Assessing the robustness of raingardens under climate change using SDSM and temporal downscaling

Abstract

Climate change is expected to lead to higher precipitation amounts and intensities causing an increase of the risk for flooding and combined sewer overflows in urban areas. To cope with these changes, water managers are requesting practical tools that can facilitate adaptive planning. This study was carried out to investigate how recent developments in downscaling techniques can be used to assess the effects of adaptive measures. A combined spatial-temporal downscaling methodology using the Statistical DownScaling Model-Decision Centric and the Generalized ExtremeValue distribution was applied to project future precipitation in the city of Bergen, Norway.A raingarden was considered a potential adaptive measure and its performance was assessed using the RECARGA simulation tool. The benefits and limitations of using the proposed method has been demonstrated and compared to current design practices in Norway. Large differences in the raingarden's performance with respect to percentage overflow and lag-time reduction was found for varying projections. This highlights the need for working with a range of possible futures. Further, it was found that K sat was the determining factor for peak-flow reduction and that different values of K sat had different benefits. Engineering flexible solutions by combining measures holding different characteristics will induce robust adaptation.

Temporal Downscaling of IDF Curves Applied to Future Performance of Local Stormwater Measures

“…The scaling model for Bergen is seen to always overestimate or underestimate the return levels and the offset is worst for higher durations and higher return periods. However, there is a higher agreement between scaled return levels and return levels estimated by GPD parameters than what has been found in earlier studies for Bergen [32] and Trondheim [33] based on AM and GEV distribution selection. The results of scaling the observed daily return levels are compared to the return levels estimated based on the GPD parameters (Figure 4).…”

“…In addition to threshold selection, the GPD parameters will be affected by choice of method for parameter estimation. For practical reasons, all GPDs was fitted by maximum likelihood estimation (MLE) in this study [32]. Furthermore, the behavior of the GPD is greatly dependent on the shape parameter.…”

“…In these studies, daily and sub-daily extremes were extracted from observational series as annual maxima (AMs), fitted to the generalized extreme value (GEV) distribution, and a scaling relationship was found between the GEV statistics of the daily and the sub-daily scales. In Bergen, the results of the scaling were found satisfying for durations as low as 15 min, but the bias increased significantly for lower durations [32]. In Trondheim, the Gumbel distribution (GEV 1) had to be assumed for the AMs due to unsatisfying fittings of the shape parameter [33].…”

“…In Trondheim, the Gumbel distribution (GEV 1) had to be assumed for the AMs due to unsatisfying fittings of the shape parameter [33]. The results suggest that further research on defining rainfall extremes on fine temporal scales and proper parameter estimation is needed in Norway to provide reliable climate projections that can be accounted for in planning of stormwater management [32,33].…”

A Comprehensive Performance Assessment of the Modified Philip–Dunne Infiltrometer

“…Our review indicates that several recent studies, including Weiss and Gulliver [29], García-Serrana et al [30], Kristvik et al [31], and Taguchi et al [32], have used the MPDI approach to measure the in situ soil hydraulic parameters. None of these studies have evaluated the sensitivity of the MPDI theory to the variations of the soil hydraulic parameters.…”