the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 May 2026
Network-Based Subbasin-Scale Mapping of Streamflow Alteration in Ontario, Canada
Abstract. Dams, reservoirs, and waterpower facilities are central to regional water management, but they can induce streamflow alterations that propagate downstream through connected river networks and complicate hydrologic modeling and aquatic ecosystem assessment. Existing streamflow indicators often quantify alteration only at discrete locations (e.g., hydrometric gauges), thus limiting spatially continuous, subbasin-scale mapping of where upstream infrastructure may influence downstream flows. We propose the Streamflow Alteration Index (SAI), a network-based screening metric that propagates point-based alteration signal sources downstream through a routing network to map potential alteration influence. The framework includes paired indices: SAI_I (0–100 %) and SAI_II (0–100 %), representing two levels of influence derived from confirmed (Level I) and potential (Level II) alteration signal sources. We implemented SAI to develop the SAI v1.0 database for Ontario, Canada, using the Ontario Lake and River Routing Product Version 2 (OLRRP v2) network and an inventory of 643 alteration signal sources compiled from provincial datasets. The resulting product provides seamless, high-resolution, network-based, subbasin-scale mapping of streamflow alteration across Ontario and enables SAI estimates at both gauged and ungauged nodes within the routing network. We validated SAI by classifying subbasins containing hydrometric gauges as natural, conditional, or altered and compared these classes against two independent references: Reference Hydrometric Basin Network (RHBN) gauges (with minimal human impact) and Water Survey of Canada (WSC) gauge “Natural” and “Regulated” labels. SAI shows strong agreement for gauges labelled natural: 86 % of RHBN natural gauges and 86 % of WSC-natural gauges are classified as natural by SAI. For WSC-regulated gauges, SAI classifies 74 % as conditional or natural. Overall, SAI v1.0 provides a practical, objective screening product for large-domain hydrologic and aquatic applications without requiring detailed dam operating information, and it offers a scalable pathway toward national- to global-scale, network-based screening products for streamflow alteration.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2026-286', Anonymous Referee #1, 03 Jun 2026
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RC2: 'Comment on essd-2026-286', Anonymous Referee #2, 13 Jul 2026
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.
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RC3: 'Comment on essd-2026-286', Anonymous Referee #3, 10 Aug 2026
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.
Citation: https://doi.org/10.5194/essd-2026-286-RC3 - AC1: 'Authors’ response to referee comments on ESSD-2026-286', Hongren Shen, 09 Sep 2026
Model code and software
streamflow-alteration-index Hongren Shen https://github.com/hongren-shen/streamflow-alteration-index/tree/main
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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?