Review of ESSD-2026-286 “Network-Based Subbasin-Scale Mapping of Streamflow Alteration in Ontario, Canada” by Shen et al.

This is a timely database that provides quantification of the local and downstream influence of reservoirs on streamflow proxy across Ontario, Canada. A Streamflow Alteration Index (SAI) is presented, but not as a hydrologic metric, as a simpler areal metric. The value of a simple metric is a spatially complete picture of reservoir influence throughout a river network. This reviewer appreciates the presentation using two types to express some uncertainty. The reviewer also agrees with the routing approach using hydrologic sequencing and nodes. Well done. This reviewer has very little feedback regarding how to improve the presentation. Below are a few specific comments to consider for perhaps some improved clarity or suggestions for next steps.  

32: What is the precision of Lehner 2024? Are these waterbodies 0.1 square km or above. Most ponds by number are smaller than that (e.g., https://doi.org/10.1029/2019GL083937). A reader may benefit from some size classification.

33: It would be more useful to express as volume (% of total water volume) and area (% of drainage area) if possible for better context.

36: other good citations regarding network fragmentation: https://doi.org/10.5194/hess-13-2413-2009  and https://doi.org/10.1126/science.1107887.

128: this reviewer agrees that a simple streamflow alteration metric is needed and useful.

171: How comprehensive are these datasets? Meaning, are there other waterbodies perhaps missed? A reader would benefit from knowledge of completeness.

527: spatially complete.

533: A spatial/static picture is valuable. How would one update the SAI for dynamic representation? A reader may benefit from a statement that with a SAI, a next step could be dynamic optimization to adjust the SAI as needed.

540: good point. Also consider some discussion around the idea that not all waterbodies are created equal, and their geometries may be another factor to consider in the SAI (https://doi.org/10.1038/s41467-018-05156-x). Also, while this approach assumes drainage area scales with streamflow, how could this SAI metric be used as a water-quality influence indicator?

The authors present two Streamflow Alteration Indices that have been developped and applied in the province of Ontario (Canada). The indices were relatively successfully compared to two Canadian databases. The approach offer strong spatial information, compared to traditional approaches that are typically applied only at gauged locations.  The methodology is sound and the manuscript is well written, with relevant figures and tables. I only have a few minor suggestions that can be found in the attached file.

This paper proposes a new method for assessing the sensitivity of runoff in Canadian watersheds to human alternations and provides a relevant dataset. The method is based on a strong assumption that runoff is proportional to catchment area; therefore, the estimated streamflow alternation Index (SAI) can be easily obtained by averaging upstream sub-basins that contain (100%) or do not contain (0%) alternation sources. This method is easy to implement, requiring only a well-developed river network system and a comprehensive inventory of alternation sources. I have two main concerns regarding this approach.

First, the assumption that streamflow is proportional to watershed area is overly simplistic. Streamflow is closely related to factors such as topography, soil type, snowmelt supply, and precipitation conditions. Please validate this assertion using hydrological models and explore potential uncertainties.

Second, the validation method is too crude. The reference dataset contains only two classification labels—“natural” and “regulated”—while the SAI values are downsampled into three labels. However, we have two categories of SAI values, ranging from 0% to 100%. This gives the impression that SAI values are largely meaningless and that we can only perform approximate classifications. For example, if two sub-basins have SAI-I values of 0.6 and 0.8, respectively, can we say that the sub-basin with an SAI-I value of 0.8 is more vulnerable than the other? If not, what is the exact meaning of the SAI value? If so, how can this be verified?

-- other comments.

L128, finally we reach the topic of this study. The previous introduction is a bit lengthy. Besides, it omits the introduction about the particularity of the study region, i.e., why do we need to implement a case study here?

L128-144, here it only highlights the merits of the new SAI metric. Before that, the authors need to explain the principle of this metric, what exact data it relies on (“whether a dam or reservoir is expected to substantially modify downstream flows” is too vague, is there a database for this purpose?), why it could address the data limitation problem, what is the magic code that makes it more efficient than the previous approaches.

L225, please add a map to show the locations/spatial coverage of these datasets.

How complete are these datasets? Is it possible that the dataset is incomplete in some particular regions or for small-scale facilities?

L272, again, we need a map to show the distributions of these three sources.

Table 1. All dams belong to Level II, even if one has the power generation purpose?

L305, “Assuming streamflow is proportional to upstream drainage area”, this is a very strong assumption.

L443, what is the criterion (level, size?) for defining a subbasin?

Fig. 2, it is difficult to see the details.