Kpogo-Nuwoklo, Komlan

Meredith, Edmund

Rust, Henning

Ulbrich, Uwe

Vagenas, Christos

Institutional Repository of the Freie Universität Berlin

Geophysical Research AbstractsVol. 19, EGU2017-10174, 2017EGU General Assembly 2017© Author(s) 2017. CC Attribution 3.0 License.High-resolution downscaling for hydrological managementUwe Ulbrich, Henning Rust, Edmund Meredith, Komlan Kpogo-Nuwoklo, and Christos VagenasFreie Universität Berlin, Institut für Meteorologie, Berlin, GermanyHydrological modellers and water managers require high-resolution climate data to model regional hydrologiesand how these may respond to future changes in the large-scale climate. The ability to successfully model suchchanges and, by extension, critical infrastructure planning is often impeded by a lack of suitable climate data. Thistypically takes the form of too-coarse data from climate models, which are not sufficiently detailed in either spaceor time to be able to support water management decisions and hydrological research.BINGO (Bringing INnovation in onGOing water management; <www.projectbingo.eu>) aims to bridge thegap between the needs of hydrological modellers and planners, and the currently available range of climate data,with the overarching aim of providing adaptation strategies for climate change-related challenges. Producingthe kilometre- and sub-daily-scale climate data needed by hydrologists through continuous simulations isgenerally computationally infeasible. To circumvent this hurdle, we adopt a two-pronged approach involving (1)selective dynamical downscaling and (2) conditional stochastic weather generators, with the former presented here.We take an event-based approach to downscaling in order to achieve the kilometre-scale input needed byhydrological modellers. Computational expenses are minimized by identifying extremal weather patterns for eachBINGO research site in lower-resolution simulations and then only downscaling to the kilometre-scale (convectionpermitting) those events during which such patterns occur. Here we (1) outline the methodology behind theselection of the events, and (2) compare the modelled precipitation distribution and variability (preconditioned onthe extremal weather patterns) with that found in observations.

High-resolution downscaling for hydrological management

https://refubium.fu-berlin.de/bitstream/fub188/20393/1/EGU2017-10174.pdf

High-resolution downscaling for hydrological management

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