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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.
Trend analysis on observations and model-based climate change simulations are two commonly used methods for climate change detection and impact analysis. Here we propose an integrated assessment and interpretation of climate change impacts as a prerequisite for stakeholder outreach and planning of suitable climate change adaptation measures. The assessment includes (i) identifying trends in mete-orological and hydrological observations and their nature, (ii) analysing the relation between the mete-orological drivers and generated run-off as an integrated catchment response and (iii) analysing how hitherto changes agree with the simulations by regional climate models (RCMs). The Lusatian river catchments of Spree and Schwarze Elster, characterised by high anthropogenic impact (e.g. mining activities) and low natural water yield, serve as study areas. The results of this study suggest that in-creases in observed temperature and potential evapotranspiration are robust while observed precipitation remained nearly unchanged (1963–2006). The RCMs agree on simulating a temperature increase but simulate opposing trends for precipitation for both past (1963–2006) and future (2018–2060) periods, the latter inducing differences in the hydrological response (actual evapotranspiration and run-off). For stakeholder outreach, we communicated a range of potential future climates and identified the statistical RCMs (STAR, WettReg) as warm and dry scenarios, and the dynamical RCMs (REMO, CCLM) as wet scenarios. Ultimately, the combined analysis of trends in observations and simulation models can be beneficial for stakeholder outreach and may increase their willingness to plan and implement suitable climate change adaptation strategies which are urgently needed within the Lusatian river catchments.
Difficulties in quantitatively closing the water balance of catchments arise when upscaling point measurements and from insufficient knowledge of the physical boundaries, inner structure, and storage volumes of natural catchments. In addition, there is a strong need for generalizing the relationship between catchment characteristics and hydrological response. Therefore, experimental catchments with well-known boundaries and conditions could contribute valuable data to hydrological and critical zone research. One of the most well-established and largest constructed catchments is the Chicken Creek catchment (6 ha including a pond, Brandenburg, Germany) representing an initial ecosystem undergoing highly dynamic ecological development starting from clearly defined starting conditions. Directly after completion of the construction, extensive monitoring equipment was installed to track the ecosystem development and to capture the
spatiotemporal variability of meteorological, hydrological, ecological, and soil conditions and vegetation succession. In this study, we focused on the water balance dynamics of the Chicken Creek catchment for the period 2005 to 2015 as influenced by ecological development. Water storage in the catchment was calculated from a three-dimensional model of groundwater volumes, soil moisture measurements, and water level recordings of the pond. The catchment water balance equation was resolved for evapotranspiration, the only part that was not measured directly. Time series of meteorological, hydrological, and ecological data for 10 yr enabled us to characterize the transient development of the catchment and to evaluate the effect of different feedback mechanisms on catchment hydrology.