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Terrestrial environmental systems are characterised by numerous feedback links between their different compartments. However, scientific research is organized into disciplines that focus on processes within the respective compartments rather than on interdisciplinary links. Major feedback mechanisms between compartments might therefore have been systematically overlooked so far. Without identifying these gaps, initiatives on future comprehensive environmental monitoring schemes and experimental platforms might fail. We performed a comprehensive overview of feedbacks between compartments currently represented in environmental sciences and explores to what degree missing links have already been acknowledged in the literature. We focused on process models as they can be regarded as repositories of scientific knowledge that compile findings of numerous single studies. In total, 118 simulation models from 23 model types were analysed. Missing processes linking different environmental compartments were identified based on a meta-review of 346 published reviews, model inter-comparison studies, and model descriptions. Eight disciplines of environmental sciences were considered and 396 linking processes were identified and ascribed to the physical, chemical or biological domain. There were significant differences between model types and scientific disciplines regarding implemented interdisciplinary links. The most wide-spread interdisciplinary links were between physical processes in meteorology, hydrology and soil science that drive or set the boundary conditions for other processes (e.g., ecological processes). In contrast, most chemical and biological processes were restricted to links within the same compartment. Integration of multiple environmental compartments and interdisciplinary knowledge was scarce in most model types. There was a strong bias of suggested future research foci and model extensions towards reinforcing existing interdisciplinary knowledge rather than to open up new interdisciplinary pathways. No clear pattern across disciplines exists with respect to suggested future research efforts. There is no evidence that environmental research would clearly converge towards more integrated approaches or towards an overarching environmental systems theory. (c) 2017 Elsevier B.V. All rights reserved.
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.
Effects of data and model simplification on the results of a wetland water resource management model
(2016)
This paper presents the development of a wetland water balance model for use in a large river basin with many different wetlands. The basic model was primarily developed for a single wetland with a complex water management system involving large amounts of specialized input data and water management details. The aim was to simplify the model structure and to use only commonly available data as input for the model, with the least possible loss of accuracy. Results from different variants of the model and data adaptation were tested against results from a detailed model. This shows that using commonly available data and unifying and simplifying the input data is tolerable up to a certain level. The simplification of the model has greater effects on the evaluated water balance components than the data adaptation. Because this simplification was necessary for large-scale use, we suggest that, for reasons of comparability, simpler models should always be applied with uniform data bases for large regions, though these should only be moderately simplified. Further, we recommend using these simplified models only for large-scale comparisons and using more specific, detailed models for investigations on smaller scales.
Effects of Data and Model Simplification on the Results of a Wetland Water Resource Management Model
(2016)
This paper presents the development of a wetland water balance model for use in a large river basin with many different wetlands. The basic model was primarily developed for a single wetland with a complex water management system involving large amounts of specialized input data and water management details. The aim was to simplify the model structure and to use only commonly available data as input for the model, with the least possible loss of accuracy. Results from different variants of the model and data adaptation were tested against results from a detailed model. This shows that using commonly available data and unifying and simplifying the input data is tolerable up to a certain level. The simplification of the model has greater effects on the evaluated water balance components than the data adaptation. Because this simplification was necessary for large-scale use, we suggest that, for reasons of comparability, simpler models should always be applied with uniform data bases for large regions, though these should only be moderately simplified. Further, we recommend using these simplified models only for large-scale comparisons and using more specific, detailed models for investigations on smaller scales.
Reliable hydrological monitoring is the basis for sound water management in drained wetlands. Since statistical methods cannot be employed for unobserved or sparsely monitored areas, the primary design (first set-up) may be arbitrary in most instances. The objective of this paper is therefore to provide a guideline for designing the initial hydrological monitoring network. A scheme is developed that handles different parts of monitoring and hydrometry in wetlands, focusing on the positioning of surface water and groundwater gauges. For placement of the former, control units are used which correspond to areas whose water levels can be regulated separately. The latter are arranged depending on hydrological response units, defined by combinations of soil type and land use, and the chosen surface water monitoring sites. A practical application of the approach is shown for an investigation area in the Spreewald region in north-east Germany. The presented scheme leaves a certain degree of freedom to its user, allowing the inclusion of expert knowledge or special concerns. Based on easily obtainable data, the developed hydrological network serves as a first step in the iterative procedure of monitoring network optimisation. Copyright (c) 2013 John Wiley & Sons, Ltd.
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.
Like almost all fields of science, hydrology has benefited to a large extent from the tremendous improvements in scientific instruments that are able to collect long-time data series and an increase in available computational power and storage capabilities over the last decades. Many model applications and statistical analyses (e.g., extreme value analysis) are based on these time series. Consequently, the quality and the completeness of these time series are essential. Preprocessing of raw data sets by filling data gaps is thus a necessary procedure. Several interpolation techniques with different complexity are available ranging from rather simple to extremely challenging approaches. In this paper, various imputation methods available to the hydrological researchers are reviewed with regard to their suitability for filling gaps in the context of solving hydrological questions. The methodological approaches include arithmetic mean imputation, principal component analysis, regression-based methods and multiple imputation methods. In particular, autoregressive conditional heteroscedasticity (ARCH) models which originate from finance and econometrics will be discussed regarding their applicability to data series characterized by non-constant volatility and heteroscedasticity in hydrological contexts. The review shows that methodological advances driven by other fields of research bear relevance for a more intensive use of these methods in hydrology. Up to now, the hydrological community has paid little attention to the imputation ability of time series models in general and ARCH models in particular.
The Northeast German Lowland Observatory (TERENO-NE) was established to investigate the regional impact of climate and land use change. TERENO-NE focuses on the Northeast German lowlands, for which a high vulnerability has been determined due to increasing temperatures and decreasing amounts of precipitation projected for the coming decades. To facilitate in-depth evaluations of the effects of climate and land use changes and to separate the effects of natural and anthropogenic drivers in the region, six sites were chosen for comprehensive monitoring. In addition, at selected sites, geoarchives were used to substantially extend the instrumental records back in time. It is this combination of diverse disciplines working across different time scales that makes the observatory TERENO-NE a unique observation platform. We provide information about the general characteristics of the observatory and its six monitoring sites and present examples of interdisciplinary research activities at some of these sites. We also illustrate how monitoring improves process understanding, how remote sensing techniques are fine-tuned by the most comprehensive ground-truthing site DEMMIN, how soil erosion dynamics have evolved, how greenhouse gas monitoring of rewetted peatlands can reveal unexpected mechanisms, and how proxy data provides a long-term perspective of current ongoing changes.
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.
Does textural heterogeneity matter? Quantifying transformation of hydrological signals in soils
(2015)
Textural heterogeneity causes complex water flow patterns and soil moisture dynamics in soils that hamper monitoring and modeling soil hydrological processes. These patterns can be generated by process based models considering soil texture heterogeneities. However, there is urgent need for tools for the inverse approach, that is, to analyze observed dynamics in a quantitative way independent from any model approach in order to identify effects of soil texture heterogeneity. Here, studying the transformation of hydrological input signals (e.g., rainfall, snow melt) propagating through the vadose zone is a promising supplement to the common perspective of mass flux considerations. In this study we applied a recently developed new approach for quantitative analysis of hydrological time series (i) to investigate the effect of soil texture on the signal transformation behavior and (ii) to analyze to what degree soil moisture dynamics from a heterogeneous profile can be reproduced by a corresponding homogenous substrate. We used simulation models to generate three data sets of soil moisture time series considering homogeneous substrates (HOM), homogeneous substrates with noise added (NOISE), and heterogeneous substrates (HET). The soil texture classes sand, loamy sand, clay loam and silt were considered. We applied a principal component analysis (also called empirical orthogonal functions) to identify predominant functional patterns and to measure the degree of signal transformation of single time series. For the HOM case 86.7% of the soil moisture dynamics were reproduced by the first two principal components. Based on these results a quantitative measure for the degree of transformation of the input signal was derived. The general nature of signal transformation was nearly identical in all textures, but the intensity of signal damping per depth interval decreased from fine to coarse textures. The same functional patterns occurred in the HET data set. However, here the signal damping of time series did not increase monotonically with soil depth. The analysis succeeded in extracting the same signal transformation behavior from the NOISE data set compared to that of the HOM case in spite of being blurred by random noise. Thus, principal component analysis proved to be a very robust tool to disentangle between independent effects and to measure the degree of transformation of the input signal. The suggested approach can be used for (i) data processing, including subtracting measurement noise (ii) identification of factors controlling soil water dynamics, (iii) assessing the mean signal transformation in heterogeneous soils based on observed soil moisture time series, and (iv) model building, calibration and evaluation. (C) 2015 Elsevier B.V. All rights reserved.
Stream restoration aims at an enhancement of ecological habitats, an increase of water retention within a landscape and sometimes even at an improvement of biogeochemical functions of lotic ecosystems. For the latter, good exchange between groundwater and stream water is often considered to be of major importance. In this study hydraulic connectivity between river and aquifer was investigated for a four years period, covering the restoration of an old oxbow after the second year. The oxbow became reconnected to the stream and the clogging layer in the oxbow was excavated. We expected increasing hydraulic connectivity between oxbow and aquifer after restoration of the stream, and decreasing hydraulic connectivity for the former shortcut due to increased clogging. To test that hypothesis, the spatial and temporal characteristics of the coupled groundwater-stream water system before and after the restoration were analysed by principal component analyses of time series of groundwater heads and stream water levels. The first component depicted between 53% and 70% of the total variance in the dataset for the different years. It captured the propagation of the pressure signal induced by stream water level fluctuations throughout the adjacent aquifer. Thus it could be used as a measure of hydraulic connectivity between stream and aquifer. During the first year, the impact of stream water level fluctuations decreased with distance from the regulated river (shortcut), whereas the hydraulic connection of the oxbow to the adjacent aquifer was very low. After restoration of the stream we observed a slight but not significant increase of hydraulic connectivity in the oxbow in the second year after restoration, but no change for the former shortcut. There is some evidence that the pattern of hydraulic connectivity at the study site is by far more determined by the natural heterogeneity of hydraulic conductivities of the floodplain sediments and the initial construction of the shortcut rather than by the clogging layer in the oxbow. (C) 2015 The Authors. Published by Elsevier B.V.
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.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected "normal" behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the "stable" principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected “normal” behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the “stable” principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
In the young moraine landscape in Northeast Germany, small glacially created ponds, the so-called kettle holes, are very abundant. They exhibit large spatial heterogeneity, seemingly rendering each kettle hole unique. However, this would not be consistent with any scientific approach. Thus, a classification scheme has been developed for kettle holes in Northeast Germany based on morphology, hydrodynamics and connection to stream networks of the kettle holes as well as size, topography and land use of the respective catchment. These indices are assumed to be related both to water quality as well as to biological issues of the kettle holes. Starting in the mid-1990s, an extensive monitoring program has been established in the federal state of Brandenburg, Germany. In this study, a subset comprising 1,316 samples from 79 kettle holes was analysed, where 21 parameters had been determined. Sampling intervals varied widely, and were between bi-weekly and three-monthly at most sites. A nonlinear principal component analysis was performed. The first four components explained 90% of the variance. These components seem to provide quantitative measures of phosphorus release from the sediments during hypoxic periods, agricultural solute input, algae primary production, and geogenic compounds. This allowed differentiating between the natural and anthropogenic impact factors on water quality. In addition, scores of single components were related to properties of the kettle holes and their environments. The results contribute to a better understanding of biological and biogeochemical processes and can be used to verify the effects of conservation and management strategies for kettle holes.
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.
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.
Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.