Measurement and simulation of evaporation from a red earth. II. Simulation using different evaporation functions

Soil Research ◽  
1982 ◽  
Vol 20 (2) ◽  
pp. 179 ◽  
Author(s):  
GG Johns
Keyword(s):  
Hydraulic Conductivity ◽  
Root Mean Square ◽  
Computer Model ◽  
Water Loss ◽  
Field Data ◽  
Mean Square ◽  
Field Evaporation ◽  
Red Earth ◽  
Water Contents ◽  
Soil Water Contents

The performance of several alternative evaporation functions for simulating water loss from a bare red earth was assessed by including them in a computer model which was used to simulate evaporation both from red earth monoliths in a glasshouse, and from a study site in the field. The coefficients in the different evaporation functions were also optimized to minimize the root mean square discrepancy (RMSD) between simulated and observed soil water contents. RMSD values for the alternative evaporation functions before optimization of coefficients ranged from 3.2 to 7.0 mm for the glasshouse data and from 4.0 to 6.6 mm for the field data. Optimization reduced these values 3.0 to 6.4 mm (glasshouse) and 3.9 to 6.1 mm (field). The sensitivity of the model to errors in hydraulic conductivity estimates was assessed. Overestimating hydraulic conductivity by 2 and 10 times increased predicted cumulative evaporation by 8 and 28% respectively. Underestimating conductivity by the same factors produced similar reductions in predicted cumulative evaporation. The model was used to test the effect of basing the simulation of field evaporation on different thicknesses of surface compartment, for two alternative evaporation functions. Optimum thicknesses of surface compartment were 20 and 30 cm, and increasing these thicknesses to 60 cm resulted in only c. 20% increase in RMSD. This effect was considerably less than the increase caused by using inferior alternate types of evaporation function.

Corrigendum - Water consumption by lucerne in central Australia

1960 ◽  
Vol 11 (5) ◽  
pp. 715
Author(s):  
EA Jackson
Keyword(s):  
Soil Water ◽  
Water Consumption ◽  
Water Loss ◽  
Growth Stages ◽  
The Mean ◽  
Central Australia ◽  
The Relationship ◽  
Water Contents ◽  
Soil Water Contents

Observations on the loss of water from soil under irrigated lucerne have been made over a 12 month period at Alice Springs. The relationship between this water loss and tank evaporation has been determined and used to calculate mean monthly requirements of lucerne in this environment. The mean annual requirement is 90 in. Data are also presented showing the rate of water loss at different soil water contents, growth stages of the crop, and soil depths.

scholarly journals Improved Calculation of Nonlinear Near-Bed Wave Orbital Velocity in Shallow Water: Validation against Laboratory and Field Data

2020 ◽  
Vol 8 (2) ◽  
pp. 81
Author(s):  
Pham Thanh Nam ◽  
Joanna Staneva ◽  
Nguyen Thi Thao ◽  
Magnus Larson
Keyword(s):  
Shallow Water ◽  
Root Mean Square ◽  
Field Data ◽  
Small Scale ◽  
Mean Square ◽  
Orbital Velocity ◽  
Flume Experiments ◽  
Wave Flume ◽  
Wave Orbital Velocity ◽  
Mean Square Errors

A new parameterization for calculating the nonlinear near-bed wave orbital velocity in the shallow water was presented. The equations proposed by Isobe and Horikawa (1982) were modified in order to achieve more accurate predictions of the peak orbital velocities. Based on field data from Egmond Beach in the Netherlands, the correction coefficient and maximum skewness were determined as functions of the Ursell number. The obtained equations were validated against measurements from Egmond Beach, and with laboratory data from small-scale wave flume experiments at Delft University of Technology and from large-scale wave flume experiments at Delft Hydraulics. Inter-comparisons with other previously developed parameterizations were also carried out. The model simulations by the present study were in good agreement with the measurements and have been improved compared to the previous ones. For Egmond Beach, the root-mean-square errors for the peak onshore (uc) and offshore (ut) orbital velocities were approximately 21%. The relative biases were small, approximately 0.013 for uc and −0.068 for ut. The coefficient of determination was in the range between 0.64 and 0.68. For laboratory experiments, the root-mean-square errors in a range of 7.2%–24% for uc, and 7.9%–15% for ut.

scholarly journals Calibration of a capacitance probe in a Paleudult

2007 ◽  
Vol 64 (6) ◽  
pp. 636-640 ◽  
Author(s):  
Cláudio Ricardo da Silva ◽  
Aderson Soares de Andrade Júnior ◽  
José Alves Júnior ◽  
Antonio Barros de Souza ◽  
Francisco de Brito Melo ◽  
...  
Keyword(s):  
Soil Water ◽  
Mean Square ◽  
Time Intervals ◽  
Site Specific ◽  
Calibration Equation ◽  
Capacitance Probe ◽  
Water Contents ◽  
Soil Water Contents ◽  
Separate Calibration ◽  
Theta Range

The use of capacitance sensors is one of the methods used to quantitatively measure soil water contents (theta, m³ m-3). Sensors provide readings at desired depths and time intervals. A capacitance probe (Diviner 2000) was calibrated for a Rhodic Paleudult from the Piaui State, Brazil. Six access tubes were installed in a 5 × 2 m grid arrangement. Three moisture levels (saturated, moist and dry) were used in two replications. Probe readings and soil samplings to determine theta were made at 0.1 m depth intervals down to a depth of 1.0 m. A power calibration equation was developed for each depth as well as for the entire soil profile (Root Mean Square Error = 0.014, R² = 0.93) for a theta range of 0.068 to 0.264 m³m-3. A separate calibration for each depth improves the correlation coefficient and minimizes RMSE. Site-specific calibration improves the accuracy for soil water monitoring.

scholarly journals A COMPARISON OF MACHINE LEARNING MODELS FOR SOIL SALINITY ESTIMATION USING MULTI-SPECTRAL EARTH OBSERVATION DATA

2021 ◽  
Vol V-3-2021 ◽  
pp. 257-263
Author(s):  
A. Zarei ◽  
M. Hasanlou ◽  
M. Mahdianpari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Soil Salinity ◽  
Field Data ◽  
Observation Data ◽  
Mean Square ◽  
Sentinel 2

Abstract. Soil salinity, a significant environmental indicator, is considered one of the leading causes of land degradation, especially in arid and semi-arid regions. In many cases, this major threat leads to loss of arable land, reduces crop productivity, groundwater resources loss, increases economic costs for soil management, and ultimately increases the probability of soil erosion. Monitoring soil salinity distribution and degree of salinity and mapping the electrical conductivity (EC) using remote sensing techniques are crucial for land use management. Salt-effected soil is a predominant phenomenon in the Eshtehard Salt Lake located in Alborz, Iran. In this study, the potential of Sentinel-2 imagery was investigated for mapping and monitoring soil salinity. According to the satellite's pass, different salt properties were measured for 197 soil samples in the field data study. Therefore several spectral features, such as satellite band reflectance, salinity indices, and vegetation indices, were extracted from Sentinel-2 imagery. To build an optimum machine learning regression model for soil salinity estimation, three different regression models, including Gradient Boost Machine (GBM), Extreme Gradient Boost (XGBoost), and Random Forest (RF), were used. The XGBoostmethod outperformed GBM and RF with the coefficient of determination (R2) more than 76%, Root Mean Square Error (RMSE) about 0.84 dS m−1, and Normalized Root Mean Square Error (NRMSE) about 0.33 dS m−1. The results demonstrated that the integration of remote sensing data, field data, and using an appropriate machine learning model could provide high-precision salinity maps to monitor soil salinity as an environmental problem.

Water consumption by lucerne in central Australia

1960 ◽  
Vol 11 (5) ◽  
pp. 715
Author(s):  
EA Jackson
Keyword(s):  
Soil Water ◽  
Water Consumption ◽  
Water Loss ◽  
Growth Stages ◽  
The Mean ◽  
Central Australia ◽  
The Relationship ◽  
Water Contents ◽  
Soil Water Contents

Observations on the loss of water from soil under irrigated lucerne have been made over a 12 month period at Alice Springs. The relationship between this water loss and tank evaporation has been determined and used to calculate mean monthly requirements of lucerne in this environment. The mean annual requirement is 90 in. Data are also presented showing the rate of water loss at different soil water contents, growth stages of the crop, and soil depths.

The Relationship of Root Mean Square Value of Electromyography and Isometric Torque of Quadriceps in Normal Subjects

2016 ◽  
Vol 26 (1) ◽  
pp. 58
Author(s):  
Qiurong XIE ◽  
Zheng JIANG ◽  
Qinglu LUO ◽  
Jie LIANG ◽  
Xiaoling WANG ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Normal Subjects ◽  
Mean Square ◽  
Isometric Torque ◽  
Relationship Of ◽  
The Relationship

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

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