., & Thomas, R. Q. (2024) · AMBIO · doi:10.1007/s13280-024-02076-7 · Cited by 9 · see citing works

Integrating sensor data and machine learning to advance the science and management of river carbon emissions

Brown, L. E., Maavara, T., Zhang, J., Chen, X., Klaar, M. J., Moshe, F. O. R., Ben-Zur, E., Stein, S., Grayson, R. P., Carter, L., et al. (2024) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2024.2429912 · Cited by 8 · see citing works

Near‐term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species

Paíz, R., Thomas, R. Q., Carey, C. C., de Eyto, E., Jones, I. D., Delany, A. D., Poole, R., Nixon, P., Dillane, M., McCarthy, V., et al. (2025) · Ecosphere · doi:10.1002/ecs2.70335 · Cited by 5 · see citing works

Understanding and mitigating global change with aquatic sensors: current challenges and future prospects

Diamond, D., Relyea, R. A., & McCaul, M. V. (2023) · Frontiers in Sensors · doi:10.3389/fsens.2023.1284043 · Cited by 5 · see citing works

An evaluation of statistical models of microcystin detection in lakes applied forward under varying climate conditions

Wilkinson, G. M., Walter, J. A., Albright, E. A., King, R. F., Moody, E. K., & Ortíz, D. (2024) · Harmful Algae · doi:10.1016/j.hal.2024.102679 · Cited by 5 · see citing works

Modelling the potential for local management practices to offset climate change impacts on freshwater macroinvertebrate communities

Orr, J. A., Bussi, G., Hughes, J., Whitehead, P. G., & Jackson, M. C. (2024) · Freshwater Biology · doi:10.1111/fwb.14222 · Cited by 5 · see citing works

LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles

McAfee, B., Pradhan, A., Neog, A., Fatemi, S., Hensley, R. T., Lofton, M. E., Karpatne, A., Carey, C. C., & Hanson, P. C. (2025) · Earth system science data · doi:10.5194/essd-17-3141-2025 · Cited by 4 · see citing works

Spatio-Temporal Variation in the Exceedance of Enterococci in Lake Burley Griffin: An Analysis of 16 Years’ Recreational Water Quality Monitoring Data

Adhikary, R. K., Starrs, D., Wright, D., Croke, B., Glass, K., & Lal, A. (2024) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph21050579 · Cited by 4 · see citing works

Integrating phytoplankton phenology, traits, and model‐data fusion to advance bloom prediction

Hipsey, M. R., Carey, C. C., Brookes, J. D., Burford, M. A., Dang, H. V., Ibelings, B. W., & Hamilton, D. P. (2025) · Limnology and Oceanography Letters · doi:10.1002/lol2.70052 · Cited by 3 · see citing works

Biodiversity forecasting in natural plankton communities reveals temperature and biotic interactions as key predictors

Merz, E., Pomati, F., Saavedra, S., & Gilarranz, L. J. (2025) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2024.2895 · Cited by 3 · see citing works

High‐frequency monitoring data reveal substantial variability in the intrinsic predictability of ecosystem dynamics

Carey, C. C., Olsson, F., Breef‐Pilz, A., & Thomas, R. Q. (2026) · Ecology · doi:10.1002/ecy.70292 · Cited by 2 · see citing works

Synthesizing data products, mathematical models, and observations for lake temperature forecasting

Holthuijzen, M., Gramacy, R. B., Carey, C. C., Higdon, D., & Thomas, R. Q. (2025) · The Annals of Applied Statistics · doi:10.1214/25-aoas2027 · Cited by 2 · see citing works

Process-Based Forecasts of Lake Water Temperature and Dissolved Oxygen Outperform Null Models, with Variability Over Time and DepthPreprint

Woelmer, W. M., Thomas, R. Q., Olsson, F., Steele, B. G., Weathers, K. C., & Carey, C. C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4699835 · Cited by 2 · see citing works

Translational Edge and Cloud Computing to Advance Lake Water Quality Forecasting

Figueiredo, R., Carey, C. C., & Thomas, R. Q. (2024) · Computing in Science & Engineering · doi:10.1109/mcse.2024.3430148 · Cited by 1 · see citing works

Fifty years of riverine harmful algal bloom modeling: A global synthesis of approaches, challenges, and opportunities

Murphy, J. C., Gorney, R. M., Lucas, L. V., Zwart, J. A., & Graham, J. L. (2026) · Water Research · doi:10.1016/j.watres.2026.126240 · Cited by 1 · see citing works

Numerical Aspects and Implementation of LAKE Scheme into a Global Atmospheric Model SLAV

Fadeev, R. Y., & Stepanenko, V. (2024) · Lobachevskii Journal of Mathematics · doi:10.1134/s1995080224602601 · Cited by 1 · see citing works

A multi-model ensemble of empirical and process-based models improves the predictive skill of near-term lake forecastsPreprint

Olsson, F., Moore, T. N., Carey, C. C., Breef‐Pilz, A., & Thomas, R. Q. (2023) · doi:10.22541/essoar.169049097.75300247/v1

Water Quality Forecasting

Sene, K. (2024) · Springer eBooks · doi:10.1007/978-3-031-58269-1_12

Day-ahead statistical forecasting of algal bloom risk to support reservoir release decisions in a highly engineered watershed

Menchú-Maldonado, M., Kaplan, D. A., Arias, M. E., Milbrandt, E. C., Morrison, E. S., Phli̇ps, E. J., & Nelson, N. G. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.124327

Developing scenario-based, near-term iterative forecasts to inform water management

Olsson, F., Thomas, R. Q., Hipsey, M. R., Delany, A. D., Sims, C., Rumbelow, A., & Carey, C. C. (2026) · Inland Waters · doi:10.1080/20442041.2026.2748305

Near‐Term Forecasts of Dissolved Organic Matter Exhibit Consistent Patterns of Accuracy Across Multiple Freshwater Reservoirs

Howard, D. W., Lofton, M. E., Thomas, R. Q., Delany, A. D., Breef‐Pilz, A., & Carey, C. C. (2026) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2025jg009064

Beyond point predictions: Probabilistic algal biomass forecasting for risk-based management via Bayesian additive regression trees

Li, Y., He, Z., Ding, K., Xiao, Z., Liang, Z., Hu, S., & Liu, Y. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.104038

Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification

Shukla, R., Carey, C. C., Breef‐Pilz, A., & Thomas, R. Q. (2026) · ACS ES&T Water · doi:10.1021/acsestwater.6c00757

Multi-Day Salinity Forecasting in the Vietnamese Mekong Delta: Horizon-Dependent Performance and Interpretation of XGBoost, LSTM, and a Weighted Hybrid

Cong, N. P., Downes, N. K., Minh, H. V. T., & Singh, S. K. (2026) · Water · doi:10.3390/w18192379

Near‐term ecological forecasts to inform environmental flows: A case study on a long‐lived riverine fish in multiple rivers

Yen, J. D. L., Todd, C. R., Amtstaetter, F., Kitchingman, A., Nystrand, M., Wootton, H. F., & Tonkin, Z. (2026) · Journal of Applied Ecology · doi:10.1111/1365-2664.70608

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

Carey, C. C., Woelmer, W. M., Lofton, M. E., Figueiredo, R., Bookout, B. J., Corrigan, R. S., Daneshmand, V., Hounshell, A. G., Howard, D. W., Lewis, A. S. L., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13604639

What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge

Olsson, F., Carey, C. C., Boettiger, C., Harrison, G., Ladwig, R., Lapeyrolerie, M., Lewis, A. S. L., Lofton, M. E., Montealegre‐Mora, F., Rabaey, J. S., et al. (2025) · Ecological Applications · doi:10.1002/eap.70004 · Cited by 11 · see citing works

Toward real-time water quality forecasts

Appling, A., Zwart, J. A., Salas, F. R., Blodgett, D. L., DeCicco, L., & Read, J. S. (2018) · ScholarsArchive (Brigham Young University)

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real timePreprint

Thomas, R. Q., Figueiredo, R., Daneshmand, V., Bookout, B. J., Puckett, L. K., & Carey, C. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.22.915538 · Cited by 8 · see citing works

Building, applying, and communicating ecosystem understanding via freshwater forecasts over time and space

Woelmer, W. M. (2023) · VTechWorks (Virginia Tech)