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HS7.2/AS1.20/CL5.16/NH1.3/NP3.6

Precipitation uncertainty and variability: observations, ensemble simulation and downscaling (co-organized)
Convener: R. Deidda  | Co-Conveners: T. Bellerby , G. Grossi , A. Langousis , X. L. Wang , S. Vogl , P. Laux 
Oral Programme
 / Fri, 27 Apr, 08:30–12:00  / 13:30–17:00  / Room 33
Poster Programme
 / Attendance Thu, 26 Apr, 17:30–19:00  / Hall A
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The assessment of precipitation variability and uncertainty is crucial in a variety of applications, such as flood risk forecasting, evaluation of the hydrological impacts of climate change, determination of design floods, and hydrological modelling in general. Within this framework, this session aims to gather contributions on research, application advances, and future needs in the understanding and modeling of precipitation variability, and its sources of uncertainty.

Specifically, contributions focusing on one or more of the following issues are particularly welcome:
- Novel studies aimed at the assessment and representation of different sources of uncertainty versus natural variability of precipitation.
- Methods to account for different accuracy in precipitation time series, e.g. due to change and improvement of observation networks.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source precipitation products.
- Estimation of precipitation variability and uncertainty in ungauged sites.
- Process conceptualization and modeling approaches at different spatial and temporal scales, including model parameter identification and calibration, and sensitivity analyses to parameterization and scales of process representation.
- Physically and statistically based approaches to downscale information from meteorological and climate models to spatial and temporal scales useful for hydrological modeling and applications.
- Modeling approaches based on ensemble simulation and methods for synthetic representation of precipitation variability and uncertainty.
- Scale invariance properties of precipitation in space and/or in time.