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  • Hydrology
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  • 1
    Language: English
    In: Journal of Hydrology, October 2015, Vol.529, pp.1754-1767
    Description: Soil moisture plays a key role in the water and energy balance in soil, vegetation and atmosphere systems. According to Wood et al. (2011) there is a grand need to increase global-scale hyper-resolution water–energy–biogeochemistry land surface modelling capabilities. These modelling capabilities should also recognize epistemic uncertainties, as well as the nonlinearity and hysteresis in its dynamics. Unfortunately, it is not clear how to parameterize hydrological processes as a function of scale, and how to test deterministic models with regard to epistemic uncertainties. In this study, high resolution long-term simulations were conducted in the highly instrumented TERENO hydrological observatory of the Wüstebach catchment. Soil hydraulic parameters were derived using inverse modelling with the Hydrus-1D model using the global optimization scheme SCE-UA and soil moisture data from a wireless soil moisture sensor network. The estimated parameters were then used for 3D simulations of water transport using the integrated parallel simulation platform ParFlow-CLM. The simulated soil moisture dynamics, as well as evapotranspiration (ET) and runoff, were compared with long-term field observations to illustrate how well the model was able to reproduce the water budget dynamics. We investigated different anisotropies of hydraulic conductivity to analyze how fast lateral flow processes above the underlying bedrock affect the simulation results. For a detail investigation of the model results we applied the empirical orthogonal function (EOF) and wavelet coherence methods. The EOF analysis of temporal–spatial patterns of simulated and observed soil moisture revealed that introduction of heterogeneity in the soil porosity effectively improves estimates of soil moisture patterns. Our wavelet coherence analysis indicates that wet and dry seasons have significant effect on temporal correlation between observed and simulated soil moisture and ET. Our study demonstrates the usefulness of the EOF and wavelet coherence methods for a more in-depth validation of spatially highly resolved hydrological 3D models.
    Keywords: 3d Hydrological Simulation ; Soil Moisture ; Eof Analysis ; Wavelet Coherence Analysis ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 2
    Language: English
    In: Journal of Hydrology, October 2015, Vol.529, pp.872-889
    Description: Many attempts have been made to characterize particle size distribution (PSD) curves using different mathematical models, which are primarily used as a basis for estimating soil hydraulic properties. The principle step in using soil PSD to predict soil hydraulic properties is determining an accurate and continuous curve for PSD. So far, the characteristics of the PSD models, their fitting accuracy, and the effects of their parameters on the shape and position of PSD curves have not been investigated. In this study all developed PSD models, their characteristics, behavior of their parameters, and their fitting capability to the UNSODA database soil samples were investigated. Results showed that beerkan estimation of soil transfer (BEST), two and three parameter Weibull, Rosin and Rammler (1 and 2), unimodal and bimodal Fredlund, and van Genuchten models were flexible over the entire range of soil PSD. Correspondingly, the BEST, two and three parameter Weibull, Rosin and Rammler (1 and 2), hyperbolic and offset renormalized log-normal models possessed a high fitting capability over the entire range of PSD. The few parameters of the BEST, Rosin and Rammler (1 and 2), and two parameter Weibull models provides ease of use in soil physics and mechanics research. Thus, they are seemingly fit with acceptable accuracy in predicting the PSD curve. Although the fractal models have physical and mathematical basis, they do not have the adequate flexibility to contribute a description of the PSD curve. Different aspects of the PSD models should be considered in selecting a model to describe a soil PSD.
    Keywords: Fitting ; Mathematical Psd Models ; Models Parameters ; Particle Size Distribution ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 3
    Language: English
    In: Journal of Hydrology, 2011, Vol.399(3), pp.410-421
    Description: ► Top soil moisture observations for estimation of hydraulic parameters. ► Simultaneous update of model states (soil moisture) and hydraulic parameters. ► SIR-PF for propagation of non-Gaussian distributions through a nonlinear model. ► Estimation of hydr. parameters driven by non linearity between SM and pressure head. In a synthetic study we explore the potential of using surface soil moisture measurements obtained from different satellite platforms to retrieve soil moisture profiles and soil hydraulic properties using a sequential data assimilation procedure and a 1D mechanistic soil water model. Four different homogeneous soil types were investigated including loamy sand, loam, silt, and clayey soils. The forcing data including precipitation and potential evapotranspiration were taken from the meteorological station of Aachen (Germany). With the aid of the forward model run, a synthetic data set was designed and observations were generated. The virtual top soil moisture observations were then assimilated to update the states and hydraulic parameters of the model by means of a particle filtering data assimilation method. Our analyses include the effect of assimilation strategy, measurement frequency, accuracy in surface soil moisture measurements, and soils differing in textural and hydraulic properties. With this approach we were able to assess the value of periodic spaceborne observations of top soil moisture for soil moisture profile estimation and identify the adequate conditions (e.g. temporal resolution and measurement accuracy) for remotely sensed soil moisture data assimilation. Updating of both hydraulic parameters and state variables allowed better predictions of top soil moisture contents as compared with updating of states only. An important conclusion is that the assimilation of remotely-sensed top soil moisture for soil hydraulic parameter estimation generates a bias depending on the soil type. Results indicate that the ability of a data assimilation system to correct the soil moisture state and estimate hydraulic parameters is driven by the non linearity between soil moisture and pressure head.
    Keywords: Soil Moisture ; Data Assimilation ; Particle Filter ; Sequential Importance Resampling ; Hydrus-1d ; Smos ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 4
    In: Global Change Biology, March 2017, Vol.23(3), pp.1338-1352
    Description: Salinity intrusion caused by land subsidence resulting from increasing groundwater abstraction, decreasing river sediment loads and increasing sea level because of climate change has caused widespread soil salinization in coastal ecosystems. Soil salinization may greatly alter nitrogen (N) cycling in coastal ecosystems. However, a comprehensive understanding of the effects of soil salinization on ecosystem N pools, cycling processes and fluxes is not available for coastal ecosystems. Therefore, we compiled data from 551 observations from 21 peer‐reviewed papers and conducted a meta‐analysis of experimental soil salinization effects on 19 variables related to N pools, cycling processes and fluxes in coastal ecosystems. Our results showed that the effects of soil salinization varied across different ecosystem types and salinity levels. Soil salinization increased plant N content (18%), soil (12%) and soil total N (210%), although it decreased soil (2%) and soil microbial biomass N (74%). Increasing soil salinity stimulated soil NO fluxes as well as hydrological and fluxes more than threefold, although it decreased the hydrological dissolved organic nitrogen () flux (59%). Soil salinization also increased the net N mineralization by 70%, although salinization effects were not observed on the net nitrification, denitrification and dissimilatory nitrate reduction to ammonium in this meta‐analysis. Overall, this meta‐analysis improves our understanding of the responses of ecosystem N cycling to soil salinization, identifies knowledge gaps and highlights the urgent need for studies on the effects of soil salinization on coastal agro‐ecosystem and microbial N immobilization. Additional increases in knowledge are critical for designing sustainable adaptation measures to the predicted intrusion of salinity intrusion so that the productivity of coastal agro‐ecosystems can be maintained or improved and the N losses and pollution of the natural environment can be minimized.
    Keywords: Costal Ecosystem ; Denitrification ; Dissimilatory Nitrate Reduction To Ammonium Dnra ; Nitrogen Cycle ; Salinity Intrusion ; Sea‐Level Rise ; Soil Salinization
    ISSN: 1354-1013
    E-ISSN: 1365-2486
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  • 5
    Language: English
    In: Journal of Hydrology, May 2016, Vol.536, pp.365-375
    Description: In distributed hydrological modelling one often faces the problem that input data need to be aggregated to match the model resolution. However, aggregated data may be too coarse for the parametrization of the processes represented. This dilemma can be circumvented by the adjustment of certain model parameters. For instance, the reduction of local hydraulic gradients due to spatial aggregation can be partially compensated by increasing soil hydraulic conductivity. In this study, we employed the information entropy concept for the scale dependent parameterization of soil hydraulic conductivity. The loss of information content of terrain curvature as consequence of spatial aggregation was used to determine an amplification factor for soil hydraulic conductivity to compensate the resulting retardation of water flow. To test the usefulness of this approach, continuous 3D hydrological simulations were conducted with different spatial resolutions in the highly instrumented Wüstebach catchment, Germany. Our results indicated that the introduction of an amplification factor can effectively improve model performances both in terms of soil moisture and runoff simulation. However, comparing simulated soil moisture pattern with observation indicated that uniform application of an amplification factor can lead to local overcorrection of soil hydraulic conductivity. This problem could be circumvented by applying the amplification factor only to model grid cells that suffer from high information loss. To this end, we tested two schemes to define appropriate location-specific correction factors. Both schemes led to improved model performance both in terms of soil water content and runoff simulation. Thus, we anticipate that our proposed scaling approach is useful for the application of next-generation hyper-resolution global land surface models.
    Keywords: Scale Dependent Parameterization ; 3d Hydrological Modelling ; Topographical Information Content ; Soil Hydraulic Conductivity ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 6
    Language: English
    In: Journal of Hydrology, 04 August 2014, Vol.516, pp.154-160
    Description: Knowledge of spatial mean soil moisture and its variability over time is needed in many environmental applications. We analyzed dependencies of soil moisture variability on average soil moisture contents in soils with and without root water uptake using ensembles of non-stationary water flow simulations by varying soil hydraulic properties under different climatic conditions. We focused on the dry end of the soil moisture range and found that the magnitude of soil moisture variability was controlled by the interplay of soil hydraulic properties and climate. The average moisture at which the maximum variability occurred depended on soil hydraulic properties and vegetation. A positive linear relationship was observed between mean soil moisture and its standard deviation and was controlled by the parameter defining the shape of soil water retention curves and the spatial variability of saturated hydraulic conductivity. The influence of other controls, such as variable weather patterns, topography or lateral flow processes needs to be studied further to see if such relationship persists and could be used for the inference of soil hydraulic properties from the spatiotemporal variation in soil moisture.
    Keywords: Soil Moisture ; Variability ; Soil Hydraulic Properties ; Climate ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 7
    Language: English
    In: Science of the Total Environment, 01 May 2018, Vol.622-623, pp.954-973
    Description: 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 intercomparison 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.
    Keywords: Review ; Interdisciplinary Links ; Integrated Environmental Modelling ; Research Needs ; Environmental Sciences ; Biology ; Public Health
    ISSN: 0048-9697
    E-ISSN: 1879-1026
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  • 8
    Language: English
    In: Journal of Hydrology, May 2015, Vol.524, pp.680-695
    Description: Spatially highly resolved mapping of aquifer heterogeneities is critical for the accurate prediction of groundwater flow and contaminant transport. Here, we demonstrate the value of using full-waveform inversion of crosshole ground penetrating radar (GPR) data for aquifer characterization. We analyze field data from the Krauthausen test site, where crosshole GPR data were acquired along a transect of 20 m length and 10 m depth. Densely spaced cone penetration tests (CPT), located close to the GPR transect, were used to validate and interpret the tomographic images obtained from GPR. A strong correlation was observed between CPT porosity logs and porosity estimates derived from GPR using the Complex Refractive Index Model (CRIM). A less pronounced correlation was observed between electrical conductivity data derived from GPR and CPT. Cluster analysis of the GPR data defined three different subsurface facies, which were found to correspond to sediments with different grain size and porosity. In conclusion, our study suggests that full-waveform inversion of crosshole GPR data followed by cluster analysis is an applicable approach to identify hydrogeological facies in alluvial aquifers and to map their architecture and connectivity. Such facies maps provide valuable information about the subsurface heterogeneity and can be used to construct geologically realistic subsurface models for numerical flow and transport prediction.
    Keywords: Heterogeneity ; Aquifer Characterization ; Geophysical Methods ; Ground Penetrating Radar ; Full-Waveform Inversion ; Cone Penetration Tests ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 9
    Language: English
    In: Journal of Hydrology, 25 February 2013, Vol.481, pp.106-118
    Description: ► We simulated soil water flow in bare and grassed soil columns of three textures. ► Typical features of soil water temporal stability were recovered in simulations. ► Simulated duration and season affected the temporal stability of soil water contents. ► Spatio-temporal variations in soil water correlated with soil hydraulic conductivity. Occurrence of temporal stability of soil water content has been observed for a range of soil and landscape conditions and is generally explained as a consequence of local and non-local controls. However, the underlying factors for this phenomenon are not completely understood and have not been quantified. This work attempts to elucidate and quantify the effects of several local controls, such as soil hydraulic properties and root water uptake, through water flow simulations. One-dimensional water flow was simulated with the HYDRUS code for bare and grassed sandy loam, loam and clay soils at different levels of variability in the saturated hydraulic conductivity . Soil water content at 0.05 and 0.60 m and the average water content of the top 1 m were analyzed. Temporal stability was characterized by calculating the mean relative differences of soil water content in 100 soil columns used for each combination of soil and season. Using log-normal distributions of resulted in mean relative differences distributions that were commonly observed in experimental studies of soil water content variability. Linear relationships were observed between scaling factor of ln and spread of the mean relative differences distributions. For the same scaling factor and soil texture, simulated shapes of the mean relative differences distributions depended on the duration of the simulation period and the season. Variation in mean relative differences was higher in coarser textures than in finer ones and more variability was seen in the topsoil than in the subsoil. Root water uptake decreased the mean relative differences variability in the root zone and increased variability below it. This work presents a preliminary research to promote the use of water flow simulations under site-specific conditions to better understand the temporal stability of soil water contents. The estimation of the spatial variability of from soil water content monitoring presents an interesting avenue for further research.
    Keywords: Soil Water Content ; Temporal Stability ; Simulations ; Local Controls ; Saturated Hydraulic Conductivity ; Geography
    ISSN: 0022-1694
    E-ISSN: 1879-2707
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  • 10
    Language: English
    Description: Neutron intensity measured by the aboveground cosmic-ray neutron intensity probe (CRP) allows estimating soil moisture content at the field scale. In this work, synthetic neutron intensities were used to remove the bias of simulated soil moisture content or update soil hydraulic properties (together with soil moisture) in the Community Land Model (CLM) using the Local Ensemble Transform Kalman Filter. The cosmic-ray forward model COSMIC was used as the non-linear measurement operator which maps between neutron intensity and soil moisture. The novel aspect of this work is that synthetically measured neutron intensity was used for real time updating of soil states and soil properties (or soil moisture bias) and posterior use for the real time scheduling of irrigation (data assimilation based real-time control approach). Uncertainty of model forcing and soil properties (sand fraction, clay fraction and organic matter density) were considered in the ensemble predictions of the soil moisture profiles. Horizontal and vertical weighting of soil moisture was introduced in the data assimilation in order to handle the scale mismatch between the cosmic-ray footprint and the CLM grid cell. The approach was illustrated in a synthetic study with the real-time irrigation scheduling of fields of citrus trees. After adjusting soil moisture content by assimilating neutron intensity, the irrigation requirements were calculated based on the water deficit method. Model bias was introduced by using coarser soil texture in the data assimilation experiments than in reality. A series of experiments was done with different combinations of state, parameter and bias estimation in combination with irrigation scheduling. Assimilation of CRP neutron intensity improved soil moisture characterization. Irrigation requirement was overestimated if biased soil properties were used. The soil moisture bias was reduced by 35% after data assimilation. The scenario of joint state-parameter estimation resulted in the best soil moisture characterization (50% decrease in root mean square error compared to open loop simulations), and the best estimate of needed irrigation amount (86% decrease in Hausdorff distance compared to open loop). The coarse scale synthetic CRP observation was proven to be useful for the fine scale soil moisture and soil properties estimation for the objective of irrigation scheduling
    Keywords: Data Assimilation ; Cosmic Ray ; Soil Moisture ; Parameter Estimation ; Bias Estimation ; Irrigation Scheduling ; Ingenieria Hidraulica
    ISSN: 0022-1694
    ISSN: 1879-2707
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