Technical note: A two-sided affine power scaling relationship to represent the concentration–discharge relationship

Neira, José Manuel Tunqui; Andréassian, Vazken; Tallec, Gaëlle; Mouchel, Jean-Marie

doi:10.5194/hess-24-1823-2020

Cited by 12 publications

(16 citation statements)

References 21 publications

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“…It is significantly interesting for ions exhibiting a descending (negative slope) chemo‐dynamic behaviour. However, Tunqui et al (2020), who applied a multi‐objective calibration on the nitrate ions, did not improve the performances of the models tested. The multi‐objective method seems less interesting for ions exhibiting a chemostatic behaviour (flow independent behaviour) or an ascending (positive slope) chemo‐dynamic behaviour.…”

Section: Resultsmentioning

confidence: 93%

“…As described by Tunqui Neira et al (2020), we use the half‐hourly (every 30 min) hydrochemical dataset collected by the in situ River Lab laboratory located at the Avenelles outlet, a sub‐catchment of the ORACLE‐Orgeval Observatory (https://gisoracle.inrae.fr/) (Floury et al, 2017; Tallec et al, 2015). The Avenelles catchment, located 70 km east of Paris (France), has an area of 46 km 2 .…”

Section: Methodsmentioning

confidence: 99%

“…The numerical criteria used for optimization was a multi‐objective function based on the performances of both simulated concentrations and loads (Tunqui Neira et al, 2020). In fact, if a more weight‐balanced representation of

C - Q

relationships may not be necessary in the case of ions showing a chemostasis behaviour with respect to discharge, it is well founded for ions showing a dilution behaviour (Tunqui Neira et al, 2020). While the multi‐objective method is not completely new, the novelty of the present work is related to its application to high frequency data and

C - Q

relationships.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective fitting of concentration‐discharge relationships

Neira

Andréassian

Tallec

et al. 2021

Hydrological Processes

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Concentration-discharge (C -Q) relationships are widely used to assess the link between hydrological and biogeochemical processes at the catchment scale. C -Q relationships are mainly calibrated using mono-objective methods to represent, either concentrations or discharge-weighted concentrations (i.e., load). Based on its wide use in hydrological modelling, we test a multi-objective calibration for the C -Q relationship parameters, using both concentration and load, and compare it to a monoobjective calibration applied on either concentrations or load. This work is carried out on a high-frequency dataset (ORACLE-Orgeval Observatory, France). Our findings show that the multi-objective calibration yield a better representation of C -Q relationships parameters during the high and low-flow events. The multi-objective calibration can be used for all forms of C -Q relationships and avoids issues of underrepresentation of dilution processes characterized by high-discharge, lowconcentration periods.

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Section: Resultsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

C - Q

C - Q

relationships.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective fitting of concentration‐discharge relationships

Neira

Andréassian

Tallec

et al. 2021

Hydrological Processes

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“…The shape of log(C)-log(Q) relationships is often described as linear (Bieroza et al, 2018;Godsey et al, 2009;Musolff et al, 2017) or segmented linear (Meybeck and Moatar, 2012;Moatar et al, 2017;Marinos et al, 2020), implying a limit on the possible log(C)-log(Q) shapes (Tunqui Neira et al, 2020) and setting assumptions such as "fixed breaking points". Here, we introduce the concept of maximum curvature (Curv max ) to quantify rather than describe the shape of "broken-stick" log(C)-log(Q) relationships without the assumption of a fixed form.…”

Section: Maximum Curvature -Curv Maxmentioning

confidence: 99%

Bending of the concentration discharge relationship can inform about in-stream nitrate removal

Dehaspe

Sarrazin

Kumar

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Nitrate (NO3-) excess in rivers harms aquatic ecosystems and can induce detrimental algae growths in coastal areas. Riverine NO3- uptake is a crucial element of the catchment-scale nitrogen balance and can be measured at small spatiotemporal scales, while at the scale of entire river networks, uptake measurements are rarely available. Concurrent, low-frequency NO3- concentration and streamflow (Q) observations at a basin outlet, however, are commonly monitored and can be analyzed in terms of concentration discharge (C–Q) relationships. Previous studies suggest that steeper positive log (C)–log (Q) slopes under low flow conditions (than under high flows) are linked to biological NO3- uptake, creating a bent rather than linear log (C)–log (Q) relationship. Here we explore if network-scale NO3- uptake creates bent log (C)–log (Q) relationships and when in turn uptake can be quantified from observed low-frequency C–Q data. To this end we apply a parsimonious mass-balance-based river network uptake model in 13 mesoscale German catchments (21–1450 km2) and explore the linkages between log (C)–log (Q) bending and different model parameter combinations. The modeling results show that uptake and transport in the river network can create bent log (C)–log (Q) relationships at the basin outlet from log–log linear C–Q relationships describing the NO3- land-to-stream transfer. We find that within the chosen parameter range the bending is mainly shaped by geomorphological parameters that control the channel reactive surface area rather than by the biological uptake velocity itself. Further we show that in this exploratory modeling environment, bending is positively correlated to percentage of NO3- load removed in the network (Lr.perc) but that network-wide flow velocities should be taken into account when interpreting log (C)–log (Q) bending. Classification trees, finally, can successfully predict classes of low (∼4 %), intermediate (∼32 %) and high (∼68 %) Lr.perc using information on water velocity and log (C)–log (Q) bending. These results can help to identify stream networks that efficiently attenuate NO3- loads based on low-frequency NO3- and Q observations and generally show the importance of the channel geomorphology on the emerging log (C)–log (Q) bending at network scales.

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“…A modified and improved version of WRTDS, which gets better predictions of riverine concentration and flux, was developed by Zhang et al [33] and Zhang & Ball [34]. Another more recent alternative methodology is the two-sided affine power scaling relationship by Tunqui et al [35], which produced better results than the linear regression.…”

Section: Introductionmentioning

confidence: 99%

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

González‐Nicolás

Schwientek

Sinsbeck

et al. 2021

Water

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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.

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Technical note: A two-sided affine power scaling relationship to represent the concentration–discharge relationship

Cited by 12 publications

References 21 publications

Multi‐objective fitting of concentration‐discharge relationships

Multi‐objective fitting of concentration‐discharge relationships

Bending of the concentration discharge relationship can inform about in-stream nitrate removal

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

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