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Landscapes that are heavily disturbed or newly formed by either natural processes or human activity are in a state
of disequilibrium. Their initial development is thus characterized by highly dynamic processes under all climatic
conditions. The primary distribution and structure of the solid phase (i.e. mineral particles forming the pore space)
is one of the decisive factors for the development of hydrological behavior of the eco-hydrological system and
therefore (co-) determining for its – more or less – stable final state. The artificially constructed ‚Hühnerwasser‘ catchment (a 6 ha area located in the open-cast lignite mine
Welzow-Süd, southern Brandenburg, Germany) is a landscape laboratory where the initial eco-hydrological development is observed since 2005. The specific formation (or construction) processes generated characteristic sediment structures and distributions, resulting in a spatially heterogeneous initial state of the catchment. We developed a structure generator that simulates the characteristic distribution of the solid phase for such constructed landscapes. The program is able to generate quasi-realistic structures and sediment compositions
on multiple spatial levels (1 cm up to ∼ 100 m scale). The generated structures can be i) conditioned to actual
measurement values (e.g., soil texture and bulk distribution); ii) stochastically generated, and iii) calculated deterministically according to the geology and technical processes at the excavation site. Results are visualized using the GOCAD software package and the free software Paraview. Based on the 3D-spatial sediment distributions, effective hydraulic van-Genuchten parameters are calculated using pedotransfer functions. The hydraulic behavior of different sediment distribution (i.e. versions or variations of the catchment’s porous body) is calculated using a numerical model developed by one of us (Caviedes-Voullième). Observation data are available from catchment monitoring are available for i) determining the boundary conditions (e.g., precipitation), and ii) the calibration / validation of the model (catchment discharge, ground water). The analysis of multiple sediment distribution scenarios should allow to approximately determine the influx of starting conditions on initial development of hydrological behavior. We present first flow modeling results for a reference (conditioned) catchment model and variations thereof. We will also give an outlook on further methodical
development of our approach.
A numerical study on the influence of mìcrotopography on raìnfall-runoff-infiltration partitioning
(2017)
Microtopographic features, although minute relative to the hillslope scales, are not insignificant in terms of runoff generation, rain-runoff-infiltration partitioning and overall hillslope hydrological signals. As-sessing the effects of such small scale features, arguably requires mathematical models that can cope with microtopraphic complexity to adequately represent surface water dynamics, which in turn deter-mine hydrological signals at the hillslope scale. In this work, rain-runoff simulations are perfomed with a 2D shallow water model on a rectangular domain representing a hillslope with an idealized 2D sinusoidal microtopography. Several combinations of slope, wavelength and amplitudes were used to create over 500 surfaces on which simulations were performed in order to assess their hydrological response in terms of rainfallrunoff-infiltration partitioning. The results were analysed through several dimensionless indices which allow to observe the dependency of characteristic hydrological responses to mi-crotopography properties. They reveal a complex dependency of hydrological signatures to surface microtopography. In particular, the results show that the fraction of rainfall that results in infiltration is increased following a particular non-linear dependency on surface smoothness. Additionally, hydrograph properties and surface flow connectivity also show emerging patterns in response to microtopography.
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.
Flash droughts are caused by a rapid depletion of soil moisture, and they severely affect vegetation growth and agricultural production. Notwithstanding the growing importance of flash droughts under the warming climate, drivers of flash droughts across the Europe are not well understood. Here we estimate the changes in flash droughts characteristics across Europe using the ERA5 reanalysis dataset for 1950–2019 period. We find a substantial increase in the frequency and spatial extent of flash droughts across Europe (with 79% of the total area) during the growing season with at-least one fourth of domain showing two-fold increase in the recent decades. Increased occurrence of flash drought is largely attributed to frequent occurrence of warmer and drier compound extremes, with a sharp gradient of changes being noticed in Mediterranean and Central European regions. Compound meteorological extremes causing the flash drought events across Europe are pre-dominantly driven by the recent climate warming. With unabated greenhouse gas emissions and current pace of climate warming, Europe is likely to face an increased occurrence of flash droughts, requiring prompt response for effective drought adaptation and management strategies.
Precipitation scenario analysis is a crucial step in flood risk assessment, in which storm events with different probabilities are defined and used as input for the hydrological/hydrodynamic calculations. Rainfall generators may serve as a basis for the precipitation analysis. With the increase in the use of high resolution spatially-explicit hydrological/hydrodynamic models in flood risk calculations, demand for synthetic gridded precipitation input is increasing. In this work, we present a dynamic spatiotemporal rainfall generator. The model is capable of generating catchment-scale rainfields containing moving storms, which enable physically-plausible and spatiotemporally coherent precipitation events. This is achieved by the tools event-based approach, where dynamic storms are identified as clusters of related data that occur at different locations in space and time, and are then used as basis for event regeneration. The implemented methodology, mainly inspired by Dierden et al. (2019), provides an improvement in the spatial coherence of precipitation extremes, which can in turn be beneficial in flood risk calculations. The model has been validated under different databases such as the radar-based RADALON dataset or spatially-interpolated historical raingauge timeseries of different catchments in Germany, which is also presented in this work. The validation indicates the models ability to adequately preserve observed storm statistics in the generated timeseries. The generator is developed as an extension to the state-of-the-science flood risk modelling tool ProMaIDes (Promaides 2023). The model also puts great focus on user accessibility with offering features such as an easy installation process, support for most operating systems, a user interface and an online user manual.
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.