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Institute
- Institut für Geowissenschaften (30) (remove)
Due to increasing demands and competition for high quality groundwater resources in many parts of the world, there is an urgent need for efficient methods that shed light on the interplay between complex natural settings and anthropogenic impacts. Thus a new approach is introduced, that aims to identify and quantify the predominant processes or factors of influence that drive groundwater and lake water dynamics on a catchment scale. The approach involves a non-linear dimension reduction method called Isometric feature mapping (Isomap). This method is applied to time series of groundwater head and lake water level data from a complex geological setting in Northeastern Germany. Two factors explaining more than 95% of the observed spatial variations are identified: (1) the anthropogenic impact of a waterworks in the study area and (2) natural groundwater recharge with different degrees of dampening at the respective sites of observation. The approach enables a presumption-free assessment to be made of the existing geological conception in the catchment, leading to an extension of the conception. Previously unknown hydraulic connections between two aquifers are identified, and connections revealed between surface water bodies and groundwater. (C) 2014 Elsevier B.V. All rights reserved.
In this paper we report on a series of replicated tracer experiments with deuterium conducted under controlled, steady stormflow conditions at the Gardsjon G1 catchment in south-western Sweden. In five different years, these experiments were carried out in a subcatchment of G1. Deuterium was applied as a narrow pulse so that distributions of water transit times could be directly inferred from the observed tracer breakthrough curves. Significantly different transit times of water were observed under similar experimental conditions. Coefficients of variation for estimated mean transit times were greater than 60%, which can be understood as a measure of the interannual variability for this type of experiments. Implications for water transit times under more natural flow conditions as wells as for future experimentation are discussed. Copyright (c) 2015 John Wiley & Sons, Ltd.
Landscapes can be viewed as spatially heterogeneous areas encompassing terrestrial and aquatic domains. To date, most landscape carbon (C) fluxes have been estimated by accounting for terrestrial ecosystems, while aquatic ecosystems have been largely neglected. However, a robust assessment of C fluxes on the landscape scale requires the estimation of fluxes within and between both landscape components. Here, we compiled data from the literature on C fluxes across the air–water interface from various landscape components. We simulated C emissions and uptake for five different scenarios which represent a gradient of increasing spatial heterogeneity within a temperate young moraine landscape: (I) a homogeneous landscape with only cropland and large lakes; (II) separation of the terrestrial domain into cropland and forest; (III) further separation into cropland, forest, and grassland; (IV) additional division of the aquatic area into large lakes and peatlands; and (V) further separation of the aquatic area into large lakes, peatlands, running waters, and small water bodies These simulations suggest that C fluxes at the landscape scale might depend on spatial heterogeneity and landscape diversity, among other factors. When we consider spatial heterogeneity and diversity alone, small inland waters appear to play a pivotal and previously underestimated role in landscape greenhouse gas emissions that may be regarded as C hot spots. Approaches focusing on the landscape scale will also enable improved projections of ecosystems’ responses to perturbations, e.g., due to global change and anthropogenic activities, and evaluations of the specific role individual landscape components play in regional C fluxes. WIREs Water 2016, 3:601–617. doi: 10.1002/wat2.1147
Deep seepage estimation is important for water balance investigations of groundwater and the vadose zone. A simplified Buckingham-Darcy method to assess time series of deep seepage fluxes was proposed by Schindler and Muller (1998). In the method dynamics of water fluxes are calculated by a soil hydraulic conductivity function. Measured soil moistures and matric heads are used as input data. Resulting time series of flux dynamics are scaled to realistic absolute levels by calibrating the method with the areal water balance. An assumption of the method is that water fluxes at different positions exhibit identical dynamics although their absolute values can differ. The aim of this study was to investigate uncertainties of that method depending on the particle size distribution and textural heterogeneity in non-layered soils. We performed a numerical experiment using the two-dimensional Richards Equation. A basic model of transient water fluxes beneath the root and capillary zone was setup and used to simulate time series of soil moisture, matric head, and seepage fluxes for 4221 different cases of particle size distribution and intensities of textural heterogeneity. Soil hydraulic parameters were predicted by the pedotransfer function Rosetta. Textural heterogeneity was modeled with Miller and Miller scaling factors arranged in spatial random fields. Seepage fluxes were calculated with the Buckingham-Darcy method from simulated soil moisture and matric head time series and compared with simulated reference fluxes. The median of Root Mean Square Error was about 0.026 cm d(-1) and the median of maximum cross correlation was 0.96 when the method was calibrated adequately. The method's performance was mainly influenced by (i) the soil textural class and (ii) the time period used for flux calibration. It performed best in sandy loam while hotspots of errors occurred in sand and silty texture. Calibrating the method with time periods that exhibit high variance of seepage fluxes yielded the best performance. The geostatistical properties of the Miller and Miller scaling field influenced the performance only slightly. However, the Miller and Miller scaling procedure generated heterogeneous flow fields that were addressed as main reason for mismatches of simulated reference fluxes and fluxes obtained with the Buckingham-Darcy method.
Hydrology is rich in methods that use information theory to evaluate monitoring networks. Yet in most existing studies, only the available data set as a whole is used, which neglects the intraannual variability of the hydrological system. In this paper, we demonstrate how this variability can be considered by extending monitoring evaluation to subsets of the available data. Therefore, we separately evaluated time windows of fixed length, which were shifted through the data set, and successively extended time windows. We used basic information theory measures and a greedy ranking algorithm based on the criterion of maximum information/minimum redundancy. The network investigated monitored surface and groundwater levels at quarter-hourly intervals and was located at an artificially drained lowland site in the Spreewald region in north-east Germany. The results revealed that some of the monitoring stations were of value permanently while others were needed only temporally. The prevailing meteorological conditions, particularly the amount of precipitation, affected the degree of similarity between the water levels measured. The hydrological system tended to act more individually during periods of no or little rainfall. The optimal monitoring setup, its stability, and the monitoring effort necessary were influenced by the meteorological forcing. Altogether, the methodology presented can help achieve a monitoring network design that has a more even performance or covers the conditions of interest (e.g., floods or droughts) best.
The classification of small catchments with respect to low flow risk is needed by water and environmental managers to plan adaptation measures for freshwater streams. In this study a new approach is presented to assess the risk of seasonal low flow in the Pleistocene landscape of the Federal State of Brandenburg in Germany. Seasonal low flow and drought in small streams is very common in this region and is predicted to increase due to climate change within the next decades. Data of 15 years (1991-2006) of daily discharge at 37 small catchments (<500 km(2)) and rainfall data from the same region were used. Principal component analyses were applied to the two data sets separately.
The first five principal components of the discharge data, principal components of a precipitation data set covering the same catchments and catchment characteristics were used to explain the patterns found. The first five discharge components explained 72.9% of the total variance in the data set. The first component reflected the general regional discharge pattern. Components 2 and 3 of the discharge data could be related to spatial patterns of precipitation. Components 4 and 5 of the discharge data reflected geohydrologic processes within the catchments. In order to identify catchments with high risk with respect to low flows, component three and five were important as they both identified catchments with faster decrease of flows during summer. These components were used to estimate low flow risk. Catchments located in the northeast of Brandenburg, especially those in the Barnim highlands north and east of Berlin, were identified to be prone to seasonal low flow. There water management measures to adapt to climate change are needed the most.
The pleistocenic landscape in North Europe, North Asia and North America is spotted with thousands of natural ponds called kettle holes. They are biological and biogeochemical hotspots. Due to small size, small perimeter and shallow depth biological and biogeochemical processes in kettle holes are closely linked to the dynamics and the emissions of the terrestrial environment. On the other hand, their intriguing high spatial and temporal variability makes a sound understanding of the terrestrial-aquatic link very difficult. It is presumed that intensive agricultural land use during the last decades has resulted in a ubiquitous high nutrient load. However, the water quality encountered at single sites highly depends on internal biogeochemical processes and thus can differ substantially even between adjacent sites. This study aimed at elucidating the interplay between external drivers and internal processes based on a thorough analysis of a comprehensive kettle hole water quality data set. To study the role of external drivers, effects of land use in the adjacent terrestrial environment, effects of vegetation at the interface between terrestrial and aquatic systems, and that of kettle hole morphology on water quality was investigated. None of these drivers was prone to strong with-in year variability. Thus temporal variability of spatial patterns could point to the role of internal biogeochemical processes. To that end, the temporal stability of the respective spatial patterns was studied as well for various solutes. All of these analyses were performed for a set of different variables. Different results for different solutes were then used as a source of information about the respective driving processes. In the Quillow catchment in the Uckermark region, about 100 km north of Berlin, Germany, 62 kettle holes have been regularly sampled since 2013. Kettle hole catchments were determined based on a groundwater level map of the uppermost aquifer. The catchments were not clearly related to topography. Spatial patterns of kettle hole water concentration of (earth) alkaline metals and chloride were fairly stable, presumably reflecting solute concentration of the uppermost aquifer. In contrast, spatial patterns of nutrients and redox-sensitive solutes within the kettle holes were hardly correlated between different sampling campaigns. Correspondingly, effects of season, hydrogeomorphic kettle hole type, shore vegetation or land use in the respective catchments were significant but explained only a minor portion of the total variance. It is concluded that internal processes mask effects of the terrestrial environment. There is some evidence that denitrification and phosphorus release from the sediment during frequent periods of hypoxia might play a major role. The latter seems to boost primary production occasionally. These processes do not follow a clear seasonal pattern and are still not well understood.
Due to increasing demands for irrigation using groundwater as a source there is an urgent need for efficient methods that shed light on the resulting anthropogenic impacts on the connected aquifers. Thus an innovative approach is introduced, that aims to identify predominant geochemical changes in the groundwater system. The approach involves a principal component analysis as a promising tool to disentangle the effects of different impacts and even to give a quantitative assessment of the respective effect strength at each site. The study was applied in an irrigation region of the Nuthe River Basin, State Brandenburg, Northeast Germany. The results identify the negative impacts on the groundwater quality in the aquifer used for irrigation. A decrease of shallow groundwater quality under irrigation due to contamination with fertilizers (NO3, Cl, K, Na) and a slight shift in the redox system is indicated. Beside this direct impact on the shallow groundwater a long-term impact on a deeper groundwater resource could be identified. There is clear evidence, that the contamination is not restricted to the shallow groundwater but that extraction from deeper wells increasingly includes deeper, uncontaminated groundwater resources into the local irrigation cycle. The approach can be used as a basic tool for the adaptation of sustainable agricultural irrigation management strategies.
Monitoring the phase space of ecosystems: Concept and examples from the Quillow catchment, Uckermark
(2016)
Ecosystem research benefits enormously from the fact that comprehensive data sets of high quality, and covering long time periods are now increasingly more available. However, facing apparently complex interdependencies between numerous ecosystem components, there is urgent need rethinking our approaches in ecosystem research and applying new tools of data analysis. The concept presented in this paper is based on two pillars. Firstly, it postulates that ecosystems are multiple feedback systems and thus are highly constrained. Consequently, the effective dimensionality of multivariate ecosystem data sets is expected to be rather low compared to the number of observables. Secondly, it assumes that ecosystems are characterized by continuity in time and space as well as between entities which are often treated as distinct units. Implementing this concept in ecosystem research requires new tools for analysing large multivariate data sets. This study presents some of them, which were applied to a comprehensive water quality data set from a long-term monitoring program in Northeast Germany in the Uckermark region, one of the LTER-D (Long Term Ecological Research network, Germany) sites. Short-term variability of the kettle hole water samples differed substantially from that of the stream water samples, suggesting different processes generating the dynamics in these two types of water bodies. However, again, this seemed to be due to differing intensities of single processes rather than to completely different processes. We feel that research aiming at elucidating apparently complex interactions in ecosystems could make much more efficient use from now available large monitoring data sets by implementing the suggested concept and using corresponding innovative tools of system analysis. (C) 2015 Elsevier Ltd. All rights reserved.
Runoff, especially during summer months, and low flows have decreased in Central and Eastern Europe during the last decades. A detailed knowledge on predictors and dependencies between meteorological forcing, catchment properties and low flow is necessary to optimize regional adaption strategies to sustain minimum runoff. The objective of this study is to identify low flow predictors for 16 small catchments in Northeast Germany and their long-term shifts between 1965 and 2006. Non-linear regression models (support vector machine regression) were calibrated to iteratively select the most powerful low flow predictors regarding annual 30-day minimum flow (AM(30)). The data set consists of standardized precipitation (SPI) and potential evapotranspiration (SpETI) indices on different time scales and lag times. The potential evapotranspiration of the previous 48 and 3 months, as well as the precipitation of the previous 3 months and last year were the most relevant predictors for AM(30). Pearson correlation (r(2)) of the final model is 0.49 and if for every year the results for all catchments are averaged r(2) increases to 0.80 because extremes are smoothing out. Evapotranspiration was the most important low flow predictor for the study period. However, distinct long-term shifts in the predictive power of variables became apparent. The potential evapotranspiration of the previous 48 months explained most of the variance, but its relevance decreased during the last decades. The importance of precipitation variables increased with time. Model performance was higher at catchments with a more damped discharge behavior. The results indicate changes in the relevant processes or flow paths generating low flows. The identified predictors, temporal patterns and patterns between catchments will support the development of low flow monitoring systems and determine those catchments where adaption measures should aim more at increasing groundwater recharge. (C) 2014 Elsevier B.V. All rights reserved.