the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Empirical stream thermal sensitivities cluster on the landscape according to geology and climate
Lillian M. McGill
E. Ashley Steel
Aimee H. Fullerton
Abstract. Climate change is modifying river temperature regimes across the world. To apply management interventions in an effective and efficient fashion, it is critical to both understand the underlying processes causing stream warming and identify the streams most and least sensitive to environmental change. Empirical stream thermal sensitivity, defined as the change in water temperature with a single degree change in air temperature, is a useful tool to characterize historical stream temperature conditions and to predict how streams might respond to future climate warming. We measured air and stream temperature across the Snoqualmie and Wenatchee basins, Washington during the years 2014–2021. We used ordinary least squares regression to calculate seasonal summary metrics of thermal sensitivity and time-varying coefficient models to derive continuous estimates of thermal sensitivity for each site. We then applied classification approaches to determine unique thermal sensitivity regimes and, further, to establish a link between environmental covariates and thermal sensitivity regime. We found a diversity of thermal sensitivity responses across our basins that differed in both timing and magnitude of sensitivity. We also found that covariates describing underlying geology and snowmelt were the most important in differentiating clusters. Our findings can be used to inform strategies for river basin restoration and conservation in the context of climate change, such as identifying climate insensitive areas of the basin that should be preserved and protected.
- Preprint
(3002 KB) -
Supplement
(923 KB) - BibTeX
- EndNote
Lillian M. McGill et al.
Status: open (until 20 Apr 2023)
-
RC1: 'Comment on hess-2022-428', Anonymous Referee #1, 01 Mar 2023
reply
In their study, McGill et al. characterized the thermal sensitivity of streams in two watersheds of the Pacific Northwest of the United States which describes how changes in stream temperature track changes in air temperature. They characterized thermal sensitivity using the conventional method looking at the slope between air and stream temperatures. They also used a novel approach using time-varying coefficients to capture how thermal sensitivity varies through the year – this is a truly interesting contribution to the field to assess in a continuous way the seasonality of thermal sensitivity. McGill et al. then performed a clustering analysis on the annual average time series of thermal sensitivities and used classification and regression trees to identify drivers of thermal sensitivity.
Overall, the manuscript is well written and beautifully illustrated. Methods are well described and sound. While results per se are mainly of regional interest, their use of time-varying coefficients offers a methodological contribution of interest to the journal. With a few revisions, this would make a quality contribution to HESS. The main points to address are the following:
1) Clearly present hypotheses/predictions underlying the study
The manuscript identified three broad research questions (lines 82-85) and most of the results section then goes on to describe observed patterns found posteriori. Using a descriptive research approach is perfectly sound but I believe the study would be more informative if it used an explanatory research approach where the goal is to understand underlying causal mechanisms. In fact, at numerous places in the manuscript, authors talked of expected results: “we expected thermal sensitivity to increase with river size” (line 418), “we expected land cover characteristics such as open water and forest cover to be important predictors” (line 426) or “expectations of a negative relationship between thermal sensitivity and groundwater influence” (line 362). While not presented this way, it appears authors had hypotheses/predictions underlying their work. I believe framing the manuscript to more clearly present hypotheses/predictions would make conclusions of broader interest to the stream temperature community in comparison to the current presentation of results which can be difficult to interpret without regional knowledge. For example, presenting results for the Chiwawa, White and Little Wenatchee rivers (lines 270), the tributaries to the mainstem and Raging River (line 259) or the Chumstick Creek (line 415) is factually correct but bears little meaning to someone unfamiliar with the study region.
Along the same lines, authors performed three distinct cluster analyses (air temperature, water temperature, thermal sensitivity) and their goal was unclear until I reached the discussion and understood that they wished to show that thermal sensitivity clusters offers additional information to what we find when studying solely air/stream temperatures. If that was one of the goals of the study, I suggest it be clearly stated and communicated as a take-home message.
2) Conclusions need to be better supported by analyses and results.
A few statements in the results are not sufficiently supported by analyses. Moreover, the results section often lacks precision and statements are often little quantified.
For example, the abstract states that thermal sensitivity regimes “differed in both timing and magnitude of sensitivity” and while Figures 4-5 offer a nice illustration of regimes, no formal analysis clearly compared the timing/magnitude of clusters. There are a few other examples in the results section:
The manuscript states that “thermal sensitivity estimates were not entirely consistent” (line 230) although it is not clear what consistent refers to and if it was quantified. Similar wording regarding a “consistent seasonal signal” (line 243) should be revised.
The manuscript states that “only SWE displayed a relationship with thermal sensitivity” (line 233) while no formal analysis was done – a visual assessment is not sufficient to determine the presence/absence of a relationship. At minimum, R² values should be presented in Figure 3 to assess the strength of relationships. I also question here the presence of the “wedge-shaped pattern” (line 234) for SWE in Figure 3 which does not stand out clearly and may be simply due to fewer data points for large SWE values. Further analysis is required to assess the strength of this relationship.
Similarly, I question some of the relationships between variables that are discussed in Table 3. It is not clear to me that we can see a “consistent negative relationship between thermal sensitivity, distance upstream and MWE” (line 236). Figures in Table 3 dot not present the correlation coefficient but my visual assessment is that it is likely close to 0 for MWE. The results section also points towards a “weakly positive and parabolic” (line 238) relationship between hydraulic conductivity and thermal sensitivity in the Snoqualmie basin, yet a linear regression is plotted in the figure in Table 3. Overall, many different relationships between thermal sensitivity and environmental variables appear to be weak and should be confirmed using statistical analyses.
Last, the paragraph from lines 256-275 should be more precise and quantify some statements such as “somewhat high mean thermal sensitivities” (line 263), “overall high thermal sensitivity and low variability” (line 267), “cluster 3 had the greatest variability through time” (line 271)
3) Better consideration of interannual variability in thermal sensitivity
The cluster analysis of thermal sensitivity relies on an annual average time series of thermal sensitivities. I suggest that the manuscript better lay out the implications of having sites with fewer years of data. Did this have a strong influence on the clustering? For example, was the clustering similar if performed using a single and most common year of data available? Section 4.5 in the discussion does a very good job of discussing limitations in general terms but adding a more formal analysis would be more convincing.
MINOR POINTS
line 52: Define thermal memory as it is not a widely accepted concept.
line 179: what is the dissimilarity matrix d^c xy ?
line 246: Although presented in Supplementary Material (Table S2), I suggest adding a sentence to give an idea of the variability in the number of clusters according to the method used.
line 249: Without regional knowledge, it is not clear from figures that “air and water temperature correspond closely with elevational gradients”.
line 302: thermal sensitivities varied substantially between sites? I suggest being more explicit as to what is being compared here.
line 306: non-redundant aspects relative to what? I suggest being more explicit as to what is being compared here.
line 323: This statement is a bit strong and little supported by results. For example, static thermal sensitivity (e.g. Table 2) may in fact align well with clusters defined using the time-varying approach, something the manuscript did not look into.
line 334: To what does the buffering refer to?
line 335: A comma is missing after “summer”
line 361: Do “summary metric regression” refer to Table 2?
line 435: Are there large dams in the two studied basins? If so, it should be clearly stated as this could explain why certain environmental variables had little influence.
line 457: What were the bandwidth and averaging periods used? I couldn’t find this information anywhere in the methodology.
Citation: https://doi.org/10.5194/hess-2022-428-RC1
Lillian M. McGill et al.
Data sets
Stream air and water data used in the analysis Lillian McGill, E. Ashley Steel, Aimee Fullerton https://lmcgill.shinyapps.io/TimeVarying_AWC/
Lillian M. McGill et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 256 | 84 | 7 | 347 | 16 | 1 | 1 |
- HTML: 256
- PDF: 84
- XML: 7
- Total: 347
- Supplement: 16
- BibTeX: 1
- EndNote: 1
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1