5.1 Table of contents (Greek group from Patras)

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2 5 Greek Part Patras

3 5.1 Table of contents (Greek group from Patras) 5.1 Table of contents (Greek group from Patras) Study area Methodology Downscaling classification and related models Downscaling from Principal Component Analysis (PCA) and k-mean clustering CP-type classification (WCS1) Downscaling from wind direction CP-type classification (WCS2) Downscaling from CP-type classification based on wind direction and cyclonic/anticyclonic conditions (WCS3) Downscaling from CP-type classification based on a reduced number of wind directions and cyclonic/anticyclonic conditions (WCS4) Objective indices of quality for the downscaling approach Models Calibration and validation Large scale pressure distribution fields Calibration of the downscaling approach Temperature and precipitation at the basin scale Hydrology at the basin scale Possible reasons of temperature and precipitation changes in case of increased CO2-concentration Validation of the downscaling approach Calibration of the hydrological model Quantification of the model error Calibration of ENNS-model Calibration of ARNO-model Impact studies (2*CO2) Changes in geopotential height and in the occurrence probabilities of the weather types Hydrometeorological impact of climate change on basin Changes in the cumulative probability of temperature Changes in the cumulative probability of precipitation Impact on basin hydrology Uncertainties in the results...49

4 5.2 Study area The description of the Mesochora basin can be found in chapter Methodology Downscaling classification and related models Downscaling from Principal Component Analysis (PCA) and k-mean clustering CP-type classification (WCS1) Principal Component Analysis (PCA) combined with k-mean clustering (Matyasovszky et al., 1994) have been used to define ten weather types for both winter (October to March) and summer (April to September), valid for whole Europe. This classification scheme is characterised in the following as WCS1). The classification of the historic pressure distribution fields as well as of GCM output for the 1*CO2 and 2*CO2 scenario, is based on the 7hPa isobar. The circulation pattern characteristics for each weather type are described in Hebenstreit (1995) Downscaling from wind direction CP-type classification (WCS2) For the estimation of wind directions geopotential height corresponding to 5hPa isobar at six grid points around the Mesohora basin with coordinates given in Table have been used. From the geopotential height, geopotential height gradients have been estimated. Tab : Coordinates of grid points used for wind direction estimation POINT LATITUDE LONGITUDE By four triplets of these points, four triangles are formed and the geopotential height values are taken as heights on each of the triangle's apex. Gradients result as the pressure difference between two points over their distance, which is taken as unit length. The gradient directions are classified in eight 45-degree angles, and is given an identifier D1, D2,...,D8 starting from East and proceeding counter-clockwise (Fig ). With this procedure a daily time series of geopotential height gradients and geopotential height gradient directions is produced. For the elevation of 5hPa isobar, geopotential height gradient is related to wind direction. Wind direction is perpendicular to gradient in such a way that in the wind direction the low-pressure field is on the left and the high on the right side. 9 [North] 135 D3 D2 45 D4 D1 [East]

5 18 [West] D5 D8 D6 D [South] Fig : Directions of geopotential height used in classification scheme WCS Downscaling from CP-type classification based on wind direction and cyclonic/anticyclonic conditions (WCS3) According to Maheras and Patrikas (1998, personal communication) an improvement of the classification scheme WCS2 could result if in addition to wind direction in a definite day the kind of the prevailing atmospheric system (cyclone or anticyclone) is taken into consideration. Conditions in a day are here considered to be cyclonic/ anticyclonic, if the geopotential height in this day is smaller/larger than the corresponding long term monthly mean of the geopotential height. This results to a classification scheme with 16 weather types, eight for cyclonic and eight for anticyclonic conditions. However, the increased number of weather types reduces the number of rainy days corresponding to each of them, which causes problems to the downscaling procedure. For this reason the number of weather types has been reduced grouping together those corresponding to directions D5 to D8 have low occurrence probability. Thus, classification scheme WCS3 is characterised by ten weather types, which are related to the geopotential height gradient as shows Table

6 Tab : Geopotential height gradient direction for the weather types of classification scheme WCS3 WEATHER TYPE 1 * D1 2 D2 3 D3 4 D4 5 D5,D6,D7,D8 6 ** D1 7 D2 8 D3 9 D4 1 D5,D6,D7,D8 * ** DIRECTION OF GEOPOTENTIAL HEIGHT GRADIENT Weather types 1-5 correspond to cyclonic conditions Weather types 6-1 correspond to anticyclonic conditions Downscaling from CP-type classification based on a reduced number of wind directions and cyclonic/anticyclonic conditions (WCS4) Given the problems resulting in downscaling due to large number of weather types, a further classification has been used. It is characterised by a smaller number of weather types and uses different weather types for downscaling temperature and precipitation. The classification scheme used for downscaling temperature distinguishes between two weather types, the first one for cyclonic and the second one for anticyclonic conditions. Cyclonic and anticyclonic conditions are defined as described previously. The classification scheme for downscaling precipitation considers cyclonic/ anticyclonic conditions as well as two wind directions, the first one including south, south east and south west winds and the second one including north, north east and north west winds. Owing to the fact that wind direction is perpendicular to geopotential height gradient, the aforementioned two wind directions are related to geopotential height gradient directions as it is shown in Table 5.3.3, which summarises the definition of WCS4.

7 Tab : Definition of classification scheme WCS4 WEATHER TYPE GRADIENT DIRECTION CYCLONI C COND. ANTICYCL. COND D3, D4, D5, D D1, D2, D7, D D3, D4, D5, D D1, D2, D7, D8 - + DOWNSCALING OF temperature temperature precipitation precipitation precipitation precipitation Objective indices of quality for the downscaling approach In Table the values of the performance indices for downscaling of precipitation (see definition of these indices in the contribution of the German group) are given. For the estimation the mean basin precipitation has been used, which is calculated taking into consideration the orography of the basin (see Annex of Second Annual Report). Classification schemes WCS3 and WCS4 appear the largest values of all performance indices, which indicates that these are the most appropriate for the Mesohora basin. However, it is difficult to decide which of them is the most appropriate because, as it can be seen directly from the definition of the indices I1 and I2, their values increase when the rainy days are concentrated in less weather types. Thus, although performance indices are higher for WCS4 it can not be said that it is a better classification scheme than WCS3. Therefore both schemes have been applied in the downscaling procedure. Tab : Values of performance indices for the classification schemes I1 I2 I3 KI I1 I2 I3 KI WCS1 WCS2 average W+S Winter Summer average W+S Winter Summer WCS3 WCS4 average W+S Winter Summer average W+S winter Summer Models For space-time modeling of temperature and precipitation conditioned on weather types the models developed by Matyasovszky et al. (1994) have been used. A short description of the model is given also by the Austrian group in the Annex of the first annual report. For rainfall - runoff simulation in Mesohora basin the ENNS model developed by the Austrian group (Nachtnebel et al., 1993) as well as the ARNO model developed by the Italian group (Todini, 1996) have been used. The models are described in the contributions of these groups.

8 5.4 Calibration and validation Large scale pressure distribution fields The historical data of pressure distribution fields used in this study have been provided by the National Center for Atmospheric Research (NCAR) in USA. They contain the geopotential height of 5hPa and 7hPa pressure level. Geopotential heights for the CO2-scenarios (1*CO2 and 2*CO2) have been taken from the results of General Circulation Model ECHAM-3 T42 of Max Plank Institute in Germany. In Fig the spatial mean of geopotential height for the period estimated from the values at the grid points given in Table 5.3.1, is compared with the GCM results for the scenario 2*CO2 and a period of twenty years. Geopotential height in 2*CO2 case is slightly increased compared to historic case (see also Table 5.4.1). However, from Fig it can be concluded that GCM values are probably overestimated. In this figure mean values of geopotential height for the weather types corresponding to classification scheme WCS3 are given for the historic, the 1*CO2 and 2*CO2 case. It can be seen that the differences between historic case and 1*CO2 are larger than the differences between 1*CO2 and 2*CO2. The fact that 1*CO2 should be closer to historic case than to 2*CO2 indicates that the geopotential height values of the aforementioned GCM are overestimated. Thus, the actual differences between historic data and 2*CO2 cases are probably smaller than those given in Table Tab : Comparison of spatially mean of geopotential height for 5hPa pressure level for a period of 2 years GEOPOTENTIAL HEIGHT GEOPOTENTIAL HEIGHT WINTER SUMMER (MEAN FOR 2 YEARS) (MEAN FOR 2 YEARS) historic m m 2*CO m m increase 1.8%.9% geopotential height winter half year historic '72-'92 2xCO 2 ( a ) time in days

9 geopotential height ( b ) summer half year historic '72-'92 2xCO time in days Fig : Change of geopotential height for the 5hPa isobar, due to CO 2 increase: a) winter half year b) summer half year spatially averaged geopotential height in m for 5hPa ( a ) winter half year historical '72-'92 1xCO 2 2xCO Weather type

10 spatially averaged geopotential height in m for 5hPa ( b ) historical '72-'92 1xCO 2 2xCO 2 summer half year Weather type Fig : Spatially averaged geopotential height (5 hpa) for different weather types: a) winter half year b) summer half year Calibration of the downscaling approach Temperature and precipitation at the basin scale The hydrological data (temperature and precipitation) for the Mesohora basin used in this study have been provided by Dr. D. Panagoulia in the frame of a cooperation with the group of University of Thessaloniki. Precipitation data (daily values) are available for nine stations in and around the Mesohora basin. The position of the precipitation stations is shown in Fig For temperature, data from the station Vakari have been used. Mean daily temperatures have been calculated as the mean of minimal and maximal temperature of a day. Temperatures in elevation zones different from the elevation of Vakari, which are required for hydrological modeling, are estimated using a lapse rate of.7 C/1m. Fig shows the annual mean temperature. The trend of temperature is statistically not significant. Fig shows

11 Matsouk Katafyto Pahtouri Paleochori Pertouli Tyrna Vakari Stournareika Altitude [m] Vathyrema Fig : Digital terrain model of the Mesohora basin with locations of stations the long term mean monthly temperature. The lowest values appear in December, January and February and the highest in June, July and August. In Table the correlation matrix of the precipitation stations for lag= is given. The strongest correlation exists between the stations Vakari and Vathirema. Both of them are also well correlated to four other stations (correlation coefficients >.7). Between the other stations the correlation is not significant, probably due to local processes resulting from the orography, which in Mesohora basin is, as Fig shows, complicated. The lag 1 correlation of the precipitation stations (Table 5.4.3) is considerably reduced. Fig (a) shows the annual precipitation height in the investigated period for five stations. Precipitation height varies considerably in the basin. The stations show different trends. Katafyto Pertouli and Vakari show an increasing trend in the investigated period whereas Pahtouri and Matsouki a decreasing trend. Fig (b) shows the distribution over the year of the basin mean precipitation. The main precipitation amount is concentrated in the period from October to February. Tab : Correlation matrix (lag=) for the precipitation stations in Mesohora basin. (KAT=Katafyto, MAT=Matsouki, PAH=Pahtouri, PAL=Paleohori, PER=Pertouli, STO=Stournareika, TYR=Tyrna, VAK=Vakari, VAT=Vathyrema) KAT MAT PAH PAL PER STO TYR VAK VAT KAT 1,526,552,437,458,543,351,543,633 MAT,526 1,762,635,659,568,487,785,753 PAL,552,762 1,583,618,611,464,781,725 PAH,437,635,583 1,689,54,66,66,687 PER,458,659,618,689 1,576,665,73,697 STO,543,568,611,54,576 1,54,64,78 TYR,351,487,464,66,665,54 1,551,6 VAK,543,785,781,66,73,64,551 1,816 N

12 VAT,633,753,725,687,697,78,6,816 1 Tab : Correlation matrix (lag=1) for some precipitation stations in Mesohora. (KAT=Katafyto, MAT=Matsouki, STO=Stournareika, VAK=Vakari) KAT MAT STO VAK KAT,273,19,213,172 MAT,467,378,4,332 STO,375,237,276,234 VAK,472,314,36, temperature ( o C ) Year Fig : Mean annual temperature 25 temperatures ( o C ) month Fig : Mean monthly temperature

13 annual precip. height (mm) PAHTOURI KATAFYTO MATSOUKI PERTOULI VAKARI year Fig a: Total precipitation height in five stations 3 precipitation ( mm ) month Fig b: Monthly mean of basin mean precipitation for the period Hydrology at the basin scale Except of precipitation, the only measured water balance component in Mesohora basin is total runoff. Fig shows the mean annual runoff in the investigated period. Fig shows the long term monthly mean of runoff. The largest runoff values appear from

14 November to March. For the estimation of the water balance components (evapotranspiration, snow melt, surface flow, interflow and base flow) the runoff model ENNS, which has been calibrated for the conditions of the Mesohora basin has been used. Fig presents mean monthly values of runoff components (surface flow, interflow and base flow) as they result from the simulation. Surface flow appears only during the winter half year and is small compared to the other components. Interflow exists only from October to April and in this period it is the most important component. Base flow exists over the whole year and has its maximal values from November to May. Fig shows the mean monthly values of actual evapotranspiration and snowmelt as they result from the simulation. Evapotranspiration follows the variation of the temperature and the water availability in the soil over the year. The maximal value of evapotranspiration ( 11mm) appears in May. Snowmelt contributes to runoff from December to April. It should be noticed that snowfall data are not available. They were hypothetically determined by considering precipitation measurements as snowfall where temperature falls below zero. The hypothesis is checked so that snow accumulation and water from snowmelt appear together when the measured runoff shows that clearly these processes are taking place.

15 Possible reasons of temperature and precipitation changes in case of increased CO2-concentration As already seen (Fig ), increased CO2-concentration in the atmosphere causes the increase of pressure. Further it causes changes of the occurrence probability of the weather types. In order to find out if the increase of pressure and the change of the occurrence probability of the weather types cause changes of temperature and precipitation, it is important to investigate if: there is a functional relationship between geopotential height and local hydrologic variables (temperature and precipitation) the value of the local variables depends on the weather types and the occurrence probability of the weather types changes significantly in case that CO2- concentration is doubled Dependence of temperature and precipitation on geopotential height Owing to the fact that with increasing CO2-concentration geopotential height of the 5hPa isobar increases, changes of temperature and precipitation at the basin scale should be expected if there is a functional relationship between geopotential height and temperature as well as geopotential height and precipitation. Fig shows the relationship between temperature and spatial mean of geopotential height for the Mesohora basin. The correlation is stronger for the summer than for the winter half-year but changes of temperature in Mesohora basin should be expected in both winter and summer half year because of the changes of the geopotential height. 35, 3, Runoff(m 3 /sec) 25, 2, 15, 1, 5,, Year Fig : Mean annual runoff

16 14 12 Runoff (m 3 /sec) Month Fig : Monthly mean of measured runoff for the period Surface Flow Inter Flow Base Flow Runoff (m 3 /sec) Month Fig : Mean monthly components of runoff

17 Snow Melt Evapotranspiration Evapotranspiration (mm) Snowmelt (mm) Month Fig : Mean monthly evapotranspiration and snowmelt C o mean daily temperature in measured (summer half year '72-'92) linear fit (R=.898) geopotential height for 5hPa in (m) Fig a: Relationship between temperature and geopotential height (summer half year)

18 C mean daily temperature in measured (winter half year '72-'92) linear fit (R=.5759) geopotential height for 5 hpa in (m) Fig b: Relationship between temperature and geopotential height (winter half year) Fig shows that for the station Katafyto in Mesohora there is no functional relationship between precipitation and geopotential height. Similar results have been obtained for all other stations. Additionally, this relationship has been calculated for each weather type of the classification schemes discussed in chapter Also in this case no better results have been obtained. Consequently, the increase of geopotential height in case of 2*CO2 do not justify changes of precipitation height. For the classification schemes WCS2, WCS3 and WCS4, which are based on the geopotential height gradient, it has been investigated if there is a functional relationship between precipitation and geopotential height gradient. The results shown in Fig have been obtained for classification scheme WCS3. The correlation estimated for the weather type 3, which corresponds to geopotential height gradient direction D3 (see Fig 5.3.1), is weak. Similar results have been obtained for the other directions as well as for the classification schemes WCS2 and WCS4. Thus, eventual changes of geopotential height gradients in case of 2*CO2 do not justify changes of precipitation height too Dependence of temperature and precipitation on weather type Owing to the fact that one of the consequences of climate change is the change of the occurrence probability of the different weather types, changes of temperature and precipitation should be expected if these local hydrological variables are related to the weather types. Fig shows the dependence of mean daily temperature on the weather types for the classification schemes WCS1, WCS2, WCS3 and WCS4. All classification schemes show that in winter half year, mean daily temperature depends stronger on the weather type than in summer. In all classification schemes, standard deviation (sd) of daily temperature conditioned on the weather type is in the winter half year large compared to the mean value. In the summer half year sd is small. In classification schemes WCS2 and WCS3 the highest temperatures appear for the directions D2, D3 and D4. In classification scheme WCS3 (Fig.

19 5.4.14(c)) this is valid for cyclonic as well as for anticyclonic conditions. Fig (c) as well as Fig (d) demonstrates also that for each weather type temperature for cyclonic conditions is lower than temperature for anticyclonic conditions. Figs and show the dependence of the mean daily precipitation on the weather type for all classification schemes used. In all classification schemes there are dominating weather types. In WCS3 there is a stronger concentration of the large precipitation heights in few weather types (3 and 4 corresponding to geopotential height gradient directions D3 and D4 and cyclonic conditions) than it is the case in WCS1 and WCS2. Particularly in WCS4, large precipitation heights are concentrated in one weather type (weather type 1 corresponding to south, southeast, southwest winds and cyclonic conditions). For WCS3 and WCS4, which according to Table have the largest performance indices and for this reason they are used below to downscale precipitation according to method of Matyasovszky et al. (1994), the probability of precipitation for the different weather types is given in Fig Precipitation probability has been calculated using the mean precipitation in the basin. Fig (a) shows that in WCS3 the weather types corresponding to geopotential height gradient directions D3 and D4 and to cyclonic conditions are those with significantly higher precipitation probability than the other weather types. Fig (b) shows that weather type 1 corresponding to south, southeast, southwest winds and cyclonic conditions have significantly higher precipitation probability than all other weather types in classification scheme WCS4. geopotential height in m winter half year '72-'89 KATAFYTO (measured) linear fit (R=-.25) precipitation height in mm Fig a: Dependence of precipitation height in Katafyto station on geopotential height for 5 hpa (winter half year)

20 geopotential height in m summer half year '72-'89 KATAFYTO (measured) linear fit (R=-.264) precipitation height in mm Fig b: Dependence of precipitation height in Katafyto station on geopotential height for 5 hpa (summer half year) daily precipitation in mm 2 PAHTOURI linear fit (R=.36) winter half year '72-'89 15 Weather type WT geopotential height gradient Fig : Dependence of precipitation height on geopotential height gradient for the classification scheme WCS3 and weather type Occurrence probability of weather types Fig shows the occurrence probability of the weather types for the classification schemes WCS1, WCS2 and WCS3 whereas Fig shows the occurrence probability for

21 WCS4. Occurrence probability for the historic case is given along with occurrence probability for the cases 1*CO2 and 2*CO2. In all classification schemes there are dominating weather types (weather types with higher occurrence probability than the others). Particularly in classification scheme WCS2 weather types 5, 6, 7 and 8, which correspond to geopotential height gradient directions D5, D6, D7 and D8 in Fig , have significantly smaller occurrence probability than the other weather types. This was the reason, why these weather types in classification scheme WCS3 were grouped together. In all classification schemes dominating weather types are the same in winter and summer half year. In WCS3 and WCS4 occurrence probability for cyclonic conditions is smaller than occurrence probability for anticyclonic conditions in both winter and summer half year. In 1*CO2 and 2*CO2 case the dominating weather types in all classification schemes are the same as for the historic data. The differences between the occurrence probability of historic and 1*CO2 case are for all classification schemes larger than the differences between 1*CO2 and 2*CO2. This is a further indication that the quality of the used GCM results is questionable, as 1*CO2 case should be closer to the historic than to 2*CO2 case. mean daily temperature in C 9 winter half year '72-'92 8 mean sd Weather type mean daily temperature in C mean sd summer half year '72-' Weather type Fig a: Dependence of mean daily temperature on weather type for classification WCS1 mean daily temperatur in C winter half year '72-'92 mean sd Weather type mean daily temperature in C summer half year '72-'92 mean sd Weather type Fig b: Dependence of mean daily temperature on weather type for classification WCS2 ( b )

22 mean daily temperature in C mean sd winter half year '72-' Weather type mean daily temperature in C summer half year '72-'92 mean sd Weather type Fig c: Dependence of mean daily temperature on weather type for classification WCS3 mean daily temperature in n C mean mean sd (winter half year '72-'92) sd (summer half year '72-'92) 1 2 Weather type Fig d: Dependence of mean daily temperature on weather type for classification WCS4

23 Mean daily precipitation height (mm) Katafyto Matsouki Pahtouri Paliochori Pertouli Stournar. Tyrna Vakari Weather type Fig a: Dependence of precipitation height on weather type for classification WCS1 mean daily precipitation (mm) winter half year '72 - '89 Katafyto Matsouki Pahtouri Paleohori Pertouli Stournareik Tyrna Vakari Vathyrema Weather type Fig b: Dependence of precipitation height on weather type for classification WCS2

24 mean daily precipitation (mm) 7 summer '72-'89 KAT MAT PAH PAL 6 PER STO TYR VAK 5 VAT WT mean daily precipitation in mm winter half year '72-'89 KAT MAT PAH PAL PER STO TYR VAK VAT Weather type Fig a: Dependence of precipitation height on weather type for classification WCS3 mean daily precipitation in mm summer half year '72-'89 KAT MAT 11 PAH PAL 1 PER STO 9 8 TYR VAK 7 VAT Weather type mean daily precipitation (mm) 24 winter '72-' KAT MAT 18 PAH PAL 16 PER STO TYR VAK 14 VAT weather type Fig b: Dependence of precipitation height on weather type for classification WCS4 probability of precipitation Weather type w inter half year '72-'89 summer half year '72-'89 Fig a: Probability of precipitation for classification concept WCS3

25 probability of precipitation winter half year '72-'89 summer half year '72-' weather type Fig b: Probability of precipitation for classification concept WCS Validation of the downscaling approach In this study the downscaling approach proposed by Matyasovszky et al. (1994) has been used. For validation the method has been used to calculate cumulative probabilities of measured temperature and precipitation. The results have been compared with the cumulative probabilities resulting from the data. Validation presented below concerns classification schemes WCS3 and WCS4, which have the largest performance indices (Table 5.3.4). Results obtained by using classification scheme WCS1 have been presented in the Annex of Second Annual Report of this project. WCS2 gives similar results as WCS3. Therefore it is not discussed separately Downscaling of temperature and precipitation based on WCS3 Fig shows the cumulative probability estimated from the temperature data and from the simulation using the method of Matyasovsky et al. (1994) for November, March, April and September. Similar results have been obtained for all months of the year. In Table the differences between measured and simulated cumulative probabilities are given. For some months these differences are 1.5 C and appear over the whole range of the temperature values. Owing to the fact that the expected changes of temperature in 2*CO2 case are of the order of 2 C, the error of the downscaling model in simulating the historic data is not negligible. It is of the order of climate change impact. The most probable reason for this error is that in Mesohora basin, temperature distribution conditioned on the weather types is not binormal. Chi-square tests showed that in many cases the conditioned empirical temperature distribution could not be approximated appropriately by the binormal distribution for the usual significance level of 5%.

26 Tab : Comparison between measured and simulated cumulative probability of temperature DIFFERENCE BETWEEN REMARKS SIMULATED AND MEASURED TEMPERATURE IN O C October +,2 over the whole interval of values November +1,5 over the whole interval of values December +1,5 over the whole interval of values January +,5 over the whole interval of values February +1,5 over the whole interval of values March April +1,5 over the upper interval of values May +1,5 over the upper interval of values June +,5 over the whole interval of values July +1,1 over the whole interval of values August +,5 over the whole interval of values September +,5 over the lower interval of values Fig shows the cumulative probability estimated from precipitation data and simulated using the model of Matyasovszky et al. (1994) for four stations in the Mesohora basin. The model does not approximate the cumulative probability of historic data appropriately. This occurs particularly for small precipitation heights. occurrence probability winter half year historic '72-'89 1xCO 2 2xCO Weather type occurrence probability summer half year historic '72-'92 1xCO 2 2xCO Weather type Fig a: Occurrence probability of different weather types for classification concept WCS1

27 occurrence probability.22 winter half year historic '72-'92 1xCO 2 2xCO Weather type occurrence probability.45 summer half year Weather type hisroric '72-'92 1xCO 2 2xCO 2 Fig b: Occurrence probability of different weather types for classification concept WCS2.35 winter half year.25 summer half year occurrence probability historic '72-'92 1xCO 2 2xCO 2 occurrence probability historic '72-'92 1xCO 2 2xCO Weather type Weather type Fig c: Occurrence probability of different weather types for classification concept WCS3.7.6 historic '72-'92 1xCO 2 2xCO 2 occurrence probability historic '72-'92 1xCO 2 2xCO 2 winter occurrence probability summer. 1 2 Weather type. 1 2 Weather type Fig a: Occurrence probability of different weather types for classification concept WCS4

28 occurrence probability historic '72-'92 1xCO 2 2xCO 2 summer occurrence probability historic '72-'92 1xCO 2 2xCO 2 winter WT Weather type Fig b: Occurrence probability of different weather types for classification concept WCS4 cumulative probability 1 November historic '72-'92 simulated cumulative probability March historic '72-'92 simulated temperature in C temperature in C Fig a: Cumulative probability for temperature in November and March cumulative probability 1 April historic '72-'92 simulated cumulative probability September historic '72-'92 simulated temperature in C temperature in C Fig b: Cumulative probability for temperature in April and September

29 The most probable reason for these deviations is the small number of days belonging to each weather type due to the relatively large number of weather types considered in this classification scheme combined with the relatively short precipitation time series, which are available Downscaling of temperature and precipitation based on WCS4 For WCS4, the cumulative probability estimated from the temperature data and from the simulation for November, March April and September is shown in Fig Comparing Figs and it can be seen that WCS4 does not give better results than WCS3. This is valid for all months of the year. Investigation of temperature distribution conditioned on weather types for WCS4 has shown that also for this classification, the distribution is not binormal. This is the most probable reason for the deviations between measured and simulated occurrence probability. Fig shows the measured and simulated occurrence probability for WCS4 and for the same stations as those in Fig for WCS3. Comparing the results in Figs and it can be seen that for WCS4 the model approximates the precipitation cumulative probability much better than for WCS3. The most probable reason is that in WCS4, the number of weather types is smaller than in WCS3 and consequently the number of days belonging to each weather type is larger than in WCS3. However, also for WCS4 there are no negligible deviations between measured and simulated cumulative probability of precipitation occurrence probability Katafyto winter half year '72-'87 historic data simulation of hist. data occurrence probability historic data simulation of hist. data Matsouki winter half year '72-' precipitation height (mm) precipitation height (mm) occurrence probability Stournareika winter half year '72-'87 historic data simulation of hist. data occurrence probability Vakari winter half year '72-'87 historic data simulation of hist. data precipitation height (mm) precipitation height (mm) Fig : Measured and simulated (for WCS3) cumulative probability of precipitation

30 1 November 1 March cumulative probability historic '72-'92 simulated cumulative probability historic '72-'92 simulated temperature in C temperature in C 1 April 1 September 8 8 cumulative probability historic '72-'92 simulated cumulative probability historic '72-'92 simulated temperature in C temperature in C Fig : Measured and simulated (for WCS4) cumulative probability of temperature

31 occurrence probability Katafyto winter half year '72-'87 historic data simulation of hist. data precipitation height (mm) occurrence probability 1. Matsouki winter half year '72-' historic data simulation of hist. data precipitation height (mm) occurrence probability 1. Stournareika.95 winter half year '72-' historic data simulation of hist. data precipitation height (mm) occurrence probability Vakari winter half year '72-'87 historic data simulation of hist. data precipitation height (mm) Fig : Measured and simulated (for WCS4) cumulative probability of precipitation Calibration of the hydrological model Quantification of the model error For the Mesohora basin two models have been used and compared. The ENNS-model (Nachtnebel et al., 1993) developed by the Austrian project partners and the ARNO-model (Todini, 1996) developed by the Italian project partners. Preliminary calibration results have been presented in previous annual reports. The main purpose of hydrological modelling in this study is to estimate the probable impact of climate changes on river basin hydrology. This can be achieved if the effect of climate change is larger than the errors of the model. In order to quantify the error of the model in this study, the following criteria are used: Monthly mean of daily squared differences (MMDSD) between measured and simulated runoff in (m 3 /s) 2. Percentage annual mean of daily differences (PAMDD) between measured and simulated runoff referred to measured runoff. Correlation coefficients (CC) between measured and simulated runoff for each year. The values of these criteria resulting for the simulation which is performed by using the optimal set of model parameters, are denoted by MMDSD, PAMDD and CC and give the range of the model error. The optimal parameter set results from the calibration Calibration of ENNS-model Table shows the optimal parameter set used to simulate runoff by means of the ENNSmodel (Nachtnebel et al.,1993). Fig shows the simulated and measured runoff for the period This is the period for which temperature and precipitation data are

32 available. Fig shows the simulation results for three years ( , , and ). In both figures there is generally good agreement between measurements and simulation, particularly in the dry period of the year. Snowmelt can not be simulated reliably as it results from the simulation of the year High values of measured runoff in May are probably caused by snowmelt, as precipitation during this period is not significant. This effect can not be described by the model because, as already referred, snowfall data are not available. Fig shows MMDSD, PAMDD and CC for the investigated period. The largest values of MMDSD result for the period of the year with the highest precipitation (compare Fig ). Correlation coefficients are for the most years larger than.7, which corresponds to a relatively high correlation. The maximal value of PAMDD is 25%. The most PAMDD values are less than 15%. Tab : Optimal set of model parameters for ENNS-model. PARAMETER SYMBOL VALUE Field Capacity FK,2 Wilting point PWP,12 Thickness of soil layer M 4, (mm) Infiltration parameter BETA 5,5 Constant for soil moist. KBF 4, reduction Threshold value for reservoir 1 H1 5, (mm) Threshold value for reservoir 2 H2 2, (mm) Constant for reservoir 1 TVS1 5, (hr) Constant for reservoir 2 TVS2 259, (hr) Storage factor for reservoir 1 TAB1 5, (hr) Storage factor for reservoir 2 TAB2 78, (hr) Storage factor for reservoir 3 TAB3 3, (hr) Storage factor for reservoir 4 TAB4 5, (hr) Min. soil temperature TSOILMIN - 5, ( O C) Max. soil temperature TSOILMAX 25 ( O C) New snow height variance Zone 1 Zone 2-3 Zone 4 Maximal degree day factor Zone 1, 2 Zone 3, 4 VAR CTMAX,25 (mm),319 (mm) 1,693 (mm) 2,3 (mm/ o C) 9,3 (mm/ o C) Minimal degree - day factor CTMIN,5 (mm/ o C) Degree day factor for melting CTNEG,5 Snow max. density SHROMAX,5

33 4 5 4 m e a s u r e d s i m u l a t e d Runoff(m 3 /sec) d a y s Fig : Runoff measured and simulated by means of the Enns model for the period Runoff(m 3 /sec) Year m easured sim ulated days Runoff(m 3 /sec) Year measured simulated days Runoff(m 3 /sec) Year m easured simulated days Fig : Runoff measured and simulated by means of the Enns model for the period

34 Calibration of ARNO-model Simulation of runoff in the Mesohora basin by means of the ARNO-model using daily values of precipitation have been presented in the previous annual report. Here an attempt has been undertaken in order to improve the simulation by using, instead of daily, hourly values of precipitation. Hourly time steps are more consistent with the concentration time of the Mesohora basin, which is less than 24 hours. For this purpose daily precipitation values were distributed uniformly over the 24 hours. In Table the optimal combination of the parameters used for the simulation on daily and hourly basis are given. Tab : Calibrated parameter values for ARNO-model. PARAMETER VALUE FOR VALUE DT=1D DT=1H potential evapotranspiration (AL,BE) , ,.5749 field capacity in mm (WM) soil saturation rating curve exponent (B) 1..2 drainage maximum value in mm/h (DRMAX) drainage exponent (CESP) drainage base curve maximum in mm/h.3.1 (DRMIN) drainage threshold in mm (SOL) threshold in mm (SOL1) percolation maximum in mm/h (PERC) percolation exponent (PESP) 2..5 groundwater linear reservoir number (NP) 1 1 groundwater linear reservoir constant in h (KFA) snow threshold temperature (TS).. FOR 45 4 squared differences (m 3 /sec) month Fig a: Enns model: Monthly mean of daily squared differences between measured and simulated runoff (MMDSD)

35 percentage differences year Fig b: Enns model: Percentage annual mean of daily differences between measured and simulated runoff (PAMDD) correlation coefficient year Fig c: Correlation coefficients between measured and simulated runoff

36 runoff ( m 3 /sec ) Year measured simulated days runoff ( m 3 /sec ) Year measured simulated days runoff ( m 3 /sec ) Year measured simulated days Fig : Simulation of runoff by means of ARNO model using one day time steps

37 runoff ( m 3 /sec ) Year measured simulated days runoff ( m 3 /sec ) Year measured simulated days runoff (m3/sec ) Year measured simulated days Fig : Simulation of runoff by means of ARNO model using one hour time steps

38 squared differences ( m 3 /sec ) period : month Fig a: Monthly mean of daily squared differences between measured and simulated (by means of ARNO model) runoff for one day time step squared differences ( m 3 /sec) period : month Fig b: Monthly mean of daily squared differences between measured and simulated (by means of ARNO model) runoff for one hour time step The optimal values of the parameters B, DRMAX, CESP, DRMIN, SOL, PERC and PESP resulted for one-day and one-hour time steps are considerably different. Fig shows the results of simulation for three years ( , , and ) based on one day time steps. Fig shows the corresponding results based on one hour time step. For the one hour time step the reaction of the basin to rainfall is much more intensive (quick increase and decrease of runoff, large peaks) than measured runoff shows, although the parameter combination used almost eliminates the surface runoff component. Runoff includes

39 only interflow and base flow. A possibility to overcome this problem is to use parameters, which reduce drastically interflow and transform it to base flow. However, strong reduction of interflow does not correspond to the conditions in the basin studied. MMDSD values given in Fig are generally smaller for one day time step. On the other hand PAMDD values given in Fig are smaller for the simulation with one hour time step. Finally, correlation coefficients for one day time steps are larger than the corresponding values for one hour time steps as Fig shows. MMDSD values of ARNO model for one day time steps are smaller than the corresponding values of ENNS-model as it results from the comparison of Figs (a) and (a). Correlation coefficients of both models are comparable (see Fig (c) and (a)), whereas PAMDD values are better for the ENNS-model (see Fig (b) and 5.4.3(a)). Thus, the quality of simulation results obtained by means of the ENNS and the ARNO model for one day time steps are comparable.,4 percentage differences,2 -,2 -,4 -, ,8 year Fig a: Percentage annual mean of daily differences between measured and simulated runoff for one day time step

40 ,4,35 percentage differences,3,25,2,15,1,5 -, ,1 year Fig b: Percentage annual mean of daily differences between measured and simulated runoff for one hour time step 1,9 correlation coefficient,8,7,6,5,4,3,2, year Fig a: Correlation coefficient between measured and simulated runoff for one day time step

41 correlation coefficient 1,9,8,7,6,5,4,3,2, year Fig b: Correlation coefficient between measured and simulated runoff for one hour time step

42 5.5 Impact studies (2*CO2) Changes in geopotential height and in the occurrence probabilities of the weather types The change of the geopotential height in 2*CO2 case compared to historic data has already been discussed in chapter However, the estimation of the cumulative probability of temperature and precipitation for the 2*CO2 case according to Matyasovszky et al. (1994), is not performed by using the original GCM output but by using values which are reduced analogously to the difference between historic and 1*CO2 values. Thus, the increase of geopotential height, by means of which the calculation of cumulative probability of temperature and precipitation in 2*CO2 case is carried out, is less than that given in Table The occurrence probabilities for the classification schemes WCS3 and WCS4 in 2*CO2 case are given in Fig They differ from the corresponding values of the historic case less than 1%. However, it should be noticed that the occurrence probabilities in 1*CO2 case, which result from the GCM geopotential heights, are probably overestimated just as they are closer to 2*CO2 case than to historic case. Therefore occurrence probabilities of 2*CO2 case can be overestimated too. Thus, their difference from the historic case can be smaller than shown in Fig The smaller are the differences between historic and 2*CO2 case, the smaller changes of the cumulative probability of temperature and precipitation in 2*CO2 case compared to the historic case must be expected Hydrometeorological impact of climate change on basin Changes in the cumulative probability of temperature Cumulative probability of temperature in 2*CO2 case has been estimated by simulating 16 years. Fig shows for the 2*CO2 and WCS3 case the simulated cumulative probability of temperature for November along with the simulated and the empirically (from the historic data) estimated probability. The differences between 2*CO2 and simulated probability of historic data are of the same order of magnitude as the differences between empirically calculated and simulated probability of the historic data. Similar results have been obtained also for other months of the year. The results obtained for WCS4 are comparable. Thus, the probable impact of the climate change on the temperature does not differs considerably from the error of the climatic model. The probable reason is that the parameters of the binormal temperature distribution for the 2*CO2 case have been estimated from the historical data, for which we have found that they are not binormaly distributed Changes in the cumulative probability of precipitation Owing to the fact that by using weather classification scheme WCS4 the error of the precipitation climatic model is smaller than shown in chapter , only this classification scheme has been used in order to estimate changes of cumulative probability of precipitation in 2*CO2 case. Fig shows the simulated cumulative probability of precipitation for the stations Matsouki and Stournareika along with the simulated and the empirically estimated probability of the historic data. The differences between simulated cumulative probability of the historic data and 2*CO2 case are significant. For given cumulative probability the precipitation height in 2*CO2 case reduces. However, the differences between 2*CO2 case and simulation of historic data is approximately equal to the difference between simulated and empirically estimated cumulative probability. Thus, the probable impact of the climate change