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Stofftransport in einem Lösseinzugsgebiet: Experimentelle Evidenz und numerische Modellierung.
(2004)
[1] This paper examines the effect of uncertain initial soil moisture on hydrologic response at the plot scale (1 m(2)) and the catchment scale (3.6 km(2)) in the presence of threshold transitions between matrix and preferential flow. We adopt the concepts of microstates and macrostates from statistical mechanics. The microstates are the detailed patterns of initial soil moisture that are inherently unknown, while the macrostates are specified by the statistical distributions of initial soil moisture that can be derived from the measurements typically available in field experiments. We use a physically based model and ensure that it closely represents the processes in the Weiherbach catchment, Germany. We then use the model to generate hydrologic response to hypothetical irrigation events and rainfall events for multiple realizations of initial soil moisture microstates that are all consistent with the same macrostate. As the measures of uncertainty at the plot scale we use the coefficient of variation and the scaled range of simulated vertical bromide transport distances between realizations. At the catchment scale we use similar statistics derived from simulated flood peak discharges. The simulations indicate that at both scales the predictability depends on the average initial soil moisture state and is at a minimum around the soil moisture value where the transition from matrix to macropore flow occurs. The predictability increases with rainfall intensity. The predictability increases with scale with maximum absolute errors of 90 and 32% at the plot scale and the catchment scale, respectively. It is argued that even if we assume perfect knowledge on the processes, the level of detail with which one can measure the initial conditions along with the nonlinearity of the system will set limits to the repeatability of experiments and limits to the predictability of models at the plot and catchment scales
A fine-grained slope that exhibits slow movement rates was investigated to understand how geohydrological processes contribute to a consecutive development of mass movements in the Vorarlberg Alps, Austria. For that purpose intensive hydrometeorological, hydrogeological and geotechnical observations as well as surveying of surface movement rates were conducted during 1998-2001. Subsurface water dynamics at the creeping slope turned out to be dominated by a three-dimensional pressure system. The pressure reaction is triggered by fast infiltration of surface water and subsequent lateral water flow in the south-western part of the hillslope. The related pressure signal was shown to propagate further downhill, causing fast reactions of the piezometric head at 5.5 m depth on a daily time scale. The observed pressure reactions might belong to a temporary hillslope water body that extends further downhill. The related buoyancy forces could be one of the driving forces for the mass movement. A physically based hydrological model was adopted to model simultaneously surface and subsurface water dynamics including evapotranspiration and runoff production. It was possible to reproduce surface runoff and observed pressure reactions in principle. However, as soil hydraulic functions were only estimated on pedotransfer functions, a quantitative comparison between observed and simulated subsurface dynamics is not feasible. Nevertheless, the results suggest that it is possible to reconstruct important spatial structures based on sparse observations in the field which allow reasonable simulations with a physically based hydrological model. Copyright (c) 2005 John Wiley & Sons, Ltd
This paper examines the effect of spatially variable initial soil moisture and spatially variable precipitation on predictive uncertainty of simulated catchment scale runoff response in the presence of threshold processes. The underlying philosophy is to use a physically based hydrological model named CATFLOW as a virtual landscape, assuming perfect knowledge of the processes. The model, which in particular conceptualizes preferential flow as threshold process, was developed based on intensive process and parameter studies and has already been successfully applied to simulate flow and transport at different scales and catchments. Study area is the intensively investigated Weiherbach catchment. Numerous replicas of spatially variable initial soil moisture or spatially variable precipitation with the same geostatistical properties are conditioned to observed soil moisture and precipitation data and serve as initial and boundary conditions for the model during repeated simulations. The effect of spatially soil moisture on modeling catchment runoff response was found to depend strongly on average saturation of the catchment. Different realizations of initial soil moisture yielded strongly different hydrographs for intermediate initial soil moisture as well as in dry catchment conditions; in other states the effect was found to be much lower. This is clearly because of the threshold nature of preferential flow as well as the threshold nature of Hortonian production of overland flow. It was shown furthermore that the spatial pattern of a key parameter (macroporosity) that determined threshold behavior is of vast importance for the model response. The estimation of these patterns, which is mostly done based on sparse observations and expert knowledge, is a major source for predictive model uncertainty. Finally, it was shown that the usage of biased, i.e. spatially homogenized precipitation, input during parameter estimation yields a biased model structure, which gives poor results when used with highly distributed input. If spatially highly resolved precipitation was used during model parameter estimation. the predictive uncertainty of the model was clearly reduced. (c) 2005 Elsevier B.V. All rights reserved
[1] Spatial patterns of land surface and subsurface characteristics often exert significant control over hydrological processes at many scales. Recognition of the dominant controls at the watershed scale, which is a prerequisite to successful prediction of system responses, will require significant progress in many different research areas. The development and improvement of techniques for mapping structures and spatiotemporal patterns using geophysical and remote sensing techniques would greatly benefit watershed science but still requires a significant synthesis effort. Effective descriptions of hydrological systems will also significantly benefit from new scaling and averaging techniques, from new mathematical description for spatial pattern/structures and their dynamics, and also from an understanding and quantification of structure and pattern-building processes in different compartments ( soils, rocks, and land surface) and at different scales. The advances that are needed to tackle these complex challenges could be greatly facilitated through the development of an interdisciplinary research framework that explores instrumentation, theory, and simulation components and that is implemented in a coordinated manner
The objective is to compare the time scale of microbial degradation of the herbicide Isoproturon at the end of earthworm burrows with the time scale of microbial degradation in the surrounding soil matrix. To this end, we developed a method which allows the observation of microbial degradation on Isoproturon in macropores under field conditions. Study area was the well-investigated Weiherbach catchment (Kraichgau, SW Germany). The topsoil of a 12 m(2) large plot parcel was removed, the parcel was covered with a tent and instrumented with TDR and temperature sensors at two depths. After preliminary investigations to optimize application and sampling techniques, the bottom of 55 earthworm burrows, located at a depth of 80-100cm, was inoculated with Isoproturon. Within an interval of 8 d, soil material from the bottom of 5-6 earthworm burrows was taken into the laboratory and analyzed for the Isoproturon concentration for investigation of the degradation kinetics. Furthermore, the degradation of Isoproturon in the soil matrix, that surrounded the macropores at the field plot, was observed in the laboratory. Microbial degradation of Isoproturon at the bottom of the earthworm burrows was with a DT-50-value of 15.6 d almost as fast as in the topsoil. In the soil matrix that closely surrounded the center of the earthworm burrows, no measurable degradation was observed within 30 d. The clearly slower degradation in the soil matrix may be likely explained by a lower microbial activity that was observed in the surrounding soil matrix. The results give evidence that deterministic modeling of the fate of pesticides once transported into heterogeneous subsoils by preferential flow requires an accuracy of a few centimeters in terms of predicting spatial locations: time scales of microbial degradation in the subsoil drop almost one order of magnitude, in case the herbicides dislocates from the bottom of an earthworm burrow a few centimeter into the surrounding soil matrix. If at all, predictions of such an accuracy can only be achieved at locations at sites where the soil hydraulic properties and the macropore system are known at a very high spatial resolution
[1] Observations of hydrological response often exhibit considerable scatter that is difficult to interpret. In this paper, we examine runoff production of 53 sprinkling experiments on the water-repellent soils in the southern Alps of Switzerland; simulated plot scale tracer transport in the macroporous soils at the Weiherbach site, Germany; and runoff generation data from the 2.3-km(2) Tannhausen catchment, Germany, that has cracking soils. The response at the three sites is highly dependent on the initial soil moisture state as a result of the threshold dynamics of the systems. A simple statistical model of threshold behavior is proposed to help interpret the scatter in the observations. Specifically, the model portrays how the inherent macrostate uncertainty of initial soil moisture translates into the scatter of the observed system response. The statistical model is then used to explore the asymptotic pattern of predictability when increasing the number of observations, which is normally not possible in a field study. Although the physical and chemical mechanisms of the processes at the three sites are different, the predictability patterns are remarkably similar. Predictability is smallest when the system state is close to the threshold and increases as the system state moves away from it. There is inherent uncertainty in the response data that is not measurement error but is related to the observability of the initial conditions.
This study presents an application of an innovative sampling strategy to assess soil moisture dynamics in a headwater of the Weißeritz in the German eastern Ore Mountains. A grassland site and a forested site were instrumented with two Spatial TDR clusters (STDR) that consist of 39 and 32 coated TDR probes of 60 cm length. Distributed time series of vertically averaged soil moisture data from both sites/ensembles were analyzed by statistical and geostatistical methods. Spatial variability and the spatial mean at the forested site were larger than at the grassland site. Furthermore, clustering of TDR probes in combination with long-term monitoring allowed identification of average spatial covariance structures at the small field scale for different wetness states. The correlation length of soil water content as well as the sill to nugget ratio at the grassland site increased with increasing average wetness and but, in contrast, were constant at the forested site. As soil properties at both the forested and grassland sites are extremely variable, this suggests that the correlation structure at the forested site is dominated by the pattern of throughfall and interception. We also found a strong correlation between average soil moisture dynamics and runoff coefficients of rainfall-runoff events observed at gauge Rehefeld, which explains almost as much variability in the runoff coefficients as pre-event discharge. By combining these results with a recession analysis we derived a first conceptual model of the dominant runoff mechanisms operating in this catchment. Finally, long term simulations with a physically based hydrological model were in good/acceptable accordance with the time series of spatial average soil water content observed at the forested site and the grassland site, respectively. Both simulations used a homogeneous soil setup that closely reproduces observed average soil conditions observed at the field sites. This corroborates the proposed sampling strategy of clustering TDR probes in typical functional units is a promising technique to explore the soil moisture control on runoff generation. Long term monitoring of such sites could maybe yield valuable information for flood warning. The sampling strategy helps furthermore to unravel different types of soil moisture variability.
Dry lands are exposed to a highly variable environment and face a high risk of degradation. The effects of climate change are likely to increase this risk; thus a profound knowledge of the system dynamics is crucial for evaluating management options. This applies particularly for the interactions between water and vegetation, which exhibit strong feedbacks. To evaluate these feedbacks and the effects of climate change on soil moisture dynamics, we developed a generic, process-based, spatially explicit soil moisture model of two soil layers, which can be coupled with vegetation models. A time scale relevant for ecological processes can be simulated without difficulty, and the model avoids complex parameterization with data that are unavailable for most regions of the world. We applied the model to four sites in Israel along a precipitation and soil type gradient and assessed the effects of climate change by comparing possible climatic changes with present climate conditions. The results show that in addition to temperature, the total amount of precipitation and its intra-annual variability are an important driver of soil moisture patterns. This indicates that particularly with regard to climate change, the approach of many ecological models that simulate water dynamics on an annual base is far too simple to make reliable predictions. Thus, the introduced model can serve as a valuable tool to improve present ecological models of dry lands because of its focus on the applicability and transferability.
The intention of the presented study is to gain a better understanding of the mechanisms that caused the bimodal rainfall-runoff responses which occurred up to the mid-1970s regularly in the Schafertal catchment and vanished after the onset of mining activities. Understanding, this process is a first step to understanding the ongoing hydrological change in this area. It is hypothesized that either subsurface stormflow, or fast displacement of groundwater, could cause the second delayed peak. A top-down analysis of rainfall-runoff data, field observations as well as process modelling are combined within a rejectionistic framework. A statistical analysis is used to test whether different predictors. which characterize the forcing. near surface water content and deeper subsurface store, allow the prediction of the type of rainfall-runoff response. Regression analysis is used with generalized linear models Lis they can deal with non-Gaussian error distributions Lis well its a non-stationary variance. The analysis reveals that the dominant predictors are the pre-event discharge (proxy of state of the groundwater store) and the precipitation amount, In the field campaign, the subsurface at a representative hillslope was investigated by means of electrical resistivity tomography in order to identify possible strata as flow paths for subsurface stormflow. A low resistivity in approximately 4 in depth-either due to a less permeable layer or the groundwater surface-was detected. The former Could serve as a flow path for subsurface stormflow. Finally, the physical-based hydrological model CATFLOW and the groundwater model FEFLOW are compared with respect to their ability to reproduce the bimodal runoff responses. The groundwater model is able to reproduce the observations, although it uses only an abstract representation of the hillslopes. Process model analysis as well Lis statistical analysis strongly suggest that fast displacement of groundwater is the dominant process underlying the bimodal runoff reactions.
Drylands worldwide are exposed to a highly variable environment and face a high risk of degradation. The effects of global climate change such as altered precipitation patterns and increased temperature leading to reduced water availability will likely increase this risk. At the same time, an elevated atmospheric CO2 level could mitigate the effects of reduced water availability by increasing the water use efficiency of plants. To prevent degradation of drylands, it is essential to understand the underlying processes that affect water availability and vegetation cover. Since water and vegetation are strongly interdependent in water-limited ecosystems, changes can lead to highly non- linear effects. We assess these effects by developing an ecohydrological model of soil moisture and vegetation cover. The water component of the model simulates the daily dynamics of surface water and water contents in two soil layers. Vegetation is represented by two functional types: shrubs and grasses. These compete for soil water and strongly influence hydrological processes. We apply the model to a Namibian thornbush savanna and evaluate the separate and combined effects of decreased annual precipitation, increased temperature, more variable precipitation and elevated atmospheric CO2 on soil moisture and on vegetation cover. The results show that two main factors control the response of plant types towards climate change, namely a change in water availability and a change in water allocation to a specific plant type. Especially, reduced competitiveness of grasses can lead to a higher risk of shrub encroachment in these systems.
Applying metrics for hydrograph comparison is a central task in hydrological modelling, used both in model calibration and the evaluation of simulations or forecasts. Motivated by the shortcomings of standard objective metrics such as the Root Mean Square Error or the Mean Peak Time Error and the advantages of visual inspection as a powerful tool for simultaneous, case-specific and multi-criteria (yet subjective) evaluation, we propose a new objective metric termed Series Distance, which is in close accordance with visual evaluation. The Series Distance is an event-based method and consists of three parts, namely a Threat Score which evaluates overall agreement of event occurrence, and the overall distance of matching observed and simulated events with respect to amplitude and timing. The novelty of the latter two is the way in which matching point pairs on the observed and simulated hydrographs are identified, namely by the same relative position in matching segments (rise or recession) of matching events. Thus, amplitude and timing errors are calculated simultaneously but separately, from point pairs that also match visually, considering complete events rather than only individual points (which is for example the case with metrics related to Peak Time Errors). After presenting the Series Distance theory, we discuss its properties and compare it to those of standard metrics and visual inspection, both at the example of simple, artificial hydrographs and an ensemble of realistic forecasts. The results suggest that the Series Distance compares and evaluates hydrographs in a way comparable to visual inspection, but in an objective, reproducible way.
Soil respiration is the second largest flux in the global carbon cycle, yet the underlying below-ground process, carbon dioxide (CO2) production, is not well understood because it can not be measured in the field. CO2 production has frequently been calculated from the vertical CO2 diffusive flux divergence, known as "soil-CO2 profile method". This relatively simple model requires knowledge of soil CO2 concentration profiles and soil diffusive properties. Application of the method for a tropical lowland forest soil in Panama gave inconsistent results when using diffusion coefficients (D) calculated based on relationships with soil porosity and moisture ("physically modeled" D). Our objective was to investigate whether these inconsistencies were related to (1) the applied interpolation and solution methods and/or (2) uncertainties in the physically modeled profile of D. First, we show that the calculated CO2 production strongly depends on the function used to interpolate between measured CO2 concentrations. Secondly, using an inverse analysis of the soil-CO2 profile method, we deduce which D would be required to explain the observed CO2 concentrations, assuming the model perception is valid. In the top soil, this inversely modeled D closely resembled the physically modeled D. In the deep soil, however, the inversely modeled D increased sharply while the physically modeled D did not. When imposing a constraint during the fit parameter optimization, a solution could be found where this deviation between the physically and inversely modeled D disappeared. A radon (Rn) mass balance model, in which diffusion was calculated based on the physically modeled or constrained inversely modeled D, simulated observed Rn profiles reasonably well. However, the CO2 concentrations which corresponded to the constrained inversely modeled D were too small compared to the measurements. We suggest that, in well-structured soils, a missing description of steady state CO2 exchange fluxes across water-filled pores causes the soil-CO2 profile method to fail. These fluxes are driven by the different diffusivities in inter- vs. intra-aggregate pores which create permanent CO2 gradients if separated by a "diffusive water barrier". These results corroborate other studies which have shown that the theory to treat gas diffusion as homogeneous process, a precondition for use of the soil-CO2 profile method, is inaccurate for pore networks which exhibit spatial separation between CO2 production and diffusion out of the soil.
This study presents an application of an innovative sampling strategy to assess soil moisture dynamics in a headwater of the Weisseritz in the German eastern Ore Mountains. A grassland site and a forested site were instrumented with two Spatial TDR clusters (STDR) that consist of 39 and 32 coated TDR probes of 60 cm length. Distributed time series of vertically averaged soil moisture data from both sites/ensembles were analyzed by statistical and geostatistical methods. Spatial variability and the spatial mean at the forested site were larger than at the grassland site. Furthermore, clustering of TDR probes in combination with long-term monitoring allowed identification of average spatial covariance structures at the small field scale for different wetness states. The correlation length of soil water content as well as the sill to nugget ratio at the grassland site increased with increasing average wetness and but, in contrast, were constant at the forested site. As soil properties at both the forested and grassland sites are extremely variable, this suggests that the correlation structure at the forested site is dominated by the pattern of throughfall and interception. We also found a very strong correlation between antecedent soil moisture at the forested site and runoff coefficients of rainfall-runoff events observed at gauge Rehefeld. Antecedent soil moisture at the forest site explains 92% of the variability in the runoff coefficients. By combining these results with a recession analysis we derived a first conceptual model of the dominant runoff mechanisms operating in this catchment. Finally, we employed a physically based hydrological model to shed light on the controls of soil- and plant morphological parameters on soil average soil moisture at the forested site and the grassland site, respectively. A homogeneous soil setup allowed, after fine tuning of plant morphological parameters, most of the time unbiased predictions of the observed average soil conditions observed at both field sites. We conclude that the proposed sampling strategy of clustering TDR probes is suitable to assess unbiased average soil moisture dynamics in critical functional units, in this case the forested site, which is a much better predictor for event scale runoff formation than pre-event discharge. Long term monitoring of such critical landscape elements could maybe yield valuable information for flood warning in headwaters. We thus think that STDR provides a good intersect of the advantages of permanent sampling and spatially highly resolved soil moisture sampling using mobile rods.