the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Surface water storage influences streamflow signatures
Abstract. Extreme flow conditions in river discharge have far-reaching environmental and economic consequences. The retention of surface water in lakes, wetlands, and floodplains can potentially moderate these extreme flows by modifying the timing, duration, and magnitude of flow generation. However, efforts to characterize the impact of surface water storage on river discharge have been limited in geographic extent. In this analysis, a suite of hydrologic signatures, quantifying components of watershed flow regimes, was calculated from daily discharge at 72 gaged watersheds across the conterminous United States. Random forest models were developed to explain variability in six hydrologic signatures related to flashiness and high and low flow conditions. In addition to traditionally considered variables such as climate, land cover, topography, and geology, a novel remote sensing (Sentinel-1 & 2) approach was used to study the contribution of surface water storage dynamics to each signature's variability. While climate variables explained much of the variability in the hydrologic signatures, models for five of the six signatures showed an improvement in explanatory power when landscape characteristics were added. Automated variable selection is part of the modeling process and can be indicative of the relative importance of certain variables over others. When all variables were considered, four of the six signature models selected remotely sensed inundation variables. The amount of semi-permanent and permanent floodplain inundation, for example, was both negatively correlated with, and showed the greatest variable importance for wet season flashiness. Further, increases in seasonal floodplain inundation were positively correlated with increases in peak flows. This suggests that the storage of surface water on floodplains is relevant to both flashiness and high flow signatures. In addition, spatial variability in the amount of semi-permanent and permanent non-floodplain water helped explain variability in the baseflow index. These findings suggest that watershed surface water storage dynamics explain a portion of streamflow signature variability. The results underscore the need for protection and restoration of surface water storage systems, such as wetlands, across watersheds.
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RC1: 'Comment on hess-2024-119', Anonymous Referee #1, 09 Jul 2024
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Major comments
The main conclusions are based on increases in the explanatory power (R2) from the model including only climate as independent variables (Mclimate) to the model including all variables as independent variables (Mall). These increases (Table 5) range from 0 to 10%, which results in adjusted R2 increases up to 0.04. These are indeed very low model improvements. To better assess if these increases are not by chance, the authors should include another method for hypothesis testing, computing the p-value and the uncertainty behind these results.
The authors model the maximum annual flow, however, none of the climatic variables are related to high precipitation events (e.g. maximum annual precipitation). Given that peak flow is often linked to peak precipitation and snowmelt (Berghuijs et al., 2016), I suggest that such variables are added to the models. Additionally, why is the maximum annual flow computed for the time scale of 30 days? How do the results change if shorter time scales are also analyzed? (e.g. maximum annual daily flow; 7-day flow).
The baseflow index is usually highly connected to the geology of a catchment (Aboelnour et al., 2021; Bloomfield et al., 2021; Briggs et al., 2022; Carlier et al., 2018). None of the independent variables to model the baseflow index includes geological characteristics. This could potentially change the results and final conclusions obtained in the manuscript.
The title could be modified as it states knowledge that is already well established, with several references in the introduction showing that surface water storage impacts streamflow. It also could be misleading as it does not focus on the mechanisms behind these impacts.
Some of the flow signatures with the strongest influences of inundation variables are related to the temporal variability at the daily scale (flashiness index) rather than to a central measure of magnitude (e.g. MAX30/area, DryMonth/area). Would other flow variables related to the temporal variability be worth investigating? (e.g. CV of MAX30/area).
Minor comments
L26-29. What is precisely “amount of semi-permanent and permanent floodplain inundation” and “increases in seasonal floodplain inundation”? If it refers to the spatial extent, is it in terms of km2 or relative to the catchment’s area?
From the Abstract alone, it is hard to get an idea of how significantly water storage influences streamflow. The Abstract would be clearer if it presented some numbers to show how much some key flow signatures are explained by the inundation variables (e.g. differences between Mclimate and Mall R2).
Fig. 1 and flow signatures. “annual actual evapotranspiration divided by annual precipitation,” Shouldn’t it be potential evapotranspiration? The aridity index usually considers potential evapotranspiration rather than actual evapotranspiration (e.g., Berghuijs et al., 2017; Gudmundsson et al., 2016; Sawicz et al., 2011). It also looks like something is off because Fig. 1 shows that mean annual actual evaporation is up to 29.8 times higher than mean annual precipitation. Values in Fig. 1 do not match the values in Table 2 (Aridity index); are they the same variable?
Section 2.2. Is the calendar year used for the computation of the hydrologic signatures?
Are the gap-filled inundation time series publicly available? It would be necessary to replicate the study.
What were the precise selection criteria behind the watersheds chosen? Why aren’t more watersheds analyzed, given that there are hundreds (or thousands) of gauges with data available?
L176-177. How precisely is the temperature CV calculated? Is it calculated from monthly temperature values considering the entire time series? The same applies to precipitation CV.
Section 2.3.2 and Table 2. Are the subsurface and topography variables computed using the mean (or median) value of all raster cells within the catchment?
What is the spatial configuration of the inundation variables? It would be great to include maps showing their spatial distribution and a brief discussion. What are the relationships between the inundation variables? E.g. how are they correlated?
Results - Section 3.2 Flashiness signatures. The authors show how the flashiness indices are only weakly correlated with climate, have the models with the lowest R2 compared to other flow signatures (Table 4), and have the highest increases in R2 when all independent variables are added. I’m guessing – Is one contributor to the weak climate signal the fact that the flashiness index focuses on the daily variability while the climate indices focus on the monthly variability (or the annual mean)? Could this contribution to the lowest Mclimate R2 indirectly explain the highest potential for R2 increases when including all variables?
Table 2; Table 4; Table 6. Is it actual or potential evapotranspiration? Does the coefficient of variation of climate variables refer to the monthly change or annual change? Also, it looks like the authors might have computed the standard deviation instead of the coefficient of variation because of the units of mm, ºC, and the high values. The coefficient of variation is unitless as it is relative to the sample mean.
I believe that the Results section would read better if the sensitivity analysis of the data (Section 3.1) was not the first result presented and discussed, given that this is not the main topic of research.
L301-303. To me, the DryMonth/area bias of -2.2% seems very low for interpreting it as a drier condition than for the longer period, given that the between-year coefficient of variation of flow indices is usually much higher than 2%. The interpretation is up to the authors, but it looks like a very small bias and perhaps not statistically significant. The bias in the baseflow index (-11.8%) might come from a larger volume of high flows (as shown with MAX30/area) rather than lower flows or a drier period. These interpretations would agree with those using the PDSI.
Parts of the results show the Pearson correlation (R) and its associated p-values without stating if the residuals follow the normal distribution or if the authors checked for outliers. It would be good to either state that the authors checked for the correlation assumptions or use Spearman correlation throughout the manuscript.
L340-341. Here the authors refer to R as the Spearman correlation, but in other parts of the manuscript as the Pearson correlation.
How correlated are the indices Precipitation (P), Aridity index (ET/P) and Water demand (P-ET)? They look very highly correlated given the similar results.
Table 6 and associated discussion. Is it possible to know whether these variables are positively or negatively associated with the flow signatures?
Fig. 4 and Fig. 5. The numbers are hard to read because they are in the middle of the plot, mixed with the observations. It would read better if the numbers were displayed on the border of the plots. Fig. 4 shows R as the Spearman correlation but the Results section uses R as the Pearson correlation.
L434-436. Here I believe it should be clarified how much the models improved with the inundation variables, providing some numbers.
L439-441. It will be easier to interpret if the authors clarify whether these inundation variables are significantly correlated with other independent variables, particularly climate.
L452-454. How do geographically isolated wetlands contribute to baseflow? Given that they are isolated, what physical phenomena could explain this contribution? It is surprising to me that geographically isolated wetlands contribute to increasing baseflow while floodplain wetlands don’t. The reasons for this difference could also be discussed.
L524-528. I believe that it should also be clarified how much the inundation variables increased the model’s explanatory power. It would be interesting if the Conclusion section also presented results for the different inundation variables (e.g. floodplain and non-floodplain).
Technical comments
L49. “endanger poverty”. Do the authors mean increase (exacerbate) poverty?
Table 5. There is a parenthesis missing it the column “RMSE LOOCV)”.
Table 6. Are the values shown unitless?
Table 3, caption. I believe that R and p should be italics.
L419. “difference between baseflow and high flow conditions (i.e., (Q10-Q95)/area).”. It looks like it should be the difference between low and high flow conditions.
L473. There’s a mistake in “including like precipitation”.
L479. “seasonal flooding” refers to the seasonal flooding extent? “reduce or otherwise impact” is confusing to me. Do the authors mean that it could also be the other way around? That is, that larger peak discharges cause larger flooding extents?
References
Aboelnour, M. A., Engel, B. A., Frisbee, M. D., Gitau, M. W., and Flanagan, D. C.: Impacts of Watershed Physical Properties and Land Use on Baseflow at Regional Scales, Journal of Hydrology: Regional Studies, 35, 100810, https://doi.org/10.1016/j.ejrh.2021.100810, 2021.
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M.: Dominant flood generating mechanisms across the United States, Geophys. Res. Lett., 43, 4382–4390, https://doi.org/10.1002/2016GL068070, 2016.
Berghuijs, W. R., Larsen, J. R., van Emmerik, T. H. M., and Woods, R. A.: A Global Assessment of Runoff Sensitivity to Changes in Precipitation, Potential Evaporation, and Other Factors, Water Resour. Res., 53, 8475–8486, https://doi.org/10.1002/2017WR021593, 2017.
Bloomfield, J. P., Gong, M., Marchant, B. P., Coxon, G., and Addor, N.: How is Baseflow Index (BFI) impacted by water resource management practices?, Hydrology and Earth System Sciences, 25, 5355–5379, https://doi.org/10.5194/hess-25-5355-2021, 2021.
Briggs, M. A., Goodling, P., Johnson, Z. C., Rogers, K. M., Hitt, N. P., Fair, J. B., and Snyder, C. D.: Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams, Hydrology and Earth System Sciences, 26, 3989–4011, https://doi.org/10.5194/hess-26-3989-2022, 2022.
Carlier, C., Wirth, S. B., Cochand, F., Hunkeler, D., and Brunner, P.: Geology controls streamflow dynamics, Journal of Hydrology, 566, 756–769, https://doi.org/10.1016/j.jhydrol.2018.08.069, 2018.
Gudmundsson, L., Greve, P., and Seneviratne, S. I.: The sensitivity of water availability to changes in the aridity index and other factors-A probabilistic analysis in the Budyko space, Geophys. Res. Lett., 43, 6985–6994, https://doi.org/10.1002/2016GL069763, 2016.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Citation: https://doi.org/10.5194/hess-2024-119-RC1 -
RC2: 'Comment on hess-2024-119', Anonymous Referee #2, 12 Jul 2024
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This is a review of the manuscript “Surface water storage influences streamflow signatures”, authored by Vanderhoof et al. and submitted to Hydrology and Earth System Sciences (HESS). The paper explores the relationship between streamflow signatures and climate, land cover, geology, topography, and surface water storage variables using random forest (RF) models for 72 catchments in the contiguous United States (CONUS). The study addresses an important subject in an important study area and should interest HESS readers. The study has promising results, however, there are methodological issues that should be carefully addressed before publication. Please, find below some suggestions that I hope will improve the overall reliability of the manuscript.
GENERAL COMMENTS
- The manuscript is well-written and sound. The introduction is informative of the study’s scope and context; the RF approach is very interesting.
- The general quality of the figures is good, but some improvements can help enhance the interpretability of the results. Please, refer to specific point-by-point comments.
- The title and abstract are not representative of the study. The title expresses well-known information about the relationship between streamflow signatures and surface water storage which is mentioned many times in the introduction. It states a strong conclusion that, in my understanding, is not supported by the results. The abstract could be improved if some numerical information about the findings were provided, such as differences in the explanatory power of the model after the inclusion of the wetlands and inundation variables.
- If I understand correctly, all of the 72 catchments are pooled together. For instance, the pooling will necessarily pool together information on small and large catchments, and also on catchments with distinct runoff generation mechanisms, and hence, with distinct responses to the explanatory variables. This pooling would result in a large variability in the hydrological signatures, which combined with a quite limited sample size (e.g., 72 samples), could result in biased estimates of the model parameters and large uncertainties. How is this issue addressed in the analysis? Please: (i) provide additional analysis; or (ii) clearly mention this as a potential limitation of the study, which currently is little discussed in the manuscript.
- The use of the random forest (RF) model is very interesting to address the research questions. However, the output from the RF model provided in the manuscript is simply the R-squared and the relative importance of each explanatory variable. This is a very limited result which is not a meaningful interpretation of the hydrological process – especially taking into account all the effort employed in the data curation. I think that the RF is a very useful tool for a first-step analysis, but complementary analysis must be done for a more meaningful interpretation of the results.
- The overall improvement in the model's performance due to the inclusion of wetlands and inundation variables is very limited since improvements are up to 10% and in most cases are less than 6%. The manuscript’s results do not corroborate the conclusions, and it is unclear if the results are indeed meaningful from a hydrological process point of view. I think that would be very useful if p-values were provided to understand if these results are indeed significant.
- There are a lot of sources of uncertainties in the methodology related to both the streamflow signatures (see Westerberg and McMillan, 2015) and also due to the modeling approach which are not accounted for in the manuscript. Those uncertainties may be large and hence the results can be misleading. This issue makes the results even less reliable and is timidly discussed in the manuscript. Please, provide uncertainty estimation or mention this as a critical limitation of the study.
- I was wondering about the choice of some explanatory variables. For instance, the 30-day annual maximum streamflow signatures are related to the extreme streamflow regime. In this case, it would be interesting to include explanatory variables related to extreme streamflow dynamics. Snow melting is indirectly accounted for by temperature, however, relevant variables such as extreme precipitation and antecedent wetness – based on soil moisture or antecedent precipitation (see Lun et al., 2021; Merz and Blöschl, 2009) are omitted in the model. Also, are the results sensitive to the choice of the 30-day window?
- The discussion provided in the manuscript is very limited and mostly based on a literature review rather than the paper results, which makes the discussion most speculative.
SPECIFIC COMMENTS
- Lines 156-158. It is not clear whether the baseflow index is derived from the UFSF, which is based on model simulations, or whether it was just calculated in a similar fashion. Please clarify this information in the revised manuscript.
- Lines 377-381. Figure 3. It is difficult to follow the results shown in Fig. 3, especially for (c), (d), and (e) frames due to the units used.
- Lines 399-408. Figures 4 and 5. The overall quality of Fig. 4 and 5 should be improved, the overlapping of axis labels with other graphical elements makes the interpretation difficult.
- Lines 163-165. It is not clear which correlation method was used. For instance, in the main text, the Pearson correlation is mentioned, however, in tables and figure captions the Spearman is mentioned. Please clarify this information in the revised manuscript. If the Pearson correlation was indeed used, please check the assumptions for its use. Also, which test is used for the correlation significance? The Pearson/Spearman correlation itself is not a statistical test.
- Lines 308-310. Indeed, the relative variations in hydrologic signature values between the long-term flow records (24 years) compared to the study period (8 years) are quite similar. This would be a pragmatic decision based on Sentinel data availability rather than a “solid justification”.
TECHNICAL COMMENTS
- Lines 387-390. Table 5. There is a missing parenthesis in the RMSE of leave-one-out cross-validation.
REFERENCES
Lun, D., Viglione, A., Bertola, M., Komma, J., Parajka, J., Valent, P., and Blöschl, G.: Characteristics and process controls of statistical flood moments in Europe – a data-based analysis, Hydrology and Earth System Sciences, 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021, 2021.Merz, R., & Blöschl, G.: Process controls on the statistical flood moments - a data based analysis. Hydrological Processes, 23, 675–696, 10.1002/hyp.7168, 2009.
Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures, Hydrology and Earth System Sciences, 19, 3951–3968, https://doi.org/10.5194/hess-19-3951-2015, 2015.
Citation: https://doi.org/10.5194/hess-2024-119-RC2
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