Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought
نویسندگان
چکیده
Abstract. General circulation models (GCMs) are the primary tools for evaluating possible impacts of climate change; however, their results coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling bias correction model outputs is thus required local applications. Apart from computationally intensive strategy dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- large-scale variables. This study compares four methods (BC), change factor mean (CFM), quantile perturbation (QP) an event-based weather generator (WG) assess impact on drought end 21st century (2071–2100) relative baseline period 1971–2000 station Uccle located Belgium. A set drought-related aspects analysed, i.e. dry day frequency, spell duration total precipitation. The applied 28-member ensemble Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced future scenarios SSP1–2.6, SSP2–4.5, SSP3–7.0 SSP5–8.5. 25-member CanESM5 GCM also used significance signals comparison internal variability climate. performance reveals that QP method outperforms others reproducing magnitude monthly pattern observed indicators. While all good agreement precipitation, differ quite largely frequency length spells. Using methods, projected increase significantly summer months, with up 19 % At same time, precipitation decrease 33 these months. Total increases winter, as it driven significant intensification extreme rather than change. Lastly, spells 9 %.
منابع مشابه
Statistical downscaling of precipitation
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ژورنال
عنوان ژورنال: Hydrology and Earth System Sciences
سال: 2021
ISSN: ['1607-7938', '1027-5606']
DOI: https://doi.org/10.5194/hess-25-3493-2021