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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.
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.
Decreasing groundwater levels in many parts of Germany and decreasing low flows in Central Europe have created a need for adaptation measures to stabilize the water balance and to increase low flows. The objective of our study was to estimate the impact of ditch water level management on stream-aquifer interactions in small lowland catchments of the mid-latitudes. The water balance of a ditch-irrigated area and fluxes between the subsurface and the adjacent stream were modeled for three runoff recession periods using the Hydrus-2D software package. The results showed that the subsurface flow to the stream was closely related to the difference between the water level in the ditch system and the stream. Evapotranspiration during the growing season additionally reduced base flow. It was crucial to stop irrigation during a recession period to decrease water withdrawal from the stream and enhance the base flow by draining the irrigated area. Mean fluxes to the stream were between 0.04 and 0.64 ls(-1) for the first 20 days of the low-flow periods. This only slightly increased the flow in the stream, whose mean was 57 ls(-1) during the period with the lowest flows. Larger areas would be necessary to effectively increase flows in mesoscale catchments.
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.