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BTU
Tagebau(folge)landschaften bieten gute Möglichkeiten den Wandel ökohydrologische Systeme aufgrund veränderter Umweltbedingungen zu untersuchen: Im Zuge des Tagebaubetriebs wird die Vegetation vollständig entfernt, nach dem Tagebau wächst die Vegetation entweder durch aktive Rekultivierung oder natürliche Sukzession wieder auf. Von Interesse ist, ob und wie bzw. wie schnell sich die Tagebauflächen von der Störung erholen und ähnliche Bedingungen wie vor dem Tagebau bzw. auf ungestörten Flächen herrschen. Klimatische,
geomorphologische und ökologische Gegebenheiten sowie die Rekultivierungsstrategie spielen eine große Rolle in der Phase der Rehabilitation und bestimmen die Rate der Wiederbesiedlung
mit Pflanzen bzw. deren Wachstum. Der NDVI (normalisierter differenzierter Vegetationsindex) bietet die Möglichkeit generelle Muster der Vegetation quantitativ zu detektieren, um die Regenerationsrate der Vegetation für verschiedene Klima- und Ökoregionen abzuschätzen. Wir analysierten den MODIS Terra NDVI (achttägliche Werte) für Tagebaulandschaften verschiedener Klimate (äquatoriale, trockene, warm gemäßigte und Schnee-Klimate nach Köppen-Geiger) im Zeitraum 2001 bis 2015. Es wurden Kohletagebaue
betrachtet, da diese gut definierte Chronosequenzen der Störung erzeugen. Bei der Analyse der NDVI-Zeitreihen sollten Charakteristiken der Rehabilitationsphase erfasst werden. Um
die räumliche Heterogenität der Zellen (ca. 250 x 250 m²) der Tagebaulandschaft abzubilden, wurde je Tagebau eine hierarchische Clusteranalyse durchgeführt. Die einzelnen Zeitreihen der Cluster wurden mit einer Methode zur Detektion von Bruchpunkten und zur Zeitreihenzerlegung auf Konsistenz bezüglich Eigenschaften der Zeitreihen (Beginn des Tagebaus, Ende des Tagebaus/Beginn der Rehabilitation, Rate der Rehabilitation) untersucht. Die Clusteranalyse führt zu einer Einordnung der Zellen in vom Tagebau nicht direkt beeinflusste Flächen, aktiven Tagebau und in der Rehabilitation befindliche Fläche verschiedenen Alters
bzw. rehabilitierte Flächen. Das Zeitfenster der Entfernung der Vegetation kann im NDVI-Signal identifiziert werden, es zeigt sich meist in einer abrupten Änderung des NDVI. Die Rehabilitationsphase hingegen verläuft graduell und kann mehrere Jahre bis Jahrzehnte
andauern. Die Zeitreihenzerlegung zeigt auf, dass in der Rehabilitationsphase der Trend dominiert, während mit Voranschreiten der Rehabilitation die Saisonalität im NDVI-Signal vorherrschend wird. Durch die ermittelte Rate der Rehabilitation können die Flächen innerhalb eines Tagebaus miteinander verglichen werden. Die mittlere Rehabilitationsrate der Tagebaue kann in Zusammenhang mit den vorherrschenden hydroklimatischen Bedingungen der Klimazonen und mit Rekultivierungsstrategien gebracht werden. Zudem ist auch eine
Betrachtung hydrometeorologischer Größen zur Erkennung von kurzzeitigen Veränderungen des Pflanzenwachstums im NDVI-Signal möglich.
Studien zum Einfluss des zukünftigen Klimawandels auf das Abflussgeschehen fokussieren oft auf die Fortpflanzung von Unsicherheiten in Modellkaskaden, berücksichtigen meist jedoch
die Wasserressourcenbewirtschaftung nur ungenügend. Wir untersuchten den Einfluss der Wasserressourcenbewirtschaftung auf die Abflussvariabilität und die Fortpflanzung von Unsicherheiten von Klimaprojektionen auf Abflusssimulationen in den Einzugsgebieten von
Spree (bis Pegel Große Tränke: 6200 km²) und Schwarzer Elster (5700 km²). Die Einzugsgebiete ähneln sich hinsichtlich Klima, Topographie, Boden und Landnutzung, jedoch ist das Spreeeinzugsgebiet stärker durch den Braunkohletagebau und die damit verbundenen Bewirtschaftung geprägt und durch einen höheren Speicherausbaugrad gekennzeichnet. Um zwischen Bewirtschaftungseinflüssen und meteorologischen Einflüssen zu separieren, wurden für den Zeitraum 1961-2005 beobachtete Abflüsse mit durch das Modell SWIM rekonstruierten natürlichen (d.h. ohne Bewirtschaftungseinfluss) Abflüssen der Vergangenheit verglichen. Mögliche Einflüsse des Klimawandels wurden für den Zeitraum 2018-2052 auf Grundlage von 3 Szenarien des statistischen Regionalmodells STAR (je 100 Realisierungen) mit SWIM (natürliche Abflüsse) und dem Langfristbewirtschaftungsmodell WBalMo (bewirtschaftete Abflüsse) modelliert. Die Analyse erfolgte mit Fokus auf Saisonalität, Oszillation, Verteilung und räumliche Variabilität der Abflüsse. Der Vergleich zwischen beobachteten
und natürlichen Abflüssen der vergangenen Jahrzehnte zeigt, dass die zwischenjährliche Abflussvariabilität im Spreeeinzugsgebiet stärker durch Grubenwassereinleitungen als durch natürliche hydrologische Prozesse bestimmt wurde. Zusätzlich führt der höhere Speicherausbaugrad dazu, dass die kurzzeitliche und saisonale Variabilität im Spreeeinzugsgebiet geringer als im Einzugsgebiet der Schwarzen Elster ist. Simulationen mit Klimaszenarien, welche
steigende Jahresmitteltemperaturen und einen Rückgang der Niederschlagsjahressummen enthalten, führen zu deutlichen Abflussrückgängen. Die Unterschiede der natürlichen Abflüsse
beider Einzugsgebiete sind gering, die Unsicherheiten der Klimaprojektionen werden durch die hydrologische Modellierung verstärkt. Die natürlichen und bewirtschafteten Abflüsse
der Schwarzen Elster unter Klimawandel unterscheiden sich kaum. Im Spreeeinzugsgebiet zeigt sich eine deutliche Verringerung der Variabilität und Unsicherheiten unter Klimawandel von den natürlichen zu den bewirtschafteten Abflüssen. Die Analysen zeigen, dass effektive Wasserressourcenbewirtschaftung die Abflussvariabilität verringern kann und damit auch
dazu beitragen kann, die sich aus Klimawandelprojektionen ergebenden Unsicherheiten zu vermindern. Einzugsgebiete mit einem hohen Ausbaugrad weisen weniger Vulnerabilität bezüglich klimatischer Änderungen auf. Dies unterstreicht die Bedeutung von Wasserresourcenbewirt-schaftungfür die Anpassung an den Klimawandel.
In landscapes with heterogeneous vegetation structure, interception and throughfall patterns produce spatiotemporal
variability of soil moisture. This variability is important for eco-hydrological processes, in particular on
small spatial scales up to the catchment scale. Throughfall depends on vegetation structure, whereas vegetation
development is presumably co-determined by the spatio-temporal distribution of throughfall itself. In addition
to vegetation structure, meteorological factors like wind speed and rainfall intensity also have an impact on
throughfall.
The objective of this study is to quantify the influence of vegetation structure and meteorological variables
on spatial (and in the long run the temporal) variability of throughfall. For that purpose, we developed an
approach combining field methods, image analysis and multivariate statistics. The 6-ha constructed catchment
‚Hühnerwasser‘ (aka Chicken Creek, southern Brandenburg, Germany) offers ideal conditions for the investigation
of eco-hydrological feedback processes. After more than 10 years of development, vegetation structure on the
catchment is spatially heterogeneous and evolves through natural succession. Furthermore, complementary
meteorological data are available on-site.
Throughfall was measured using 50 tipping-bucket rain gauges, which are aligned along two transects in 0.5
and 1 m heights, covering the dominating vegetation types on the catchment (e.g., robinia, sallow thorn, reed,
reedgrass, herbs). The spatial distribution of vegetation structures around each measurement site was recorded
with hemispheric photographs, which were subsequently analyzed using image processing techniques. Two
weather stations provide reference values for precipitation and relevant meteorological variables for wind speed
and direction, air humidity, temperature and irradiation.
The amount and distribution of precipitation measured in scarcely vegetated areas of the catchment widely
correspond with values from the reference weather stations. Under dense vegetation, very heterogeneous values
were recorded, which can be explained by i) canopy interception, and ii) fetching effects. The results of this study
can serve as basis for interception models and may also contribute to complex eco-hydrological models.
Climate change impact studies are associated with error propagation and amplification of uncertainties through model chains from global climate models down to impact (e.g. hydrological) models. The effect of water management, which reduces discharge variability, is often not considered in climate change impact studies. Here, we investigated how water resources management influences discharge variability and uncertainty propagation of climate change scenarios by combining the analyses of observed flow records and model-based climate change impact simulations. Two neighbouring catchments, the Schwarze Elster River (Germany) and the Spree River (Germany and Czech Republic) which are similar in climate, topography and land use, but different in terms of water resources management were chosen as study area. The intense water resources management in the Spree River catchment includes a high reservoir capacity, water use in terms of mining discharges and water withdrawals by power plants as well as water transfers.
The analysis of historical flow records focusses on variability indices (Parde index, Richards-Baker-Flashiness Index, Interquartile Ratio and Baseflow Index). The climate change impact simulations were carried out using a model cascade of (i) the statistical regional model STAR (100 stochastically generated realizations each for 3 scenarios with different prescribed temperature trend), (ii) the hydrological models SWIM and EGMO, and (iii) the water resources management model WBalMo.
The analysis of the observed discharges reveals that the annual discharge variability in the Spree catchment is dominated by mining activities rather than natural rainfall-runoff processes. Due to the high reservoir capacity in the Spree catchment its discharge is characterised by less seasonality and short-term variability compared to the Schwarze Elster. Simulations with climate change scenarios assuming increasing temperature and decreasing precipitation result in pronounced reductions of discharge in both catchments. The differences in potential natural discharges between the Schwarze Elster and the Spree catchments as projected by the hydrological models SWIM and EGMO are marginal. The uncertainties related to the climate projection are propagated through the hydrological models. In the Schwarze Elster catchment, the managed discharges simulated by WBalMo are comparable to the potential natural discharges. In the Spree River however, the short-term variability is moderated by water resources management and managed discharge under climate change is less affected by amplification of uncertainties through model chains.
The results of the study, which combines the analyses of observed flow records and model-based climate change impact simulations, imply that generally, effective water resources management reducing discharge variability hence also reduces uncertainty related to climate change impacts on river discharge. Catchments with a high storage ratio are thus less vulnerable to changing climate conditions. This underlines the role of water resources management in coping with climate change impacts. Yet, due to decreasing reservoir volumes in drought periods, reservoir management alone cannot compensate strong changes in climate conditions over long time periods.
Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff,
erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution.
We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative weights, which we implemented through sigmoid functions. Secondly, the branching of the first and last box is constrained to preserve the rainfall event durations generated by the Poisson rectangular pulse model.
The event-based continuous time step rainfall generator has been developed and tested using 10 min and hourly rainfall data of four stations in North-Eastern Germany. The model performs well in comparison to observed rainfall in terms of event durations and mean event intensities as well as wet spell and dry spell durations. It is currently being tested using data from other stations across Germany and in different climate zones. Furthermore, the rainfall event generator is being applied in modelling approaches aimed at understanding the impact of rainfall variability on hydrological processes.
Assessing ecohydrological systems that undergo state transitions due to environmental change is becoming
increasingly important. One system that can be used to study severe disturbances are post-mining landscapes as they usually are associated with complete removal of vegetation and afterwards subsequent ecosystem restoration or spontaneous rehabilitation in line with natural succession.
Within this context it is of interest, whether and how (fast) the land cover in these areas returns to conditions
comparable to those in the undisturbed surrounding or those prior mining. Many aspects of mine site rehabilitation
depend on climatic, geomorphic and ecological settings, which determine at which rate vegetation may be reestablished. In order to identify general patterns of vegetation establishment, we propose to use NDVI (Normalized Difference Vegetation Index) time series for mine affected land to estimate rate of recovery across climate regions and ecoregions. In this study we analysed the MODIS Terra Satellite 8 day-composite NDVI for areas influenced by surface mining in different climates from 2001 to 2015. The locations have been chosen based on their extent and the
data availability of mining and rehabilitation activities. We selected coal extraction as a case study as strip mining
generates well-defined chronosequences of disturbance. The selected mining areas are located in equatorial, arid, warm temperate or snow climates with different precipitation and temperature conditions according to the Köppen-Geiger classification. We analysed the NDVI time series regarding significant characteristics of the re-vegetation phase. We applied hierarchical cluster analysis to capture the spatial heterogeneity between different pixels (ca. 250 * 250 m2 each) in and around each open cast mine. We disentangled seasonality, trend and residual components in the NDVI time
series by Seasonal and Trend decomposition using LOESS.
As expected the time of the removal of vegetation can be clearly identified from the NDVI time series and provides
the starting point of disturbance. The cluster analysis allowed us to distinguish between the non-mining land, the
mine and the restored land of different ages. Based on these clusters, the time series decomposition revealed the
dominance of the trend of increasing NDVI in areas undergoing the restoration process as well as the prevailing
seasonality of the oldest restored sites. The determined phase of a dominant trend component, lasting until the
NDVI is in the range of the surrounding landscape or the pre-mining conditions, is in the scale of a decade. The impacts of different hydroclimatic regimes and different rehabilitation strategies on long term NDVI development are currently being investigated. Furthermore, coherence analysis will be applied to quantify short term influences of hydrometeorological variables on vegetation development.