Effects of climate scenarios on the hydropower sector – needs and challenges in development projects and long time forecasting COST VALUE-2012 End User Needs for Regional Climate Change Scenarios Kiel, 7-9 march 2012 Eli Alfnes, Statkraft Energi AS

Baltic Cable Head Office Offices Power production hydro, gas, wind and bio Subsidiari companies Trondheim Småkraft Fjordkraft Oslo Stockholm Skagerak Energi London Brussel Amsterdam Düsseldorf Marbella Tirana Podgorica STATKRAFT IN EUROPE Lyon Ankara Istanbul

BEYOND EUROPE - SN POWER Peru Chile Nepal The Philippines Laos * India Sri Lanka * 20% i THPC eies av Statkraft SF Singapore

HYDROPOWER PRODUCTION 4 Statkraft presentation Typical profiles for Norway Week no

CLIMATE PROJECTIONS - WHEN New power plant projects Economy, design and dimension Hydro and wind Rehabilitation and revision of power plants Dam constructions Dam safety Power turbine installations Financial market operations on long time bases 5 Statkraft presentation

CLIMATE INFORMATION - WHAT Hydropower generation Runoff / inflow Precipitation and temperature Evaporation, snow and glacier balance Annual water volume Seasonal profile / variation Occurrence of succeeding wet or dry years Dams As Hydro Extreme floods Financial market operations As Hydro Changes in the demand and supply pattern 6 Statkraft presentation

Historical data as ensembles Hydrological models (HBV) Present state Models for production planning Changes in water availability WHERE ARE STATKRAFT TODAY 7 Statkraft presentation Delta change for climate projections -> Runoff time series Time horizon: ~ 50 years Changes in water volume and seasonal profile Power plant and catchment scale Climate change (%) from gridded runoff maps from NVE and SMHI Time horizon: ~ 25 years Changes in water volume Norway + Sweden Investment decisions Financial market operations

8 Statkraft presentation Which method to achieve the best possible description of the uncertainty / risk Are the climate model realizations equally god / equally likely Guidance to select a handful of models / realizations Want the expectation value for runoff and a measure on the risk which the uncertainty brings Snow Soil moisture Runoff Evaporation Global climate modelRegional climate model Emission scenario Hydrological model Interface

CHALLENGES Simulated runoff from hydrological models using downscaled GCM/RCM results as input must have the same statistical properties and seasonal profile as when driven by observed meteorological data Not sufficient for hydrological modelling that mean values and standard deviations of monthly meteorological data are reproduced (Beldring et al. 2008) Downscaling methods – what do they do with the climate signal How is the climate signal preserved throughout downscaling ? How does the bias correction alter the dependency between climate elements obtained from the RCM (or GCM for stat. emp. downscaling) ? Transferring the climate signal to the hydrological models Delta change – losing information of changes in frequency Transient scenarios – statistics and seasonal profiles must agree with the observations in the reference period 9 Statkraft presentation

CHALLENGES 10 Statkraft presentation More and more realizations of climate models How do they differ – strengths and weaknesses How to select representative scenarios Uncertainty – how to deal with that Methods for quantifying the uncertainty Evapotranspiration - the joker? Neither atmospheric models nor hydrological models simulate observed evaporation correctly under present climate (Engeland et al. 2004).

SUMMARY Parameters of interest Runoff Precipitation and temperature Evaporation for ‘validation’ Time frame 25 – 50 years (+ 15 years before and after) Daily values Maintain dependencies between parameters Consistence between downscaled climate scenarios and observations Expectation ‘value’ and measure of uncertainties and risk Guidance to which models where, for what and at which time frames 11 Statkraft presentation

12 Statkraft presentation Thank you for your attention !

13 Statkraft presentation Projected runoff 2050 for Hølen (western Norway), ECHAM/OPYC3 IPCC SRES B2 – HIRHAM met.no EXAMPLE – HBV SIMULATIONS ON CATCHMENTS HBV-simulated runoff HBV-models calibrated against observed runoff 80 years of historical data homogenized to today's climate Simulated runoff for and as reference 1 RCM Delta change Predict runoff for 2050 Directly comparable with today’s situation Achieve changes in annual runoff level and seasonal profile Miss out changes in frequency inter- and intra annual patterns, extremes etc. Uncertainty in climate change not considered HBV-model performance uncertain snow and glacier evaporation Production models Cost-benefit analysis for power plant rehab

14 Statkraft presentation EXAMPLE – RUNOFF MAPS NORWAY Runoff change maps in mm and percentage 6 bias corrected GCM/RCM realizations HBV-simulations - 1x1 km grid 30-year time periods representing 2035 and 2085, and reference periods and Aggregating area values of runoff change - typically inflow catchments to hydropower stations. Used in Long time price models and Cost-benefit analysis

15 Statkraft presentation Source: SMHI report No EXAMPLE – RUNOFF MAPS SWEDEN Percentile maps of change in annual runoff (%) 16 (12) bias corrected GCM/RCM realizations HBV-simulations on catchment level Raster 1x1 km Median, max, min, 25- and 75-percentile and mean 30-year time periods representing 2030, 2040, …, 2080 Extracting the change for selected areas typically inflow catchment to hydropower stations Used in Long time price models Cost-benefit analysis

FROM TODAY TO TOMORROW Today’s method only gives us part of the picture Need to address the uncertainty and the change in frequency but at which scale and to which level Not realistic to run all possible scenarios and downscalings through the entire line of models. How best to capture the changes of interest 16 Statkraft presentation