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Urban air pollution is a substantial threat to human health. Traffic emissions remain a large contributor to air pollution in urban areas. The mobility restrictions put in place in response to the COVID-19 pandemic provided a large-scale real-world experiment that allows for the evaluation of changes in traffic emissions and the corresponding changes in air quality. Here we use observational data, as well as modelling, to analyse changes in nitrogen dioxide, ozone, and particulate matter resulting from the COVID-19 restrictions at the height of the lockdown period in Spring of 2020. Accounting for the influence of meteorology on air quality, we found that reduction of ca. 30-50 % in traffic counts, dominated by changes in passenger cars, corresponded to reductions in median observed nitrogen dioxide concentrations of ca. 40 % (traffic and urban background locations) and a ca. 22 % increase in ozone (urban background locations) during weekdays. Lesser reductions in nitrogen dioxide concentrations were observed at urban background stations at weekends, and no change in ozone was observed. The modelled reductions in median nitrogen dioxide at urban background locations were smaller than the observed reductions and the change was not significant. The model results showed no significant change in ozone on weekdays or weekends. The lack of a simulated weekday/weekend effect is consistent with previous work suggesting that NOx emissions from traffic could be significantly underestimated in European cities by models. These results indicate the potential for improvements in air quality due to policies for reducing traffic, along with the scale of reductions that would be needed to result in meaningful changes in air quality if a transition to sustainable mobility is to be seriously considered. They also confirm once more the highly relevant role of traffic for air quality in urban areas.
Silicon (Si) speciation and availability in soils is highly important for ecosystem functioning, because Si is a beneficial element for plant growth. Si chemistry is highly complex compared to other elements in soils, because Si reaction rates are relatively slow and dependent on Si species. Consequently, we review the occurrence of different Si species in soil solution and their changes by polymerization, depolymerization, and condensation in relation to important soil processes. We show that an argumentation based on thermodynamic endmembers of Si dependent processes, as currently done, is often difficult, because some reactions such as mineral crystallization require months to years (sometimes even centuries or millennia). Furthermore, we give an overview of Si reactions in soil solution and the predominance of certain solid compounds, which is a neglected but important parameter controlling the availability, reactivity, and function of Si in soils. We further discuss the drivers of soil Si cycling and how humans interfere with these processes. The soil Si cycle is of major importance for ecosystem functioning; therefore, a deeper understanding of drivers of Si cycling (e.g., predominant speciation), human disturbances and the implication for important soil properties (water storage, nutrient availability, and micro aggregate stability) is of fundamental relevance.
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.
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 presence of impermeable surfaces in urban areas hinders natural drainage and directs the surface runoff to storm drainage systems with finite capacity, which makes these areas prone to pluvial flooding. The occurrence of pluvial flooding depends on the existence of minimal areas for surface runoff generation and concentration. Detailed hydrologic and hydrodynamic simulations are computationally expensive and require intensive resources. This study compared and evaluated the performance of two simplified methods to identify urban pluvial flood-prone areas, namely the fill–spill–merge (FSM) method and the topographic wetness index (TWI) method and used the TELEMAC-2D hydrodynamic numerical model for benchmarking and validation. The FSM method uses common GIS operations to identify flood-prone depressions from a high-resolution digital elevation model (DEM). The TWI method employs the maximum likelihood method (MLE) to probabilistically calibrate a TWI threshold (τ) based on the inundation maps from a 2D hydrodynamic model for a given spatial window (W) within the urban area. We found that the FSM method clearly outperforms the TWI method both conceptually and effectively in terms of model performance.
The co-occurrence of warm spells and droughts can lead to detrimental socio-economic and ecological impacts, largely surpassing the impacts of either warm spells or droughts alone. We quantify changes in the number of compound warm spells and droughts from 1979 to 2018 in the Mediterranean Basin using the ERA5 data set. We analyse two types of compound events: 1) warm season compound events, which are extreme in absolute terms in the warm season from May to October and 2) year-round deseasonalised compound events, which are extreme in relative terms respective to the time of the year. The number of compound events increases significantly and especially warm spells are increasing strongly – with an annual growth rates of 3.9 (3.5) % for warm season (deseasonalised) compound events and 4.6 (4.4) % for warm spells –, whereas for droughts the change is more ambiguous depending on the applied definition. Therefore, the rise in the number of compound events is primarily driven by temperature changes and not the lack of precipitation. The months July and August show the highest increases in warm season compound events, whereas the highest increases of deseasonalised compound events occur in spring and early summer. This increase in deseasonalised compound events can potentially have a significant impact on the functioning of Mediterranean ecosystems as this is the peak phase of ecosystem productivity and a vital phenophase.
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.
Root water uptake is an essential process for terrestrial plants that strongly affects the spatiotemporal distribution of water in vegetated soil. Fast neutron tomography is a recently established non-invasive imaging technique capable to capture the 3D architecture of root systems in situ and even allows for tracking of three-dimensional water flow in soil and roots. We present an in vivo analysis of local water uptake and transport by roots of soil-grown maize plants—for the first time measured in a three-dimensional time-resolved manner. Using deuterated water as tracer in infiltration experiments, we visualized soil imbibition, local root uptake, and tracked the transport of deuterated water throughout the fibrous root system for a day and night situation. This revealed significant differences in water transport between different root types. The primary root was the preferred water transport path in the 13-days-old plants while seminal roots of comparable size and length contributed little to plant water supply. The results underline the unique potential of fast neutron tomography to provide time-resolved 3D in vivo information on the water uptake and transport dynamics of plant root systems, thus contributing to a better understanding of the complex interactions of plant, soil and water.
Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil water content at a horizontal scale of hectometres (the “field scale”) and depths of tens of centimetres (“the root zone”). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign carried out between May and July 2019 that featured a dense network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within one square kilometre. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects (heterogeneity in space, e.g. soil organic matter) and dynamic effects (heterogeneity in time, e.g. barometric pressure). Based on the homogenized neutron data, we investigate the robustness of a calibration approach that uses a single calibration parameter across all CRNS stations. Finally, we benchmark two different interpolation techniques for obtaining spatio-temporal representations of soil moisture: first, ordinary Kriging with a fixed range; second, spatial interpolation complemented by geophysical inversion (“constrained interpolation”). To that end, we optimize the parameters of a geostatistical interpolation model so that the error in the forward-simulated neutron count rates is minimized, and suggest a heuristic forward operator to make the optimization problem computationally feasible. Comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach is superior for representing horizontal soil moisture gradients at the hectometre scale. The study demonstrates how a CRNS network can be used to generate coherent, consistent, and continuous soil moisture patterns that could be used to validate hydrological models or remote sensing products.
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)