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  • Wiley Online Library  (46)
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  • 1
    In: Hydrological Processes, 15 February 2014, Vol.28(4), pp.1899-1915
    Description: Temporal stability of soil water content (TS SWC) is an often‐observed phenomenon, which characterization finds multiple applications. Climate and variability in soil properties are usually mentioned as factors of TS SWC, but their effects are far from clear. The objective of this work was to use SWC modeling to evaluate the effects of climate and soil hydraulic properties on the TS of soil water at different measurement schedules. We selected four representative climates found in USA and simulated the multiyear SWC dynamics for sandy loam, loam, and silty clay loam soils, all having the lognormal spatial distribution of the saturated hydraulic conductivity. The CLIMGEN and the HYDRUS6 codes were used to generate weather patterns and to simulate SWC, respectively. Four different methods were applied to select the representative location (RL). The low probability of having the same variability of mean relative differences of soil water under different climates was found in most of the cases. The probability that the variance of mean relative differences depended on sampling frequency was generally higher than 91% for the three soils. The interannual difference in mean relative differences variation from short and intensive summer campaigns was highly probable for all climates and soils. The RLs changed as climate and measurement scheduling changed, and they were less pronounced for coarse‐textured soils. The RL selection methods based solely on bias provided more consistency as compared with other methods. The TS appears to be the result of the interplay between climate, soil properties, and survey protocols. One implication of this factor interaction effect on TS SWC is that a simulation study can be useful to decide on the feasibility of including a search for TS‐based RLs for a specific site. Copyright © 2013 John Wiley & Sons, Ltd.
    Keywords: Temporal Stability ; Soil Water Content ; Simulations ; Climate ; Water Retention ; Saturated Hydrauic Conductivity
    ISSN: 0885-6087
    E-ISSN: 1099-1085
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  • 2
    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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  • 3
    In: Global Change Biology, October 2017, Vol.23(10), pp.4068-4083
    Description: Animal manure application as organic fertilizer does not only sustain agricultural productivity and increase soil organic carbon () stocks, but also affects soil nitrogen cycling and nitrous oxide (NO) emissions. However, given that the sign and magnitude of manure effects on soil NO emissions is uncertain, the net climatic impact of manure application in arable land is unknown. Here, we performed a global meta‐analysis using field experimental data published in peer‐reviewed journals prior to December 2015. In this meta‐analysis, we quantified the responses of NO emissions to manure application relative to synthetic N fertilizer application from individual studies and analyzed manure characteristics, experimental duration, climate, and soil properties as explanatory factors. Manure application significantly increased NO emissions by an average 32.7% (95% confidence interval: 5.1–58.2%) compared to application of synthetic N fertilizer alone. The significant stimulation of NO emissions occurred following cattle and poultry manure applications, subsurface manure application, and raw manure application. Furthermore, the significant stimulatory effects on NO emissions were also observed for warm temperate climate, acid soils ( 〈 6.5), and soil texture classes of sandy loam and clay loam. Average direct NO emission factors (s) of 1.87% and 0.24% were estimated for upland soils and rice paddy soils receiving manure application, respectively. Although manure application increased stocks, our study suggested that the benefit of increasing stocks as sinks could be largely offset by stimulation of soil NO emissions and aggravated by emissions if, particularly for rice paddy soils, the stimulation of emissions by manure application was taken into account. The uncertain manure effects on NO emissions constrain evaluation of the net climatic impact of manure application in arable lands. A global meta‐analysis was performed to quantify the overall responses of NO emissions to manure application relative to synthetic N fertilizer in agricultural soils. Manure application on average significantly increased NO emissions by 32.7% as compared to synthetic N fertilizer alone, and the sign and magnitude of NO emissions were dependent on manure characteristics, climate, and soil properties. The benefit of C sequestration could be largely offset by stimulation of soil NO emissions and aggravated by CH emissions if, particularly for rice paddy soils, the stimulation of CH emissions by manure application was taken into account.
    Keywords: Animal Manure ; Emission Factor ; Greenhouse Gas Balance ; Manure Characteristics ; Meta‐Analysis ; Nitrous Oxide ; Soil Ph ; Soil Texture
    ISSN: 1354-1013
    E-ISSN: 1365-2486
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  • 4
    In: Geophysical Research Letters, 16 July 2015, Vol.42(13), pp.5299-5308
    Description: Previous studies of streamwater transit time distributions (TTDs) used isotope tracer information from open precipitation (OP) as inputs to lumped watershed models that simulate the stream isotopic composition to estimate TTD. However, in forested catchments passage of rainfall through the canopy will alter the tracer signature of throughfall (TF) via interception. Here we test the effect of using TF instead of OP on TTD estimates. We sampled a 0.39 km catchment (Wüstebach, Germany) for a 19 month period using weekly precipitation and stream isotope data to evaluate changes in stream isotope simulation and TTDs. We found that TF had different effects on TTDs for δO and δH, with TF leading to up to 4 months shorter transit times. TTDs converged for both isotopes only when using TF. TF improved the stream isotope simulations. These results demonstrate the importance of canopy‐induced isotope tracer changes in estimating streamwater TTDs in forested catchments. TTDs are affected by throughfall isotope data A simple correction factor can partly account for throughfall effects Using throughfall isotope data is necessary for accurate TTD estimates
    Keywords: Interception ; Throughfall ; Transit Time Distribution ; Stable Isotopes ; Isotope Hydrology ; Catchment Hydrology
    ISSN: 0094-8276
    E-ISSN: 1944-8007
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  • 5
    In: Geophysical Research Letters, 16 August 2018, Vol.45(15), pp.7571-7579
    Description: For the first time, we combine depth‐specific soil information obtained from the quantitative inversion of ground‐based multicoil electromagnetic induction data with the airborne hyperspectral vegetation mapping of 1 × 1‐m pixels including Sun‐induced fluorescence () to understand how subsurface structures drive above‐surface plant performance. Hyperspectral data were processed to quantitative and selected biophysical canopy maps, which are proxies for actual photosynthetic rates. These maps showed within‐field spatial patterns, which were attributed to paleo‐river channels buried at around 1‐m depth. The soil structures at specific depths were identified by quantitative electromagnetic induction data inversions and confirmed by soil samples. Whereas the upper plowing layer showed minor correlation to the plant data, the deeper subsoil carrying vital plant resources correlated substantially. Linking depth‐specific soil information with plant performance data may greatly improve our understanding and the modeling of soil‐vegetation‐atmosphere exchange processes. Plants interact with soil. This is intuitive, although we know little about the subsurface structure because we cannot see it. At first glance, all soil may look the same, yet healthy plants can survive beside withered ones. We investigate the soil‐plant interaction in an agricultural field situated in an area characterized by ancient (paleo‐) river channels. These channels formed in sandy‐gravelly material due to melting water after last glaciation, were then filled up with fine aeolien sediments, overlaid with soils up to 1 m thick, and are no longer visible at the surface. However, crops grow in meandering/braiding patterns that can be seen on satellite images, for example. To explain this, the subsurface structural geometry must be known. We combine ground‐based electromagnetic induction data inversion results with airborne hyperspectral measurements to reveal the soil depths driving plant performance (photosynthetic activity and growth). Contrary to expectations, the deeper subsoil and not the plowing layer controls plant performance at the investigated site. Plants above the buried paleoriver channels find nutrients and water, whereas the surrounding plants in gravelly soil suffer, especially during drought. These results improve our understanding of soil‐plant interaction, which may improve soil‐vegetation‐atmosphere exchange process modeling and harvest predictability. Soil structures at depth were obtained by quantitative 3‐D electromagnetic induction data inversions not by apparent electrical conductivity maps Deeper subsoil characteristics correlate with airborne Sun‐induced fluorescence data indicating soil moisture effects on plant performance Quantitative inverted electrical conductivity model together with plant data help to inform and improve soil‐vegetation‐atmosphere models
    Keywords: Soil And Plant Interaction ; Ground‐Based Electromagnetic Induction Measurements And Quantitative Inversions ; Airborne Hyperspectral Measurements And Quantitative Plant Performance Data ; Relating Soil Structures At Specific Depths And Plant Performance ; Quantitative Quasi‐3d Emi Inversions Capture Responsible Soil Depths Driving Plant Performance ; Sun‐Induced Fluorescence Data May Contain Soil Moisture Information Beside Photosynthetic Activity
    ISSN: 0094-8276
    E-ISSN: 1944-8007
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  • 6
    Language: English
    In: ChemInform, 11 May 2010, Vol.41(19), pp.no-no
    Description: ChemInform is a weekly ing Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
    Keywords: Theoretical Chemistry ; Review ; Environmental Protection ; Waste Gas Purification ; Waste Water Purification
    ISSN: 0931-7597
    E-ISSN: 1522-2667
    Source: John Wiley & Sons, Inc.
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  • 7
    In: Water Resources Research, October 2012, Vol.48(10), pp.n/a-n/a
    Description: An adequate characterization of river bed hydraulic conductivities () is crucial for a proper assessment of river‐aquifer interactions. However, river bed characteristics may change over time due to dynamic morphological processes like scouring or sedimentation what can lead to erroneous model predictions when static leakage parameters are assumed. Sequential data assimilation with the ensemble Kalman filter (EnKF) allows for an update of model parameters in real‐time and may thus be capable of assessing the transient behavior of . Synthetic experiments with a three‐dimensional finite element model of the Limmat aquifer in Zurich were used to assess the performance of data assimilation in capturing time‐variant river bed properties. Reference runs were generated where followed different temporal and/or spatial patterns which should mimic real‐world sediment dynamics. Hydraulic head () data from these reference runs were then used as input data for EnKF which jointly updated and . Results showed that EnKF is able to capture the different spatio‐temporal patterns of in the reference runs well. However, the adaptation time was relatively long which was attributed to the fast decrease of ensemble variance. To improve the performance of EnKF also an adaptive filtering approach with covariance inflation was applied that allowed a faster and more accurate adaptation of model parameters. A sensitivity analysis indicated that even for a low amount of observations a reasonable adaptation of towards the reference values can be achieved and that EnKF is also able to correct for a biased initial ensemble of . EnKF is able to recalibrate time‐variable river bed conductivities in real‐time Adaptation time and accuracy can be improved with adaptive covariance inflation Reasonable results were also obtained for a low number of observations
    Keywords: Covariance Inflation ; Data Assimilation ; Parameter Estimation ; River Aquifer Interaction ; River Bed Conductivity
    ISSN: 0043-1397
    E-ISSN: 1944-7973
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  • 8
    In: Water Resources Research, June 2013, Vol.49(6), pp.3747-3755
    Description: In soils, the isotopic composition of water (H and O) provides qualitative (e.g., location of the evaporation front) and quantitative (e.g., evaporation flux and root water uptake depths) information. However, the main disadvantage of the isotope methodology is that contrary to other soil state variables that can be monitored over long time periods, H and O are typically analyzed following destructive sampling. Here we present a nondestructive method for monitoring soil liquid water H and O over a wide range of water availability conditions and temperatures by sampling water vapor equilibrated with soil water using gas‐permeable polypropylene tubing and a cavity ring‐down laser absorption spectrometer. By analyzing water vapor H and O sampled with the tubing from a fine sand for temperatures ranging between 8°C and 24°C, we demonstrate that our new method is capable of monitoring H and O in soils online with high precision and after calibration, also with high accuracy. Our sampling protocol enabled detecting changes of H and O following nonfractionating addition and removal of liquid water and water vapor of different isotopic compositions. Finally, the time needed for the tubing to monitor these changes is compatible with the observed variations of H and O in soils under natural conditions. Soil water isotopic compositions are usually measured by destructive sampling We present a new method for monitoring soil water isotopic compositions The new method is field deployable, user friendly, and affordable
    Keywords: Cavity Ring‐Down Spectroscopy ; Soil Water Vapor ; Evaporation Front ; Equilibrium Fractionation ; Kinetic Fractionation ; Nondestructive Sampling
    ISSN: 0043-1397
    E-ISSN: 1944-7973
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  • 9
    In: Water Resources Research, May 2012, Vol.48(5), pp.n/a-n/a
    Description: Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread‐skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of interest is a weighted average of pdfs centered around the individual (possibly bias‐corrected) forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts, and reflect the individual models skill over a training (calibration) period. The original BMA approach presented by Raftery et al. (2005) assumes that the conditional pdf of each individual model is adequately described with a rather standard Gaussian or Gamma statistical distribution, possibly with a heteroscedastic variance. Here we analyze the advantages of using BMA with a flexible representation of the conditional pdf. A joint particle filtering and Gaussian mixture modeling framework is presented to derive analytically, as closely and consistently as possible, the evolving forecast density (conditional pdf) of each constituent ensemble member. The median forecasts and evolving conditional pdfs of the constituent models are subsequently combined using BMA to derive one overall predictive distribution. This paper introduces the theory and concepts of this new ensemble postprocessing method, and demonstrates its usefulness and applicability by numerical simulation of the rainfall‐runoff transformation using discharge data from three different catchments in the contiguous United States. The revised BMA method receives significantly lower‐prediction errors than the original default BMA method (due to filtering) with predictive uncertainty intervals that are substantially smaller but still statistically coherent (due to the use of a time‐variant conditional pdf). Conditional distributions in BMA are shown to not be normal and time‐constant Particle filtering significantly reduces the forecast error of the BMA ensemble Forecast spread is improved with mixture models derived from particle filtering
    Keywords: Bayesian Model Averaging ; Gaussian Mixture Modeling ; Mcmc ; Data Assimilation ; Hydrology ; Predictive Uncertainty Estimation
    ISSN: 0043-1397
    E-ISSN: 1944-7973
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  • 10
    In: Water Resources Research, August 2015, Vol.51(8), pp.6549-6563
    Description: NMR relaxometry has developed into a method for rapid pore‐size determination of natural porous media. Nevertheless, it is prone to uncertainties because of unknown surface relaxivities which depend mainly on the chemical composition of the pore walls as well as on the interfacial dynamics of the pore fluid. The classical approach for the determination of surface relaxivities is the scaling of NMR relaxation times by surface to volume ratios measured by gas adsorption or mercury intrusion. However, it is preferable that a method for the determination of average pore sizes uses the same substance, water, as probe molecule for both relaxometry and surface to volume measurements. One should also ensure that in both experiments the dynamics of the probe molecule takes place on similar length scales, which are in the order of some microns. Therefore, we employed NMR diffusion measurements with different observation times using bipolar pulsed field gradients and applied them to unconsolidated sediments (two purified sands, two natural sands, and one soil). The evaluation by Mitra's short‐time model for diffusion in restricted environments yielded information about the surface to volume ratios which is independent of relaxation mechanisms. We point out that methods based on NMR diffusometry yield pore dimensions and surface relaxivities consistent with a pore space as sampled by native pore fluids via the diffusion process. This opens a way to calibrate NMR relaxation measurements with other NMR techniques, providing information about the pore‐size distribution of natural porous media directly from relaxometry. Surface relaxivity serves as calibration parameter for NMR relaxation measurements Pore surface measurement method affects the surface relaxivity determination NMR diffusion experiments lead to best calibration results
    Keywords: Surface Relaxivity ; Porous Media ; Soil ; Pfg Nmr ; S/V Ratio ; Nmr Relaxation Times
    ISSN: 0043-1397
    E-ISSN: 1944-7973
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