Suchergebnisse

In diesem Bericht werden die derzeitigen Verfahren zur Ausweisung nitratbelasteter Gebiete für den Grundwasserschutz in Deutschland bewertet und neue räumliche statistische Methoden für die Umsetzung bis 2028 vorgestellt. Es werden Defizite der derzeitigen Methoden aufgezeigt und geostatistische Regressions-Kriging-Verfahren als geeignete Alternativen vorgeschlagen.

Entre las consecuencias más preocupantes del calentamiento climático en Chile central figuran la pérdida de hielo y el retroceso de los glaciares andinos debido a sus efectos sobre la disponibilidad del recurso hídrico. En este contexto, el comportamiento de los glaciares cubiertos ha sido poco estudiado pese a que constituyen el 14% de la superficie glaciar en los Andes de Santiago y un porcentaje mayor de las zonas de ablación donde se concentra la pérdida de hielo. Utilizando métodos geodésicos, este estudio calculó el balance de masa neto del glaciar del Pirámide (4,6 km2), el glaciar cubierto más grande de la cuenca del río Yeso, la cual abastece la ciudad de Santiago de agua potable. Con el fin de obtener la diferencia en altura entre los años 1965 y 2000, se prepararon modelos digitales de elevación (MDE) a través de la interpolación de curvas de nivel de una carta topográfica (Instituto Geográfico Militar, IGM) y la restitución fotogramétrica de fotografías aéreas estereoscópicas (Servicio Aerofotogramétrico de la Fuerza Aérea, SAF). Se alcanzaron precisiones de 10,6 m (IGM) y 7,5 m (SAF) para los MDE y 12,5 m para la diferencia entre los MDE, resultando en un margen de error de 4,0 m al 95% de confianza para la diferencia de altura promedio en 40 puntos fijos. El descenso de altura total de -9,69 m como promedio de la superficie del glaciar equivale a unbalance de masa neto anual de -0,249 m a-1 equivalente en agua (e.a.), o una pérdida de 40 millones de m3 de agua (±40% al 95% de confianza). Ello es alrededor del 23% de la masa de hielo del glaciar del Pirámide y corresponde a una escorrentía promedio potencial del orden de 100 l s-1 durante verano, lo que subraya la importancia de este recurso hídrico no renovable para la disponibilidad de agua en la cuenca.PALABRAS CLAVE: Glaciar Cubierto, Balance de Masa, Cambio Climático, Andes de Santiago

Abstract. There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce. The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available. The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Cross-validation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions. The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The systematic comparisons of 12 models provided valuable evidence that the respective error-propagation was not only determined by the degree of positional inaccuracy inherent in the landslide data, but also by the spatial representation of landslides and the environment, landslide magnitude, the characteristics of the study area, the selected classification method and an interplay of predictors within multiple variable models. Based on the results, we deduced that a direct propagation of minor to moderate inventory-based positional errors into modelling results can be partly counteracted by adapting the modelling design (e.g. generalization of input data, opting for strongly generalizing classifiers). Since positional errors within landslide inventories are common and subsequent modelling and validation results are likely to be distorted, the potential existence of inventory-based positional inaccuracies should always be considered when assessing landslide susceptibility by means of empirical models.

Trockenheiten und Hitzewellen beeinflussen unsere Gesellschaft und die Vegetation. Insbesondere im Zusammenhang mit dem Klimawandel sind die Auswirkungen auf die Vegetation von besonderer Bedeutung. Im globalen Kohlenstoffkreislauf sind terrestrische Ökosysteme normalerweise Senken von Kohlenstoffdioxid, können sich aber während und nach Klimaextremereignissen in Kohlenstoffquellen verwandeln. Ein entscheidender Aspekt hierbei ist die Rolle verschiedener Pflanzenarten und Vegetationstypen auf verschiedenen Skalen, die die Auswirkungen auf den Kohlenstoffkreislauf beeinflussen. Obwohl durch physiologische Unterschiede zwischen verschiedenen Pflanzenarten unterschiedliche Reaktionen auf Extremereignisse naheliegen, sind diese Unterschiede auf globaler Ebene nicht systematisch ausgewertet und vollständig verstanden. Ein weiter Aspekt ist, dass Klimaextremereignissen von Natur aus multivariat sind. Beispielsweise kann heiße Luft mehr Wasser aufnehmen als kalte Luft. Extremereignisse mit starken Auswirkungen waren in der Vergangenheit häufig multivariat, wie beispielsweise in Europa 2003, Russland 2012, oder den USA 2012. Diese multivariate Natur von Klimaextremen erfordert eine multivariate Perspektive auf diese Ereignisse. Bisher werden meistens einzelne Variablen zu Detektion von Extremereignissen genutzt und keine Kovariation oder Nichtlinearitäten berücksichtigt. Neue generische Workflows, die solche multivariaten Strukturen berücksichtigen, müssen erst entwickelt oder aus anderen Disziplinen übertragen werden, um uns eine multivariate Perspektive auf Klimaextreme zu bieten. Das übergeordnete Ziel der Dissertation ist es, die Erkennung und das Verständnis von Klimaextremen und deren Auswirkungen auf die Vegetation zu verbessern, indem eine breitere multivariate Perspektive ermöglicht wird, die bisherige Ansätze zur Erkennung von Extremereignissen ergänzt.

Trockenheiten und Hitzewellen beeinflussen unsere Gesellschaft und die Vegetation. Insbesondere im Zusammenhang mit dem Klimawandel sind die Auswirkungen auf die Vegetation von besonderer Bedeutung. Im globalen Kohlenstoffkreislauf sind terrestrische Ökosysteme normalerweise Senken von Kohlenstoffdioxid, können sich aber während und nach Klimaextremereignissen in Kohlenstoffquellen verwandeln. Ein entscheidender Aspekt hierbei ist die Rolle verschiedener Pflanzenarten und Vegetationstypen auf verschiedenen Skalen, die die Auswirkungen auf den Kohlenstoffkreislauf beeinflussen. Obwohl durch physiologische Unterschiede zwischen verschiedenen Pflanzenarten unterschiedliche Reaktionen auf Extremereignisse naheliegen, sind diese Unterschiede auf globaler Ebene nicht systematisch ausgewertet und vollständig verstanden. Ein weiter Aspekt ist, dass Klimaextremereignissen von Natur aus multivariat sind. Beispielsweise kann heiße Luft mehr Wasser aufnehmen als kalte Luft. Extremereignisse mit starken Auswirkungen waren in der Vergangenheit häufig multivariat, wie beispielsweise in Europa 2003, Russland 2012, oder den USA 2012. Diese multivariate Natur von Klimaextremen erfordert eine multivariate Perspektive auf diese Ereignisse. Bisher werden meistens einzelne Variablen zu Detektion von Extremereignissen genutzt und keine Kovariation oder Nichtlinearitäten berücksichtigt. Neue generische Workflows, die solche multivariaten Strukturen berücksichtigen, müssen erst entwickelt oder aus anderen Disziplinen übertragen werden, um uns eine multivariate Perspektive auf Klimaextreme zu bieten. Das übergeordnete Ziel der Dissertation ist es, die Erkennung und das Verständnis von Klimaextremen und deren Auswirkungen auf die Vegetation zu verbessern, indem eine breitere multivariate Perspektive ermöglicht wird, die bisherige Ansätze zur Erkennung von Extremereignissen ergänzt.

Brazil possesses the largest freshwater resources worldwide. However, it is already facing severe water problems due to an adverse distribution of water availability and demand, which becomes especially apparent in the densely populated Southeast. This area is home to 75 % of Brazil's population and the location of two of the world's largest megacities, São Paulo and Rio de Janeiro. Their regional water demands serve various purposes, including drinking water, agricultural and industrial water, and hydropower supply for the metropole regions. At the same time, the region belongs to the Atlantic rainforest, the Mata Atlântica. The remaining patches of the pristine Mata Atlântica biome host worldwide outstanding biodiversity, which depends on the unique hydro-climatic conditions of the region. Nevertheless, little is known about the ecohydrological processes and dynamics in the region and their alteration under climate change. This, however, is an essential requirement for making informed decisions in river basin management that aims at safeguarding water-related ecosystem functions and services to simulate processes at different spatial and temporal scales. This dissertation contributes to the understanding of ecohydrological process dynamics in the Atlantic rainforest of Brazil. It creates a crucial prerequisite for science-based environmental planning and knowledge-based river basin management. The results indicate major challenges for water-resources management in the Southeastern Mata Atlântica for the upcoming decades. The methods and tools developed and used in this work can be easily applied and transferred to sub-tropical and tropical regions worldwide. They allow planning and mitigation for future climate, land-use, and management changes to sustain water-related ecosystem functions and services and preserve the beauty of unique biomes, such as the Mata Atlântica.

Brazil possesses the largest freshwater resources worldwide. However, it is already facing severe water problems due to an adverse distribution of water availability and demand, which becomes especially apparent in the densely populated Southeast. This area is home to 75 % of Brazil's population and the location of two of the world's largest megacities, São Paulo and Rio de Janeiro. Their regional water demands serve various purposes, including drinking water, agricultural and industrial water, and hydropower supply for the metropole regions. At the same time, the region belongs to the Atlantic rainforest, the Mata Atlântica. The remaining patches of the pristine Mata Atlântica biome host worldwide outstanding biodiversity, which depends on the unique hydro-climatic conditions of the region. Nevertheless, little is known about the ecohydrological processes and dynamics in the region and their alteration under climate change. This, however, is an essential requirement for making informed decisions in river basin management that aims at safeguarding water-related ecosystem functions and services to simulate processes at different spatial and temporal scales. This dissertation contributes to the understanding of ecohydrological process dynamics in the Atlantic rainforest of Brazil. It creates a crucial prerequisite for science-based environmental planning and knowledge-based river basin management. The results indicate major challenges for water-resources management in the Southeastern Mata Atlântica for the upcoming decades. The methods and tools developed and used in this work can be easily applied and transferred to sub-tropical and tropical regions worldwide. They allow planning and mitigation for future climate, land-use, and management changes to sustain water-related ecosystem functions and services and preserve the beauty of unique biomes, such as the Mata Atlântica.

Abstract. The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.

Abstract
Both the frequency and intensity of hot temperature extremes are expected to increase in the coming decades, challenging various socioeconomic sectors including public health. Therefore, societal attention data available in real time, such as Google search attention, could help monitor heat-wave impacts in domains with lagged data availability. Here, we jointly analyze societal attention and health impacts of heat waves in Germany at weekly time scales. We find that Google search attention responds similarly to hot temperatures as indicators of public health impacts, represented by excess mortality and hospitalizations. This emerges from piecewise linear relationships of Google search attention to and health impacts of temperature. We can then determine temperature thresholds above which both attention and public health are affected by heat. More generally, given the clear and similar response of societal indicators to heat, we conclude that heat waves can and should be defined from a joint societal and meteorological perspective, whereby temperatures are compared with thresholds established using societal data. A better joint understanding of societal attention and health impacts offers the potential to better manage future heat waves.

Abstract. Knowing the source and runout of debris flows can help in planning
strategies aimed at mitigating these hazards. Our research in this paper
focuses on developing a novel approach for optimizing runout models for
regional susceptibility modelling, with a case study in the upper Maipo
River basin in the Andes of Santiago, Chile. We propose a two-stage
optimization approach for automatically selecting parameters for estimating
runout path and distance. This approach optimizes the random-walk and Perla
et al.'s (PCM) two-parameter friction model components of the open-source
Gravitational Process Path (GPP) modelling framework. To validate model
performance, we assess the spatial transferability of the optimized runout
model using spatial cross-validation, including exploring the model's
sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance, larger samples sizes (i.e. ≥80) had higher model performance and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using the open-source R software and the System for Automated Geoscientific Analyses geographic information system (SAGA-GIS) will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.