the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 May 2024
Investigation of Different Roughness Approaches and Vegetation Height Effects on rain-induced overland flow
Abstract. Overland flow is a critical aspect of the hydrological cycle, and understanding its dynamics is crucial for managing water-related issues such as flooding and soil erosion. This paper investigates the impact of various roughness estimation methods on simulating overland flow during intense rain events, with a specific focus on the influence of vegetation. The study assesses various approaches to vary roughness as a function of water sheet thickness and vegetation height, including two different constant Manning's coefficients, a linear approach, an exponential function, a power law function, an empirical formula, and a physics-based approach. The investigation emphasizes the importance of accurate roughness estimation for improving the reliability of hydrological models and enhancing flood prediction capabilities. Experimental data from artificial rainfall experiments on 22 different natural hillslopes in Germany are used to calibrate the OpenLISEM hydrological model, adjusting parameters such as saturated hydraulic conductivity and soil suction at the wetting front. Subsequently, various Manning's coefficient estimation methods are applied, and the model's performance is evaluated numerically.
Preliminary results indicate satisfactory calibration outcomes, with NSE values ranging from 0.75 to 0.95 in most cases for various sites. To validate the models, 100 different experimental rainfall events are used for each roughness method. Validation findings suggest that the physics-based approach, the linear function, and constant Manning roughness, demonstrate the best performance based on NSE values. According to our results, areas with more vegetation coverage demonstrate higher saturated hydraulic conductivity value, indicating that, for two sites with the same soil type, the locations with dense vegetation exhibit higher infiltration parameters. Consequently, it is crucial to evaluate the influence of vegetation on runoff, considering not only its effects on Manning's coefficient but also on saturated hydraulic conductivity.
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RC1: 'Comment on egusphere-2024-1276', Anonymous Referee #1, 22 Oct 2024
This manuscript compares performance of several overland flow models of previously published experimental data. The manuscript suffers from poor organization, and it is difficult to sift through details to get the broad picture, and, inversely, to find details that are sprinkled throughout. The focus on detailed reporting of statistical fit parameters throughout limits the utility of the manuscript because it comes at the expense of clarity in the reasons for the performances. The introduction does not separate detailed thoughts from main ideas, and it even contains methodological choices mixed in with the background. Two of the three objectives listed in bullet points too broad to be tested. The discussion is written mostly from the perspective of which models perform better practically, not on what modeling concepts best match the physical processes they are intended to mirror. This perspective is especially limiting given that the paper is focused mainly on one set of experimental results—no matter how good those experiments are. The conclusions oddly emphasize the importance of modeling infiltration, rather than on roughness as emphasized in the introduction.
Detailed comments
L28 citation please for constant velocity profile with emergent vegetation
L58 citation please for OpenLISEM. The Jetten (2002) ref is for LISEM. Maybe that’s being picky, but it’s important to be precise about the theory and methods.
References to the theoretical origins of LISEM would be much better. The model predates 2002. Also Jetten 2002 is difficult to obtain.
L100 the first two sentences are unrelated to Methods
L143 assumed by who and for what purposes?
L153 impact on what?
L159-165 are not study site.
Fig 3 is the overland flow mm/min? It would be better if the rain and flow were the same units, but at least the time basis for the flow must be specified.
The naming of the experiments is difficult to access. Why not use more intuitive names instead of number codes?
Percent bias is not defined anywhere. It is also inconsistently named, e.g., as “percentage of bias,” “bias percentage,” and “pBias.”
Saxton and Willey incomplete reference
L225 this paragraph is an example of how poor organization makes it difficult to follow this manuscript. In six sentences, there is a note on anomalously low NSE at one site, a comparison of runoff and rainfall rates at one site, a discussion of how runoff quantity affects model fits, a description of a figure that duplicates figure captions, discussion of differences in Ksat obtained by various methods, and a note that best-fit soil moisture at two sites was different than elsewhere. There is no way to assimilate this information to form a comprehensive picture, and there is no hope of later referring to these disorganized facts.
The results are wordy, overdetailed, and repetitive. For example, most of p 12 is not needed: Table 4 is not needed, L275-280 not needed. L285-288 not needed. Most instances of the word “value” are not needed.
Fig 8 and 9 lack Y axis labels and their meaning is obscure unless the reader remembers how many models there are. Overall, these figures are of low value.
Fig 10 why are some cells green?
L321 if these sites should not be compared to the others, they should not be presented in a figure comparing the sites.
Discussion on the importance of antecedent conditions ought not lead with a citation to a catchment modeling text. Catchment responses are not the subject of this work.
The frequent citation of Feldman et al. (2023) indicates insufficient original content in this manuscipt.
The discussion is just as disorganized as the rest of the manuscript. For example, section 5.3 on vegetation coverage is mainly focused on Ksat and infiltration. I thought this paper was about roughness?
Citation: https://doi.org/10.5194/egusphere-2024-1276-RC1 -
AC1: 'Reply on RC1', Azam Masoodi, 06 Nov 2024
We appreciate the reviewer’s insightful and constructive feedback and intend to revise the paper accordingly. The misalignment between the introduction and conclusion may have resulted from an evolving focus in our study. Initially, our aim was to evaluate how different roughness estimation methods affect the accuracy of model predictions when compared to physical measurements. However, our results revealed that, in the presence of vegetation, saturated hydraulic conductivity plays a critical role that cannot be ignored. This insight led us to emphasize infiltration parameters in the conclusions, which may have created some confusion for readers.
To address this, we will revwrite the abstract, introduction, and discussion to clarify the role of infiltration in our study and refine the stated objectives to provide a clearer framework for readers.
Additionally, we compared our results with a related study by Feldman et al. (2023), as both studies share the same experimental data set and a similar objective but use different approaches. Feldman et al. extrapolated a nearly constant infiltration rate for the falling limb of the hydrograph, which allowed them to separate the impact of roughness on hydrograph shape from infiltration effects. Our approach, however, considers infiltration across the entire hydrograph. To highlight the differences between these approaches and the importance of considering infiltration effects, we referenced to this paper frequently. However, we did not emphasize the relation to the paper enough in the introduction, which will be changed in a revised version.
Below are our responses to the detailed comments, along with our plan to improve the manuscript’s clarity and organization.
Detailed Comments
Comment L28: The sentence has been modified for better clarity, and a relevant citation has been added at this point in the text.
Comment L58: OpenLISEM is the open-source version of LISEM. We explained about it in L58 and updated the citation in L60 and 63.
Comment L100: these sentences were deleted.
Comment L143: This text “Nevertheless, they do not provide information on the ℎ₀ value. Consequently, this parameter is assumed to be 5 times the plant height for each experimental site” was changed to “As Feldmann et al., (2023) did not provide information on the ℎ₀ value, we assumed ℎ₀ to be five times the plant height to apply the Kadlec’s method in our study.”
Comment L153: “on overland flow” was inserted to the text.
Comment L159-165: We changed the title of this section to “Study site and experimental setup”
Comment on Figure 3: The picture was modified.
Comment on Experiment Naming: A table describing each experiment was added in Section 2.3 for easier access.
Comment on Percentage of Bias: It was modified in the manuscript.
Comment on Saxton and Willey Reference: The reference was completed.
Comment on Figures 8 and 9: These figures are modified.
Comment on Figure 10: An explanation for the green cells is added below the figure.
Comment on L321: We removed these sites from the Figure. 11
Comment on antecedent conditions: We have modified the discussion on antecedent conditions and removed the citations related to catchment modeling.
Citation: https://doi.org/10.5194/egusphere-2024-1276-AC1
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AC1: 'Reply on RC1', Azam Masoodi, 06 Nov 2024
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RC2: 'Comment on egusphere-2024-1276', Anonymous Referee #2, 22 Nov 2024
Especially in light of the increase in heavy rainfall events with enormous damage potential and the associated great efforts of the federal states to create high-resolution heavy rainfall hazard maps, the further improvement of hydrological and hydraulic modeling is of great importance. Particularly with regard to the quantification resp. modeling of the influence of different vegetation conditions on runoff formation and flow parameters of overland flow, I also see an urgent need for research. In this respect, the authors’ commitment is very welcome, and the results can provide valuable input both from a scientific perspective and for practical application. In the publication of the research results, I still see potential for improvement overall, in order to present the findings more transparently and comprehensibly for the professional community.
Before I list my specific comments and questions in detail, please allow me to address some general critical-constructive comments:
The title raised my expectation of an examination of the physical (and particularly the hydraulic) processes of surface runoff. However, the methodological focus is exclusively on data-driven analyses with little to no engagement with the actual processes. The influence of vegetation height as an input parameter is also only evaluated in a rudimentary or purely model-based manner. Therefore, a title such as “Investigation of Different Roughness Approaches in Hydrological Run-off Modelling” would be more appropriate.
Regardless of the chosen title, I would have fundamentally wished for a deeper engagement with the physical phenomena and their modeling representation when evaluating the predictive capability of the investigated model approaches. This is especially relevant in terms of comparing the simulated and measured hydrographs, where differences between model and measurement set-up could also influence the results. The possible influence of the microscale surface structure, which together with the vegetation determines the flow resistance (friction and drag), should also be mentioned more visibly.
The presentation of the literature review on vegetation hydraulics in the introduction does not differentiate between studies on channel/river hydraulics and those on overland flow (thin-layer surface runoff) due to heavy rainfall. However, the boundary conditions of the mostly highly empirical studies are completely different in terms of slope, flow depths, vegetation situations, and thus only allow limited mutual conclusions.
Since the present contribution exclusively uses field experiments with very low flow depths of a few centimeters for the validation of the approaches, and there is (presumably) hardly any significant submergence of the vegetation, the presentation of the modeling approaches and third-party research results could perhaps be limited to the investigated ranges to avoid misunderstandings (in the sense of “less is more”).
I am somewhat uncertain to what extent the NSA value is sufficient as a key parameter for evaluating the model quality. Especially when it is balanced over the entire hydrograph, the advantages and disadvantages of the methods and, above all, the reasons for deviations are difficult or impossible to understand. Therefore, the red thread for comparative evaluation and the conclusions were only partially comprehensible to me. More comparative illustrations as shown in Figure 3 and a detailed process-related discussion of agreements and deviations might be helpful.
Line 1: Titel misleading (see comments above)
Lines 19-21: Is this conclusion a new insight (sounds like) or just the confirmation of an expected dependency? Is it really the result of your model applications or rather of the measured data on the plot (Accounting inflow/outflow on field plot)? Are there possible other effects (e.g. increasing pressure due to higher waterlevels) or model-wise dependencies which have an impact on infiltration?
Introduction: Many sources refer to studies of river hydraulics (not overland flow) with other boundary conditions (e.g. slope, water depth). This should be highlighted or even reduced on relevant approaches (see comments above). A compact overview can be found in DWA-M 524.
Line 35: Oberle et al. (2021) presents a comprehensive literature study on hydraulic flow resistance of overland flow. In addition, a recommendation for depth-dependent roughness values is derived from different laboratory experiments with artificial grass (Yörük, Karantounias, Ruiz Rodriguez).
Line 42: “approximated” instead of “effectively characterized”.
Line 70: This was not the main focus of the paper. Investigation of vegetation effects… reference to modelling is missing.
Chapter 2: General it would be easier to read if the model and the study site would be explained one after the other. Like e.g.: Presentation of Study site and its boundary conditions -> leads to this model -> leads to different approaches which have been tested -> leads to sequence of modelling.
Or the other way around: These are approaches from literature which we want to examine. For that we build this model based on this study site…
Chapter 2.1: The model looks diagonally inclined although it seems that the study sites of Ries et al. are sloped orthogonal. If it´s like that the deviation of the model from the original (different specific discharge distributed over the plot) and impact on results needs to be discussed. Furthermore, the discharge of the model at the outlet is measured instant in contrast to the set-up from Ries where the discharge measurement is from my understanding 10-20 m (drainage tubes) after the plot. This should lead to distorted results later in the evaluation and needs to be discussed. Also, a rough overview of ranges of discharge, velocities and water depth on the plot should be given. Maybe some maximum values for a better understanding.
Line 85: Size 1x1. Noticeably coarse resolution in hydraulic. Have cell size sensitivities been checked?
Line 86: Possibly misleading. Micro depressions where the water is retained and infiltrating after a rain event are not accounted for in the roughness factor. Only water that can later drain away is considered as well as small geometrical variances (drag force) additionally to frictional resistance.
Fig. 1: A picture of the experimental setup of Ries et al. (2020) next to the model would be helpful for the reader.
Lines 93 – 96: The model-related influence of Manning on infiltration rate should be discussed. How are the parameters related to each other in the model?
Line 105: Resistance factors for river hydraulics (different slope, water depth). Needs to be mentioned.
Line 110: Feldmann's approach/procedure should be explained in more detail, as comparison to him is a bigger part in the paper.
Table 1. Site 19. n Chow. Format not in line with others.
Line 115: Oberle et al. (2021) recommends a roughness spectrum based on experimental experiments to simulate overland flow in 2D hydrodynamic models. Investigations had been done by different authors.
Eq. 1: Should be 1 / nmanning = … ?
Line 121: Are higher flow depths in the model even achieved or is the linear method also a “constant method”, since only flow depths smaller than the vegetation height occur?
Line 123: Strickler k -> kStr
Line 126: It´s not clear whether the actual vegetation footage from Reis is considered using these equations. If it´s like that more information is needed how it´s done and what blockage factors have been calculated. What value was used for Cd?
Line 136: Explanation c and d. One sentence how Feldmann calculated them and what is the value from them.
Line 145: Oberle at al. (2021) did not confirm factor 5. As no vegetation height was considered in the study at that time. Short side note: New measurement results based on experiments on a natural plot (grassland, different vegetation conditions) will soon be published (see also Oberle et al. 2024 Dresden Wasserbaukolloqium).
Line 147: plant basal cover can be much lower than canopy cover depending on vegetation type. It´s to be expected that plant coverage of Ries is canopy cover? Needs to be addressed that there are uncertainties.
Chapter 2.3: Also, if possible a range of expected velocities, water depth or at least the measured specific discharge in the experiments would add benefit to the readers. Would it make sense to separate the sites into the ones with arable land (with and without vegetation) and grassland? Maybe you find a trend by looking at them isolated rather than all mixed up.
Chapter 3: How does the start value of Manning effects the calibration of ksat and Psi? Have the final ksat and psi been checked for plausibility? In addition to the NSE, an isolated consideration of the specific characteristics of the hydrograph seems beneficial. (see comments above)
Fig. 3: unit? Also, more of these graphs would give greater insight. Moreover, differences in rising and falling limb could be influenced by differences between model and experimental set-up and should be discussed. Also, on the rising limb micro depressions could have an impact (smooth model in contrast to nature). Fundamental discussion (instead of only focusing on NSE & bias) against the background of the methodology used would be desirable.
Fig. 4 & 5: Systematic differences are noticeable. These should be discussed and evaluated. Site seems to have a greater influence on the parameters than the different methods.
Fig. 10: Please check if the unit is correct? Discharge for the whole plot or specific discharge? Why is 3 parameters so bad? Discussion should be more profound.
Fig. 11: Show also psi per site? See also comments to Lines 19-21
Line 352: As mentioned above: Are you sure that submerged vegetation has been investigated or are the water depths shallower?
Chapter 5.3: Site (soil type?) was more dominant than vegetation and should be put into relation.
Line 412: Is this new insight (see comments above)?
Line 416: e.g. RoGer-Modell?
Citation: https://doi.org/10.5194/egusphere-2024-1276-RC2 -
AC2: 'Reply on RC2', Azam Masoodi, 17 Dec 2024
We appreciate the reviewer’s insightful comments and have made efforts to address them comprehensively to improve the manuscript. What we will emphasize especially is, that we are using a model approach that can be used for upscaling. The OpenLISEM model as we are validating in this study can be used as is for the field and even the landscape scale. Regarding microdepressions, since they are a subscale phenomenon for the ultimate target scale, we incorporated their effects into the Manning's coefficient parameterization. This allowed us to model surface roughness using waterdepth-dependent or constant Manning coefficients, ensuring that all flow-affecting parameters were represented within the model's constraints. We will elaborate further on the limitations of this approach and its implications for the comparison between observed and simulated hydrographs. In the introduction, we will also clearly differentiate between river hydraulics studies and those relevant to overland flow, emphasizing the distinct boundary conditions and ensuring the literature review aligns with the study’s scope.
Given the low flow depths in our experiments, we acknowledge that submerged vegetation conditions were not observed. To avoid misunderstandings, we will limit the presentation of modeling approaches and results to those directly relevant to the ranges of flow depths and vegetation conditions in our study. Discussions of submerged vegetation or different boundary conditions will be excluded or noted as beyond the scope.
General Comments:
Title Suggestion:
We will change the title as proposed “Investigation of Different Roughness Approaches in Hydrological Flow Modelling through Vegetation” in order to more clarity.
Considering micro depression:
Since no DEM data was available, we incorporated the effects of micro depressions as part of the Manning's coefficient parameterization. The surface roughness is thus modeled using water-depth-dependent or constant Manning coefficients, ensuring that all flow-affecting parameters are represented as accurately as possible within the constraints of the model. We will elaborate further on the limitations of this approach and its implications for the comparison between modeled and measured hydrographs in the revised manuscript.
Literature differentiation:
We will revise the introduction to distinctly differentiate between river hydraulics studies and those relevant to overland flow. Also, we will explain about boundary conditions of different approaches.
Limiting data to investigate submerged vegetation:
We evaluated the model's results concerning water depth and acknowledge that the field experiments in our study focus on very low flow depths, with minimal or no significant submergence of vegetation. To prevent potential misunderstandings, we will restrict the presentation of modeling approaches and results to those directly relevant to the investigated ranges of flow depths and vegetation conditions. Discussions of submerged vegetation or scenarios involving significantly different boundary conditions will be either excluded or explicitly noted as beyond the scope of this study.
Comparative Illustrations:
Given the large number of models, it would not be practical to present all hydrographs in detail. Instead, we have provided a representative example in Figure 3 to illustrate the model's behavior and included results focusing on the falling limb of the hydrograph in Figure 10 to highlight specific aspects of model performance. To enhance clarity and address your concern, we will present additional hydrographs as supplemental material. So, we will discuss the differences between observed and simulated hydrographs in greater detail, highlighting the agreements, deviations, and potential reasons behind them.
Specific Comments
Line 1: The title will be changed.
Lines 19-21: Is this conclusion a new insight (sounds like) or just the confirmation of an expected dependency? Is it really the result of your model applications or rather of the measured data on the plot (Accounting inflow/outflow on field plot)?
Ans: As explained in the discussion, Ksat was estimated through the model's calibration process and subsequently used to evaluate the model’s ability to simulate hydrographs. This conclusion is based on our calibration results, which showed a clear relationship between dense vegetation and higher saturated hydraulic conductivity. While this finding aligns with the expected dependency between vegetation coverage and infiltration capacity, it provides a model-based confirmation of this relationship under the specific conditions of our field experiments. Thus, the result is both a validation of expected trends and a contribution to understanding the importance of incorporating Ksat variability in hydrological models.
Lines 19-21: Are there possible other effects (e.g. increasing pressure due to higher waterlevels) or model-wise dependencies which have an impact on infiltration?
Ans: It is important to acknowledge the inherent limitations of hydrological models, which may influence this conclusion. For instance, this model does not explicitly consider the effects of increased water pressure at higher water levels, which could also impact infiltration dynamics. We will address these limitations and their potential implications for the results in the revised discussion section.
Introduction: We will revise the introduction to focus specifically on studies relevant to overland flow, emphasizing the key differences between river hydraulics and overland flow in terms of boundary conditions, such as slope, water depth, and flow dynamics. This will ensure that the introduction aligns more closely with the scope of the research.
Line 35: The text will be changed the text to “Oberle et al. (2021) present a comprehensive literature review on the hydraulic flow resistance of overland flow. Furthermore, recommendations for depth-dependent roughness values are derived based on various laboratory experiments conducted with artificial grass (Karantounias, 1974; Yörük, 2020; Ruiz Rodriguez et al., 2017).
Line 42: We will use “approximated” in the text.
Line 70: The objective will be changed to:
- Modelling the overland flow to compare and validation of different approaches of Manning’s coefficient estimation.
- Evaluate the impact of incorporating vegetation density into the saturated hydraulic conductivity parameter in the model.
- Investigation of initial soil moisture conditions on the model performance.
Chapter 2: We will restructure Chapter 2 to first present the study site, followed by an explanation of the modeling approaches, and finally the sequence of modeling steps.
Chapter 2.1: The model looks diagonally inclined although it seems that the study sites of Ries et al. (2020) are sloped orthogonal. If it´s like that the deviation of the model from the original (different specific discharge distributed over the plot) and impact on results needs to be discussed.
Ans: We will address this in the paper with the following explanation: “It should be noted that the DEM prepared for the simulations is oriented diagonally, whereas the study sites had orthogonal slopes. When using the diagonal orientation, the rising limb of the hydrograph tends to exhibit lower values compared to the orthogonal case, while the diagonal orientation also produces a higher peak value. However, given the small size of the study area, this discrepancy is not significant. Recognizing this potential source of error, we have presented the model results accordingly.”
Chapter 2.1: Furthermore, the discharge of the model at the outlet is measured instant in contrast to the set-up from Ries where the discharge measurement is from my understanding 10-20 m (drainage tubes) after the plot. This should lead to distorted results later in the evaluation and needs to be discussed. Also, a rough overview of ranges of discharge, velocities and water depth on the plot should be given. Maybe some maximum values for a better understanding.
Ans: We believe that the data presented in their study are modified and that their time series do not reflect this discrepancy.
Line 85: Since the DEM which used in the model is flat and without micro depression the result is not sensitive to the cell size based on our brief sensitivity analyses.
Line 86: Unfortunately, Reis et al. (2020) did not provide any DEM of the study sites. As a result, we had to account for the micro-depressions as part of Manning’s coefficient. Usually, OpenLISEM is applied to larger areas, like catchment scale. Using a resolution which can represent micro depressions will not lead to scalable results for future applications. To limit the number of compared variables, we left a test of scale effects out intentionally and kept this condition consistent across all experiments and methods. We will emphasize the aim to validate roughness approaches that will be ultimately used on the field to landscape level.
Fig. 1: We will insert one picture of Reis et al., (2020) study.
Lines 93 – 96: The two parameters are not directly related, while the infiltration rate (Green-Ampt model) is subtracted at each timestep from the surface flow, the Manning roughness is only used for the surface flow. However, a higher infiltration rate reduces the surface water depth and the flow velocity slows down. To differentiate between the processes, we check for the water balance as a model performance indicator. We will include this interplay into the discussion.
Line 105: It will be mentioned as “Resistance factors significantly impact flow velocity, discharge, and energy loss. Bed resistance results from water-bed interactions, while slope influences the energy gradient driving the flow. Resistance decreases with an increased hydraulic radius, as deeper water encounters less friction compared to shallow water. Additional factors, such as temperature and viscosity, also contribute. Hydraulic equations, such as Manning’s equation, incorporate these factors to predict flow behavior under varying conditions. Although this equation has been developed for channel flow, most hydrological models, including OpenLISEM uses this equation also for surface runoff on upslope areas.”
Line 110: We will explain the relation of this study to the study of Feldmann et al., (2023) in more detail in the introduction. The key difference between the studies lies in the different handling of infiltration. We will add in the introduction: “The core idea of their study was to use the shape of the hydrograph to estimate surface roughness. They assumed that during the descending limb of the hydrograph, a nearly constant infiltration rate can be extrapolated using the fitted Horton function. This approach separates the effects of roughness from infiltration, enabling a clearer determination of roughness. To describe the infiltration process in artificial rainfall experiments, a Hortonian curve was applied to calculate the maximum possible infiltration rate by fitting the equation to the difference between rainfall and observed discharge. Each experiment produced a solution space representing several acceptable roughness functions, all with minimal differences in result quality. They reduced the solution space in three steps: exclusion of low roughness values, based on the rising hydrograph; comparison across experiments, to identify consistent patterns within the same site; and cross-site comparison, to further refine and validate the results across sites with similar properties. While this method helps to investigate the roughness effect alone, the result cannot be transferred to ungauged situations in the field to landscape scale”
Table 1: It will be edited.
Line 115: It will be modified as “Oberle et al. (2021) proposed a roughness spectrum based on experimental studies for simulating overland flow”
Eq.1: fixed.
Line 121: We checked the maximum water depth in our models and found that it is consistently less than the vegetation height. Therefore, we agree that under these conditions, the linear method behaves similarly to a "constant method." We will address this limitation explicitly in the discussion. This also explains why the three methods Chow, Linear, and Nepf produced similar results in our analysis.
Line 123: fixed.
Line 126: The blockage factor was determined based on the vegetation cover presented by Reis et al. (2021). For the drag coefficient (Cd), we assumed that the stem of vegetation is cylindrical in shape, and therefore a Cd value of 1 was used.
Line 136: This text was inserted to the paper: “The parameters c and d represent coefficients used to define the roughness function. All possible combinations of c and d within a "reasonable range" were systematically tested by Feldmann et al. (2023). By comparing the observed hydrograph with the simulated hydrograph generated using the tested c and d values, the combination that best replicates the behavior of the falling limb was identified by them.”
Line 145: We will modify in the text “Oberle et al. (2021) reviewed experimental datasets from various studies on artificial grass, and our assumption aligns with the range they presented. Furthermore, Hinsberger et al. (2022) cited studies by Augustijn et al. (2008) and Huthoff et al. (2007), which demonstrate that a high submergence ratio exceeds a value of 5.”
Line 147: We will explain about it: “Since the plant coverage reported by Reis et al. (2020) appears to represent canopy cover, uncertainties arise in the calculation of resistance coefficients. This is because the basal elements of the vegetation are not accounted for, which may result in an incomplete representation of flow resistance.”
Chapter 2.3: The velocity of flow and water depth were not measured in the experiments. However, we will insert the range of mean cumulative overland flow and volumetric soil moisture to the paper. One advantage of the experimental basis by Reis et al, is the broad range of different soils, vegetation types and regions. By isolating arable fields and grasslands, the pairing approach used in the experiments would be compromised, making it impossible to distinguish between vegetation and soil effects.
Chapter 3: How does the start value of Manning effects the calibration of ksat and Psi?
Ans: We calculated the Manning’s coefficient using the relevant equations and then calibrated the Ksat and Psi parameters for run 2. Later experiments are used for validation using the calibrated values for Ksat and Psi. We will revise l 170-176 to explain the calibration procedure in more clearly.
Chapter 3: Have the final ksat and psi been checked for plausibility?
Ans: Since direct measurements for Ksat and Psi are unavailable, verifying these parameters against actual values was not possible. However, the calibrated values fall within a reasonable range, as we will show in the result section by a comparison with the calibrated results with values derived from the soil properties using the SPAW software (Saxton and Willey, 2006), following the suggestion of the authors of OpenLISEM.
Chapter 3: In addition to the NSE, an isolated consideration of the specific characteristics of the hydrograph seems beneficial.
Ans: We do not want to discuss the result of a single experiment in too much depth, since 138 experiments are available. We will present additional hydrographs as supplemental material and analyze the three parts of the hydrograph (rising limb, plateau and falling limb) separately. We will present the results for the calibration as uncertainty bands in an additional figure.
Fig. 3: The unit will be modified. A representative example will be provided to show the complete hydrographs for one site, along with an explanation of the behavior of the hydrographs
Fig. 4 & 5: As mentioned above, the infiltration submodel and the surface flow submodel are separated. A strong influence of the selected roughness method on the parameters of the infiltration model would mean, that parameters are not as independent, as they should be. The infiltration is heavily depending on the soil properties and the existence of a macro pore regime which is also depending on vegetation. The vegetation and soil properties differ between sites. We will describe this result in course of the result section and discuss the meaning of the result in the discussion.
Fig. 10: Please check if the unit is correct? Discharge for the whole plot or specific discharge?
Ans: We checked the unit again and can confirm it correctness, as it shows the specific discharge. We will expand the figure caption accordingly
Fig. 10: Why is 3 parameters so bad?
Ans: The three parameters did not perform well because the Manning’s coefficient (n) was calculated as a calibrated parameter in the model. This indicates that calibrating both n and Ksat simultaneously is not an effective approach. Adding more calibration parameters can increase model complexity, but this does not always lead to better results. We will discuss this aspect in chapter 5.1 in the revised version.
Fig. 11: We will include Psi in a revised version of the figure.
Line 352: We will change the line to: “This concurs with previous studies advocating for roughness determination based on the relation vegetation height and water table depth.”
Chapter 5.3: The effects of soil type on infiltration have been previously demonstrated and are accounted for by Ksat in the equation. In this study, we aim to show that, even within the same location, Ksat values vary with different vegetation cover. We will clarify the role of soil type vs. vegetation in this chapter. However, in flood forecasting scenarios, Ksat is usually only derived from the soil type, but is also, as we show in this study heavily influenced by the vegetation. The vegetation changes the runoff behavior more by its effect on infiltration then on roughness.
Line 412: Actually, our results are a confirmation to highlight the importance of considering vegetation effects when estimating saturated hydraulic conductivity.
Line 416: We will add the following sentence: “Modular modelling frameworks with the ability to model space continuous surface runoff together with infiltration methods like CMF (Kraft et al., 2011) or potentially RoGER (Steinbrich et al., 2016) and SUMMA (Clark et al., 2015) can help to decouple the interwoven processes of friction and infiltration at the hillslope scale. OpenLISEM is one of the few established readymade models that includes detailed surface runoff, infiltration and erosion mechanisms.”
Citation: https://doi.org/10.5194/egusphere-2024-1276-AC2
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AC2: 'Reply on RC2', Azam Masoodi, 17 Dec 2024
Status: closed
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RC1: 'Comment on egusphere-2024-1276', Anonymous Referee #1, 22 Oct 2024
This manuscript compares performance of several overland flow models of previously published experimental data. The manuscript suffers from poor organization, and it is difficult to sift through details to get the broad picture, and, inversely, to find details that are sprinkled throughout. The focus on detailed reporting of statistical fit parameters throughout limits the utility of the manuscript because it comes at the expense of clarity in the reasons for the performances. The introduction does not separate detailed thoughts from main ideas, and it even contains methodological choices mixed in with the background. Two of the three objectives listed in bullet points too broad to be tested. The discussion is written mostly from the perspective of which models perform better practically, not on what modeling concepts best match the physical processes they are intended to mirror. This perspective is especially limiting given that the paper is focused mainly on one set of experimental results—no matter how good those experiments are. The conclusions oddly emphasize the importance of modeling infiltration, rather than on roughness as emphasized in the introduction.
Detailed comments
L28 citation please for constant velocity profile with emergent vegetation
L58 citation please for OpenLISEM. The Jetten (2002) ref is for LISEM. Maybe that’s being picky, but it’s important to be precise about the theory and methods.
References to the theoretical origins of LISEM would be much better. The model predates 2002. Also Jetten 2002 is difficult to obtain.
L100 the first two sentences are unrelated to Methods
L143 assumed by who and for what purposes?
L153 impact on what?
L159-165 are not study site.
Fig 3 is the overland flow mm/min? It would be better if the rain and flow were the same units, but at least the time basis for the flow must be specified.
The naming of the experiments is difficult to access. Why not use more intuitive names instead of number codes?
Percent bias is not defined anywhere. It is also inconsistently named, e.g., as “percentage of bias,” “bias percentage,” and “pBias.”
Saxton and Willey incomplete reference
L225 this paragraph is an example of how poor organization makes it difficult to follow this manuscript. In six sentences, there is a note on anomalously low NSE at one site, a comparison of runoff and rainfall rates at one site, a discussion of how runoff quantity affects model fits, a description of a figure that duplicates figure captions, discussion of differences in Ksat obtained by various methods, and a note that best-fit soil moisture at two sites was different than elsewhere. There is no way to assimilate this information to form a comprehensive picture, and there is no hope of later referring to these disorganized facts.
The results are wordy, overdetailed, and repetitive. For example, most of p 12 is not needed: Table 4 is not needed, L275-280 not needed. L285-288 not needed. Most instances of the word “value” are not needed.
Fig 8 and 9 lack Y axis labels and their meaning is obscure unless the reader remembers how many models there are. Overall, these figures are of low value.
Fig 10 why are some cells green?
L321 if these sites should not be compared to the others, they should not be presented in a figure comparing the sites.
Discussion on the importance of antecedent conditions ought not lead with a citation to a catchment modeling text. Catchment responses are not the subject of this work.
The frequent citation of Feldman et al. (2023) indicates insufficient original content in this manuscipt.
The discussion is just as disorganized as the rest of the manuscript. For example, section 5.3 on vegetation coverage is mainly focused on Ksat and infiltration. I thought this paper was about roughness?
Citation: https://doi.org/10.5194/egusphere-2024-1276-RC1 -
AC1: 'Reply on RC1', Azam Masoodi, 06 Nov 2024
We appreciate the reviewer’s insightful and constructive feedback and intend to revise the paper accordingly. The misalignment between the introduction and conclusion may have resulted from an evolving focus in our study. Initially, our aim was to evaluate how different roughness estimation methods affect the accuracy of model predictions when compared to physical measurements. However, our results revealed that, in the presence of vegetation, saturated hydraulic conductivity plays a critical role that cannot be ignored. This insight led us to emphasize infiltration parameters in the conclusions, which may have created some confusion for readers.
To address this, we will revwrite the abstract, introduction, and discussion to clarify the role of infiltration in our study and refine the stated objectives to provide a clearer framework for readers.
Additionally, we compared our results with a related study by Feldman et al. (2023), as both studies share the same experimental data set and a similar objective but use different approaches. Feldman et al. extrapolated a nearly constant infiltration rate for the falling limb of the hydrograph, which allowed them to separate the impact of roughness on hydrograph shape from infiltration effects. Our approach, however, considers infiltration across the entire hydrograph. To highlight the differences between these approaches and the importance of considering infiltration effects, we referenced to this paper frequently. However, we did not emphasize the relation to the paper enough in the introduction, which will be changed in a revised version.
Below are our responses to the detailed comments, along with our plan to improve the manuscript’s clarity and organization.
Detailed Comments
Comment L28: The sentence has been modified for better clarity, and a relevant citation has been added at this point in the text.
Comment L58: OpenLISEM is the open-source version of LISEM. We explained about it in L58 and updated the citation in L60 and 63.
Comment L100: these sentences were deleted.
Comment L143: This text “Nevertheless, they do not provide information on the ℎ₀ value. Consequently, this parameter is assumed to be 5 times the plant height for each experimental site” was changed to “As Feldmann et al., (2023) did not provide information on the ℎ₀ value, we assumed ℎ₀ to be five times the plant height to apply the Kadlec’s method in our study.”
Comment L153: “on overland flow” was inserted to the text.
Comment L159-165: We changed the title of this section to “Study site and experimental setup”
Comment on Figure 3: The picture was modified.
Comment on Experiment Naming: A table describing each experiment was added in Section 2.3 for easier access.
Comment on Percentage of Bias: It was modified in the manuscript.
Comment on Saxton and Willey Reference: The reference was completed.
Comment on Figures 8 and 9: These figures are modified.
Comment on Figure 10: An explanation for the green cells is added below the figure.
Comment on L321: We removed these sites from the Figure. 11
Comment on antecedent conditions: We have modified the discussion on antecedent conditions and removed the citations related to catchment modeling.
Citation: https://doi.org/10.5194/egusphere-2024-1276-AC1
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AC1: 'Reply on RC1', Azam Masoodi, 06 Nov 2024
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RC2: 'Comment on egusphere-2024-1276', Anonymous Referee #2, 22 Nov 2024
Especially in light of the increase in heavy rainfall events with enormous damage potential and the associated great efforts of the federal states to create high-resolution heavy rainfall hazard maps, the further improvement of hydrological and hydraulic modeling is of great importance. Particularly with regard to the quantification resp. modeling of the influence of different vegetation conditions on runoff formation and flow parameters of overland flow, I also see an urgent need for research. In this respect, the authors’ commitment is very welcome, and the results can provide valuable input both from a scientific perspective and for practical application. In the publication of the research results, I still see potential for improvement overall, in order to present the findings more transparently and comprehensibly for the professional community.
Before I list my specific comments and questions in detail, please allow me to address some general critical-constructive comments:
The title raised my expectation of an examination of the physical (and particularly the hydraulic) processes of surface runoff. However, the methodological focus is exclusively on data-driven analyses with little to no engagement with the actual processes. The influence of vegetation height as an input parameter is also only evaluated in a rudimentary or purely model-based manner. Therefore, a title such as “Investigation of Different Roughness Approaches in Hydrological Run-off Modelling” would be more appropriate.
Regardless of the chosen title, I would have fundamentally wished for a deeper engagement with the physical phenomena and their modeling representation when evaluating the predictive capability of the investigated model approaches. This is especially relevant in terms of comparing the simulated and measured hydrographs, where differences between model and measurement set-up could also influence the results. The possible influence of the microscale surface structure, which together with the vegetation determines the flow resistance (friction and drag), should also be mentioned more visibly.
The presentation of the literature review on vegetation hydraulics in the introduction does not differentiate between studies on channel/river hydraulics and those on overland flow (thin-layer surface runoff) due to heavy rainfall. However, the boundary conditions of the mostly highly empirical studies are completely different in terms of slope, flow depths, vegetation situations, and thus only allow limited mutual conclusions.
Since the present contribution exclusively uses field experiments with very low flow depths of a few centimeters for the validation of the approaches, and there is (presumably) hardly any significant submergence of the vegetation, the presentation of the modeling approaches and third-party research results could perhaps be limited to the investigated ranges to avoid misunderstandings (in the sense of “less is more”).
I am somewhat uncertain to what extent the NSA value is sufficient as a key parameter for evaluating the model quality. Especially when it is balanced over the entire hydrograph, the advantages and disadvantages of the methods and, above all, the reasons for deviations are difficult or impossible to understand. Therefore, the red thread for comparative evaluation and the conclusions were only partially comprehensible to me. More comparative illustrations as shown in Figure 3 and a detailed process-related discussion of agreements and deviations might be helpful.
Line 1: Titel misleading (see comments above)
Lines 19-21: Is this conclusion a new insight (sounds like) or just the confirmation of an expected dependency? Is it really the result of your model applications or rather of the measured data on the plot (Accounting inflow/outflow on field plot)? Are there possible other effects (e.g. increasing pressure due to higher waterlevels) or model-wise dependencies which have an impact on infiltration?
Introduction: Many sources refer to studies of river hydraulics (not overland flow) with other boundary conditions (e.g. slope, water depth). This should be highlighted or even reduced on relevant approaches (see comments above). A compact overview can be found in DWA-M 524.
Line 35: Oberle et al. (2021) presents a comprehensive literature study on hydraulic flow resistance of overland flow. In addition, a recommendation for depth-dependent roughness values is derived from different laboratory experiments with artificial grass (Yörük, Karantounias, Ruiz Rodriguez).
Line 42: “approximated” instead of “effectively characterized”.
Line 70: This was not the main focus of the paper. Investigation of vegetation effects… reference to modelling is missing.
Chapter 2: General it would be easier to read if the model and the study site would be explained one after the other. Like e.g.: Presentation of Study site and its boundary conditions -> leads to this model -> leads to different approaches which have been tested -> leads to sequence of modelling.
Or the other way around: These are approaches from literature which we want to examine. For that we build this model based on this study site…
Chapter 2.1: The model looks diagonally inclined although it seems that the study sites of Ries et al. are sloped orthogonal. If it´s like that the deviation of the model from the original (different specific discharge distributed over the plot) and impact on results needs to be discussed. Furthermore, the discharge of the model at the outlet is measured instant in contrast to the set-up from Ries where the discharge measurement is from my understanding 10-20 m (drainage tubes) after the plot. This should lead to distorted results later in the evaluation and needs to be discussed. Also, a rough overview of ranges of discharge, velocities and water depth on the plot should be given. Maybe some maximum values for a better understanding.
Line 85: Size 1x1. Noticeably coarse resolution in hydraulic. Have cell size sensitivities been checked?
Line 86: Possibly misleading. Micro depressions where the water is retained and infiltrating after a rain event are not accounted for in the roughness factor. Only water that can later drain away is considered as well as small geometrical variances (drag force) additionally to frictional resistance.
Fig. 1: A picture of the experimental setup of Ries et al. (2020) next to the model would be helpful for the reader.
Lines 93 – 96: The model-related influence of Manning on infiltration rate should be discussed. How are the parameters related to each other in the model?
Line 105: Resistance factors for river hydraulics (different slope, water depth). Needs to be mentioned.
Line 110: Feldmann's approach/procedure should be explained in more detail, as comparison to him is a bigger part in the paper.
Table 1. Site 19. n Chow. Format not in line with others.
Line 115: Oberle et al. (2021) recommends a roughness spectrum based on experimental experiments to simulate overland flow in 2D hydrodynamic models. Investigations had been done by different authors.
Eq. 1: Should be 1 / nmanning = … ?
Line 121: Are higher flow depths in the model even achieved or is the linear method also a “constant method”, since only flow depths smaller than the vegetation height occur?
Line 123: Strickler k -> kStr
Line 126: It´s not clear whether the actual vegetation footage from Reis is considered using these equations. If it´s like that more information is needed how it´s done and what blockage factors have been calculated. What value was used for Cd?
Line 136: Explanation c and d. One sentence how Feldmann calculated them and what is the value from them.
Line 145: Oberle at al. (2021) did not confirm factor 5. As no vegetation height was considered in the study at that time. Short side note: New measurement results based on experiments on a natural plot (grassland, different vegetation conditions) will soon be published (see also Oberle et al. 2024 Dresden Wasserbaukolloqium).
Line 147: plant basal cover can be much lower than canopy cover depending on vegetation type. It´s to be expected that plant coverage of Ries is canopy cover? Needs to be addressed that there are uncertainties.
Chapter 2.3: Also, if possible a range of expected velocities, water depth or at least the measured specific discharge in the experiments would add benefit to the readers. Would it make sense to separate the sites into the ones with arable land (with and without vegetation) and grassland? Maybe you find a trend by looking at them isolated rather than all mixed up.
Chapter 3: How does the start value of Manning effects the calibration of ksat and Psi? Have the final ksat and psi been checked for plausibility? In addition to the NSE, an isolated consideration of the specific characteristics of the hydrograph seems beneficial. (see comments above)
Fig. 3: unit? Also, more of these graphs would give greater insight. Moreover, differences in rising and falling limb could be influenced by differences between model and experimental set-up and should be discussed. Also, on the rising limb micro depressions could have an impact (smooth model in contrast to nature). Fundamental discussion (instead of only focusing on NSE & bias) against the background of the methodology used would be desirable.
Fig. 4 & 5: Systematic differences are noticeable. These should be discussed and evaluated. Site seems to have a greater influence on the parameters than the different methods.
Fig. 10: Please check if the unit is correct? Discharge for the whole plot or specific discharge? Why is 3 parameters so bad? Discussion should be more profound.
Fig. 11: Show also psi per site? See also comments to Lines 19-21
Line 352: As mentioned above: Are you sure that submerged vegetation has been investigated or are the water depths shallower?
Chapter 5.3: Site (soil type?) was more dominant than vegetation and should be put into relation.
Line 412: Is this new insight (see comments above)?
Line 416: e.g. RoGer-Modell?
Citation: https://doi.org/10.5194/egusphere-2024-1276-RC2 -
AC2: 'Reply on RC2', Azam Masoodi, 17 Dec 2024
We appreciate the reviewer’s insightful comments and have made efforts to address them comprehensively to improve the manuscript. What we will emphasize especially is, that we are using a model approach that can be used for upscaling. The OpenLISEM model as we are validating in this study can be used as is for the field and even the landscape scale. Regarding microdepressions, since they are a subscale phenomenon for the ultimate target scale, we incorporated their effects into the Manning's coefficient parameterization. This allowed us to model surface roughness using waterdepth-dependent or constant Manning coefficients, ensuring that all flow-affecting parameters were represented within the model's constraints. We will elaborate further on the limitations of this approach and its implications for the comparison between observed and simulated hydrographs. In the introduction, we will also clearly differentiate between river hydraulics studies and those relevant to overland flow, emphasizing the distinct boundary conditions and ensuring the literature review aligns with the study’s scope.
Given the low flow depths in our experiments, we acknowledge that submerged vegetation conditions were not observed. To avoid misunderstandings, we will limit the presentation of modeling approaches and results to those directly relevant to the ranges of flow depths and vegetation conditions in our study. Discussions of submerged vegetation or different boundary conditions will be excluded or noted as beyond the scope.
General Comments:
Title Suggestion:
We will change the title as proposed “Investigation of Different Roughness Approaches in Hydrological Flow Modelling through Vegetation” in order to more clarity.
Considering micro depression:
Since no DEM data was available, we incorporated the effects of micro depressions as part of the Manning's coefficient parameterization. The surface roughness is thus modeled using water-depth-dependent or constant Manning coefficients, ensuring that all flow-affecting parameters are represented as accurately as possible within the constraints of the model. We will elaborate further on the limitations of this approach and its implications for the comparison between modeled and measured hydrographs in the revised manuscript.
Literature differentiation:
We will revise the introduction to distinctly differentiate between river hydraulics studies and those relevant to overland flow. Also, we will explain about boundary conditions of different approaches.
Limiting data to investigate submerged vegetation:
We evaluated the model's results concerning water depth and acknowledge that the field experiments in our study focus on very low flow depths, with minimal or no significant submergence of vegetation. To prevent potential misunderstandings, we will restrict the presentation of modeling approaches and results to those directly relevant to the investigated ranges of flow depths and vegetation conditions. Discussions of submerged vegetation or scenarios involving significantly different boundary conditions will be either excluded or explicitly noted as beyond the scope of this study.
Comparative Illustrations:
Given the large number of models, it would not be practical to present all hydrographs in detail. Instead, we have provided a representative example in Figure 3 to illustrate the model's behavior and included results focusing on the falling limb of the hydrograph in Figure 10 to highlight specific aspects of model performance. To enhance clarity and address your concern, we will present additional hydrographs as supplemental material. So, we will discuss the differences between observed and simulated hydrographs in greater detail, highlighting the agreements, deviations, and potential reasons behind them.
Specific Comments
Line 1: The title will be changed.
Lines 19-21: Is this conclusion a new insight (sounds like) or just the confirmation of an expected dependency? Is it really the result of your model applications or rather of the measured data on the plot (Accounting inflow/outflow on field plot)?
Ans: As explained in the discussion, Ksat was estimated through the model's calibration process and subsequently used to evaluate the model’s ability to simulate hydrographs. This conclusion is based on our calibration results, which showed a clear relationship between dense vegetation and higher saturated hydraulic conductivity. While this finding aligns with the expected dependency between vegetation coverage and infiltration capacity, it provides a model-based confirmation of this relationship under the specific conditions of our field experiments. Thus, the result is both a validation of expected trends and a contribution to understanding the importance of incorporating Ksat variability in hydrological models.
Lines 19-21: Are there possible other effects (e.g. increasing pressure due to higher waterlevels) or model-wise dependencies which have an impact on infiltration?
Ans: It is important to acknowledge the inherent limitations of hydrological models, which may influence this conclusion. For instance, this model does not explicitly consider the effects of increased water pressure at higher water levels, which could also impact infiltration dynamics. We will address these limitations and their potential implications for the results in the revised discussion section.
Introduction: We will revise the introduction to focus specifically on studies relevant to overland flow, emphasizing the key differences between river hydraulics and overland flow in terms of boundary conditions, such as slope, water depth, and flow dynamics. This will ensure that the introduction aligns more closely with the scope of the research.
Line 35: The text will be changed the text to “Oberle et al. (2021) present a comprehensive literature review on the hydraulic flow resistance of overland flow. Furthermore, recommendations for depth-dependent roughness values are derived based on various laboratory experiments conducted with artificial grass (Karantounias, 1974; Yörük, 2020; Ruiz Rodriguez et al., 2017).
Line 42: We will use “approximated” in the text.
Line 70: The objective will be changed to:
- Modelling the overland flow to compare and validation of different approaches of Manning’s coefficient estimation.
- Evaluate the impact of incorporating vegetation density into the saturated hydraulic conductivity parameter in the model.
- Investigation of initial soil moisture conditions on the model performance.
Chapter 2: We will restructure Chapter 2 to first present the study site, followed by an explanation of the modeling approaches, and finally the sequence of modeling steps.
Chapter 2.1: The model looks diagonally inclined although it seems that the study sites of Ries et al. (2020) are sloped orthogonal. If it´s like that the deviation of the model from the original (different specific discharge distributed over the plot) and impact on results needs to be discussed.
Ans: We will address this in the paper with the following explanation: “It should be noted that the DEM prepared for the simulations is oriented diagonally, whereas the study sites had orthogonal slopes. When using the diagonal orientation, the rising limb of the hydrograph tends to exhibit lower values compared to the orthogonal case, while the diagonal orientation also produces a higher peak value. However, given the small size of the study area, this discrepancy is not significant. Recognizing this potential source of error, we have presented the model results accordingly.”
Chapter 2.1: Furthermore, the discharge of the model at the outlet is measured instant in contrast to the set-up from Ries where the discharge measurement is from my understanding 10-20 m (drainage tubes) after the plot. This should lead to distorted results later in the evaluation and needs to be discussed. Also, a rough overview of ranges of discharge, velocities and water depth on the plot should be given. Maybe some maximum values for a better understanding.
Ans: We believe that the data presented in their study are modified and that their time series do not reflect this discrepancy.
Line 85: Since the DEM which used in the model is flat and without micro depression the result is not sensitive to the cell size based on our brief sensitivity analyses.
Line 86: Unfortunately, Reis et al. (2020) did not provide any DEM of the study sites. As a result, we had to account for the micro-depressions as part of Manning’s coefficient. Usually, OpenLISEM is applied to larger areas, like catchment scale. Using a resolution which can represent micro depressions will not lead to scalable results for future applications. To limit the number of compared variables, we left a test of scale effects out intentionally and kept this condition consistent across all experiments and methods. We will emphasize the aim to validate roughness approaches that will be ultimately used on the field to landscape level.
Fig. 1: We will insert one picture of Reis et al., (2020) study.
Lines 93 – 96: The two parameters are not directly related, while the infiltration rate (Green-Ampt model) is subtracted at each timestep from the surface flow, the Manning roughness is only used for the surface flow. However, a higher infiltration rate reduces the surface water depth and the flow velocity slows down. To differentiate between the processes, we check for the water balance as a model performance indicator. We will include this interplay into the discussion.
Line 105: It will be mentioned as “Resistance factors significantly impact flow velocity, discharge, and energy loss. Bed resistance results from water-bed interactions, while slope influences the energy gradient driving the flow. Resistance decreases with an increased hydraulic radius, as deeper water encounters less friction compared to shallow water. Additional factors, such as temperature and viscosity, also contribute. Hydraulic equations, such as Manning’s equation, incorporate these factors to predict flow behavior under varying conditions. Although this equation has been developed for channel flow, most hydrological models, including OpenLISEM uses this equation also for surface runoff on upslope areas.”
Line 110: We will explain the relation of this study to the study of Feldmann et al., (2023) in more detail in the introduction. The key difference between the studies lies in the different handling of infiltration. We will add in the introduction: “The core idea of their study was to use the shape of the hydrograph to estimate surface roughness. They assumed that during the descending limb of the hydrograph, a nearly constant infiltration rate can be extrapolated using the fitted Horton function. This approach separates the effects of roughness from infiltration, enabling a clearer determination of roughness. To describe the infiltration process in artificial rainfall experiments, a Hortonian curve was applied to calculate the maximum possible infiltration rate by fitting the equation to the difference between rainfall and observed discharge. Each experiment produced a solution space representing several acceptable roughness functions, all with minimal differences in result quality. They reduced the solution space in three steps: exclusion of low roughness values, based on the rising hydrograph; comparison across experiments, to identify consistent patterns within the same site; and cross-site comparison, to further refine and validate the results across sites with similar properties. While this method helps to investigate the roughness effect alone, the result cannot be transferred to ungauged situations in the field to landscape scale”
Table 1: It will be edited.
Line 115: It will be modified as “Oberle et al. (2021) proposed a roughness spectrum based on experimental studies for simulating overland flow”
Eq.1: fixed.
Line 121: We checked the maximum water depth in our models and found that it is consistently less than the vegetation height. Therefore, we agree that under these conditions, the linear method behaves similarly to a "constant method." We will address this limitation explicitly in the discussion. This also explains why the three methods Chow, Linear, and Nepf produced similar results in our analysis.
Line 123: fixed.
Line 126: The blockage factor was determined based on the vegetation cover presented by Reis et al. (2021). For the drag coefficient (Cd), we assumed that the stem of vegetation is cylindrical in shape, and therefore a Cd value of 1 was used.
Line 136: This text was inserted to the paper: “The parameters c and d represent coefficients used to define the roughness function. All possible combinations of c and d within a "reasonable range" were systematically tested by Feldmann et al. (2023). By comparing the observed hydrograph with the simulated hydrograph generated using the tested c and d values, the combination that best replicates the behavior of the falling limb was identified by them.”
Line 145: We will modify in the text “Oberle et al. (2021) reviewed experimental datasets from various studies on artificial grass, and our assumption aligns with the range they presented. Furthermore, Hinsberger et al. (2022) cited studies by Augustijn et al. (2008) and Huthoff et al. (2007), which demonstrate that a high submergence ratio exceeds a value of 5.”
Line 147: We will explain about it: “Since the plant coverage reported by Reis et al. (2020) appears to represent canopy cover, uncertainties arise in the calculation of resistance coefficients. This is because the basal elements of the vegetation are not accounted for, which may result in an incomplete representation of flow resistance.”
Chapter 2.3: The velocity of flow and water depth were not measured in the experiments. However, we will insert the range of mean cumulative overland flow and volumetric soil moisture to the paper. One advantage of the experimental basis by Reis et al, is the broad range of different soils, vegetation types and regions. By isolating arable fields and grasslands, the pairing approach used in the experiments would be compromised, making it impossible to distinguish between vegetation and soil effects.
Chapter 3: How does the start value of Manning effects the calibration of ksat and Psi?
Ans: We calculated the Manning’s coefficient using the relevant equations and then calibrated the Ksat and Psi parameters for run 2. Later experiments are used for validation using the calibrated values for Ksat and Psi. We will revise l 170-176 to explain the calibration procedure in more clearly.
Chapter 3: Have the final ksat and psi been checked for plausibility?
Ans: Since direct measurements for Ksat and Psi are unavailable, verifying these parameters against actual values was not possible. However, the calibrated values fall within a reasonable range, as we will show in the result section by a comparison with the calibrated results with values derived from the soil properties using the SPAW software (Saxton and Willey, 2006), following the suggestion of the authors of OpenLISEM.
Chapter 3: In addition to the NSE, an isolated consideration of the specific characteristics of the hydrograph seems beneficial.
Ans: We do not want to discuss the result of a single experiment in too much depth, since 138 experiments are available. We will present additional hydrographs as supplemental material and analyze the three parts of the hydrograph (rising limb, plateau and falling limb) separately. We will present the results for the calibration as uncertainty bands in an additional figure.
Fig. 3: The unit will be modified. A representative example will be provided to show the complete hydrographs for one site, along with an explanation of the behavior of the hydrographs
Fig. 4 & 5: As mentioned above, the infiltration submodel and the surface flow submodel are separated. A strong influence of the selected roughness method on the parameters of the infiltration model would mean, that parameters are not as independent, as they should be. The infiltration is heavily depending on the soil properties and the existence of a macro pore regime which is also depending on vegetation. The vegetation and soil properties differ between sites. We will describe this result in course of the result section and discuss the meaning of the result in the discussion.
Fig. 10: Please check if the unit is correct? Discharge for the whole plot or specific discharge?
Ans: We checked the unit again and can confirm it correctness, as it shows the specific discharge. We will expand the figure caption accordingly
Fig. 10: Why is 3 parameters so bad?
Ans: The three parameters did not perform well because the Manning’s coefficient (n) was calculated as a calibrated parameter in the model. This indicates that calibrating both n and Ksat simultaneously is not an effective approach. Adding more calibration parameters can increase model complexity, but this does not always lead to better results. We will discuss this aspect in chapter 5.1 in the revised version.
Fig. 11: We will include Psi in a revised version of the figure.
Line 352: We will change the line to: “This concurs with previous studies advocating for roughness determination based on the relation vegetation height and water table depth.”
Chapter 5.3: The effects of soil type on infiltration have been previously demonstrated and are accounted for by Ksat in the equation. In this study, we aim to show that, even within the same location, Ksat values vary with different vegetation cover. We will clarify the role of soil type vs. vegetation in this chapter. However, in flood forecasting scenarios, Ksat is usually only derived from the soil type, but is also, as we show in this study heavily influenced by the vegetation. The vegetation changes the runoff behavior more by its effect on infiltration then on roughness.
Line 412: Actually, our results are a confirmation to highlight the importance of considering vegetation effects when estimating saturated hydraulic conductivity.
Line 416: We will add the following sentence: “Modular modelling frameworks with the ability to model space continuous surface runoff together with infiltration methods like CMF (Kraft et al., 2011) or potentially RoGER (Steinbrich et al., 2016) and SUMMA (Clark et al., 2015) can help to decouple the interwoven processes of friction and infiltration at the hillslope scale. OpenLISEM is one of the few established readymade models that includes detailed surface runoff, infiltration and erosion mechanisms.”
Citation: https://doi.org/10.5194/egusphere-2024-1276-AC2
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AC2: 'Reply on RC2', Azam Masoodi, 17 Dec 2024
Model code and software
modified_manning_console Philipp Kraft https://github.com/philippkraft/openlisem/tree/modified_manning_console
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