Intensity-duration-frequency curves at the global scale

Courty, Laurent Guillaume; Wilby, Robert L.; Hillier, John K.; Slater, Louise

doi:10.1088/1748-9326/ab370a

Cited by 72 publications

(53 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The insights presented in this study are specific to Western Norway and Svalbard SON-3DP but the UNSEEN-trends approach is transferrable to other regions, temporal resolutions and spatial extent of events, seasons and climate variables. Global validation of the independence, model stability and model fidelity will show in which regions the approach may challenge the robustness of design level estimates, with a potentially high utility in supporting data-scarce regions 63 . Furthermore, the large sample size may allow estimation of extremes using empirical approaches that avoid assumptions about underlying distributions and their nonstationarity, thereby offering the possibility of improved design estimates 10 and empirical attribution of physical mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes

et al. 2020

Self Cite

View full text Add to dashboard Cite

Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction system SEAS5. We fit the GEV distribution to the UNSEEN ensemble with a time covariate to facilitate detection of changes in 100-year precipitation values over a period of 35 years (1981–2015). Applying UNSEEN trends to 3-day precipitation extremes over Western Norway substantially reduces uncertainties compared to estimates based on the observed record and returns no significant linear trend over time. For Svalbard, UNSEEN trends suggests there is a significant rise in precipitation extremes, such that the 100-year event estimated in 1981 occurs with a return period of around 40 years in 2015. We propose a suite of methods to evaluate UNSEEN and highlight paths for further developing UNSEEN trends to investigate non-stationarities in climate extremes.

show abstract

Section: Discussionmentioning

confidence: 99%

Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…This issue was addressed by replacing missing IMERG estimates of the P indices with equivalent estimates from ERA5, resulting in a duplication of ERA5 at high latitudes. P distributional characteristics associated with different durations often exhibit similar spatial patterns 27,86 . To allow the 3-hourly estimates to take advantage of the much better availability of daily P observations (Figs.…”

Section: Methodsmentioning

confidence: 99%

“… Courty et al . 27 used P estimates between 1979 and 2018 from the ERA5 reanalysis 28 to derive probability distribution parameters of annual P maxima for the entire globe. Reanalyses assimilate vast amounts of in situ and satellite observations into numerical weather prediction models 29 to produce a temporally and spatially consistent continuous record of the state of the atmosphere, ocean, and land surface.…”

Section: Background and Summarymentioning

confidence: 99%

PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

et al. 2020

View full text Add to dashboard Cite

We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R2) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 h−1 threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone. The PPDIST dataset is available via www.gloh2o.org/ppdist.

show abstract

“…Different hydrologic products can be derived based on the high-frequency rainfall data, such as 5 min rainfall data. Intensity-duration-frequency (IDF) curves are an example of such a product that is used as one of the inputs in engineering applications for the design hyetograph definition or rational equation (e.g., [1]). Therefore, knowledge about changes in the rainfall processes related to the high-frequency data is important in order to evaluate the validity of the constructed IDF curves and to test if there are any changes in the IDF analysis results when using some additional years (e.g., the last few years).…”

Section: Introductionmentioning

confidence: 99%

Investigation of Trends, Temporal Changes in Intensity-Duration-Frequency (IDF) Curves and Extreme Rainfall Events Clustering at Regional Scale Using 5 min Rainfall Data

Bezak

Mikoš

2019

Water

View full text Add to dashboard Cite

High-frequency rainfall data is needed in different practical hydrologic applications, such as the construction of the intensity-duration-frequency curves (IDF). This paper presents an investigation of trends (station-wise and regional) for several rainfall durations that were constructed based on the 5 min rainfall data. Moreover, changes in IDF results for two 22-year sub-samples were also analyzed. Additionally, changes in extreme events clustering at the regional scale were also analyzed. Ten rainfall stations (44 years of data 1975-2018) located in Slovenia (central EU, approx. 20,000 km 2 ) were used in this study. Results indicate that no clear pattern in the detected trends can be found based on the analyzed stations. However, all the statistically significant trends at the significance level of 0.05 for the 5 min rainfall data were negative. Moreover, regional trends for this duration were also statistically significant. The changes in the design rainfall events between two equal sub-samples were between −30% and 60%. The investigation of changes in extreme rainfall event clustering indicated that extreme 5, 30, and 60 min events could more frequently occur a few days earlier in spring or summer compared to the past period. On the other hand, longer duration events (i.e., 360 and 720 min) tend to more frequently occur a few days later in autumn compared to the past. In most cases, changes are not statistically significant.

show abstract

Intensity-duration-frequency curves at the global scale

Cited by 72 publications

References 55 publications

Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes

Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes

PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

Investigation of Trends, Temporal Changes in Intensity-Duration-Frequency (IDF) Curves and Extreme Rainfall Events Clustering at Regional Scale Using 5 min Rainfall Data

Contact Info

Product

Resources

About