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Article

Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies

1
Department of Stochastic Simulation & Safety Research for Hydrosystems (LS3)/SimTech, Institute for Modelling Hydraulic and Environmental Systems (IWS), University of Stuttgart, 70569 Stuttgart, Germany
2
Center for Applied Geoscience, Eberhard Karls University of Tübingen, 72074 Tübingen, Germany
3
Department of Hydrogeology and Geothermal Energy, LGRB—Geological Survey of the Federal State of Baden-Württemberg, Ruppmannstr. 21, 70565 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Renata J. Romanowicz
Water 2021, 13(13), 1723; https://doi.org/10.3390/w13131723
Received: 4 May 2021 / Revised: 15 June 2021 / Accepted: 16 June 2021 / Published: 22 June 2021
(This article belongs to the Section Hydrology)
Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs. We propose a simple stochastic time-series model to represent the export regime of a catchment beyond simple regression. We propose how to get the required data with the least effort when the use of high-frequency in situ analyzers is not feasible or restricted. Sampling strategies based on extreme events are essential for reducing the prediction uncertainty of the catchment behavior. View Full-Text
Keywords: concentration–discharge; catchment; hydrology; optimal design of experiments; event-based sampling strategies; high-frequency in situ analyzers concentration–discharge; catchment; hydrology; optimal design of experiments; event-based sampling strategies; high-frequency in situ analyzers
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MDPI and ACS Style

Gonzalez-Nicolas, A.; Schwientek, M.; Sinsbeck, M.; Nowak, W. Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies. Water 2021, 13, 1723. https://doi.org/10.3390/w13131723

AMA Style

Gonzalez-Nicolas A, Schwientek M, Sinsbeck M, Nowak W. Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies. Water. 2021; 13(13):1723. https://doi.org/10.3390/w13131723

Chicago/Turabian Style

Gonzalez-Nicolas, Ana, Marc Schwientek, Michael Sinsbeck, and Wolfgang Nowak. 2021. "Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies" Water 13, no. 13: 1723. https://doi.org/10.3390/w13131723

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