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Article

Deriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zones

1
Leibniz-Institute of Freshwater Ecology and Inland Fisheries, 12489 Berlin, Germany
2
Center for Agricultural Technology Augustenberg, 76227 Karlsruhe, Germany
3
Faculty of Biology, Department of Aquatic Ecology, University of Duisburg-Essen, 45141 Essen, Germany
4
Department of Geography, Humboldt-University of Berlin, 12489 Berlin, Germany
*
Author to whom correspondence should be addressed.
Water 2020, 12(3), 617; https://doi.org/10.3390/w12030617
Received: 7 January 2020 / Revised: 7 February 2020 / Accepted: 11 February 2020 / Published: 25 February 2020
(This article belongs to the Special Issue Monitoring, Modelling and Management of Water Quality)
Bayesian networks (BN) have increasingly been applied in water management but not to estimate the efficacy of riparian buffer zones (RBZ). Our methodical study aims at evaluating the first BN to predict the RBZ efficacy to retain sediment and nutrients (dissolved, total, and particulate nitrogen and phosphorus) from widely available variables (width, vegetation, slope, soil texture, flow pathway, nutrient form). To evaluate the influence of parent nodes and how the number of states affects prediction errors, we used a predefined general BN structure, collected 580 published datasets from North America and Europe, and performed classification tree analyses and multiple 10-fold cross-validations of different BNs. These errors ranged from 0.31 (two output states) to 0.66 (five states). The outcome remained unchanged without the least influential nodes (flow pathway, vegetation). Lower errors were achieved when parent nodes had more than two states. The number of efficacy states influenced most strongly the prediction error as its lowest and highest states were better predicted than intermediate states. While the derived BNs could support or replace simple design guidelines, they are limited for more detailed predictions. More representative data on vegetation or additional nodes like preferential flow will probably improve the predictive power. View Full-Text
Keywords: model evaluation; nitrogen; nutrient retention; phosphorus; sediment model evaluation; nitrogen; nutrient retention; phosphorus; sediment
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MDPI and ACS Style

Gericke, A.; Nguyen, H.H.; Fischer, P.; Kail, J.; Venohr, M. Deriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zones. Water 2020, 12, 617. https://doi.org/10.3390/w12030617

AMA Style

Gericke A, Nguyen HH, Fischer P, Kail J, Venohr M. Deriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zones. Water. 2020; 12(3):617. https://doi.org/10.3390/w12030617

Chicago/Turabian Style

Gericke, Andreas, Hong H. Nguyen, Peter Fischer, Jochem Kail, and Markus Venohr. 2020. "Deriving a Bayesian Network to Assess the Retention Efficacy of Riparian Buffer Zones" Water 12, no. 3: 617. https://doi.org/10.3390/w12030617

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