Articles | Volume 26, issue 8
Hydrol. Earth Syst. Sci., 26, 1993–2017, 2022
https://doi.org/10.5194/hess-26-1993-2022

Special issue: Frontiers in the application of Bayesian approaches in water...

Hydrol. Earth Syst. Sci., 26, 1993–2017, 2022
https://doi.org/10.5194/hess-26-1993-2022
Research article
25 Apr 2022
Research article | 25 Apr 2022

Spatially referenced Bayesian state-space model of total phosphorus in western Lake Erie

Timothy J. Maguire et al.

Related subject area

Subject: Rivers and Lakes | Techniques and Approaches: Modelling approaches
Future water temperature of rivers in Switzerland under climate change investigated with physics-based models
Adrien Michel, Bettina Schaefli, Nander Wever, Harry Zekollari, Michael Lehning, and Hendrik Huwald
Hydrol. Earth Syst. Sci., 26, 1063–1087, https://doi.org/10.5194/hess-26-1063-2022,https://doi.org/10.5194/hess-26-1063-2022, 2022
Short summary
Physical controls and a priori estimation of raising land surface elevation across the southwestern Bangladesh delta using tidal river management
Md Feroz Islam, Paul P. Schot, Stefan C. Dekker, Jasper Griffioen, and Hans Middelkoop
Hydrol. Earth Syst. Sci., 26, 903–921, https://doi.org/10.5194/hess-26-903-2022,https://doi.org/10.5194/hess-26-903-2022, 2022
Short summary
Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling
Sam Anderson and Valentina Radić
Hydrol. Earth Syst. Sci., 26, 795–825, https://doi.org/10.5194/hess-26-795-2022,https://doi.org/10.5194/hess-26-795-2022, 2022
Short summary
Synthesizing the impacts of baseflow contribution on concentration–discharge (CQ) relationships across Australia using a Bayesian hierarchical model
Danlu Guo, Camille Minaudo, Anna Lintern, Ulrike Bende-Michl, Shuci Liu, Kefeng Zhang, and Clément Duvert
Hydrol. Earth Syst. Sci., 26, 1–16, https://doi.org/10.5194/hess-26-1-2022,https://doi.org/10.5194/hess-26-1-2022, 2022
Short summary
Calibrating 1D hydrodynamic river models in the absence of cross-section geometry using satellite observations of water surface elevation and river width
Liguang Jiang, Silja Westphal Christensen, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 25, 6359–6379, https://doi.org/10.5194/hess-25-6359-2021,https://doi.org/10.5194/hess-25-6359-2021, 2021
Short summary

Cited articles

Auger-Méthé, M., Newman, K., Cole, D., Empacher, F., Gryba, R., King, A. A., Leos-Barajas, V., Mills Flemming, J., Nielsen, A., and Petris, G.: A guide to state–space modeling of ecological time series, Ecol. Monogr., 91, e01470, https://doi.org/10.1002/ecm.1470, 2021. 
Bolsenga, S. J. and Herdendorf, C. E.: Lake Erie and Lake St. Clair Handbook, Wayne State University Press, ISBN 0-8143-2470-3, 1993. 
Brooks, B. W., Lazorchak, J. M., Howard, M. D. A., Johnson, M. V, Morton, S. L., Perkins, D. A. K., Reavie, E. D., Scott, G. I., Smith, S. A., and Steevens, J. A.: Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems?, Environ. Toxicol. Chem., 35, 6–13, 2016. 
Buckland, S. T., Newman, K. B., Thomas, L., and Koesters, N. B.: State-space models for the dynamics of wild animal populations, Ecol. Model., 171, 157–175, 2004. 
Durbin, J. and Koopman, S. J.: Time series analysis by state space methods, Oxford University Press, ISBN 0-1996-4117-X, 2012. 
Download
Short summary
Water within large water bodies is constantly moving. Consequently, water movement masks causal relationships that exist between rivers and lakes. Incorporating water movement into models of nutrient concentration allows us to predict concentrations at unobserved locations and at observed locations on days not sampled. Our modeling approach does this while accommodating nutrient concentration data from multiple sources and provides a way to experimentally define the impact of rivers on lakes.