scholarly journals Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

2017 ◽  
Vol 31 (3) ◽  
pp. 433-445
Author(s):  
Yifan Yan ◽  
Jianli Liu ◽  
Jiabao Zhang ◽  
Xiaopeng Li ◽  
Yongchao Zhao
Keyword(s):  
Confidence Interval ◽  
Least Squares ◽  
Water Content ◽  
Soil Water ◽  
Nonlinear Least Squares ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Retention Curve ◽  
Least Squares Algorithm ◽  
Soil Hydraulic

AbstractNonlinear least squares algorithm is commonly used to fit the evaporation experiment data and to obtain the ‘optimal’ soil hydraulic model parameters. But the major defects of nonlinear least squares algorithm include non-uniqueness of the solution to inverse problems and its inability to quantify uncertainties associated with the simulation model. In this study, it is clarified by applying retention curve and a modified generalised likelihood uncertainty estimation method to model calibration. Results show that nonlinear least squares gives good fits to soil water retention curve and unsaturated water conductivity based on data observed by Wind method. And meanwhile, the application of generalised likelihood uncertainty estimation clearly demonstrates that a much wider range of parameters can fit the observations well. Using the ‘optimal’ solution to predict soil water content and conductivity is very risky. Whereas, 95% confidence interval generated by generalised likelihood uncertainty estimation quantifies well the uncertainty of the observed data. With a decrease of water content, the maximum of nash and sutcliffe value generated by generalised likelihood uncertainty estimation performs better and better than the counterpart of nonlinear least squares. 95% confidence interval quantifies well the uncertainties and provides preliminary sensitivities of parameters.

scholarly journals Effects of various input levels and different soil water retention curve models on water content estimation using different statistical methods

2015 ◽  
Vol 47 (2) ◽  
pp. 312-332 ◽  
Author(s):  
Hossein Bayat ◽  
Eisa Ebrahimi
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Water Retention ◽  
Water Retention Curve ◽  
Model Parameters ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve ◽  
Input Variables ◽  
Gardner Model

This study investigated the impact of different input variables on the predictability of the water content using soil water retention curve (SWRC) models. The particle and aggregate size distribution model parameters were calculated by fitting the Perrier model to the related distributions for 75 soil samples. Nine SWRC models were fitted to the experimental data and their coefficients were obtained. The regression method was used to estimate the coefficients for nine SWRC models at three input levels. Cluster analysis classified the SWRC models into more homogeneous groups according to the accuracy of their predictions. The SWRC estimated using the Gardner model had the highest accuracy, but it was not an appropriate model for the soils because of its low fitting accuracy. Boltzman, Campbell, and Fermi models obtained the highest accuracy after the Gardner model. The Durner model yielded the lowest prediction accuracy due to the lack of correlation between the input variables and coefficients in this model. Thus, the water content predictions obtained using different SWRC models varied because different input variables were employed.

Periodicity analysis and a model structure for consumer behavior on hotel online search interest in the US

2017 ◽  
Vol 29 (5) ◽  
pp. 1486-1500 ◽  
Author(s):  
Juan Liu ◽  
Xue Li ◽  
Ya Guo
Keyword(s):  
Consumer Behavior ◽  
Least Squares ◽  
Nonlinear Least Squares ◽  
Model Parameters ◽  
Model Structure ◽  
Online Search ◽  
Content Type ◽  
The Usa ◽  
Frequency Components ◽  
Least Squares Algorithm

Purpose This paper aims to analyze and model consumer behavior on hotel online search interest in the USA. Design/methodology/approach Discrete Fourier transform was used to analyze the periodicity of hotel search behavior in the USA by using Google Trends data. Based on the obtained frequency components, a model structure was proposed to describe the search interest. A separable nonlinear least squares algorithm was developed to fit the data. Findings It was found that the major dynamics of the search interest was composed of nine frequency components. The developed separable nonlinear least squares algorithm significantly reduced the number of model parameters that needed to be estimated. The fitting results indicated that the model structure could fit the data well (average error 0.575 per cent). Practical implications Knowledge of consumer behavior on online search is critical to marketing decision because search engine has become an important tool for customers to find hotels. This work is thus very useful to marketing strategy. Originality/value This research is the first work on analyzing and modeling consumer behavior on hotel online search interest.

scholarly journals Temporal stability of electrical conductivity in a sandy soil

2016 ◽  
Vol 30 (3) ◽  
pp. 349-357 ◽  
Author(s):  
Aura Pedrera-Parrilla ◽  
Eric C. Brevik ◽  
Juan V. Giráldez ◽  
Karl Vanderlinden
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Precision Agriculture ◽  
Temporal Stability ◽  
Principal Component ◽  
Model Parameters ◽  
Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

scholarly journals Using an inverse modelling approach to evaluate the water retention in a simple water harvesting technique

2009 ◽  
Vol 6 (3) ◽  
pp. 4265-4306 ◽  
Author(s):  
K. Verbist ◽  
W. M. Cornelis ◽  
D. Gabriels ◽  
K. Alaerts ◽  
G. Soto
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Retention ◽  
Measurement Techniques ◽  
Inverse Modelling ◽  
Model Parameters ◽  
Arid Zones ◽  
Water Harvesting ◽  
Data Set

Abstract. In arid and semi-arid zones runoff harvesting techniques are often applied to increase the water retention and infiltration on steep slopes. Additionally, they act as an erosion control measure to reduce land degradation hazards. Nevertheless, few efforts were observed to quantify the water harvesting processes of these techniques and to evaluate their efficiency. In this study a combination of detailed field measurements and modelling with the HYDRUS-2D software package was used to visualize the effect of an infiltration trench on the soil water content of a bare slope in Northern Chile. Rainfall simulations were combined with high spatial and temporal resolution water content monitoring in order to construct a useful dataset for inverse modelling purposes. Initial estimates of model parameters were provided by detailed infiltration and soil water retention measurements. Four different measurement techniques were used to determine the saturated hydraulic conductivity (Ksat) independently. Tension infiltrometer measurements proved a good estimator of the Ksat value and a proxy for those measured under simulated rainfall, whereas the pressure and constant head well infiltrometer measurements showed larger variability. Six different parameter optimization functions were tested as a combination of soil-water content, water retention and cumulative infiltration data. Infiltration data alone proved insufficient to obtain high model accuracy, due to large scatter on the data set, and water content data were needed to obtain optimized effective parameter sets with small confidence intervals. Correlation between observed soil water content and simulated values was as high as R2=0.93 for ten selected observation points used in the model calibration phase, with overall correlation for the 22 observation points equal to 0.85. Model results indicate that the infiltration trench has a significant effect on soil water storage, especially at the base of the trench.

scholarly journals Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Arid Regions ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Model Parameters ◽  
Precipitation Radar ◽  
Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

Root effects on soil water and hydraulic properties

Biologia ◽  
2007 ◽  
Vol 62 (5) ◽  
Author(s):  
Horst Gerke ◽  
Rolf Kuchenbuch
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Content ◽  
Bulk Density ◽  
Soil Water ◽  
Soil Water Content ◽  
Hydraulic Properties ◽  
Soil Hydraulic Properties ◽  
Soil Hydraulic ◽  
Root Effects

AbstractPlants can affect soil moisture and the soil hydraulic properties both directly by root water uptake and indirectly by modifying the soil structure. Furthermore, water in plant roots is mostly neglected when studying soil hydraulic properties. In this contribution, we analyze effects of the moisture content inside roots as compared to bulk soil moisture contents and speculate on implications of non-capillary-bound root water for determination of soil moisture and calibration of soil hydraulic properties.In a field crop of maize (Zea mays) of 75 cm row spacing, we sampled the total soil volumes of 0.7 m × 0.4 m and 0.3 m deep plots at the time of tasseling. For each of the 84 soil cubes of 10 cm edge length, root mass and length as well as moisture content and soil bulk density were determined. Roots were separated in 3 size classes for which a mean root porosity of 0.82 was obtained from the relation between root dry mass density and root bulk density using pycnometers. The spatially distributed fractions of root water contents were compared with those of the water in capillary pores of the soil matrix.Water inside roots was mostly below 2–5% of total soil water content; however, locally near the plant rows it was up to 20%. The results suggest that soil moisture in roots should be separately considered. Upon drying, the relation between the soil and root water may change towards water remaining in roots. Relations depend especially on soil water retention properties, growth stages, and root distributions. Gravimetric soil water content measurement could be misleading and TDR probes providing an integrated signal are difficult to interpret. Root effects should be more intensively studied for improved field soil water balance calculations.

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