This session will address the understanding of sources of predictability and quantification and reduction of predictive uncertainty of hydrological extremes in operational hydrologic forecasting. Including uncertainty estimation in operational forecasting systems is becoming a more common practice. However, a significant research challenge and central interest of this session is to understand the sources of predictability and development of approaches, methods and techniques to enhance predictability (e.g. accuracy, reliability etc.) and quantify and reduce predictive uncertainty in general. Providing uncertainty estimates for integrated catchment models involving forecasting models, either as a cascade or as alternative models, can prove particularly challenging and are an issue of interest to the session. Data assimilation or pre-/post-processing in real-time can provide important ways of improving the quality (e.g. accuracy, reliability) and reducing the uncertainty of hydrological forecasts. Methods that help update forecasts in real-time to reduce bias and increase accuracy, and case study demonstrations of their use, are of interest to this session.
The models involved with the methods for predictive uncertainty, data assimilation, post-processing and decision-making may include catchment models, runoff routing models, groundwater models, coupled meteorological-hydrological models as well as combinations of these. Demonstrations of the sources of predictability and subsequent reduction in predictive uncertainty at different scales through improved representation of model process (physics, parameterization, numerical solution, data support and calibration) and error, forcing and initial state are of special interest to the session.
Contributions are expected to address the following issues:
(i) Sources of predictability (model, forcing, initial conditions)
(ii) Quantification and reduction of predictive uncertainty
(iii) Real-time data assimilation
(iii) Untangling sources of uncertainty in the meteorological-hydrological forecasting chain
(iv) Effect of (improved) representation of model process on forecast quality and predictive uncertainty
(v) Methods for preparing meteorological predictions as input to real-time hydrological probability forecasts
(vi) Verification (methods) of hydrologic forecasts
(vii) Case studies of the above

Solicited speaker is Maurizio Mazzoleni (from Uppsala University) who will give a talk about Real-time assimilation of crowdsourced observations in hydrological and hydraulic models.

Co-organized as NH1.34
Convener: Oldrich Rakovec | Co-conveners: Hamid Moradkhani, Albrecht Weerts
| Tue, 09 Apr, 14:00–15:30
Room 2.31
| Attendance Tue, 09 Apr, 16:15–18:00
Hall A

Attendance time: Tuesday, 9 April 2019, 16:15–18:00 | Hall A

Chairperson: Albrecht Weerts
A.132 |
Zhengke Pan, Pan Liu, Shida Gao, Lei Cheng, Jie Chen, and Xiaojing Zhang
A.134 |
Cosmo Ngongondo, Yanlai Zhou, and Chong-Yu Xu
A.135 |
Zhenwu Wang, Rolf Hut, and Nick van de Giesen
A.136 |
François Bourgin, Lionel Berthet, Renaud Marty, Olivier Piotte, Julie Viatgé, and Charles Perrin
A.137 |
Verification of the Impact-Based Flood Forecasting in El Salvador.
Jose Valles, Rochelle Campbell, Celina Kattan, and Roberto Ceron
A.138 |
Shima Azimi, Christian Massari, Alireza Darian, Sara Modanesi, Bernhard Marschallinger, and Wolfgang Wagner
A.139 |
Yosuke Nakamura, Toshio Koike, Kazuyuki Nakamura, Shiori Abe, Takahiro Sayama, and Koji Ikeuchi
A.140 |
Esraa Tarawneh, Jonathan Bridge, and Neil Macdonald
A.141 |
Martina Kauzlaric, Andreas Paul Zischg, and Markus Mosimann
A.142 |
Mojca Šraj, Sašo Petan, Mira Kobold, Nejc Bezak, and Mitja Brilly
A.143 |
Yiqun Sun, Koen Valk, Claudia Brauer, Weimin Bao, and Albrecht Weerts
A.144 |
Jenny Sjåstad Hagen, Andrew Cutler, Patricia Trambauer, Albrecht Weerts, Pablo Suarez, and Dimitri Solomatine