Journal articles on the topic 'Soil surface parameter'
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Schreiner, Simon, Dubravko Culibrk, Michele Bandecchi, Wolfgang Gross, and Wolfgang Middelmann. "Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors." at - Automatisierungstechnik 69, no. 4 (April 1, 2021): 325–35. http://dx.doi.org/10.1515/auto-2020-0042.
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Abstract This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.
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Dumbrovský, Miroslav, Ivana Kameníčková, Jana Podhrázská, František Pavlík, and Veronika Sobotková. "Evaluation of soil conservation technologies from the perspective of selected physical soil properties and infiltration capacity of the soil." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 1 (2011): 37–48. http://dx.doi.org/10.11118/actaun201159010037.
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This paper evaluates different technologies of soil cultivation (conventional and minimization) in terms of physical properties and water regime of soils, where infiltration of surface water is a major component of subsurface water. Soil physical properties (the current humidity, reduced bulk density, porosity, water retention capacity of soil, pore distribution and soil aeration) is determined from soil samples taken from the organic horizon according to standard methodology. To observe the infiltration characteristics of surface layers of topsoil, the drench method (double ring infiltrometers) was used. For the evaluation of field measurements of infiltration, empirical and physically derived equations by Kostiakov and Philip and the three-parameter Philip-type equation were used. The Philip three-parameter equation provides physical based parameters near the theoretical values, a good estimation of saturated hydraulic conductivity Ks and sorptivity C1. The parameter S of Philip’s equation describes the real value of the sorptivity of the soil. Experimental research work on the experimental plots H. Meziříčko proceeded in the years 2005–2008.
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Marthews, T. R., C. A. Quesada, D. R. Galbraith, Y. Malhi, C. E. Mullins, M. G. Hodnett, and I. Dharssi. "High-resolution hydraulic parameter maps for surface soils in tropical South America." Geoscientific Model Development 7, no. 3 (May 6, 2014): 711–23. http://dx.doi.org/10.5194/gmd-7-711-2014.
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Abstract. Modern land surface model simulations capture soil profile water movement through the use of soil hydraulics sub-models, but good hydraulic parameterisations are often lacking, especially in the tropics. We present much-improved gridded data sets of hydraulic parameters for surface soil for the critical area of tropical South America, describing soil profile water movement across the region to 30 cm depth. Optimal hydraulic parameter values are given for the Brooks and Corey, Campbell, van Genuchten–Mualem and van Genuchten–Burdine soil hydraulic models, which are widely used hydraulic sub-models in land surface models. This has been possible through interpolating soil measurements from several sources through the SOTERLAC soil and terrain data base and using the most recent pedotransfer functions (PTFs) derived for South American soils. All soil parameter data layers are provided at 15 arcsec resolution and available for download, this being 20x higher resolution than the best comparable parameter maps available to date. Specific examples are given of the use of PTFs and the importance highlighted of using PTFs that have been locally parameterised and that are not just based on soil texture. We discuss current developments in soil hydraulic modelling and how high-resolution parameter maps such as these can improve the simulation of vegetation development and productivity in land surface models.
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Marthews, T. R., C. A. Quesada, D. R. Galbraith, Y. Malhi, C. E. Mullins, M. G. Hodnett, and I. Dharssi. "High-resolution hydraulic parameter maps for surface soils in tropical South America." Geoscientific Model Development Discussions 6, no. 4 (December 17, 2013): 6741–74. http://dx.doi.org/10.5194/gmdd-6-6741-2013.
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Abstract. Modern land surface model simulations capture soil profile water movement through the use of soil hydraulics sub-models, but good hydraulic parameterisations are often lacking, especially in the tropics. We present much-improved gridded datasets of hydraulic parameters for surface soil for the critical area of tropical South America, describing soil profile water movement across the region to 30 cm depth. Optimal hydraulic parameter values are given for the Brooks and Corey, Campbell, van Genuchten–Mualem and van Genuchten–Burdine soil hydraulic models, which are widely-used hydraulic sub-models in Land Surface Models. This has been possible through interpolating soil measurements from several sources through the SOTERLAC soil and terrain database and using the most recent pedotransfer functions (PTFs) derived for South American soils. All soil parameter data layers are provided at 15 arcsec resolution and available for download, this being 20 × higher resolution than the best comparable parameter maps available to date. Specific examples are given of the use of PTFs and the importance highlighted of using PTFs that have been locally-parameterised and that are not just based on soil texture. Details are provided specifically on how to assemble the ancillary data files required for grid-based vegetation simulation using the Joint UK Land Environment Simulator (JULES). We discuss current developments in soil hydraulic modelling and how high-resolution parameter maps such as these can improve the simulation of vegetation development and productivity in land surface models.
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Mohanty, Binayak P., and Jianting Zhu. "Effective Hydraulic Parameters in Horizontally and Vertically Heterogeneous Soils for Steady-State Land–Atmosphere Interaction." Journal of Hydrometeorology 8, no. 4 (August 1, 2007): 715–29. http://dx.doi.org/10.1175/jhm606.1.
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Abstract In this study, the authors investigate effective soil hydraulic parameter averaging schemes for steady-state flow in heterogeneous shallow subsurfaces useful to land–atmosphere interaction modeling. “Effective” soil hydraulic parameters of the heterogeneous shallow subsurface are obtained by conceptualizing the soil as an equivalent homogeneous medium. It requires that the effective homogeneous soil discharges the same mean surface moisture flux (evaporation or infiltration) as the heterogeneous media. Using the simple Gardner unsaturated hydraulic conductivity function, the authors derive the effective value for the saturated hydraulic conductivity Ks or the shape factor α under various hydrologic scenarios and input hydraulic parameter statistics. Assuming one-dimensional vertical moisture movement in the shallow unsaturated soils, both scenarios of horizontal (across the surface landscape) and vertical (across the soil profile) heterogeneities are investigated. The effects of hydraulic parameter statistics, surface boundary conditions, domain scales, and fractal dimensions in case of nested soil hydraulic property structure are addressed. Results show that the effective parameters are dictated more by the α heterogeneity for the evaporation scenario and mainly by Ks variability for the infiltration scenario. Also, heterogeneity orientation (horizontal or vertical) of soil hydraulic parameters impacts the effective parameters. In general, an increase in both the fractal dimension and the domain scale enhances the heterogeneous effects of the parameter fields on the effective parameters. The impact of the domain scale on the effective hydraulic parameters is more significant as the fractal dimension increases.
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Fang, Xu, Anna Muntwyler, Pascal Schneider, Iso Christl, Peng Wang, Fang-Jie Zhao, and Ruben Kretzschmar. "Exploring Key Soil Parameters Relevant to Arsenic and Cadmium Accumulation in Rice Grain in Southern China." Soil Systems 6, no. 2 (April 14, 2022): 36. http://dx.doi.org/10.3390/soilsystems6020036.
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Paddy soils in some areas of southern China are contaminated by arsenic (As) and cadmium (Cd), threatening human health via the consumption of As- and/or Cd-tainted rice. To date, a quantitative understanding of how soil characteristics control As and Cd accumulation in rice grains under field conditions is still deficient. Based on 31 paired soil-grain samples collected in southern China, we statistically explored which soil parameter or parameter combination from various soil analyses best estimates As and Cd in rice. We found that CaCl2 extraction of field-moist soil collected at rice harvest provided the best estimation (R2adj = 0.47–0.60) for grain Cd followed by dry soil CaCl2 extraction (R2adj = 0.38–0.49), where CaCl2 extractable Cd from moist or dry soil was the dominant soil parameter. Compared to soil totals, parameters from neither dry soil ascorbate-citrate extraction nor anoxic soil incubation improved model performance for grain As (R2adj ≤ 0.44), despite their closer relevance to soil redox conditions during plant As uptake. A key role of soil-available sulfur in controlling grain As was suggested by our models. Our approach and results may help develop potential soil amendment strategies for decreasing As and/or Cd accumulation in soils.
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Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: idealized twin experiments." Hydrology and Earth System Sciences Discussions 8, no. 1 (January 28, 2011): 1433–68. http://dx.doi.org/10.5194/hessd-8-1433-2011.
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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, initial soil moisture condition and atmospheric forcing. A physically-based land surface model is used for this purpose. Using a series of idealized twin experiments, model generated near-surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The single imperfect parameter can be successfully estimated using the EnKF. Results show that all the three estimated parameters converge toward their respective true values, while the root mean squared errors (RMSE) of soil moisture associated with these parameters is on average reduced by 54% and 53% comparing with the non-parameter-estimation benchmark RMSE for near-surface layer and root zone layer, respectively. The performance of simultaneous multi-parameter estimation is significant degraded, mainly because the inherent balance relationship of these parameters is broken and the degree of freedom increases in assimilation processes. By introducing constraints between estimated parameters, the performance of the constraint-based simultaneous multi-parameter estimations are as good as that of single-parameter cases even assimilating temporal-sparse observations. In terms of the relative root mean squared error (RRE), the constraint-based estimation cases can achieve 36% to 53% in near-surface layer and 25% to 50% in root zone layer for different assimilation intervals ranging from 1-day to 40-days. This result suggests that the greatest advantage of this method can be displayed with a proper temporal-sparse assimilation interval of 10-days as actual measurement interval of conventional in situ soil moisture observations. As these obtained constraints are mostly in statistical sense, this constraint-based simultaneous state-parameter estimation scheme is supposed to be suitable for other land surface models in soil moisture assimilation applications.
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ZHENG, Xingming, Kai ZHAO, and Xiaojie LI. "Accuracy Analysis of Agriculture Soil Surface Roughness Parameter." Journal of Geo-information Science 15, no. 5 (2013): 752. http://dx.doi.org/10.3724/sp.j.1047.2013.00752.
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Lehrsch, G. A., F. D. Whisler, and M. J. M. Römkens. "Selection of a Parameter Describing Soil Surface Roughness." Soil Science Society of America Journal 52, no. 5 (September 1988): 1439–45. http://dx.doi.org/10.2136/sssaj1988.03615995005200050044x.
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Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments." Hydrology and Earth System Sciences 15, no. 8 (August 3, 2011): 2437–57. http://dx.doi.org/10.5194/hess-15-2437-2011.
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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.
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Mölders, Nicole. "Plant- and Soil-Parameter-Caused Uncertainty of Predicted Surface Fluxes." Monthly Weather Review 133, no. 12 (December 1, 2005): 3498–516. http://dx.doi.org/10.1175/mwr3046.1.
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Abstract Simulated surface fluxes depend on one or more empirical plant or soil parameters that have a standard deviation (std dev). Thus, simulated fluxes will have a stochastic error (or std dev) resulting from the parameters’ std dev. Gaussian error propagation (GEP) principles are used to calculate the std dev for fluxes predicted by the hydro–thermodynamic soil–vegetation scheme to identify prediction limitations due to stochastic errors, parameterization weaknesses, and critical parameters, and to prioritize which parameters to measure with higher accuracy. Relative errors of net radiation, sensible, latent, and ground heat flux, on average, are 7%, 10%, 6%, and 26%, respectively. The analysis identified the parameterization of thermal conductivity as the dominant influence on the std dev of ground heat flux. For net radiation, critical parameters are vegetation fraction and ground emissivity; for sensible and latent heat fluxes, vegetation fraction. Minimum stomatal resistance and leaf area index dominate the std dev of stomatal resistance for most vegetation and soil types. The empirical parameters with the highest relative error are not necessarily the greatest contributors to the std dev of the predicted flux. Based on the analysis high priority should be given to measurements of vegetation fraction, ground emissivity, minimum stomatal resistance, leaf area index in general, and the permanent wilting point and field capacity for clay and clay loam. Moreover, further specification of clay-type soils and tundra-type vegetation may improve the accuracy of the lower boundary condition in Arctic numerical weather prediction. Since GEP showed itself able to identify critical parameters and (parts of) parameterizations, GEP analysis could form a basis for parameterization intercomparisons and for parameter determination aimed at improving models.
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Peng, Fei, and Guodong Sun. "Identifying Sensitive Model Parameter Combinations for Uncertainties in Land Surface Process Simulations over the Tibetan Plateau." Water 11, no. 8 (August 19, 2019): 1724. http://dx.doi.org/10.3390/w11081724.
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Model parameters are among the primary sources of uncertainties in land surface models (LSMs). Over the Tibetan Plateau (TP), simulations of land surface processes, which have not been well captured by current LSMs, can significantly affect the accurate representations of the weather and climate impacts of the TP in numerical weather prediction and climate models. Therefore, to provide guidelines for improving the performance of LSMs over the TP, it is essential to quantify the uncertainties in the simulated land surface processes associated with model parameters and detect the most sensitive parameters. In this study, five observational sites were selected to well represent the land surfaces of the entire TP. The impacts of 28 uncertain parameters from the common land model (CoLM) on the simulated surface heat fluxes (including sensible and latent heat fluxes) and soil temperature were quantified using the approach of conditional nonlinear optimal perturbation related to parameters (CNOP-P). The results showed that parametric uncertainties could induce considerable simulation uncertainties in surface heat fluxes and soil temperature. Thus, errors in parameters should be reduced. To inform future parameter estimation efforts, a three-step sensitivity analysis framework based on the CNOP-P was applied to identify the most sensitive parameter combinations with four member parameters for sensible and latent heat fluxes as well as soil temperature. Additionally, the most sensitive parameter combinations were screened out and showed variations with the target state variables and sites. However, the combinations also bore some similarities. Generally, three or four members from the most sensitive combinations were soil texture related. Furthermore, it was only at the wetter sites that parameters related to vegetation were contained in the most sensitive parameter combinations. In the future, studies on parameter estimations through multiobjective or single-objective optimization can be conducted to improve the performance of LSMs over the TP.
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Jozefaciuk, Grzegorz, Kamil Skic, Agnieszka Adamczuk, Patrycja Boguta, and Krzysztof Lamorski. "Structure and Strength of Artificial Soils Containing Monomineral Clay Fractions." Materials 14, no. 16 (August 19, 2021): 4688. http://dx.doi.org/10.3390/ma14164688.
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Structure and strength are responsible for soil physical properties. This paper determines in a uniaxial compression test the strength of artificial soils containing different proportions of various clay-size minerals (cementing agents) and silt-size feldspar/quartz (skeletal particles). A novel empirical model relating the maximum stress and the Young’s modulus to the mineral content basing on the Langmuir-type curve was proposed. By using mercury intrusion porosimetry (MIP), bulk density (BD), and scanning electron microscopy (SEM), structural parameters influencing the strength of the soils were estimated and related to mechanical parameters. Size and shape of particles are considered as primary factors responsible for soil strength. In our experiments, the soil strength depended primarily on the location of fine particles in respect to silt grains and then, on a mineral particle size. The surface fractal dimension of mineral particles played a role of a shape parameter governing soil strength. Soils containing minerals of higher surface fractal dimensions (rougher surfaces) were more mechanically resistant. The two latter findings appear to be recognized herein for the first time.
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Zhu, Xiufang, Yaozhong Pan, Junxia Wang, and Ying Liu. "A Cuboid Model for Assessing Surface Soil Moisture." Remote Sensing 11, no. 24 (December 16, 2019): 3034. http://dx.doi.org/10.3390/rs11243034.
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This study proposes a cuboid model for soil moisture assessment. In the model, the three edges were the meteorological, soil, and vegetation feature parameters highly related to soil moisture, and the edge lengths represented the degree of influence of each feature parameter on soil moisture. Soil moisture is assessed by the cuboid diagonal, which is referred to as the cuboid soil moisture index (CSMI) in this paper. The model was applied and validated in the Huang-Huai-Hai Plain. The results showed that (1) the difference in land surface temperature between day and night (ΔLST), land surface water index (LSWI), and accumulated precipitation (AP) were most closely correlated with soil moisture observation data in our study area, and were therefore selected as soil, crop, and meteorological system parameters to participate in CSMI calculations, respectively. (2) CSMI-1, with a cuboid length coefficient of 2/1/2, was the best model. The correlation of soil moisture derived from CSMI-1 with observed values was 0.64, 0.60, and 0.52 at depths of 10 cm, 20 cm, and 50 cm, respectively. (3) CSMI-1 had good applicability to the evaluation of soil moisture under different vegetation coverage. When the normalized difference vegetation index (NDVI)was 0–0.7, CSMI-1 was highly correlated with soil moisture at a significance level of 0.01. (4) The three-dimensional (3D) CSMI model can be easily converted to a two-dimensional (2D) model to adapt to different surface conditions (as long as the weight coefficient of one parameter is set to 0). Irrigation information (if available) can be considered as artificial recharge precipitation added in the AP to improve the accuracy of soil moisture inversion. This study provides a reference for soil moisture inversion using optical remote sensing images by integrating soil, vegetation, and meteorological feature parameters.
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Richter, H., A. W. Western, and F. H. S. Chiew. "The Effect of Soil and Vegetation Parameters in the ECMWF Land Surface Scheme." Journal of Hydrometeorology 5, no. 6 (December 1, 2004): 1131–46. http://dx.doi.org/10.1175/jhm-362.1.
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Abstract Numerical Weather Prediction (NWP) and climate models are sensitive to evapotranspiration at the land surface. This sensitivity requires the prediction of realistic surface moisture and heat fluxes by land surface models that provide the lower boundary condition for the atmospheric models. This paper compares simulations of a stand-alone version of the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface scheme, or the Viterbo and Beljaars scheme (VB95), with various soil and vegetation parameter sets against soil moisture observations across the Murrumbidgee River catchment in southeast Australia. The study is, in part, motivated by the adoption of VB95 as the operational land surface scheme by the Australian Bureau of Meteorology in 1999. VB95 can model the temporal fluctuations in soil moisture, and therefore the moisture fluxes, fairly realistically. The monthly model latent heat flux is also fairly insensitive to soil or vegetation parameters. The VB95 soil moisture is sensitive to the soil and, to a lesser degree, the vegetation parameters. The model exhibits a significant (generally wet) bias in the absolute soil moisture that varies spatially. The use of the best Australia-wide available soils and vegetation information did not improve VB95 simulations consistently, compared with the original model parameters. Comparisons of model and observed soil moistures revealed that more realistic soil parameters are needed to reduce the model soil moisture bias. Given currently available continent-wide soils parameters, any initialization of soil moisture with observed values would likely result in significant flux errors. The soil moisture bias could be largely eliminated by using soil parameters that were derived directly from the actual soil moisture observations. Such parameters, however, are only available at very few point locations.
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Orth, Rene, Emanuel Dutra, and Florian Pappenberger. "Improving Weather Predictability by Including Land Surface Model Parameter Uncertainty." Monthly Weather Review 144, no. 4 (April 1, 2016): 1551–69. http://dx.doi.org/10.1175/mwr-d-15-0283.1.
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Abstract The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogeneous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration, and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs the authors find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. The authors demonstrate the robustness of these findings by comparing multiple best-performing parameter sets and multiple randomly chosen parameter sets. The authors find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, the authors construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system.
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Zhang, Xiong, Beshoy Riad, and Eduardo E. Alonso. "Calibration of BBM Parameters using the Modified State Surface Approach." E3S Web of Conferences 382 (2023): 15001. http://dx.doi.org/10.1051/e3sconf/202338215001.
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The Barcelona Basic Model (BBM) developed by Alonso et al. [1] is the first and the mostwidely used elasto-plastic model for unsaturated soils. The BBM successfully explained many key features of unsaturated soils and received extensive acceptance. However, there is lack of a well-establishedmethod for selecting parameter values for the BBM from laboratory tests, although a variety of methods have been recently developed for calibrating model parameters for the BBM. Concerns still exist on the correctness and robustness of such parameter value selection procedures. The above statements were evidenced by a recent benchmark exercise on selection of parameter values for the BBM organized within a "Marie Curie" Research Training Network on "Mechanics of Unsaturated Soils for Engineering"(MUSE)[2]. Experienced constitutive modelers in unsaturated soils from 7 prestigious teams were provided with the same experimental results on an unsaturated soil to calibrate the parameter values in the BBM. Theoretically, the calibrated parameters from different teams are expected to be the same or at leastvery close. However, the selected parameter values by the 7 teams are surprisingly widely different. This paper first discussed the limitations in the existing methods to calibrate the parameter values in the BBM. A novel approach was then proposed to calibrate the parameter values for the BBM. The approach takes advantage of the close-form solution of the BBM, which is derived based upon a newly proposed Modified State Surface Approach to study the unsaturated soils [3-6]. The same experimental data, used in the MUSE benchmark exercise, were reanalysed using the proposed approach to calibrate parameters for the BBM. The results were compared with those in the MUSE benchmark exercise from which thesimplicity, effectiveness, and robustness of the proposed method were evaluated.
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Tong, J. X., J. Z. Yang, and B. X. Hu. "Parameter identification and analysis of soluble chemical transfer from soil to surface runoff." Hydrology and Earth System Sciences Discussions 9, no. 3 (March 27, 2012): 3901–31. http://dx.doi.org/10.5194/hessd-9-3901-2012.
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Abstract. A two-layer mathematical model is used to predict the chemical transfer from the soil into the surface runoff with ponding water. There are two incomplete infiltration-related parameter γ and runoff-related parameter α in the analytical solution to the model, which were assumed to be constant in previous studies (Tong et al., 2010). In this study, experimental data are used to identify the variable γ and α based on the analytical solution. The soil depth of the mixing zone is kept to be constant in different experiments, and the values of γ and α before the surface runoff occurs are constant and equal to their values at the moment the runoff starts. From the study results, it is found that γ will decrease with the increase of the surface runoff time, the increase of the ponding-water depth, hp, or with the decrease of the initial volumetric water content. The variability of γ will decrease with the increase of the initial volumetric water content. Similarly, α will decrease with time for the initially unsaturated experimental soils, but will increase with time for the initially saturated experimental soils. The larger the infiltration, the less chemical concentration in the surface runoff is. The analytical solution is not valid for experimental soil without any infiltration if α is expected to be less or equal to 1. The results will help to quantify chemical transfer from soil into runoff, a significant problem in agricultural pollution management.
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Peng, X., R. Horn, D. Deery, M. B. Kirkham, and J. Blackwell. "Influence of soil structure on the shrinkage behaviour of a soil irrigated with saline - sodic water." Soil Research 43, no. 4 (2005): 555. http://dx.doi.org/10.1071/sr04116.
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Soil structural properties of swelling/shrinking soils play an important role in assessing hydraulic properties. However, the effect of shrinkage/swelling processes on structure formation and strength especially in saline–sodic soils, such as a Typic Chromexert, has not yet been clarified. In this study, we investigate the changes in the shrinkage pattern after applying saline sewage water and use a 3-parameter sigmoidal curve model to fit its shrinkage data. Our aims were to determine the overall effect of sewage water application on soil structure and shrinkage processes after applying saline–sodic water and to evaluate soil shrinkage behaviour through parameters in relation to soil properties. Three plots within the FILTER Project, which were irrigated for summer and winter irrigated cropping with around 1000 m3/ha every 2 weeks with different saline sewage effluent concentrations for >5 years, were sampled from the top horizon to 1.00 m depth. The exchangeable sodium percentage is greatly decreased due to the application of low salt concentration except in the deep horizon. Soil structural properties such as aggregate strength and hydraulic properties are improved, especially in topsoil horizon. The stabilised soil structure reduces the volume change of structural shrinkage. Three parameters of the shrinkage model, defined as α, m, and n, present different physical meanings in relation to soil structure. Parameters α and m have similar functions, both a significantly exponential relationship with aggregate strength and a linear relationship with structural and residual shrinkages, whereas parameter n has a significantly linear relationship with aggregate strength and with the slope of the proportional shrinkage. The relation between parameters of the model and shrinkage behaviour facilitates the prediction of changes in pore water and soil structure and will be a useful tool for modelling water flow in non-rigid soils.
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Baroni, G., A. Facchi, C. Gandolfi, B. Ortuani, D. Horeschi, and J. C. Van Dam. "Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity." Hydrology and Earth System Sciences Discussions 6, no. 3 (June 4, 2009): 4065–105. http://dx.doi.org/10.5194/hessd-6-4065-2009.
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Abstract. Data of soil hydraulic properties forms often a limiting factor in unsaturated zone modelling, especially at the larger scales. Investigations for the hydraulic characterization of soils are time-consuming and costly, and the accuracy of the results obtained by the different methodologies is still debated. However, we may wonder how the uncertainty in soil hydraulic parameters relates to the uncertainty of the selected modelling approach. We performed an intensive monitoring study during the cropping season of a 10 ha maize field in Northern Italy. These data were used to: i) compare different methods for determining soil hydraulic parameters and ii) evaluate the effect of the uncertainty in these parameters on different outputs (i.e. evapotranspiration, water content in the root zone, fluxes through the bottom boundary of the root zone) of two hydrological models with different complexity: SWAP, a widely used model of soil moisture dynamics in unsaturated soils based on Richards equation, and ALHyMUS, a conceptual model of the same dynamics based on a reservoir cascade scheme. We employed five direct and indirect methods to determine soil hydraulic parameters for each horizon of the experimental field. Two methods were based on a parameter optimization of: a) laboratory measured retention and hydraulic conductivity data and b) field measured retention and hydraulic conductivity data. Three methods were based on the application of widely used Pedo-Transfer Functions: c) Rawls and Brakensiek; d) HYPRES; and e) ROSETTA. Simulations were performed using meteorological, irrigation and crop data measured at the experimental site during the period June–October 2006. Results showed a wide range of soil hydraulic parameter values evaluated with the different methods, especially for the saturated hydraulic conductivity Ksat and the shape parameter α of the Van Genuchten curve. This is reflected in a variability of the modeling results which is, as expected, different for each model. The variability of the simulated water content in the root zone and of the fluxes at the root zone bottom for different soil hydraulic parameter sets is found to be often larger than the difference between modeling results of the two models using the same soil hydraulic parameter set. Also we found that a good agreement in simulated soil moisture patterns may occur even if evapotranspiration and percolation fluxes are significantly different. Therefore multiple output variables should be considered to test the performances of methods and models.
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Jin, Menglin, and Shunlin Liang. "An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations." Journal of Climate 19, no. 12 (June 15, 2006): 2867–81. http://dx.doi.org/10.1175/jcli3720.1.
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Abstract Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply constant, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, and 0.96 for bare soil as in the National Center for Atmospheric Research (NCAR) Community Land Model version 2 (CLM2). This is the so-called constant-emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the constant emissivity induces errors in modeling the surface energy budget, especially over large arid and semiarid areas where ɛ is far smaller than unity. One feasible solution to this problem is to apply the satellite-based broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities (ɛλ) in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity (ɛ) required by land surface models. The observed emissivity data show strong seasonality and land-cover dependence. Specifically, emissivity depends on surface-cover type, soil moisture content, soil organic composition, vegetation density, and structure. For example, broadband ɛ is usually around 0.96–0.98 for densely vegetated areas [(leaf area index) LAI > 2], but it can be lower than 0.90 for bare soils (e.g., desert). To examine the impact of variable surface broadband emissivity, sensitivity studies were conducted using offline CLM2 and coupled NCAR Community Atmosphere Models, CAM2–CLM2. These sensitivity studies illustrate that large impacts of surface ɛ occur over deserts, with changes up to 1°–2°C in ground temperature, surface skin temperature, and 2-m surface air temperature, as well as evident changes in sensible and latent heat fluxes.
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Robertson, D., M. Wood, and Q. J. Wang. "Estimating hydraulic parameters for a surface irrigation model from field conditions." Australian Journal of Experimental Agriculture 44, no. 2 (2004): 173. http://dx.doi.org/10.1071/ea02191.
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Border-check irrigation is the most common method of irrigating pastures in Northern Victoria. To make the best use of a border-check irrigation system, consideration needs to be given to the irrigation schedule and irrigation event management. Surface irrigation models can provide an inexpensive and rapid method for identifying optimal irrigation event performance. The most common difficulty encountered when using surface irrigation models is determining appropriate hydraulic parameters. Two experiments were conducted to investigate the relationship between hydraulic parameters of the Analytical Irrigation Model and easily observable field conditions. The field experiments were performed at Tatura, Victoria, on 12 irrigation bays characterised by a Lemnos loam, a red duplex soil, sown to perennial pasture. For each experiment, 3 replicates of 4 treatments were applied. The first experiment found a linear relationship between field soil water deficit, approximated by crop water use less effective rainfall, and the initial infiltration depth. The second experiment found no relationship between pasture height and the model surface roughness parameter. An alternative to estimate the surface roughness parameter is suggested, which involves making an early observation of irrigation advance and solving for the unknown roughness parameter. The parameter estimation method developed in this paper can assist in improving the management of border-check irrigation on Lemnos loam soil, which covers about 125 000 hectares in the Goulburn Valley. However, field-testing of the approach on commercial farms and other soil types is required.
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Soet, M., R. J. Ronda, J. N. M. Stricker, and A. J. Dolman. "Land surface scheme conceptualisation and parameter values for three sites with contrasting soils and climate." Hydrology and Earth System Sciences 4, no. 2 (June 30, 2000): 283–94. http://dx.doi.org/10.5194/hess-4-283-2000.
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Abstract. The objective of the present study is to test the performance of the ECMWF land surface module (LSM) developed by Viterbo and Beljaars (1995) and to identify primary future adjustments, focusing on the hydrological components. This was achieved by comparing off-line simulations against observations and a detailed state-of-the-art model over a range of experimental conditions. Results showed that the standard LSM, which uses fixed vegetation and soil parameter values, systematically underestimated evapotranspiration, partly due to underestimating bare soil evaporation, which appeared to be a conceptual problem. In dry summer conditions, transpiration was seriously underestimated. The bias in surface runoff and percolation was not of the same sign for all three locations. A sensitivity analysis, set up to explore the impact of using standard parameter values, found that implementing specific soil hydraulic properties had a significant effect on runoff and percolation at all three sites. Evapotranspiration, however affected only slightly at the temperate humid climate sites. Under semi-arid conditions, introducing site specific soil hydraulic properties plus a realistic rooting depth improved simulation results considerably. Future adjustments to the standard LSM should focus on parameter values of soil hydraulic functions and rooting depths and, conceptually, on the bare soil evaporation parameterisation and the soil bottom boundary condition. Implications of changing soil hydraulic properties for future large-simulations were explored briefly. For Europe, soil data requirements can be fulfilled partly by the recent data base HYPRES. Sandy and loamy sand soils will then cover about 65% of Europe, whereas in the present model 100% of the area is loam. Keywords: land surface model; soil hydraulic properties; water balance simulation
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Silburn, DM, and RJ Loch. "Evaluation of the CREAMS model. I. Sensitivity analysis of the soil erosion sedimentation component for aggregated clay soils." Soil Research 27, no. 3 (1989): 545. http://dx.doi.org/10.1071/sr9890545.
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The sensitivity of the soil erosion component of the CREAMS model to changes in various input parameters was assessed in the range of parameter values suited to erosion from aggregated clay soils. Predictions of total sediment yield were sensitive to changes in a number of parameters, and interactions between parameter values were observed, e.g., for situations when either detachment of sediment or transport capacity of overland flow limited sediment yield. The CREAMS model was classified as: (i) sensitive to: specific gravity of sediment (Sgi), slope steepness; (ii) sensitive under some conditions, moderately sensitive under others to: total runoff (Vu); Universal Soil Loss Equation factors of erodibility (K), cover (C) and support practices (P); Manning-type available shear parameter (nbov); (iii) moderately sensitive to: peak runoff rate (�p), storm erosivity (EI30), slope length, sediment size distribution and kinematic viscosity. The model was judged to be 'sensitive' to a parameter when change in that parameter caused an equal or greater relative change in predicted sediment yield.
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Hossain, F., E. N. Anagnostou, and K. H. Lee. "A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model." Nonlinear Processes in Geophysics 11, no. 4 (September 24, 2004): 427–40. http://dx.doi.org/10.5194/npg-11-427-2004.
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Abstract. This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM). The scheme is assessed within a Monte Carlo (MC) simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linear deterministic behavior between soil moisture and land surface parameters. Uncertainty in soil moisture simulation (model output) is approximated through a Hermite polynomial chaos expansion of normal random variables that represent the model's parameter (model input) uncertainty. The unknown coefficients of the polynomial are calculated using limited number of model simulation runs. The calibrated polynomial is then used as a fast-running proxy to the slower-running LSM to predict the degree of representativeness of a randomly sampled model parameter set. An evaluation of the scheme's efficiency in sampling is made through comparison with the fully random MC sampling (the norm for GLUE) and the nearest-neighborhood sampling technique. The scheme was able to reduce computational burden of random MC sampling for GLUE in the ranges of 10%-70%. The scheme was also found to be about 10% more efficient than the nearest-neighborhood sampling method in predicting a sampled parameter set's degree of representativeness. The GLUE based on the proposed sampling scheme did not alter the essential features of the uncertainty structure in soil moisture simulation. The scheme can potentially make GLUE uncertainty estimation for any LSM more efficient as it does not impose any additional structural or distributional assumptions.
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Teuling, Adriaan J., Remko Uijlenhoet, Bart van den Hurk, and Sonia I. Seneviratne. "Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters." Journal of Hydrometeorology 10, no. 3 (June 1, 2009): 751–65. http://dx.doi.org/10.1175/2008jhm1033.1.
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Abstract Integration of simulated and observed states through data assimilation as well as model evaluation requires a realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using parameters from three different LSMs participating in the European Land Data Assimilation System (ELDAS) project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect soil moisture more strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the ELDAS LSMs differ strongly, most notably in the available moisture content between the wilting point and the critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated parameter range. The parsimonious framework presented here can be used to investigate first-order parameter sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent with many other studies using different LSMs under a more limited range of possible forcing conditions.
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Gehne, Maria, Thomas M. Hamill, Gary T. Bates, Philip Pegion, and Walter Kolczynski. "Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System." Monthly Weather Review 147, no. 4 (April 1, 2019): 1319–40. http://dx.doi.org/10.1175/mwr-d-18-0057.1.
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Abstract The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is underdispersive near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters, with physically based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from two land surface models (LSMs). Perturbations to roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction, and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed using a stochastic perturbation scheme. The results show that surface perturbations, through their impact on 2-m temperature spread, have a modest positive impact on the skill of short-range ensemble forecasts. However, adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a greater impact on the forecast reliability than surface perturbations, indicating that systematic bias in the model needs to be addressed as well.
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Kovář, P., D. Vaššová, and M. Janeček. "Surface runoff simulation to mitigate the impact of soil erosion, case study of Třebsín (Czech Republic)." Soil and Water Research 7, No. 3 (July 10, 2012): 85–96. http://dx.doi.org/10.17221/50/2011-swr.
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The relation between soil erosion and its redistribution on land strictly depends on the process of surface runoff formation during intensive rainfall. Therefore, interrupting and reducing continuous surface runoff, using adequate conservation measures, may be implemented in order to reduce the shear stress of flowing water. This paper describes the outcomes of the KINFIL model simulation in assessing the runoff from extreme rainfall on hill slopes. The model is a physically based and parameter distributed 3D model that was applied at the Třebsín experimental station in the Czech Republic. This model was used for the first time to simulate the impact of surface runoff caused by natural or sprinkler-made intensive rains on four of the seven different experimental plots. The plots involved in the analysis contain a variety of soils which are covered with different field crops. At this stage, the model parameters comprise saturated hydraulic conductivity, field capacity, sorptivity, plot geometry and surface roughness reflecting the Třebsín experimental plots. These parameters were verified on observed data. All seven plots had the same slope angle, but two of them were vulnerable to surface runoff due to their soil hydraulic parameters. There were rapidly increasing depths and velocities which consequently caused a higher shear stress for splashing soil particles downstream. The paper provides further information and data concerning the relationships between the depth of water and its velocity on the slopes of certain roughness. It also provides information concerning shear stress and shear velocity values, compared with their critical values depending on the soil particle distribution. This approach is more physically based than the traditional method of Universal Soil Loss Equation (USLE).
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Szerakowska, Sylwia, Maria Jolanta Sulewska, Edward Stanisław Oczeretko, Jerzy Trzciński, and Barbara Woronko. "Application of Fractal Geometry in the Evaluation of Surface Microtexture of Soil Particles." Applied Mechanics and Materials 797 (November 2015): 238–45. http://dx.doi.org/10.4028/www.scientific.net/amm.797.238.
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The shape of particles building the solid phase of soils is an important factor influencing soil behaviour. Three parameters defining the characteristics of particle shape: roundness, angularity and texture are the most commonly analyzed. The most difficult issue is texture determination due to its complex nature. Quantitative evaluation of this parameter creates serious problems, however, is not impossible. A new mathematical tool, such as fractal geometry, may be helpful. Through the use of power law, fractal analysis allows to designate fractal dimension that specifies the complexity of the tested object.
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Holford, ICR, and AD Doyle. "Influence of intensity/quantity characteristics of soil phosphorus tests on their relationships to phosphorus responsiveness of wheat under field conditions." Soil Research 30, no. 3 (1992): 343. http://dx.doi.org/10.1071/sr9920343.
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Six soil phosphorus tests (lactate, Brayl, Bray2, neutral fluoride, Olsen and Colwell) were regressed against potassium chloride-soluble phosphorus (intensity) and isotopically exchangeable phosphorus (quantity) measured in 59 soils of the northern and central wheat belts of New South Wales. Wheat nutrition experiments on these soils during 1986-89 measured yield responses to phosphate and nitrogen fertilizers. Soil tests varied widely in their correlations with yield responsiveness to phosphate, with the lactate and Bray2 tests accounting for more than twice the variance accounted for by other soil tests. The intensity parameter was also highly correlated but the quantity parameter was not. All soil tests, except Bray1, were very highly correlated with the intensity parameter, so this relationship did not differentiate the relative efficacies of the soil tests. Soil tests were less correlated with the quantity parameter, but those soil tests (neutral fluoride, Olsen and Colwell) that were most highly correlated (r2 > 0.62) with this parameter were most weakly correlated (r2 < 0.29) with yield response. It was concluded therefore that exchangeable phosphorus is not a satisfactory measure of the quantity factor and that an effective soil test for wheat-growing soils will be highly correlated with intensity but not necessarily with exchangeable phosphorus. The critical value of the lactate test was the same (17 mg/kg) as in previous studies with wheat but was lower (14 mg/kg) in 1989 when very low in-crop rainfall occurred. With deeper sampling (15 cm rather than 10 cm) the lactate test was slightly less accurate and the critical value was lower (11 mg/kg).
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Bayad, Mohamed, Henry Wai Chau, Stephen Trolove, Jim Moir, Leo Condron, and Moussa Bouray. "The Relationship between Soil Moisture and Soil Water Repellency Persistence in Hydrophobic Soils." Water 12, no. 9 (August 19, 2020): 2322. http://dx.doi.org/10.3390/w12092322.
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In this work, we modelled the response of soil water repellency (SWR) persistence to the decrease in moisture in drying soils, and we explored the implication of soil particle size distribution and specific surface area on the SWR severity and persistence. A new equation for the relationship between SWR persistence and soil moisture (θ) is described in this paper. The persistence of SWR was measured on ten different hydrophobic soils using water drop penetration time (WDPT) at decreasing levels of gravimetric water content. The actual repellency persistence showed a sigmoidal response to soil moisture decrease, where Ra(θ)=Rp/1+eδ(θ−θc). The suggested equation enables one to model the actual SWR persistence (Ra) using θ, the potential repellency (Rp) and two characteristic parameters related to the shape of the response curve. The two parameters are the critical soil moisture θc, where the Ra increase rate reaches its maximum, and the parameter δ affecting the steepness of the curve at the inflexion point of the sigmoidal curve. Data shows that both soil carbon and texture are controlling the potential SWR in New Zealand pastures.
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Maggioni, V., E. N. Anagnostou, and R. H. Reichle. "The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling." Hydrology and Earth System Sciences 16, no. 10 (October 4, 2012): 3499–515. http://dx.doi.org/10.5194/hess-16-3499-2012.
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Abstract. The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.
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Liu, Yongwei, Wen Wang, and Yiming Hu. "Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River basin." Journal of Hydrology and Hydromechanics 65, no. 2 (June 1, 2017): 123–33. http://dx.doi.org/10.1515/johh-2017-0011.
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Abstract This paper investigates the impact of surface soil moisture assimilation on the estimation of both parameters and states in the Soil and Water Assessment Tool (SWAT) model using the ensemble Kalman filter (EnKF) method in upper Huai River basin. The investigation is carried out through a series of synthetic experiments and real world tests using a merged soil moisture product (ESA CCI SM) developed by the European Space Agency, and considers both the joint state-parameter updating and only state updating schemes. The synthetic experiments show that with joint state-parameter update, the estimation of model parameter SOL_AWC (the available soil water capacity) and model states (the soil moisture in different depths) can be significantly improved by assimilating the surface soil moisture. Meanwhile, the runoff modeling for the whole catchment is also improved. With only state update, the improvement on runoff modeling shows less significance and robustness. Consistent with the synthetic experiments, the assimilation of the ESA CCI SM with joint state-parameter update shows considerable capability in the estimation of SOL_AWC. Both the joint state-parameter update and the only state update scheme could improve the streamflow modeling although the optimal model and observation error parameters for them are quite different. However, due to the high vegetation coverage of the study basin, and the strong spatial mismatch between the satellite and the model simulated soil moisture, it is still challenging to significantly benefit the runoff estimates by assimilating the ESA CCI SM.
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Yang, Yukun, Baoqin Wen, Longpeng Ding, Liqiao Li, Xinghua Chen, and Jingbin Li. "Soil Particle Modeling and Parameter Calibration for Use with Discrete Element Method." Transactions of the ASABE 64, no. 6 (2021): 2011–23. http://dx.doi.org/10.13031/trans.14083.
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HighlightsSoil particle shapes were statistically analyzed, and four representative particles were obtained.A particle model was established using three-dimensional non-contact surface topography.This study used a response surface design method to calibrate significant soil parameters.The simulation parameters were verified by rotary tiller experiment.Abstract. The discrete element method (DEM) has broad prospects for application in soil-tool simulations. To ensure the reliability of simulations, appropriate simulation parameters and particle modeling are essential. Therefore, in this article, a method combining simulation and actual tests is proposed to calibrate the critical soil parameters. First, the effect of soil particle shape on particle contact was considered. Soil particle shapes were statistically analyzed using an improved GrabCut algorithm and k-means algorithm. Four representative soil particles were obtained. Second, a soil particle model was established by microscope and three-dimensional non-contact surface topography. Finally, taking the angle of repose as the response value, the three parameters with significant effects on the angle of repose, i.e., soil shear modulus, Hertz-Mindlin with Johnson-Kendall-Roberts contact model (JKR), and soil-soil restitution coefficient, were obtained via a Plackett-Burman experiment. The optimal value intervals of the significant parameters were determined by the steepest climbing test. A polynomial regression model between the angle of repose and the three significant parameters was established with a Box-Behnken experiment using three factors and three levels. The interactions between the three significant parameters were not significant, as revealed by response surface analysis. The optimal values of the significant parameters were obtained by taking the actual angle of repose as the target and resulted in a soil shear module of 9.8 MPa, JKR of 0.063, and soil-soil restitution coefficient of 0.478. To verify the reliability of the calibrated parameters, the soil angles of repose from the simulation and from actual tests were compared and analyzed. For a simulated angle of repose of 38.5°, the actual angle of repose was 38.6°, and the relative error was 0.26%. DEM was also used to simulate a rotary tiller with the calibrated parameters. The maximum error of the simulated soil throwing angle was less than 10% when compared with the actual throwing angle. The experimental results showed that the calibrated parameters were accurate and can provide a reference for the selection of soil discrete element parameters. Keywords: Angle of repose, Numerical simulation, Parameter calibration, Shape survey, Soil.
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Arsenault, Kristi R., Grey S. Nearing, Shugong Wang, Soni Yatheendradas, and Christa D. Peters-Lidard. "Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation." Journal of Hydrometeorology 19, no. 5 (May 1, 2018): 815–30. http://dx.doi.org/10.1175/jhm-d-17-0205.1.
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Abstract The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include the dynamic simulation of vegetation and carbon to 43 primary user-specified parameters. This is done using 32 years’ worth of data from 10 international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes.
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Fér, Miroslav, Martin Leue, Radka Kodešová, Horst H. Gerke, and Ruth H. Ellerbrock. "Droplet infiltration dynamics and soil wettability related to soil organic matter of soil aggregate coatings and interiors." Journal of Hydrology and Hydromechanics 64, no. 2 (June 1, 2016): 111–20. http://dx.doi.org/10.1515/johh-2016-0021.
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Abstract The organo-mineral coatings of soil aggregates, cracks, and biopores control sorption and macropore-matrix exchange during preferential flow, in particular in the clay-illuvial Bt-horizon of Luvisols. The soil organic matter (SOM) composition has been hypothesized to explain temporal changes in the hydraulic properties of aggregate surfaces. The objective of this research was to find relations between the temporal change in wettability, in terms of droplet infiltration dynamics, and the SOM composition of coated and uncoated aggregate surfaces. We used 20 to 40 mm sized soil aggregates from the Bt2 horizon of a Haplic Luvisol from loess that were (i) coated, (ii) not coated (both intact), and (iii) aggregates from which coatings were removed (cut). The SOM composition of the aggregate surfaces was characterized by infrared spectroscopy in the diffuse reflection mode (DRIFT). A potential wettability index (PWI) was calculated from the ratio of hydrophobic and hydrophilic functional groups in SOM. The water drop penetration times (WDPT) and contact angles (CA) during droplet infiltration experiments were determined on dry and moist aggregate samples of the three types. The decrease in the CA with time was described using the power function (CA(t) = at−b). For dry aggregates, the WDPT values were larger for coated as compared to uncoated regions on the aggregate surfaces, and increased with increasing PWI value (R2 = 0.75). The a parameter was significantly related to the WDPT (R2 = 0.84) and to the PWI (R2 = 0.64). The relations between the b parameter and the WDPT (R2 = 0.61) and the PWI (R2 = 0.53) were also significant. The WDPT values of wet soil aggregates were higher than those of dry aggregates due to high water contents, which limited the droplet infiltration potential. At the wet aggregate surfaces, the WDPT values increased with the PWI of the SOM (R2 = 0.64). In contrast to dry samples, no significant relationships were found between parameters a or b of CA(t) and WDPT or PWI for wet aggregate surfaces. The results suggest that the effect of the SOM composition of coatings on surface wettability decreases with increasing soil moisture. In addition to the dominant impact of SOM, the wettability of aggregate surfaces could be affected by different mineralogical compositions of clay in coatings and interiors of aggregates. Particularly, wettability of coatings could be decreased by illite which was the dominant clay type in coatings. However, the influence of different clay mineral fractions on surface wettability was not due to small number of measurements (2 and 1 samples from coatings and interiors, respectively) quantified.
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Soylu, M. E., E. Istanbulluoglu, J. D. Lenters, and T. Wang. "Quantifying the impact of groundwater depth on evapotranspiration in a semi-arid grassland region." Hydrology and Earth System Sciences 15, no. 3 (March 7, 2011): 787–806. http://dx.doi.org/10.5194/hess-15-787-2011.
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Abstract. Interactions between shallow groundwater and land surface processes play an important role in the ecohydrology of riparian zones. Some recent land surface models (LSMs) incorporate groundwater-land surface interactions using parameterizations at varying levels of detail. In this paper, we examine the sensitivity of land surface evapotranspiration (ET) to water table depth, soil texture, and two commonly used soil hydraulic parameter datasets using four models with varying levels of complexity. The selected models are Hydrus-1D, which solves the pressure-based Richards equation, the Integrated Biosphere Simulator (IBIS), which simulates interactions among multiple soil layers using a (water-content) variant of the Richards equation, and two forms of a steady-state capillary flux model coupled with a single-bucket soil moisture model. These models are first evaluated using field observations of climate, soil moisture, and groundwater levels at a semi-arid site in south-central Nebraska, USA. All four models are found to compare reasonably well with observations, particularly when the effects of groundwater are included. We then examine the sensitivity of modelled ET to water table depth for various model formulations, node spacings, and soil textures (using soil hydraulic parameter values from two different sources, namely Rawls and Clapp-Hornberger). The results indicate a strong influence of soil texture and water table depth on groundwater contributions to ET. Furthermore, differences in texture-specific, class-averaged soil parameters obtained from the two literature sources lead to large differences in the simulated depth and thickness of the "critical zone" (i.e., the zone within which variations in water table depth strongly impact surface ET). Depending on the depth-to-groundwater, this can also lead to large discrepancies in simulated ET (in some cases by more than a factor of two). When the Clapp-Hornberger soil parameter dataset is used, the critical zone becomes significantly deeper, and surface ET rates become much higher, resulting in a stronger influence of deep groundwater on the land surface energy and water balance. In general, we find that the simulated sensitivity of ET to the choice of soil hydraulic parameter dataset is greater than the sensitivity to soil texture defined within each dataset, or even to the choice of model formulation. Thus, our findings underscore the need for future modelling and field-based studies to improve the predictability of groundwater-land surface interactions in numerical models, particularly as it relates to the parameterization of soil hydraulic properties.
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Vázquez, Eva Vidal, Sidney Rosa Vieira, Isabella Clerici De Maria, and Antonio Paz González. "Geostatistical analysis of microrelief of an oxisol as a function of tillage and cumulative rainfall." Scientia Agricola 66, no. 2 (April 2009): 225–32. http://dx.doi.org/10.1590/s0103-90162009000200012.
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Surface roughness can be influenced by type and intensity of soil tillage among other factors. In tilled soils microrelief may decay considerably as rain progresses. Geostatistics provides some tools that may be useful to study the dynamics of soil surface variability. The objective of this study was to show how it is possible to apply geostatistics to analyze soil microrelief variability. Data were taken at an Oxisol over six tillage treatments, namely, disk harrow, disk plow, chisel plow, disk harrow + disk level, disk plow + disk level and chisel plow + disk level. Measurements were made initially just after tillage and subsequently after cumulative natural rainfall events. Duplicated measurements were taken in each one of the treatments and dates of samplings, yielding a total of 48 experimental surfaces. A pin microrelief meter was used for the surface roughness measurements. The plot area was 1.35 × 1.35 m and the sample spacing was 25 mm, yielding a total of 3,025 data points per measurement. Before geostatistical analysis, trend was removed from the experimental data by two methods for comparison. Models were fitted to the semivariograms of each surface and the model parameters were analyzed. The trend removing method affected the geostatistical results. The geostatistical parameter dependence ratio showed that spatial dependence improved for most of the surfaces as the amount of cumulative rainfall increased.
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Munoz-Martin, Joan Francesc, Nereida Rodriguez-Alvarez, Xavier Bosch-Lluis, and Kamal Oudrhiri. "Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals." Remote Sensing 15, no. 8 (April 11, 2023): 2013. http://dx.doi.org/10.3390/rs15082013.
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Single-pass soil moisture retrieval has been a key objective of Global Navigation Satellite System-Reflectometry (GNSS-R) for the last decade. Achieving this goal will allow small satellites with GNSS-R payloads to perform such retrievals at high temporal resolutions. Properly modeling the soil surface roughness is key to providing high-quality soil moisture estimations. In the present work, the Physical Optics and Geometric Optics models of the Kirchhoff Approximation are implemented to the coherent and incoherent components of the reflectometry measurements collected by the SMAP radar receiver (SMAP-Reflectometry or SMAP-R). Two surface roughness products are retrieved and compared for a single-polarization approach, critical for single-polarization GNSS-R instruments that target soil moisture retrievals. Then, a polarization decoupling model is implemented for a dual-polarization retrieval approach, where the ratio between two orthogonal polarizations is evaluated to estimate soil moisture. Differences between linear and circular polarization ratios are evaluated using this decoupling parameter, and the theoretical soil moisture error with varying decoupling parameters is analyzed. Our results show a 1-sigma soil moisture error of 0.08 cm3/cm3 for the dual-polarization case for a fixed polarization decoupling value used for the whole Earth, and a 2-sigma error of 0.08 cm3/cm3 when the measured reflectivity and the VOD are used to estimate the polarization decoupling parameter.
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Huang, Maoyi, Zhangshuan Hou, L. Ruby Leung, Yinghai Ke, Ying Liu, Zhufeng Fang, and Yu Sun. "Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model: Evidence from MOPEX Basins." Journal of Hydrometeorology 14, no. 6 (November 22, 2013): 1754–72. http://dx.doi.org/10.1175/jhm-d-12-0138.1.
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Abstract In this study, the authors applied version 4 of the Community Land Model (CLM4) integrated with an uncertainty quantification (UQ) framework to 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning a wide range of climate and site conditions to investigate the sensitivity of runoff simulations to major hydrologic parameters and to assess the fidelity of CLM4, as the land component of the Community Earth System Model (CESM), in capturing realistic hydrological responses. They found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture–related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, water-limited hydrologic regime and finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study evaluated the parameter identifiability of hydrological parameters from streamflow observations at selected MOPEX basins and demonstrated the feasibility of parameter inversion/calibration for CLM4 to improve runoff simulations. The results suggest that in order to calibrate CLM4 hydrologic parameters, model reduction is needed to include only the identifiable parameters in the unknowns. With the reduced parameter set dimensionality, the inverse problem is less ill posed.
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Ta, Na, Chutian Zhang, Hongru Ding, and Qingfeng Zhang. "Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion." Open Geosciences 12, no. 1 (July 6, 2020): 232–41. http://dx.doi.org/10.1515/geo-2020-0036.
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AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.
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Van Diest, H., and J. Kesselmeier. "Soil atmosphere exchange of carbonyl sulfide (COS) regulated by diffusivity depending on water-filled pore space." Biogeosciences 5, no. 2 (April 1, 2008): 475–83. http://dx.doi.org/10.5194/bg-5-475-2008.
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Abstract. The exchange of carbonyl sulfide (COS) between soil and the atmosphere was investigated for three arable soils from Germany, China and Finland and one forest soil from Siberia for parameterization in the relation to ambient carbonyl sulfide (COS) concentration, soil water content (WC) and air temperature. All investigated soils acted as sinks for COS. A clear and distinct uptake optimum was found for the German, Chinese, Finnish and Siberian soils at 11.5%, 9%, 11.5%, and 9% soil WC, respectively, indicating that the soil WC acts as an important biological and physical parameter for characterizing the exchange of COS between soils and the atmosphere. Different optima of deposition velocities (Vd) as observed for the Chinese, Finnish and Siberian boreal soil types in relation to their soil WC, aligned at 19% in relation to the water-filled pore space (WFPS), indicating the dominating role of gas diffusion. This interpretation was supported by the linear correlation between Vd and bulk density. We suggest that the uptake of COS depends on the diffusivity dominated by WFPS, a parameter depending on soil WC, soil structure and porosity of the soil.
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Van Diest, H., and J. Kesselmeier. "Soil atmosphere exchange of Carbonyl Sulfide (COS) regulated by diffusivity depending on water-filled pore space." Biogeosciences Discussions 4, no. 5 (October 12, 2007): 3701–22. http://dx.doi.org/10.5194/bgd-4-3701-2007.
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Abstract. The exchange of carbonyl sulfide (COS) between soil and the atmosphere was investigated for three arable soils from Germany, China and Finland and one forest soil from Siberia for parameterization in the relation to ambient carbonyl sulfide (COS) concentration, soil water content (WC) and air temperature. All investigated soils acted as significant sinks for COS. A clear and distinct uptake optimum was found for the German, Chinese, Finnish and Siberian soils at 11.5%, 9%, 11.5%, and 9% soil WC, respectively, indicating that the soil WC acts as an important biological and physical parameter for characterizing the exchange of COS between soils and the atmosphere. Different optima of deposition velocities (Vd) as observed for the Chinese, Finnish and Siberian boreal soil types in relation to their soil WC, aligned at 19% in relation to the water-filled pore space (WFPS), indicating the dominating role of gas diffusion. This interpretation was supported by the linear correlation between Vd and bulk density. We suggest that the uptake of COS depends on the diffusivity dominated by WFPS, a parameter depending on soil WC, soil structure and porosity of the soil.
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Silburn, D. M. "Hillslope runoff and erosion on duplex soils in grazing lands in semi-arid central Queensland. II. Simple models for suspended and bedload sediment." Soil Research 49, no. 2 (2011): 118. http://dx.doi.org/10.1071/sr09069.
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The use of simple models of soil erosion which represent the main effects of management in grazing lands in northern Australia is limited by a lack of measured parameter values. In particular, parameters are needed for erosion models (sediment concentration v. cover equations) used in daily soil-water balance models. For this research, we specifically avoided equations that use rainfall and runoff rates (e.g. peak flow), as current daily models are limited in their ability to estimate these rates. The resulting models will therefore give poor estimates of soil losses for individual events, but should give good estimates of long-term average erosion and management influences. Runoff and erosion data were available for 7 years on 12 hillslope plots with cover of 10–80%, with and without grazing, with and without tree canopy cover, on a variety of soils according to various soil classification systems. Soils were grouped into those derived from sandstone (SS), mudstone (MS), and eroded mudstone (MSe). These data were used to determine two parameters, i.e. (i) efficiency of entrainment for bare soil and (ii) a cover factor, for simple models of bedload and suspended sediment concentrations. Methods used to fit parameters affected the results; optimising to obtain the minimum sum of squares of errors in soil losses gave better results than fitting an exponential equation to sediment concentration–cover data. The use of a linear slope factor in the sediment concentration models was confirmed with data from plots with slopes 4–8%. Parameters for the bedload sediment concentration model were the same for SS, MS, and MSe soils. Parameters for the suspended sediment concentration model were the same for SS and MS soils, but the MSe soil had a greater efficiency of entrainment for bare soil (about double). The sediment concentration–cover relationships and fitted cover factors were different for suspended and bedload sediment. Thus, the resulting modelled proportion of sediment as suspended load changed with cover, from ~0.3 for bare soil to 0.9 at 80% cover, mimicking the measured data. The cover factor was lower than published values for cultivated soils, indicating less reduction in sediment concentration with greater cover. A compilation of parameter values for the sediment concentration model from published and unpublished sources in grazing and cropping lands is provided.
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Alkassem Alosman, Mohamed, Stéphane Ruy, Samuel Buis, Patrice Lecharpentier, Jean Bader, François Charron, and Albert Olioso. "An Improved Method to Estimate Soil Hydrodynamic and Hydraulic Roughness Parameters by Using Easily Measurable Data During Flood Irrigation Experiments and Inverse Modelling." Water 10, no. 11 (November 5, 2018): 1581. http://dx.doi.org/10.3390/w10111581.
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Surface irrigation is known as a highly water-consuming system and needs to be optimized to save water. Models can be used for this purpose but require soil parameters at the field scale. This paper aims to estimate effective soil parameters by combining: (i) a surface flow-infiltration model, namely CALHY; (ii) an automatic fitting algorithm based on the SIMPLEX method; and (iii) easily accessible and measurable data, some of which had never been used in such a process, thus minimizing parameter estimation errors. The validation of the proposed approach was performed through three successive steps: (1) examine the physical meaning of the fitted parameters; (2) verify the accuracy of the proposed approach using data that had not been served in the fitting process; and (3) validate using data obtained from independent irrigation events. Three parameters were estimated with a low uncertainty: the saturated hydraulic conductivity Ks, the hydraulic roughness k, and the soil water depletion ∆θ. The estimation uncertainty of the soil surface depressional storage parameter H0 was of the same order of magnitude of its value. All experimental datasets were simulated very well. Performance criteria were similar during both the fitting and validation stages.
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Schweppe, Robert, Stephan Thober, Sebastian Müller, Matthias Kelbling, Rohini Kumar, Sabine Attinger, and Luis Samaniego. "MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models." Geoscientific Model Development 15, no. 2 (January 31, 2022): 859–82. http://dx.doi.org/10.5194/gmd-15-859-2022.
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Abstract. Distributed environmental models such as land surface models (LSMs) require model parameters in each spatial modeling unit (e.g., grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimensionality of the parameter space in these models is to use regularization techniques. One such highly efficient technique is the multiscale parameter regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of NetCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model (mHM; https://www.ufz.de/mhm, last access: 16 January 2022). By using this tool for the generation of continental-scale soil hydraulic parameters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.
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Silburn, DM, and DM Freebairn. "Evaluations of the CREAMS model. III. Simulation of the hydrology of vertisols." Soil Research 30, no. 5 (1992): 547. http://dx.doi.org/10.1071/sr9920547.
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The CREAMS hydrology model was evaluated for two Vertisols, each with three fallow management strategies, by comparing predictions of runoff, soil moisture and drainage with 5-8 years of measured data. Model parameter values were derived by: (i) using a combination of measured site characteristics and published values, and (ii) optimizing selected parameters, particularly the runoff parameter (curve number). With parameter values from published sources, runoff was overpredicted by 1 to 39%; good estimates of total soil moisture were obtained. Using optimized curve numbers, runoff was predicted well (daily, r2 = 0.83; monthly, r2 = 0.92; annual, r2 = 0.94). Total soil moisture values were predicted well, the main source of error being from overprediction of transpiration. Errors in predicted runoff caused little of the error in predicted total soil moisture. The distribution of soil moisture in the soil was poorly predicted. Drainage predictions were similar to estimates from steady-state solute mass balance. Optimized curve numbers derived in this study provide parameter values for modelling the water balance of self-mulching Vertisols. Values of other model parameters, derived from field measurements and published sources were near optimal, and predictions were not improved by adjusting the more sensitive of these parameters. The model is considered adequate for many practical applications. Some enhancements to the model are suggested.
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Gao, Chen, Min Xu, Hanzeyu Xu, and Wei Zhou. "Retrieving Photometric Properties and Soil Moisture Content of Tidal Flats Using Bidirectional Spectral Reflectance." Remote Sensing 13, no. 7 (April 6, 2021): 1402. http://dx.doi.org/10.3390/rs13071402.
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Moisture content in tidal flats changes frequently and spatially on account of tidal fluctuations, which greatly influence the reflectance of the tidal flat surface. Precise prediction of the spatial-temporal variation of tidal flats’ moisture content is an important foundation of surface bio-geophysical information research by remote sensing. In this paper, we first measured the multi-angle reflectance of soil samples obtained from tidal flats in the northeastern Dongtai, Jiangsu Province, China, in the laboratory. Then, based on the particle swarm optimization (PSO) algorithm, we retrieved the photometric characteristics of the soil surface by employing the SOILSPECT bidirectional reflectance model. Finally, the soil moisture content was retrieved by introducing the equivalent water thickness of the soil. The results showed that: (i) A significant correlation existed between the retrieved equivalent water thickness and the measured soil moisture content. The SOILSPECT model is capable of estimating soil moisture with high precision by using multi-angle reflectance. (ii) Retrieved values of single scattering albedo (ω) were consistent with the variation of soil moisture content. The roughness parameter (h) and the asymmetry factor (Θ) were consistent with the structure and particle composition of the soil surface in dry soil samples. (iii) When the soil samples were soaked with water, the roughness parameter (h) and the type of scattering on the soil surface both showed irregular changes. These results support the importance of using the measured soil particle size as one of the parameters for the retrieval of soil moisture content, which is a method that should be used cautiously, especially in tidal flats.
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Sufardi, Sufardi, Teti Arabia, Khairullah Khairullah, Karnilawati Karnilawati, Sahbudin Sahbudin, and Zainabun Zainabun. "Charge Characteristics and Cation Exchanges Properties of Hilly Dryland Soils Aceh Besar, Indonesia." Aceh International Journal of Science and Technology 9, no. 2 (September 7, 2020): 90–101. http://dx.doi.org/10.13170/aijst.9.2.17565.
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Soil surface charge and cation exchange are important parameters of soil fertility in tropical soils. This study was conducted to investigate characteristics of surface charges and cation exchanges on four soil orders of the dryland in Aceh Besar district. The soil order includes Entisols Jantho (05o16’58.41” N; 95o37’51.82” E), Andisols Saree (05o27'15.6" N; 95o44'09,1" E), Inceptisols Cucum (05º18’18,37” N; 95º32’48,04” E), dan Oxisols Lembah Seulawah (05o27’19,4” N; 95o46’19,2” E). The charge characteristics of surface charge are evaluated from the parameter of DpH (pHH2O-pHKCl), variable charge (Vc), permanent charge (Pc), and point of zero charges (PZC). In contrast, cation exchange properties are evaluated from several soil chemical properties, such as soil organic matter (SOM), base saturation (BS), cation exchange capacity (CEC), and effective CEC (ECEC). The results show that the four pedons of soil in the hilly dryland of Aceh Besar include a variable charge because it has a PZC, which is characterized by a negative surface charge with a PZC of pHH2O and has CEC dependent soil pH. PZC value varies from 3.21 – 5.26 and sequentially PZC Andisols Oxisols Entisols Inceptisols. The total CEC value differs considerably from ECEC and the sum of cations. CEC total of the soils varies from 12.8 – 34.4 cmol kg-1, whereas the ECEC values vary from 2.72 – 8.66 cmol kg-1. The highest variable charge percentage is found in Andisols Saree. In contrast, the highest permanent charge is found in Inceptisols Cucum and is positively correlated with pHH20, PZC, CEC, and sums of cations or ECEC. Improving soil quality in hilly dryland soils in Aceh Besar District can be done by decreasing the PZC status of soils with organic amendments and fertilizers or increasing the pH by using liming.
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CHE, AILAN, XIANQI LUO, JINGHUA QI, and DEYONG WANG. "STUDY ON CORRELATION BETWEEN SHEAR WAVE VELOCITY AND GROUND PROPERTIES FOR GROUND LIQUEFACTION INVESTIGATION OF SILTS." International Journal of Modern Physics B 22, no. 31n32 (December 30, 2008): 5705–10. http://dx.doi.org/10.1142/s0217979208051042.
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Shear wave velocity (V s ) of soil is one of the key parameters used in assessment of liquefaction potential of saturated soils in the base with leveled ground surface; determination of shear module of soils used in seismic response analyses. Such parameter can be experimentally obtained from laboratory soil tests and field measurements. Statistical relation of shear wave velocity with soil properties based on the surface wave survey investigation, and resonant column triaxial tests, which are taken from more than 14 sites within the depth of 10 m under ground surface, is obtained in Tianjin (China) area. The relationship between shear wave velocity and the standard penetration test N value (SPT-N value) of silt and clay in the quaternary formation are summarized. It is an important problem to research the effect of shear wave velocity on liquefaction resistance of saturated silts (sandy loams) for evaluating liquefaction resistance. According the results of cyclic triaxial tests, a correlation between liquefaction resistance and shear wave velocity is presented. The results are useful for ground liquefaction investigation and the evaluation of liquefaction resistance.