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ECMWF | Reading | 25-28 November 2019
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Workshop description
ECMWF hosted the H SAF and HEPEX joint workshop on “Satellite inspired hydrology in an uncertain future”, with the following objectives:
To review the status of the hydrological variables retrieval algorithms and to prepare future satellites (e.g. METEOSAT Third Generation, METOP‐SG, SWOT)
To characterize the hydrological variable accuracy and to discuss requirements and validation metrics
To review the status of modelling and data assimilation for hydrological and NWP applications and to share new ideas
To strengthen links between the H SAF and the Risk management, Hydrological and NWP communities to promote a consistent Earth system approach.
The EUMETSAT Satellite Application Facility (SAF) for the support to operational hydrology and water management (H SAF) aims to provide new satellite‐derived products from existing and future satellites with sufficient time and space resolution to satisfy the needs of operational hydrology. H SAF focuses on operational satellite derived products of precipitation, snow and soil moisture as well as the continuous validation of these products.
The HEPEX (Hydrological Ensemble Prediction Experiment) is a community of researchers and practitioners for hydrologic ensemble prediction. It is a voluntarily initiative with many people contributing and working on specific topics related to hydrological forecasting and hydrometeorological ensemble prediction. HEPEX seeks to advance the science and practice of hydrologic ensemble prediction and its usage for risk-based decision making by engaging in several ongoing activities.
Presentations and recordings
Monday 25 November 2019
Workshop information | n/a | |
ECMWF Welcome | n/a | |
EUMETSAT Welcome | n/a | |
H SAF Welcome | n/a | |
HEPEX Welcome | ||
Soil moisture products | ||
Soil moisture products: Quality assessment and hydrovalidation | ||
HSAF Precipitation products | ||
H SAF precipitation products: Quality assessment and hydro validation | ||
HSAF snow cover products: From developing to operation stage | ||
Operational validation of H SAF snow products |
Tuesday 26 November 2019
Challenges for the HEPEX community in the coming years | ||
Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces | ||
Joint assimilation of soil moisture and flood extent maps retrieved from satellite earth observation into a conceptual hydrological model for improving flood prediction: a proof of concept study. | ||
An automatic system for flood mapping based on Sentinel-1 data | ||
Precipitation measurements for hydrological applications | ||
Projected Advances in the Remote Sensing of Precipitation | ||
Snow depth observations from Sentinel-1 over the Northern Hemisphere mountain ranges | ||
The impact of satellite data assimilation on hydrologic model performance | ||
Characterization and monitoring of heavy precipitation events in the Mediterranean area using the H-SAF precipitation products | ||
EO-based retrieval of snow cover, overview of selected snow products and their quality assessment | ||
Improving the snowmelt modelling in mesoscale Hydrological Model (mHM) with satellite based dynamically calculated degree-day factor | ||
Combining Passive and Active Microwave Remote Sensing Data to Assess the Impact of Forest Fires on the Hydrology of Boreal Forests |
Wednesday 27 November 2019
Optimization of the satellite datasets to study the water cycle at the global scale | n/a | |
SMOS Soil Moisture and its potential within the Copernicus Emergency Management Service for Flood Forecasting at ECMWF | ||
Precipitation data assimilation at ECMWF | ||
Impact of UKV soil moisture data assimilation on potential operational forecast of river flows | ||
Satellite data and operational hydrology - A WMO perspective | ||
Assimilation of SCATSAR-SWI with SURFEX: Resolution studies over Austria | n/a | |
Development of snow depth assimilation for the Met Office UK forecasting system | ||
Snow processes in bucket-type hydrological models – does increased realism lead to better simulations? | ||
Improving hydrological prediction through data assimilation: results from the IMPREX and eWaterCycle II projects | ||
The use of H-SAF soil moisture products for event-based hydrological modelling in Liguria (north of Italy) | ||
Assimilation of flood maps derived from satellite SAR data into a flood forecasting model | ||
Understanding Water Availability Within Ugandan through the Drought and Flood Mitigation Service |
Thursday 28 November 2019
Hydrological data assimilation using machine learning | ||
Retrieval of soil moisture using neural networks | ||
On the impacts of location, timing, and frequency of inundation extent assimilation on flood forecast skill | ||
Sequential and variational assimilation of satellite snow data through a conceptual hydrological model in a mountainous catchment | ||
Implementation of a coupled land-atmosphere modeling system within a northwestern Mexican river basin | n/a | |
H SAF soil moisture products | ||
H SAF precipitation products | ||
H SAF snow products | ||
HEPEX products |
Posters
EUMETSAT HSAF SNOW COVER PRODUCTS: H10 and H34 | |
Overview of the Met Office land surface data assimilation system | |
The H SAF H64 soil moisture-precipitation integrated product:development and preliminary results | |
Preparing for SWOT data assimilation in a coupled atmosphere-surface-hydrology-hydrodynamics prediction system | |
H SAF root-zone soil moisture products from ASCAT assimilation | |
Development of a machine learning hydrological forecast model using in-situ and remote sensing data | |
Towards a HEPEX-HSAF testbed for the development of data assimilation techniques in combination with remote sensing data for improving operational flow forecasting systems | |
On the use of EUMETSAT-H SAF soil moisture records for monitoring flood and drought periods 2011 to 2018 in Central Europe | |
Ensemble streamflow data assimilation with NOAA’s National Water Model: Novel methods and evaluation applied to hurricane Florence hindcasts. | |
Data assimilation of remotely sensed soil moisture in hydrological modeling to improve flood forecasting | |
The new H-SAF H67 and H68 precipitation products |
Workshop report
Organising committee
David Fairbairn, Patricia de Rosnay, Fredrik Wetterhall
Scientific organising committee
Zuhal Akyurek, Rodolfo Alvarado, Luca Ciabatta, Simone Gabellani, Flavio Gattari, Ali Nadir Arslan, Silvia Puca, Paolo Sanò, Aynur Sensoy
Sessions
1. Remote sensing, hydrological modelling and data assimilation
- Remote sensing techniques and products for hydrological variables, including soil moisture, precipitation and snow, especially for ensemble forecasting
- Reviewing current and planning for future satellite missions
- Assimilation and use of remotely sensed observations in land surface and hydrological models
2. Hydrological validation and benchmarking
- Validating products against ground measurements, satellite products or other hydrological models
- Evaluating a products performance against a suitable benchmark i.e. a model or data set with a defined expectation of performance
- Post-processing techniques for downscaling remote sensing to local applications
- Exploring novel validation techniques using spatial and temporal data
3. Hydrological data assimilation for NWP
- Assimilating remotely sensed and ground based observations into NWP models
- Validating the impact of hydrological assimilation on NWP forecast skill
- Coupled data assimilation approaches in NWP e.g. land-atmosphere
- Multi-sensor and ensemble data assimilation procedures
4. Impacts of hydrological uncertainty, hydrological forecasting and modelling
- Monitoring and forecasting of floods, droughts and other hydrological hazards
- Monitoring and modelling impacts of hydrological variability on water resources (e.g. rivers, reservoirs), including both natural (e.g. droughts) and anthropogenic (e.g. dams) influences;
- Incorporating uncertainty in hydrological forecasting (assessing uncertainties in initial conditions, parameter values, tendencies et cetera)
- Monitoring and modelling impacts of climate change on water resources
5. Novel hydrological data sources and assimilation techniques
- Novel data assimilation techniques, including coupled data assimilation approaches
- Integrating novel observational data in hydrological ensemble prediction systems (probabilistic-based inundation maps, crowdsourced data, citizen science approaches, etc.)
- New available data sources for hydrological data assimilation
- Linking large scale data and models to local knowledge and needs in hydrological forecasting and decision-making
- From traditional flood forecasting to impact-based forecasting (including e.g. inundation mapping, economic assessment, decision-based analysis.
