Abstract
Abstract. Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change in extremes at the event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes of seasonal precipitation. The methodology is very robust against the actual magnitude of the expected changes as well as the direction of the changes (increase or decrease), even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.
Highlights
Climate change impacts water management worldwide as the water cycle is an essential part of the climate system
Fine-resolution precipitation time series for future climates are not observable, and a multitude of statistical approaches have been developed to enable generation of time series with properties that for a large range of metrics have the same characteristics as the expected future precipitation (Willems, 1999; Olsson and Burlando, 2002; Cowpertwait, 2006; Molnar and Burlando, 2008; Burton et al, 2010; Willems et al, 2012; Sørup et al, 2016a)
We develop and demonstrate a novel non-linear methodology that perturbs existing precipitation time series to reflect complex expectations to precipitation in a changed future climate
Summary
Climate change impacts water management worldwide as the water cycle is an essential part of the climate system. The planning horizon for water infrastructure is often very long, making reliable predictions of future climate crucial (Arnbjerg-Nielsen et al, 2015b). In the design of water infrastructure, precipitation data are needed. For urban infrastructure the time resolution of precipitation data needed for design and planning is much finer than what is provided by climate models (Berndtsson and Niemczynowicz, 1988; Schilling, 1991). Do not translate directly into changes in floods or overflows from structures. To determine these changes, urban hydrological models have to be run, driven by the changed precipitation (Olsson et al, 2009; Willems et al, 2012). Fine-resolution precipitation time series for future climates are not observable, and a multitude of statistical approaches have been developed to enable generation of time series with properties that for a large range of metrics have the same characteristics as the expected future precipitation (Willems, 1999; Olsson and Burlando, 2002; Cowpertwait, 2006; Molnar and Burlando, 2008; Burton et al, 2010; Willems et al, 2012; Sørup et al, 2016a)
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