The Potential of Land-Use Change to Mitigate Water Scarcity in Northeast Germany – a Review

Climate change is expected to increase water scarcity in northeast Germany. Land-use change is one of the options of mitigation that is intensely discussed in this region. This review aims at giving a compilation of existing data and modelling studies in order to investigate the potential and the limits of the land-use change approach

Comparative simulation of the nitrogen dynamics using the INCA model and a neural network analysis: implications for improved nitrogen modelling

International audienceContinuing deposition of nitrogen in forested catchments affects stream and groundwater quality. However, the dependence of nitrogen dynamics on climatic and hydrological boundary conditions is still poorly understood. These dynamics have been investigated by applying the process-oriented Integrated Nitrogen in CAtchments (INCA) model and an artificial neural network to the data set from the forested Steinkreuz catchment in South Germany. The data comprise daily values of precipitation, air temperature and discharge of the catchment runoff. The INCA model simulated the mean nitrate concentration in the stream as well as seasonal fluctuations but it underestimated the short-term variance of the observed stream water nitrate concentration, especially the pronounced concentration peaks in late summer. In contrast, the artificial neural network matched the short-term dynamics using non-linear regressions with stream discharge and air temperature data. The results provide strong evidence that the short-term dynamics of stream nitrate concentration during storm-flow were generated in the riparian zone, which is less than 1% of the catchment area, and is not considered explicitly in the INCA model. The concentration peaks have little effect on the catchment's nitrogen budget and the shallow groundwater data suggest that the short-term hydrological dynamics also govern groundwater recharge in the upland parts of the catchment. This substantial underestimate by the INCA model parameterisation is balanced by a corresponding underestimate of denitrification in clayey layers of the deeper aquifer. A better understanding of these processes is necessary to improve long-term risk assessments. Keywords: catchment, runoff, nitrogen, INCA, artificial neural network, flushin

Groundwater flow reversal between small water bodies and their adjoining aquifers: A numerical experiment

The countless kettle holes in the Late Pleistocene landscapes of Northern Europe are hotspots for biodiversity and biogeochemical processes. As a rule, they are hydraulically connected to the shallow groundwater system. The rapid, intensive turnover of carbon, nutrients and pollutants in the kettle holes therefore has a major impact on the quality of the shallow groundwater downstream. As a result of high-evapotranspiration rates from their riparian vegetation or strong storm events, the process of downstream groundwater flow may stagnate and reverse back towards the kettle hole, making interactions between the groundwater and kettle hole more complex. Furthermore, the highly heterogeneous soil landscape in the catchment contributes to this complexity. Therefore, the present study aims to enhance our understanding of this complicated interaction. To this end, 24 model variants were integrated into HydroGeoSphere, capturing a wide range of uncertainties in quantifying the extent and timing of groundwater flow reversal between a kettle hole and the adjacent aquifer. The findings revealed that the groundwater flow reversal lasted between 1 month and 19 years at most and occurred in a distance of more than 140 m downstream of the kettle hole. Our results demonstrated that the groundwater flow reversal arises especially often in areas where the shallow aquifer possesses low-hydraulic conductivity. There may also be a recurrent circulating flow between the groundwater and kettle hole, resulting in solute turnover within the kettle hole. This holds particularly true in dry periods with medium to low-water levels within the kettle hole and a negative water balance. However, shallow groundwater flow reversals are not necessarily a consequence of seasonal effects. In this respect, the properties of the local shallow aquifer by far outweigh the effect of the kettle hole location in the regional flow regime

Efficiently Enumerating Hitting Sets of Hypergraphs Arising in Data Profiling

We devise an enumeration method for inclusion-wise minimal hitting sets in hypergraphs. It has delay O(mk* +1 · n2) and uses linear space. Hereby, n is the number of vertices, m the number of hyperedges, and k* the rank of the transversal hypergraph. In particular, on classes of hypergraphs for which the cardinality k* of the largest minimal hitting set is bounded, the delay is polynomial. The algorithm solves the extension problem for minimal hitting sets as a subroutine. We show that the extension problem is W[3]-complete when parameterised by the cardinality of the set which is to be extended. For the subroutine, we give an algorithm that is optimal under the exponential time hypothesis. Despite these lower bounds, we provide empirical evidence showing that the enumeration outperforms the theoretical worst-case guarantee on hypergraphs arising in the profiling of relational databases, namely, in the detection of unique column combinations

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Time series of groundwater and stream water quality often exhibit substantial temporal and spatial variability, whereas typical existing monitoring data sets, e.g. from environmental agencies, are usually characterized by relatively low sampling frequency and irregular sampling in space and/or time. This complicates the differentiation between anthropogenic influence and natural variability as well as the detection of changes in water quality which indicate changes in single drivers. We suggest the new term "dominant changes" for changes in multivariate water quality data which concern (1) multiple variables, (2) multiple sites and (3) long-term patterns and present an exploratory framework for the detection of such dominant changes in data sets with irregular sampling in space and time. Firstly, a non-linear dimension-reduction technique was used to summarize the dominant spatiotemporal dynamics in the multivariate water quality data set in a few components. Those were used to derive hypotheses on the dominant drivers influencing water quality. Secondly, different sampling sites were compared with respect to median component values. Thirdly, time series of the components at single sites were analysed for long-term patterns. We tested the approach with a joint stream water and groundwater data set quality consisting of 1572 samples, each comprising sixteen variables, sampled with a spatially and temporally irregular sampling scheme at 29 sites in northeast Germany from 1998 to 2009. The first four components were interpreted as (1) an agriculturally induced enhancement of the natural background level of solute concentration, (2) a redox sequence from reducing conditions in deep groundwater to post-oxic conditions in shallow groundwater and oxic conditions in stream water, (3) a mixing ratio of deep and shallow groundwater to the streamflow and (4) sporadic events of slurry application in the agricultural practice. Dominant changes were observed for the first two components. The changing intensity of the first component was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase in the second component at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer

Differentiating between crop and soil effects on soil moisture dynamics

There is an urgent need to develop sustainable agricultural land use schemes. Intensive crop production has induced increased greenhouse gas emissions and enhanced nutrient and pesticide leaching to groundwater and streams. Climate change is also expected to increase drought risk as well as the frequency of extreme precipitation events in many regions. Consequently, sustainable management schemes require sound knowledge of site-specific soil water processes that explicitly take into account the interplay between soil heterogeneities and crops. In this study, we applied a principal component analysis to a set of 64 soil moisture time series from a diversified cropping field featuring seven distinct crops and two weeding management strategies. Results showed that about 97 % of the spatial and temporal variance of the data set was explained by the first five principal components. Meteorological drivers accounted for 72.3 % of the variance and 17.0 % was attributed to different seasonal behaviour of different crops. While the third (4.1 %) and fourth (2.2 %) principal components were interpreted as effects of soil texture and cropping schemes on soil moisture variance, respectively, the effect of soil depth was represented by the fifth component (1.7 %). However, neither topography nor weed control had a significant effect on soil moisture variance. Contrary to common expectations, soil and rooting pattern heterogeneity seemed not to play a major role. Findings of this study highly depend on local conditions. However, we consider the presented approach generally applicable to a large range of site conditions.</p

Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land

Explanation of header and data: Site is either west-facing or east-facing (see "Measurement site"); DistToEdge is the distance to the zero line (edge) in m, negative values are in the forest, positive values are in the arable land, zero is the edge; Repetition is the number of repetitions in the lab; Depth is measured in cm and is the depth of soil sampling ±3 cm; Ctot is the percentage (%) of total soil carbon content in the tested soil sample; Ntot is the percentage (%) of total soil nitrogen content in the tested soil sample and pH is the numeric scale to specify the acidity or basicity of the soil sample in solution

Nonlinear effects of environmental drivers shape macroinvertebrate biodiversity in an agricultural pondscape

Agriculture is a leading cause of biodiversity loss and significantly impacts freshwater biodiversity through many stressors acting locally and on the landscape scale. The individual effects of these numerous stressors are often difficult to disentangle and quantify, as they might have nonlinear impacts on biodiversity. Within agroecosystems, ponds are biodiversity hotspots providing habitat for many freshwater species and resting or feeding places for terrestrial organisms. Ponds are strongly influenced by their terrestrial surroundings, and understanding the determinants of biodiversity in agricultural landscapes remains difficult but crucial for improving conservation policies and actions. We aimed to identify the main effects of environmental and spatial variables on α-, β-, and γ-diversities of macroinvertebrate communities inhabiting ponds (n = 42) in an agricultural landscape in the Northeast Germany, and to quantify the respective roles of taxonomic turnover and nestedness in the pondscape. We disentangled the nonlinear effects of a wide range of environmental and spatial variables on macroinvertebrate α- and β-biodiversity. Our results show that α-diversity is impaired by eutrophication (phosphate and nitrogen) and that overshaded ponds support impoverished macroinvertebrate biota. The share of arable land in the ponds' surroundings decreases β-diversity (i.e., dissimilarity in community), while β-diversity is higher in shallower ponds. Moreover, we found that β-diversity is mainly driven by taxonomic turnover and that ponds embedded in arable fields support local and regional diversity. Our findings highlight the importance of such ponds for supporting biodiversity, identify the main stressors related to human activities (eutrophication), and emphasize the need for a large number of ponds in the landscape to conserve biodiversity. Small freshwater systems in agricultural landscapes challenge us to compromise between human demands and nature conservation worldwide. Identifying and quantifying the effects of environmental variables on biodiversity inhabiting those ecosystems can help address threats impacting freshwater life with more effective management of pondscapes

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Time series of groundwater and stream water quality often exhibit substantial temporal and spatial variability, whereas typical existing monitoring data sets, e.g. from environmental agencies, are usually characterized by relatively low sampling frequency and irregular sampling in space and/or time. This complicates the differentiation between anthropogenic influence and natural variability as well as the detection of changes in water quality which indicate changes in single drivers. We suggest the new term dominant changes for changes in multivariate water quality data which concern (1) multiple variables, (2) multiple sites and (3) long-term patterns and present an exploratory framework for the detection of such dominant changes in data sets with irregular sampling in space and time. Firstly, a non-linear dimension-reduction technique was used to summarize the dominant spatiotemporal dynamics in the multivariate water quality data set in a few components. Those were used to derive hypotheses on the dominant drivers influencing water quality. Secondly, different sampling sites were compared with respect to median component values. Thirdly, time series of the components at single sites were analysed for long-term patterns. We tested the approach with a joint stream water and groundwater data set quality consisting of 1572 samples, each comprising sixteen variables, sampled with a spatially and temporally irregular sampling scheme at 29 sites in northeast Germany from 1998 to 2009. The first four components were interpreted as (1) an agriculturally induced enhancement of the natural background level of solute concentration, (2) a redox sequence from reducing conditions in deep groundwater to post-oxic conditions in shallow groundwater and oxic conditions in stream water, (3) a mixing ratio of deep and shallow groundwater to the streamflow and (4) sporadic events of slurry application in the agricultural practice. Dominant changes were observed for the first two components. The changing intensity of the first component was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase in the second component at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer.</p