Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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- 174 - Expected Changes in Water Resources Availability and Water Quality with Respect to Climate Change in the Elbe River Basin Valentina Krysanova and Fred Hattermann Potsdam Institute for Climate Impact Research, P.O. Box 601203, Telegrafenberg, 14412 Potsdam krysanova@pik-potsdam.de 1. Introduction Reliable modelling of climate-water interactions at the river basin and regional scale requires development of advanced modelling approaches at scales relevant for assessing the potential effects of climate change on hydrological cycle. These approaches should represent the atmospheric, surface and subsurface hydrological processes, and take into account their characteristic temporal and spatial scales of occurrence. The paper presents a climate change impact assessment for the Elbe River basin in Germany. The method used for the study combines (a) a statistical downscaling method driven by GCMpredicted temperature trend for producing climate scenarios, and (b) an ecohydrological spatially semi-distributed river basin model. In addition, a conditioned Monte Carlo simulation was implemented in the downscaling procedure, so that 100 realizations of the climate scenario were produced to investigate the uncertainty of the model predictions. 2. Case study basin The study area is the German part of the Elbe River basin (about 100.000 km 2 ). The long-term mean annual precipitation in the basin is 659 mm. The long-term mean discharge of the Elbe River is 716 m 3 s -1 at the mouth, and the specific discharge is 6.2 l s -1 km -2 , which corresponds to the mean annual runoff of 10.06 x 10 9 m 3 , or 29.7 % of the annual precipitation. A primary reason for selecting this river basin as the case study region is its vulnerability against water stress in dry periods. Due to the position of the basin around the boundary between, on the one hand, the relatively wet maritime climate in western Europe and, on the other hand, the more continental climate in eastern Europe with longer dry periods, the annual long-term average precipitation is relatively small. Therefore the Elbe river basin is classified as the driest among the five largest river basins in Germany (Rhine, Danube, Elbe, Weser and Ems) with all resulting problems and conflicts. The region is representative of semi-humid landscapes in Europe, where water availability during the summer season is the limiting factor for plant growth and crop yield. The drainage basin is densely populated, and includes two large metropolitan areas: Berlin and Hamburg. Within Europe the Elbe River basin has the second lowest water availability per capita. Due to possible change in circulation patterns and local orographical conditions the amount of precipitation will most likely decrease in the Elbe region (Werner & Gerstengarbe, 1997). 3. Climate scenario Currently the resolution of General Circulation Models (GCMs) is too rough for correct representation of hydrological cycle variations within river basins. The 10 km climate model resolution, which is not yet achieved, is a critical threshold, since at this scale the climate model outputs become comparable with the scale of hydrological cycle variations within catchments, and climate variables could be predicted without the need for downscaling. The problem can be partly solved by applying downscaling methods to transform the GCM outputs onto the regional or river basin scale. Two main types of downscaling methods are in use: the deterministic dynamical downscaling method and the statistical downscaling method. The deterministic downscaling models are applied by nesting their grid structure into the grid structure of GCMs, whereas the outputs of GCMs are taken as boundary conditions to calculate climate input data for regional applications. This type of models is still under development. The statistical downscaling method makes use of the correlation between the large-scale climate patterns (where the results of GCMs are relatively reliable) and their regional representation, considering consistency in frequency distribution, annual and interannual variability and persistency of the main climate characteristics. The advantage of this method is that its results are relatively robust as long as the basic climate correlations in the observed and scenario periods do not differ. The method takes the results of GCMs as boundary and initial conditions, and therefore the inherent GCM uncertainty is transferred to the regional scale as well. The applied climate scenario was produced by the statistical downscaling method from the ECHAM4- OPYC3 GCM, which was driven by the IPCC emission scenario A1 (F.-W. Gerstengarbe and P. Werner). The climate change scenario is characterized by an increase in temperature by 1.4°C until 2050, and a moderate decrease in mean annual precipitation in the basin corresponding to the observed regional climate trend with notable subregional differences. A conditioned Monte Carlo simulation was implemented in the downscaling procedure, so that 100 realizations of the scenario were produced to investigate the uncertainty of the method. 4. Ecohydrological river basin model and simulation experiments The continuous-time spatially semi-distributed processbased ecohydrological model SWIM (Soil and Water Integrated Model, Krysanova et al., 1998 & 2000) integrating hydrology, nutrient cycling and vegetation growth at the river basin scale, was used in the study. The modelling system includes an interface to the Geographic Information System GRASS (Geographic Resources Analysis Support System) (GRASS4.1, 1993). The spatial disaggregation scheme has three levels: basin – subbasins – hydrotopes. The subbasin map can be produced by using the r.watershed operation in GRASS or input from other sources, and the hydrotope map is usually produced by overlaying the subbasin, land use and soil maps. The SWIM/GRASS interface allows to extract spatially distributed parameters of elevation, land use, soil
and vegetation, and to derive the hydrotope structure and the routing structure for the basin under study. In advance, the model was extensively validated in the Elbe River basin using the multi-scale, multi-criteria and multisite validation method (Hattermann et al., 2004), and has proven to be able to reproduce well the observed hydrological characteristics (river discharge, groundwater table) in meso- and large basins, water quality characteristics (concentration, load) in mesoscale basins, and the regional crop yields. The uncertainty of climate impacts was evaluated using 100 realizations of the climate scenario. The modelling with SWIM was used to transform the uncertainties in climate input represented by 100 realizations into hydrological responses like evapotranspiration, surface and subsurface runoff, river discharge and groundwater recharge. In addition, some water quality responses were evaluated, considering diffuse sources of pollution and assuming unchanging agriculture management practices: nitrogen wash-off with surface runoff and interflow and leaching to groundwater. Point sources of pollution have not been evaluated. The model results were subsequently analyzed considering seasonal dynamics, trends, histograms for the set of 100 simulations, and spatial patterns in different subregions. 5. Results and conclusions The hydrological and water quality responses and the propagation of uncertainty differ in three Elbe sub-regions: the mountainous area, the loess sub-region, and the lowland area due to differences in geomorphological and climate conditions. According to the simulation results, actual evapotranspiration is expected to decrease on average by 4%, with significant subregional differences. Namely, a moderate increase up to ≈100 mm y -1 is expected in north-western part of the basin, and a decrease up to ≈120 mm y -1 was simulated for the loess subregion located in the central part of the basin (Saxony-Anhalt). Runoff and groundwater recharge show a decreasing trend, whereas groundwater recharge responded most sensitively to the anticipated climate change (-37% on average). Groundwater recharge decreased practically everywhere, whereas lower absolute changes were simulated in the loess area, where it is very low anyway due to soil properties. - 175 - The impact of drier climate on diffuse pollution from agriculture is expected to be positive (under the same land use & land management practices), because this type of pollution is highly correlated with hydrological processes intensity. The uncertainty in hydrological and water quality responses was evaluated. It was shown that the uncertainty in lowland is in general higher than that in mountainous area. The overall result of the study is that the mean water discharge and the mean groundwater recharge in the Elbe basin will be most likely decreased, and diffuse source pollution will be diminished, but the uncertainty in hydrological response to changing climate is generally higher than the uncertainty in climate input. Development of climate models with 10 km resolution would allow to verify the regional climate scenario as well as its hydrological consequences. References GRASS4.1. Reference Manual. US Army Corps of Engineers, Construction Engineering Research Laboatories, Champaign, Illinois, 1993. Hattermann, F., V. Krysanova, F. Wechsung, M. Wattenbach, Multiscale and multicriterial hydrological validation of the ecohydrological model SWIM. In: A.E. Rizzoli, A.J. Jakeman (eds.), Integrated assessment and decision support. Proc. of the 1st biennial meeting of the Int. Env. Modelling and Software Society, vol. 1, 281-286, 2002. Krysanova, V., Müller-Wohlfeil, D.I. & Becker, A. Development and test of a spatially distributed hydrological / water quality model for mesoscale watersheds. Ecological Modelling, 106, 261-289, 1998. Krysanova, F. Wechsung, J. Arnold, R. Srinivasan, J. Williams, PIK Report Nr. 69 "SWIM (Soil and Water Integrated Model), User Manual", 239p., 2000. Werner, P.C. & F.-W. Gerstengarbe, A proposal for the development of climate scenarios. Climate Change, 8, 3, 171-182, 1997.
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Fourth Stu
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Preface The 4 th Study</str
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- I - Table of Abstracts Title Auth
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- III - Sensitivity in Calculation
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- V - Parameter Estimation of the S
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- VII - The Realism of the ECHAM5.2
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Adam, W. K. .......................
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- 1 - Activities of the GEWEX Hydro
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eference site archive (Cabauw, Neth
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- 5 - Remote Sensing of Atmospheric
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minute averages around the satellit
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- 9 - Precipitation Type Statistics
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- 11 - Assimilation of New Land Sur
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Multichannel Microwave Radiometer f
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output has been processed in an equ
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height dependence of the Z-R-relati
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- 19 - CERES and Surface Radiation
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- 21 - Coastal Wind Mapping from Sa
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- 23 - Clouds and Water Vapor over
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- 25 - Broadband Cloud Albedo from
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- 27 - Observation of Clouds and Wa
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shows a ground-track of the vessel
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- 31 - Determination and Comparison
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- 33 - Spatial Variability of Snow
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- 35 - EVA-GRIPS: Regional Evaporat
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- 37 - LITFASS-2003 - A Land Surfac
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-39- Calibrated Surface Temperature
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- 41 - The Marine Boundary Layer -
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- 43 - Characteristics of the Atmos
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Figure 2. Surface stress from two d
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During the experiment the water was
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While the number of smallest drops
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SCANDIA will not be supported any l
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- 53 - Relationships Between Precip
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- 55 - Analysis of the Role of Atmo
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- 57 - Meteorological Peculiarities
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Baltic Sea Inflow Events Jan Piechu
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- 61 - The Different Baltic Inflows
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- 63 - Observations of Turbulent Ki
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- 65 - The Influence of Synoptic Si
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-67- Improved Method for the Determ
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-69- The Helicopter-Borne Turbulenc
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- 71 - CEOP Reference Site Data fro
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- 73 - The BALTEX Hydrological Data
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- 75 - Hydrological and Hydrochemic
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-77- Sea-level Monitoring at MARNET
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1 0.8 0.6 0.4 0.2 GO(CLD-M) ERA40 S
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Figure 2. Monhly mean values of lat
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In the case of an ideal fit, the co
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- 85 - Influence of Atmospheric For
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The largest inter-annual variabilit
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References Andrejev, O, Sokolov, A.
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On the other hand, if the stratific
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Cyberska 1989, Cyberska and Krzymin
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- 95 - Continental-Scale Water-Bala
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- 97 - ICTS (Inter-CSE Transferabil
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The subsurface flow calculation is
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functions only data with higher qua
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- 103 - Modelling the Impact of Ine
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- 105 - Objective Calibration of th
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- 107 - Validation of Boundary Laye
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- 109 - Simulated Dynamical Process
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spreads along isobaths (fig.2). As
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than the precipitation pattern of J
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Figure 2. Long-term variability in
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obvious from temperature charts. Ho
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shortening of the winter seasons by
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Maximum 5 day precipitation (mm) Nu
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Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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