Big data og data assimilering i hydrologisk modellering Henrik Madsen DHI Hydrologidag 2019, Odense, 22. oktober 2019
Big data og data assimilering i hydrologiskmodellering
Henrik MadsenDHI
Hydrologidag 2019, Odense, 22. oktober 2019
Transforming data into operational decisions
Only 5-10% of data collected in a typical utility are utilised
for actionable information
(Global Water Intelligence, 2016)
© DHI #2
Predictive models
© DHI #5
Use o
f p
hysic
s-b
ase
d kn
ow
led
ge
Use of data
Low
High
HighLow
Physic
s-b
ased
models
Data-based
models
Hybrid physics and
data-based models
Adapted from Karpatne et al. (2017)
Physic
s-b
ased
models
Accu
racy
Use of data
Low
High
HighLow
Adapted from Read and Kumar (2019)
Big Data challenges
• Data sources represent
o Different temporal dynamics
o Different spatial resolution
(supporting scale)
o Different measurement
uncertainties and representation
errors
© DHI
© DHI
Hydraulic Head
Cosmic Ray
Soil Moisture (3 depths)
River Discharge
Ahlergaarde, West Denmark
1055 km2
SMOS soil
moisture
High-resolution soil moisture product based on Sentinel-1
© DHI
10 % 90 %Relative Soil Moisture Content
1 km
10 m
© DHI
#12
1. Describe
2. Diagnose
3. Predict
4. Prescribe
Physical System
Sensors
Automation &
control
System Loads Forecast
Model
Optimisation Model
Other data
Process Model(s)
Digital Twin