Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

264

146

Abstract. In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on smallscale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

Section: Rainfall Downscalingmentioning

confidence: 99%

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

Cristiano

Veldhuis

Giesen

2017

264

146

“…Entekhabi et al, 1989;Cowpertwait, 1991Cowpertwait, , 2010Cowpertwait et al, 2002;Fowler et al, 2005;Burton et al, 2008;Paschalis et al, 2014;Sørup et al, 2016). Calibration of the generators is typically performed by comparing generated series to observed series and adjusting relevant parameters prior to climate projection.…”

Section: Introductionmentioning

confidence: 99%

Event-based stochastic point rainfall resampling for statistical replication and climate projection of historical rainfall series

Thorndahl

Andersen²,

Larsen³

2017

Abstract. Continuous and long rainfall series are a necessity in rural and urban hydrology for analysis and design purposes. Local historical point rainfall series often cover several decades, which makes it possible to estimate rainfall means at different timescales, and to assess return periods of extreme events. Due to climate change, however, these series are most likely not representative of future rainfall. There is therefore a demand for climate-projected long rainfall series, which can represent a specific region and rainfall pattern as well as fulfil requirements of long rainfall series which includes climate changes projected to a specific future period. This paper presents a framework for resampling of historical point rainfall series in order to generate synthetic rainfall series, which has the same statistical properties as an original series. Using a number of key target predictions for the future climate, such as winter and summer precipitation, and representation of extreme events, the resampled historical series are projected to represent rainfall properties in a future climate. Climate-projected rainfall series are simulated by brute force randomization of model parameters, which leads to a large number of projected series. In order to evaluate and select the rainfall series with matching statistical properties as the key target projections, an extensive evaluation procedure is developed.

“…In the literature some studies apply hydro-climatic regimes for this classification (Gelati et al, 2010;Svoboda et al, 2016), while others apply event statistics Sørup et al, 2016a). For any given application, the most appropriate classification depends on the data available.…”

Section: Framework For Determining State Of Individual Eventsmentioning

confidence: 99%

“…Expectations to precipitation at event level under climate change are often non-linear with the anticipation that changes in occurrence and size of extreme events will be higher than changes in seasonal or yearly precipitation (Boberg et al, 2010). This is a problem often solved by weather generators or other similar downscaling techniques (Fowler et al, 2007;Burton et al, 2010), but these often have difficulty in presenting realistic time series at the sub-hourly to hourly timescales relevant for urban infrastructure (Segond et al, 2006;Verhoest et al, 2010;Sørup et al, 2016a). Several studies have tested the applicability of Markov models for simulation of high-resolution precipitation series (Srikanthan and McMahon, 1983;Thyregod et al, 1998;Ailliot et al, 2009;Gelati et al, 2010;Sørup et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…To determine these changes, urban hydrological models have to be run, driven by the changed precipitation Willems et al, 2012). By definition, fine-resolution precipitation time series for future climates are not observable, and hence 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). Expectations to precipitation at event level under climate change are often non-linear with the anticipation that changes in occurrence and size of extreme events will be higher than changes in seasonal or yearly precipitation (Boberg et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

Sørup

Georgiadis

Gregersen

et al. 2017

Self Cite

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.