A comparison of methods to estimate future sub-daily design rainfall

Li, J.; Johnson, Fiona; Evans, Jason P.

doi:10.1016/j.advwatres.2017.10.020

Cited by 27 publications

(21 citation statements)

References 48 publications

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“…Application of this scaling rate to precipitation intensities is valid assuming that extreme precipitation amounts are controlled by the local moisture availability and are not influenced by the large-scale atmospheric circulation patterns. In reality, however, physical processes interact and higher scaling rates are also found (Barbero et al, 2018;Blenkinsop et al, 2018;Manola et al, 2018;Lenderink et al, 2017;Zhang et al, 2017). The CC relationship is determined by the annual timescale.…”

Section: Step 3: Precipitation Scaling By the Clausius-clapeyron Relamentioning

confidence: 99%

“…First, the 10 min precipitation events in the time series are identified by using a peak-over-threshold method (threshold = 0.1 mm h −1 and time between successive events > 12 h). Next, the precipitation events and corresponding temperatures are classified in moving temperature bins and each bin is sorted from low to high (Manola et al, 2018). Finally, the magnification of the 90th, 95th and 99th percentile precipitation amount for increasing temperature bins is investigated.…”

Section: Informative Assumptionmentioning

confidence: 99%

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Uncovering the shortcomings of a weather typing method

Uytven

Niel

Willems

2020

Hydrol. Earth Syst. Sci.

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Abstract. In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated simultaneously. This paper illustrates the verification and evaluation of the downscaling assumptions for a weather typing method. Using the observations and outputs of a global climate model ensemble, the skill of the method is evaluated for precipitation downscaling in central Belgium during the winter season (December to February). Shortcomings of the studied method have been uncovered and are identified as biases and a time-variant predictor–predictand relationship. The predictor–predictand relationship is found to be informative for historical observations but becomes inaccurate for the projected climate model output. The latter inaccuracy is explained by the increased importance of the thermodynamic processes in the precipitation changes. The results therefore question the applicability of the weather typing method for the case study location. Besides the shortcomings, the results also demonstrate the added value of the Clausius–Clapeyron relationship for precipitation amount scaling. The verification and evaluation of the downscaling assumptions are a tool to design a statistical downscaling ensemble tailored to end-user needs.

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Section: Step 3: Precipitation Scaling By the Clausius-clapeyron Relamentioning

confidence: 99%

Section: Informative Assumptionmentioning

confidence: 99%

Uncovering the shortcomings of a weather typing method

Uytven

Niel

Willems

2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…Second, when considering high temporal (i.e., sub-daily) and spatial simulations (i.e., 12 km), gridded observational products offer daily temporal resolution and spatial resolution~25 km (at the time this work is being written) in the studied area. This would involve an "upscaling" of the original simulations [56].…”

Section: Observed Data Setsmentioning

confidence: 99%

On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

et al. 2019

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The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDEX are used to drive the CETEMPS hydrological model CHyM. HCS is assessed by using monthly mean discharge and a hydrological-stress index. HCS shows a large spatial and seasonal variability where the summer results are affected by the largest decrease of mean discharge (down to −50%). QM produces a small alteration of the original CCS, which generates a generally wetter HCS, especially during the spring season.

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“…Several studies have suggested ways to simulate future rainfall series with high resolution through weather generators on the basis of climate projections [29][30][31]. Various applications and methodologies to update IDF curves in a changing climate are presented and discussed in some recent studies [32][33][34][35][36][37]. However, the data obtained from climate models are of course not deterministic estimates and there is considerable uncertainty [38].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Future Design Rainfall Depths in Korea

et al. 2019

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One of the most common ways to investigate changes in future rainfall extremes is to use future rainfall data simulated by climate models with climate change scenarios. However, the projected future design rainfall intensity varies greatly depending on which climate model is applied. In this study, future rainfall Intensity–Duration–Frequency (IDF) curves are projected using various combinations of climate models. Future Ensemble Average (FEA) is calculated using a total of 16 design rainfall intensity ensembles, and uncertainty of FEA is quantified using the coefficient of variation of ensembles. The FEA and its uncertainty vary widely depending on how the climate model combination is constructed, and the uncertainty of the FEA depends heavily on the inclusion of specific climate model combinations at each site. In other words, we found that unconditionally using many ensemble members did not help to reduce the uncertainty of future IDF curves. Finally, a method for constructing ensemble members that reduces the uncertainty of future IDF curves is proposed, which will contribute to minimizing confusion among policy makers in developing climate change adaptation policies.

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A comparison of methods to estimate future sub-daily design rainfall

Cited by 27 publications

References 48 publications

Uncovering the shortcomings of a weather typing method

Uncovering the shortcomings of a weather typing method

On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

Uncertainty Quantification of Future Design Rainfall Depths in Korea

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