Simulated Climate Change and Its Effects on the Hydrology of The
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Eberhard Karls Universit¨atT¨ubingen Mathematisch-Naturwissenschaftliche Fakult¨at Fachbereich Geowissenschaften Institut f¨urGeographie Simulated climate change and its effects on the hydrology of the Saale River catchment Diploma thesis by David Sch¨afer Supervisors: Prof. Dr. Volker Hochschild Universit¨atT¨ubingen Dr. Andreas Marx Helmholtz Zentrum f¨urUmweltforschung UFZ Contents Acknowledgments IV Summary V 1 Introduction 1 2 Methods 7 2.1 The Saale Catchment . 7 2.1.1 Hydrography . 7 2.1.2 Climatology . 11 2.1.3 Geology and Geomorphology . 12 2.1.4 Soils . 16 2.2 Climate Models . 18 2.2.1 SRES Emission Scenarios . 18 2.2.2 Global Circulation Models . 20 2.2.3 Regional Climate Models . 21 2.3 Bias Correction . 23 2.4 The hydrological model mHM-UFZ . 24 2.4.1 Model Structure . 25 2.4.2 Model Parameters . 25 2.4.3 Parameter Regionalization . 28 2.4.4 Parameter Calibration . 29 2.5 Data Availability . 29 2.5.1 Physiographical Characteristics . 29 2.5.2 Land Cover . 31 2.5.3 Meteorological Data . 31 2.5.4 Discharge Data . 32 2.6 Statistics . 33 2.6.1 Model Performance Criteria . 33 2.6.2 Data Analysis . 34 2.6.3 Spatial Statistics . 35 2.7 Data and Model Preprocessing . 36 I 3 Climate Model Data 37 3.1 Model Evaluation . 37 3.1.1 Precipitation . 38 3.1.2 Temperature . 41 3.1.3 Potential Evaporation . 42 3.2 Validation of Bias Correction . 42 3.3 Projected Climate Change . 49 3.3.1 Temperature . 49 3.3.2 Precipitation . 50 4 Hydrological Modelling 55 4.1 Model Validation . 55 4.1.1 Gauge Calbe-Grizehne, Saale . 56 4.1.2 Gauge Halle-Trotha, Saale . 58 4.1.3 Gauge Laucha, Unstrut . 59 4.1.4 Gauge Rudolstadt, Saale . 61 4.1.5 Gauge Hadmersleben, Bode . 62 4.1.6 General modelling performance . 64 4.2 Hydrological Impacts of Climate Change . 64 4.2.1 Gauge Calbe-Grizehne, Saale . 64 4.2.2 Gauge Halle-Trotha, Saale . 67 4.2.3 Gauge Laucha, Unstrut . 69 4.2.4 Gauge Rudolstadt, Saale . 72 4.2.5 Gauge Hadmersleben, Bode . 74 4.2.6 Common trends in hydrological projections . 77 5 Discussion 79 6 Conclusions 86 II Statement of Authorship Except where reference is made in the text of this thesis, the entire work contains no material published elsewhere or already submitted for the award of any other degree in a tertiary institution. III Acknowledgments In first place a want to thank my supervisors, Prof. Dr. Volker Hoschschild and Dr. Andreas Marx for making this cross-institutional diploma thesis possible. In particular, I wish to thank Dr. Andreas Marx for providing the topic, every day guidance, constructive feedback and the large amount of patience with a not always purposefully working student. I want to thank further all members of the Stochastic Hydrology and Water Resources Management working group of the Department Computational Hydrosystems at the Helmholtz Centre for Environmental Research - UFZ. Special thank to the group leader, Dr. Luis E. Samaniego-Eguiguren for sharing the hydrological model and all necessary data and to Matthias Zink for the detailed introduction to the modelling procedure. I am grateful to all people who guided me throughout the last year and are directly or indirectly part in this work's development process. Particular thank to my friend Marie Felicitas Busch for proofreading and day-in, day-out love and support. I am further indebted to my mother and grand-mother for providing the solid and secure background, that I experienced during my entire life. Last but not least I want to thank my office colleague Greta J¨ackel for the relaxed, in times serious-constructive or just humorous working atmosphere. The data used in this work was gathered from a number of different agencies and author- ities: The Regional Climate Model data was provided by the EU-Project ENSEMBLES, input data to the hydrological model by the Bundesanstalt f¨urGeowissenschaften und Rohstoffe (geological and soil map), Bundesamt f¨urKartographie und Geod¨asie (Digital Elevation Model), Landesbetrieb f¨urHochwasserschutz und Wasserwirtschaft Sachsen-An- halt (gauging data), Th¨uringerLandesanstalt f¨urUmwelt und Geologie (gauging data), Deutscher Wetterdienst (meteorological data) and the National Aeronautics and Space Administration (Landsat TM5 land cover data). IV Summary Longterm weather observations clearly indicate changing climatic conditions in Germany. Available measurement data indicates an increase of annual average temperature of 1◦ K and a moderate increase of annual precipitation (9%) during the last century (Sch¨onwiese et al. (2006)). Both, increases of temperature and precipitation are mainly based on higher surpluses during the winter seasons until now. Available computer-based climate simulations project an ongoing warming trend and further changes in the seasonal dis- tribution of precipitation. These changes in atmospheric conditions had and will have consequences on water resources IPCC (2007). As extreme changes in water availability may have severe consequences, such as an higher number or intensity of floods during winter and/or water shortage in summer, knowledge about possible impacts of climate change on river hydrology is of great socio-economical relevance. In this study the output from four methodological similar climate model combinations is compared and evaluated against observation. Differences in simulated future changes in the climatology of the Saale River basin, situated in central Germany, and their translation into river hydrology are quantified. A bias correction is applied and its effects on RCM simulated meteorology in present and future, as well as consequences for results from the hydrological modelling are reviewed. In order to estimate regional variations of climate change impact and performance differences due to catchments size, the study area is further divided into five sub-catchments covering areas between less than 900 km2 to more than 23700 km2. Four Regional Climate Models (RCM) from the ENSEMBLES- Project (van der Linden and Mitchell (2009)), downscaling A1B boundary conditions from two different Global Circulation Models (GCM), are used to force the hydrological model mHM. A bias correction is applied and evaluated for the 1961-1990 reference, a 1980-2009 control and two scenario periods (2011-2040 and 2061-2090). Subsequently both datasets for any RCM are used to generate in overall eight synthetic discharge timeseries at five different gauging stations. Modelled discharge is evaluated against observation in the reference and for changes in mean discharge in both scenario periods. All RCM significantly overestimate past annual precipitation (+24% to + 57%). The bias correction applied is suitable to reduce these deviations from observational data for the 1961-1990 correction period considerably (3% and 6%), without substantial side-ef- fects on RCM dynamics. Forcing the hydrological model with uncorrected RCM data reproduces the inherent input data biases, certain regional performance differences can V be stated but non of the simulation results is in a reasonable range. Corrected RCM data performed rather well, modelled streamflow is in most cases close to observation. Catchment size does influence model performance, biases are considerably higher at the two gauging stations covering areas less than about 6000 km2. An increase of annual mean temperature, ranging from 0.8◦ K to 1.8◦ K in the first and from 2.5◦ K to 3.9◦ K in the second scenario period, is simulated by all models. Changes in yearly precipitation range from 0% to 4% in the 2011-2040 and from 4% to 9% in the 2061-2090 period. These differences are mainly based on upward trends in autumn and winter during the first, and additionally increasing spring precipitation totals in the second scenario period. Climate dynamics translates into higher discharge during winter and lower during spring, with a positive trend in annual mean streamflow in the first but without clear signal in the second scenario period. Differences in the sub-catchments are rather small. The bias cor- rection applied is hardly altering climate change impact on hydrology in the first scenario period but, depending on RCM, more substantially in the second. It could be shown that simulated impact of climate change is quite sensitive to forcing RCM, whereas the general tendency of changes, positive or negative in arithmetic sign, is basically controlled by driving GCM. VI 1 Introduction Problem Formulation Climate change, a topic gaining much attention during the recent years, is now well supported by all available longterm weather records. The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC (2007)) reported an increase of average surface temperatures over Europe of 0.9◦ K for the 20th century and an even higher upward trend during the last two decades. The warming is not uniformly distributed, temperature increases are in general more pronounced in winter and more substantial in central and north-eastern Europe than in the Mediterranean. Precipitation measurements indicate even stronger regional differences. Winter rainfall amounts are increasing in northern and atlantic Europe, whereas total yearly precipitation is decreasing in the southern part of the continent (IPCC (2007)). Temperature trends in Germany are slightly higher than in continental average. Within the period from 1901 to 2000 mean surface temperatures increased about 1◦ K in all seasons. A higher inter-annual variability is reported for the 1981-2000 time-slice, temperatures increased disproportionally during winter and are stagnating in autumn (Sch¨onwiese et al. (2006)). Averaged total yearly precipitation amounts rose about 9% during the 20th century, highest gains are observed for winter (19%), while in summer a slight but statistically not significant decrease (-3%) is reported. The altered meteorology is very likely to be reflected in river discharge. Petrow and Merz (2009) proved upward flood trends, especially during winter for a large number of gauging stations all over Germany.