Estimating Present and Future IDF Curves Using Global Precipitation and Climate Projection Data
Creators: name: Blagojević, Jovan
name: Peleg, Nadav
name: Athanasoios, Paschalis
name: Marra, Francesco
name: Molnar, Peter
Publisher: name: ETH Zurich
Year: 2024
Subjects: Rainfall extremes
Climate Change

description: In this study we present a new methodology for obtaining present and future Intensity-Duration-Frequency (IDF) curves from global precipitation and climate datasets by combining stochastic rainfall modelling and climate projection based adjustments of the rainfall frequency-intensity relations.
With a warming climate, large regions of the world are projected to experience an intensification of extreme rainfall events. This phenomenon is particularly pronounced in the tropical belt, where temperature-rainfall scaling rates have been observed to deviate from the commonly referenced Clausius-Clapeyron (CC) relationship of approximately 7% rainfall intensification per degree Celsius increase. In some cases, scaling rates have reached 2 to 3 times the CC value, while in other instances, no scaling has been observed. At the same time, these tropical regions are expected to experience significant population growth and urbanization in the near future, highlighting the need for a deeper understanding of rainfall intensity-frequency relationships and their projected changes to address rainfall-related hazards.
Global precipitation datasets (eg. CMORPH, GSMaP or MERRA2), often derived from satellite observations and/or coarse-scale climate modelling, provide an accessible means for rainfall-hazard risk related studies in data-scarce regions. However, these datasets tend to missrepresent the short-duration, high-intensity rainfall events, especially those lasting one hour or less, and which belong to the tails of the distributions (i.e., return levels higher than 30-year).
We introduce a methodology for estimating present IDF curves using openly available global datasets and then fitting a joint distribution rainfall-magnitude-temperature model to make predictions for the future IDF curves, based on projected dew point temperature changes from climate models. In the first step, we use high temporal resolution satellite remote sensing rainfall data (GSMaP) to train a stochastic rainfall generator model - the point process Bartlet-Lewis model. The ensemble data generated using the rainfall simulator is used to develop more accurate present climate IDF curves. In the next step, using the dew point temperature as a covariate for modelling the rainfall intensity and adapting the TENAX model procedure, we fit a temperature-dependent rainfall intensity magnitude model to the present climate IDF distribution. The fitting procedure is conducted based on the present climate dew point temperature distribution and the observed rainfall-temperature scaling. Using this fitted model, we then use future-climate dew point temperature distributions, combined with the projected number of rainy days, to construct future climate IDF curves.
Our findings demonstrate that the proposed methodology produces IDF curves with greater accuracy compared to those derived directly from raw global rainfall datasets. Furthermore, future rainfall intensification appears to be highly spatially variable in the tropics, highlighting the importance of site-specific modeling and local measurements and predictions.
description: ZHydro2024 - Abstract Booklet