the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving understanding of groundwater flow in an alpine karst system by reconstructing its geologic history using conduit network model ensembles
Chloé Fandel
Ty Ferré
François Miville
Philippe Renard
Abstract. Reconstructing the geologic history of a karst area can advance understanding of the system’s present-day hydrogeologic functioning, and help predict the location of unexplored conduits. This study tests competing hypotheses describing past conditions controlling cave formation in an alpine karst catchment, by comparing an ensemble of modelled networks to the observed network map. The catchment, the Gottesacker karst system (Germany/Austria), is drained by three major springs and a paleo-spring, and includes the partially explored Hölloch cave, which consists of an active section whose formation is well-understood, and an inactive section whose formation is the subject of debate. Two hypotheses for the formation of the inactive section are: 1) glaciation obscured the three present-day springs, leaving only the paleo-spring, or 2) the lowest of the three major springs (Sägebach) is comparatively young, so its subcatchment previously drained to the paleo-spring. These hypotheses were tested using the pyKasso Python library (built on anisotropic fast marching methods) to generate two ensembles of networks, one representing each scenario. Each ensemble was then compared to the known cave map. The simulated networks generated under Hypothesis 2 match the observed cave map more closely than those generated under Hypothesis 1. This supports the conclusion that the Sägebach spring is young, and suggests that the cave likely continues southwards. Finally, this study extends the applicability of model ensemble methods from situations where the geologic setting is known but the network is unknown, to situations where the network is known but the geologic evolution is not.
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Chloé Fandel et al.
Status: final response (author comments only)
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RC1: 'Comment on hess-2022-280', Anonymous Referee #1, 04 Oct 2022
This research article employs a probabilistic approach to test two hypotheses regarding the geological evolution of caves in an alpine karst catchment, thus in my opinion, the study fits well within the scope of the HESS journal. In general, the article is well written, and its objective is clear. The methodology consists of techniques already proposed and tested in previous studies, however, the application of such techniques in the context of geological evolution assessment is quite novel. In my opinion, the paper can be accepted for publication provided that the present comments/suggestions are correctly addressed:
- In the document the authors state that the simulation of the networks for a resolution of 50x50 meters can be performed in less than 2 minutes. In this sense, what is the limiting factor for not using a map with a higher resolution?
- In Figure 3, how is the expected network defined?
- In Figure 3, is it possible to draw the expected network with different plotting settings to enhance its visibility? As it is, the expected network can be confused with the simulated conduits.
- Could you provide an interpretation of the variability of the simulated conduits? Is it an indicator of the uncertainty of the expected conduit? Could you include a metric to measure such variability?
- In line 243 the authors mention that there is an additional scenario that is not explored in this study. Is there any justification for not analyzing it?
- In line 125 the authors wrote that in this study you are considering that conduits form preferentially in the direction of the maximum downward hydraulic gradient. Could you please add some references to studies where this assumption is also employed? Alternatively, could you please add some words explaining the reasoning behind this assumption?
- In Section 6 (Findings) you do some references to Figure 4. However, they seem to refer to elements in Figure 5.
- There is a missing closing bracket in line 112.
Citation: https://doi.org/10.5194/hess-2022-280-RC1 -
AC1: 'Reply on RC1', Nico Goldscheider, 22 Nov 2022
We appreciate the insightful and constructive comments from Reviewer 1, and we agree that addressing them will result in stronger work overall. In the following paragraphs, we provide specific responses and also explain the planned modifications in the revised paper:
Comment: In the document the authors state that the simulation of the networks for a resolution of 50x50 meters can be performed in less than 2 minutes. In this sense, what is the limiting factor for not using a map with a higher resolution?
Response: The basic reason for not including a higher resolution is that a high resolution is not needed to answer the question that is treated in this paper. We compared the results of a higher-resolution model with 5x5 meter cells to the result of the lower-resolution model with 50x50 meter cells. The higher resolution significantly increased the computation time (nearly 50 minutes compared to less than 2 minutes), and produced conduit networks that followed the same general paths and orientations as those in the low-resolution model. Because the goal of this paper is to reconstruct the regional-scale geologic events controlling past conduit evolution, and not to generate detailed cave maps, we chose to use the more rapid lower-resolution model, which provided enough information for our purposes. Higher-resolution 3D models would bring additional information if more detailed input data were considered, but this is not relevant to our primary question.
Comment: In Figure 3, how is the expected network defined?
Response: The expected network was originally defined by Chen and Goldscheider (2014), and refined by Chen et al. (2018), based on geologic mapping by Wagner (1950), predominant fracture orientations documented by Cramer (1959), several decades of speleological investigations by the regional caving club (Höhlenverein Sonthofen), 18 quantitative multi-tracer tests by Goldscheider (2005), Göppert and Goldscheider (2008), and Sinreich et al. (2002), and hydrogeological field observations by Goldscheider (2005). There is a sentence to this effect around line 80, but we will edit it to make it more clear that this refers to the expected conduit network.
Comment: In Figure 3, is it possible to draw the expected network with different plotting settings to enhance its visibility? As it is, the expected network can be confused with the simulated conduits.
Response: Yes, thank you for pointing this out. We will make the expected network a different color from the simulated networks.
Comment: Could you provide an interpretation of the variability of the simulated conduits? Is it an indicator of the uncertainty of the expected conduit? Could you include a metric to measure such variability?
Responses:
Question 1: The variability in the simulated conduits is a result of the model’s stochastic nature. Stochasticity can be introduced in several ways, but for this study, in the simulations of the competing scenarios under consideration, the primary source of stochasticity is the fracture network. For each conduit simulation, a new fracture network is generated, based on the descriptive statistics obtained from field and aerial photo mapping of the actual fracture network. The conduits then form preferentially along fractures, resulting in slightly different networks for each simulation. This is described around line 130, but the explanation will be further improved in the revised paper.
Question 2: In regions of the model strongly controlled by fractures, there will be more variability in the ensemble in response to the different fracture networks in each simulation. In regions of the model where other factors (such as gradient, existing conduits, or obstacles) are more dominant, there will be less variability in the ensemble. The “fuzziness” of the ensemble maps (more fuzzy = more variability in where different model simulations predict conduits will go) is an indicator of model uncertainty. Regions where the different model simulations all predict different conduit paths would be regions where we have low confidence in the model’s predictive abilities, and where we would be interested in acquiring more data. Regions where the different model iterations cluster tightly around a single path would be regions where we have higher confidence that a conduit is indeed in the model-predicted location, and we would not prioritize collecting additional data. This is discussed in more depth in Fandel et al. (2021). We will add text with a brief overview of the source of variability, pointing readers to our previous work for a more in-depth explanation.
Question 3: This is an excellent question, and is one that we have been exploring at length, and do not yet have a satisfying answer to. Quantifying the similarity or dissimilarity of conduit networks is challenging. One approach is to represent the networks as mathematical objects: graphs with nodes connected by edges. Statistics can then be computed describing the geometry and topology of these graphs. Collon et al. (2017) describe the statistics most relevant to describing karst networks, and we have incorporated their functions to calculate these statistics into the pyKasso model code. However, these statistics quantify strong differences in the topology of the networks and are more relevant to classifying different types of cave networks, or to determining how closely modeled networks match mapped networks. When attempting to quantify the degree of variability in our simulated conduits, the models in our ensembles are statistically similar enough to each other that we cannot distinguish between them based on available geometrical and topological metrics. If, given these considerations, it would still be interesting to include this information, we could insert a table showing summary statistics for our ensembles. Identifying statistical metrics that can usefully describe differences between models of the same network in an ensemble is an area of future research that we are highly interested in pursuing further, but we consider that our results are not mature enough and that that topic is beyond the scope of this paper.
Comment: In line 243 the authors mention that there is an additional scenario that is not explored in this study. Is there any justification for not analyzing it?
Response: The scenario in question is that there were several overlapping phases of karstification, in which different combinations of springs were either exposed or occluded. While the two scenarios we explored were supported by enough geologic, hydrogeologic, and geomorphologic observations to define clear hypotheses, this is not the case with the additional scenario. Furthermore, one of the two tested scenarios delivered cave patterns that match the observed cave pattern very well, suggesting that this is how the caves have essentially formed. For these reasons, we decided not to test the additional scenario in detail. We agree that the statement about the “additional scenario” in line 243 causes confusion and distracts from the logical sequence of our research work. Therefore, we will delete this statement and not mention any “additional scenarios” in the revised paper.
Comment: In line 125 the authors wrote that in this study you are considering that conduits form preferentially in the direction of the maximum downward hydraulic gradient. Could you please add some references to studies where this assumption is also employed? Alternatively, could you please add some words explaining the reasoning behind this assumption?
Response: The hydraulic gradient is the driving force of groundwater flow (i.e., groundwater always flows from high to low hydraulic potential), and flowing groundwater containing CO2 is the key agent of cave formation (speleogenesis) in limestone. Fracture characteristics, lithological differences and several other aspects and processes also influence the formation and orientation of conduits, but the hydraulic gradient is the key driver in most cases. This is discussed in classical scientific literature about caves (e.g. Palmer 2007) but also in numerous studies dealing with the modeling of speleogenesis (papers by Dreybrodt, Gabrovsek and others). In shallow and mostly unsaturated settings, such as our example, hydraulic gradients largely coincide with topographic gradients; in deep and artesian settings, however, groundwater can flow upwards, but it always follows the hydraulic gradient, and the conduit network reflects this flow direction. In the revised paper, we will briefly explain and discuss this important aspect, supported by relevant additional references.
Comment: In Section 6 (Findings) you do some references to Figure 4. However, they seem to refer to elements in Figure 5.
Response: Thank you for catching this mistake! We will correct the figure references.
Comment: There is a missing closing bracket in line 112.
Response: Thank you for catching this. We will insert the missing bracket.
Citation: https://doi.org/10.5194/hess-2022-280-AC1
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RC2: 'Comment on hess-2022-280', Anonymous Referee #2, 17 Oct 2022
NB: I have received an email notification, but I did not read the referee1’s comments to avoid any influence in my judgment. Some repetitions could thus appear in the following (or I could have a completely different opinion, I don’t know).
The present work is devoted to the study of two hypotheses on the Hölloch cave karstogenesis process with a geometric modelling approach. The Hölloch cave is part of the large Gottesacker karst system and contains 2 parts from a karstogenesis point of view: an active one and an inactive one. The authors recall two explanations proposed by speleologists, which are not exhaustive and/or excluding, for the co-existence of the 2 parts. Then, they translate these hypotheses into input constraints (i.e., inlet/outlet pairings) for a stochastic modelling method of karstic conduit paths. Then, observing the results, they comment on both hypotheses.
The topic addressed in this paper is, I think, completely relevant for HESS. The English is good and the reading easy. However, I have unfortunately two major concerns that prevent me to recommend its publication: 1) the lightness of the contribution as compared to the recent papers already published by the same authors on the same topic and the same case study; 2) what I would qualify as an over-exploitation (or even mis-exploitation (?)) of the modelling approach. More details are further provided.
The submitted paper is a follow-up of two papers recently published in Hydrogeology journal in 2021 and 2022: Fandel, C., Ferré, T., Chen, Z., Renard, P., Goldscheider, N., 2020. A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits. Hydrogeol. J. 229–248. https://doi.org/10.1007/s10040-020-02227-6 and Fandel, C., Miville, F., Ferré, T., Goldscheider, N., Renard, P., 2022. The stochastic simulation of karst conduit network structure using anisotropic fast marching, and its application to a geologically complex alpine karst system. Hydrogeol. J. 927–946. https://doi.org/10.1007/s10040-022-02464-x . To perform properly this review, I have also carefully read both these papers to clearly understand the added value of this new one.
In this new submission, the concepts, tools, and data are identical to those presented in these two previous papers. The 2021 paper already presented the SKS approach (which was initially published by Borghi et al., 2012 and used in an inverse approach in 2016) to generate ensemble of models. The main contribution of this first paper was the application to the Gottesacker case study. The innovation of the previous 2022 paper was to use an anisotropic fast marching algorithm (provided by an already existing external library: skfmm) instead of the initial version that used an isotropic fast marching . This change was necessary to consider the hydraulic gradient when using a 2D method. A deepest comment of this paper is out of the scope of this review and will not be provided. But this previous paper also proposed a very short sensitivity analysis, and, in particular, it explored the effect of various pairing of sources and sinks on the resulting networks for the Gottesacker karst (fig. 18 of 2022’s paper). Note that the modelling results are compared to a “reference” which is not the real karstic system, but a conceptual representation proposed by Chen et al. (2018) allowing to reproduce the hydrological observations. In the present work, the two hypotheses for the formation of the inactive karst conduits are translated in the modelling approach as changes in the position and pairing of inlets/outlets. They are described in 22 lines page 9 + the figure 4. All the remaining of the methodology (page 1-8) is a rewording of the previous papers. As well as a large part of the discussion and of the “messages” of the paper. As the inlets-outlets pairing effect was already discussed in the previous 2022 paper, the example of a pairing guided by a speleogenetical history would have been more pertinent as 15 additional lines in the previous paper than as a “new” 16 pages paper, avoiding the large number of redundancies.
My second main concern is about the interest of using the model to answer the scientific question “should the inactive conduit having been formed during the glaciation or by a late opening of the QS spring?”. Indeed, the inactive conduit is oriented north-south. The modelling algorithm searches a path between an inlet and an outlet, with a secondary influence of fractures (randomly generated) and a pseudo-hydraulic gradient. If you do not put an inlet / outlet on the south of the paleo-spring, there is no reason (theoretically and numerically) to generate a path aligned with the inactive conduit: from top view, the path “south -> QO spring” is opposite to the input (= topographic) gradient. No need for the model to see that. Thus, as QO is the only outlet in hypothesis 1, only an inlet located approximately in its south could explain such a path. As the test performed by the authors does not propose this solution, it is obvious that they are not going to generate a consistent solution. In addition to this first point, in the hypothesis 2, some paths are generated accurately, but it is because the authors let the spring QA exist: thus, some paths are generated between QO and QA which is a main direction aligned with the inactive part. Again, this is consistent with the topographic gradient and the direction QO-QA, no need for the model to guess this hypothesis is consistent with the observations.
But, and this is really important, what about the direction of fluxes, never discussed (a limitation due to the fact they simplify in 2d)? Indeed, in hypothesis 2 these paths are obviously going from QO (higher) to QA (lower): in that case, QO is not the spring of the inactive part but an “entrance”. What about the field data? Are the inactive conduits indeed sloping towards QA or in the opposite direction? Were there indices of QO being an inlet and not an outlet? It is not said so, in the text, where QO is presented as an outlet. If the conduits are sloping towards QA, then QO cannot be their previous outlet, and the tests performed for hypothesis 1 were, from start, bound to fail (again, no need to perform any computation to conclude that). If, oppositely, the inactive conduits are sloping towards QO, then their “dead-end” should be the place of a past inlet. And paths will be generated between them and QO, independently of the presence or not of the paleo-glacier (thus independently of both hypotheses). Most of all, it will imply that what the authors consider as a proof for benefit of hypothesis 2 is wrong as hypothesis 2 generates conduits sloping in the opposite direction.
Still on this second aspect: by definition, stochastic simulations are stochastic, not determinist. Thus, they do not aim to find the “true location” of a conduit but to propose equiprobable paths given a limited knowledge. In the present approach, the paths are found by fast marching. The cost is a combination between aerial distance (in 2D here) and topographical gradient (because it is what is used here), and fractures. Only the fractures change from one simulation to another, thus, it just allows to create a kind of a glow around the two main elements of the cost function: gradient and distance between the two points of the considered pair. Thus, as soon as a hypothesis is consistent with these two elements, no further ensemble simulation is required. The stochastic approach does not seem to provide any particular interest in that precise case.
To finish with the modelling approach used here, the 2D approach simplification seems not appropriate as it completely ignores the staggering of the karstic conduits (illustrated in theory by the authors in their figure 1). The Borghi et al. algorithm was however 3D and other works by other authors proposed 3D approaches, why not having worked with them? In this precise case, the topographic gradient is used to mimic the hydraulic gradient, but what does the 2D maps represent? Do the authors consider that the conduits are vertical until they reach the 2D map level? In that case, should the paths computed by the model be seen as conduits developed along the piezometric surface? If yes, which one: today’s surface or a paleo-water table? The paper is clear: the authors use only something which they associate to today’s surface, but it is not consistent with their own initial illustration in figure1. If there is a scenario of water-level dropdown like the theory in figure1, this level is moving through time, and thus the “2D equivalent level” (and map) is changing… This is not at all considered by the model here. Another critical point related to this : if what we see in 2D maps are the conduits developed inside the limestone level after the conduits have vertically crossed the highest part to reach the water-table, then on this virtual surface, everything should be limestones => the geological units presented on this 2D maps have no meaning, more precisely, the sandstone and overlying units should be ignored: they have been crossed by the conduits vertically and on the virtual surface we consider, which is below, we are always inside the limestones (except when the limestones are completely eroded). Saying the same differently: the 2D units presented on the maps seem to be what is seen at the surface, while the network develops at depth, inside the Schrattenkalk. This problem was already there in the previous papers.
These are the main reasons for my recommendation. Below are some additional comments, provided following the order of the paper.
“On the flow” comments:
The figure 1 is misleading: it is presenting the principles of a two-phase karstification due to a drop in the base level, resulting in a two-level network. But the used approach in the paper is 2D and can not consider different levels of karstification as elevation is not taken into account in a map view. It is indirectly considered with the anisotropic fast marching approach, where only a global gradient, here parallel to the topography, is introduced to try mimicking the gravity effect. Using such a figure is not appropriate if it does not correspond to the spirit of what is feasible with the approach and if it does not correspond to what is tested/investigated in the following.
Section 2, section 4 and section 5.2 almost say differently the same things: the hypotheses should not be repeated and split in various incomplete parts but regrouped in a single complete section.
Line 53: “this study presents a model-based approach to reconstructing the geologic processes driving cave formation”. I disagree, the modelling approach builds networks by considering the influence of user-defined geologic influencing factors. It does not reconstruct a geologic process, as no physical rules/equation are used.
Line 57: why only two hypotheses? Why only the QS being younger? What about the “ages” of inlets? Inlets could also change depending on the erosion of overlying deposits…
Figure 2:
- use different colours for active vs. inactive conduits.
- Currently and considering the conceptual schema of Chen et al., QO is linked to QS: on the figure it is only a stream: what happens at depth? Do we have information about the connections between the QO point on map and QS point?
- The consistency between the 2D map and schematic cross-section is not clear : contrary to the map, on the cross section, the marls do not outcrop in the valley, as well as the sandstone on the Hoher Ifen, the marls at the same point, then again the sandstone at Gottesacker, etc… Looking at the geological map provided in the 2021 paper, the 2D map used here is also not consistent : limestones should outcrop almost everywhere. The geology is not complex, and the rasterizing effect does not justify all these differences. As this seems to be used in the cost function, this is questioning.
- On the cross section, N1, N6, N11 and N16 should be indicated to help the reading.
Holloch cave: show developed cross-sections of the conduits if possible (vertical organisation?)
Line 107: hypothesis 2 not clear: if the connection is from QO to QA, what should today explain the dis-activation as QA still exist?
Line 127: in 2022 paper, the hydraulic gradient was said to coincide with the elevation of the bottom surface of the limestone for the synthetic case. Here, it is said it is the topography, with the justification “it is simpler to calculate” (line 123): how do you explain such differences? The cross-section in figure 2 is not so evident with different level of erosion for limestone (the Hoher Ifen for example and its surroundings). “Simpler to calculate” is not relevant if the consequences are large (see remarks in above comments).
Line 136: “The conduits leading to each outlet can be simulated in separate iterations, to represent springs of different ages”: in SKS there are not 1 iteration but several ones as you simulate one path between 2 points, and then a second one which is influenced by the first one (second iteration). The authors explain it well in their first paper, thus here this sentence is unclear: what was the real message to pass?
Section 5 and 5.1: a resume of previous papers (but see point 1 of the main comments).
Line 155: referring to Fandel et al 2021 for a detailed description of geology is strange (and partly unfair) as the geology was there taken from the previous works of Goldscheider et al., Chen et al., etc.
Line 157: the model is said “sliced using the topography surface”: why that? the karstic system does not develop at the topographic surface (see also detailed comment above).
Line 158: cells of 50x50m seem quite big for the level of detailed searched here… (the difference in source elevation is largely lower which can induce large border effect in the method, see previous comment on the 2D approximation).
Line 161-171: all already said in previous papers.
Line 163: “expected paths”. These paths are not the karstic conduits in itself (the superposition of the Holloch cave map show this clearly) but a equivalent conceptual model allowing to reproduce the hydraulic response. Why do the authors try to fit it instead of the conduit real paths if they have them? Also, if we have not the real paths, as the concepts guiding the conduit modelling approach is not the same than trying to find an equivalent hydraulic model, why trying to compare both model results? They do not have the same purpose.
Line 169: “These results support placing confidence in the ability of pyKasso-generated ensembles to simulate”: I find it an “over-interpretation”. The end of the sentence: “particularly when the inlet/outlet assignments are fixed” confirms what I said above about the importance of inlet/outlets pairing issue.
Figure 3: Already there in Fandel et al 2022 (said by the author in the legend): why spending so much time on already presented works and results?
Line 192: what proves that the other inlets N1, N6 and N11 exist at the time of the glacier? Is there any field element for that?
Line 194: “The existing inlets remained the same”: why, what support this assumption?
Findings: See the critics in my top comments.
Discussion: apart for the large number of repeated points from previous papers:
- Line 251-257: globally break open doors.
- Line 261: “This is likely a limitation of the model’s ability and our simplified assumptions to predict exact conduit locations rather than general orientations and connections” As I said in the above comments, the goal of stochastic approach is precisely NOT to predict the exact conduit location. If you want an exact prediction, you have to use a deterministic approach. I am surprised by this sentence.
Section 7.1: Not very informative, it is globally a rewording of what was previously said.
Citation: https://doi.org/10.5194/hess-2022-280-RC2 -
AC2: 'Reply on RC2', Nico Goldscheider, 22 Nov 2022
We do not agree with the assessment of the paper in RC2. This comment appears to be based on a fundamental misunderstanding of the principles underlying the work presented in this paper. The referee comment is also very long (and we do appreciate the time and effort invested by the reviewer). If required, we could answer on every single comment. However, for reasons of clarity and comprehensibility, we prefer to answer only the main points and hope to clear up the misunderstanding at hand in this way.
Comment: This paper is not a new contribution because the same work was already presented in two previous papers.
Response: We disagree. This paper is indeed the third in a series, each of which builds on the preceding work. Some minor repetition is therefore necessary so that readers unfamiliar with the first two papers can understand the third. The work presented in this paper is, however, completely novel and fundamentally different from the work presented in the previous two papers. The first paper was focused on developing modeling capabilities in the Gottesacker karst system, while the second one was focused on developing anisotropic fast marching methods for conduit simulations. This paper focuses on applying these methods to understand the geologic history and conduit evolution of the Gottesacker karst system. This is the first time that a stochastic conduit simulation algorithm has been applied to questions about past geologic conditions. Usually, simulations focus on projecting conduit maps based on the geologic setting, whereas here we attempt to reconstruct the geologic evolution based on conduit maps. This is also only the second application of anisotropic fast marching algorithms for conduit simulation. To our knowledge, no other published work uses anisotropic fast marching algorithms to simulate conduits, nor does any published work use conduit simulations to understand past geologic conditions.
Comment: The stochastic modeling approach used in the paper is not appropriate for the hypothesis testing it is applied to in this study because: a) the modeling does not add any information that was not available from topographic maps and spring locations; and b) the model is 2D while the real conduits are 3D.
Response: We disagree. Point a) This comment seems to indicate that it is obvious, without doing any simulations, which of the hypotheses presented in our paper is most likely to be correct, which is, however, not the case. The comment also suggests that the stochastic element of the simulations does not add any useful information, which is also not correct. As described in the paper and in our responses to RC1, the fracture network affects the conduit simulations, and there are significant differences in different parts of the study area in the level of agreement across the ensemble of simulated conduit networks. In some areas, each simulation in the ensemble gave a different path, resulting in a swath across which the presence of a conduit is roughly equiprobable, and orientation of that conduit is difficult to pinpoint. In other areas, almost all the simulations agree, indicating a higher degree of certainty about where the conduit is most likely to be and what orientation it is in. In our simulations of our two hypotheses, none of the conduits simulated under scenario 1 match the mapped conduits, while the conduits simulated under scenario 2 are tightly clustered around the mapped conduits. This increases our confidence in our results, whereas if the ensembles had been spread out, we would have less confidence in our results.
Point b) This comment seems to miss a key point. The model used in this paper, although it yields only a 2D conduit map, is in fact closer to a “two and half dimensional” model, because the elevations of the inlets and outlets are considered, as is the topographic gradient of the catchment. The comment asks why we have not used other algorithms which are 3D, such as SKS, presented by Borghi et al. (2012). We in fact did use SKS as a starting point. However, the existing 3D conduit simulation algorithms such as SKS are isotropic and do not perform well in catchments with steep gradients. Our anisotropic 2D version is currently better-able to capture the three-dimensional conduit network than the isotropic 3D algorithm.
To summarize, we find that the points made in RC2 are based on a misinterpretation of our work, and we disagree with the comment’s assessment of our paper. Beyond this disagreement, we thank the reviewer who made a very thorough and serious review of the paper. Many of the other comments can easily be taken into account in a revised version of the paper to improve clarity and avoid such misinterpretation.
Citation: https://doi.org/10.5194/hess-2022-280-AC2
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AC3: 'Comment on hess-2022-280 - Comment for the handling editor', Nico Goldscheider, 22 Nov 2022
Dear Alberto Guadagnini,
We have answered the referee comments RC1 and RC2. While RC1 includes insightful and constructive comments whose implementation will help to further improve our paper, RC2 appears to be based on a fundamental misunderstanding of the principles underlying the work presented in this paper. RC2 is also very long. In our response, for reasons of clarity, we decided to focus on the major points in RC2 and hope to clear up the misunderstandings at hand in this way.
We hope that you will also read and evaluate our paper, the review comments and our answers personally and/or ask a third reviewer for an independent evaluation.
Best regards, Nico Goldscheider
For the team of authors: Chloé Fandel, Ty Ferré, François Miville, Philippe Renard
Citation: https://doi.org/10.5194/hess-2022-280-AC3
Chloé Fandel et al.
Video supplement
Popular science film about out research in this test site (in German, no direct relation to this paper) Austrian TV, with Nico Goldscheider https://www.pm-wissen.com/umwelt/v/aa-24mv4em992112/
Chloé Fandel et al.
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