the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Annual memory in the terrestrial water cycle
Abstract. The water balance of catchments will, in many cases, strongly depend on its state in the recent past (e.g., previous days). Processes causing significant hydrological memory may persist at longer timescales (e.g., annual). The presence of such memory could prolong drought and flood risks and affect water resources over long periods, but the global universality, strength, and origin of long memory in the water cycle remain largely unclear. Here, we quantify annual memory in the terrestrial water cycle globally using autocorrelation applied to annual time series of water balance components. These timeseries of streamflow, global gridded precipitation, GLEAM potential and actual evaporation, and a GRACE-informed global terrestrial water storage reconstruction indicate that, at annual timescales, memory is typically absent in precipitation but strong in terrestrial water stores (rootzone moisture and groundwater). Outgoing fluxes (streamflow and evaporation) positively scale with storage, so they also tend to hold substantial annual memory. As storage mediates flow extremes, such memory also often occurs in annual extreme flows and is especially strong for low flows and in large catchments. Our model experiments show that storage-discharge relationships that are hysteretic and strongly nonlinear are consistent with these observed memory behaviours, whereas non-hysteretic and linear drainage fails to reconstruct these signals. Thus, a multi-year slow dance of terrestrial water stores and their outgoing fluxes is common, it is not simply mirroring precipitation memory, and appears to be caused by hysteretic storage and drainage mechanisms that are incorporable in hydrological models.
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Status: open (until 20 Nov 2024)
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RC1: 'Comment on egusphere-2024-2954', Michael Roderick, 09 Oct 2024
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See report.
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AC1: 'Reply on RC1', Wouter Berghuijs, 09 Oct 2024
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Please see the enclosed document for our response
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AC1: 'Reply on RC1', Wouter Berghuijs, 09 Oct 2024
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RC2: 'Comment on egusphere-2024-2954', Anonymous Referee #2, 14 Nov 2024
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This is a very interesting paper on the long-term memory in precipitation, evaporation, streamflow and storage. The analyses are clearly described. The writing is clear and the figures are all very informative. I have no major comments and highly recommend publication of the manuscript in HESS.
My main comment is related to the structure of the paper. The model part is mentioned at the end of the introduction but is not part of the methods and almost came as a surprise to me. I would move some of the parts of the model section into the methods (e.g., the analyses and the description of the three model structures) and then divide the results into those related to the data analyses and those related to the model results. That way, a) the model section has a better division of methods and results/discussion, and b) the model part is part of the methods and doesn’t appear to almost come as an afterthought. The model results are very interesting but I think that they could use a bit more discussion, e.g., it would be useful to highlight which lumped bucket type models have the tested structures, to highlight that this type of analysis may help to determine what type of model structure one needs to use, and that it means that any model structure testing with short datasets needs to be done with care!
My other comment relates to the datasets used. I fully agree with the choice of the datasets for ET and storage but it would be useful if there was at least some critical reflection of the datasets. Afterall, there is some “modelling” already involved in getting the “data”. Thus, as with any data, there are some uncertainties in the data. There is currently no discussion on how this may influence the outcomes.
Minor suggestions –really just suggestions:
- L98: Explain why you left out these arctic areas, rather than only stating that you left them out.
- L126, L188: Considering all uncertainties, I would not include the decimal.
- L173: Is this 67% of the 79% or 67% of all the pixels? This could be worded more clearly.
- L176, L181: Replace ‘Spearman rank coefficient’ by a symbol, but ideally not rho (see comment below).
- L195, L199: Add symbol after ‘mean’ for greater clarity.
- L209: I don’t think that the header is very fitting to the contents of the section. In fact, I think that you can just leave the header out and include the text as a continuation of the previous section.
- L226: It would be helpful for the readers if you gave your thoughts on why the larger catchments have a stronger long-term memory. Is it the presence or importance of larger (alluvial) aquifers? The fact that there are likely more (large) lakes for larger catchments? Or that larger catchments are overall flatter?
- L236: I found it a bit confusing that rho is use for both the memory (ry) and the Spearman rank correlation (r). Consider using rs for the Spearman rank correlation instead.
- L239-240: Are these very low correlations statistically significant?
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AC2: 'Reply on RC2', Wouter Berghuijs, 15 Nov 2024
reply
Please see the enclosed document for our response
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