Climate Change and Water Cycle: Effect to Water Resources and their Utilization in Finland (CLIMWATER)

Transcription

1 Climate Change and Water Cycle: Effect to Water Resources and their Utilization in Finland (CLIMWATER) Final Seminar of the Ficca Programme J. Pulliainen (FMI), L. Backman, J. Kaurola, T. Olsson, A. Arslan, P. Raisanen, M. Salminen, K. Luojus, M. Takala, A. Karpechko, J. Lemmetyinen, J. Ikonen B. Vehviläinen (SYKE), J. Jakkila, N. Veijalainen, T. Dubrovin, K. Heinilä, S. Metsämäki, H. Mela, H. Saarikoski, M. Pihlajamäki, M. Marttunen

2 Work Packages 1. Advanced analysis of 30-year-long time-series of geophysical variables relevant to water cycle at terrestrial region - How reliable is available observational data including their spatial characteristics 2. Evaluation of climate model simulations - How reliable are GCM and RCM predictions (analysis of historical data sets, also topic of WP3) 3. Evaluation and improvement of the ECHAM5 global circulation model and the REMO regional model 4. Derivation of forty-year projections of seasonal behaviour of precipitation and temperature and their uncertainty estimates - How to obtain feasible predictions of variables relevant to hydrology: bias correction to precipitation and temperature predictions (also topic of WP5) 5. Determination of hydrological scenarios for Finland 6. Analysis of results for end-use applications 7. Evaluation of water course regulation adaptation strategies - How should the future water management practices consider climate change effects

3 Climate scenarios Emission scenario (A1B) Global Climate Models (GCMs) Boundary conditions Regional Climate Models (RCMs) Watershed Simulation and Forecasting System (WSFS) Hydrological model Observations Temperature Precipitation Wind speed and specific humidity Daily data Bias correction DBS/Gaussian DBS/Gamma Mean correction Hydrological scenarios: Continuous time series for Water levels and Discharges Analyses and further studies

4 WP 1: Advanced analysis of 30- year time-series of geophysical variables relevant to water cycle at terrestrial region WP 2: Evaluation of climate model simulations How reliable are climate model simulations?

5 GlobSnow CMIP5 Examples mean March SWE Had CMIP5 Had CMIP3 Had CMIP

6 CMIP5 vs. GlobSnow SWE (March & April)

7 CMIP5 vs. GlobSnow SWE (March & April)

8 WP 2: Evaluation of climate model simulations WP 3: Evaluation and improvement of the ECHAM5 global circulation model and the REMO regional model WP 4: Derivation of forty-year projections of seasonal behaviour of precipitation and temperature and their uncertainty estimates

9 WP 5: Determination of hydrological scenarios for Finland WP 6: Analysis of results for end-use applications

10 Evaluation of snow melt time in ECHAM5 Räisänen, P., and coauthors: Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric GCM, 2014: Geosci. Model. Dev., (in print). Snow melts too early (except for Eastern Siberia) Difference in snow-off day: ECHAM5 satellite data A slight cold bias in spring (large bias in E. Siberia) Difference in April-May temperature: ECHAM5 obs

11 Snow melts early though it is cold 2-m temperature ( C) 10 Composite analysis for Sodankylä air temperature too low in the snow melt season 0 observations ECHAM5 Recommendations for future model development: day wrt. snow-off -Surface heterogeneity: separate surface temperature for snow-covered and snow-free parts of a model grid cell -Shading of snow by forest canopy should be accounted for - Improved treatment of snow albedo (P. Räisänen et al., manuscript in preparation)

12 ClimWater WP4, WP5 and WP6: Derivation of projections of seasonal behaviour of precipitation and temperature Determination of hydrological scenarios Analysis of results for end use applications Juho Jakkila, Noora Veijalainen, Taru Olsson, Leif Backman ja Bertel Vehviläinen IL ja SYKE,

13 Climate scenarios Emission scenario (A1B) Global Climate Models (GCMs) Boundary conditions Regional Climate Models (RCMs) Watershed Simulation and Forecasting System (WSFS) Hydrological model Observations Temperature Precipitation Wind speed and specific humidity Daily data Bias correction DBS/Gaussian DBS/Gamma Mean correction Hydrological scenarios: Continuous time series for Water levels and Discharges Analyses and further studies

14 Hydrological scenarios in ClimWaterproject Bias corrections for Regional Climate Models (RCMs) 5 scenarios (ENSEMBLES-project), A1B emission scenario Bias corrections for input variables of WSFS Precipitation and Temperature Wind Speed and Specific Humidity Requirements of bias corrected data as input to hydrological model Correct precipitation sums in different seasons Extreme precipitations close to observed values Correct temperature distributions, especially close to zero Hydrological scenarios Changes in hydrological variables Water level, discharge, snow water equivalent, etc. Changes in floods in different seasons in 67 sites Changes in flood areas and damages in a few study sites

15 Temperature and precipitation corrections The distribution based scaling (DBS) method* was implemented for five climate scenarios Temperature distribution (top) normal distribution (left) +separating wet and dry days (right) Precipitation distribution (bottom) single gamma distribution (left) double gamma distribution, 95 percentile (right) * Yang et al Distribution-based scaling to improve usability of regional climate model projections for hydrological 15 climate change impacts studies. Hyd.Res. 41, 211.

16 Annual precipitation and temperature Monthly mean precipitation (left) and temperature (right) during the control period Observations in black Uncorrected RCMs in solid lines Adjusted RCMs in dashed and dotted lines (overlaps observations) 16

17 Seasonal trends Seasonal trends in observed (red, ) and RCM simulated temperatures in Lentua basin during Raw RCM daily temperatures in black, bias corrected data in blue and green For most scenarios the DBS method preserves sufficiently well the temperature precipitation trends 17

18 Hydrological simulations The DBS bias correction method significantly improves the simulated discharges(upper fig.) Changes in annual mean discharges by are moderate or small Seasonal distribution of discharges will change significantly (lower fig.) Larger winter discharge Less spring snowmelt discharge Smaller summer discharge 18

19 Changes in discharges of River Loimijoki Daily distributions and with 3 scenarios Wide range of changes in 3 different scenarios HIRHAM ARPEGE Floods decrease Lower discharges in summer season HadRM HadCM Smaller floods in spring Larger and more frequent floods in summer, autumn and winter HIRHAM BCM Floods increase Largest floods in January-March 19

20 Flood discharges Floods are sensitive to biases in temperature and precipitation Floods will change differently in different parts of Finland Decreasing floods in snowmelt flood dominated areas Increasing floods in large outflow rivers of the lake area Variability between climate scenarios with different climate models is large Remaining biases caused some scenarios to produce misleading results in some areas 20

21 How reliable are obtained hydrological scenarios based on comparison with satellite data analysis Using advanced satellite data-derived information on snow cover characteristics (from WP1) Ensemble mean predictions of five RCM and hydrological model combinations Comparison with satellite data-derived fractional snow cover (FSC)

22 WP 7: Evaluation of water course regulation adaptation strategies

23 ClimWater WP7: Evaluation of water course regulation adaptation strategies. Recent climate change modelling suggests that flood risks will increase significantly in the Kokemäenjoki River Basin due to increased winter floods and frazil ice Cities of Pori and Huittinen are significant flood risk areas The lakes are threatened by low water levels during summer The aim of WP7: to develop, together with relevant stakeholders, lake regulation practices to minimize the overall damage of an extreme flood event in the river basin to integrate the results into policy-making by facilitating a dialogue between scientists, policymakers and key interest groups

24 Study design

25 Kohdevesistö: Kokemäenjoen vesistö 25

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27 Alternative regulation strategies and estimation of direct flood damages Hundreds of millions euros Direct flood damages (M ) 200,0 180,0 160,0 140,0 120,0 100,0 80,0 60,0 40,0 20,0 0,0 Strategy 1 Strategy 2 Strategy 3 Lakes River Total Outflow from each lake is regulated independently, according to current regulation permit The water exceeded the current river defence structures in Pori and caused significant damage Lake outflows was reduced to minimize the total flood damage in the whole watershed Flood damages at lakes Pori and Huittinen saved from major damages Total damage minimization as in strategy 2 Lake water levels are lowered cm already in autumn -- preparation for winter flood Total flood damages reduced further 27

28 Thank You for Your Attention!