@phdthesis{Hartmann2024,
  author    = {Hartmann, Anne},
  title     = {Tracing the evolution of hillslope structure and hillslope hydrological response over ten millennia in two glacial forefields of different geology},
  doi       = {10.25932/publishup-62862},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-628629},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XVIII, 138, XLIV},
  year      = {2024},
  abstract  = {Assessing the impact of global change on hydrological systems is one of the greatest hydrological challenges of our time. Changes in land cover, land use, and climate have an impact on water quantity, quality, and temporal availability. There is a widespread consensus that, given the far-reaching effects of global change, hydrological systems can no longer be viewed as static in their structure; instead, they must be regarded as entire ecosystems, wherein hydrological processes interact and coevolve with biological, geomorphological, and pedological processes. To accurately predict the hydrological response under the impact of global change, it is essential to understand this complex coevolution. The knowledge of how hydrological processes, in particular the formation of subsurface (preferential) flow paths, evolve within this coevolution and how they feed back to the other processes is still very limited due to a lack of observational data. At the hillslope scale, this intertwined system of interactions is known as the hillslope feedback cycle. This thesis aims to enhance our understanding of the hillslope feedback cycle by studying the coevolution of hillslope structure and hillslope hydrological response. Using chronosequences of moraines in two glacial forefields developed from siliceous and calcareous glacial till, the four studies shed light on the complex coevolution of hydrological, biological, and structural hillslope properties, as well as subsurface hydrological flow paths over an evolutionary period of 10 millennia in these two contrasting geologies. The findings indicate that the contrasting properties of siliceous and calcareous parent materials lead to variations in soil structure, permeability, and water storage. As a result, different plant species and vegetation types are favored on siliceous versus calcareous parent material, leading to diverse ecosystems with distinct hydrological dynamics. The siliceous parent material was found to show a higher activity level in driving the coevolution. The soil pH resulting from parent material weathering emerges as a crucial factor, influencing vegetation development, soil formation, and consequently, hydrology. The acidic weathering of the siliceous parent material favored the accumulation of organic matter, increasing the soils' water storage capacity and attracting acid-loving shrubs, which further promoted organic matter accumulation and ultimately led to podsolization after 10 000 years. Tracer experiments revealed that the subsurface flow path evolution was influenced by soil and vegetation development, and vice versa. Subsurface flow paths changed from vertical, heterogeneous matrix flow to finger-like flow paths over a few hundred years, evolving into macropore flow, water storage, and lateral subsurface flow after several thousand years. The changes in flow paths among younger age classes were driven by weathering processes altering soil structure, as well as by vegetation development and root activity. In the older age class, the transition to more water storage and lateral flow was attributed to substantial organic matter accumulation and ongoing podsolization. The rapid vertical water transport in the finger-like flow paths, along with the conductive sandy material, contributed to podsolization and thus to the shift in the hillslope hydrological response. In contrast, the calcareous site possesses a high pH buffering capacity, creating a neutral to basic environment with relatively low accumulation of dead organic matter, resulting in a lower water storage capacity and the establishment of predominantly grass vegetation. The coevolution was found to be less dynamic over the millennia. Similar to the siliceous site, significant changes in subsurface flow paths occurred between the young age classes. However, unlike the siliceous site, the subsurface flow paths at the calcareous site only altered in shape and not in direction. Tracer experiments showed that flow paths changed from vertical, heterogeneous matrix flow to vertical, finger-like flow paths after a few hundred to thousands of years, which was driven by root activities and weathering processes. Despite having a finer soil texture, water storage at the calcareous site was significantly lower than at the siliceous site, and water transport remained primarily rapid and vertical, contributing to the flourishing of grass vegetation. The studies elucidated that changes in flow paths are predominantly shaped by the characteristics of the parent material and its weathering products, along with their complex interactions with initial water flow paths and vegetation development. Time, on the other hand, was not found to be a primary factor in describing the evolution of the hydrological response. This thesis makes a valuable contribution to closing the gap in the observations of the coevolution of hydrological processes within the hillslope feedback cycle, which is important to improve predictions of hydrological processes in changing landscapes. Furthermore, it emphasizes the importance of interdisciplinary studies in addressing the hydrological challenges arising from global change.},
  language  = {en}
}
@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{Zhou2024,
  author    = {Zhou, Xiangqian},
  title     = {Modeling of spatially distributed nitrate transport to investigate the effects of drought and river restoration in the Bode catchment, Central Germany},
  doi       = {10.25932/publishup-62105},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-621059},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VIII, 168},
  year      = {2024},
  abstract  = {The European Water Framework Directive (WFD) has identified river morphological alteration and diffuse pollution as the two main pressures affecting water bodies in Europe at the catchment scale. Consequently, river restoration has become a priority to achieve the WFD's objective of good ecological status. However, little is known about the effects of stream morphological changes, such as re-meandering, on in-stream nitrate retention at the river network scale. Therefore, catchment nitrate modeling is necessary to guide the implementation of spatially targeted and cost-effective mitigation measures. Meanwhile, Germany, like many other regions in central Europe, has experienced consecutive summer droughts from 2015-2018, resulting in significant changes in river nitrate concentrations in various catchments. However, the mechanistic exploration of catchment nitrate responses to changing weather conditions is still lacking. Firstly, a fully distributed, process-based catchment Nitrate model (mHM-Nitrate) was used, which was properly calibrated and comprehensively evaluated at numerous spatially distributed nitrate sampling locations. Three calibration schemes were designed, taking into account land use, stream order, and mean nitrate concentrations, and they varied in spatial coverage but used data from the same period (2011-2019). The model performance for discharge was similar among the three schemes, with Nash-Sutcliffe Efficiency (NSE) scores ranging from 0.88 to 0.92. However, for nitrate concentrations, scheme 2 outperformed schemes 1 and 3 when compared to observed data from eight gauging stations. This was likely because scheme 2 incorporated a diverse range of data, including low discharge values and nitrate concentrations, and thus provided a better representation of within-catchment heterogenous. Therefore, the study suggests that strategically selecting gauging stations that reflect the full range of within-catchment heterogeneity is more important for calibration than simply increasing the number of stations. Secondly, the mHM-Nitrate model was used to reveal the causal relations between sequential droughts and nitrate concentration in the Bode catchment (3200 km2) in central Germany, where stream nitrate concentrations exhibited contrasting trends from upstream to downstream reaches. The model was evaluated using data from six gauging stations, reflecting different levels of runoff components and their associated nitrate-mixing from upstream to downstream. Results indicated that the mHM-Nitrate model reproduced dynamics of daily discharge and nitrate concentration well, with Nash-Sutcliffe Efficiency ≥ 0.73 for discharge and Kling-Gupta Efficiency ≥ 0.50 for nitrate concentration at most stations. Particularly, the spatially contrasting trends of nitrate concentration were successfully captured by the model. The decrease of nitrate concentration in the lowland area in drought years (2015-2018) was presumably due to (1) limited terrestrial export loading (ca. 40\% lower than that of normal years 2004-2014), and (2) increased in-stream retention efficiency (20\% higher in summer within the whole river network). From a mechanistic modelling perspective, this study provided insights into spatially heterogeneous flow and nitrate dynamics and effects of sequential droughts, which shed light on water-quality responses to future climate change, as droughts are projected to be more frequent. Thirdly, this study investigated the effects of stream restoration via re-meandering on in-stream nitrate retention at network-scale in the well-monitored Bode catchment. The mHM-Nitrate model showed good performance in reproducing daily discharge and nitrate concentrations, with median Kling-Gupta values of 0.78 and 0.74, respectively. The mean and standard deviation of gross nitrate retention efficiency, which accounted for both denitrification and assimilatory uptake, were 5.1 ± 0.61\% and 74.7 ± 23.2\% in winter and summer, respectively, within the stream network. The study found that in the summer, denitrification rates were about two times higher in lowland sub-catchments dominated by agricultural lands than in mountainous sub-catchments dominated by forested areas, with median ± SD of 204 ± 22.6 and 102 ± 22.1 mg N m-2 d-1, respectively. Similarly, assimilatory uptake rates were approximately five times higher in streams surrounded by lowland agricultural areas than in those in higher-elevation, forested areas, with median ± SD of 200 ± 27.1 and 39.1 ± 8.7 mg N m-2 d-1, respectively. Therefore, restoration strategies targeting lowland agricultural areas may have greater potential for increasing nitrate retention. The study also found that restoring stream sinuosity could increase net nitrate retention efficiency by up to 25.4 ± 5.3\%, with greater effects seen in small streams. These results suggest that restoration efforts should consider augmenting stream sinuosity to increase nitrate retention and decrease nitrate concentrations at the catchment scale.},
  language  = {en}
}
@phdthesis{Li2024,
  author    = {Li, Yunfei},
  title     = {On the influence of density and morphology on the Urban Heat Island intensity},
  doi       = {10.25932/publishup-62150},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-621504},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xviii, 119},
  year      = {2024},
  abstract  = {The urban heat island (UHI) effect, describing an elevated temperature of urban areas compared with their natural surroundings, can expose urban dwellers to additional heat stress, especially during hot summer days. A comprehensive understanding of the UHI dynamics along with urbanization is of great importance to efficient heat stress mitigation strategies towards sustainable urban development. This is, however, still challenging due to the difficulties of isolating the influences of various contributing factors that interact with each other. In this work, I present a systematical and quantitative analysis of how urban intrinsic properties (e.g., urban size, density, and morphology) influence UHI intensity. To this end, we innovatively combine urban growth modelling and urban climate simulation to separate the influence of urban intrinsic factors from that of background climate, so as to focus on the impact of urbanization on the UHI effect. The urban climate model can create a laboratory environment which makes it possible to conduct controlled experiments to separate the influences from different driving factors, while the urban growth model provides detailed 3D structures that can be then parameterized into different urban development scenarios tailored for these experiments. The novelty in the methodology and experiment design leads to the following achievements of our work. First, we develop a stochastic gravitational urban growth model that can generate 3D structures varying in size, morphology, compactness, and density gradient. We compare various characteristics, like fractal dimensions (box-counting, area-perimeter scaling, area-population scaling, etc.), and radial gradient profiles of land use share and population density, against those of real-world cities from empirical studies. The model shows the capability of creating 3D structures resembling real-world cities. This model can generate 3D structure samples for controlled experiments to assess the influence of some urban intrinsic properties in question. [Chapter 2] With the generated 3D structures, we run several series of simulations with urban structures varying in properties like size, density and morphology, under the same weather conditions. Analyzing how the 2m air temperature based canopy layer urban heat island (CUHI) intensity varies in response to the changes of the considered urban factors, we find the CUHI intensity of a city is directly related to the built-up density and an amplifying effect that urban sites have on each other. We propose a Gravitational Urban Morphology (GUM) indicator to capture the neighbourhood warming effect. We build a regression model to estimate the CUHI intensity based on urban size, urban gross building volume, and the GUM indicator. Taking the Berlin area as an example, we show the regression model capable of predicting the CUHI intensity under various urban development scenarios. [Chapter 3] Based on the multi-annual average summer surface urban heat island (SUHI) intensity derived from Land surface temperature, we further study how urban intrinsic factors influence the SUHI effect of the 5,000 largest urban clusters in Europe. We find a similar 3D GUM indicator to be an effective predictor of the SUHI intensity of these European cities. Together with other urban factors (vegetation condition, elevation, water coverage), we build different multivariate linear regression models and a climate space based Geographically Weighted Regression (GWR) model that can better predict SUHI intensity. By investigating the roles background climate factors play in modulating the coefficients of the GWR model, we extend the multivariate linear model to a nonlinear one by integrating some climate parameters, such as the average of daily maximal temperature and latitude. This makes it applicable across a range of background climates. The nonlinear model outperforms linear models in SUHI assessment as it captures the interaction of urban factors and the background climate. [Chapter 4] Our work reiterates the essential roles of urban density and morphology in shaping the urban thermal environment. In contrast to many previous studies that link bigger cities with higher UHI intensity, we show that cities larger in the area do not necessarily experience a stronger UHI effect. In addition, the results extend our knowledge by demonstrating the influence of urban 3D morphology on the UHI effect. This underlines the importance of inspecting cities as a whole from the 3D perspective. While urban 3D morphology is an aggregated feature of small-scale urban elements, the influence it has on the city-scale UHI intensity cannot simply be scaled up from that of its neighbourhood-scale components. The spatial composition and configuration of urban elements both need to be captured when quantifying urban 3D morphology as nearby neighbourhoods also cast influences on each other. Our model serves as a useful UHI assessment tool for the quantitative comparison of urban intervention/development scenarios. It can support harnessing the capacity of UHI mitigation through optimizing urban morphology, with the potential of integrating climate change into heat mitigation strategies.},
  language  = {en}
}
@phdthesis{Zhang2023,
  author    = {Zhang, Xiaolin},
  title     = {Evaluation of nitrogen dynamics in high-order streams and rivers based on high-frequency monitoring},
  doi       = {10.25932/publishup-60764},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-607642},
  school      = {Universit{\"a}t Potsdam},
  pages     = {X, 113},
  year      = {2023},
  abstract  = {Nutrient storage, transform and transport are important processes for achieving environmental and ecological health, as well as conducting water management plans. Nitrogen is one of the most noticeable elements due to its impacts on tremendous consequences of eutrophication in aquatic systems. Among all nitrogen components, researches on nitrate are blooming because of widespread deployments of in-situ high-frequency sensors. Monitoring and studying nitrate can become a paradigm for any other reactive substances that may damage environmental conditions and cause economic losses. Identifying nitrate storage and its transport within a catchment are inspiring to the management of agricultural activities and municipal planning. Storm events are periods when hydrological dynamics activate the exchange between nitrate storage and flow pathways. In this dissertation, long-term high-frequency monitoring data at three gauging stations in the Selke river were used to quantify event-scale nitrate concentration-discharge (C-Q) hysteretic relationships. The Selke catchment is characterized into three nested subcatchments by heterogeneous physiographic conditions and land use. With quantified hysteresis indices, impacts of seasonality and landscape gradients on C-Q relationships are explored. For example, arable area has deep nitrate legacy and can be activated with high intensity precipitation during wetting/wet periods (i.e., the strong hydrological connectivity). Hence, specific shapes of C-Q relationships in river networks can identify targeted locations and periods for agricultural management actions within the catchment to decrease nitrate output into downstream aquatic systems like the ocean. The capacity of streams for removing nitrate is of both scientific and social interest, which makes the quantification motivated. Although measurements of nitrate dynamics are advanced compared to other substances, the methodology to directly quantify nitrate uptake pathways is still limited spatiotemporally. The major problem is the complex convolution of hydrological and biogeochemical processes, which limits in-situ measurements (e.g., isotope addition) usually to small streams with steady flow conditions. This makes the extrapolation of nitrate dynamics to large streams highly uncertain. Hence, understanding of in-stream nitrate dynamic in large rivers is still necessary. High-frequency monitoring of nitrate mass balance between upstream and downstream measurement sites can quantitatively disentangle multi-path nitrate uptake dynamics at the reach scale (3-8 km). In this dissertation, we conducted this approach in large stream reaches with varying hydro-morphological and environmental conditions for several periods, confirming its success in disentangling nitrate uptake pathways and their temporal dynamics. Net nitrate uptake, autotrophic assimilation and heterotrophic uptake were disentangled, as well as their various diel and seasonal patterns. Natural streams generally can remove more nitrate under similar environmental conditions and heterotrophic uptake becomes dominant during post-wet seasons. Such two-station monitoring provided novel insights into reach-scale nitrate uptake processes in large streams. Long-term in-stream nitrate dynamics can also be evaluated with the application of water quality model. This is among the first time to use a data-model fusion approach to upscale the two-station methodology in large-streams with complex flow dynamics under long-term high-frequency monitoring, assessing the in-stream nitrate retention and its responses to drought disturbances from seasonal to sub-daily scale. Nitrate retention (both net uptake and net release) exhibited substantial seasonality, which also differed in the investigated normal and drought years. In the normal years, winter and early spring seasons exhibited extensive net releases, then general net uptake occurred after the annual high-flow season at later spring and early summer with autotrophic processes dominating and during later summer-autumn low-flow periods with heterotrophy-characteristics predominating. Net nitrate release occurred since late autumn until the next early spring. In the drought years, the late-autumn net releases were not so consistently persisted as in the normal years and the predominance of autotrophic processes occurred across seasons. Aforementioned comprehensive results of nitrate dynamics on stream scale facilitate the understanding of instream processes, as well as raise the importance of scientific monitoring schemes for hydrology and water quality parameters.},
  language  = {en}
}
@phdthesis{Riebold2023,
  author    = {Riebold, Johannes},
  title     = {On the linkage between future Arctic sea ice retreat, the large-scale atmospheric circulation and temperature extremes over Europe},
  doi       = {10.25932/publishup-60488},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604883},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 126},
  year      = {2023},
  abstract  = {Extreme weather and climate events are one of the greatest dangers for present-day society. Therefore, it is important to provide reliable statements on what changes in extreme events can be expected along with future global climate change. However, the projected overall response to future climate change is generally a result of a complex interplay between individual physical mechanisms originated within the different climate subsystems. Hence, a profound understanding of these individual contributions is required in order to provide meaningful assessments of future changes in extreme events. One aspect of climate change is the recently observed phenomenon of Arctic Amplification and the related dramatic Arctic sea ice decline, which is expected to continue over the next decades. The question to what extent Arctic sea ice loss is able to affect atmospheric dynamics and extreme events over mid-latitudes has received a lot of attention over recent years and still remains a highly debated topic. In this respect, the objective of this thesis is to contribute to a better understanding on the impact of future Arctic sea ice retreat on European temperature extremes and large-scale atmospheric dynamics. The outcomes are based on model data from the atmospheric general circulation model ECHAM6. Two different sea ice sensitivity simulations from the Polar Amplification Intercomparison Project are employed and contrasted to a present day reference experiment: one experiment with prescribed future sea ice loss over the entire Arctic, as well as another one with sea ice reductions only locally prescribed over the Barents-Kara Sea.\% prescribed over the entire Arctic, as well as only locally over the Barent/Karasea with a present day reference experiment. The first part of the thesis focuses on how future Arctic sea ice reductions affect large-scale atmospheric dynamics over the Northern Hemisphere in terms of occurrence frequency changes of five preferred Euro-Atlantic circulation regimes. When compared to circulation regimes computed from ERA5 it shows that ECHAM6 is able to realistically simulate the regime structures. Both ECHAM6 sea ice sensitivity experiments exhibit similar regime frequency changes. Consistent with tendencies found in ERA5, a more frequent occurrence of a Scandinavian blocking pattern in midwinter is for instance detected under future sea ice conditions in the sensitivity experiments. Changes in occurrence frequencies of circulation regimes in summer season are however barely detected. After identifying suitable regime storylines for the occurrence of European temperature extremes in winter, the previously detected regime frequency changes are used to quantify dynamically and thermodynamically driven contributions to sea ice-induced changes in European winter temperature extremes. It is for instance shown how the preferred occurrence of a Scandinavian blocking regime under low sea ice conditions dynamically contributes to more frequent midwinter cold extreme occurrences over Central Europe. In addition, a reduced occurrence frequency of a Atlantic trough regime is linked to reduced winter warm extremes over Mid-Europe. Furthermore, it is demonstrated how the overall thermodynamical warming effect due to sea ice loss can result in less (more) frequent winter cold (warm) extremes, and consequently counteracts the dynamically induced changes. Compared to winter season, circulation regimes in summer are less suitable as storylines for the occurrence of summer heat extremes. Therefore, an approach based on circulation analogues is employed in order to quantify thermodyamically and dynamically driven contributions to sea ice-induced changes of summer heat extremes over three different European sectors. Reduced occurrences of blockings over Western Russia are detected in the ECHAM6 sea ice sensitivity experiments; however, arguing for dynamically and thermodynamically induced contributions to changes in summer heat extremes remains rather challenging.},
  language  = {en}
}
@phdthesis{Duy2023,
  author    = {Duy, Nguyen Le},
  title     = {Hydrological processes in the Vietnamese Mekong Delta},
  doi       = {10.25932/publishup-60260},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-602607},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxi, 153},
  year      = {2023},
  abstract  = {Understanding hydrological processes is of fundamental importance for the Vietnamese national food security and the livelihood of the population in the Vietnamese Mekong Delta (VMD). As a consequence of sparse data in this region, however, hydrologic processes, such as the controlling processes of precipitation, the interaction between surface and groundwater, and groundwater dynamics, have not been thoroughly studied. The lack of this knowledge may negatively impact the long-term strategic planning for sustainable groundwater resources management and may result in insufficient groundwater recharge and freshwater scarcity. It is essential to develop useful methods for a better understanding of hydrological processes in such data-sparse regions. The goal of this dissertation is to advance methodologies that can improve the understanding of fundamental hydrological processes in the VMD, based on the analyses of stable water isotopes and monitoring data. The thesis mainly focuses on the controlling processes of precipitation, the mechanism of surface-groundwater interaction, and the groundwater dynamics. These processes have not been fully addressed in the VMD so far. The thesis is based on statistical analyses of the isotopic data of Global Network of Isotopes in Precipitation (GNIP), of meteorological and hydrological data from Vietnamese agencies, and of the stable water isotopes and monitoring data collected as part of this work. First, the controlling processes of precipitation were quantified by the combination of trajectory analysis, multi-factor linear regression, and relative importance analysis (hereafter, a model-based statistical approach). The validity of this approach is confirmed by similar, but mainly qualitative results obtained in other studies. The total variation in precipitation isotopes (δ18O and δ2H) can be better explained by multiple linear regression (up to 80\%) than single-factor linear regression (30\%). The relative importance analysis indicates that atmospheric moisture regimes control precipitation isotopes rather than local climatic conditions. The most crucial factor is the upstream rainfall along the trajectories of air mass movement. However, the influences of regional and local climatic factors vary in importance over the seasons. The developed model-based statistical approach is a robust tool for the interpretation of precipitation isotopes and could also be applied to understand the controlling processes of precipitation in other regions. Second, the concept of the two-component lumped-parameter model (LPM) in conjunction with stable water isotopes was applied to examine the surface-groundwater interaction in the VMD. A calibration framework was also set up to evaluate the behaviour, parameter identifiability, and uncertainties of two-component LPMs. The modelling results provided insights on the subsurface flow conditions, the recharge contributions, and the spatial variation of groundwater transit time. The subsurface flow conditions at the study site can be best represented by the linear-piston flow distribution. The contributions of the recharge sources change with distance to the river. The mean transit time (mTT) of riverbank infiltration increases with the length of the horizontal flow path and the decreasing gradient between river and groundwater. River water infiltrates horizontally mainly via the highly permeable aquifer, resulting in short mTTs (<40 weeks) for locations close to the river (<200 m). The vertical infiltration from precipitation takes place primarily via a low-permeable overlying aquitard, resulting in considerably longer mTTs (>80 weeks). Notably, the transit time of precipitation infiltration is independent of the distance to the river. All these results are hydrologically plausible and could be quantified by the presented method for the first time. This study indicates that the highly complex mechanism of surface-groundwater interaction at riverbank infiltration systems can be conceptualized by exploiting two-component LPMs. It is illustrated that the model concept can be used as a tool to investigate the hydrological functioning of mixing processes and the flow path of multiple water components in riverbank infiltration systems. Lastly, a suite of time series analysis approaches was applied to examine the groundwater dynamics in the VMD. The assessment was focused on the time-variant trends of groundwater levels (GWLs), the groundwater memory effect (representing the time that an aquifer holds water), and the hydraulic response between surface water and multi-layer alluvial aquifers. The analysis indicates that the aquifers act as low-pass filters to reduce the high-frequency signals in the GWL variations, and limit the recharge to the deep groundwater. The groundwater abstraction has exceeded groundwater recharge between 1997 and 2017, leading to the decline of groundwater levels (0.01-0.55 m/year) in all considered aquifers in the VMD. The memory effect varies according to the geographical location, being shorter in shallow aquifers and flood-prone areas and longer in deep aquifers and coastal regions. Groundwater depth, season, and location primarily control the variation of the response time between the river and alluvial aquifers. These findings are important contributions to the hydrogeological literature of a little-known groundwater system in an alluvial setting. It is suggested that time series analysis can be used as an efficient tool to understand groundwater systems where resources are insufficient to develop a physical-based groundwater model. This doctoral thesis demonstrates that important aspects of hydrological processes can be understood by statistical analysis of stable water isotope and monitoring data. The approaches developed in this thesis can be easily transferred to regions in similar tropical environments, particularly those in alluvial settings. The results of the thesis can be used as a baseline for future isotope-based studies and contribute to the hydrogeological literature of little-known groundwater systems in the VMD.},
  language  = {en}
}
@phdthesis{Calitri2023,
  author    = {Calitri, Francesca},
  title     = {Co-evolution of erosion rates, weathering and profile development in soil landscapes of hummocky ground moraines},
  doi       = {10.25932/publishup-60138},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-601387},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XXVII, 163, V},
  year      = {2023},
  abstract  = {Soil is today considered a non-renewable resource on societal time scale, as the rate of soil loss is higher than the one of soil formation. Soil formation is complex, can take several thousands of years and is influenced by a variety of factors, one of them is time. Oftentimes, there is the assumption of constant and progressive conditions for soil and/or profile development (i.e., steady-state). In reality, for most of the soils, their (co-)evolution leads to a complex and irregular soil development in time and space characterised by "progressive" and "regressive" phases. Lateral transport of soil material (i.e., soil erosion) is one of the principal processes shaping the land surface and soil profile during "regressive" phases and one of the major environmental problems the world faces. Anthropogenic activities like agriculture can exacerbate soil erosion. Thus, it is of vital importance to distinguish short-term soil redistribution rates (i.e., within decades) influenced by human activities differ from long-term natural rates. To do so, soil erosion (and denudation) rates can be determined by using a set of isotope methods that cover different time scales at landscape level. With the aim to unravel the co-evolution of weathering, soil profile development and lateral redistribution on a landscape level, we used Pluthonium-239+240 (239+240Pu), Beryllium-10 (10Be, in situ and meteoric) and Radiocarbon (14C) to calculate short- and long-term erosion rates in two settings, i.e., a natural and an anthropogenic environment in the hummocky ground moraine landscape of the Uckermark, North-eastern Germany. The main research questions were: 1. How do long-term and short-term rates of soil redistributing processes differ? 2. Are rates calculated from in situ 10Be comparable to those of using meteoric 10Be? 3. How do soil redistribution rates (short- and long-term) in an agricultural and in a natural landscape compare to each other? 4. Are the soil patterns observed in northern Germany purely a result of past events (natural and/or anthropogenic) or are they imbedded in ongoing processes? Erosion and deposition are reflected in a catena of soil profiles with no or almost no erosion on flat positions (hilltop), strong erosion on the mid-slope and accumulation of soil material at the toeslope position. These three characteristic process domains were chosen within the CarboZALF-D experimental site, characterised by intense anthropogenic activities. Likewise, a hydrosequence in an ancient forest was chosen for this study and being regarded as a catena strongly influenced by natural soil transport. The following main results were obtained using the above-mentioned range of isotope methods available to measure soil redistribution rates depending on the time scale needed (e.g., 239+240Pu, 10Be, 14C): 1. Short-term erosion rates are one order of magnitude higher than long-term rates in agricultural settings. 2. Both meteoric and in situ 10Be are suitable soil tracers to measure the long-term soil redistribution rates giving similar results in an anthropogenic environment for different landscape positions (e.g., hilltop, mid-slope, toeslope) 3. Short-term rates were extremely low/negligible in a natural landscape and very high in an agricultural landscape - -0.01 t ha-1 yr-1 (average value) and -25 t ha-1 yr-1 respectively. On the contrary, long-term rates in the forested landscape are comparable to those calculated in the agricultural area investigated with average values of -1.00 t ha-1 yr-1 and -0.79 t ha-1 yr-1. 4. Soil patterns observed in the forest might be due to human impact and activities started after the first settlements in the region, earlier than previously postulated, between 4.5 and 6.8 kyr BP, and not a result of recent soil erosion. 5. Furthermore, long-term soil redistribution rates are similar independently from the settings, meaning past natural soil mass redistribution processes still overshadow the present anthropogenic erosion processes. Overall, this study could make important contributions to the deciphering of the co-evolution of weathering, soil profile development and lateral redistribution in North-eastern Germany. The multi-methodological approach used can be challenged by the application in a wider range of landscapes and geographic regions.},
  language  = {en}
}
@phdthesis{Schmitz2023,
  author    = {Schmitz, Se{\´a}n},
  title     = {Using low-cost sensors to gather high resolution measurements of air quality in urban environments and inform mobility policy},
  doi       = {10.25932/publishup-60105},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-601053},
  school      = {Universit{\"a}t Potsdam},
  pages     = {180},
  year      = {2023},
  abstract  = {Air pollution has been a persistent global problem in the past several hundred years. While some industrialized nations have shown improvements in their air quality through stricter regulation, others have experienced declines as they rapidly industrialize. The WHO's 2021 update of their recommended air pollution limit values reflects the substantial impacts on human health of pollutants such as NO2 and O3, as recent epidemiological evidence suggests substantial long-term health impacts of air pollution even at low concentrations. Alongside developments in our understanding of air pollution's health impacts, the new technology of low-cost sensors (LCS) has been taken up by both academia and industry as a new method for measuring air pollution. Due primarily to their lower cost and smaller size, they can be used in a variety of different applications, including in the development of higher resolution measurement networks, in source identification, and in measurements of air pollution exposure. While significant efforts have been made to accurately calibrate LCS with reference instrumentation and various statistical models, accuracy and precision remain limited by variable sensor sensitivity. Furthermore, standard procedures for calibration still do not exist and most proprietary calibration algorithms are black-box, inaccessible to the public. This work seeks to expand the knowledge base on LCS in several different ways: 1) by developing an open-source calibration methodology; 2) by deploying LCS at high spatial resolution in urban environments to test their capability in measuring microscale changes in urban air pollution; 3) by connecting LCS deployments with the implementation of local mobility policies to provide policy advice on resultant changes in air quality. In a first step, it was found that LCS can be consistently calibrated with good performance against reference instrumentation using seven general steps: 1) assessing raw data distribution, 2) cleaning data, 3) flagging data, 4) model selection and tuning, 5) model validation, 6) exporting final predictions, and 7) calculating associated uncertainty. By emphasizing the need for consistent reporting of details at each step, most crucially on model selection, validation, and performance, this work pushed forward with the effort towards standardization of calibration methodologies. In addition, with the open-source publication of code and data for the seven-step methodology, advances were made towards reforming the largely black-box nature of LCS calibrations. With a transparent and reliable calibration methodology established, LCS were then deployed in various street canyons between 2017 and 2020. Using two types of LCS, metal oxide (MOS) and electrochemical (EC), their performance in capturing expected patterns of urban NO2 and O3 pollution was evaluated. Results showed that calibrated concentrations from MOS and EC sensors matched general diurnal patterns in NO2 and O3 pollution measured using reference instruments. While MOS proved to be unreliable for discerning differences among measured locations within the urban environment, the concentrations measured with calibrated EC sensors matched expectations from modelling studies on NO2 and O3 pollution distribution in street canyons. As such, it was concluded that LCS are appropriate for measuring urban air quality, including for assisting urban-scale air pollution model development, and can reveal new insights into air pollution in urban environments. To achieve the last goal of this work, two measurement campaigns were conducted in connection with the implementation of three mobility policies in Berlin. The first involved the construction of a pop-up bike lane on Kottbusser Damm in response to the COVID-19 pandemic, the second surrounded the temporary implementation of a community space on B{\"o}ckhstrasse, and the last was focused on the closure of a portion of Friedrichstrasse to all motorized traffic. In all cases, measurements of NO2 were collected before and after the measure was implemented to assess changes in air quality resultant from these policies. Results from the Kottbusser Damm experiment showed that the bike-lane reduced NO2 concentrations that cyclists were exposed to by 22 ± 19\%. On Friedrichstrasse, the street closure reduced NO2 concentrations to the level of the urban background without worsening the air quality on side streets. These valuable results were communicated swiftly to partners in the city administration responsible for evaluating the policies' success and future, highlighting the ability of LCS to provide policy-relevant results. As a new technology, much is still to be learned about LCS and their value to academic research in the atmospheric sciences. Nevertheless, this work has advanced the state of the art in several ways. First, it contributed a novel open-source calibration methodology that can be used by a LCS end-users for various air pollutants. Second, it strengthened the evidence base on the reliability of LCS for measuring urban air quality, finding through novel deployments in street canyons that LCS can be used at high spatial resolution to understand microscale air pollution dynamics. Last, it is the first of its kind to connect LCS measurements directly with mobility policies to understand their influences on local air quality, resulting in policy-relevant findings valuable for decisionmakers. It serves as an example of the potential for LCS to expand our understanding of air pollution at various scales, as well as their ability to serve as valuable tools in transdisciplinary research.},
  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}
}