Disentangling Scatter in Long-Term Concentration-Discharge Relationships: the Role of Event Types

Saavedra, Felipe; Musolff, Andreas; von Freyberg, Jana; Merz, Ralf; Basso, Stefano; Tarasova, Larisa

doi:https://doi.org/10.5194/egusphere-2022-205

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https://doi.org/10.5194/egusphere-2022-205

Preprints

25 Apr 2022

| 25 Apr 2022

Disentangling Scatter in Long-Term Concentration-Discharge Relationships: the Role of Event Types

Felipe Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova

Abstract. Relationships between nitrate concentrations and discharge rates (C-Q) at the catchment outlet can provide insights into sources, mobilization and biogeochemical transformations of nitrate within the catchment. Nitrate C-Q relationships often exhibit considerable scatter that might be related to variable hydrologic conditions during runoff events at sampling time, corresponding to variable sources and flow paths despite similar discharge rates. Although the origins of this scatter was investigated in individual catchments, the role of different runoff event types on the C-Q relationships across a large dataset of catchments was not yet evaluated.

In order to better understand the role of different runoff events in shaping long-term C-Q relationships, we analyzed low-frequency nitrate data from 184 German catchments, and quantified the deviation of samples collected during different types of events from the long-term power-law C-Q relationships. In most of the catchments, snow-impacted events produce positive deviations of concentrations, indicating an increased nitrate mobilization compared to the long-term pattern. In contrast, negative deviations occur mostly for rainfall-induced events with dry antecedent conditions, indicating lower nitrate concentrations. Pronounced differences in event runoff coefficients among different event types indicate their contrasting levels of hydrologic connectivity that in turn might play a key role controlling nitrate transport due to the activation of faster flow paths between sources and streams. Our study demonstrates using long-term, low-frequency nitrate data that runoff event types shape observed scatter in long-term C-Q relationships according to their level of hydrologic connectivity.

Received: 13 Apr 2022 – Discussion started: 25 Apr 2022

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Journal article(s) based on this preprint

12 Dec 2022

Disentangling scatter in long-term concentration–discharge relationships: the role of event types

Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova

Hydrol. Earth Syst. Sci., 26, 6227–6245, https://doi.org/10.5194/hess-26-6227-2022,https://doi.org/10.5194/hess-26-6227-2022, 2022

Short summary

Felipe Saavedra et al.

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-205', Anonymous Referee #1, 03 Jun 2022

General Comments:

This study presents a unique, spatially and temporally extensive dataset of nitrate C-Q relationships across 184 German catchments of varying size and land cover/land use from 2000-2015. The authors found that the degree of catchment hydrologic connectivity (and the closely related factor of runoff event type) strongly regulates the pattern of catchment nitrate export. Divergence of event-scale nitrate C-Q responses from the more generalized long-term response were attributed to a combination of catchment topographic properties and event type. The study dataset is impressive, providing a long-term view of catchment nitrate responses across gradients of event type, topography, and land use. The paper is very well written, making it both easy and enjoyable to read; there are only a few places in the manuscript where grammatical clarifications are needed. The statistical analyses presented in the paper are well-presented and wholly appropriate for the research questions that are being asked. Overall, this paper represents a meaningful addition to the existing C-Q literature and I recommend it for publication with only a few minor revisions. My main concern with the paper is that the potential influence of C-Q hysteresis on the observed patterns is not addressed anywhere in the paper. For example, if a particular catchment (or a particular event type) is characterized by a strong hysteresis signal, then the observed C-Q pattern (i.e., dilution or enrichment) would be highly influenced by the timing of sample collection. If a strong sampling bias exists where samples are more frequently collected on the rising limb relative to the falling limb (or vice versa), then the observed catchment or event C-Q signal might be confounded by the presence of hysteresis processes. I would not expect this to be an issue for the long-term C-Q pattern, but it may be an issue for the event-scale patterns and this would, in turn, cause a problem for the interpretation of the “Δres50” term presented in the paper. Given the low frequency of sample collection in this dataset (biweekly to monthly), it seems likely that a particular runoff event would be represented in the dataset by a sample collected either on the rising limb OR the falling limb but not both. It would be fascinating to see an additional analysis of this extensive dataset that incorporates the potential influence of sample timing on the hydrograph, but this is likely beyond the scope of this paper in its current form. However, I do think the authors need to include at least some discussion of the potential influence of this potential “hysteresis effect bias” associated with low-frequency event sampling.

Specific Comments:

Introduction: Very well-written and cited, providing a concise but informative review of the relevant C-Q literature. However, the Introduction focuses heavily (almost exclusively) on the hydrologic drivers of observed C-Q patterns, with little mention of the role of biogeochemical drivers. Particularly in the case of nitrate, biogeochemical drivers—emphasizing the “bio” aspect-- can also influence C-Q patterns. Because this paper focuses solely on nitrate concentrations, I think it is worth mentioning the potential role of biogeochemical processes as drivers of the observed C-Q patterns (this might fit well in the paragraph starting on L56 or after). For example, seasonality of microbial processes might influence soil nitrate concentrations and affect the observed patterns of C-Q especially during seasonal events (e.g., rain-on-snow). Similarly, one might expect the “C” side of the nitrate C-Q relationship to be strongly influenced by the timing of nitrogen fertilizer applications in agricultural catchments. In each of these two examples, the biogeochemical drivers exert as much (of not more) control on the C-Q relationship as the hydrologic drivers. Salli Thompson’s 2011 paper “Relative dominance of hydrologic versus biogeochemical factors on solute export across impact gradients” might be a useful paper to consider here.

Methods: If it is possible with your dataset to quantify the proportion of rising limb and falling limb samples, it would be good to include that quantitative information in the Methods section. If the proportions of the two are widely unbalanced, then the potential influence of that sampling bias on your results should be discussed in the Discussion. If it is not possible to determine the rising- or falling-limb status of samples in your dataset, then a brief acknowledgement of the implications of this should still be included in the Methods.

L134: What is meant here by “precipitation attribution”? Does this mean precipitation classification as rain or snow? Otherwise, I’m not sure what precipitation would be attributed to.

L243-247: For these correlation analyses, how did you account for the potential interaction between catchment topographic characteristics and land use? For example, one would expect at least some of the flatter catchments to also be used for agriculture (indeed, Figure S4 seems to indicate this). Thus, a simple correlation between median catchment slope and nitrate C-Q response is not straightforward if it does not somehow control for potential biases due to land use effects on nitrate availability.

L253: “Instead, we observed strong...”? It seems like a word is missing here…

L264-266: It would provide useful context here to also provide the ranges around these median values, not only the medians themselves.

L276-296: These two paragraphs are basically invoking the same hydrologic driver for the observed C-Q patterns: catchment wetness status associated with a given event type. But catchment wetness also changes during events, and this is where the need to consider potential hysteresis effects / sampling biases becomes important. I am not sure where a discussion of this issue fits in best in the Discussion section, but it should be included somewhere.

L358: The word “wetness” is not needed here.

L373: “… controls of the variability of C-Q …” I think another “of” needs to be added here.

L414: The word “prompt” does not make sense here. I’m not sure what you’re trying to convey with that word, but “prompt” doesn’t work. Do you mean “prone”?

L432: Do you mean “increase” instead of “increment”?

L457-458: I generally agree, but it’s also important to consider that the Δres50 metric uses INDIVIDUAL grab sample deviations from the long-term C-Q pattern, whereas event-scale metrics like runoff coefficient integrate hydrologic conditions across an entire event. So accounting for potential biases due to the timing of sample collection and hysteresis become important to consider.

L464: Again, I’m not sure what you mean by “prompt” here.

Citation: https://doi.org/10.5194/egusphere-2022-205-RC1
- AC1: 'Reply on RC1', Felipe Saavedra, 18 Jul 2022
  
  We thank the reviewer for a positive evaluation of our manuscript and a comprehensive review. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/egusphere-2022-205-AC1
- AC3: 'Reply on RC1', Felipe Saavedra, 18 Jul 2022
  
  We thank the reviewer for a positive evaluation of our manuscript and a comprehensive review. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/egusphere-2022-205-AC3
RC2:
'Comment on egusphere-2022-205', Anonymous Referee #2, 15 Jun 2022

Disentangling Scatter in Long-Term Concentration-Discharge Relationships: the Role of Event Types

The paper aims to provide explanation for deviations from long-term C-Q behaviour for different types of hydrological conditions. The authors claim that they are first in doing so, but the only novel thing in this study is a large number of catchments that are investigated. The discussion and implications are pretty much the same as in other studies by the research team, highlighting the incremental character of this study. Thus, to grant the publication of this paper, the authors need to convince the readers about novelty of their work, in light of recent publications in this field.

I understand that the authors want to show off the contributions from their own team, but there are plenty other papers, not published by your group, that you could refer to in your discussion.

Specific comments

Line 16 grammar

Line 16 how about Winter et al? This topic seems to have been already covered by your colleagues, so what is the novel aspect of this study? There have been also other paper studying how different storm event response contribute to scatter in C-Q data making this statement untrue, please update the list of previous studies on the topic in the introduction

Line 22 ‘indicating low nitrate concentrations’ – this does not make sense

It is not clear if you analyse high-frequency or low-frequency C-Q data, this should be clarified at the very beginning of the paper. Without this information it is difficult to judge the quality of your hypotheses.

Figure 1 should be part of methods or results but not introduction

Hypothesis 1 is not clear. Do you mean individual C-Q points?

Not clear how daily discharge data can provide information about short storm events with duration of hours?

In this sense, using a term ‘event classification’ is misleading. I would rather use classification of ‘hydrological conditions’.

Since you have low-frequency samples they are sampled randomly over the hydrograph. So samples that belong to the same hydrological condition can have been sampled on a rising, falling limb of the hydrograph or baseflow conditions. Thus, some of your scatter in each hydrological condition group can be attributed to when on the hydrograph your samples were taken. Please clarify.

I have just noticed that Reviewer 1 expressed similar concerns regarding the role of C-Q hysteresis. This is a key weakness of your approach.

Citation: https://doi.org/10.5194/egusphere-2022-205-RC2
- AC2: 'Reply on RC2', Felipe Saavedra, 18 Jul 2022
  
  We thank the reviewer for a comprehensive review. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/egusphere-2022-205-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-205', Anonymous Referee #1, 03 Jun 2022

General Comments:

This study presents a unique, spatially and temporally extensive dataset of nitrate C-Q relationships across 184 German catchments of varying size and land cover/land use from 2000-2015. The authors found that the degree of catchment hydrologic connectivity (and the closely related factor of runoff event type) strongly regulates the pattern of catchment nitrate export. Divergence of event-scale nitrate C-Q responses from the more generalized long-term response were attributed to a combination of catchment topographic properties and event type. The study dataset is impressive, providing a long-term view of catchment nitrate responses across gradients of event type, topography, and land use. The paper is very well written, making it both easy and enjoyable to read; there are only a few places in the manuscript where grammatical clarifications are needed. The statistical analyses presented in the paper are well-presented and wholly appropriate for the research questions that are being asked. Overall, this paper represents a meaningful addition to the existing C-Q literature and I recommend it for publication with only a few minor revisions. My main concern with the paper is that the potential influence of C-Q hysteresis on the observed patterns is not addressed anywhere in the paper. For example, if a particular catchment (or a particular event type) is characterized by a strong hysteresis signal, then the observed C-Q pattern (i.e., dilution or enrichment) would be highly influenced by the timing of sample collection. If a strong sampling bias exists where samples are more frequently collected on the rising limb relative to the falling limb (or vice versa), then the observed catchment or event C-Q signal might be confounded by the presence of hysteresis processes. I would not expect this to be an issue for the long-term C-Q pattern, but it may be an issue for the event-scale patterns and this would, in turn, cause a problem for the interpretation of the “Δres50” term presented in the paper. Given the low frequency of sample collection in this dataset (biweekly to monthly), it seems likely that a particular runoff event would be represented in the dataset by a sample collected either on the rising limb OR the falling limb but not both. It would be fascinating to see an additional analysis of this extensive dataset that incorporates the potential influence of sample timing on the hydrograph, but this is likely beyond the scope of this paper in its current form. However, I do think the authors need to include at least some discussion of the potential influence of this potential “hysteresis effect bias” associated with low-frequency event sampling.

Specific Comments:

Introduction: Very well-written and cited, providing a concise but informative review of the relevant C-Q literature. However, the Introduction focuses heavily (almost exclusively) on the hydrologic drivers of observed C-Q patterns, with little mention of the role of biogeochemical drivers. Particularly in the case of nitrate, biogeochemical drivers—emphasizing the “bio” aspect-- can also influence C-Q patterns. Because this paper focuses solely on nitrate concentrations, I think it is worth mentioning the potential role of biogeochemical processes as drivers of the observed C-Q patterns (this might fit well in the paragraph starting on L56 or after). For example, seasonality of microbial processes might influence soil nitrate concentrations and affect the observed patterns of C-Q especially during seasonal events (e.g., rain-on-snow). Similarly, one might expect the “C” side of the nitrate C-Q relationship to be strongly influenced by the timing of nitrogen fertilizer applications in agricultural catchments. In each of these two examples, the biogeochemical drivers exert as much (of not more) control on the C-Q relationship as the hydrologic drivers. Salli Thompson’s 2011 paper “Relative dominance of hydrologic versus biogeochemical factors on solute export across impact gradients” might be a useful paper to consider here.

Methods: If it is possible with your dataset to quantify the proportion of rising limb and falling limb samples, it would be good to include that quantitative information in the Methods section. If the proportions of the two are widely unbalanced, then the potential influence of that sampling bias on your results should be discussed in the Discussion. If it is not possible to determine the rising- or falling-limb status of samples in your dataset, then a brief acknowledgement of the implications of this should still be included in the Methods.

L134: What is meant here by “precipitation attribution”? Does this mean precipitation classification as rain or snow? Otherwise, I’m not sure what precipitation would be attributed to.

L243-247: For these correlation analyses, how did you account for the potential interaction between catchment topographic characteristics and land use? For example, one would expect at least some of the flatter catchments to also be used for agriculture (indeed, Figure S4 seems to indicate this). Thus, a simple correlation between median catchment slope and nitrate C-Q response is not straightforward if it does not somehow control for potential biases due to land use effects on nitrate availability.

L253: “Instead, we observed strong...”? It seems like a word is missing here…

L264-266: It would provide useful context here to also provide the ranges around these median values, not only the medians themselves.

L276-296: These two paragraphs are basically invoking the same hydrologic driver for the observed C-Q patterns: catchment wetness status associated with a given event type. But catchment wetness also changes during events, and this is where the need to consider potential hysteresis effects / sampling biases becomes important. I am not sure where a discussion of this issue fits in best in the Discussion section, but it should be included somewhere.

L358: The word “wetness” is not needed here.

L373: “… controls of the variability of C-Q …” I think another “of” needs to be added here.

L414: The word “prompt” does not make sense here. I’m not sure what you’re trying to convey with that word, but “prompt” doesn’t work. Do you mean “prone”?

L432: Do you mean “increase” instead of “increment”?

L457-458: I generally agree, but it’s also important to consider that the Δres50 metric uses INDIVIDUAL grab sample deviations from the long-term C-Q pattern, whereas event-scale metrics like runoff coefficient integrate hydrologic conditions across an entire event. So accounting for potential biases due to the timing of sample collection and hysteresis become important to consider.

L464: Again, I’m not sure what you mean by “prompt” here.

Citation: https://doi.org/10.5194/egusphere-2022-205-RC1
- AC1: 'Reply on RC1', Felipe Saavedra, 18 Jul 2022
  
  We thank the reviewer for a positive evaluation of our manuscript and a comprehensive review. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/egusphere-2022-205-AC1
- AC3: 'Reply on RC1', Felipe Saavedra, 18 Jul 2022
  
  We thank the reviewer for a positive evaluation of our manuscript and a comprehensive review. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/egusphere-2022-205-AC3
RC2:
'Comment on egusphere-2022-205', Anonymous Referee #2, 15 Jun 2022

Disentangling Scatter in Long-Term Concentration-Discharge Relationships: the Role of Event Types

The paper aims to provide explanation for deviations from long-term C-Q behaviour for different types of hydrological conditions. The authors claim that they are first in doing so, but the only novel thing in this study is a large number of catchments that are investigated. The discussion and implications are pretty much the same as in other studies by the research team, highlighting the incremental character of this study. Thus, to grant the publication of this paper, the authors need to convince the readers about novelty of their work, in light of recent publications in this field.

I understand that the authors want to show off the contributions from their own team, but there are plenty other papers, not published by your group, that you could refer to in your discussion.

Specific comments

Line 16 grammar

Line 16 how about Winter et al? This topic seems to have been already covered by your colleagues, so what is the novel aspect of this study? There have been also other paper studying how different storm event response contribute to scatter in C-Q data making this statement untrue, please update the list of previous studies on the topic in the introduction

Line 22 ‘indicating low nitrate concentrations’ – this does not make sense

It is not clear if you analyse high-frequency or low-frequency C-Q data, this should be clarified at the very beginning of the paper. Without this information it is difficult to judge the quality of your hypotheses.

Figure 1 should be part of methods or results but not introduction

Hypothesis 1 is not clear. Do you mean individual C-Q points?

Not clear how daily discharge data can provide information about short storm events with duration of hours?

In this sense, using a term ‘event classification’ is misleading. I would rather use classification of ‘hydrological conditions’.

Since you have low-frequency samples they are sampled randomly over the hydrograph. So samples that belong to the same hydrological condition can have been sampled on a rising, falling limb of the hydrograph or baseflow conditions. Thus, some of your scatter in each hydrological condition group can be attributed to when on the hydrograph your samples were taken. Please clarify.

I have just noticed that Reviewer 1 expressed similar concerns regarding the role of C-Q hysteresis. This is a key weakness of your approach.

Citation: https://doi.org/10.5194/egusphere-2022-205-RC2
- AC2: 'Reply on RC2', Felipe Saavedra, 18 Jul 2022
  
  We thank the reviewer for a comprehensive review. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/egusphere-2022-205-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Publish subject to revisions (further review by editor and referees) (27 Jul 2022) by Genevieve Ali

AR by Felipe Saavedra on behalf of the Authors (02 Aug 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (09 Aug 2022) by Genevieve Ali

RR by Anonymous Referee #1 (06 Sep 2022)

RR by Camille Minaudo (21 Sep 2022)

Suggestions for revision or reasons for rejection

------------------ General comment ------------------
Saavedra et al. provide a comprehensive study on nitrate concentration-discharge relationships. With an original methodology, they successfully identify key relationships between C-Q patterns and hydrological event types. The approach is sound, the results and interpretations are well conducted and interesting for the readership of HESS. I have raised some relatively minor issues, which must be properly addressed prior to publication.

------------------ Major comments ------------------
L139: could you please represent these 4 regions on the map (Fig 2a)? For someone outside Germany, this could be useful to better understand the interpretation from Fig 5a.
L177: “neutral (b~0) indicates a weak dependency of C and Q.”. This could also be because the long-term CQ relationship is the combination of different opposing events or seasonal patterns vs storm events: for instance, at the seasonal scale there is a dilution pattern, which combines with enrichment during storm events. Since the entire study here has for objective to disentangle the scatter in C-Q plots, we expect from the authors to carefully chose their words in this particular case. Please, consider rephrasing.
L187-197: this methodological approach is absolutely key for the robustness of the entire study, because a different choice (for instance compute deviation from CQ relationship from non-event observations) would likely lead to different outcomes. It is unclear to me where to find the results from this important bootstrapping analysis. Please make the result from this analysis much clearer as the reader shouldn’t have any doubt about your methodological choices.
L312: “Event runoff coefficients exhibit a larger variability across event types than across catchments for most of the catchments.”. Please place this sentence after the following one “Catchment median event runoff coefficients exhibit a coefficient of variation of 41% across catchments. Nevertheless, median runoff coefficients of event types exhibit coefficients of variation in different catchments from 12% to 118%, with a median value of 67% across catchment” as it is very hard to get as it is now.

L344-346: “Lower nitrate concentration during runoff events with dry antecedent conditions can be explained by low pre-event conditions linked to hydrological and biogeochemical drivers in addition to possible dilution during runoff events.”. What are exactly these “hydrological and biogeochemical drivers” being mentioned? Please be specific and name directly these processes.

L353: “due to a more efficient removal”. Please explain the driver behind, because a more efficient removal necessarily removes more nitrate! Please be more specific with the name of the processes behind (e.g. denitrification, biological uptake, … etc).

L378-379: “Most of nitrate samples during no event conditions coincide with low rates of discharge (Fig. 4a) as well as Rainfall events with dry antecedent conditions (i.e., Rain.dry.patchy and Rain.dry.uniform).”. How could a sample taken under no-event condition coincide with some rainfall events? Please clarify or revise.

L439-440: “We acknowledge that catchment characteristics might be highly correlated (Fig. S4).”. It is indeed needed to do so, but this sentence seems lost in the paragraph, although one can understand with the end of the paragraph what was meant. Please revise this sentence, because the reader should not have to read the end of the paragraph to understand the meaning of the sentences found mid-paragraph.

------------------ Minor comments ------------------
L29: I’m not native English speaker, but are you sure “portend” is appropriate in this sentence?
L30: “particularly of nitrate”. I agree excessive nitrate concentrations are partly responsible, but please, temper this statement as P is in general the main driver for eutrophication in freshwater- ecosystems.
L55: “The time of fertilizer”. Consider “timing” instead of “time”
L57: “On the other hand,” Please remove
L83: “C-Q relationships are more positive due to the accumulation in soil during dry periods of nitrate from atmospheric deposition”. Consider rephrasing to “C-Q relationships are more positive due to the accumulation of nitrate in the soil during dry periods by atmospheric deposition”
L84: “Eurpe,” Typo. Please correct
L167: “This implies that only events longer than 1 day are captured.”. Can we reliably detect a hydrological event of 2 days with daily data? Is there a minimum number of daily observations needed to detect an “event”?

L248: Please insert a “the” in between “considerable scatter in” and “regressions”

L276: Please guide the reader, this paragraph is a bit lost in between two bigger paragraphs and the reader needs to read the end of it to understand what it is about (i.e. the timing of sampling during rising/falling limb of the hydrograph).
L277: “Fig. S6b” Please sort the figures numbering from SI accordingly to their appearance in the text.
L278-281: this sentence is very long and difficult to read. Please simplify to help the reader.
L285-286: maybe give numbers to make it clearer?
L295: “however the last feature shows” which feature is being mentioned? Please be more specific.
L308: “A nitrate surplus is strongly related only to Rain.wet residuals.” Please revise to “Nitrate surplus is significantly related only to Rain.wet residuals.”

L338: “movilized”. Please revise

L339: “In adition, the movilized water during this events is less affected by biogeochemical processes due to lower microbial activity induced by low temperature during snow-impacted events.”. Could you add at least 1 reference to support this statement?

L341-374: could you make this a single paragraph? Splitting the discussion in so many short paragraphs makes it difficult to follow.
L355-356: “Moreover, during runoff events with dry antecedent conditions nitrate concentrations can decrease below pre-event concentration level.”. Isn’t it redundant with previous statements in previous paragraph?

L449-454: these sentences are a repetition of the lines 439-444. Please delete and make sure this paragraph still makes sense.

L481: “a substantial decline in seasonal snowpack accumulation and earlier snowmelt onset in Central Europe”. Yes but wouldn’t the frequency of rain on snow events increase? Please make sure of this aspect, which would completely change the interpretation of the outcome paragraph.

L519-520: “Moreover, we inferred using catchment descriptors physical mechanisms that explain the spatial variability of this scatter.”. Please reorder or revise, something is missing or misplaced in this sentence.

Hide

ED: Publish subject to minor revisions (review by editor) (04 Oct 2022) by Genevieve Ali

AR by Felipe Saavedra on behalf of the Authors (13 Oct 2022) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (31 Oct 2022) by Genevieve Ali

AR by Felipe Saavedra on behalf of the Authors (08 Nov 2022) Manuscript

Journal article(s) based on this preprint

12 Dec 2022

Disentangling scatter in long-term concentration–discharge relationships: the role of event types

Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova

Hydrol. Earth Syst. Sci., 26, 6227–6245, https://doi.org/10.5194/hess-26-6227-2022,https://doi.org/10.5194/hess-26-6227-2022, 2022

Short summary

Felipe Saavedra et al.

Supplement

https://doi.org/10.5194/egusphere-2022-205-supplement

Data sets

WQQDB - water quality and quantity data base Germany Andreas Musolff https://doi.org/10.4211/hs.a42addcbd59a466a9aa56472dfef8721

CCDB - catchment characteristics data base Germany Ebeling, P., R. Kumar, A. Musolff https://doi.org/10.4211/hs.82f8094dd61e449a826afdef820a2c19

Model code and software

Classified runoff events Tarasova L., Basso, S., Wendi, D., Viglione, A., Kumar, R., and R. Merz (2020) https://zenodo.org/record/3575024

Felipe Saavedra et al.

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Nitrate contamination of rivers from agricultural sources is a challenging problem for water quality management. During runoff events of different generation processes, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using data for 184 German catchments we demonstrate that wetness conditions during runoff events at the time of sampling are a key control of nitrate transport from catchments to streams.


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