Models for spatially interpolating hourly precipitation data and temporally disaggregating daily precipitation to hourly data are developed for application to multisite scenarios at the watershed scale. The intent is to create models to produce data which are valid input for a hydrologic rainfall-runoff model, from daily data produced by a stochastic weather generator. These models will be used to determine the potential effects of climate change on local precipitation events. A case study is presented applying these models to the Upper Thames River basin in Ontario, Canada; however, these models are generic and applicable to any watershed with few changes. <br /><br /> Some hourly precipitation data were required to calibrate the temporal disaggregation model. Spatial interpolation of this hourly precipitation data was required before temporal disaggregation could be completed. Spatial interpolation methods were investigated and an inverse distance method was applied to the data. Analysis of the output from this model confirms that isotropy is a valid assumption for this application and illustrates that the model is robust. The results for this model show that further study is required for accurate spatial interpolation of hourly precipitation data at the watershed scale. <br /><br /> An improved method of fragments is used to perform temporal disaggregation on daily precipitation data. A parsimonious approach to multisite fragment calculation is introduced within this model as well as other improvements upon the methods presented in the literature. The output from this model clearly indicates that spatial and temporal variations are maintained throughout the disaggregation process. Analysis of the results indicates that the model creates plausible precipitation events. <br /><br /> The models presented here were run for multiple climate scenarios to determine which GCM scenario has the most potential to affect precipitation. Discussion on the potential impacts of climate change on the region of study is provided. Selected events are examined in detail to give a representation of extreme precipitation events which may be experienced in the study area due to climate change.

Models for spatially interpolating hourly precipitation data and temporally disaggregating daily precipitation to hourly data are developed for application to multisite scenarios at the watershed scale. The intent is to create models to produce data which are valid input for a hydrologic rainfall-runoff model, from daily data produced by a stochastic weather generator. These models will be used to determine the potential effects of climate change on local precipitation events. A case study is presented applying these models to the Upper Thames River basin in Ontario, Canada; however, these models are generic and applicable to any watershed with few changes. <br /><br /> Some hourly precipitation data were required to calibrate the temporal disaggregation model. Spatial interpolation of this hourly precipitation data was required before temporal disaggregation could be completed. Spatial interpolation methods were investigated and an inverse distance method was applied to the data. Analysis of the output from this model confirms that isotropy is a valid assumption for this application and illustrates that the model is robust. The results for this model show that further study is required for accurate spatial interpolation of hourly precipitation data at the watershed scale. <br /><br /> An improved method of fragments is used to perform temporal disaggregation on daily precipitation data. A parsimonious approach to multisite fragment calculation is introduced within this model as well as other improvements upon the methods presented in the literature. The output from this model clearly indicates that spatial and temporal variations are maintained throughout the disaggregation process. Analysis of the results indicates that the model creates plausible precipitation events. <br /><br /> The models presented here were run for multiple climate scenarios to determine which GCM scenario has the most potential to affect precipitation. Discussion on the potential impacts of climate change on the region of study is provided. Selected events are examined in detail to give a representation of extreme precipitation events which may be experienced in the study area due to climate change.

This doctoral thesis is based on three publications (two peer-reviewed, one submitted). Its objective was to test existing methods and to develop innovative methods for generating highly resolved climate data with focus on the spatio-temporal distribution of precipitation as both, the spatial and temporal resolution as well as the length of such data sets are limited. For this purpose, satellite and radar-based remote sensing data, ground-based station data, and modelling methods were applied and combined. The Free State of Saxony (Germany) served as an investigation area as its mountainous regions are prone to heavy precipitation events and related (flash) floods like e.g., in 2002, in 2010, and in 2013. Two approaches were developed to generate hourly data when there are no station data available or only daily data. The first approach applies four different algorithms to estimate area-wide rain rates by using the satellite data of Meteosat Second Generation (MSG-1) and compares them to the gauge adjusted radar data product RADOLAN RW. The analyses of five spatial und six temporal integration steps by means of four fit scores and statistical relations show a stepwise improvement. That means, the integration leads to increasing probability of detection (POD) and critical success index (CSI), decreasing false alarm ratio (FAR) and Bias, and improved statistical relations especially for heavy rain rates. The best results are achieved for the lowest resolution of 120 km × 120 km and 24 h. However, this resolution is too low for applications in (flash) flood risk management for small and medium sized catchments. Such satellite-based estimated rain rates may serve as a data source for unobserved regions or as an indicator for large catchments or longer periods. A second approach comprises the newly developed Euclidean distance model (EDM) that generates hourly climate data by means of a temporal disaggregation procedure. The delivered data are point data for the climate variables temperature, precipitation, sunshine duration, relative humidity, and wind speed. They show high correlations and conserve (i) the statistics in comparison to the observed hourly data and (ii) also the consistency over all disaggregated climate elements. The results reveal that the EDM performs best for climate elements with a continuous diurnal cycle like temperature, for the winter half-year, and when the basic climate stations are characterised by similar climate conditions. The EDM proves to be a very robust, flexible and fast working model. Hence, the work presented here succeeded in developing two innovative locally-independent approaches that are applicable to the climate data of any region or station without complex model parametrisation. Simultaneously, the method can be applied to future daily climate data allowing the generation of hourly data that are needed for climate impact models. / Diese Dissertation basiert auf drei Publikationen (zwei begutachtet, eine eingereicht). Ziel war es, existierende Methoden zur Generierung hochaufgelöster Klimadaten zu untersuchen und innovative Methoden zu entwickeln mit dem Fokus auf der raumzeitlichen Niederschlagsverteilung, da sowohl die räumliche und zeitliche Auflösung als auch die Länge solcher Datenreihen begrenzt sind. Hierfür wurden satelliten- und radarbasierte Fernerkundungsdaten, Bodenstationsdaten sowie Modellierungsverfahren angewendet und kombiniert. Als Untersuchungsgebiet wurde der Freistaat Sachsen (Deutschland) gewählt, da dessen Gebirgsregionen starkregen- und damit hochwassergefährdet sind, wie bei den Hochwasserereignissen von 2002, 2010 und 2013 sichtbar wurde. Es wurden zwei Ansätze entwickelt, die die Generierung von Stundendaten ermöglichen, wenn keine Daten oder nur Tagesdaten vorhanden sind. Der erste Ansatz verwendet vier verschiedene Algorithmen zum Abschätzen flächendeckender Niederschlagsintensitäten unter Verwendung der Daten des Satelliten Meteosat Second Generation (MSG-1) und vergleicht diese mit den an Bodenstationsdaten angeeichten Radardaten des RADOLAN RW Produktes. Die Analysen von fünf räumlichen und sechs zeitlichen Integrationsstufen mit Hilfe von vier Fit Scores und statistischer Kennwerte zeigen eine schrittweise Verbesserung der Ergebnisse. Das heißt, dass durch Integration steigende Werte der probability of detection (POD) und des critical success index (CSI), sinkende Werte der false alarm ratio (FAR) und des Bias sowie verbesserte statistische Kennwerte erreicht werden. Dies gilt insbesondere für Starkniederschlagsintensitäten. Die besten Ergebnisse werden bei der niedrigsten Auflösung von 120 km × 120 km und 24 h erreicht. Jedoch ist diese Auflösung für Anwendungen des Hochwasserrisikomanagements kleiner und mittlerer Einzugsgebiete zu gering. Solche satellitenbasierten Niederschlagsintensitäten können als Datenquelle für unbeobachtete Regionen oder als Indikator für große Einzugsgebiete oder längere Zeitintervalle dienen. Ein zweiter Ansatz beinhaltet das neu entwickelte Euclidean distance model (EDM), das mittels zeitlicher Disaggregierung stündliche Klimadaten generiert. Die erzeugten Daten sind punktbezogene Daten der Klimavariablen Temperatur, Niederschlag, Sonnenscheindauer, relative Feuchte und Windgeschwindigkeit. Sie weisen hohe Korrelationen auf und sie wahren (i) die statistischen Kenngrößen im Vergleich mit den beobachteten Stundendaten und (ii) die Konsistenz über alle Klimaelemente hinweg. Die Ergebnisse zeigen, dass das EDM für Klimaelemente mit einem kontinuierlichen Tagesgang, wie z.B. die Temperatur, für das Winterhalbjahr und bei der Verwendung von Basisstationen mit ähnlicher klimatischer Charakteristik die besten Ergebnisse liefert. Das EDM erweist sich als ein sehr robustes, flexibles und schnell arbeitendes Modell. Somit ist es mit der hier vorliegenden Arbeit gelungen, zwei innovative Ansätze zu entwickeln, die ohne komplexe Modellparametrisierung auf Daten einer jeden Klimaregion oder Klimastation angewendet werden können.