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In the following article, the focus is on the transformative potentials created by so-called persistence avant-gardes and prevention innovators. The text extends Bluhdorn's guiding concept of narratives of hope (Bluhdorn 2017; Bluhdorn and Butzlaff 2019) by considering those groups that are marginalized within debates on socio-ecological transformation. With a closer look at the narratives of prevention and blockade that these actors engage, the ambiguous nature of postgrowth avant-gardes is carved out. Their discursive, argumentative, and effective inhibition of transitory policies is interpreted as a pro-active potential, rather than a mere obstacle to socio-ecological transformation. Adding a geographical perspective, the paper pleads for a more precise theoretical penetration of the ambivalent figure of avantgardes when analyzing processes of local and regional postgrowth.
Many researchers and politicians believe that the COVID-19 crisis may have opened a "window of opportunity " to spur sustainability transformations. Still, evidence for such a dynamic is currently lacking. Here, we propose the linkage of "big data " and "thick data " methods for monitoring debates on transformation processes by following the COVID-19 discourse on ecological sustainability in Germany. We analysed variations in the topics discussed by applying text mining techniques to a corpus with 84,500 newspaper articles published during the first COVID-19 wave. This allowed us to attain a unique and previously inaccessible "bird's eye view " of how these topics evolved. To deepen our understanding of prominent frames, a qualitative content analysis was undertaken. Furthermore, we investigated public awareness by analysing online search behaviour. The findings show an underrepresentation of sustainability topics in the German news during the early stages of the crisis. Similarly, public awareness regarding climate change was found to be reduced. Nevertheless, by examining the newspaper data in detail, we found that the pandemic is often seen as a chance for sustainability transformations-but not without a set of challenges. Our mixed-methods approach enabled us to bridge knowledge gaps between qualitative and quantitative research by "thickening " and providing context to data-driven analyses. By monitoring whether or not the current crisis is seen as a chance for sustainability transformations, we provide insights for environmental policy in times of crisis.
Trends in streamflow, rainfall and potential evapotranspiration (PET) time series, from 1970 to 2017, were assessed for five important hydrological basins in Southeastern Brazil. The concept of elasticity was also used to assess the streamflow sensitivity to changes in climate variables, for annual data and 5-, 10- and 20-year moving averages. Significant negative trends in streamflow and rainfall and significant increasing trend in PET were detected. For annual analysis, elasticity revealed that 1% decrease in rainfall resulted in 1.21-2.19% decrease in streamflow, while 1% increase in PET induced different reductions percentages in streamflow, ranging from 2.45% to 9.67%. When both PET and rainfall were computed to calculate the elasticity, results were positive for some basins. Elasticity analysis considering 20-year moving averages revealed that impacts on the streamflow were cumulative: 1% decrease in rainfall resulted in 1.83-4.75% decrease in streamflow, while 1% increase in PET induced 3.47-28.3% decrease in streamflow. This different temporal response may be associated with the hydrological memory of the basins. Streamflow appears to be more sensitive in less rainy basins. This study provides useful information to support strategic government decisions, especially when the security of water resources and drought mitigation are considered in face of climate change.
Distributed environmental models such as land surface models (LSMs) require model parameters in each spatial modeling unit (e.g., grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimensionality of the parameter space in these models is to use regularization techniques. One such highly efficient technique is the multiscale parameter regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of NetCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model (mHM; https://www.ufz.de/mhm, last access: 16 January 2022). By using this tool for the generation of continental-scale soil hydraulic parameters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.
From gustiness to dustiness
(2022)
This study delivers the first empirical data-driven analysis of the impact of turbulence induced gustiness on the fine dust emissions from a measuring field. For quantification of the gust impact, a new measure, the Gust uptake Efficiency (GuE) is introduced. GuE provides a percentage of over- or under-proportional dust uptake due to gust activity during a wind event. For the three analyzed wind events, GuE values of up to 150% could be found, yet they significantly differed per particle size class with a tendency for lower values for smaller particles. In addition, a high-resolution correlation analysis among 31 particle size classes and wind speed was conducted; it revealed strong negative correlation coefficients for very small particles and positive correlations for bigger particles, where 5 mu m appears to be an empirical threshold dividing both directions. We conclude with a number of suggestions for further investigations: an optimized field experiment setup, a new particle size ratio (PM1/PM0.5 in addition to PM10/PM2.5), as well as a comprehensive data-driven search for an optimal wind gust definition in terms of soil erosivity.
The biodiversity of tundra areas in northern high latitudes is threatened by invasion of forests under global warming. However, poorly understood nonlinear responses of the treeline ecotone mean the timing and extent of tundra losses are unclear, but policymakers need such information to optimize conservation efforts. Our individual-based model LAVESI, developed for the Siberian tundra-taiga ecotone, can help improve our understanding. Consequently, we simulated treeline migration trajectories until the end of the millennium, causing a loss of tundra area when advancing north. Our simulations reveal that the treeline follows climate warming with a severe, century-long time lag, which is overcompensated by infilling of stands in the long run even when temperatures cool again. Our simulations reveal that only under ambitious mitigation strategies (relative concentration pathway 2.6) will ~30% of original tundra areas remain in the north but separated into two disjunct refugia.
A reliable estimation of flood impacts enables meaningful flood risk management and rapid assessments of flood impacts shortly after a flood. The flood in 2021 in Central Europe and the analysis of its impacts revealed that these estimations are still inadequate. Therefore, we investigate the influence of different data sets and methods aiming to improve flood impact estimates. We estimated economic flood impacts to private households and companies for a flood event in 2013 in Germany using (a) two different flood maps, (b) two approaches to map exposed objects based on OpenStreetMap and the Basic European Asset Map, (c) two different approaches to estimate asset values, and (d) tree-based models and Stage-Damage-Functions to describe the vulnerability. At the macro scale, water masks lead to reasonable impact estimations. At the micro and meso-scale, the identification of affected objects by means of water masks is insufficient leading to unreliable estimations. The choice of exposure data sets is most influential on the estimations. We find that reliable impact estimations are feasible with reported numbers of flood-affected objects from the municipalities. We conclude that more effort should be put in the investigation of different exposure data sets and the estimation of asset values. Furthermore, we recommend the establishment of a reporting system in the municipalities for a fast identification of flood-affected objects shortly after an event.
Agriculture in India accounts for 18% of greenhouse gas (GHG) emissions and uses significant land and water. Various socioeconomic factors and food subsidies influence diets in India. Indian food systems face the challenge of sustainably nourishing the 1.3 billion population. However, existing studies focus on a few food system components, and holistic analysis is still missing. We identify Indian food systems covering six food system components: food consumption, production, processing, policy, environmental footprints, and socioeconomic factors from the latest Indian household consumer expenditure survey. We identify 10 Indian food systems using k-means cluster analysis on 15 food system indicators belonging to the six components. Based on the major source of calorie intake, we classify the ten food systems into production-based (3), subsidy-based (3), and market-based (4) food systems. Home-produced and subsidized food contribute up to 2000 kcal/consumer unit (CU)/day and 1651 kcal/CU/day, respectively, in these food systems. The calorie intake of 2158 to 3530 kcal/CU/day in the food systems reveals issues of malnutrition in India. Environmental footprints are commensurate with calorie intake in the food systems. Embodied GHG, land footprint, and water footprint estimates range from 1.30 to 2.19 kg CO(2)eq/CU/day, 3.89 to 6.04 m(2)/CU/day, and 2.02 to 3.16 m(3)/CU/day, respectively. Our study provides a holistic understanding of Indian food systems for targeted nutritional interventions on household malnutrition in India while also protecting planetary health.
Aerosol emissions from human activities are extensive and changing rapidly over Asia. Model simulations and satellite observations indicate a dipole pattern in aerosol emissions and loading between South Asia and East Asia, two of the most heavily polluted regions of the world. We examine the previously unexplored diverging trends in the existing dipole pattern of aerosols between East and South Asia using the high quality, two-decade long ground-based time series of observations of aerosol properties from the Aerosol Robotic Network (AERONET), from satellites (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)), and from model simulations (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The data cover the period since 2001 for Kanpur (South Asia) and Beijing (East Asia), two locations taken as being broadly representative of the respective regions. Since 2010 a dipole in aerosol optical depth (AOD) is maintained, but the trend is reversed-the decrease in AOD over Beijing (East Asia) is rapid since 2010, being 17% less in current decade compared to first decade of twenty-first century, while the AOD over South Asia increased by 12% during the same period. Furthermore, we find that the aerosol composition is also changing over time. The single scattering albedo (SSA), a measure of aerosol's absorption capacity and related to aerosol composition, is slightly higher over Beijing than Kanpur, and has increased from 0.91 in 2002 to 0.93 in 2017 over Beijing and from 0.89 to 0.92 during the same period over Kanpur, confirming that aerosols in this region have on an average become more scattering in nature. These changes have led to a notable decrease in aerosol-induced atmospheric heating rate (HR) over both regions between the two decades, decreasing considerably more over East Asia (- 31%) than over South Asia (- 9%). The annual mean HR is lower now, it is still large (>= 0.6 K per day), which has significant climate implications. The seasonal trends in AOD, SSA and HR are more pronounced than their respective annual trends over both regions. The seasonal trends are caused mainly by the increase/decrease in anthropogenic aerosol emissions (sulfate, black carbon and organic carbon) while the natural aerosols (dust and sea salt) did not change significantly over South and East Asia during the last two decades. The MERRA-2 model is able to simulate the observed trends in AODs well but not the magnitude, while it also did not simulate the SSA values or trends well. These robust findings based on observations of key aerosol parameters and previously unrecognized diverging trends over South and East Asia need to be accounted for in current state-of-the-art climate models to ensure accurate quantification of the complex and evolving impact of aerosols on the regional climate over Asia.
Climate change threatens to undermine efforts to eradicate extreme poverty. However, climate policies could impose a financial burden on the global poor through increased energy and food prices. Here, we project poverty rates until 2050 and assess how they are influenced by mitigation policies consistent with the 1.5 degrees C target. A continuation of historical trends will leave 350 million people globally in extreme poverty by 2030. Without progressive redistribution, climate policies would push an additional 50 million people into poverty. However, redistributing the national carbon pricing revenues domestically as an equal-per-capita climate dividend compensates this policy side effect, even leading to a small net reduction of the global poverty headcount (-6 million). An additional international climate finance scheme enables a substantial poverty reduction globally and also in Sub-Saharan Africa. Combining national redistribution with international climate finance thus provides an important entry point to climate policy in developing countries. Ambitious climate policies can negatively impact the global poor by affecting income, food and energy prices. Here, the authors quantify this effect, and show that it can be compensated by national redistribution of the carbon pricing revenues in combination with international climate finance.
Inventory of dams in Germany
(2021)
Dams are an important element of water resources management. Data about dams are crucial for practitioners, scientists, and policymakers for various purposes, such as seasonal forecasting of water availability or flood mitigation. However, detailed information on dams on the national level for Germany is so far not freely available. We present the most comprehensive open-access dam inventory for Germany (DIG) to date. We have collected and combined information on dams using books, state agency reports, engineering reports, and internet pages. We have applied a priority rule that ensures the highest level of reliability for the dam information. Our dam inventory comprises 530 dams in Germany with information on name, location, river, start year of construction and operation, crest length, dam height, lake area, lake volume, purpose, dam structure, and building characteristics. We have used a global, satellite-based water surface raster to evaluate the location of the dams. A significant proportion (63 %) of dams were built between 1950-2013. Our inventory shows that dams in Germany are mostly single-purpose (52 %), 53% can be used for flood control, and 25% are involved in energy production. The inventory is freely available through GFZ (GeoForschungsZentrum) Data Services (https://doi.org/10.5880/GFZ.4.4.2020.005)
How fast the Northern Hemisphere (NH) forest biome tracks strongly warming climates is largely unknown. Regional studies reveal lags between decades and millennia. Here we report a conundrum: Deglacial forest expansion in the NH extra-tropics occurs approximately 4000 years earlier in a transient MPI-ESM1.2 simulation than shown by pollen-based biome reconstructions. Shortcomings in the model and the reconstructions could both contribute to this mismatch, leaving the underlying causes unresolved. The simulated vegetation responds within decades to simulated climate changes, which agree with pollen-independent reconstructions. Thus, we can exclude climate biases as main driver for differences. Instead, the mismatch points at a multi-millennial disequilibrium of the NH forest biome to the climate signal. Therefore, the evaluation of time-slice simulations in strongly changing climates with pollen records should be critically reassessed. Our results imply that NH forests may be responding much slower to ongoing climate changes than Earth System Models predict. <br /> Deglacial forest expansion in the Northern Hemisphere poses a conundrum: Model results agree with the climate signal but are several millennia ahead of reconstructed forest dynamics. The underlying causes remain unsolved.
Air pollution is a pressing issue that is associated with adverse effects on human health, ecosystems, and climate. Despite many years of effort to improve air quality, nitrogen dioxide (NO2) limit values are still regularly exceeded in Europe, particularly in cities and along streets. This study explores how concentrations of nitrogen oxides (NOx = NO + NO2) in European urban areas have changed over the last decades and how this relates to changes in emissions. To do so, the incremental approach was used, comparing urban increments (i.e. urban background minus rural concentrations) to total emissions, and roadside increments (i.e. urban roadside concentrations minus urban background concentrations) to traffic emissions. In total, nine European cities were assessed. The study revealed that potentially confounding factors like the impact of urban pollution at rural monitoring sites through atmospheric transport are generally negligible for NOx. The approach proves therefore particularly useful for this pollutant. The estimated urban increments all showed downward trends, and for the majority of the cities the trends aligned well with the total emissions. However, it was found that factors like a very densely populated surrounding or local emission sources in the rural area such as shipping traffic on inland waterways restrict the application of the approach for some cities. The roadside increments showed an overall very diverse picture in their absolute values and trends and also in their relation to traffic emissions. This variability and the discrepancies between roadside increments and emissions could be attributed to a combination of local influencing factors at the street level and different aspects introducing inaccuracies to the trends of the emis-sion inventories used, including deficient emission factors. Applying the incremental approach was evaluated as useful for long-term pan-European studies, but at the same time it was found to be restricted to certain regions and cities due to data availability issues. The results also highlight that using emission inventories for the prediction of future health impacts and compliance with limit values needs to consider the distinct variability in the concentrations not only across but also within cities.
The sensitivity of key hydrologic variables and hydropower generation to climate change in the Lake Malawi and Shire River basins is assessed. The study adapts the mesoscale Hydrological Model (mHM) which is applied separately in the Upper Lake Malawi and Shire River basins. A particular Lake Malawi model, which focuses on reservoir routing and lake water balance, has been developed and is interlinked between the two basins. Climate change projections from 20 Coordinated Regional Climate Downscaling Experiment (CORDEX) models for Africa based on two scenarios (RCP4.5 and RCP8.5) for the periods 2021-2050 and 2071-2100 are used. An annual temperature increase of 1 degrees C decreases mean lake level and outflow by 0.3 m and 17%, respectively, signifying the importance of intensified evaporation for Lake Malawi's water budget. Meanwhile, a +5% (-5%) deviation in annual rainfall changes mean lake level by +0.7 m (-0.6 m). The combined effects of temperature increase and rainfall decrease result in significantly lower flows in the Shire River. The hydrological river regime may change from perennial to seasonal with the combination of annual temperature increase and precipitation decrease beyond 1.5 degrees C (3.5 degrees C) and -20% (-15%). The study further projects a reduction in annual hydropower production between 1% (RCP8.5) and 2.5% (RCP4.5) during 2021-2050 and between 5% (RCP4.5) and 24% (RCP8.5) during 2071-2100. The results show that it is of great importance that a further development of hydro energy on the Shire River should take into account the effects of climate change, e.g., longer low flow periods and/or higher discharge fluctuations, and thus uncertainty in the amount of electricity produced.
Increasing arctic coastal erosion rates imply a greater release of sediments and organic matter into the coastal zone. With 213 sediment samples taken around Herschel Island-Qikiqtaruk, Canadian Beaufort Sea, we aimed to gain new insights on sediment dynamics and geochemical properties of a shallow arctic nearshore zone. Spatial characteristics of nearshore sediment texture (moderately to poorly sorted silt) are dictated by hydrodynamic processes, but ice-related processes also play a role. We determined organic matter (OM) distribution and inferred the origin and quality of organic carbon by C/N ratios and stable carbon isotopes delta C-13. The carbon content was higher offshore and in sheltered areas (mean: 1.0 wt.%., S.D.: 0.9) and the C/N ratios also showed a similar spatial pattern (mean: 11.1, S.D.: 3.1), while the delta C-13 (mean: -26.4 parts per thousand VPDB, S.D.: 0.4) distribution was more complex. We compared the geochemical parameters of our study with terrestrial and marine samples from other studies using a bootstrap approach. Sediments of the current study contained 6.5 times and 1.8 times less total organic carbon than undisturbed and disturbed terrestrial sediments, respectively. Therefore, degradation of OM and separation of carbon pools take place on land and continue in the nearshore zone, where OM is leached, mineralized, or transported beyond the study area.
The Flood Damage Database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. The main purpose of development of HOWAS 21 was to support forensic flood analysis and the derivation of flood damage models. HOWAS 21 was first developed for Germany and currently almost exclusively contains datasets from Germany. However, its scope has recently been enlarged with the aim to serve as an international flood damage database; e.g. its web application is now available in German and English. This paper presents the recent advancements of HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data. The data applications indicate a large potential of the database for fostering a better understanding and estimation of the consequences of flooding.
Increasing interests in hydrocarbon resources at depths have drawn greater attentions to the deeply-buried carbonate reservoirs in the Tarim Basin in China. In this study, the cyclic dolomite rocks of Upper Cambrian Lower Qiulitag Group from four outcrop sections in northwestern Tarim Basin were selected to investigate and evaluate the petrophysical properties in relation to depositional facies and cyclicity. The Lower Qiulitag Group includes ten lithofacies, which were deposited in intermediate to shallow subtidal, restricted shallow subtidal, intertidal, and supratidal environments on a carbonate ramp system. These lithofacies are vertically stacked into repeated shallowing-upward, meter-scale cycles which are further grouped into six third-order depositional sequences (Sq1 to Sq6). There are variable types of pore spaces in the Lower Qiulitag Group dolomite rocks, including interparticle, intraparticle, and fenestral pores of primary origin, inter crystal, and vuggy pores of late diagenetic modification. The porosity in the dolomites is generally facies-selective as that the microbially-originated thrombolites and stromatolites generally yield a relatively high porosity. In contrast, the high-energy ooidal grainstones generally have very low porosity. In this case, the microbialite-based peritidal cycles and peritidal cycle-dominated highstand (or regressive) successions have relatively high volumes of pore spaces, although highly fluctuating (or vertical inhomogeneous). Accordingly, the grainstone-based subtidal cycles and subtidal cycle-dominated transgressive successions generally yield extremely low porosity. This scenario indicates that porosity development and preservation in the thick dolomite successions are primarily controlled by depositional facies which were influenced by sea-level fluctuations of different orders and later diagenetic overprinting.
Elevation-dependent compensation effects in snowmelt in the Rhine River Basin upstream gauge Basel
(2021)
In snow-dominated river basins, floods often occur during early summer, when snowmelt-induced runoff superimposes with rainfall-induced runoff. An earlier onset of seasonal snowmelt as a consequence of a warming climate is often expected to shift snowmelt contribution to river runoff and potential flooding to an earlier date. Against this background, we assess the impact of rising temperatures on seasonal snowpacks and quantify changes in timing, magnitude and elevation of snowmelt. We analyse in situ snow measurements, conduct snow simulations and examine changes in river runoff at key gauging stations. With regard to snowmelt, we detect a threefold effect of rising temperatures: snowmelt becomes weaker, occurs earlier and forms at higher elevations. Due to the wide range of elevations in the catchment, snowmelt does not occur simultaneously at all elevations. Results indicate that elevation bands melt together in blocks. We hypothesise that in a warmer world with similar sequences of weather conditions, snowmelt is moved upward to higher elevation. The movement upward the elevation range makes snowmelt in individual elevation bands occur earlier, although the timing of the snowmelt-induced runoff stays the same. Meltwater from higher elevations, at least partly, replaces meltwater from elevations below.
The study examined the potential future changes of drought characteristics in the Greater Lake Malawi Basin in Southeast Africa. This region strongly depends on water resources to generate electricity and food. Future projections (considering both moderate and high emission scenarios) of temperature and precipitation from an ensemble of 16 bias-corrected climate model combinations were blended with a scenario-neutral response surface approach to analyses changes in: (i) the meteorological conditions, (ii) the meteorological water balance, and (iii) selected drought characteristics such as drought intensity, drought months, and drought events, which were derived from the Standardized Precipitation and Evapotranspiration Index. Changes were analyzed for a near-term (2021-2050) and far-term period (2071-2100) with reference to 1976-2005. The effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble to reproduce observed drought characteristics as compared to raw climate projections was also investigated. Results suggest that the bias-correction improves the climate models in terms of reproducing temperature and precipitation statistics but not drought characteristics. Still, despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher drought intensity and longer events. Drought intensity is projected to increase between +25 and +50% during 2021-2050 and between +131 and +388% during 2071-2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, the number of drought events decreases. Projected droughts based on the high emission scenario are 1.7 times more severe than droughts based on the moderate scenario. That means that droughts in this region will likely become more severe in the coming decades. Despite the inherent high uncertainties of climate projections, the results provide a basis in planning and (water-)managing activities for climate change adaptation measures in Malawi. This is of particular relevance for water management issues referring hydro power generation and food production, both for rain-fed and irrigated agriculture.
When inferring on the magnitude of future heat-related mortality due to climate change, human adaptation to heat should be accounted for. We model long-term changes in minimum mortality temperatures (MMT), a well-established metric denoting the lowest risk of heat-related mortality, as a function of climate change and socio-economic progress across 3820 cities. Depending on the combination of climate trajectories and socio-economic pathways evaluated, by 2100 the risk to human health is expected to decline in 60% to 80% of the cities against contemporary conditions. This is caused by an average global increase in MMTs driven by long-term human acclimatisation to future climatic conditions and economic development of countries. While our adaptation model suggests that negative effects on health from global warming can broadly be kept in check, the trade-offs are highly contingent to the scenario path and location-specific. For high-forcing climate scenarios (e.g. RCP8.5) the maintenance of uninterrupted high economic growth by 2100 is a hard requirement to increase MMTs and level-off the negative health effects from additional scenario-driven heat exposure. Choosing a 2 degrees C-compatible climate trajectory alleviates the dependence on fast growth, leaving room for a sustainable economy, and leads to higher reductions of mortality risk.
Coastal areas are particularly sensitive because they are complex, and related land use conflicts are more intense than those in noncoastal areas. In addition to representing a unique encounter of natural and socioeconomic factors, coastal areas have become paradigms of progressive urbanisation and economic development. Our study of the infrastructural mega project of Patimban Seaport in Indonesia explores the factors driving land use changes and the subsequent land use conflicts emerging from large-scale land transformation in the course of seaport development and mega project governance. We utilised interviews and questionnaires to investigate institutional aspects and conflict drivers. Specifically, we retrace and investigate the mechanisms guiding how mega project governance, land use planning, and actual land use interact. Therefore, we observe and analyse where land use conflicts emerge and the roles that a lack of stakeholder interest involvement and tenure-responsive planning take in this process. Our findings reflect how mismanagement and inadequate planning processes lead to market failure, land abandonment and dereliction and how they overburden local communities with the costs of mega projects. Enforcing a stronger coherence between land use planning, participation and land tenure within the land governance process in coastal land use development at all levels and raising the capacity of stakeholders to interfere with governance and planning processes will reduce conflicts and lead to sustainable coastal development in Indonesia.
In der derzeitigen Wahrnehmung werden die Sommer dürrer, heißer und extremer – dieser Eindruck verstärkt sich im urbanen Raum durch das Auftreten von Hitzeinseleffekten in dicht bebauten Gebieten. Um das wirkliche Ausmaß der Dürre bewerten zu können, wurden Zeitreihendaten von 31 urbanen Klimastationen (DWD) für den Zeitraum 1950 bis 2019 mittels des standardisierten Niederschlagsindex (SPI) bezüglich Dürrelängen, Dürreextrema, Hitzewellen und gleichzeitig auftretenden Hitze- und Dürremonaten ausgewertet.
Die Analyse zeigt eine große Heterogenität innerhalb von Deutschland: In den meisten Städten trat 2018 eine lange Dürre von einer durchschnittlichen Dauer von 6 Monaten auf, gleichzeitig gehörte das Jahr 2018 nur bei einem Drittel der Städte zu den drei Jahren mit den längsten Dürren seit 1950. Bei den meisten betrachteten Stationen traten die längsten Dürren in den Jahren 1953, 1971 und 1976 auf. Bei einigen südlichen und mitteldeutschen Städten kann man eine statistisch signifikante Zunahme der Anzahl der Dürremonate pro Dekade seit 1950 verzeichnen. Andere Städte, eher im Norden und Nordwesten gelegen, zeigen nur in den letzten zwei Dekaden eine Zunahme oder gar keinen Trend. Die Compoundanalyse von gleichzeitig auftretenden Hitze- und Dürremonaten zeigt bei den meisten Stationen eine starke Zunahme innerhalb der letzten zwei Dekaden, wobei die beiden Komponenten regional mit einem sehr unterschiedlichen Anteil zur Zunahme der Compoundereignisse beitragen.
Extreme Regenereignisse von kurzer Dauer im Bereich von Stunden und darunter rücken aufgrund der dadurch bedingten Schäden durch Sturzfluten und auch wegen ihrer möglichen Intensivierungen durch den anthropogenen Klimawandel immer stärker in den Fokus. Die vorliegende Studie untersucht auf Basis von teilweise sehr langen (> 50 Jahre) und zeitlich hochaufgelösten Zeitreihen (≤ 15 Minuten) mögliche Trends in Starkregenintensitäten für Stationen aus schweizerischen und österreichischen Alpenregionen sowie für das Emscher-Lippe-Gebiet in Nordrhein-Westfalen. Es wird deutlich, dass es eine Zunahme der extremen Niederschlagsintensitäten gibt, welche gut durch die Erwärmung des regionalen Klimas erklärt werden kann: Die Analysen langfristiger Trends der Überschreitungssummen und Wiederkehrniveaus zeigen zwar erhebliche Unsicherheiten, lassen jedoch eine Zunahme in einer Größenordnung von 30 % pro Jahrhundert erkennen. Zudem wird in diesem Beitrag, basierend auf einer "mittleren" Klimasimulation für das 21. Jahrhundert, für ausgewählte Stationen der Emscher-Lippe-Region eine Projektion für extreme Niederschlagsintensitäten in sehr hoher zeitlicher Auflösung beschrieben. Dabei wird ein gekoppeltes räumliches und zeitliches "Downscaling" angewendet, dessen entscheidende Neuerung die Berücksichtigung der Abhängigkeit der lokalen Regenintensität von der Lufttemperatur ist. Dieses Verfahren beinhaltet zwei Schritte: Zuerst werden großräumige Klimafelder in täglicher Auflösung durch Regression mit den Temperatur- und Niederschlagswerten der Stationen statistisch verbunden (räumliches Downscaling). Im zweiten Schritt werden dann diese Stationswerte mithilfe eines sogenannten multiplikativen stochastischen Kaskadenmodells (MC) auf eine zeitliche Auflösung von 10 Minuten disaggregiert (zeitliches Downscaling). Die neuartige, temperatursensitive Variante berücksichtigt zusätzlich die Lufttemperatur als erklärende Variable für die Niederschlagsintensitäten. Dadurch wird der mit einer Erwärmung zu erwartende höhere atmosphärische Feuchtegehalt, welcher sich aus der Clausius-Clapeyron-Beziehung (CC) ergibt, mit in das zeitliche Downscaling einbezogen.
Für die statistische Auswertung der extremen kurzfristigen Niederschläge wurden die oberen Quantile (99,9 %), Überschreitungssummen (ÜS, P > 5 mm) und 3-jährliche Wiederkehrniveaus (WN) einer Dauerstufe von ≤ 15-Minuten betrachtet. Diese Auswahl erlaubt die gleichzeitige Analyse sowohl von Extremwertstatistiken als auch von deren langfristigen Trends; leichte Abweichungen von dieser Wahl beeinflussen die Hauptergebnisse nur unwesentlich. Nur durch die Hinzunahme der Temperatur wird die beobachtete Temperaturabhängigkeit der extremen Quantile (CC-Scaling) gut wiedergegeben. Bei Vergleich von Beobachtungsdaten und Gegenwartssimulationen der Modellkaskade zeigt das temperatursensitive Verfahren konsistente Ergebnisse. Im Vergleich zu den Entwicklungen der letzten Jahrzehnte werden für die Zukunft ähnliche oder sogar noch stärkere Anstiege der extremen Niederschlagsintensitäten projiziert. Dies ist insofern bemerkenswert, als diese anscheinend hauptsächlich durch die örtliche Temperatur bestimmt werden, denn die projizierten Trends der Niederschlags-Tageswerte sind für diese Region vernachlässigbar.
Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07%; R-2 = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.
The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25% in 68% and 52% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25% at 60% and 65% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment.
Bilingualer Unterricht gilt als das Erfolgsmodell für den schulischen Fremdsprachenerwerb in Deutschland und die Beherrschung einer Fremdsprache in Wort und Schrift ist eine entscheidende berufsqualifizierende Kompetenz in unserer globalisierten Welt. Insbesondere die Verzahnung fachlicher und sprachlicher Inhalte im Kontext Bilingualen Unterrichts scheint gewinnbringend für den Fremdspracherwerb zu sein. Dabei ist die Diskrepanz zwischen den zumeist noch geringen fremdsprachlichen Fähigkeiten der Lernenden und den fachlichen Ansprüchen des Geographieunterrichts eine große Herausforderung für fachliches Lernen im bilingualen Sachfachunterricht. Es stellt sich die Frage, wie der Bilinguale Unterricht gestaltet sein muss, um einerseits geographische Themen fachlich komplex behandeln zu können und andererseits die Lernenden fremdsprachlich nicht zu überfordern.
Im Rahmen einer Design-Based-Research-Studie im bilingualen Geographieunterricht wurde untersucht, wie fachliches Lernen im bilingualen Geographieunterricht durch den Einsatz beider beteiligter Sprachen (Englisch/Deutsch) gefördert werden kann.
Auf Grundlage eines theoretisch fundierten Kenntnisstands zum Bilingualen Unterricht und zum Lernen mit Fachkonzepten im Geographieunterricht wurde eine Lernumgebung konzipiert, im Unterricht erprobt und weiterentwickelt, in der Strategien des Sprachwechsels zum Einsatz kommen.
Die Ergebnisse der Studie sind kontextbezogene Theorien einer zweisprachigen Didaktik für den bilingualen Geographieunterricht und Erkenntnisse zum Lernen mit Fachkonzepten im Geographieunterricht am Beispiel des geographischen Konzepts Wandel. Produkt der Studie ist eine unterrichtstaugliche Lernumgebung zum Thema Wandlungsprozesse an ausgewählten Orten für den bilingualen Geographieunterricht mit didaktischem Konzept, Unterrichtsmaterialien und -medien.
Floodplains have been degraded in Central Europe for centuries, resulting in less dynamic and less diverse ecosystems than in the past. They provide essential ecosystem services like nutrient retention to improve overall water quality and thus fulfill naturally what EU legislation demands, but this service is impaired by reduced connectivity patterns. Along the second-longest river in Europe, the Danube, restoration measures have been carried out and are planned for the near future in the Austrian Danube Floodplain National Park in accordance with navigation purposes. We investigated nutrient retention capacity in seven currently differently connected side arms and the effects of proposed restoration measures using two complementary modeling approaches. We modeled nutrient retention capacity in two scenarios considering different hydrological conditions, as well as the consequences of planned restoration measures for side arm connectivity. With existing monitoring data on hydrology, nitrate, and total phosphorus concentrations for three side arms, we applied a statistical model and compared these results to a semi-empirical retention model. The latter was originally developed for larger scales, based on transferable causalities of retention processes and set up for this floodplain with publicly available data. Both model outcomes are in a comparable range for NO3-N (77-198 kg ha(-1)yr(-1)) and TP (1.4-5.7 kg ha(-1)yr(-1)) retention and agree in calculating higher retention in floodplains, where reconnection allows more frequent inundation events. However, the differences in the model results are significant for specific aspects especially during high flows, where the semi-empirical model complements the statistical model. On the other hand, the statistical model complements the semi-empirical model when taking into account nutrient retention at times of no connection between the remaining water bodies left in the floodplain. Overall, both models show clearly that nutrient retention in the Danube floodplains can be enhanced by restoring lateral hydrological reconnection and, for all planned measures, a positive effect on the overall water quality of the Danube River is expected. Still, a frequently hydrologically connected stretch of national park is insufficient to improve the water quality of the whole Upper Danube, and more functional floodplains are required.
Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.
The world is facing a triple burden of undernourishment, obesity, and environmental impacts from agriculture while nourishing its population. This burden makes sustainable nourishment of the growing population a global challenge. Addressing this challenge requires an understanding of the interplay between diets, health, and associated environmental impacts (e.g., climate change). For this, we identify 11 typical diets that represent dietary habits worldwide for the last five decades. Plant-source foods provide most of all three macronutrients (carbohydrates, protein, and fat) in developing countries. In contrast, animal-source foods provide a majority of protein and fat in developed ones. The identified diets deviate from the recommended healthy diet with either too much (e.g., red meat) or too little (e.g., fruits and vegetables) food and nutrition supply. The total calorie supplies are lower than required for two diets. Sugar consumption is higher than recommended for five diets. Three and five diets consist of larger-than-recommended carbohydrate and fat shares, respectively. Four diets with a large share of animal-source foods exceed the recommended value of red meat. Only two diets consist of at least 400 gm/cap/day of fruits and vegetables while accounting for food waste. Prevalence of undernourishment and underweight dominates in the diets with lower calories. In comparison, a higher prevalence of obesity is observed for diets with higher calories with high shares of sugar, fat, and animal-source foods. However, embodied emissions in the diets do not show a clear relation with calorie supplies and compositions. Two high-calorie diets embody more than 1.5 t CO<mml:semantics>2</mml:semantics>eq/cap/yr, and two low-calorie diets embody around 1 t CO<mml:semantics>2</mml:semantics>eq/cap/yr. Our analysis highlights that sustainable and healthy diets can serve the purposes of both nourishing the population and, at the same time, reducing the environmental impacts of agriculture.
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
(2023)
Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected "normal" behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the "stable" principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
A growing focus is being placed on both individuals and communities to adapt to flooding as part of the Sendai Framework for Disaster Risk Reduction 2015-2030. Adaptation to flooding requires sufficient social capital (linkages between members of society), risk perceptions (understanding of risk), and self-efficacy (self-perceived ability to limit disaster impacts) to be effective. However, there is limited understanding of how social capital, risk perceptions, and self-efficacy interact. We seek to explore how social capital interacts with variables known to increase the likelihood of successful adaptation. To study these linkages we analyze survey data of 1010 respondents across two communities in Thua Tien-Hue Province in central Vietnam, using ordered probit models. We find positive correlations between social capital, risk perceptions, and self-efficacy overall. This is a partly contrary finding to what was found in previous studies linking these concepts in Europe, which may be a result from the difference in risk context. The absence of an overall negative exchange between these factors has positive implications for proactive flood risk adaptation.
Flood risk management in Germany follows an integrative approach in which both private households and businesses can make an important contribution to reducing flood damage by implementing property-level adaptation measures. While the flood adaptation behavior of private households has already been widely researched, comparatively less attention has been paid to the adaptation strategies of businesses. However, their ability to cope with flood risk plays an important role in the social and economic development of a flood-prone region. Therefore, using quantitative survey data, this study aims to identify different strategies and adaptation drivers of 557 businesses damaged by a riverine flood in 2013 and 104 businesses damaged by pluvial or flash floods between 2014 and 2017. Our results indicate that a low perceived self-efficacy may be an important factor that can reduce the motivation of businesses to adapt to flood risk. Furthermore, property-owners tended to act more proactively than tenants. In addition, high experience with previous flood events and low perceived response costs could strengthen proactive adaptation behavior. These findings should be considered in business-tailored risk communication.
Glaciated high-alpine areas are fundamentally altered by climate change, with well-known implications for hydrology, e.g., due to glacier retreat, longer snow-free periods, and more frequent and intense summer rainstorms. While knowledge on how these hydrological changes will propagate to suspended sediment dynamics is still scarce, it is needed to inform mitigation and adaptation strategies. To understand the processes and source areas most relevant to sediment dynamics, we analyzed discharge and sediment dynamics in high temporal resolution as well as their patterns on several spatial scales, which to date few studies have done.
We used a nested catchment setup in the Upper Ötztal in Tyrol, Austria, where high-resolution (15 min) time series of discharge and suspended sediment concentrations are available for up to 15 years (2006–2020). The catchments of the gauges in Vent, Sölden and Tumpen range from 100 to almost 800 km2 with 10 % to 30 % glacier cover and span an elevation range of 930 to 3772 m a.s.l. We analyzed discharge and suspended sediment yields (SSY), their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. We complemented our analysis by linking the observations to satellite-based snow cover maps, glacier inventories, mass balances and precipitation data.
Our results indicate that the areas above 2500 m a.s.l., characterized by glacier tongues and the most recently deglaciated areas, are crucial for sediment generation in all sub-catchments. This notion is supported by the synchronous spring onset of sediment export at the three gauges, which coincides with snowmelt above 2500 m but lags behind spring discharge onsets. This points at a limitation of suspended sediment supply as long as the areas above 2500 m are snow-covered. The positive correlation of annual SSY with glacier cover (among catchments) and glacier mass balances (within a catchment) further supports the importance of the glacier-dominated areas. The analysis of short-term events showed that summer precipitation events were associated with peak sediment concentrations and yields but on average accounted for only 21 % of the annual SSY in the headwaters. These results indicate that under current conditions, thermally induced sediment export (through snow and glacier melt) is dominant in the study area.
Our results extend the scientific knowledge on current hydro-sedimentological conditions in glaciated high-alpine areas and provide a baseline for studies on projected future changes in hydro-sedimentological system dynamics.
Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data covering the time frame 1869-2016. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions.
Our results show that runoff seasonality of snow-dominated rivers decreases. Runoff increases in winter and spring, while discharge decreases in summer and at the beginning of autumn. We attribute this redistribution of annual flow mainly to reservoir constructions in the Alpine ridge. During the course of the last century, large fractions of the Alpine rivers were dammed to produce hydropower. In recent decades, runoff changes induced by reservoir constructions seem to overlap with changes in snow cover. We suggest that Alpine signals propagate downstream and affect runoff far outside the Alpine area in river segments with mixed flow regimes. Furthermore, our results hint at more (intense) rain-fall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation.
The study examined the potential future changes of drought characteristics in the Greater Lake Malawi Basin in Southeast Africa. This region strongly depends on water resources to generate electricity and food. Future projections (considering both moderate and high emission scenarios) of temperature and precipitation from an ensemble of 16 bias-corrected climate model combinations were blended with a scenario-neutral response surface approach to analyses changes in: (i) the meteorological conditions, (ii) the meteorological water balance, and (iii) selected drought characteristics such as drought intensity, drought months, and drought events, which were derived from the Standardized Precipitation and Evapotranspiration Index. Changes were analyzed for a near-term (2021–2050) and far-term period (2071–2100) with reference to 1976–2005. The effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble to reproduce observed drought characteristics as compared to raw climate projections was also investigated. Results suggest that the bias-correction improves the climate models in terms of reproducing temperature and precipitation statistics but not drought characteristics. Still, despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher drought intensity and longer events. Drought intensity is projected to increase between +25 and +50% during 2021–2050 and between +131 and +388% during 2071–2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, the number of drought events decreases. Projected droughts based on the high emission scenario are 1.7 times more severe than droughts based on the moderate scenario. That means that droughts in this region will likely become more severe in the coming decades. Despite the inherent high uncertainties of climate projections, the results provide a basis in planning and (water-)managing activities for climate change adaptation measures in Malawi. This is of particular relevance for water management issues referring hydro power generation and food production, both for rain-fed and irrigated agriculture.
Die Arbeit gibt einen Einblick in die Verständigungspraxen bei Stadtführungen mit (ehemaligen) Obdachlosen, die in ihrem Selbstverständnis auf die Herstellung von Verständnis, Toleranz und Anerkennung für von Obdachlosigkeit betroffene Personen zielen. Zunächst wird in den Diskurs des Slumtourismus eingeführt und, angesichts der Vielfalt der damit verbundenen Erscheinungsformen, Slumming als organisierte Begegnung mit sozialer Ungleichheit definiert. Die zentralen Diskurslinien und die darin eingewobenen moralischen Positionen werden nachvollzogen und im Rahmen der eigenommenen wissenssoziologischen Perspektive als Ausdruck einer per se polykontexturalen Praxis re-interpretiert. Slumming erscheint dann als eine organisierte Begegnung von Lebensformen, die sich in einer Weise fremd sind, als dass ein unmittelbares Verstehen unwahrscheinlich erscheint und genau aus diesem Grund auf der Basis von gängigen Interpretationen des Common Sense ausgehandelt werden muss. Vor diesem Hintergrund untersucht die vorliegende Arbeit, wie sich Teilnehmer und Stadtführer über die Erfahrung der Obdachlosigkeit praktisch verständigen und welcher Art das hierüber erzeugte Verständnis für die im öffentlichen Diskurs mit vielfältigen stigmatisierenden Zuschreibungen versehenen Obdachlosen ist. Dabei interessiert besonders, in Bezug auf welche Aspekte der Erfahrung von Obdachlosigkeit ein gemeinsames Verständnis möglich wird und an welchen Stellen dieses an Grenzen gerät. Dazu wurden die Gesprächsverläufe auf neun Stadtführungen mit (ehemaligen) obdachlosen Stadtführern unterschiedlicher Anbieter im deutschsprachigen Raum verschriftlicht und mit dem Verfahren der Dokumentarischen Methode ausgewertet. Die vergleichende Betrachtung der Verständigungspraxen eröffnet nicht zuletzt eine differenzierte Perspektive auf die in den Prozessen der Verständigung immer schon eingewobenen Anerkennungspraktiken. Mit Blick auf die moralische Debatte um organisierte Begegnungen mit sozialer Ungleichheit wird dadurch eine ethische Perspektive angeregt, in deren Zentrum Fragen zur Vermittlungsarbeit stehen.
Die Gesellschaft befindet sich längst in einem digitalen Transformationsprozess. Alle gesellschaftlichen Bereiche verändern sich. Man spricht von einer Kultur der Digitalität, die den Leitmedienwechsel vom gedruckten Buch hin zum vernetzten digitalen Endgerät beschreibt. Auch die Institution „Schule“ muss sich diesem Wandel öffnen. Einen wesentlichen Schritt stellt das Strategiepapier der Kultusministerkonferenz „Bildung in der digitalen Welt“ aus dem Jahr 2017 dar. Darin legt sie die wesentlichen Handlungsfelder zu einem digitalen Wandel fest und erweitert den Bildungsauftrag um die „Kompetenzen in der digitalen Welt“.
Das sog. SAMR-Modell stellt dabei ein geeignetes Umsetzungs- und Reflektionswerkzeug für den Einsatz digitaler Medien dar. Es strukturiert den Einsatz auf vier Stufen. Die beiden unteren Stufen (Substitution und Augmentation) schreiben der Art und Weise, wie die digitalen Medien genutzt werden, eine Ersatz- oder Verbesserungsfunktion des analogen Lernwerkzeuges zu. Ziel des Modells ist es aber, mithilfe hinzugewonnener digitaler Möglichkeiten, Lernen neu zu gestalten. Da das Modell aus den USA stammt, weist es weder direkten Bezüge zum Strategiepapier der Kultusministerkonferenz noch zu den Bildungsstandards der Geographie auf.
Diese wissenschaftliche Arbeit stellt diese Bezüge her. Ziel ist es, auf der Grundlage des SAMR-Modells ein Handlungskonzept für Geographielehrkräfte zu entwickeln. Es zeigt auf, wie sie sowohl fachliche Kompetenzen als auch Kompetenzen in der digitalen Welt systematisch bei den Lernenden fördern können.
Pokhara (ca. 850 m a.s.l.), Nepal's second-largest city, lies at the foot of the Higher Himalayas and has more than tripled its population in the past 3 decades. Construction materials are in high demand in rapidly expanding built-up areas, and several informal settlements cater to unregulated sand and gravel mining in the Pokhara Valley's main river, the Seti Khola. This river is fed by the Sabche glacier below Annapurna III (7555 m a.s.l.), some 35 km upstream of the city, and traverses one of the steepest topographic gradients in the Himalayas. In May 2012 a sudden flood caused >70 fatalities and intense damage along this river and rekindled concerns about flood risk management. We estimate the flow dynamics and inundation depths of flood scenarios using the hydrodynamic model HEC-RAS (Hydrologic Engineering Center’s River Analysis System). We simulate the potential impacts of peak discharges from 1000 to 10 000 m3 s−1 on land cover based on high-resolution Maxar satellite imagery and OpenStreetMap data (buildings and road network). We also trace the dynamics of two informal settlements near Kaseri and Yamdi with high potential flood impact from RapidEye, PlanetScope, and Google Earth imagery of the past 2 decades. Our hydrodynamic simulations highlight several sites of potential hydraulic ponding that would largely affect these informal settlements and sites of sand and gravel mining. These built-up areas grew between 3- and 20-fold, thus likely raising local flood exposure well beyond changes in flood hazard. Besides these drastic local changes, about 1 % of Pokhara's built-up urban area and essential rural road network is in the highest-hazard zones highlighted by our flood simulations. Our results stress the need to adapt early-warning strategies for locally differing hydrological and geomorphic conditions in this rapidly growing urban watershed.
Quantifying the extremeness of heavy precipitation allows for the comparison of events. Conventional quantitative indices, however, typically neglect the spatial extent or the duration, while both are important to understand potential impacts. In 2014, the weather extremity index (WEI) was suggested to quantify the extremeness of an event and to identify the spatial and temporal scale at which the event was most extreme. However, the WEI does not account for the fact that one event can be extreme at various spatial and temporal scales. To better understand and detect the compound nature of precipitation events, we suggest complementing the original WEI with a “cross-scale weather extremity index” (xWEI), which integrates extremeness over relevant scales instead of determining its maximum.
Based on a set of 101 extreme precipitation events in Germany, we outline and demonstrate the computation of both WEI and xWEI. We find that the choice of the index can lead to considerable differences in the assessment of past events but that the most extreme events are ranked consistently, independently of the index. Even then, the xWEI can reveal cross-scale properties which would otherwise remain hidden. This also applies to the disastrous event from July 2021, which clearly outranks all other analyzed events with regard to both WEI and xWEI.
While demonstrating the added value of xWEI, we also identify various methodological challenges along the required computational workflow: these include the parameter estimation for the extreme value distributions, the definition of maximum spatial extent and temporal duration, and the weighting of extremeness at different scales. These challenges, however, also represent opportunities to adjust the retrieval of WEI and xWEI to specific user requirements and application scenarios.
In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Delta t ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Delta t. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t - 1 to t (LK-Lin1) and t - 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models.
Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms.
The most severe flood events in Turkey were determined for the period 1960-2014 by considering the number of fatalities, the number of affected people, and the total economic losses as indicators. The potential triggering mechanisms (i.e., atmospheric circulations and precipitation amounts) and aggravating pathways (i.e., topographic features, catchment size, land use types, and soil properties) of these 25 events were analyzed. On this basis, a new approach was developed to identify the main influencing factor per event and to provide additional information for determining the dominant flood occurrence pathways for severe floods. The events were then classified through hierarchical cluster analysis. As a result, six different clusters were found and characterized. Cluster 1 comprised flood events that were mainly influenced by drainage characteristics (e.g., catchment size and shape); Cluster 2 comprised events aggravated predominantly by urbanization; steep topography was identified to be the dominant factor for Cluster 3; extreme rainfall was determined as the main triggering factor for Cluster 4; saturated soil conditions were found to be the dominant factor for Cluster 5; and orographic effects of mountain ranges characterized Cluster 6. This study determined pathway patterns of the severe floods in Turkey with regard to their main causal or aggravating mechanisms. Accordingly, geomorphological properties are of major importance in large catchments in eastern and northeastern Anatolia. In addition, in small catchments, the share of urbanized area seems to be an important factor for the extent of flood impacts. This paper presents an outcome that could be used for future urban planning and flood risk prevention studies to understand the flood mechanisms in different regions of Turkey.
Food system innovations will be instrumental to achieving multiple Sustainable Development Goals (SDGs). However, major innovation breakthroughs can trigger profound and disruptive changes, leading to simultaneous and interlinked reconfigurations of multiple parts of the global food system. The emergence of new technologies or social solutions, therefore, have very different impact profiles, with favourable consequences for some SDGs and unintended adverse side-effects for others. Stand-alone innovations seldom achieve positive outcomes over multiple sustainability dimensions. Instead, they should be embedded as part of systemic changes that facilitate the implementation of the SDGs. Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging. Trade-offs with undesirable consequences are manageable through the development of well planned transition pathways, careful monitoring of key indicators, and through the implementation of transparent science targets at the local level.
Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
Although sedimentary ancient DNA (sedaDNA) has been increasingly used to study paleoecological dynamics (Schulte et al., 2020), the approach has rarely been compared with the traditional method of pollen analysis for investigating past changes in the vegetation composition and diversity of Arctic treeline areas. Here, we provide a history of latitudinal floristic composition and species diversity based on a comparison ofsedaDNA and pollen data archived in three Siberian lake sediment cores spanning the mid-Holocene to the present (7.6-0 cal ka BP), from northern typical tundra to southern open larch forest in the Omoloy region. Our results show that thesedaDNA approach identifies more plant taxa found in the local vegetation communities, while the corresponding pollen analysis mainly captures the regional vegetation development and has its limitations for plant diversity reconstruction. Measures of alpha diversity were calculated based onsedaDNA data recovered from along a tundra to forest tundra to open larch forest gradient. Across all sites,sedaDNA archives provide a complementary record of the vegetation transition within each lake's catchment, tracking a distinct latitudinal vegetation type range from larch tree/alder shrub (open larch forest site) to dwarf shrub-steppe (forest tundra) to wet sedge tundra (typical tundra site). By contrast, the pollen data reveal an open landscape, which cannot distinguish the temporal changes in compositional vegetation for the open larch forest site and forest-tundra site. IncreasingLarixpollen percentages were recorded in the forest-tundra site in the last millenium although noLarixDNA was detected, suggesting that thesedaDNA approach performs better for tracking the local establishment ofLarix. Highest species richness and diversity are found in the mid-Holocene (before 4.4 ka) at the typical tundra site with a diverse range of vegetational habitats, while lowest species richness is recorded for the forest tundra where dwarf-willow habitats dominated the lake's catchment. During the late Holocene, strong declines in species richness and diversity are found at the typical tundra site with the vegetation changing to relatively simple communities. Nevertheless, plant species richness is mostly higher than at the forest-tundra site, which shows a slightly decreasing trend. Plant species richness at the open larch forest site fluctuates through time and is higher than the other sites since around 2.5 ka. Taken together, there is no evidence to suggest that the latitudinal gradients in species diversity changes are present at a millennial scale. Additionally, a weak correlation between the principal component analysis (PCA) site scores ofsedaDNA and species richness suggests that climate may not be a direct driver of species turnover within a lake's catchment. Our data suggest thatsedaDNA and pollen have different but complementary abilities for reconstructing past vegetation and species diversity along a latitude.
Contemporary drought impact assessments have been constrained due to data availability, leading to an incomplete representation of impact trends. To address this, we present a novel method for the comprehensive and near-real-time monitoring of drought socio-economic impacts based on media reports. We tested its application using the case of the exceptional 2018/19 German drought. By employing text mining techniques, 4839 impact statements were identified, relating to livestock, agriculture, forestry, fires, recreation, energy and transport sectors. An accuracy of 95.6% was obtained for their automatic classification. Furthermore, high levels of performance in terms of spatial and temporal precision were found when validating our results against independent data (e.g. soil moisture, average precipitation, population interest in droughts, crop yield and forest fire statistics). The findings highlight the applicability of media data for rapidly and accurately monitoring the propagation of drought consequences over time and space. We anticipate our method to be used as a starting point for an impact-based early warning system.
Quantitative detection and attribution of groundwater level variations in the Amu Darya Delta
(2020)
In the past few decades, the shrinkage of the Aral Sea is one of the biggest ecological catastrophes caused by human activity. To quantify the joint impact of both human activities and climate change on groundwater, the spatiotemporal groundwater dynamic characteristics in the Amu Darya Delta of the Aral Sea from 1999 to 2017 were analyzed, using the groundwater level, climate conditions, remote sensing data, and irrigation information. Statistics analysis was adopted to analyze the trend of groundwater variation, including intensity, periodicity, spatial structure, while the Pearson correlation analysis and principal component analysis (PCA) were used to quantify the impact of climate change and human activities on the variabilities of the groundwater level. Results reveal that the local groundwater dynamic has varied considerably. From 1999 to 2002, the groundwater level dropped from -189 cm to -350 cm. Until 2017, the groundwater level rose back to -211 cm with fluctuation. Seasonally, the fluctuation period of groundwater level and irrigation water was similar, both were about 18 months. Spatially, the groundwater level kept stable within the irrigation area and bare land but fluctuated drastically around the irrigation area. The Pearson correlation analysis reveals that the dynamic of the groundwater level is closely related to irrigation activity within the irrigation area (Nukus: -0.583), while for the place adjacent to the Aral Sea, the groundwater level is closely related to the Large Aral Sea water level (Muynak: 0.355). The results of PCA showed that the cumulative contribution rate of the first three components exceeds 85%. The study reveals that human activities have a great impact on groundwater, effective management, and the development of water resources in arid areas is an essential prerequisite for ecological protection.
Nature-based solutions (NBS) are inspired and supported by nature but designed by humans. Historically, governmental stakeholders have aimed to control nature using a top-down approach; more recently, environmental governance has shifted to collaborative planning. Polycentric governance and co-creation procedures, which include a large spectrum of stakeholders, are assumed to be more effective in the management of public goods than traditional approaches. In this context, NBS projects should benefit from strong collaborative governance models, and the European Union is facilitating and encouraging such models. While some theoretical approaches exist, setting-up the NBS co-creation process (namely co-design and co-implementation) currently relies mostly on self-organized stakeholders rather than on strategic decisions. As such, systematic methods to identify relevant stakeholders seem to be crucial to enable higher planning efficiency, reduce bottlenecks and time needed for planning, designing, and implementing NBS. In this context, this contribution is based on the analysis of 16 NBS and 359 stakeholders. Real-life constellations are compared to theoretical typologies, and a systematic stakeholder mapping method to support co-creation is presented. Rather than making one-fit-all statements about the "right" stakeholders, the contribution provides insights for those "in charge" to strategically consider who might be involved at each stage of the NBS project.