Download 7 PROGNOS - Raoul-Marie Couture NIVA

Predicting lake responses to change using
(near) real-time models
Raoul-Marie Couture
Donald Pierson (Leader)
Eleanor Jennings
Elvira de Eyto
Erik Jeppesen
Gideon Gal
Vannforsk nettverkstreff og seminar
Tirsdag 29. november, Clarion Hotel
Royal Christiania, Oslo
Consortium description
ACRONYM
JPI topic
PROGNOS
2-extremes
Predicting In-Lake Responses to
Change using near real-time models
INVESTIGATOR
Coordination
Partners
Water quality monitoring;
Sensor networks;
Economically-optimized water management;
DOC; Cyanobacteria blooms; Climate change.
INSTITUTION
COUNTRY
Donald Pierson
Uppsala University
Sweden
Raoul-Marie Couture and
Isabel Seifert-Dähnn
Norwegian Institute for Water Research – NIVA
Eleanor Jennings
Dundalk Insitute of Technology
Ireland
Elvira de Eyto
Marine Insitute
Ireland
Erik Jeppesen and Dennis Trolle
Aarhus University
Denmark
Karsten Bolding and Jorn Bruggeman
Bolding & Bruggeman ApS
Denmark
Gideon Gal
Israel Oceanographic and Limnological Research
Norway
Israel
Stakeholders
— Stockholm Vatten (Water utlity, Lake Mälaren)
— Irish Water (Water Utility)
— Israeli Water Authority (Water utilty, Lake Kinneret)
— Bolding & Bruggman (SME)
— LakeLand Instruments (SME)
— Luode Consulting (SME)
— We are looking for a Norwegian stakeholder/case study
Objectives
— Issues Relevant for Water Quality
— High DOC concentrations
— Algal Blooms
— Demonstrate the value of High Frequecy (HF) water quality
monitoring to support water managment decisions
— Information on the present state of the lake
— Introduce HF data in water quality models and enable
short-term water quality forecasts.
— Information on the near-future state of the lake
— Enhanced information to support water management decisions
— Leverage HF water quality monitoring data
Why High Frequency Monitoring?
— Water quality is shaped by episodic events that are
not easily captured by routine monitoiring programs
— These events occur along a continum of frequency
and intensity
Extreme events – large storms river flows etc
— Less extreme, yet important for affecting lake processes
—
—
—
—
Wind gusts and stills
Heating and cooling events
Threshold Events – Stratification, Ice break-up
— Frequenct of episodic events expected to increase as
a consequence of climate change
Why Modelling?
• HF monitoring reveals what is happening in real-time
• Management must react fast
• Most useful in system with directional water flow
• What if modelling could predict near-future conditions,
alike to weather forecast ?
• Forecasts to react to upcoming e.g. thaw, rain event
• Response (and resilience) to statistically likely extreme events
• Model simulations can be improved if models are updated
with HF monitoring data
• Initial Conditions
• Parameter values
• Extract value from HF monitoring data by using with
models
Coupling of Monitoring and Modelling
Weather
Data
Weather
forecast
Lake Water
QualityModel
Setup
Simulation
Short-Term
Forecast
Water Managment
Decision Support
Parameter
Estimation
Long-Term
Forecast
HF Monitoring
Data
Effect of Large Rain Event on Lake Feeagh, Ireland
•
•
•
•
High rainfall June 21
Pulse of DOC to lake
Deepening in stratification
Decrease in stability that
persisted for rest of summer
Jennings et al. Freshwater Biology 57, 589-601, 2012
Effect of Wind Driven Upwelling Lake Erken Sweden
Buoy Temperature Profiles
HF monitoring data
(Norwegian test site: Lake Langtjern)
Flow, CDOM, Temp, pH
On-site weather station
Lake sensors: temperature and oxygen
All-year lake buoy
sensors at 8 depths
Updated hourly at
www.aquamonitor.no
Lake water quality model - MyLake
Saloranta and Andersen. Ecological Modelling, 2007, 207 (1), 45-60.
Couture et al. Journal of Geophysical Research, 2015, 120 (11), 2441–2456.
PROGNOS model - lake temperature vs time
Planned infrastructure
Innovation
— Forecasts for adaptive water management
— Methods of data assimilation into models
— Reducing model time steps to match HF data streams
— Routinely process data, run simulations, and produce
forecasts
— Improve simulations of phytoplankton blooms
— Simulate DOC, with emphasis on drinking water quality
— Cost benefit analysis working with information from
major water supplies
Need for a Norwegian case study in PROGNOS
— Optimize water withdrawal and use
We focus on lakes à DOC and algal bloom
— Added value of HF data stream
— Existing HF sensor arrays and data streams
— Towards a model-based decision support systems
— Cost Benefit Analysis
— Demonstrate the benefit of WQ forecasts for water
treatment costs
— Network with peers in Sweden, Denmark, Ireland and Israel
—
Join us in Oslo, June 13-15 2017
for the PROGNOS annual meeting
NIVA’s team
Acknowledgements
Project management
Raoul-Marie Couture (PL for Norway)
Isabel Seifert-Dähn (WP5 leader)
http://prognoswater.org/
@PROGNOSproject
[email protected]
Collaborators
Jose-Luis Guerrero (hydrology, scientific computing)
Heleen deWit (catchment DOC)
Kari Austnes (exteme events)
Funding