| 09 Dec 2025
Influence of rainfall event characteristics and antecedent conditions on subsurface stormflow response of two forested hillslopes
Abstract. Subsurface stormflow (SSF) is a critical runoff-producing mechanism in many upland and mountainous environments, yet the complex relationships between antecedent conditions, rainfall characteristics and SSF response are still not fully understood. Worldwide, the small number of SSF collection systems (trenches), as well as the generally small number of investigated SSF events limit our ability to generalize the findings and explore the influence of a broader range of storm sizes, intensities, antecedent wetness conditions and different hydrogeologic settings. In this study we present a comprehensive analysis of rainfall and SSF event characteristics as well as antecedent conditions, based on data collected at two forested hillslope sites, where SSF was monitored in research trenches over a 2-year period. Our results show that SSF volume is primarily controlled by total rainfall (Ptot) and antecedent wetness, with volumes being up to three orders of magnitude larger under wet initial conditions. At one trench, the volume increased gradually with Ptot, whereas at the other trench SSF volume displayed a threshold-like behaviour, likely linked to the irregular topography of the underlying bedrock. The precipitation threshold varied between ca. 15 and 20 mm for wet and dry antecedent conditions, respectively. Peak SSF flow rates of smaller events were influenced by Ptot and antecedent conditions, but for larger events (Ptot > ca. 20 mm), rainfall intensity was one of the dominant controls along with the rainfall amount preceding peak rainfall intensity. The steepness of the rising limb of the SSF hydrograph was correlated with Ptot and rainfall intensity. The antecedent soil moisture index (ASI) together with Ptot showed a high correlation with most SSF characteristics. The seasonal analysis revealed that, statistically, the largest SSF volumes occurred in winter, while the highest peak flows and rising rates were observed in spring and summer.
Competing interests: One of the (co-)author is a member of the editorial board of Hydrology and Earth System Sciences. One co-author is Chief Executive Editor of the journal Hydrology and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
I applaud the authors' effort to analyse subsurface storm flow (SSF), given that this is an underresearched topic. In general, the experimental setup is sound, but still the findings are heavily biased (as often in hydrological research). This bias results from the fact that we can rather easily analyse 100 storms to quantify the influence of storm characteristics and antecedent moisture conditions. From this must follow, even without analysing the data, that storm and antecedent conditions have an influence. This, however, does not answer the question of whether this influence is large at all (as put forward in the manuscript), because the influence of other drivers was not analysed. They could have, and likely have, a much larger influence, but we cannot analyse 100 soils, 100 forest types, or 100 catchment sizes as easily as 100 storms, and then still do not capture the strong interaction of soils, forest types, and catchment sizes. This bias, which does not diminish the scientific value and quality of this study, should be prominently displayed because, very likely, subsequent modelling based on the study results will conclude that soils, forest use, or catchment size have only a minor effect, although the scientific foundation to support this is weak or even lacking.
The study, like all studies, depends on assumptions. The assumptions were justified. However, I missed a critical assessment of how much the findings were influenced by the assumptions and would differ under deviating assumptions. In particular, I found the definition of rain events (break 6 h) critical, given that the duration of runoff events was much longer, with the falling limb of the hydrograph lasting 240 h. These short rain events were visible on the hydrograph, but they were not independent of the previous rain that caused the falling limb. The authors use the antecedent soil moisture conditions (ASI) to correct for this discrepancy between definitions of rain events and runoff events, but I wonder whether this attempt to correct one deficit by introducing another yields helpful and applicable findings. This notion can be supported by several arguments:
Details
L 18-19: No; rainfall and moisture were the only variables analysed. Hence, it is not known whether they primarily control SSF.
L 22: This number is misleading because it depends on definitions and assumptions that are not explicitly stated in the abstract.
L 35: Doesn't the condition "overlain by a more permeable layer" always exist due to the wetting front? Isn't the more general condition leading to SSF that conductivity plus sorptivity (sensu Philip) is smaller than rain intensity?
L 56: The event is not yet defined. Furthermore, this sentence suggests that the events of precipitation and SSF agree, whereas later, SSF lasts much longer than precipitation.
L 72: ASI is not defined yet (only in the abstract).
L 86: I would argue that the complex relationships are a result of a complex definition of rain events being part of an SSL event.
L 121: Does 'concave' apply along or across the slope? To me, Fig. 1 suggests a convex shape across the slope (diverging water), not a concave slope. I may misinterpret Fig. 1.
L 125: The unit should be mm/yr.
L 130 ff: The soil material is either described as periglacial deposits or as colluvium. Both terms appear to be used interchangeably. In my understanding, both are different (Pleistocene vs. Holocene; existing along the entire slope vs. existing in toe-slope positions; coarse stones are possible vs. coarse stones are usually depleted compared to upslope soils).
L 160: I wonder why Robinia is defined to the species level while all other trees are only defined to the genus level.
Table 1: Depth is not clear. Is this the upper or the lower boundary or the midpoint? It may be worth noting whether the size classes follow the 2/50, the 2/63 or any other system.
L 218 ff: This is a strange definition of an event that must yield a complex behaviour, as complained about on L. 86. Please note that for erosion events, also a minimum dry period of 6 h is until today a generally accepted requirement going back to Wischmeier (1959), who developed this from only 20 m long plots. These two identical definitions of a dry period separating runoff events are in conflict because the travel time of surface runoff is several orders of magnitude shorter than that of SSF.
L 264: 'Non-triggering' is also a strange category. It assumes that rain does not contribute to SSF when it does not trigger a noticeable peak in SSF. From water balance considerations, the non-triggering events during the falling limb of the hydrograph should also contribute to SSF. Or where else does the water go? Ignoring these events must increase the unexplained fraction of SSF.
L 277: Shouldn't it read "trenchflow rate"? The omission of the term 'rate' is particularly confusing, as rates are often abbreviated with lowercase letters, while sums are abbreviated with uppercase letters.
L 281: This assumption is in conflict with the general understanding of hydrology, which suggests that soil moisture changes due to rain. Why not use Vtot/(Ptot-VWC change)? This would lead to a more realistic estimate of the contributing area.
L 301: Why should the average VWC be important and not the total? In your assessment, Htot is constant, and hence the difference between average and total does not appear. However, when transferring your results to different catchments, the difference becomes important. I would even expect that a contrast leasing to contrasting conductivities is important. A contrast would neither be captured by the average nor the total but by SD or similar metrics.
L 313: Why is D used here and not Htot? Is there a difference between the two? Explain.
L 318 + 328: Shouldn't ASI minus PWP (permanent wilting point) be used instead of arbitrarily adjusting ASI? From a theoretical perspective, ASI should not be the correct parameter. Furthermore, I do not see a justification for a fractional adjustment factor to correct this.
L 334: This is clearly a spurious correlation that cannot be interpreted because an identical variable is used on both sides of the equation (see Pearson, 1897, or specifically in hydrological research: Kenney, 1982). For some specific cases, a correction exists (Kenney, 1982; Kanaroglou, 1996).
Fig. 8: Many of these correlations are at least partly spurious because both sides of the equation contain common elements. E.g., the calculation of Ptot depends on the intensities. The significance, as denoted, is then meaningless. This makes interpretation of the figure impossible because the degree to which common numbers were used on both sides differs (I5 contributes considerably less to Ptot than I60; hence, the difference between correlation coefficients may only be due to a different fraction of the spurious component). Furthermore, the table is clearly multiple testing, which would require an adjustment to maintain the alpha level. Less adjustment would be necessary if the authors did not weaken the statistical power of their analysis by testing parameters like I5 or I10, for which I can see no hydrological argument as to why these parameters should generally drive SSF. Also, no confidence intervals are given. Hence, it cannot be decided whether one correlation coefficient is larger than the other. The text wrongly assumes that the larger value is always better.
L 440: Does the area really change, or does this only reflect the shortcomings of Eqn 1?
Table 3: r² and the parameters of Eqn 4 are spurious. How can the r² of Eqn 5 be calculated when the text says that Eqn 5 was obtained by rearranging Eqn 4? Is r² then the Nash-Sutcliffe efficiency? Calculation Eqn 5 directly would be advantageous because it avoids the spurious step.
L 4 66: No confidence intervals are given to support this statement.
L 478: same remark as above
L 508-509: Where is this shown? A test like the Hotelling test must be applied to support such a statement. I am unsure whether the Hotelling test can be applied to Spearman's correlation or if an analogous test exists. If not, Spearman appears unsuitable for the intended task.
Fig. 19: Again, confidence intervals would be required to support the interpretation made in the text.
L 738: This statement is only true for SSF volume as defined in this study. Whether this is generally true remains unknown.
L 742: The contributing area was unfortunately not quantified. Only a minimum area was quantified. By omitting the adjective, a wrong impression is given (not to mention that the adjective is wrong as well, because the equation makes an unrealistic assumption).
L 764: R. C.?
L 767: and beta.
Table A1: The assumed porosities for the two lowest depths of T3-B3 are very unlikely. For the measured bulk density, this would only be possible if the density of the solids were 3.2 kg/L, which is considerably above that of the main minerals in soils. Something else must be wrong in the calculations if they require unreasonable porosity to yield reasonable results. In contrast, the measured porosities are in line with the measured BDs.
Â
References:
Auerswald, K., Gu Q.-L.: Reassessment of the hydrologic soil group for runoff modelling, Soil and Tillage Research, 212, 2021. https://doi.org/10.1016/j.still.2021.105034
Kanaroglou, P.S.: On spurious correlation in geographical problems, The Canadian Geographer, 40, 194–202, 1996.
Kenney, B.C.: Beware of spurious self-correlations!, Water Resources Research, 18, 1041–1048, 1982.
Pearson, K.: Mathematical contributions to the theory of evolution – On a form of spurious correlation which may arise when indices are used in the measurement of organs, Proceedings of the Royal Society of London, 60, 489–498, 1897.
Wischmeier, W. H.: A rainfall erosion index for a universal soil-loss equation, Soil Sci. Soc. Am. Proc., 23, 246–249, 1959.