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Quantifying Hydrological Uncertainties under Climate Change using High-Resolution Numerical Models
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  • Jorge Sebastian Moraga,
  • Nadav Peleg,
  • Peter Molnar,
  • Simone Fatichi,
  • Paolo Burlando
Jorge Sebastian Moraga
ETH Zürich D-BAUG

Corresponding Author:[email protected]

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Nadav Peleg
University of Lausanne
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Peter Molnar
ETH Zürich D-BAUG
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Simone Fatichi
National University of Singapore
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Paolo Burlando
ETH Zürich D-BAUG
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Abstract

Modelling the response of hydrological processes to the changing climate requires the use of a chain of numerical models, each of which contributes some degree of uncertainty to the final outputs. As a result, hydrological projections, despite the progressive increase in the accuracy of the models along the chain, can still display high levels of uncertainty, especially at small temporal and spatial scales. The randomness intrinsic to climate phenomena, known as internal climate variability, is also a component contributing to the uncertainty of the hydrological projections. Unlike the uncertainties emerging from the climate and hydrological models, the internal climate variability is irreducible. In this work, we quantify and partition the uncertainty of hydrological processes in two mountainous catchments in Switzerland, emerging from climate models and internal variability, across a broad range of scales. To that end, we used high-resolution ensembles of climate and hydrological data, produced by a two-dimensional weather generator (AWE-GEN-2d) and a distributed hydrological model (Topkapi-ETH). We quantified the uncertainty in hydrological projections towards the end of the century through the estimation of the values of signal-to-noise ratios (STNR). We found small STNR values (<-1) in the projection of annual streamflow for most sub-catchments in both study sites that are dominated by the large natural variability of precipitation (explains ~70% of total uncertainty). Furthermore, we investigated in detail specific hydrological components that are critical in the model chain. For example, snowmelt or liquid precipitation exhibits robust change signals, which translates into high STNR values for streamflow during warm seasons and at higher elevations, together with a larger contribution of climate model uncertainty, suggesting that an improvement of the involved models has the potential of significantly narrowing the uncertainty. In contrast, extreme flows show low STNR values due to large internal climate variability across all elevations, which limits the possibility of narrowing their estimation uncertainty in a warming climate.
26 Jan 2022Submitted to Hydrological Processes
27 Jan 2022Submission Checks Completed
27 Jan 2022Assigned to Editor
27 Jan 2022Reviewer(s) Assigned
09 May 2022Review(s) Completed, Editorial Evaluation Pending
20 May 2022Editorial Decision: Revise Major
27 Jul 20221st Revision Received
02 Aug 2022Reviewer(s) Assigned
02 Aug 2022Submission Checks Completed
02 Aug 2022Assigned to Editor
10 Aug 2022Review(s) Completed, Editorial Evaluation Pending
11 Aug 2022Editorial Decision: Revise Minor
22 Aug 20222nd Revision Received
30 Aug 2022Submission Checks Completed
30 Aug 2022Assigned to Editor
30 Aug 2022Reviewer(s) Assigned
30 Aug 2022Review(s) Completed, Editorial Evaluation Pending
30 Aug 2022Editorial Decision: Accept