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White mica and tourmaline are the dominant hydrothermal alteration minerals at the world-class Panasqueira W-Sn-Cu deposit in Portugal. Thus, understanding the controls on their chemical composition helps to constrain ore formation processes at this deposit and determine their usefulness as pathfinder minerals for mineralization in general. We combine whole-rock geochemistry of altered and unaltered metasedimentary host rocks with in situ LA-ICP-MS measurements of tourmaline and white mica from the alteration halo. Principal component analysis (PCA) is used to better identify geochemical patterns and trends of hydrothermal alteration in the datasets. The hydrothermally altered metasediments are enriched in As, Sn, Cs, Li, W, F, Cu, Rb, Zn, Tl, and Pb relative to unaltered samples. In situ mineral analyses show that most of these elements preferentially partition into white mica over tourmaline (Li, Rb, Cs, Tl, W, and Sn), whereas Zn is enriched in tourmaline. White mica has distinct compositions in different settings within the deposit (greisen, vein selvages, wall rock alteration zone, late fault zone), indicating a compositional evolution with time. In contrast, tourmaline from different settings overlaps in composition, which is ascribed to a stronger dependence on host rock composition and also to the effects of chemical zoning and microinclusions affecting the LA-ICP-MS analyses. Hence, in this deposit, white mica is the better recorder of the fluid composition. The calculated trace-element contents of the Panasqueira mineralizing fluid based on the mica data and estimates of mica-fluid partition coefficients are in good agreement with previous fluid-inclusion analyses. A compilation of mica and tourmaline trace-element compositions from Panasqueira and other W-Sn deposits shows that white mica has good potential as a pathfinder mineral, with characteristically high Li, Cs, Rb, Sn, and W contents. The trace-element contents of hydrothermal tourmaline are more variable. Nevertheless, the compiled data suggest that high Sn and Li contents are distinctive for tourmaline from W-Sn deposits.
Human-induced climate change is impacting the global water cycle by, e.g., causing changes in precipitation patterns, evapotranspiration dynamics, cryosphere shrinkage, and complex streamflow trends. These changes, coupled with the increased frequency and severity of extreme hydrometeorological events like floods, droughts, and heatwaves, contribute to hydroclimatic disasters, posing significant implications for local and global infrastructure, human health, and overall productivity.
In the tropical Andes, climate change is evident through warming trends, glacier retreats, and shifts in precipitation patterns, leading to altered risks of floods and droughts, e.g., in the upper Amazon River basin. Projections for the region indicate rising temperatures, potential glacier disappearance or substantial shrinkage, and altered streamflow patterns, highlighting challenges in water availability due to these expected changes and growing human water demand. The evolving trends in hydroclimatic conditions in the tropical Andes present significant challenges to socioeconomic and environmental systems, emphasizing the need for a comprehensive understanding to guide effective adaptation policies and strategies in response to the impacts of climate change in the region.
The main objective of this thesis is to investigate current hydrological dynamics in the tropical Andes of Peru and Ecuador and their responses to climate change. Given the scarcity of hydrometeorological data in the region, this objective was accomplished through a comprehensive data preparation and analysis in combination with hydrological modeling using the Soil and Water Assessment Tool (SWAT) eco-hydrological model. In this context, the initial steps involved assessing, identifying, and/or generating more reliable climate input data to address data limitations.
The thesis introduces RAIN4PE, a high-resolution precipitation dataset for Peru and Ecuador, developed by merging satellite, reanalysis, and ground-based data with surface elevation through the random forest method. Further adjustments of precipitation estimates were made for catchments influenced by fog/cloud water input on the eastern side of the Andes using streamflow data and applying the method of reverse hydrology. RAIN4PE surpasses other global and local precipitation datasets, showcasing superior reliability and accuracy in representing precipitation patterns and simulating hydrological processes across the tropical Andes. This establishes it as the optimal precipitation product for hydrometeorological applications in the region.
Due to the significant biases and limitations of global climate models (GCMs) in representing key atmospheric variables over the tropical Andes, this study developed regionally adapted GCM simulations specifically tailored for Peru and Ecuador. These simulations are known as the BASD-CMIP6-PE dataset, and they were derived using reliable, high-resolution datasets like RAIN4PE as reference data. The BASD-CMIP6-PE dataset shows notable improvements over raw GCM simulations, reflecting enhanced representations of observed climate properties and accurate simulation of streamflow, including high and low flow indices. This renders it suitable for assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.
In addition to generating more accurate climatic input data, a reliable hydrological model is essential for simulating watershed hydrological processes. To tackle this challenge, the thesis presents an innovative multiobjective calibration framework integrating remote sensing vegetation data, baseflow index, discharge goodness-of-fit metrics, and flow duration curve signatures. In contrast to traditional calibration strategies relying solely on discharge goodness-of-fit metrics, this approach enhances the simulation of vegetation, streamflow, and the partitioning of flow into surface runoff and baseflow in a typical Andean catchment. The refined hydrological model calibration strategy was applied to conduct reliable simulations and understand current and future hydrological trajectories in the tropical Andes.
By establishing a region-suitable and thoroughly tested hydrological model with high-resolution and reliable precipitation input data from RAIN4PE, this study provides new insights into the spatiotemporal distribution of water balance components in Peru and transboundary catchments. Key findings underscore the estimation of Peru's total renewable freshwater resource (total river runoff of 62,399 m3/s), with the Peruvian Amazon basin contributing 97.7%. Within this basin, the Amazon-Andes transition region emerges as a pivotal hotspot for water yield (precipitation minus evapotranspiration), characterized by abundant rainfall and lower atmospheric water demand/evapotranspiration. This finding underlines its paramount role in influencing the hydrological variability of the entire Amazon basin.
Subsurface hydrological pathways, particularly baseflow from aquifers, strongly influence water yield in lowland and Andean catchments, sustaining streamflow, especially during the extended dry season. Water yield demonstrates an elevation- and latitude-dependent increase in the Pacific Basin (catchments draining into the Pacific Ocean), while it follows an unimodal curve in the Peruvian Amazon Basin, peaking in the Amazon-Andes transition region. This observation indicates an intricate relationship between water yield and elevation.
In Amazon lowlands rivers, particularly in the Ucayali River, floodplains play a significant role in shaping streamflow seasonality by attenuating and delaying peak flows for up to two months during periods of high discharge. This observation underscores the critical importance of incorporating floodplain dynamics into hydrological simulations and river management strategies for accurate modeling and effective water resource management.
Hydrological responses vary across different land use types in high Andean catchments. Pasture areas exhibit the highest water yield, while agricultural areas and mountain forests show lower yields, emphasizing the importance of puna (high-altitude) ecosystems, such as pastures, páramos, and bofedales, in regulating natural storage.
Projected future hydrological trajectories were analyzed by driving the hydrological model with regionalized GCM simulations provided by the BASD-CMIP6-PE dataset. The analysis considered sustainable (low warming, SSP1-2.6) and fossil fuel-based development (high-end warming, SSP5-8.5) scenarios for the mid (2035-2065) and end (2065-2095) of the century. The projected changes in water yield and streamflow across the tropical Andes exhibit distinct regional and seasonal variations, particularly amplified under a high-end warming scenario towards the end of the century. Projections suggest year-round increases in water yield and streamflow in the Andean regions and decreases in the Amazon lowlands, with exceptions such as the northern Amazon expecting increases during wet seasons. Despite these regional differences, the upper Amazon River's streamflow is projected to remain relatively stable throughout the 21st century. Additionally, projections anticipate a decrease in low flows in the Amazon lowlands and an increased risk of high flows (floods) in the Andean and northern Amazon catchments.
This thesis significantly contributes to enhancing climatic data generation, overcoming regional limitations that previously impeded hydrometeorological research, and creating new opportunities. It plays a crucial role in advancing hydrological model calibration, improving the representation of internal hydrological processes, and achieving accurate results for the right reasons. Novel insights into current hydrological dynamics in the tropical Andes are fundamental for improving water resource management. The anticipated intensified changes in water flows and hydrological extreme patterns under a high-end warming scenario highlight the urgency of implementing emissions mitigation and adaptation measures to address the heightened impacts on water resources.
In fact, the new datasets (RAIN4PE and BASD-CMIP6-PE) have already been utilized by researchers and experts in regional and local-scale projects and catchments in Peru and Ecuador. For instance, they have been applied in river catchments such as Mantaro, Piura, and San Pedro to analyze local historical and future developments in climate and water resources.
Floods continue to be the leading cause of economic damages and fatalities among natural disasters worldwide. As future climate and exposure changes are projected to intensify these damages, the need for more accurate and scalable flood risk models is rising. Over the past decade, macro-scale flood risk models have evolved from initial proof-of-concepts to indispensable tools for decision-making at global-, nationaland, increasingly, the local-level. This progress has been propelled by the advent of high-performance computing and the availability of global, space-based datasets. However, despite such advancements, these models are rarely validated and consistently fall short of the accuracy achieved by high-resolution local models. While capabilities have improved, significant gaps persist in understanding the behaviours of such macro-scale models, particularly their tendency to overestimate risk. This dissertation aims to address such gaps by examining the scale transfers inherent in the construction and application of coarse macroscale models. To achieve this, four studies are presented that, collectively, address exposure, hazard, and vulnerability components of risk affected by upscaling or downscaling.
The first study focuses on a type of downscaling where coarse flood hazard inundation grids are enhanced to a finer resolution. While such inundation downscaling has been employed in numerous global model chains, ours is the first study to focus specifically on this component, providing an evaluation of the state of the art and a novel algorithm. Findings demonstrate that our novel algorithm is eight times faster than existing methods, offers a slight improvement in accuracy, and generates more physically coherent flood maps in hydraulically challenging regions. When applied to a case study, the algorithm generated a 4m resolution inundation map from 30m hydrodynamic model outputs in 33 s, a 60-fold improvement in runtime with a 25% increase in RMSE compared with direct hydrodynamic modelling. All evaluated downscaling algorithms yielded better accuracy than the coarse hydrodynamic model when compared to observations, demonstrating similar limits of coarse hydrodynamic models reported by others. The substitution of downscaling into flood risk model chains, in place of high-resolution modelling, can drastically improve the lead time of impactbased forecasts and the efficiency of hazard map production. With downscaling, local regions could obtain high resolution local inundation maps by post-processing a global model without the need for expensive modelling or expertise.
The second study focuses on hazard aggregation and its implications for exposure, investigating implicit aggregations commonly used to intersect hazard grids with coarse exposure models. This research introduces a novel spatial classification framework to understand the effects of rescaling flood hazard grids to a coarser resolution. The study derives closed-form analytical solutions for the location and direction of bias from flood grid aggregation, showing that bias will always be present in regions near the edge of inundation. For example, inundation area will be positively biased when water depth grids are aggregated, while volume will be negatively biased when water elevation grids are aggregated. Extending the analysis to effects of hazard aggregation on building exposure, this study shows that exposure in regions at the edge of inundation are an order of magnitude more sensitive to aggregation errors than hazard alone. Among the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. The study provides the first mathematical proof and generalizeable treatment of flood hazard grid aggregation, demonstrating important mechanisms to help flood risk modellers understand and control model behaviour.
The final two studies focus on the aggregation of vulnerability models or flood damage functions, investigating the practice of applying per-asset functions to aggregate exposure models. Both studies extend Jensen’s inequality, a well-known 1906 mathematical proof, to demonstrate how the aggregation of flood damage functions leads to bias. Applying Jensen’s proof in this new context, results show that typically concave flood damage functions will introduce a positive bias (overestimation) when aggregated. This behaviour was further investigated with a simulation experiment including 2 million buildings in Germany, four global flood hazard simulations and three aggregation scenarios. The results show that positive aggregation bias is not distributed evenly in space, meaning some regions identified as “hot spots of risk” in assessments may in fact just be hot spots of aggregation bias. This study provides the first application of Jensen’s inequality to explain the overestimates reported elsewhere and advice for modellers to minimize such artifacts.
In total, this dissertation investigates the complex ways aggregation and disaggregation influence the behaviour of risk models, focusing on the scale-transfers underpinning macro-scale flood risk assessments. Extending a key finding of the flood hazard literature to the broader context of flood risk, this dissertation concludes that all else equal, coarse models overestimate risk. This dissertation goes beyond previous studies by providing mathematical proofs for how and where such bias emerges in aggregation routines, offering a mechanistic explanation for coarse model overestimates. It shows that this bias is spatially heterogeneous, necessitating a deep understanding of how rescaling may bias models to effectively reduce or communicate uncertainties. Further, the dissertation offers specific recommendations to help modellers minimize scale transfers in problematic regions. In conclusion, I argue that such aggregation errors are epistemic, stemming from choices in model structure, and therefore hold greater potential and impetus for study and mitigation. This deeper understanding of uncertainties is essential for improving macro-scale flood risk models and their effectiveness in equitable, holistic, and sustainable flood management.
In dieser Arbeit wurde eine reaktive Wand in einem kleinskaligen Laborma\ss stab (Länge~=~40\,cm) entwickelt, die Eisen- und Sulfatbelastungen aus sauren Minenabwässern (engl. \textit{acid mine drainage} (AMD)) mit einer Effizienz von bis zu 30.2 bzw. 24.2\,\% über einen Zeitraum von 146~Tagen (50\,pv) abreinigen können sollte. Als reaktives Material wurde eine Mischung aus Gartenkompost, Buchenholz, Kokosnussschale und Calciumcarbonat verwendet. Die Zugabebedingungen waren eine Eisenkonzentration von 1000\,mg/L, eine Sulfatkonzentration von 3000\,mg/L und ein pH-Wert von 6.2.
Unterschiede in der Materialzusammensetzung ergaben keine grö\ss eren Änderungen in der Sanierungseffizienz von Eisen- und Sulfatbelastungen (12.0 -- 15.4\,\% bzw. 7.0 -- 10.1\,\%) über einen Untersuchungszeitraum von 108~Tagen (41 -- 57\,pv). Der wichtigste Einflussfaktor auf die Abreinigungsleistung von Sulfat- und Eisenbelastungen war die Verweilzeit der AMD-Lösung im reaktiven Material. Diese kann durch eine Verringerung des Durchflusses oder eine Erhöhung der Länge der reaktiven Wand (engl. \textit{Permeable Reactive Barrier} (PRB)) erhöht werden. Ein halbierter Durchfluss erhöhte die Sanierungseffizienzen von Eisen und Sulfat auf 23.4 bzw. 32.7\,\%. Weiterhin stieg die Sanierungseffizienz der Eisenbelastungen auf 24.2\,\% bei einer Erhöhung der Sulfatzugabekonzentration auf 6000\,mg/L. Saure Startbedingungen (pH~=~2.2) konnten, durch das Calciumcarbonat im reaktiven Material, über einen Zeitraum von 47~Tagen (24\,pv) neutralisiert werden. Durch die Neutralisierung der sauren Startbedingungen wurde Calciumcarbonat in der \gls{prb} verbraucht und Calcium-Ionen freigesetzt, die die Sulfatsanierungseffizienz erhöht haben (24.9\,\%). Aufgrund einer Vergrö\ss erung der \gls{prb} in Breite und Tiefe und einer 2D-Parameterbestimmung konnten Randläufigkeiten beobachtet werden, ohne deren Einfluss sich die Sanierungseffizienz für Eisen- und Sulfatbelastungen erhöht (30.2 bzw. 24.2\,\%). \par
Zur \textit{in-situ} Überwachung der \gls{prb} wurden optische Sensoren verwendet, um den pH-Wert, die Sauerstoffkonzentration und die Temperatur zu ermitteln. Es wurden, nach dem Ort und der Zeit aufgelöst, stabile Sauerstoffkonzentrationen und pH-Verläufe detektiert. Auch die Temperatur konnte nach dem Ort aufgelöst ermittelt werden. Damit zeigte diese Arbeit, dass optische Sensoren zur Überwachung der Stabilität einer \gls{prb} für die Reinigung von \gls{amd} verwendet werden können. \par
Mit dem Simulationsprogramm MIN3P wurde eine Simulation erstellt, die die entwickelte PRB darstellt. Die Simulation kann die erhaltenen Laborergebnisse gut wiedergeben. Anschlie\ss end wurde eine simulierte \gls{prb} bei unterschiedlichen Filtergeschwindigkeiten ((4.0 -- 23.5)~$\cdot~\mathrm{10^{-7}}$\,m/s) und Längen der PRB (25 -- 400\,cm) untersucht. Es wurden Zusammenhänge der untersuchten Parameter mit der Sanierungseffizienz von Eisen- und Sulfatbelastungen ermittelt. Diese Zusammenhänge können verwendet werden, um die benötigte Verweilzeit der AMD-Lösung in einem zukünftigen PRB-System, die für die maximal mögliche Sanierungsleistung notwendig ist, zu berechnen.
Water stored in the unsaturated soil as soil moisture is a key component of the hydrological cycle influencing numerous hydrological processes including hydrometeorological extremes. Soil moisture influences flood generation processes and during droughts when precipitation is absent, it provides plant with transpirable water, thereby sustaining plant growth and survival in agriculture and natural ecosystems.
Soil moisture stored in deeper soil layers e.g. below 100 cm is of particular importance for providing plant transpirable water during dry periods. Not being directly connected to the atmosphere and located outside soil layers with the highest root densities, water in these layers is less susceptible to be rapidly evaporated and transpired. Instead, it provides longer-term soil water storage increasing the drought tolerance of plants and ecosystems.
Given the importance of soil moisture in the context of hydro-meteorological extremes in a warming climate, its monitoring is part of official national adaption strategies to a changing climate. Yet, soil moisture is highly variable in time and space which challenges its monitoring on spatio-temporal scales relevant for flood and drought risk modelling and forecasting.
Introduced over a decade ago, Cosmic-Ray Neutron Sensing (CRNS) is a noninvasive geophysical method that allows for the estimation of soil moisture at relevant spatio-temporal scales of several hectares at a high, subdaily temporal resolution. CRNS relies on the detection of secondary neutrons above the soil surface which are produced from high-energy cosmic-ray particles in the atmosphere and the ground. Neutrons in a specific epithermal energy range are sensitive to the amount of hydrogen present in the surroundings of the CRNS neutron detector. Due to same mass as the hydrogen nucleus, neutrons lose kinetic energy upon collision and are subsequently absorbed when reaching low, thermal energies. A higher amount of hydrogen therefore leads to fewer neutrons being detected per unit time. Assuming that the largest amount of hydrogen is stored in most terrestrial ecosystems as soil moisture, changes of soil moisture can be estimated through an inverse relationship with observed neutron intensities.
Although important scientific advancements have been made to improve the methodological framework of CRNS, several open challenges remain, of which some are addressed in the scope of this thesis. These include the influence of atmospheric variables such as air pressure and absolute air humidity, as well as, the impact of variations in incoming primary cosmic-ray intensity on observed epithermal and thermal neutron signals and their correction. Recently introduced advanced neutron-to-soil moisture transfer functions are expected to improve CRNS-derived soil moisture estimates, but potential improvements need to be investigated at study sites with differing environmental conditions. Sites with strongly heterogeneous, patchy soil moisture distributions challenge existing transfer functions and further research is required to assess the impact of, and correction of derived soil moisture estimates under heterogeneous site conditions. Despite its capability of measuring representative averages of soil moisture at the field scale, CRNS lacks an integration depth below the first few decimetres of the soil. Given the importance of soil moisture also in deeper soil layers, increasing the observational window of CRNS through modelling approaches or in situ measurements is of high importance for hydrological monitoring applications.
By addressing these challenges, this thesis aids to closing knowledge gaps and finding answers to some of the open questions in CRNS research. Influences of different environmental variables are quantified, correction approaches are being tested and developed. Neutron-to-soil moisture transfer functions are evaluated and approaches to reduce effects of heterogeneous soil moisture distributions are presented. Lastly, soil moisture estimates from larger soil depths are derived from CRNS through modified, simple modelling approaches and in situ estimates by using CRNS as a downhole technique. Thereby, this thesis does not only illustrate the potential of new, yet undiscovered applications of CRNS in future but also opens a new field of CRNS research. Consequently, this thesis advances the methodological framework of CRNS for above-ground and downhole applications. Although the necessity of further research in order to fully exploit the potential of CRNS needs to be emphasised, this thesis contributes to current hydrological research and not least to advancing hydrological monitoring approaches being of utmost importance in context of intensifying hydro-meteorological extremes in a changing climate.
Bayesian geomorphology
(2020)
The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.
Leitfaden für die Erstellung von kommunalen Aktionsplänen zur Steigerung der urbanen Klimaresilienz
(2024)
Die durch Klimaveränderungen hervorgerufenen Auswirkungen auf Menschen und Umwelt werden immer offensichtlicher: Neben der gesundheitlichen Gefährdung durch Hitzewellen, die deutschlandweit seit einigen Jahren eine steigende Rate an Todes- und Krankheitsfällen zur Folge hat sind in den letzten Jahren zunehmend Starkniederschläge und daraus resultierenden Überschwemmungen bzw. Sturzfluten aufgetreten. Diese ziehen zum Teil immensen wirtschaftlichen Schäden, aber auch Beeinträchtigungen für die menschliche Gesundheit – sowohl physisch als auch psychisch – sowie gar Todesopfer nach sich. Es ist davon auszugehen, dass diese Extremwetterereignisse zukünftiger noch häufiger auftreten werden.
Um die Bevölkerung besser vor den Folgen dieser Wetterextreme zu schützen, sind neben Klimaschutzmaßnahmen auch Vorsorge- und Anpassungsmaßnahmen zur Steigerung der kommunalen Klimaresilienz dringend notwendig. Dazu bedarf es einerseits einer Auseinandersetzung mit den eigenen kommunalen Risiken und daraus resultierenden Handlungsbedarfen, und andererseits eines interdisziplinären, querschnittsorientierten und prozessorientierten Planens und Handelns. Aktionspläne sollen diese beiden Aspekte bündeln.
In den letzten Jahren sind einige kommunale und kommunenübergreifende (Hitze-) aufgestellt worden. Diese unterscheiden sich jedoch in ihrem Inhalt und Umfang zum Teil erheblich. Mit dem vorliegenden Leitfaden soll eine effektive Hilfestellung geschaffen werden, um Kommunen bzw. die kommunale Verwaltung auf dem Weg zum eigenen Aktionsplan zu unterstützt. Dabei fokussiert der Leitfaden auf die Herausforderungen, die sich durch vermehrte Hitze- und Starkregenereignisse ergeben. Er stützt sich auf schon vorhandene Arbeitshilfen, Handlungsempfehlungen, Leitfäden und weitere Hinweise und verweist an vielen Stellen auch darauf. So soll ein praxistauglicher Leitfaden entstehen, der flexibel anwendbar ist. Mit Hilfe des vorliegenden Leitfadens können Kommunen ihre Aktivitäten auf Hitze oder Starkregen fokussieren oder einen umfassenden Aktionsplan für beide Themenbereiche erstellen.
Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve - FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.
Land degradation and water availability in semi-arid regions are interdependent challenges for management that are influenced by climatic and anthropogenic changes. Erosion and high sediment loads in rivers cause reservoir siltation and decrease storage capacity, which pose risk on water security for citizens, agriculture, and industry. In regions where resources for management are limited, identifying spatial-temporal variability of sediment sources is crucial to decrease siltation. Despite widespread availability of rigorous methods, approaches simplifying spatial and temporal variability of erosion are often inappropriately applied to very data sparse semi-arid regions. In this work, we review existing approaches for mapping erosional hotspots, and provide an example of spatial-temporal mapping approach in two case study regions. The barriers limiting data availability and their effects on erosion mapping methods, their validation, and resulting prioritization of leverage management areas are discussed.
Understanding the hydrologic connectivity between kettle holes and shallow groundwater, particularly in reaction to the highly variable local meteorological conditions, is of paramount importance for tracing water in a hydro(geo)logically complex landscape and thus for integrated water resource management. This article is aimed at identifying the dominant hydrological processes affecting the kettle holes' water balance and their interactions with the shallow groundwater domain in the Uckermark region, located in the north-east of Germany. For this reason, based on the stable isotopes of oxygen (delta O-18) and hydrogen (delta H-2), an isotopic mass balance model was employed to compute the evaporative loss of water from the kettle holes from February to August 2017. Results demonstrated that shallow groundwater inflow may play the pivotal role in the processes taking part in the hydrology of the kettle holes in the Uckermark region. Based on the calculated evaporation/inflow (E/I) ratios, most of the kettle holes (86.7%) were ascertained to have a partially open, flow-through-dominated system. Moreover, we identified an inverse correlation between E/I ratios and the altitudes of the kettle holes. The same holds for electrical conductivity (EC) and the altitudes of the kettle holes. In accordance with the findings obtained from this study, a conceptual model explaining the interaction between the shallow groundwater and the kettle holes of Uckermark was developed. The model exhibited that across the highest altitudes, the recharge kettle holes are dominant, where a lower ratio of E/I and a lower EC was detected. By contrast, the lowest topographical depressions represent the discharge kettle holes, where a higher ratio of E/I and EC could be identified. The kettle holes existing in between were categorized as flow-through kettle holes through which the recharge takes place from one side and discharge from the other side.