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Table 6. Regional climate models used to derive change factors Model Institution Reference CLM Swiss Institute of Technology (ETHZ) Jaeger et al. (2008) M-REMO Max Planck Institute for Meteorology (MPI) Jacob et al. (2001) RACMO Koninklijk Nederlands Meteorologisch Instituut (KNMI) van Meijgaard et al. (2008) The three sets of monthly CFs were averaged to obtain a single set of monthly precipitation and temperature CFs for each catchment, which were then applied to observed data (1961–1990) in order to generate climate change (2036–2065) precipitation and temperature series. The monthly temperature and precipitation CFs over the IP are shown in Figure 7 and Figure 8, respectively. The temperature series was used to generate reference evapotranspiration estimates for the climate change scenario. Irrigation water demand in the IP will be higher because of increased evapotranspiration and reduced precipitation. Monthly changes in calculated evapotranspiration were applied to total water demand, and the climate change precipitation series were subtracted to obtain future irrigation water demands. Changes in seasonal temperature patterns will also affect power demand patterns. Power demand time series for the climate change scenario were estimated from the corresponding temperature series as described in Section 3.3.1. A comparison of the estimated annual energy inflows, energy demand, and irrigation demand for the control and climate change scenarios is shown in Figure 6. Energy inflow [GWh/week] 2000 1500 1000 500 a 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Energy demand [GWh/week] 6500 6000 b 5500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total irrigation [GWh/week] 1000 500 2036−2065 1961−1990 c 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 6. Changes in energy inflows, energy demand and irrigation demand. 18
Figure 7. Temperature change factors 2036–2065 v. 1961–1990 [°C]. Numbers in parenthesis show the min and max change factor found. Figure 8. Precipitation change factors 2036–2065 v. 1961–1990 [%]. Numbers in parenthesis show the min and max change factor found.
Page 1: A framework for joint management of
Page 4 and 5: Silvio Javier Pereira-Cardenal A fr
Page 7 and 8: Acknowledgements I will always be g
Page 9 and 10: with the highest price, which can o
Page 11 and 12: og dermed værdien af vandkraft på
Page 13: Table of contents 1 Introduction ..
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Page 18 and 19: Recognizing the problem’s complex
Page 20 and 21: whereas medium- and long-term tradi
Page 22 and 23: allocations contribute to the compe
Page 25 and 26: 3 Case study: The Iberian Peninsula
Page 27 and 28: Production of biofuels, which is en
Page 29 and 30: Figure 2. Mean annual irrigation re
Page 31 and 32: Price [EUR/MWh] 80 60 40 20 Price P
Page 33: Table 4. Characteristics of generat
Page 38 and 39: The constraints are mathematical ex
Page 40 and 41: The weekly transition probability m
Page 42 and 43: values θ (€/MWh) for each inflow
Page 44 and 45: generate the backward openings (see
Page 46 and 47: The change in the objective functio
Page 48 and 49: where represents the standard dev
Page 51 and 52: 5 Results and Discussion The main f
Page 53 and 54: ment decision (and their constraint
Page 55 and 56: 15000 a Power market 20000 a Power
Page 57 and 58: Relative storage 1 0.8 0.6 0.4 Tajo
Page 59: eservoirs, although some level of a
Page 62 and 63: The suggested method relies on math
Page 65 and 66: 8 References Al-Sunaidy A, Green R
Page 67 and 68: Hoffman AR (2010) The Water-Energy
Page 69 and 70: Schaefli B, Hingray B, Musy A (2007
Page 71: 9 Papers I. Pereira-Cardenal, S.J.,

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