This project has received funding from the Horizon 2020 programme under grant agreement n°776787.

The content of this presentation reflects only the author’s view. The European Commission is not responsible for any use that may be made of the information it contains.

Hydro-climate forecasts for the upcoming spring flood season

Ilias Pechlivanidis (SMHI)

with contributions from Fredrik Almen, Thomas Bosshard, and Louise Crochemore (SMHI)

and Andrea Manrique and Francesc Roura Adserias (BSC)

Agenda

Why focusing on the HP sector?

Understanding the needs in HP

Our approach to address the needs in HP

Analysis of past meteorological status

Forecasts for the upcoming spring flood season (May-July)

Q&A

WHY FOCUSING ON THE HP SECTOR?

Why focusing on the HP sector?

Patsialis, T.; Kougias, I.; Kazakis, N.; Theodossiou, N.; Droege, P. Supporting Renewables’ Penetration in Remote Areas through the Transformation of Non-Powered Dams. Energies 2016, 9, 1054.

Why focusing on the HP sector?

https://s2s4e.eu/climate-services/case-studies

Issued date Lead

time

Variables Characteristics

Feb 2015 3 - precipitation

- snow water

equivalent

- inflows

System: ECMWF

SEAS5

Period: May-July 2015 Mar 2015 2

Apr 2015 1

Case study: Combined snowmelt and high precipitation in north Sweden

Skill (fRPSS) 1 month

(issued in

April)

2 months

(issued in

March)

3 months

(issued in

February)

Precipitation 0.13 0.10 -0.06

SWE 0.81 0.43 0.21

Inflows 0.67 0.48 0.27

UNDERSTANDING THE NEEDS IN HP

Understanding the needs in HP

Asymmetric distribution of inflows and demand over the year

Current operational systems

o Climatological P/T ensemble used to run a hydrological model

o Little improvement in forecasts over the last 30 years

Understanding the needs in HP

Information requested:

Inflows to reservoir

Precipitation

Snow (& Temperature)

Onset of spring flood

Forecast skill

…

Initial conditions

Knowledge of weather

during the forecast period

OUR APPROACH TO ADDRESS THE NEEDS IN HP

35 408 catchments with median size 215 km2

http://hypeweb.smhi.se/ Hydrological model: E-HYPE v.3

Addressing the needs in HP

Hundecha et al. (2016) A regional parameter estimation scheme for a pan-European multi-basin model. J. of Hydrol.: Reg. Studies

Topography: HydroSHEDS, GWD-LR

Land use: ESA CCI, FAO

Soil: HWSD + WISE + FAO

Lakes and reservoirs: GLWD, GRanD

Water flow: GRDC etc.

Evaporation: MODIS

Addressing the needs in HP SUB-SEASONAL

SEASONAL

May

Long term

mean Week 1

Week 2

Week 3

Week 4

SNOW MAX ANOMALY

May

Week 1 Week 2

Week 3

Week 4

INFLOW ANOMALY

Week 1

Week 2

Week 1

Week 2

June

June

Integration for the 1st time of sub-seasonal to seasonal predictions of indicators for the HP sector

Decision Support Tool v1.4.0

https://s2s4e-dst.bsc.es/

ANALYSIS OF PAST METEOROLOGICAL STATUS

March 2020 February 2020 January 2020

Wind speed forecasts Precipitation reanalysis

System used:

JRA-55

March 2020 February 2020 January 2020

System used:

JRA-55

Wind speed forecasts Temperature forecasts

FORECASTS FOR THE UPCOMING SPRING FLOOD SEASON

Above

Below

Predicted tercile Probability range Extremes

50% to 100%

34% to 49%

Max (p90)

Min (p10)

Normal

Wind speed forecasts Precipitation forecasts

SUB-SEASONAL

Prediction system used:

NCEP CFSv2

Maps show areas where

skill (fRPSS) > 0

Probability terms

Enhanced : 34% - 49%

High: 50% - 70%:

Very High: Greater than 70%

4 - 10 May 27 April - 3 May 20 - 26 April

Predicted tercile Probability range Extremes

Wind speed forecasts Temperature forecasts

SUB-SEASONAL

Prediction system used:

NCEP CFSv2

Maps show areas where

skill (fRPSS) > 0

Probability terms

Enhanced : 34% - 49%

High: 50% - 70%:

Very High: Greater than 70%

Above

Below

50% to 100%

34% to 49%

Max (p90)

Min (p10)

Normal

4 - 10 May 27 April - 3 May 20 - 26 April

Above

Below

Predicted tercile Probability range Extremes

50% to 100%

34% to 49%

Max (p90)

Min (p10)

Normal

Wind speed forecasts Precipitation forecasts Probability terms

Enhanced : 34% - 49%

High: 50% - 70%:

Very High: Greater than 70%

SEASONAL

Prediction system used:

ECMWF SEAS5

Maps show areas where

skill (fRPSS) > 0

July 2020 June 2020 May 2020

Probability terms

Enhanced : 34% - 49%

High: 50% - 70%:

Very High: Greater than 70%

SEASONAL

Prediction system used:

ECMWF SEAS5

Maps show areas where

skill (fRPSS) > 0

Predicted tercile Probability range Extremes

Wind speed forecasts Temperature forecasts Above

Below

50% to 100%

34% to 49%

Max (p90)

Min (p10)

Normal

July 2020 June 2020 May 2020

Probability terms

Enhanced : 34% - 49%

High: 50% - 70%:

Very High: Greater than 70%

SEASONAL

Prediction system used:

ECMWF SEAS5

Maps show areas where

skill (fRPSS) > 0

Wind speed forecasts Snow max anomaly forec.

July 2020 June 2020 May 2020

Above

Below

Predicted tercile

Normal

Probability terms

Enhanced : 34% - 49%

High: 50% - 70%:

Very High: Greater than 70%

SEASONAL

Prediction system used:

ECMWF SEAS5

Maps show areas where

skill (fRPSS) > 0

Wind speed forecasts Inflow anomaly forecasts Above

Below

Predicted tercile

Normal

July 2020 June 2020 May 2020

● Webinar series

○ https://s2s4e.eu/climate-services/webinar-series

● Monthly sub-seasonal to seasonal outlooks, published mid-month:

○ s2s4e.eu/climate-services/outlooks

● Case studies demonstrating the potential of the forecasts in the DST

○ s2s4e.eu/climate-services/case-studies

● Reports demonstrating the science and value of the DST

○ s2s4e.eu/climate-services/public-deliverables

Additional services

Q&A

Thank you

Get in touch for more

information!

Public reports of the project will be available for download on the S2S4E website: www.s2s4e.eu

Project coordinator: Albert Soret, Barcelona Supercomputing Center (BSC) Contact us: s2s4e@bsc.es

Follow us on Facebook and Twitter! @s2s4e

This project has received funding from the Horizon 2020 programme under grant agreement n°776787.

The content of this presentation reflects only the author’s view. The European Commission is not responsible

for any use that may be made of the information it contains.

The SMHI Hydrology R&D unit

ADDITIONAL SLIDES

● Tercile probabilities: Probability (number of members) of temperature being

in the lower, middle or upper tercile of the system’s climatology.

p90

p66

p33

p10

Forecast products

● Probability of extremes: Probability (number of

members) of temperatures exceeding the system’s

climatological 10th/90th percentile. The triangles

are shown when the probability is larger than 25%

● Relative measure of the quality a system’s forecasts for the time period

and location

● Typically measured on the system’s hindcast

● For tercile probabilities: Fair Ranked probability skill score (fair RPSS)

● For extremes (p10, p90): Fair Brier Skill Score (fair BSS)

1993

2019 2017

● compared to using a

climatological forecast

● Skill score= (1- (Sfcst /

Sclim))*100

● exceeding the 10th

and 90th percentiles

Skill scores

Coordinated by Barcelona Supercomputing Centre (Spain), and consists of:

The S2S4E Consortium

● Sub-seasonal and seasonal predictions

for the energy sector

● The Decision Support Tool (DST)

launched on 20 June 2019 and has

been operational since then.

● Monthly outlooks and quarterly

webinars to present forecasts to users

S2S4E: Sub-seasonal to seasonal climate predictions for energy