edge end-to-end demonstrator for improved decision making ......• global land cover data v2.2...

EDgEEnd-to-end

Demonstrator for improved decision

making in the water sector in Europe

Christel PRUDHOMMEEDgE Technical lead and deputy PI

Glenn Watts (WP1 lead), Luis Samaniego (WP2 lead), Justin

Sheffield (WP2) & Matt Fry (WP3 lead)

SIS meeting, UK, 17-19 October 2016

paceOn behalf of for the European Commission

EDgE vision

• Fully integrated project

• Co-design of products and

services

• Iterative development

• Case studies

• User guidance

HydrologicalModelling

For ClimateImpact

Indicators

StakeholderView

Web DeliverySystem

Define

Case Studies

Design Implement

2


Phase 1 Design

Focus Group 1

Feb/Mar ‘16

Indicators & Interface

requirementsJun ‘16

Phase 2 Testing

Sep ‘16

Focus Group 2

Oct ‘16

Mar ‘17

Case studies

Feb ’17

Phase 3 Implementing

Focus Group 3

Jul ‘17 Dec ‘17

1 2 3

4 5

6

7

8 9 10

EDgE timeline


User needs

• Who are the users?

• How are we engaging?

• How can we measure the benefits?

4


Who are the users?

Norway – local council officer

• Needs to understand future risk

from climate

• Little technical knowledge

• Needs clear information for

local politicians

• Little money available

5


Who are the users?

UK – water supply planner

• Good hydrological knowledge but

little knowledge of climate change

• Wants to run existing supply system

models to understand climate

change impact

• Wants hydrological information in

an easy-to-use format

• Needs to be able to present results

to senior managers

6


Who are the users?

Spain – river basin planner

• Multiple pressures on water

resources

• Mainly interested in seasonal

forecasts

• Developing interest in long-term

change but little scientific

knowledge of climate change

• Wants clear information to present

to decision-making forum

7


Who are the users?

Norway – hydropower planner

• Good hydrological knowledge

• Wants to understand long-term

impact especially of changes in

snow melt

• Wants high quality flow data for

existing models

8


How are we engaging?

• 4 focus groups

• Led by user-focused organisations

• Engagement tailored for the group’s needs

• Face-to-face meetings throughout the project

• Presenting ideas and choices

9


What users use?

EXISTING CLIMATE SERVICES Provider Scope REFERENCE (web link)

UK drought portal CEH 1 https://eip.ceh.ac.uk/droughts

Monthly Hydrological Summary of the UK

CEH 1

Water situation report for England

EA 1 https://www.gov.uk/government/collections/water-situation-reports-for-england

UK Climate Projections EA / Met Office 7 http://ukclimateprojections.metoffice.gov.uk/21708

EUROPEAN FLOOD AWARENESS SYSTEM (EFAS) system

Copernicus Emergency Management Service / ECMWF

4 https://www.efas.eu/http://emergency.copernicus.eu/

US drought monitor National Drought Mitigation Center

3 http://droughtmonitor.unl.edu/

US Seasonal Drought Outlook Climate Prediction Center's (CPC)

5 http://www.cpc.ncep.noaa.gov/products/expert_assessment/sdo_summary.php

European Drought Observatory JRC 2,4 (3) http://edo.jrc.ec.europa.eu/edov2/php/index.php?id=1000

UFZ drought monitor UFZ 3 http://www.ufz.de/index.php?en=37937

Climate-ADAPT European Commission / EEA

(1),2,(7),8

http://climate-adapt.eea.europa.eu/knowledge/tools/map-viewer

UK Hydrological Outlook NERC/ CEH 3,5 http://www.hydoutuk.net/

Prototype Météo France (FP7 EUPORIAS)

Météo-France 5 http://www.euporias.eu/prototype/water

Prototype IRSTEA IRSTEA 5 http://hepex.irstea.fr/hepex-seasonal-hydrological-forecasting-workshop/

Case study S-ClimWaRe (FP7 EUPORIAS)

Not defined. Collaboration AEMET, DGA and Cetaqua

5 http://sclimware.euporias.eu/

DRIAS CLIMAT Météo-France 7 http://www.drias-climat.fr/

Climate, snow, river flow, flood and drought monitor

NVE, MET, Norwegian mapping Authority, Railroad and Road authority

1,3 http://www.xgeo.no/index.html?p=fag (Limited version available in English)

Hydrological real time observations

NVE http://www2.nve.no/h/hd/plotreal/Q/index.html

National Drought Indicator System

Spanish Ministry of Agriculture, Food and

http://www.magrama.gob.es/es/ministerio/servicios/publicaciones/rev_numero.asp?codrevista=MSS

https://www.efas.eu/

http://droughtmonitor.unl.edu/

http://www.xgeo.no/index.html?p=fag


What users want?

Skilful seasonal hydrological forecasts

Change in evapotranspiration

Change in water temperature

High resolution information about future climate

Change in drought indices

Norway Spain UK

Metrics

11


Simplicity

No post-processing

Interactive maps

Maps and graphs for download

Maps of thresholds or triggers

Ability to chose own scenario

River basin district, administrative boundaries and 5km gridded data

EDgE results in wider context

Skill assessment

Norway Spain UK

Interface

SpanishNorwegian

What users want?

12


Measuring the benefits

• Cetaqua – Spanish consultant focused

on water economics

• General market analysis across Europe

• Understanding:

• Current use of hydroclimatological

information

• Economic value of sectors

• How EDgE can add value to

decisions

• Risks and constraints

13


What’s next for coproduction and testing?

FG 2: Test

interface

Learn

ing

an

d f

eed

back

Updated

Interface

Focus Group

Uncertainty/

skill

14


EDgE modelling chain concept

15

• Physiographic data

• Hydrologic & climate information

• Modelling chain to estimate tECVs

• Source code to estimate SCII indicators

Ingredients


State-of-the-Art modelling chain

AGU Fall Meeting (Samaniego et al. 2016)

16

• 350000+ lines of source code

• svn.ufz.de/EDgE/wiki/WikiStart

High Resolution 30 TB 300 GB750 GB


EDgE domain

17

• EU 28 member countries

• Associated countries: Switzerland, Norway, Albania, The

former Yugoslav Republic of

Macedonia, Montenegro, Serbia,

Bosnia and Herzegovina, Kosovo,

Andorra, Monaco, San Marino, and

the Vatican

• Entire river basins

• High resolution: 5 x 5 km2


Seamless model parameters

18

• CORINE Land use data v18 4

(EC-EEA)

• Global land cover data v2.2

(ESA)

• Global 3D Soil Information System

(ISRIC)

• Digital elevation model EU-DEM v1

(EEA)

• Global 30 Arc-Second Elevation

(USGS)

• Pan-European River and Catchment

Database v2.1

(EC-JRC)

• Hydrogeological Map of Europe v11

(BGR IHME)

Open source data


Forcings for CP and SF

19

CMIP5 (ISI-MIP) dataset: 3.5 TBGFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM,

NorESM1-M

Seasonal Forecast dataset: 20 TBNMME: CanCM4, GFDL(FLOR) (12 real.)

ECMWF: ECMF, LFPW (15 real.)

GFDL-ESM2M

monthly precipitation [mm] 2003/04

Meteo France LFPW forecast 6.1.1991

(from 1.1.1991)


High resolution streamflow

20

European

floods

2002

ln (Q) Q

3

5

20

50

150

400

1100

3000

8100


SF SCIIs: Drought event Aug. 2003

21

Denotes the percentage of

realizations of monthly

forecasted SM for every

quintile category and lead-

time.

The cut-offs for quintiles

are the 20th, 40th, 60th, and

80th monthly SM

percentiles estimated for

each calendar month over

the reference period.

Def. SCII12

E-OBS_mHM_mSMI_08_2003

SMI

Q1


SF SCIIs: Drought Event Aug. 2003

22

ecmf_sm_quantile_dist_08_2003_lead_from_1month_1quantile

Denotes the percentage of

realizations of monthly

forecasted SM for every

quintile category and lead-time.

The cut-offs for quintiles are

the 20th, 40th, 60th, and 80th

monthly SM percentiles

estimated for each calendar

month over the reference

period.

Def. SCII12

%


Importance of uncertainty and skill

• Many challenges in decision making:

• uncertainty in predictions

• uncertainty about the uncertainty (rare but high impact events)

• transient changes (e.g., versus changes in 50 years)

• different needs in terms of precision/resolution and accuracy

• Users should be able to:

• understand that there are uncertainties

• understand the source of uncertainties and how big they are

• acknowledge (be comfortable) that uncertainties will remain high

We believe that this will improve their decision-making and we will test

this through the case studies.

23


Sources of uncertainty and skill

Historic Simulation Seasonal Forecasts Climate Projections

• Climate models

• Natural variability

• Scenario

• Hydro models/parameters

• Climate models

• Initial conditions

• Hydro models/parameters

• Initial conditions

• Hydrological models

• Parameters

• Observations

24


Approach to uncertainties and skill in EDgE

Objectives of the uncertainty analysis:

• Quantify uncertainty and skill across

space/time dimensions

• Identify the source of the uncertainty

and skill across each dimension

• Interface with WP1 to understand user

needs and capacity

• and with WP3 on how to represent and

visualize uncertainties and skill

25


Example Analysis – Hydrological Model Skill

European Water Archive > 4000 stations• Skill evaluated against

continental databases

• e.g. GRDC/EWA database of

discharge data

• Set of standard skill metrics

such as NSE, RMSE, Corr.

26


Example Analysis – Seasonal Forecasts

Seasonal Forecast Skill – evaluated against E-OBS precipitation and temperature, and GRDC discharge

Model 1 Model 2 Model 3 Model 4

Tem

po

ral

ag

gre

gati

on

Tem

pera

ture

Pre

cip

itati

on

Lead time 0 – 180 days

More skill

Less skill

27


Example Analysis – Climate Projection Unc.

Different Variables and Indicators

Fra

cti

on

al

Un

cert

ain

ty

Year

Identifying the source of uncertainty from

scenario choice, natural variability and model uncertainty

Mediterranean

Australia

SPI12

(meteo. drought)

Freq of

meteo. drought

Soil Moisture

Anom.

Freq of

SM drought

Water Stress

Index

28


Co-generation of uncertainty representation

Indicators Interface

Case Studies

Capturing the Stakeholder View

Evidence Gathering & testingFocus Groups

Live demo and

Exercises

Display

examples

Voting sheets for

preferred options

29


The EDgE demonstrator

Live demo

30

http://192.171.173.135/Apps/#map

http://192.171.173.135/Apps/#climate-change


Upcoming work

• Seasonal Forecasting

• Download functionality

• Skill and uncertainty

• Metadata catalogue

• User experience

31


To conclude on EDgE

HydrologicalModelling

For ClimateImpact

Indicators

StakeholderView

Web DeliverySystem

Define

Case Studies

Design Implement

An hydro-climate service

co-designed with

stakeholders

to break down barriers for

users of different

backgrounds

to access and understand

state-of-the art hydrological

predictions

32

THANKS

Questions?



netCDF file store

Metadata catalogue

(xml, ISO19115)THREDDS Server stack

WMS (etc)

Custom jsonAPI for

indicators

Custom download

tools

Catalogue viewer/ editor

Bespoke mapping

EDgE Demonstrator

http download

Other user tools / DSS


• Prioritised requirements

• Maps and graphs of climate change

data

• Iterative software development

• 2 open webinars with EDgE

stakeholders (26th August, 8th

September)

• First major iteration complete

• Seasonal forecast data and download

tools still to be developed


edge end-to-end demonstrator for improved decision making ......• global land cover data v2.2...

Documents