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

Ana Gonzalez-Nicolas,Wolfgang Nowak,Marc Schwientek,Michael Sinsbeck

doi:10.3390/w13131723

Ana Gonzalez-Nicolas, Wolfgang Nowak + Show 2 more

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https://doi.org/10.3390/w13131723

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Abstract

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.

Highlights

The contributions of our work are fourfold: (i) We introduce a simple stochastic time-series model to characterize the export regime of a catchment subject to hysteresis; (ii) We demonstrate the robustness of our model even with only small data sets; (iii) We explore how many C–Q samples can be enough to characterize the export regime of a catchment sufficiently well; and (iv) We recommend sampling strategies that optimize the characterization of the export regime with the least sampling effort
Namely Regime and Memory Model (RMM) and linear regression, agreed that the Ammer catchment shows a chemostatic regime with α f ull = 0.011 and αreg = 0.180, respectively, when all samples were used for the analysis
We proposed a better and more robust alternative to model the behavior of a catchment

Summary

Introduction

Export regimes can be classified as chemodynamic when concentration varies with discharge (positively or negatively), and as chemostatic when the concentration is not affected by discharge [1,2,3,4,5,6,7]. Chemodynamic export regimes can be classified as dilution behavior and mobilization behavior. The catchment shows mobilization behavior if the compound concentration increases with discharge. This can occur for compounds mobilized by fast runoff components (e.g., nitrate by interflow, particle-bound pollutants by surface runoff) [7]. A chemostatic export regime presents a washout of pollutants at a relatively constant concentration (the compound concentration varies only slightly with discharge)

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