The management of New Zealand’s freshwater resources has come under increasing pressure from different industrial and environmental stakeholders. Land use change and the pressure it can put on water resources has been a significant issue regarding resource management in New Zealand. A significant mechanism driving land use change has been the growth of forestry, dairy farming, and other agricultural industries. Improvements in agricultural and forestry science and irrigation techniques have allowed new, previously less arable areas of New Zealand to be subject to land use change, such as the conversion of tussock grassland to pasture in steep, mountainous regions in the South Island. Studies regarding the effects of land use change in such catchments, especially with focus on flood hydrology, appear to be limited, despite the importance of managing catchment headwaters to minimise flood risk downstream. The TopNet model was used in this research project to evaluate the potential effects of land use change on flood hydrology in mountain catchments. It is a semi-distributed continuous rainfall-runoff model developed by the National Institute of Water and Atmospheric Research (NIWA). It has been widely used in New Zealand, and applications have included modelling water yield and the effect of climate change in catchment networks. However, it was not developed specifically for predicting flood flows. Hence, testing the model for flood peak prediction in mountainous catchments was also performed, and may show that TopNet can be a useful tool in resource management in New Zealand. The Ahuriri and Pelorus River catchments were used in this investigation. Both are steep catchments located in the South Island. The Ahuriri River catchment, in the Waitaki Basin on the eastern side of the Southern Alps, is a semi-arid catchment dominated by tussock grassland. The surrounding catchments are heavily influenced by infrastructure for hydroelectric power (HEP) generation and more recently irrigation for dairy farming. The Pelorus River catchment is located at the northern end of the South Island. It is primarily covered in native forest, but adjacent catchments are subject to agricultural and forestry development. The ability of the TopNet model for each catchment to predict flood flows were tested using a selection of historical flood events. Rainfall input to the model was at a daily timestep from the virtual climate station network (VCSN), and the method of disaggregating the daily estimate into an hourly rainfall series to be used by the model was found to have a significant influence on flood prediction. Where an accurate historical rainfall record was provided from a rainfall gauge station within the catchments, the disaggregation of the daily rainfall estimate based on the station data produced a significantly more accurate flood prediction when compared to predictions made using a stochastic disaggregation of the daily rainfall estimate. The TopNet models were modified to reflect land use change scenarios: the conversion of tussock grassland to pasture and the afforestation of tussock in the Ahuriri River catchment, and the conversion of forested land to pasture and the harvest of plantation forestry in the Pelorus River catchment. Following a past study into modelling the effects of land use change using TopNet, three key model parameters were modified to reflect each land use scenario: saturated hydraulic conductivity KS, canopy storage capacity, and the canopy enhancement factor. Past studies suggested a wide range of suitable values for KS, although also acknowledged that KS depends heavily on the specific catchment characteristics. A sensitivity analysis showed that KS had a significant influence on flood peak prediction in TopNet. It is recommended that further investigation be conducted into suitable values for KS. TopNet appeared to predict the effect of land use change on flood magnitude in mountainous catchments conservatively. Past studies of land use change suggested that the effect on flood flows should be significant, whereas TopNet generally predicted small changes in flood peaks for the scenarios in each catchment. However, this may suggest that the topography, geology, and soil properties of steep catchments are more important to flood hydrology than land cover. Further investigation into the effect of such catchment characteristics is recommended. Nevertheless, TopNet was shown to have the potential to be a useful tool for evaluating and managing the effects of land use change on the flood hydrology of mountainous catchments in New Zealand.

Floods are a significant threat to communities around the world and require substantial resources and infrastructure to predict. Limited local resources in developing nations make it difficult to build and maintain dense sensor networks like those present in the United States, creating a large disparity in flood prediction across borders. To address this disparity, I operated the Iowa Flood Center Top Layer model to predict floods in Puerto Rico without relying on in-situ data measurements. Instead, all model forcing was provided by satellite remote sensing datasets that offer near-global coverage. I used three datasets gathered via satellite remote sensing to build and operate watershed streamflow models: elevation data obtained by the Space Shuttle Endeavour through the Shuttle Radar Topography Mission (SRTM), rainfall estimates gathered by a constellation of satellites through the Global Precipitation Measurement Mission (GPM), and evapotranspiration rate estimates collected by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Aqua and Terra satellites. While these satellite remote sensing datasets make observations of nearly the entire world, their spatiotemporal resolution is coarse compared to conventional on-the-ground measurements. Hydrologic models were assembled for 75 basins upstream of streamflow gages monitored by the United States Geologic Survey (USGS). Model simulations were compared to real-time measurements at these gages. Continuous simulations spanning 58 months achieve poor Nash Sutcliffe Efficiency and Klinge Gupta Efficiency of -112.0 and -0.5, respectively. The sources of error that influence model performance were investigated, underlining some limitations of relying solely on satellite data for operational flood prediction efforts.

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.