HS4.3 | Probabilistic hydro-meteorological forecasts: ensembles, assimilation, predictive uncertainty, verification and decision making
EDI
Probabilistic hydro-meteorological forecasts: ensembles, assimilation, predictive uncertainty, verification and decision making
Convener: Ruben ImhoffECSECS | Co-conveners: Annie Yuan-Yuan ChangECSECS, Shaun Harrigan, Schalk Jan van Andel, Kolbjorn Engeland
Orals
| Tue, 16 Apr, 14:00–15:45 (CEST)
 
Room 2.15
Posters on site
| Attendance Mon, 15 Apr, 10:45–12:30 (CEST) | Display Mon, 15 Apr, 08:30–12:30
 
Hall A
Posters virtual
| Attendance Mon, 15 Apr, 14:00–15:45 (CEST) | Display Mon, 15 Apr, 08:30–18:00
 
vHall A
Orals |
Tue, 14:00
Posters on site |
Mon, 10:45
Posters virtual
Mon, 14:00
This session brings together scientists, forecasters, practitioners and stakeholders interested in exploring the use of ensemble hydro-meteorological forecast and data assimilation techniques in hydrological applications: e.g., flood control and warning, reservoir operation for hydropower and water supply, transportation, and agricultural management. It will address the understanding of sources of predictability and quantification and reduction of predictive uncertainty of hydrological extremes in deterministic and ensemble hydrological forecasting. 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. Ensemble data assimilation, NWP preprocessing, multi-model approaches or hydrological postprocessing can provide important ways of improving the quality (e.g. accuracy, reliability) and increasing the value (e.g. impact, usability) of deterministic and ensemble hydrological forecasts. The models involved with the methods for predictive uncertainty, data assimilation, post-processing and decision-making may include machine learning models, ANNs, catchment models, runoff routing models, groundwater models, coupled meteorological-hydrological models as well as combinations (multimodel) of these. Demonstrations of the sources of predictability and subsequent quantification and 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.

Orals: Tue, 16 Apr | Room 2.15

Chairpersons: Ruben Imhoff, Annie Yuan-Yuan Chang, Kolbjorn Engeland
14:00–14:05
5-minute convener introduction
Development of techniques and workflows for hydro-meteorological ensemble forecasting systems
14:55–15:00
Discussion with all presenters
Ensemble verification methods and hydro-meteorological forecasting system evaluation
15:40–15:45
Discussion with all presenters

Posters on site: Mon, 15 Apr, 10:45–12:30 | Hall A

Display time: Mon, 15 Apr 08:30–Mon, 15 Apr 12:30
Chairpersons: Schalk Jan van Andel, Shaun Harrigan, Ruben Imhoff
Ensemble verification methods and hydro-meteorological forecasting system evaluation
EGU24-13749
|
ECS
|
Highlight
|
OSPP
|
On-site presentation
The impact of streamflow forecast errors on economic outcomes in future climates
Parthkumar Modi et al.
EGU24-16763
|
ECS
|
On-site presentation
Evaluation of Hydro-meteorological Ensemble Forecasts in Small Catchments in Saxony
Mohamed Elghorab et al.
Development of techniques and workflows for hydro-meteorological ensemble forecasting systems
EGU24-14893
|
ECS
|
OSPP
|
On-site presentation
A Study of Rainfall-Runoff Process considering two uncertainties in Basin with multiple dams
Ojiro Furuoka and Tomohito Yamada

Posters virtual: Mon, 15 Apr, 14:00–15:45 | vHall A

Display time: Mon, 15 Apr 08:30–Mon, 15 Apr 18:00
Chairpersons: Shaun Harrigan, Kolbjorn Engeland, Annie Yuan-Yuan Chang
Ensemble verification methods and hydro-meteorological forecasting system evaluation
EGU24-10587
|
Virtual presentation
Hydrological modelling of the extreme flooding event of July 2021
Pierre Baguis et al.