the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Mar 2024
Actionable human-water systems modeling under uncertainty
Abstract. This paper develops an actionable interdisciplinary model that thoroughly quantifies and assesses uncertainties in water resources allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and microeconomic ensemble experiments with a widely used Decision Support System for water resources planning and management. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple climatic scenarios and policies (multi-scenario, multi-forcing) across systems, so as to identify plausible futures where water management policies meet or miss their objectives, and explore potential tipping points. The application of methods is exemplified through a study conducted in the Douro River Basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger nonlinear water allocation changes in the Decision Support Systems (DSS); which can be further aggravated by the nonlinear adaptive responses of irrigators to water shortages. For example, while some irrigators barely experience economic losses (average profit and employment fall by <0.5 %) under mild water allocation reductions of 5 % or lower, profit and employment fall up to 12 % (~24x more) where water allocation is reduced by 10 % or less (~2x more). This substantiates the relevance of informing on the potential natural and socioeconomic impacts of adaptation strategies, and related uncertainties, towards identifying robust decisions.
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RC1: 'Comment on hess-2024-61', Anonymous Referee #1, 16 Mar 2024
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This is a good paper that is well structured, argued and delivered. The combined models remain a contribution in the space and the justification for its development and design are well supported. The insights from both the study and its applicability to a range of contexts, decision-makers, and stakeholders—clearly articulated in the replication text—is highly useful and a little unusual in economics. Liked that a lot.
My only real concern then is the use of the term uncertainty here. If we assume a Knightian approach to the ideas, as I would usually so that we’re all clear on my stance here, then uncertainty is the consummate unknown in that we are not even aware we are unaware. If, as stated here, the concept of probabilities and data can be used to construct scenarios and outcomes that parameterise the conditions then we are dealing with risk. The distinction is important when building these ideas out and analysing them in such a manner as detailed here. And they make a big difference to the interpretation and usefulness of the results. In my view you can’t have a DSS built on uncertainty because it is unknown and as such cannot be parameterised-the whole point of the term.
Thus, I would like the authors to explain clearly why they are comfortable with this approach or if they agree with my views and will update the term use and constructs. It would want to be a very good argument if they are to convince me the existing approach is appropriate. As an author in this area, copping a lot of flak from engineers on this exact topic, the argument should be made, agreed with, and then worked back into a subsequent version of the paper.
Otherwise, the work is solid and well-constructed/nicely written up. I congratulate the authors and wish them well with the paper going forward.
Moderate revisions needed.
Citation: https://doi.org/10.5194/hess-2024-61-RC1 -
RC2: 'Comment on hess-2024-61', Anonymous Referee #2, 25 Apr 2024
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I agree with the underlying premise of this paper – that the use of DSS’s by policymakers needs to include an extensive scenario analysis to explore the uncertainty (or confidence) in the outputs. The authors appear to claim that they are doing a much better job of encompassing all uncertainties than has been done previously. I would question this. There has been a lot of work done on uncertainty and scenario analysis. The authors may be able to claim that theirs is the best approach so far, but this is merely claimed – there is no evidence to support this. While obtaining such evidence is intrinsically impossible, a more comprehensive literature review that discusses the various approaches that have been employed so far (e.g. Bayesian networks, coupled complex models, agent-based models) may help give credibility to the authors’ claims. While not necessarily relevant, the author may find the following papers useful:
- Hamilton et al (2019) A framework for characterising and evaluating the effectiveness of environmental modelling, Environmental Modelling and Software 118, 83-98 https://doi.org/10.1016/j.envsoft.2019.04.008
- Maier et al. (2016) An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together? Environmental Modelling and Software, 81, 154-164. https://doi.org/10.1016/j.envsoft.2016.03.014
- Guillaume, J., "Designing a knowledge system for managing deep uncertainty?" (2022). International Congress on Environmental Modelling and Software. 12. https://scholarsarchive.byu.edu/iemssconference/2022/Stream-D/12
These are just papers that I am familiar with (for the record, I know the authors, but I am not a co-author of these papers).
One question is whether the authors have just created another DSS that includes assessment of uncertainty or if this is actively being used by policymakers. Is there any evidence that the DSS is actually being used? If not, this is just another study in the academic arena and doesn’t address the lack of uptake by policymakers. It is reassuring that there is an author who is not an academic on this paper, but there have been other papers that include non-academic authors and this by itself doesn’t necessarily result in the adoption of the work by policymakers.
I think the paper should be framed as an example of how to improve DSSs by taking more careful consideration of uncertainty, including consideration of multiple scenarios.
Specific comments
- Page 1, line 14: Are you sure that you thoroughly quantified and assessed the uncertainty? Is there no possibility that you missed a source of uncertainty? Recommend deleting “thoroughly” as it is not really needed.
- Page 2: Font is far too small in some of the panels. Suggest simplifying the panels and increasing the font size.
- Page 2, line 32: I would delete “(nonlinear change)” as it is not really necessary in this sentence. Also “unexpected, sometimes abrupt, change” would be better
- Page 2, lines 35-36: I think “that gives a false appearance of uncertainty reduction” would be better phrasing.
- Page 3, line 46: “Parameters” would be better than “constants” here as calibrating constants would mean they are not constant. This would also agree with use of “parameter later in the paper (e.g. lines 49, 51)
- Page 3, lines 56-57: Suggest stating the papers cited here are examples.
- Page 3, lines 68-69: I would question this in terms of DSS. In terms of policymakers and what they use for planning and management, then maybe, but DSS themselves have been explored using ensemble research. I agree with the statement in the following sentence, but this sentence misses the mark. I suggest deleting it.
- Page 3, line 74: not "concealed" as this implies that academics are hiding these methods. "confined" would be better
- Page 4, lines 97-100: are these numbers known to 6 or 7 significant figures? I would think the uncertainty in these values would be considerably larger than 0.1 million m3/year.
- Page 4, lines 99-100: Having these periods overlap (1940-2005 and 1980-2005) is not ideal. Better to give the resources from 1940-1979 and 1980-2005.
- Page 4, line 101: Would be good to have a citation to support the “increasing both in frequency and intensity”. Otherwise, evidence for this should be shown in the paper.
- Page 8, line 169: how do the predictions by these 4 models compare with the predictions from the ensemble of models used by the IPCC? With 4 GCMs and 3 emission scenarios, this means 12 climate scenarios
- Page 11, lines 268-269: This sentence needs rephrasing
- Page 11, line 270: Need to define variables
- Table 1: need to define variables. As far as I can see, only mu_i has been defined.
- Figure 3: font size on the axes is too small. At the moment, this plot is not very helpful. Maybe better to give a cumulative frequency (or flow duration) curve?
- Caption of Figure 4: averaged across 4 GCMs and 8 GHMs, so an average of 32 sets of model outputs? What is the standard deviation of this set of results? Estimate of uncertainty in the mean?
- Page 14, line 331: I don't find Figure 5 particularly informative. Can these results be better represented? At the moment, 3 pages of very small figures is not working.
- Page 14, line 333: similarly for Figure 6. Need a summarising figure in the paper. The individual plots can be given in supplementary material, but not in the actual paper.
- Page 17, Figure 5: The legend indicates these are Delta values - what is the change with respect to. Is this current profit? If so, over what period?
Citation: https://doi.org/10.5194/hess-2024-61-RC2
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