Using high-resolution stochastic climate ensembles to model the impacts and uncertainty of hydrology in mountainous catchments
| Autor: | Jorge Sebastian Moraga, Nadav Peleg, Peter Molnar, Simone Fatichi, Paolo Burlando |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Zdroj: | IAHS Scientific Assembly 2022 |
| Popis: | Hydrological projections in the context of a changing climate may display high levels of uncertainty, particularly when examining small temporal and spatial scales. To project the response of hydrological processes to the increasing global temperatures, scientists and practitioners rely on chains of numerical models, each contributing some degree of uncertainty to the overall outputs. Furthermore, the randomness intrinsic to climate phenomena, known as internal climate variability, contributes to the uncertainty of the hydrological projections in the form of an irreducible stochasticity. In this work, we quantify the impacts and partition the uncertainty of hydrological processes emerging from climate models and internal variability for two mountainous catchments in the Swiss Alps and across a broad range of scales. To that end, we used high-resolution ensembles of climate and hydrological data produced using a two-dimensional stochastic 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 ( IAHS Scientific Assembly 2022 |
| Databáze: | OpenAIRE |
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