Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

By Hayley J. Fowler, Stephen Blenkinsop, C. Tebaldi. 2007

2,246 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7c574d7a59028b508faeb84fb80b5e2b6cc2a68e

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Citations in the corpus

The role of forest genetic resources in responding to biotic and abiotic factors in the context of anthropogenic climate change (2014, background): “One problem is that the selection between different global climate models (GCMs) and the methods for downscaling to detailed geographic levels are still areas of active research and thereby introduce uncertainty in modelling, especially for marginal environments (Fowler et al., 2007).”
The role of uncertainty in climate change adaptation strategies—A Danish water management example (2013, methodology): “Data from climate models are typically downscaled and statistically corrected (e.g. Wilby et al. 2002; Christensen et al. 2007b; Fowler et al. 2007; Kilsby et al. 2007) but different downscaling methods may give different input data, thus total errors can arise from both sources (e.g. Sunyer et al.…”
Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India (2017, background): “It has been observed that atmospheric temperatures are rising and precipitation patterns are changing (Joos et al. 2001, Kahya and Kalaycı 2004, Dibike and Coulibaly 2005, Holman 2005, Gosain et al. 2006, Fowler et al. 2007, Barkhordarian et al. 2012, Sethi et al. 2015).”
Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method (2021, background): “Most bias-correction methods assume stationarity of model errors over time (Roberts et al. 2019), which can be problematic for bias-correcting future climate model output over multi-decadal time spans (Cannon et al. 2015; Fowler et al. 2007).”
Projected changes of extreme weather events in the eastern United States based on a high resolution climate modeling system (2012, background): “methods [3].”
A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities (2019, background): “The spatial resolution of GCMs is quite coarse (around 100–300 km(2)) [1], and the direct use of their outputs is not recommended [185].”
Description and validation of a two-step analogue/regression downscaling method (2013, background): “There are two main downscaling approaches (Murphy 1999; Fowler et al. 2007).”
Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows (2013, neutral): “A further consideration is that although statistical downscaling methods are much simpler than the dynamic downscaling methods, they could underestimate the variance and fail to reproduce the extreme events of hydroclimatic variables (Fowler et al ., 2007).”
Statistical emulation of climate model projections based on precomputed GCM runs (2014, methodology): “In the previous section we compared our climate model emulation approach to pattern scaling, the most commonly used approach for emulation of climatemodel output in the impacts assessment community (see, e.g., Santer et al. 1990; Hulme and Raper 1995; Hulme and Brown 1998; Cabre et al. 2010; Dessai ”
Convection‐permitting modeling with regional climate models: Latest developments and next steps (2021, background): “They also advance the closing of the gap between GCMs and end-users (Fowler et al., 2007; Maraun et al., 2010), thanks to the use of explicit deep convection and higher horizontal resolutions.”
Statistical downscaling of precipitation using quantile regression (2013, methodology): “Dynamical downscaling involves the use of high-resolution, limited-area climate models within the domain of interest, whereas in statistical downscaling relatively simple statistical models are used to represent the link between atmospheric circulation variables, presumably well simulated by the GCM”
Adoption Versus Adaptation, with Emphasis on Climate Change (2012, background): “For example, in response to heat waves, moderating electricity use during peak hours is a response at the intensive margin, whereas extensive-margin responses might include building new power plants or adopting new electricity pricing schemes, such as peak-load pricing.”