Dear associate editor,

We are thankful for the supportive and constructive comments you and two reviewers provided for our manuscript. We thoroughly revised the manuscript based on the review, and hope that our efforts, with the insight from your end, have improved the manuscript.

Below, you will find the original comments of Reviewer 1 in bold, followed by our detailed responses and descriptions of revisions to the manuscript. We have attached a document (Supplement_Reviewer1.pdf) containing the revised Supplementary Material, which allows for better visualization of the figures included in this response, as well as the updated Figures 2 and 6 of the manuscript.

Kind regards,

Alexandre Lhosmot, on behalf of all authors

The paper by Lhosmot and co-authors presents considerable empirical data from the well studied Scotty Creek watershed where research has been ongoing for decades. In this work, the authors collate water balance data across temporal and spatial scales to answer questions regarding hydrological partitioning and dominant flux, and also touch on issues related to basin delineation, data products versus observations and other issues. 

First, I want to acknowledge that there is a lot of field data that has gone into this work and it is important to acknowledge the true challenges that this entails. The method section provides sufficient detail for the readers to evaluate where this data came from, how it was processed, and points to companion papers for further information. In short, I see no substantive issues with the data presented or the approaches used to assemble it. 

Thanks.

The paper is quite long because of all the data presented. Data is presented in both tabular and figure format directly, and I’d suggest the author to select one for the main part of the manuscript and move the other to the SI. Perhaps the figures as opposed to the tables. 

We agree with the reviewer. We moved Tables 2 and 3 to the Supplementary Material as Tables S1 and S2, respectively.

While water balances are interesting for those looking at long-term studies impacts of hydrological change, the most important contributions come from the insights of the authors. My question to the authors is: what new insights are there in this paper? For me, the discussion section does not advance much on the results, restating general patterns without a critical assessment of what insights can be gained from this data set. The general fluxes patterns are not new, but is there something that can be gained by linking this small scale with larger scale data? What here advances our understanding of process from this well studied system? 

This study provides key insights into how the hydrological responses of rapidly thawing boreal peatland complexes–at both sub-basin and basin scales–are shaped by complex factors (e.g., hydrological connectivity) that extend beyond year-to-year changes in precipitation and evapotranspiration (ET). Further details are provided in response to the following reviewer comment.

In addition, much is made of the changes in runoff ratio at the basin scale, but again, this is speculative at the moment talking about changes in drainage efficiency, changing plant communities, etc. Can the authors convince us that this is the case. The runoff ratios are presented on an annual basis, so I’m unsure if that plant community and peat drying hypothesis holds. Is the change in the ratio largely due to differences in the freshet SWE to Q in May? Or is it the rest of the year? These are important issues considering that these changes in runoff ratios are more regional. These ecosystems are changing remarkably fast, but how can something as complex as hydrological response (and runoff which integrates a lot of processes) be linked to that. I would have liked some additional analysis here, perhaps looking at previous season wetness or fall wetness which may influence the following spring runoff. There is talk of regional teleconnections that at this long-term scale could be explored even simply. 

We conducted two additional analyses to address this comment and set of questions. Specifically, we examined (1) runoff ratios during different periods of the year, and (2) current-year and previous-year influences on effective precipitation (precipitation minus ET) on annual runoff.

(1) We calculated runoff ratios for the spring freshet (using April-May rainfall and the late-winter SWE) and summer (June-September). Both periods exhibit an increasing trend from 1996 to 2011 (Figure 1), reinforcing that runoff increased independently of precipitation changes. Runoff ratios during spring freshet and summer periods show two distinct phases: 1996-2011/2012 with an overall increase in runoff ratio, and post-2012 with generally lower and more variable runoff ratios (Figure 1). Similar runoff ratios between spring freshet and summer periods suggests that a common driver or set of drivers, likely hydrological connectivity and drainage efficiency, influences both spring freshet and summer runoff patterns.

Figure 1. Annual, spring freshet (April–May), and summer (June–September) runoff ratios at Scotty Creek basin. The late winter SWE is included in the spring freshet runoff ratio calculation. The dashed vertical gray line corresponds to 2012. 

We also examined the difference between spring freshet (April-May) and summer (June-September) runoff ratios (Figure 2). Runoff ratio differences for most years remain within the narrow range of -0.1 to +0.1. Before 2012, only  2008 stands out, but more pronounced run ratio differences became more frequent after 2012. The generally greater decline in summer runoff ratio compared to spring freshet suggests pronounced wetland drying during summer months, likely caused by their temporary disconnection from the effective drainage area (Connon et al., 2015). From 2010 to 2018, wetland drying associated with hummock development has been described previously in the headwater portion of the Scotty Creek basin (Haynes et al., 2020, 2022). Such widespread wetland drying may help explain the overall decrease in runoff ratio at the basin scale observed since 2012.

Figure 2. Difference between spring freshet and summer runoff ratios.

(2): To assess the role of previous year’s hydrological conditions on annual runoff, we created scatter plots of annual basin runoff (hydrological year, October to September) as a function of:

(a) current year’s effective precipitation,

(b) previous year’s effective precipitation,

(c) cumulative current and previous year’s effective precipitation.

Effective precipitation is defined here as the difference between precipitation (late winter SWE and rainfall) and ET from BESS for a hydrological year. Our analysis suggests that current year’s effective precipitation provides the best linear correlation with runoff, while previous year’s effective precipitation has no explanatory power (R² = 0.0, Figure 3).

Figure 3. Annual runoff vs. (a) current year’s effective precipitation, (b) previous year’s effective precipitation, and (c) cumulative effective precipitation from current and previous years. 

However, correlation between current-year effective precipitation and runoff remains weak (R² = 0.2), suggesting that other processes, such as changes in landscape hydrological connectivity, might play a more important role in controlling runoff. Additionally, cross-correlation analysis reveals that previous years’ effective precipitation cannot explain basin runoff (Figure 4). The best correlation (Pearson’s correlation = 0.45) was with the current year's effective precipitation.

Figure 4. Cross-correlation plot between annual basin runoff and effective precipitation. 

Figure 1, 2, 3 and 4 have been added to the Supplementary Material as Figure S5, S6, S7 and S8, respectively. Additional analyses (1) and (2) led to modifications in the revised version of the manuscript (line numbers refer to the original submission):

Starting at line 48 (Abstract): “Overall, we demonstrate how the hydrological responses of rapidly thawing boreal peatland complexes–at both sub-basin and basin scales–are shaped by complex factors that extend beyond year-to-year changes in precipitation and ET. ”

Starting at line 563 (Discussion), all of Section 4.3 has been revised: “Understanding long-term runoff dynamics of thawing boreal peatland complexes remains challenging due to strong ecohydrological feedbacks (Shirley et al., 2022; Song et al., 2024; Walvoord and Kurylyk, 2016). Variability in precipitation regimes may have influenced runoff ratio dynamics as suggested by the peak in runoff ratio in 2020 (0.5–0.8), the rainiest year in 1996-2022 period (Figure 6-b). Under wet conditions, ephemerally connected wetlands can increase the effective drainage area (Connon et al., 2015), whereas during dry periods, some wetlands become hydrologically disconnected, thereby reducing the runoff ratio. The lower runoff ratio in summer compared to the spring freshet is consistent with intensified wetland drying during the summer months (Figure S5 and S6).

The weak correlation between current-year effective precipitation (precipitation minus ET) and runoff (R² = 0.2, Figure S7) suggests that other processes such as rapid changes in land cover and hydrological connectivity may have played a more dominant role in controlling runoff. Additionally, cross-correlation analysis showed that current-year effective precipitation provides the best linear correlation with runoff, while antecedent wetness offers no explanatory power (Figure S8).

From a landscape perspective, in the headwater portion of the Scotty Creek basin, Haynes et al. (2022) estimated a 1.4 % forest loss between 2010 and 2018. Rapid permafrost thaw and connection of wetlands to the drainage network are expected to increase hydrological connectivity, leading to an increase in permanent and transient runoff (Connon et al., 2014, 2015; Haynes et al., 2018). However, despite these changes, the average runoff ratio over the 2013–2022 period was lower than during the 2003–2012 period (Figure 6-b). Meanwhile, drying of hydrologically connected wetlands has been reported at Scotty Creek between 2010 and 2018 (Haynes et al., 2018), which facilitated the development of individual hummock landforms indicative of drier near-surface peat layers (Haynes et al., 2022). Wetland drying limits the saturation of permeable near-surface peat layers, which can coincide with a decrease in drainage efficiency. For example, the high hydraulic conductivity of the near-surface peat layer promotes more effective drainage compared to deeper peat layers (Ingram, 1978; Morris et al., 2011; Quinton et al., 2008). Vegetation succession occurring within approximately a decade following wetland initiation can lead to vertical peat accumulation above the water table. This elevation of the peat surface contributes to reduced saturation in near-surface peat layers by hydrologically decoupling them from the saturated underlying peat (Errington et al., 2024). Therefore, the decrease in runoff ratio observed after 2012 may have been attributed to reduced drainage efficiency resulting from wetland drying.

Our analysis indicates that competing influences of wetland expansion (which increases hydrological connectivity) and wetland drying (which reduces hydrological connectivity) are key drivers of long-term runoff variability in boreal peatland complexes near the southern permafrost limit. Accordingly, sustained long-term hydrological monitoring is essential to disentangle the respective impacts of precipitation and land cover changes on runoff ratio within such rapidly changing landscapes. A sub-basin-scale modeling study suggests that replacing 50 % of forested peat plateaus with wetlands leads to a reduced runoff ratio following wetland drainage. This change was attributed to increased surface storage capacity, reduced runoff efficiency, and higher landscape evapotranspiration, assuming no increase in precipitation (Stone et al., 2019).

In the boreal biome, wetlands exhibit higher mid-day ET than adjacent forests during the growing season (Helbig et al., 2020a). Projections for the 21st century indicate that wetland ET will exceed forest ET by more than 20 % across approximately one-third of the boreal biome under the Representative Concentration Pathways (RCP) 4.5 scenario, and up to two-thirds under the RCP 8.5 scenario (Helbig et al., 2020b). While wetland expansion at the expense of forested peat plateaus increases ET, it may significantly influence both the water balance and the regional climate (Helbig et al., 2016). These findings highlight the continued need for long-term measured and modeled ET comparisons, as ET is likely to play a crucial role in shaping the future water balance of boreal peatland complexes near the southern permafrost limit.” 

Starting at line 675 (Conclusions): “This study contributes to a better understanding of the hydrological response of small-scale basins (here: ‘sub-basins’) within the headwater portion of a meso-scale basin (here: ‘basin’) in the Taiga Plains in northwestern Canada. We provide insights into how the hydrological responses of rapidly thawing boreal peatland complexes–at both sub-basin and basin scales–are shaped by complex factors (e.g., changes in land cover and hydrological connectivity) that extend beyond year-to-year changes in precipitation and ET. Specifically, we find that:

•     determining runoff in low-relief landscapes such as thawing boreal peatland complexes is challenging because

○      sub-basin and basin boundaries and resulting drainage areas must be approached with caution since permafrost ridges act as barriers isolating wetlands from the effective drainage network, and

○      of difficulties in integrating spring freshet runoff into the growing season water balance.

•     the small-scale headwater portion is representative of the corresponding meso-scale basin. At both scales, our analysis shows that

○      ET is the dominating water loss, on average more than twice than runoff,

○      growing season (sub-basin) and annual water balance components temporal dynamics (basin) are similar,

○      spring freshet peaks are similar, except for the driest year, when basin runoff is more than ten times lower than sub-basin runoff, and

○      spring freshet contributions to runoff in the April-September period are similar.

• long-term changes in basin-scale runoff ratio cannot be explained by precipitation and ET alone. The increase in runoff ratio from 1996 to 2012 likely reflects enhanced hydrological connectivity and wetland drainage. In contrast, the shift to a lower mean runoff ratio from 2013 to 2022 may be attributed to wetland drying following loss of connection to the drainage network. We propose that wetland drying, observed at the headwater sub-basin scale, accounts for the declining runoff ratio at the basin scale. At the same time, new isolated wetlands are forming, and additional wetlands may be becoming connected to the drainage system. Thus, the observed changes in runoff ratio likely reflect the competing influences of wetland drying and the emergence of new hydrological connections.”

Section 4.5 is perhaps not needed. Much of this has been touched on in the introduction to the paper so the authors may consider trimming or deleting. I’m not sure how much value it has. 

We shortened this section by removing its first paragraph (L647 to 651, original submission) which was more general and somewhat redundant with the Introduction section. We clarified the remaining part of Section 4.5 as follows:

Starting at line 652 (original submission): “Non-linear hydrological responses such as changes in runoff ratio, ET and water table position to variations in precipitation and hydrological connectivity driven to by permafrost thaw are linked to shifts in soil physical properties, microbial communities and vegetation composition and structure. These interconnected changes collectively influence ecosystem services at multiple scales, including local (e.g., subsistence activities), regional (e.g., water storage) and global levels (e.g., carbon storage as reflected in the net ecosystem carbon balance [NECB]; Camill et al., 2001; Chapin et al., 2006; Ernakovich et al., 2022; Jones et al., 2022; Li et al., 2023; Shirley et al., 2022). Assessing whether thawing boreal peatland complexes act as a net source or sink of carbon (NECB), once both vertical and lateral fluxes are considered, is therefore an important avenue of research (Song et al., 2024). For example, a recent review showed that dissolved organic carbon concentration can be elevated in sporadic and discontinuous permafrost areas and tend to increase with permafrost thaw (Heffernan et al., 2024). Thus, understanding the mechanisms driving runoff, such as the spring freshet, is essential for quantifying lateral carbon exports to NECB (Chapin et al., 2006; Gandois et al., 2021; Laudon et al., 2004).

Long-term hydrological monitoring is also essential for understanding how gradual changes (e.g., vegetation shift, increasing T_air) are interlinked with more frequent and intense pulse disturbance events (e.g., weather extremes, abrupt permafrost thaw, wildfires) (Li et al., 2023). Wildfires have been shown to accelerate permafrost thaw (Gibson et al., 2018), posing an increasing threat to ecosystem services. The year 2023 set a record for surface burned across Canada (MacCarthy et al., 2024; Wang et al., 2024). As water table position and moisture can constitute an indicator of fire risk, understanding the water balance dynamics of peatland dominated basins may help in managing fire risk (Kartiwa et al., 2023; Mortelmans et al., 2024). In October 2022, the Scotty Creek basin was impacted by a late-season wildfire. While the wetland flux tower and several cutthroat flumes remained intact (Figure S1), the landscape flux tower was destroyed and rebuilt in March 2023. Our work, which contributes to understanding the hydrological response of a rapidly thawing boreal peatland complex, can serve as a baseline for understanding the combined effects of permafrost thaw accelerated by wildfire.”

Finally, there is very limited discussion of error or uncertainty here. I am not advocating for a full uncertainty or error analysis, but the authors could be a bit more forthcoming about this, particularly given the levels of interpolation and reliance on point measurements. Again, I understand the authors know the data well, but I’m somewhat surprised that there is little discussion on this considering some things like SWE come from a site far from the basins. I’m sure there has been some assessment done as to how effective this is other than citing another paper. 

We have modified the Methods section to better emphasize that all data presented at sub-basin scale including snow water equivalent (SWE) were measured within or near the three sub-basins in both the wetlands and the forest:

Starting at line 301 (original submission): “Precipitation (P_SUB-BASIN) including R and SWE in late March just before the start of snowmelt (i.e., SWE_MAX) was obtained from rain gauge measurements (R_WEST = R_EAST = R_SOUTH), and calculated as weighted mean for each sub-basins (SWE_{MAX_SUB-BASIN}) according to sub-basin specific cover areas (i.e., wetland [A_WET] and forest areal coverage [A_FOR]) and associated measured SWE from late-winter snow surveys (i.e., forest [SWE_{MAX_FOR}] and wetland SWE [SWE_{MAX_WET}]), respectively:”

To better constrain the sub-basin water balance in May, the snowmelt rate for the 2014-2016 period was estimated using a simple snowmelt model based on the temperature index equation (see the following response).

At the basin scale, we chose to use SWE data from Fort Simpson due to time series continuity and their strong correlation with SWE data from Scotty Creek (Connon et al., 2021). Fort Simpson A station is operated by NAVCAN for the purposes of air traffic safety, while the ‘Climate’ station is operated by ECCC for the purposes of climate monitoring. Although the two stations are located right beside each other, the gauges are different, and the recorded values are different. The Climate station likely produces more reliable data. We slightly refined the Methods section by adding information on data handling: 

Starting at line 133 (original submission): “Data from the Fort Simpson Climate station, WMO ID: 71365, was gap-filled with data from the Fort Simpson A station, WMO ID: 71946, Environment and Climate Change Canada, climate.weather.gc.ca.”

Could you not have used a snowmelt model? You use a complex model for ET at the basin scale so I’m unsure why you can’t apply a simple melt model to appropriately partition SWE at least at the monthly level. 

At the basin scale, since we reported the annual (hydrological year) water balance, snowmelt timing was not an issue. At the sub-basin scale, accounting for all the SWE in May resulted in an overestimation of its contribution for that month, however, this was discussed in this study. Incorporating a snowmelt model would reduce the water balance residual in May but would not provide additional insights within the scope of this study. We performed a simple snowmelt model using the temperature index equation (Pomeroy and Brun, 2001; Fontrodona-Bach et al., 2025): 

Snowmelt (mm of water equivalent) = C_f × (T_air - T_threshold)

where, C_f is the melt factor (mm °C^-1 day^-1), T_air is the daily mean air temperature measured at Scotty Creek, T_threshold is the threshold temperature at which snow begins to melt. T_threshold ranged from -1 to +1 °C (Fontrodona-Bach et al., 2025 [preprint], https://doi.org/10.5194/egusphere-2025-1214 and references therein). 

C_f was estimated using the relationship between snow density and C_f described by Rango and Martinec (1995) (10.1111/j.1752-1688.1995.tb03392.x). Given that snow density at Scotty Creek ranges from 0.11 to 0.29 (Connon et al,. 2021), the corresponding C_f values were estimated to range from 1 to 3 mm °C^-1 day^-1. 

To account for these uncertainties, we performed 10,000 simulations using a Monte Carlo approach, where C_f​ and T_thresholds were randomly sampled within their respective ranges (Figure 5, added to the Supplementary Material as Figure S3).

Figure 5. Cumulative snowmelt evolution estimates for 2014, 2015, and 2016. The colored line represents the median of the 10,000 simulations, while the shaded area represents the interquartile range (25th to 75th percentiles). The vertical black dashed line marks May 1st.

Accordingly, we added Figure 5 and the snowmelt model description to the Supplementary Material and added the snowmelt model outputs in the discussion as follows (section 4.1):

Starting at line 514 (original submission): “Regarding monthly water balance, high residuals observed in May for all three years (Figure 4-a, b, c) might be explained by the inclusion of snowmelt input through SWE that month. Due to limited data availability,  SWE_MAX, estimated in late March just before the onset of snowmelt, served as a proxy for snowmelt input in the water balance in May, highlighting the challenge of appropriately accounting for the spring freshet in the growing season water balance through observations. To shed light on this challenge, we estimated the amount of snowmelt at the end of April using a simple temperature index model (Figure S3). The estimated snowmelt amounts (median [25th-75th percentiles] from 10,000 Monte Carlo simulations) at the end of April were 105 [78-136] mm in 2014, 187 [138-238] mm in 2015, and 125 [92-159] mm in 2016. These ranges correspond closely to the SWE_MAX measured each year (102 mm, 167 mm and 128 mm in 2014, 2015 and 2016, respectively), suggesting that only a small portion of SWE_MAX contributed to the May water balance. This would reduce the high residuals in the May water balance in the West sub-basin estimated as +149 mm (2014), +176 mm (2015) and +117 mm (2016).”

Line 503-504. This value for black spruce transpiration seems unusually low and does not agree with more modern and methodologically detailed work of Perron et al. (2023) from Scotty Creek. 

We agree with the reviewer. 

Starting at line 502 (original submission): “For example, higher wetland (2.9 ± 1 mm day^-1) than forest ET (1.7 ± 0.6 mm day^-1) at Scotty Creek was reported for June-mid July 2013 (Warren et al., 2018), with transpiration from black spruce and tamarack accounting for only approximately 6 % to 12 % of forest ET (Perron et al., 2023).”

Do you examine any patterns in the timing of the hydrograph at the basin scale? You suggest that snowmelt has advanced 25 days since an earlier period? Perhaps no but if you discuss it I’m unsure why you didn’t evaluate it. 

We reported a 25-day advance in snowmelt based on the second half of the 21st century in an Arctic tundra landscape (Pohl et al., 2007). At Scotty Creek, Chasmer et al. (2017) observed an earlier snow melt (16 days) during the 2000-2009 period compared to 1970-1979. We concurrently observed that at Scotty Creek runoff increased approximately 15 days earlier during the 2009-2022 period compared 1995-2008 (Figure 6, added to the Supplementary Material as Figure S4). However, this earlier increase is modest, i.e. at only ten liters per second.

Figure 6. Composite hydrographs for 14-year periods of 1995-2008 and 2009-2022 at Scotty Creek basin outlet. Colored bands correspond to 95 % confidence intervals. The inlet shows a zoom on the spring freshet onset.

Additionally, we observe that the spring freshet peak at the Scotty Creek basin outlet has increased from 3.7 to 4.5 m³ s^-1 over the 1995-2008 to 2009-2022 periods, respectively (Figure 6). Similar observations were made at the Jean Marie River basin outlet, close to Scotty Creek (Connon et al., 2021).

As snow melts more rapidly in wetlands than in forests, the high rate of wetland expansion associated with forest loss might favor an earlier and shorter snowmelt in the future (Quinton et al., 2019). Consequently, we modified as follows in the manuscript: 

Starting at line 518 (original submission): “Despite the observational challenges, particular attention should be paid to this snowmelt period, which is profoundly influenced by climate warming. Firstly, the spring freshet is shown to occur earlier in the Arctic-boreal region (Chasmer and Hopkinson, 2017; Mack et al., 2021; Pohl et al., 2007; Woo et al., 2008). At Scotty Creek, an earlier snowmelt of 16 days was observed during the 2000-2009 period compared to the 1970-1979 period (Chasmer et al., 2017). Consistently, the Scotty Creek basin hydrograph analysis revealed an earlier increase in discharge (~15 days) during the 2009-2022 period compared to the 1995-2008 period (Figure S4). Secondly, earlier snowmelt leads to a longer snowmelt period, as projected for the Liard River watershed, resulting in a more gradual snowmelt (Woo et al., 2008). However, an increase in wetland extent caused by forested peat plateau collapse can contribute to shorter snowmelt period since snow melts faster in wetlands than in forest stands (Connon et al., 2021; Quinton et al., 2019). Shorter snowmelt periods can result in higher spring freshet peaks, as observed at Scotty Creek and the adjacent Jean Marie River meso-scale basin (Connon et al., 2021).”

Figure 2 - it is a bit hard to see some of the runoff values or distinguish them, particularly in 2022. Some of this is due to the distracting nature that the uncertainty in the runoff area for EAST - perhaps think of a way to make this more clear? Two lines? A dashed line? I’ll leave it up to the authors but the shaded yellow is a bit distracting. 

We replaced the yellow band for the East sub-basin by two lines representing the runoff from two drainage area estimates, i.e., from this study (yellow line) and Connon et al. (2015) (dashed line). We lightened the shade of green for ET and darkened the shade of gray for basin-scale runoff to improve clarity and contrast. The updated Figure 2 of the manuscript is attached at the end of the "Supplement_Reviewer1.pdf"  (page 13).

Why was the BESS data not corrected to the observations? Claiming that landcover differences are the reason that it underestimates ET is simply guessing and can easily be tested. My guess is that like many land models it is just underestimating ET. 

We did not correct the BESS data to the observations because the monthly relationship between measured ET and BESS ET was based on only three growing seasons (May–September, 2014–2016), with a relatively low coefficient of determination (R² = 0.44; see Figure 5-b in the manuscript).

Additionally, independent estimates by Hayashi et al. (2004), based on a chemical method at the basin scale, reported annual ET values between 280 and 300 mm for 1999–2002. These are consistent with BESS estimates over our study period (223–311 mm; mean ± std: 261 ± 22 mm), supporting the overall magnitude of the BESS ET estimates.

While land cover differences—such as more mineral uplands and fewer wetlands in the northern part of the Scotty Creek basin—may contribute to lower BESS ET, underestimation was also observed in the headwater sub-basins. This suggests that the bias may be at least partly inherent to the BESS model rather than solely due to land cover differences. However, the pixels representing the headwater portion cover a larger area than the three sub-basins. 

We refined the BESS ET estimate for the headwater portion by updating the number of pixels considered. One pixel located outside the basin boundary was removed (see Figure S2 of the Supplementary Material). This correction did not affect the ET values.

We have modified the corresponding paragraph in Section 4.2 of the manuscript as follows:

Starting at line 554 (original submission): “Our results show that modeled ET obtained with the BESS model at basin scale underestimated (annually ~100 mm) observed ET (Figure 5-b). Given that wetland ET is higher than forest ET (Helbig et al., 2016b; Perron et al., 2023), the underestimation of ET might be related to land cover heterogeneity at basin scale. The northern, i.e., downstream, portion of the basin is dominated by mineral uplands with better drainage and mainly covered by deciduous or mixed forest stands (Chasmer et al., 2014). Consistently, ET estimations from a chemical method at the Scotty Creek basin scale ranged from 280 to 300 mm year-1 for the 1999-2002 period (Hayashi et al., 2004). However, modeled ET was lower than observed ET at the sub-basin scale, probably underestimating the contribution of wetlands (Figure 5-c). Although this difference may stem from tendency of the BESS model to underestimate the spatial variability of ET in wetland-rich landscapes such as boreal peatland complexes near the southern permafrost limit, we cannot disentangle the extent to which it reflects a general underestimation of ET versus a specific underestimation in wetlands.”

Check colour scheme on Figure 6 (Rain vs SWE and 2022 which doesn’t match the others)

We modified the legend to better match the figure. The updated Figure 6 of the manuscript is attached at the end of the "Supplement_Reviewer1.pdf" (page 14).

Dear associate editor,

We are thankful for the supportive and constructive comments you and two reviewers provided for our manuscript. We thoroughly revised the manuscript based on the review, and hope that our efforts, with the insight from your end, have improved the manuscript.

Below, you will find the original comments of Reviewer 2 in bold, followed by our detailed responses and descriptions of revisions to the manuscript. We have attached the Supplementary Material file, which has been substantially revised. 

Kind regards,

Alexandre Lhosmot, on behalf of all authors

Lhosmot et al., present water balance studies for a well-instrumented and studied permafrost-affected boreal peatland complex. They quantify the impact of uncertainty in catchment delineation on water budgets, and compare water budgets carried out at different scales and with locally-observed and publicly available observational and simulated data. The find, among other things, that evapotranspiration is the dominant outgoing water flux but that this variable is considerably underestimated in publicly available simulated data. The results also highlight the difficulty of obtaining exact estimates of water fluxes such as discharge in a low relief landscape which is hydrologically dominated by spring freshet, as illustrated by high water balance residuals during this period.

The study provides new and valuable insights on the hydrologic behaviour of rapidly changing permafrost peatland complexes, as well as on the limitations of what information can be retrieved from detailed field-observations of water balance components as well as publicly available monitoring data. I find that the scientific rigour is high, based on the choice and descriptions of methods and the tight link to previous relevant studies from the same, and other similar areas. I have a few minor comments that I recommend that the authors address before publication of this nice manuscript.

Thanks.

L190: It is unclear to me if this instrument failure regards just one or both of the eddy covariance stations. Recommend clarification.

We clarified as follows: 

Starting at line 188 (original submission): "Due to instrument failure on the landscape flux tower, CO₂ and H₂O_(g) molar densities were measured with an enclosed CO₂/H₂O_(g) infrared gas analyzer (LI7200; LI-COR Biosciences Inc., Lincoln, NE) between March and August 2015."

Section 2.4: I read it this section several times to understand what was done here, so there might be a good idea to see if it is possible to make this easier to read (although, I might just have been tired!). On L246, it is stated that 15 rating curves were obtained, one for each flume 2014-2016. There were five flumes, so I suppose that you mean one rating curve per flume and year 2014-2016?

We corrected the manuscript text as follows: 

Starting at line 246 (original submission): “Twelve rating curves to convert WTP to half-hour discharge estimates were obtained from manual discharge and WTP measurements made during and shortly after snowmelt in late April to early May (spring freshet) and late May (baseflow) in 2014-2016, respectively. For the West sub-basin, we used one rating curve per year for each of the two outlets (West1 and West2), thus six rating curves in total. For the South sub-basin, we used one rating curve per year at the South1 outlet (thus three rating curves in total) and a single rating curve at the South2 outlet, created in 2015 and used for all three years. The East sub-basin consisted of one outlet, which was monitored in 2014 and 2015, with one rating curve per year (no data was available for 2016).”

I’m also curious about the gap-filling, especially since the West sub-basin series had 75% of data gap-filled by method 1 plus 14% gap-filled by method 2 (if I understand the text correctly). This adds up to almost 90% gap-filled data for this sub-basin, yet this is the sub-basin that you choose to show monthly water balance for in section 3.3. A motivation for your trust in this gap-filled data is warranted, as is a motivation for choosing to focus on this particular sub-basin in section 3.3, given this gap-filled data. Can you show that the rating curves from wetland WTP generate data that is well correlated with that from original rating curves?

Gap-filling percentages were first re-examined, and an error resulting in minor inaccuracies in the reporting of gap-filled flow rate data across all sub-basins was corrected. The updated values are now reported in the manuscript accordingly (see Table below). However, the issue that reviewer 2 has rightly underlined remains valid: the west sub-basin is still 79 % gap-filled.

Here the updated values of discharge gap-filling percentage: