Intercomparison of regional-scale hydrological models and robust climate impact projections

Transcription

1 Intercomparison of regional-scale hydrological models and robust climate impact projections Valen&na Krysanova & Regional-scale water sector team in ISI-MIP

2 OUTLINE Climate Change and Sustainable Development Regional-scale water sector modelling in ISI-MIP (Inter-Sectoral Impact Model Intercomparison project) EvaluaBon of impact models performance Impacts and uncertainbes Cross-scale intercomparison of impacts Special Issue in ClimaBc Change & Outlook

3 Climate Change & Sustainable Development: dual relationship On the one hand, climate change influences key natural resources and human living condibons, and thereby also the basis for social and economic development. On the other hand, society s prioribes on sustainable development influence both the GHG emissions (! CC) and the vulnerability. GHG Emissions Climate Change Climate Change Impacts: Living Conditions Natural Resources Sustainable Development Vulnerability IPCC, AR4, WG3

4 CC & SD: dual relationship CLIMATE Climate variability Climate change Climate Hazards Risk of Impacts Exposure of n. & h. systems Vulnerability of n. & h. systems SOCIO-ECONOMIC PROCESSES Socioeconomic pathways Governance AdaptaDon & MiDgaDon Sustainable development Emissions & Land use change IPCC 2014, Döll et al. 2015, modified

5 The world is becoming less sustainable Adjusted net savings: national accounts are adjusted, taking into account education, depletion of natural resources and pollution of the environment N.B. Trend has nothing to do with trend UNDP: The 2010 Human Development Report on sustainability and climate change

6 CC & SD: joint policies are needed Climate policies can be more effecdve when included in broader strategies designed to make nabonal and regional development paths more sustainable. This is because the impacts of climate change, climate policy responses, and associated socio-economic development affect the ability of countries to achieve sustainable development goals. RecogniBon of the dual relabonship between SD and CC indicates that there is a need for the explorabon of policies that jointly address Sustainable development and Climate change.

7 Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) is a community-driven modelling effort bringing together impact modellers from many countries across sectors and scales to create more consistent and comprehensive projecbons of climate change impacts. ISI-MIP is coordinated by PIK, with a support from IIASA. Based on common ensembles of climate scenarios, the impacts are simulated for different sectors using mulbple impact models and policyrelevant metrics, and uncertainbes are evaluated. First results of ISI-MIP from the global-scale models are already published (PNAS, 111, 2013). An overview of the Regional-scale hydrological model intercomparison in ISI-MIP will be presented.

8 Twelve river basins for the modelling Rhine Niger Lena, Ganges Darling MacKenzie Amazon Tagus Blue Nile Yellow, Yangtze Mississippi

9 Basins Rhine Tagus Niger Blue Nile Ganges Yellow Yangtze Lena Darling MacKenzie Mississippi Amazon Gauges Lobith Almourol Lokoja Koulikoro Khartoum El Deim Farakka Tangnaiha Cuntan Stolb Louth Ar. Red River Alton SP Olivenca Niger Blue Nile Basins Simulations completed with 9 models Rhine Tagus Models VIC X X X X X X X X X X X X X SWIM X X X X X X X X X X X X WaterGAP3 X X X X X X X X X X X mhm X X X X X X X X X HYMOD X + X X X X + X X X X + X X HBV X + X X X X X X X X X SWAT X X X X X X HYPE X X X X X X ECOMAG X X Applications Work flow: Climate scenario data are provided by ISI-MIP; Calibration & validation of models (driven by WATCH climate); Application of climate scenarios (5 GCMs, 4 RCPs); Analysis of impacts and unceratainties, & cross-scale comparison. Ganges Yellow Yangtze Lena Darling MacKenzie Mississippi Amazon = 80

10 Evaluation of regional models: monthly and seasonal dynamics (5 criteria) γ PBIAS [%] RSR KGE good interm poor good Series1 Interm. Series2 poor Series3 NSE NSE KGE RSR PBIAS [%] γ > 0.7 > 0.7 < 0.6 +/- 15% > 0.9 < 0.5 < 0.5 > 0.8 > 30% < 0.8 monthly seasonal Huang et al. SI

11 Evaluation of models: floods and low flow (7 criteria) Δ Flood [%] ΔFHV10 [%] ΔFHV5 [%] good good Series1 Interm. Series2 interm poor poor Series3 ΔFHV2 [%] ΔFHV2 [%] ΔFHV5 [%] ΔFHV10 [%] Δ Flood [%] +/- 25% +/- 25% +/- 25% +/- 25% Δ Low Flow [%] NSEIQ Δ FLV [%] Δ Low Flow [%] > 0.7 +/- 25% +/- 25% < 0.5 > 50% > 50% > 50% > 50% > 50% > 50% Δ FLV [%] Series1 good Series2 Interm. interm Series3 poor NSEIQ Huang et al. SI

12 Comparison of validation runs ( ): global and regional models glob Rhine Mississippi reg obs Yangtze Ganges Hattermann et al. SI

13 Impacts with high and moderate certainties (8.5) Tagus Lena Mackenzie Yangtze Ganges Rhine GCMs HF MF LF HF MF LF HF MF LF HF MF LF HF MF LF HF MF LF GFDL x x HADGEM x IPSL x x -1-2 MIROC x 1 x -1 NORESM x Niger Blue Nile Darling Amazon Mississippi Yellow GCMs HF MF LF HF MF LF HF MF LF HF MF LF HF MF LF HF MF LF GFDL 1 2 x x x x HADGEM x x x x x 1 1 x x x x IPSL x MIROC x 2 x x NORESM x x x x x ( h i g h G C M u n c e r t a i n t y ) no trend high HM uncert. 2 All HMs: stat. sign. positive trends 1 1 HM sign. positive trend, at least 1 insign. trend 0 No stat. sign. trends -1 1 HM sign. negative trend, at least 1 insign. trend -2 All HMs: stat. sign. negative trends x Disagreement: stat. sign. opposite trends Vetter et al. SI

14 Sources of uncertainty: decomposition of variance (ANOVA), 4 examples Rhine Niger: GCM prevails GCM+HM GCM+RCP GCM+RCP Amazon: GCM & RCP Ganges GCM GCM GCM+HM GCM RCP HM Vetter et al. SI

15 Triangle of uncertainty: where are the basins placed? Uncertainty sources on average: highest from GCMs (54%), followed by RCPs (30%) and HMs (16%). Median Flow HM Vetter et al. SI

16 Changes in seasonal runoff simulated by global & regional models: medians and ranges Rhine * Tagus * Mississippi * Yangtze * similar (2 crit.) * similar (1 crit.) ( to ) Hattermann et al. SI

17 Changes in seasonal runoff simulated by global & regional models: medians Rhine Tagus m3/s Reg Glob Mississippi Yangtze m3/s Reg Glob Differences in change signals: Rhine 45%, Tagus 59%, Mississippi 51%, Yangtze: 861 & -156 m3/s

18 Special Issue: 15 papers Evaluation of models 12 stat. indices, return periods MacKenzie, Lena seas. dyn. Blue Nile Amazon Yangtze 1-2 basins, multimodels Comparison of impacts 5-12 basins, multimodels extremes droughts IHA water balance HMs & parametrization extremes: impacts Sources of uncertainty Global Regional comparisons seasonal dynamics ANOVA: 3 sources droughts validation & seasonal dynamics

19 Papers for the Climatic Change Special Issue 1. Editorial introductory paper, V. Krysanova & F. Hattermann 2. Evaluation of regional hydrological models, Sh. Huang, R. Kumar et al. 3. Bias correction of global weather data (Upper Amazon case), M. Strauch et al. 4. Climate change impacts on stream flow seasonality, S. Eisner, M. Flörke et al. 5. Analysis of hydrological extremes, I. Pechlivanidis, Y. Hundecha et al. 6. Assessment of hydro-meteorological droughts, L. Samaniego, R. Kumar et al. 7. Analysis of hydrological alteration under climate change, T. Yang, X. Wang et al. 8. Sensitivity of water budget components to CC, V. Mishra, H. Shah, et al. 9. Evaluation of sources of uncertainty in hydrological trends, T. Vetter et al. 10. Uncertainty related to hydrological models, A. Chamorro-Chavez, P. Kraft et al. 11. Water balance components for the Blue Nile basin, A. Dinkneh, T. Alemayehu et al. 12. Trends in water resources for the Upper Yangtze river basin, Su Buda et al. 13. Water regime of great Arctic river basins, A. Gelfan, I. Pechlivanidis et al. 14. Global-regional comparison of validation runs and impacts, F. Hattermann et al. 15. Global-regional comparison of impacts on extremes, S. Gosling, R. Danker et al.

20 Conclusions Performance of regional models for monthly/seasonal flow & high flow is good, but it is poor for low flow. Performance of regional models (seasonal dynamics) is much better than that of global models. Robust projections found: Impacts with a high certainty: for Tagus, Lena & MacKenzie. Impacts with a moderate certainty: Ganges, Yangtze & Rhine. Uncertainty sources: generally the highest from GCMs (54% for MF), followed by RCPs (30%) and HMs (16%). Improvement of model performance is needed for: GCMs: in Africa, Australia & the Amazon basin; HMs: in snow-dominated basins & for low flow. Cross-scale comparison of impacts simulated by global & regional models: medians are comparable for some of the basins and differ for the others; the uncertainty ranges are higher for the global model outputs.

21 Outlook: further steps Improvement of the model performance: - spatially-distributed validation, - improvement of model structure (snow-ice processes, vegetation), - low flow. Impacts on extremes: floods and droughts, especially in highly vulnerable regions. Cross-sectoral impact assessment: - water and crops (integrated models: SWIM, SWAT, LPJmL or offline coupling), - water and biomes.

22 Thanks!