the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Water partitioning in a Neotropical Savanna forest (Cerrado s.s.): interception responses at different time-scales using adapted versions of the Rutter and the Gash models
- 1University of São Paulo, Department of Hydraulics Engineering and Sanitation, São Carlos, 13566-590, Brazil
- 2Delft University of Technology, Water Resources Section, Delft, 2628 CN, the Netherlands
- 3Federal University of Mato Grosso do Sul, Campo Grande, 79070-900, Brazil
- 4Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Şirul Beethoven 1, 500123, Brasov, Romania
- 5School of Forest Resources and Conservation, Newins-Ziegler Hall, University of Florida, Gainesville, Florida, 32611, USA
- 1University of São Paulo, Department of Hydraulics Engineering and Sanitation, São Carlos, 13566-590, Brazil
- 2Delft University of Technology, Water Resources Section, Delft, 2628 CN, the Netherlands
- 3Federal University of Mato Grosso do Sul, Campo Grande, 79070-900, Brazil
- 4Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Şirul Beethoven 1, 500123, Brasov, Romania
- 5School of Forest Resources and Conservation, Newins-Ziegler Hall, University of Florida, Gainesville, Florida, 32611, USA
Abstract. Cerrado is the broadest Savanna ecosystem of South America and has an important role in our global climate. How rainfall finds it way through the vegetation layers of the undisturbed Cerrado forest is of utmost importance to understand the evaporation process and the water availability in this unique ecosytem. Nonetheless, only few studies consider the partitioning of rainfall in the Cerrado. And if they do, these studies are limited by only considering interception by the canopy, while the forest floor can intercept a significant amount as well. Additionally, the studies often apply canopy interception models that were calibrated on short term monitoring. Hence evaluating how interception models perform at different time-scales and how the interception process responds to seasonal changes is poorly understood for the Cerrado forest. In this study we aimed to evaluate the canopy and forest floor interception estimates at different time-scales and its seasonal response for an undisturbed Cerrado s.s. forest in Brazil. Two commonly used interception models (Rutter and Gash) were adapted to include forest floor interception using observations of both canopy and forest floor interception during a 32 months study period. Our results show that the models are suitable to estimate throughfall and infiltration at daily basis, but not the evaporative processes. We confirmed that both models had limitations to simulate very high interception rates on an event scale. Nonetheless, both models are able to reproduce the total interception well at monthly scale (R2 = 0.7–0.97, NSE = 0.63–0.85), and they can represent seasonal trends in the interception process in Cerrado s.s. forests. Nevertheless, the Rutter model seems to perform better when seasonal parameters are used than the Gash model, but both models are equally valuable to inter-annual analysis when non-seasonal parameters are used.
Lívia M. P. Rosalem et al.
Status: final response (author comments only)
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RC1: 'Comment on hess-2022-59', Anonymous Referee #1, 11 May 2022
This manuscript describes measurement and modeling of interception loss from the canopy and forest floor. As is typical in published applications of Rutter-Gash models, the models performed poorly at the scale of individual events and better when accumulated over longer timescales as presented in Table 5. The manuscript also presents some potentially important modifications to the standard Rutter-Gash approach by modifying the model of canopy storage, modifying the evaporation rate from trunks, and adding explicit measurement of evaporation from wet forest floor. The manuscript does not evaluate the behavior of the models in relation to the standard ones, so the importance of the modifications is difficult to understand. In addition, the manuscript omits some details about parameter estimation and presents some model modifications vaguely and in terms of programming techniques, so the methods are not possible to follow comprehensively. The discussion touches on some important questions pertaining to the applicability of the modeling assumptions, such as time scale of forest-floor evaporation and assumptions embedded in the Gash model, and mismatches between data needs and data availability, such as radiation budgets relevant for the forest floor. However, the manuscript mostly disregards these important points and the poor by-storm performance, and makes detailed interpretation of the effects of seasonal variation of parameters. Given that the models mostly cannot reproduce per-storm interception loss, and the time-integrated performance is driven by essentially the regression between rainfall and throughfall expressed in the calibrated canopy storage capacity, I do not agree with the conclusions that the modeling is useful for understanding canopy processes. These are not unique problems for this study and field experience is valuable for developing the next generation of models, but the modeling presented in this manuscript is not well described or developed.
This manuscript needs a thorough edit for English: there are many errors in word choice, grammar, and spelling.
L34 these are rainforest numbers that are not likely relevant to the field site here
L74 The logic of the theoretical work should be justified here. The objective based on the study site, L72, is not as important as the theoretical implications of behavior of the model, and there are modifications to the basic models that are not explained here at all. Similarly, the model introduction L55-64 should focus on the shortcomings of existing models that need to be overcome, not on basics.
L94 15,000 stems per ha? This is 1.5 trees per square meter; is that correct?
L98 What use is PAR for this study?
L114 why “also”?
L119 according to DBH, but by what criteria and why?
L120 this reference does not provide details of this device. A few critical details about how a LID works are needed for this part to be understandable
L123 But there was only one August during the study, correct?
L137 upper envelopes and linear regressions are not the same. Stem storage capacity has generally not been estimated by this technique in the past for several important reasons. This needs justification and explanation.
L150 These two ad-hoc modifications to the Rutter model are not sufficiently described or justified. There is no way to understand exactly what "priority" means in terms of the water and energy balances.
L152 What about the Tf records indicate dynamic storage?
L153 this term needs explicit definition. I do not know what Cc is supposed to represent
L156 What is the purpose of Ccmax exactly and how was it implemented? Rutter's model explicitly allows storage C to exceed storage capacity S, with no explicit upper limit. Adding a cap here appears to reduce the dynamic storage, not enable it. Why only "before the end of the storm"?
L159 what is Cmax. the same as Ccmax?
L160 I don't understand this parameter identification technique
L176 I do not understand how data were used to estimate Cfmax
L179 Most of the parameters referred to in this sentence are not defined
L185 I can't find where these parameters were estimated
L199 something is wrong. This describes an experiment 8 months long but fig 3 says 3 years
L204 The original Gash model was applied at the monthly time scale. There is no time scale assumed by Gash
L214 This conflicts slightly with L145. I assume this means stomatal resistance was zero. There are no details about assumptions and about parameters relating to aerodynamic resistance
L220 Fig 3: L218 told us to expect net radiation here
L224 more similar than what?
L242 Why does the Gash model predict essentially the same canopy evaporation every storm except for very small storms?
L264 I think it is unlikely that measurements of forest-floor evaporation were better than the throughfall measurements.
L265 It is not obvious to me that canopy evaporation is modeled better.
L280 As described earlier, it did exactly the opposite: it prevented accumulation
L284 How? Shouldn't mean daily Rutter be the same as daily Gash?
L344 This problem is severe for the modeling assumptions and needs detailed theoretical discussion
L348 I do not understand this sentence
L353 I agree. I think this makes the Gash model irrelevant for this process. Lack of data to estimate energy budgets and vapor exchange at the scale of the forest floor are also major problems for applying a Rutter model. Given that Rutter does not seem to be helpful for estimating canopy evaporation on a storm scale, it seems even less likely to be helpful for forest-floor evaporation.
L397 I don’t think the evaporation model was useful for small storms, either. There was not systematic negative bias in estimates, so that means the small storms were overestimated.
L425 I would agree if the models were useful at short timescales, but they are not. If the processes are not correctly modeled, how can we extrapolate using the model?
L510 I do not agree. The models get major masses correct, but due to calibration (e.g, of canopy storage). The models are not likely to be of any predictive value should, for example, vegetation structure change.
The journal names are missing for many References.
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RC2: 'Comment on hess-2022-59', Anonymous Referee #2, 28 Jun 2022
Comments on "Water partitioning in a Neotropical Savanna forest (Cerrado s.s.): interception responses at different time-scales using adapted versions of the Rutter and the Gash models" submitted to HESS-D by Rosalem et al.
[General comment]
This paper measured the interception processes including forest floor interception loss for a period of 32 months, and applied Rutter and Gash models. Although the methodology used in this study should be evaluated carefully, I recognized that the efforts to conduct measurements would be very intensive, and would agree the importance to understand interception phenomena in Cerrado region. However, the logic of this manuscript is not constructed well, so readers cannot understand what is the most important findings in this research. The main description seems to be the model performances for daily, biweekly and monthly basis. I understood the differences in performance among different time scales, but I do not think that the differences are important to clarify the interception process. The important thing must be what factors resulted in the differences and what processes affect the model performance. I would like to recommend that authors, at first, write a paper to show interception process in this site in detail based only on measurement data without applying any models. The interception loss in this site would be affected by rainfall characteristics (e.g., rainfall duration, rainfall amount, rainfall intensity, etc)? Or, other micrometeorological factors (e.g., wind speed, vapor pressure deficit, net radiation, etc) influence the interception process? Based on these knowledges, I believe that authors could develop suitable models to simulate the interception process. I hope my comments will help to do substantial revisions.
[Specific comments]
Could you add the LAI data? (line 92-93)
I cannot catch up how to calculate the spatially representative amount of throughfall. Is that the average of four Davis, five manual gutters and three automatic gutters? (line 100-119)
Because the canopy in this site is discontinuous (line 93), I could expect that the spatial heterogeneity of throughfall would be very high. Please show the differences in throughfall amount measured by four Davis, five manual gutters and three automatic gutters. The differences are related to the canopy openness? Also, a total of 12 measurements of throughfall are safely enough to obtain spatial representative value?
As Rosalem et al. (2018), published in Ecohydrology, pointed out, Davis gauge underestimates the inflow of water flux with increasing intensity (please see FIGURE 3 in Rosalem et al., 2018). I would like to confirm that authors applied the same correction to throughfall measurements in this paper. If not, application must be required.
Three gutters are connected to three Davis gauge? If so, I am wondering that the one tip amount of 0.048 mm is too small to detect the correct amount. As Rosalem et al. (2018) showed, the underestimation by Davis gauge is relatively high. How many pulses generated by the gauge were recorded in 10-min intervals? The time between tips, equivalent to 600 second divided by accumulated pulse count, should be more than 1.0-1.5 second. Please note that, if authors used other rainfall gauge, similar issue exists and should be investigated.
How did authors calculate stemflow amount in the stand scale?
Three automatic collectors of stemflow is connected to Davis gauge? The same issue mentioned above, underestimation of inflow with increasing intensity, must be checked.
Forest floor interception was measured by two LIDs (line 121-122). Please show the evidence indicating two LIDs could safely measure the spatial representative value of forest floor interception. This is very critical, because high spatial heterogeneity of throughfall could be expected from the disconnected canopy in this site.
Figure 2: The forest floor evaporation was calculated considering potential evaporation (Ep), but how did you calculate Ep above forest floor? Did you measure net radiation above the forest floor?
Please add the description to explain how to calculate the aerodynamic conductance above canopy and forest floor (line 214-215).
How did you obtain the interception ratio of 33%? (line 253) Throughfall was described as 70-72% (line 228), so 33% interception is too large. There is a description of 40% interception in conclusion section (line 481). Maybe the target rainfall events are different among parts, but it is difficult to understand.
I cannot understand why Ec is calculated as the difference Pg and the sum of F, Ts and Ef. I recommend that basic equation showing rainwater balance should be added in the M&M section.
Discussion about the stemflow is not directly related to this paper (line 300-313). If authors show the data of canopy structure, bark, and so on, it is useful. Unfortunately, the current MS did not include any data, so I recommend to remove this part.
I felt that the much sentences in the current MS are related to model performance (line 237-245, 268-273, 293-299, 320-328, 335-342, 368-380, 435-454, Table 6, 7, 8, 9). Similar descriptions are found among parts, so I recommend reconstruction of the logic. In my opinion, it would be better that "discussion" should be separated from the "result" section. Then, descriptions of model performance should move to discussion section, and more concise discussion is recommended. Rather than differences in model performance, the reason for the difference and factors affecting it are more important to understand the interception process in this site.
Looking at appendix C, there are high correlations between observation and model output for throughfall and stemflow. However, the correlation for interception loss is very low. Could you explain this point?
[Technical corrections]
Line 98: "average PAR (photosynthetic active radiation) of 1041.8 ± 427.4 μmol.m-2.s-1", I cannot understand the duration of average.
Line 199-200: "From June of 2019 to May of 2019, we used to calibrate our Rutter and Gash models, while the second part, from June of 2019 up to of 07th February of 2020, .." Please check the consistency of months. The current description is strange.
Figure 3, y-axis title of the upper panel: "Potencial" should be "Potential".
Figure 3, lower panel: Is this net radiation? Solar radiation was measured at 2 m height (Table 1). More than 600 W m-2 value is too high for solar radiation above the forest floor. Please check carefully.
Lívia M. P. Rosalem et al.
Data sets
Dataset of "Water partitioning in a Neotropical Savanna forest (Cerrado s.s.): seasonal and non-seasonal responses at different time-scales using adapted versions of the Rutter and the Gash models Lívia Rosalem https://www.hydroshare.org/resource/9134e6dc7cc94a999ee005966d0399f5/
Lívia M. P. Rosalem et al.
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