@phdthesis{Schmidt2024,
  author    = {Schmidt, Lena Katharina},
  title     = {Altered hydrological and sediment dynamics in high-alpine areas - Exploring the potential of machine-learning for estimating past and future changes},
  doi       = {10.25932/publishup-62330},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-623302},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxi, 129},
  year      = {2024},
  abstract  = {Climate change fundamentally transforms glaciated high-alpine regions, with well-known cryospheric and hydrological implications, such as accelerating glacier retreat, transiently increased runoff, longer snow-free periods and more frequent and intense summer rainstorms. These changes affect the availability and transport of sediments in high alpine areas by altering the interaction and intensity of different erosion processes and catchment properties. Gaining insight into the future alterations in suspended sediment transport by high alpine streams is crucial, given its wide-ranging implications, e.g. for flood damage potential, flood hazard in downstream river reaches, hydropower production, riverine ecology and water quality. However, the current understanding of how climate change will impact suspended sediment dynamics in these high alpine regions is limited. For one, this is due to the scarcity of measurement time series that are long enough to e.g. infer trends. On the other hand, it is difficult - if not impossible - to develop process-based models, due to the complexity and multitude of processes involved in high alpine sediment dynamics. Therefore, knowledge has so far been confined to conceptual models (which do not facilitate deriving concrete timings or magnitudes for individual catchments) or qualitative estimates ('higher export in warmer years') that may not be able to capture decreases in sediment export. Recently, machine-learning approaches have gained in popularity for modeling sediment dynamics, since their black box nature tailors them to the problem at hand, i.e. relatively well-understood input and output data, linked by very complex processes. Therefore, the overarching aim of this thesis is to estimate sediment export from the high alpine {\"O}tztal valley in Tyrol, Austria, over decadal timescales in the past and future - i.e. timescales relevant to anthropogenic climate change. This is achieved by informing, extending, evaluating and applying a quantile regression forest (QRF) approach, i.e. a nonparametric, multivariate machine-learning technique based on random forest. The first study included in this thesis aimed to understand present sediment dynamics, i.e. in the period with available measurements (up to 15 years). To inform the modeling setup for the two subsequent studies, this study identified the most important predictors, areas within the catchments and time periods. To that end, water and sediment yields from three nested gauges in the upper {\"O}tztal, Vent, S{\"o}lden and Tumpen (98 to almost 800 km² catchment area, 930 to 3772 m a.s.l.) were analyzed for their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. The findings suggest that the areas situated above 2500 m a.s.l., containing glacier tongues and recently deglaciated areas, play a pivotal role in sediment generation across all sub-catchments. In contrast, precipitation events were relatively unimportant (on average, 21 \% of annual sediment yield was associated to precipitation events). Thus, the second and third study focused on the Vent catchment and its sub-catchment above gauge Vernagt (11.4 and 98 km², 1891 to 3772 m a.s.l.), due to their higher share of areas above 2500 m. Additionally, they included discharge, precipitation and air temperature (as well as their antecedent conditions) as predictors. The second study aimed to estimate sediment export since the 1960s/70s at gauges Vent and Vernagt. This was facilitated by the availability of long records of the predictors, discharge, precipitation and air temperature, and shorter records (four and 15 years) of turbidity-derived sediment concentrations at the two gauges. The third study aimed to estimate future sediment export until 2100, by applying the QRF models developed in the second study to pre-existing precipitation and temperature projections (EURO-CORDEX) and discharge projections (physically-based hydroclimatological and snow model AMUNDSEN) for the three representative concentration pathways RCP2.6, RCP4.5 and RCP8.5. The combined results of the second and third study show overall increasing sediment export in the past and decreasing export in the future. This suggests that peak sediment is underway or has already passed - unless precipitation changes unfold differently than represented in the projections or changes in the catchment erodibility prevail and override these trends. Despite the overall future decrease, very high sediment export is possible in response to precipitation events. This two-fold development has important implications for managing sediment, flood hazard and riverine ecology. This thesis shows that QRF can be a very useful tool to model sediment export in high-alpine areas. Several validations in the second study showed good performance of QRF and its superiority to traditional sediment rating curves - especially in periods that contained high sediment export events, which points to its ability to deal with threshold effects. A technical limitation of QRF is the inability to extrapolate beyond the range of values represented in the training data. We assessed the number and severity of such out-of-observation-range (OOOR) days in both studies, which showed that there were few OOOR days in the second study and that uncertainties associated with OOOR days were small before 2070 in the third study. As the pre-processed data and model code have been made publically available, future studies can easily test further approaches or apply QRF to further catchments.},
  language  = {en}
}
@phdthesis{Luna2023,
  author    = {Luna, Lisa Victoria},
  title     = {Rainfall-triggered landslides: conditions, prediction, and warning},
  doi       = {10.25932/publishup-60092},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-600927},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 119},
  year      = {2023},
  abstract  = {Rainfall-triggered landslides are a globally occurring hazard that cause several thousand fatalities per year on average and lead to economic damages by destroying buildings and infrastructure and blocking transportation networks. For people living and governing in susceptible areas, knowing not only where, but also when landslides are most probable is key to inform strategies to reduce risk, requiring reliable assessments of weather-related landslide hazard and adequate warning. Taking proper action during high hazard periods, such as moving to higher levels of houses, closing roads and rail networks, and evacuating neighborhoods, can save lives. Nevertheless, many regions of the world with high landslide risk currently lack dedicated, operational landslide early warning systems. The mounting availability of temporal landslide inventory data in some regions has increasingly enabled data-driven approaches to estimate landslide hazard on the basis of rainfall conditions. In other areas, however, such data remains scarce, calling for appropriate statistical methods to estimate hazard with limited data. The overarching motivation for this dissertation is to further our ability to predict rainfall-triggered landslides in time in order to expand and improve warning. To this end, I applied Bayesian inference to probabilistically quantify and predict landslide activity as a function of rainfall conditions at spatial scales ranging from a small coastal town, to metropolitan areas worldwide, to a multi-state region, and temporal scales from hourly to seasonal. This thesis is composed of three studies. In the first study, I contributed to developing and validating statistical models for an online landslide warning dashboard for the small town of Sitka, Alaska, USA. We used logistic and Poisson regressions to estimate daily landslide probability and counts from an inventory of only five reported landslide events and 18 years of hourly precipitation measurements at the Sitka airport. Drawing on community input, we established two warning thresholds for implementation in the dashboard, which uses observed rainfall and US National Weather Service forecasts to provide real-time estimates of landslide hazard. In the second study, I estimated rainfall intensity-duration thresholds for shallow landsliding for 26 cities worldwide and a global threshold for urban landslides. I found that landslides in urban areas occurred at rainfall intensities that were lower than previously reported global thresholds, and that 31\% of urban landslides were triggered during moderate rainfall events. However, landslides in cities with widely varying climates and topographies were triggered above similar critical rainfall intensities: thresholds for 77\% of cities were indistinguishable from the global threshold, suggesting that urbanization may harmonize thresholds between cities, overprinting natural variability. I provide a baseline threshold that could be considered for warning in cities with limited landslide inventory data. In the third study, I investigated seasonal landslide response to annual precipitation patterns in the Pacific Northwest region, USA by using Bayesian multi-level models to combine data from five heterogeneous landslide inventories that cover different areas and time periods. I quantitatively confirmed a distinctly seasonal pattern of landsliding and found that peak landslide activity lags the annual precipitation peak. In February, at the height of the landslide season, landslide intensity for a given amount of monthly rainfall is up to ten times higher than at the season onset in November, underlining the importance of antecedent seasonal hillslope conditions. Together, these studies contributed actionable, objective information for landslide early warning and examples for the application of Bayesian methods to probabilistically quantify landslide hazard from inventory and rainfall data.},
  language  = {en}
}
@phdthesis{Bamberg2014,
  author    = {Bamberg, Marlene},
  title     = {Planetary mapping tools applied to floor-fractured craters on Mars},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-72104},
  school      = {Universit{\"a}t Potsdam},
  year      = {2014},
  abstract  = {Planetary research is often user-based and requires considerable skill, time, and effort. Unfortunately, self-defined boundary conditions, definitions, and rules are often not documented or not easy to comprehend due to the complexity of research. This makes a comparison to other studies, or an extension of the already existing research, complicated. Comparisons are often distorted, because results rely on different, not well defined, or even unknown boundary conditions. The purpose of this research is to develop a standardized analysis method for planetary surfaces, which is adaptable to several research topics. The method provides a consistent quality of results. This also includes achieving reliable and comparable results and reducing the time and effort of conducting such studies. A standardized analysis method is provided by automated analysis tools that focus on statistical parameters. Specific key parameters and boundary conditions are defined for the tool application. The analysis relies on a database in which all key parameters are stored. These databases can be easily updated and adapted to various research questions. This increases the flexibility, reproducibility, and comparability of the research. However, the quality of the database and reliability of definitions directly influence the results. To ensure a high quality of results, the rules and definitions need to be well defined and based on previously conducted case studies. The tools then produce parameters, which are obtained by defined geostatistical techniques (measurements, calculations, classifications). The idea of an automated statistical analysis is tested to proof benefits but also potential problems of this method. In this study, I adapt automated tools for floor-fractured craters (FFCs) on Mars. These impact craters show a variety of surface features, occurring in different Martian environments, and having different fracturing origins. They provide a complex morphological and geological field of application. 433 FFCs are classified by the analysis tools due to their fracturing process. Spatial data, environmental context, and crater interior data are analyzed to distinguish between the processes involved in floor fracturing. Related geologic processes, such as glacial and fluvial activity, are too similar to be separately classified by the automated tools. Glacial and fluvial fracturing processes are merged together for the classification. The automated tools provide probability values for each origin model. To guarantee the quality and reliability of the results, classification tools need to achieve an origin probability above 50 \%. This analysis method shows that 15 \% of the FFCs are fractured by intrusive volcanism, 20 \% by tectonic activity, and 43 \% by water \& ice related processes. In total, 75 \% of the FFCs are classified to an origin type. This can be explained by a combination of origin models, superposition or erosion of key parameters, or an unknown fracturing model. Those features have to be manually analyzed in detail. Another possibility would be the improvement of key parameters and rules for the classification. This research shows that it is possible to conduct an automated statistical analysis of morphologic and geologic features based on analysis tools. Analysis tools provide additional information to the user and are therefore considered assistance systems.},
  language  = {en}
}