the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Feb 2024
Vegetation Response to Climatic Variability: Implications for Root Zone Storage and Streamflow Predictions
Abstract. This paper investigates the influence of multi-decadal climatic variability on the temporal evolution of root zone storage capacities (Sr,max) and its implications for streamflow predictions at the catchment scale. Through a comprehensive analysis of 286 catchments across Europe and the US, we analyse the deviations in evaporative ratios (IE) from expected values based on catchment aridity (IA) and their subsequent impact on Sr,max predictions. Our findings reveal that while catchments do not strictly adhere to their specific parametric Budyko curves over time, the deviations in IE are generally very minor, with an average ΔIE = 0.01 and an interquartile range IQR= -0.01 to 0.03. Consequently, these minor deviations lead to limited changes in predictions of Sr,max, mostly ranging between -10.5 and +21.5 mm (-5.1 % to +9.9 %). When these uncertainties in Sr,max are incorporated into hydrological models, the impact on streamflow predictions is found to be marginal, with the most significant shifts in monthly evaporation and streamflow not exceeding 4 % and 12 %, respectively. Our study underscores the utility of parametric Budyko-style equations for first order estimates of future Sr,max in hydrological models, even in the face of climate change and variability. This research contributes to a more nuanced understanding of hydrological responses to changing climatic conditions and offers valuable insights for future climate impact studies in hydrology.
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RC1: 'Comment on egusphere-2024-115', Andrew Guswa, 24 Mar 2024
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Review of egusphere-2024-115
Tempel et al.
Vegetation response to climatic variability: Implications for root zone storage and streamflow predictions
Review by Andrew J. Guswa, Smith College, aguswa@smith.edu
In this paper, the authors compare the response of long-term, actual evapotranspiration (E_a) to changes in climate (precipitation, P, and/or potential evapotranspiration, E_p) across 286 (mostly humid) catchments. They do this in two ways:
1) they fit a parametric Budyko curve (with parameter, w) to a decade of data, and then use that Budyko curve to estimate E_a for future decades with new P and E_p.
2) they use the data from future decades to determine E_a directly from the water balance, presuming zero change in storage over that long time.
They quantify the difference between those two values of E_a as delta_E_a. And they also quantify the change in the ratio of E_a to P (I_E) as delta_I_E.
The authors use those two values of long-term E_a (or I_E) to estimate two values of maximum root depth (Sr,max) and the difference between them: delta_Sr,max.
They show that the differences in Sr,max from the two methods are small, as are the subsequent effects on hydrologic modeling when the uncertainty in Sr,max is incorporated into a process-based model.
The study nicely integrates large datasets and modeling to elucidate the minor importance of those differences in Sr,max to hydrologic modeling. The paper is clear, well-written, and the figures are compelling. I have a few minor comments (see below), and I also think the authors could discuss further the implications of the simplification they employ to estimate Sr,max.
Regarding the latter, the authors simplify daily actual evapotranspiration (E_a_daily) to be equal to daily potential evapotranspiration scaled by the decadal ratio of actual to potential ET. They then uses a daily water balance to determine the necessary Sr,max to deliver that daily evapotranspiration. Using a constant ratio to convert potential ET into actual, however, does not necessarily reflect the behavior of catchment vegetation. As a counterpoint, one might expect potential ET to be met fully during periods of low E_p (and plentiful water) and actual ET to approach zero during periods of high-demand/drought. Thus, another way that one could determine the requisite Sr,max – as opposed to equations 4 and 5 – would be to simplify the system such that
- E_a = E_p if water is available in storage
- E_a = 0 when the water in storage is depleted
- Find the value of Sr,max such that the (long-term sum of E_a)/(long-term sum of E_p) equals that desired long-term ratio
Such an approach may better represent vegetation response (albeit a little extreme, along the lines of Milly, 1994), and would be more consistent with the complementary hypothesis for evaporation (see multiple references by Szilagyi)
It may be that the resulting Sr,max does not differ much from that determined from equations 4 and 5, due to the self-limiting process of ET (e.g., whether one removes 5 mm on day one and then zero on day two or 2.5 mm on day 1 and another 2.5 mm on day 2 may not matter). However, it would be interesting to compare and to see if there is a difference, especially for the monthly/seasonal results, where the differences may have an even larger effect.
I understand this may be beyond the scope of the paper. Nevertheless, given the significance of equations 4 and 5 on the central message of this paper, I recommend that the authors spend more time discussing those simplifications, alternative simplifications (such as that above), and the potential implications on the results and conclusions.
Relatedly, I think the abstract and discussion would benefit from additional acknowledgment that the catchments used in this study are both snow-free and relatively aseasonal. Thus, the conclusions may not be extensible to snow-dominated watersheds and/or those with strong seasonality, such as a Mediterranean climate.
Minor comments
Title
Given the nature of the datasets used, I think a more representative title for this work would be “Catchment response to climatic variability: Implications for root zone storage and streamflow predictions.” The CAMELS datasets are catchment-based, and the authors are not isolating specific vegetation responses.
Nomenclature
I found it somewhat confusing that the meanings of the subscripts modifying evapotranspiration (E) and aridity index (I) were not consistent. When A was used as a subscript, it meant “actual” when modifying evapotranspiration; however, it meant “aridity” when modifying the index, which – in turn – meant it signified potential (not actual) ET. Thus, I_A was not the analog to E_A; rather I_E was the analog to E_A. Perhaps I_A could be used to indicate the evaporative index based on actual ET, whereas I_P could indicate the evaporative index based on potential ET.
Line 52-60 The authors present their methods of determining Sr,max from a daily water balance (see above). In essence, the Sr,max is the storage volume needed to ensure that daily ET can be met. However, that value represents a minimum value for Sr,max, which – of course – could be larger. It might be worth a comment to that effect, especially since those values of Sr,max are then used in a hydrologic model with a very different mathematics.
Lines 165-172 The numbering scheme used in this paragraph does not exactly match the numbering of the methods sections to which it refers.
Lines 299-307 I particularly appreciate that the authors sought explanatory variables, such as aridity index, for their results. I expected aridity to be a controlling factor, and it was interesting to learn that it was not.
Line 431 dangling phrase, “the more equilibrated scenario A”
Equation 5
As written, the equation is circular. What should be used as the argument of the inequalities on the RHS is the integral from t0 to t of (P_daily - E_A_daily) dt rather than S_D,j,i(t)
References
The reference for Dralle, et al. 2021 is missing from the reference list
Figure 7
I recognize that figure 7 is intended to explain the methodology and not results. Even so, I recommend that the qualitative character of the distributions for delta_I_E reflect the results of this paper. That is, the distribution for scenario A should be narrower than that for scenario B; and the mean for scenario B could even be shifted away from zero (compare Figure 7 and Figure 11). As is, the figure gives the false impression that the uncertainty across all catchments is greater than across the Meuse watershed alone.
Citation: https://doi.org/10.5194/egusphere-2024-115-RC1
Data sets
CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. G. Coxon et al. https://essd.copernicus.org/articles/12/2459/2020/
The CAMELS data set: catchment attributes and meteorology for large-sample studies. N. Addor et al. https://hess.copernicus.org/articles/21/5293/2017/
Model code and software
WFLOW FlexTopo W. van Verseveld et al. https://zenodo.org/records/7040513
Interactive computing environment
Paper Vegetation Response Python notebooks Nienke Tempel https://github.com/nienketempel/Paper-Vegetation-Response-2024-
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