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Institute
BTU
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.
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.
Niedrigwasser ist ein integraler Bestandteil des Abflussregimes, der einen zeitlich und regional begrenzten Zustand in einem oberirdischen Gewässer beschreibt, bei dem der Wasserstand oder der Abfluss einen bestimmten Schwellenwert erreicht oder unterschreitet. Die Kenntnis der Niedrigwasserverhältnisse in einem Gewässer hat damit große Bedeutung für seine zielgerichtete wasserwirtschaftliche Nutzung und ökologische Bewertung.
Niedrigwasser ist in erster Linie bedingt durch Niederschlagsmangel, kann sich jedoch auch durch den Rückhalt des Niederschlags in der Schneedecke, in Gletschern oder im gefrorenen Untergrund ergeben. Zudem kann es anthropogen durch diverse Bewirtschaftungsmaßnahmen im Gewässer oder dessen Einzugsgebiet verstärkt oder abgeschwächt werden.
In diesem Merkblatt werden zunächst die wichtigsten Kenngrößen für das Niedrigwasser definiert: Niedrigwasserabfluss bzw. -wasserstand, Unterschreitungsdauer, Abflussdefizit sowie aus der Dauerlinie abgeleitete Quantile. Für die Ermittlung der Unterschreitungsdauer und des Abflussdefizits sind Schwellenwerte erforderlich. Sie sollten in Abhängigkeit von der Zielstellung der Untersuchung festgelegt werden.
Es werden sowohl die natürlichen und anthropogenen Einflussgrößen auf das Niedrigwasser diskutiert, als auch die genannten Kenngrößen und deren Ableitung aus der Ganglinie vorgestellt. Darauf aufbauend entwickeln sich inhaltlich die Kernthemen des Merkblatts: (1) Wahrscheinlichkeitsanalyse von Niedrigwasserabflüssen und (2) Wahrscheinlichkeitsanalyse von Unterschreitungsdauern und Abflussdefiziten. Mit Hilfe eines Verfahrensablaufs und anhand von Beispielen wird dem Leser schrittweise das methodische Vorgehen vermittelt – von der Festlegung der Kenngröße bis hin zur Ermittlung ihrer Quantile und deren Konfidenzgrenzen. Beide Verfahren setzen Stationarität der zugrundeliegenden Serien voraus, sodass im dritten Kernthema des Merkblatts (instationäre Wahrscheinlichkeitsanalyse) diskutiert wird, welche Möglichkeiten bestehen, um mit signifikanten Instationaritäten im Rahmen der Wahrscheinlichkeitsanalyse umzugehen.
Nach mehrjähriger Arbeit der DWA-Arbeitsgruppe HW-3.1
„Niedrigwasser“ hat die DWA im Oktober 2020 den Gelbdruck
für das Merkblatt DWA-M 541 „Statistische Analyse von
Niedrigwasserkenngrößen“ veröffentlicht. Mehr als 100 Anmerkungen
gingen zum Gelbdruck ein, die die Arbeitsgruppe akribisch
geprüft hat. Der Weißdruck des Merkblattes steht nun kurz
bevor. Der Beitrag stellt die inhaltlichen Kernpunkte des Merkblattes
vor, schildert die Entstehung des Merkblattes und bietet
einen Ausblick auf die kommenden Aufgaben der Arbeitsgruppe.
To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipi-tation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological pro-cesses. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution.
To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model (“constrained cascade”) which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid func-tions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipita-tion events of different durations. The constrained cascade model was evaluated in its ability to dis-aggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event char-acteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cas-cade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship.
To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfac-torily in terms of the temporal pattern of the precipitation time series, event characteristics and the in-tensity-frequency relationship.
Revising time series of the Elbe river discharge for flood frequency determination at gauge Dresden
(2009)
Highly disturbed soils and substrates used in land rehabilitation undergo rapid changes after the first wetting events which in turn can lead to ecosystem degradation. Such changes were detected during the early development of the
constructed Hühnerwasser (“Chicken Creek”) catchment in Lusatia, Germany. Surface substrates consisting of
quaternary sandy sediments formed surface seals during the first rainfall events leading to reduced infiltration and
substantially increased surface runoff. Subsequently biological soil crusts formed and stabilised the surface. The
aim of this study is to investigate the factors that cause the hydraulic conductivity to decrease using undisturbed
and disturbed soil samples. Based on the hypothesis that physical and biological crusts lower the hydraulic conductivity, the first set of experiments with undisturbed soil cores from the Hühnerwasser catchment were carried out to measure the saturated hydraulic conductivity using the constant head method. Measurements were done with intact cores and repeated after the surface crust was removed. As the quaternary glacial sediments tend to display hard setting behaviour, we further hypothesised that the mobilisation of fine particles within the cores lead to pore clogging and that wet-dry cycles will therefore decrease hydraulic conductivity. A second set of experiments using the same methodology consisted of five
repeated measurements of hydraulic conductivity after each drying cycle. These measurements were done with
undisturbed core samples as well as repacked cores in order to assess how dry packing affects the dynamics of the
hydraulic conductivity somewhat similar to the situation during the first wetting after completion of the catchment
construction. For all experiments, the temporal evolution of hydraulic conductivity was measured and the turbidity
of the effluent was recorded. The results clearly demonstrated that the substrate is highly unstable. The first set of experiments showed that the removal of the crust lead generally to a decrease in hydraulic conductivity. The process of crust removal represented a severe disturbance of the surface soil which to our understanding causes particle mobilisation and subsequent pore clogging. The first hypothesis could neither be rejected nor accepted. The second set of
experiments showed that the hydraulic conductivity significantly dropped in particular after the first drying event. This was observed for both undisturbed and repacked samples. The following drying cycles further decreased the
hydraulic conductivity in the repacked samples. The decrease in hydraulic conductivity was positively correlated to turbidity values in the effluent of the samples, indicating particle mobilisation in all samples. The results imply that hydraulic properties in such substrates undergo rapid changes that depend on the temporal dynamics of atmospheric drivers, precipitation and evaporative demand, controlling the degree of wetness and the rate and degree of drying during the very early stage after placement. Associated with the dynamics of the atmospheric drivers are the biological changes due to the formation of biological soil crusts and the establishment of vegetation, both of them contributing to the stabilisation of hydraulic properties.