Hellenic Agricultural Organization Demeter Land Reclamation Institute Hellas. Forschungzentrum Juelich GmbH Agrosphere Institute (IBG 3) Germany

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1 Hellenic Agricultural Organization Demeter Land Reclamation Institute Hellas Forschungzentrum Juelich GmbH Agrosphere Institute (IBG 3) Germany Assessing the potential effects of climate change in the hydrologic budget of a large Mediterranean basin: The case of River Pinios basin, central Greece Tziritis, E., Pisinaras, V., Kunkel, R., Panagopoulos, A., Arampatzis, G., Wendland, F.

2 INTRODUCTION Water Resources Management Demanding and complex task due to its competitive and diverse aspects Additional uncertainty stresses like climate change increase complexity, especially in Mediterranean regions which already suffer from significant environmental pressures Acquisition of accurate and reliable data is imperative in order to successfully apply the management plans In that frame, our study Aims to provide quantitative information about the foreseen impacts of climate change at catchment scale in terms of hydrological budget

3 INTRODUCTION Goals of study Estimation of changes in the critical climatic parameters of precipitation and temperature, for two future projected periods ( and ), based on data from 4 different RCMs 1 GCMs 2 scenarios Calculation of mean annual total runoff (Qt) values for the reference and projected periods Assessment of climate change impact to River Pinios basin, as a function of total runoff (Qt) 1 Regional Climate Models 2 Global Circulation Models

4 STUDY AREA / River Pinios basin Pinios Basin is considered as one of the highest productive basins in Greece Total surface area is km2 approximately It is characterized by highly diversified geological, hydrological and hydrogeological conditions Marked by the systematic exploitation of water resources since early 60 s. Due to water resources mismanagement signs of overexploitation have appeared since mid 80 s and a deficient water balance has been established in the last 3 decades March, 2014, Filoxenia Conference Centre, Nicosia, Cyprus

5 METHODOLOGY / The GROWA hydrological model GROWA (Kunkel & Wendland 2002) is a grid based empirical model with a temporal resolution of one year Determinates long term mean annual values of water balance components Water balance modeling is performed in three steps: 1. Determination of mean annual actual evapotranspiration (Eta) (Renger and Wessolek, 1996) 2. Grid wise calculation of total runoff, as the difference between the precipitation and actual evapotranspiration 3. Separation* of total runoff into water components: a) Direct runoff (surface and sub surface flow) b) Groundwater runoff * based on several geo factors (e.g. degree of sealing, artificial drainage, petrographic properties, depth of groundwater, perching water and slope) Water balance components considered in GROWA (Wendland et al. 2010)

6 METHODOLOGY / GROWA model input data 1. Climate precipitation temperature PET (Thornwaite method) 2. Soil texture organic content effective field capacity (efc) soil depth (Grid 100x100m) 3. Land use (CORINE classification rooting depth) 4. Topography (Digital Elevation Model, slope, aspect) 5. Geology (lithological classification) 6. Hydrology and Hydrogeology artificial drainage and wetlands hydro lithological classification piezometric level

7 METHODOLOGY / GROWA model input data Input Data used for model runs and relevant sources Climate Soil Land Use Input Data Reference period ( ) Recorded values from various sources Projected period A ( ) Projected values from *RCM-GCM models *Joint Research Center (JRC) *CORINE classifications Projected period B ( ) Projected values from *RCM-GCM models Topography Geology Hydrology, Hydrogeology *Digital Elevation Model (DEM) from 1:50,000 scale topographic maps *1:50,000 geological maps (Institute of Geological Exploration and Minerals) *Various sources *source Climatic data Rest input data Variable Constant

8 METHODOLOGY / Projection of climatic data 1 st step Data collection Data collection from 4 RCM GCM combinations from ENSEMBLES project for A1B CO 2 emissions scenario: 1. HIRHAM5 driven by ARPEGE (HA) 2. RACMO2 driven by ECHAM5 (RA) 3. REMO driven by ECHAM5 (REE) 4. RCA driven by HadCM3 (RH) 2 nd step Bias correction Projection of climatic data was performed in 3steps Bias correction with a scaling factor approach for each calendar month P P pr, cor pr, raw P * P ref, obs (1) (2) ref, raw where: pr corresponds to projected RCM data ( ), ref to reference period data ( ), obs to observed data and cor to bias corrected data and raw to raw RCM data. T T ( T T ) pr, cor pr, raw ref, obs ref, raw

9 METHODOLOGY / Projection of climatic data 3 rd step Topographic correction and Spatial interpolation Topographic correction and spatial interpolation of projected values with IDW method Summer Precipitation (Apr Sep) Potential Evapotranspiration

10 RESULTS / Total Runoff (Qt) for the reference period ( ) Mean annual values Precipitation Temperature PET Total Runoff Highlights 701 mm/y 14.6 o C 900 mm/y 324 mm/y Lowest values at the eastern part of the basin (less than 100 mm/y) A decrease trend from the mountainous to the plains areas and from the SW to NE Low levels of total runoff (less than 200 mm/y) cover nearly 70% of the total areal extent

11 RESULTS / Projected period A ( ) Total Runoff (mm/y) Mean Dif Dif% Reference ( ) 324 HIRHAM5-ARPEGE (HA) RACMO2-ECHAM5 (RA) REMO-ECHAM5 (REE) RCA-HADCM3 (RH)

12 RESULTS / Projected period B ( ) Mean Total Runoff (mm/y) Mean Dif Dif% Reference ( ) 324 HIRHAM5-ARPEGE (HA) RACMO2-ECHAM5 (RA) REMO-ECHAM5 (REE) RCA-HADCM3 (RH)

13 RESULTS / Highlights of Total Runoff (Qt) variation rates % Qt decrement HIRHAM5-ARPEGE (HA) RACMO2-ECHAM5 (RA) REMO-ECHAM5 (REE) RCA-HadCM3 (RH) HA RA REE RH Significant differences of Total Runoff (Qt) values and variations rates between the examined models Variations are greater during period A (compared to ref. period) than in period B (compared to period A) Qt reduction of HIRHAM5 ARPEGE (HA) models is major for period A (62%), but minor for period B (4%); Qt reduction rates of RACMO2 ECHAM5 (RA) and REMO ECHAM5 (REE) models are similar for period A but differ for period B. Qt results with RCA HadCM3 (RH) models are by far more optimistic with a final decrease of 22% and nearly stable variation rates for both projection periods.

14 RESULTS / HIRHAM5 ARPEGE (HA) models Period A ( ) Period B ( ) 80% of area coverage has low Qt values (<100mm/y) Same spatial distribution of Qt with period A (only 4% decrease) Located mainly in plain parts but also extend in mountainous and semi mountainous areas March, 2014, Filoxenia Conference Centre, Nicosia, Cyprus

15 RESULTS / RACMO2 ECHAM5 (RE) models Period A ( ) Period B ( ) Progressive zonal decrease trend from SW to NE Low Qt values extend to N but mainly to W, including a part of western Thessaly sub basin Elevated values at the western mountainous areas Low values (100mm/y) located in eastern part (mainly eastern Thessaly sub basin) March, 2014, Filoxenia Conference Centre, Nicosia, Cyprus

16 RESULTS / REMO ECHAM5 (REE) models Period A ( ) Period B ( ) Progressive zonal decrease trend from SW to NE similar to RE models Low Qt values exhibit an increase in spatial coverage Low Qt values located mainly in E. Thessaly sub basin Few local Hot spots probably interpolation artifact More dense spatial distribution in northern part March, 2014, Filoxenia Conference Centre, Nicosia, Cyprus

17 RESULTS / RCA HadCM3 (RC) models Period A ( ) Period B ( ) Qt spatial distribution exhibits great differences compared to previous models Zonal distribution (SW NE) / different of previous models (increase in Pinios River delta area) Elevated values have wider areal coverage (W and NE) Low Qt values (100mm/y) cover the entire eastern Thessaly sub basin and its fringes to west Low values (100mm/y) are only 7% and located mainly in some parts of eastern Thessaly sub basin March, 2014, Filoxenia Conference Centre, Nicosia, Cyprus

18 CONCLUSIONS Precipitation and Temperature data from 4 different RCM GCM combinations accounting for two projected periods ( and ) were used as a basis for the assessment of the potential changes in the water budget of River Pinios Basin Despite the different sources of uncertainties incorporated in climate change impacts assessment, simulation results with GROWA model showed that: The four RCM GCM combinations lead to a considerable decrease in Qt by the end of 2080 (22% to 66%) The greater impact is expected at plain areas especially at eastern Thessaly sub basin Mountainous areas retain their elevated total runoff values impact is moderate The contrast of total runoff values between areas of high and low altitude is enhanced

19 SUGGESTIONS Design and implement integrated set of measures in the direction of adaptation and preparedness, so that the anticipated impacts would be minimized and controllable. Such measures should include: groundwater artificial recharge rational exploitation of water reserves environmental monitoring of water resources (quality and quantity) targeted technical interventions public awareness and participation..moreover we should consider: Restructure of cropping pattern in the framework of the new Common Agricultural Policy (CAP): shifting to crops and varieties of low irrigation demands but high market value application of deficient irrigation techniques as a tool for the reduction of water demand

20 Assessing the potential effects of climate change in the hydrologic budget of a large Mediterranean basin: The case of River Pinios basin, central Greece

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23 METHODOLOGY / Projected climate collection and bias correction Eq.1 where: ETa hrel Ps Pw Wpl ETp Dp a,b,c,d,e,f (mm/y) (mm/y) (mm/y) mean annual actual evapotranspiration correction factor for consideration of relief areas precipitation level in hydrologic summer period precipitation level in hydrologic winter period plant available soil water (mm) mean annual potential evapotranspiration (mm/y) degree of sealing coefficients depending on soil cover

24 METHODOLOGY / Projected climate collection and bias correction

25 METHODOLOGY / Downscaling of climatic data General categories of climate data downscaling methods: Dynamic downscaling: referring to the use of Regional Climate Models (RCMs), or Limited-Area Models (LAMs) Statistical or Empirical downscaling: referring to the use of statistical techniques to translate GCM or RCM climate data output onto a finer resolution Current state of the art implies the application of both approaches

26 METHODOLOGY / bias correction Correction of projected RCM climate data using the scaling factor approach for each calendar month. (1) P P pr, cor pr, raw ref, obs ref, raw T T ( T T ) (2) pr, cor pr, raw ref, obs ref, raw Where: pr corresponds to projected RCM data ( ) ref to reference period data ( ), obs to observed data and cor to bias corrected data and raw to raw RCM data. * P P Why linear scaling? It is simple, it accounts for potential future changes in climate dynamics, while the degree of consistency between the variability of corrected and raw RCM data remains high.

27 METHODOLOGY / Spatial Interpolation of climatic data Summer Precipitation (Apr Sep) Winter Precipitation (Oct Mar) IDW method PET Temperature