Presentation by Albrecht Weerts (Deltares) at the Symposium on catchment hydrology and WFlow, during Delft Software Days - Edition 2017. Tuesday, 24 October 2017, Delft.

1. 14 november 2017
State updating using OpenDA-WFLOW
Albrecht Weerts

2. Overview
• OpenDA
• Coupling OpenDA-WFLOW
• Example(s)
14 november 2017

3. OpenDA
What is OpenDA?
• A generic toolbox for data-assimilation
• set of interfaces that define interactions between components
• library of data-assimilation algorithms
• Open source
Why OpenDA?
• More efficient than development
for each application
• Avoid spaghetti code
• Shared knowledge between applications
• Development of algorithms with e.g. universities
• Easier to test, which should result in fewer bugs
• Supported & maintained (DHI, Delft University, Deltares,
VORTECH, TNO, eScience Center,..)
www.openda.org

4. OpenDA-WFLOW
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Drost et al., (2015)
OpenDA is an open source
toolbox for data assimilation
and parameter calibration in
a generic modeling context.
OpenDA
Method
(e.g. EnKF)
Java
Thrift
Java
Client
Thrift
python
Server
BMI
python
Model
(e.g. Wflow)
BMI
model
Factory
Java
https://github.com/openstreams/wflow
BMI=> http://csdms.colorado.edu/wiki/BMI_Description
Weerts et al., (in prep., 2017)

5. Configuration OpenDA
• Algorithm
Decide which algorithm to use?
• Multiple available (EnKF, DEnKF, AEnKF EnSR, PF, etc)
• Model Factory + Stochastic Modeller
Decide which states to update with which measurement?
• not trivial (e.g. leave out soil moisture state when using
discharge obs.?)
• localization (autozhang, others need to be implemented)
• Physical limits:
• Min / Max (currently uniform over wflow model domain)
• Specification noise:
• input or states variables (multiplicative or adative noise)
• (gaussian) correlated in space/time
Use parallel setup (ThreadStochModel)
• Stochastic Observer
• noise distribution obs.(multiplicative or adative noise)
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6. Examples
1) Distributed noise modelling
2) (single) catchment scale DA
• Effect different algorithms
• hourly time step
3) Continental DA
• Show potential DA for updating large scale distributed
hydrological models for flood/drought forecasting
> No measured forcing used
> Test case IMPREX (see also globalfloodforecasting.com)
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Rakovec et al 2012
Rakovec et al 2015

7. Distributed noise field (OpenDA noise model)
• To create an ensemble the wflow model you can perturb the inputs
or the model states
• White Gaussian noise
• Gaussian noise correlated in space (define decorrelation
length) and time
• Additive or Multiplicative
Tuesday, November 14,

8. Operational hourly forecasting Meuse
12 September CAHMDA-
Rakovec et al. (2012) studied effect of update
frequency, number and location of streamflow gauges
wflow_hbv
1 km2

9. Operational hourly forecasting Meuse-II
12 September CAHMDA-
Fig. 8. Real world experiment, simulation period from 15 August 2002 to 15 January 2003. Root-mean-square error at
Tabreux for different discharge observation vectors. Forecast issued every 6 h. EnKF assimilation every 24 h (left),
12 h (centre), 6 h (right).
Conclusions:
• Best results in terms of the RMSE were achieved using
all observations, which includes all six discharge
gauges.
• Given the travel time of the catchment, an updating
frequency of 12 h seems to be the most appropriate
• Most sensitivity in routing stores

10. Running OpenDA-WFLOW experiment:
globalfloodforecast.com
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11. OpenDA-WFLOW at continental scales
• wflow_w3ra+wflow_routing coupled via BMI
• model timestep = day
• Model resolution currently 0.5 degree (to be upgraded either locally ~1-
2 km2 or global 5-10km2)
• 64 ensemble members
• Perturbation of the forcing (precipitation, temperature)
• Assimilate 17 discharge measurements across Europe
(Germany, France, Belgium, The Netherlands, Norway, Sweden,Serbia)
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12. Example 1
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bias correction correction poor forcing data?
• analysis
1 day ahead forecast/simulation
with state updating
no assimilation

13. Example 2
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filter out noise
Timing issue?
Model time step to coarse?

14. Same ongoing and past example applications
• European scale (globalfloodforecast.com)
• Magdalena Colombia 4km2 Patricia Lopez Lopez Earth2Observe project
• Rhine River 1.44 km2 Bart van Osnabrugge IMPREX project
• Meuse River 1.44 km2 Oldrich Rakovec FC2015 project
• Snare river (Canada) 1km2 David Casson IHE-student
• Ibaizabal (Spain) 1km2 Deltares /SENER
• Karasu (Turkey) 1km2 Aynur Sensoy/Bulut Akkol Anadolu University
• Etc.. 14 november 2017

15. Conclusions
• OpenDA-WFLOW fully open source
• documentation ongoing, see Weerts et al. in prep for GMD and
wflow docs to be updated
• Can run via wflow executable or via wflow python code (fast
prototyping)
• To be used standalone or integrated in Delft-FEWS
• Can be run in parallel to reduce computational time
• Can be combined with EPS (see globalfloodforecast.com for
example)
• Developments/Improvements pending ongoing/future projects
If interested please contact: albrecht.weerts@deltares.nl
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