Soil Moisture,Temperature, Electrical Conductivity and Heat Flux Hourly Data for Dry Creek Experimental Watershed, Southwest Idaho, Bogus Ridge Weather Station Pits 1 and 2

2018 ◽  
Author(s):  
James P. McNamara
2009 ◽  
Vol 48 (12) ◽  
pp. 2474-2486 ◽  
Author(s):  
Kun Yang ◽  
Jun Qin ◽  
Xiaofeng Guo ◽  
Degang Zhou ◽  
Yaoming Ma

Abstract To clarify the thermal forcing of the Tibetan Plateau, long-term coarse-temporal-resolution data from the China Meteorological Administration have been widely used to estimate surface sensible heat flux by bulk methods in many previous studies; however, these estimates have seldom been evaluated against observations. This study at first evaluates three widely used bulk schemes against Tibet instrumental flux data. The evaluation shows that large uncertainties exist in the heat flux estimated by these schemes; in particular, upward heat fluxes in winter may be significantly underestimated, because diurnal variations of atmospheric stability were not taken into account. To improve the estimate, a new method is developed to disaggregate coarse-resolution meteorological data to hourly according to statistical relationships derived from high-resolution experimental data, and then sensible heat flux is estimated from the hourly data by a well-validated flux scheme. Evaluations against heat flux observations in summer and against net radiation observations in winter indicate that the new method performs much better than previous schemes, and therefore it provides a robust basis for quantifying the Tibetan surface energy budget.


2021 ◽  
Vol 64 (1) ◽  
pp. 287-298
Author(s):  
Ruixiu Sui ◽  
Jonnie Baggard

HighlightsWe developed and evaluated a variable-rate irrigation (VRI) management method for five crop years in the Mississippi Delta.VRI management significantly reduced irrigation water use in comparison with uniform-rate irrigation (URI). There was no significant difference in grain yield and irrigation water productivity between VRI and URI management.Soil apparent electrical conductivity (ECa) was used to delineate irrigation management zones and generate VRI prescriptions.Sensor-measured soil water content was used in irrigation scheduling.Abstract. Variable-rate irrigation (VRI) allows producers to site-specifically apply irrigation water at variable rates within a field to account for the temporal and spatial variability in soil and plant characteristics. Developing practical VRI methods and documenting the benefits of VRI application are critical to accelerate the adoption of VRI technologies. Using apparent soil electrical conductivity (ECa) and soil moisture sensors, a VRI method was developed and evaluated with corn and soybean for five crop years in the Mississippi Delta. Soil ECa of the study fields was mapped and used to delineate VRI management zones and create VRI prescriptions. Irrigation was scheduled using soil volumetric water content measured by soil moisture sensors. A center pivot VRI system was employed to deliver irrigation water according to the VRI prescription. Grain yield, irrigation water use, and irrigation water productivity in the VRI treatment were determined and compared with that in a uniform-rate irrigation (URI) treatment. Results showed that the grain yield and irrigation water productivity between the VRI and URI treatments were not statistically different with both corn and soybean crops. The VRI management significantly reduced the amount of irrigation water by 22% in corn and by 11% in soybean (p = 0.05). Adoption of VRI management could improve irrigation water use efficiency in the Mississippi Delta. Keywords: Soil electrical conductivity, Soil moisture sensor, Variable rate irrigation, Water management.


Irriga ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 1-15
Author(s):  
Iug Lopes ◽  
Abelardo A. A. Montenegro

SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1     IUG LOPES2 E ABELARDO ANTONIO DE ASSUNÇÃO MONTENEGRO3   1Paper extracted from the doctoral thesis of the first author. 2Department of Agronomy, Instituto Federal de Educação, Ciência e Tecnologia Baiano, BR 349, Km 14 - Zona Rural, CEP: 47600-000, Bom Jesus da Lapa - BA, Brazil; [email protected] - ORCID: 0000-0003-0592-4774. 3Department of Agricultural Engineering, Universidade Federal Rural de Pernambuco, Rua Dom Manoel de Medeiros, Dois Irmão, CEP: 52171-900, Recife - PE, Brazil; [email protected] -ORCID: 0000-0002-5746-8574.     1 ABSTRACT   Spatial information on soil characteristics is essential to proper decision-making regarding to the environment and land use management. The objective of this work was the investigation of cross - variance between soil moisture and apparent soil electrical conductivity (CEa), under different land uses in an alluvial valley of Pernambuco. The study was developed at the Advanced Research Unit of Universidade Federal Rural de Pernambuco (UFRPE), located at  Brígida River Basin, municipality of Panamirim-PE. Soil samples were collected in a regular mesh of 20 x 10 m, for soil moisture by gravimetric method and, following a regular 10 x 10 m mesh, CEa measurements were performed using EM38® device. Cross-semivariograms were assessed and spatial dependence was verified by geostatistical procedures. It was verified in geostatistical procedures  low variation for soil moisture and intermediate variation for CEa. The use of geostatistics allowed identification of covariance between soil moisture and ECa, as well as spatial dependence for both variables, for agricultural areas. It was verified that soil moisture, even at levels close to residual, constitutes a relevant secondary component for increasing soil salinity maps precision, and hence to precision agriculture.   Keywords: geostatistics, semi-arid, precision agriculture     LOPES, I. E MONTENEGRO, A. A. DE A. DEPENDÊNCIA ESPACIAL DA UMIDADE DO SOLO E CONDUTIVIDADE ELÉTRICA EM REGIÃO ALUVIAL     2 RESUMO   Informações espaciais sobre as características do solo são essenciais para uma tomada de decisão adequada em relação ao meio ambiente e ao gerenciamento do uso do solo. O objetivo deste trabalho foi investigar a variância cruzada entre a umidade do solo e a condutividade elétrica aparente do solo (CEa), sob diferentes usos do solo em um vale aluvial de Pernambuco. O estudo foi desenvolvido na Unidade de Pesquisa Avançada da Universidade Federal Rural de Pernambuco (UFRPE), localizada na bacia do rio Brígida, município de Panamirim-PE. As amostras de solo foram coletadas em uma malha regular de 20 x 10 m, para a umidade do solo pelo método gravimétrico e, seguindo uma malha regular de 10 x 10 m, as medidas de CEa foram realizadas usando o dispositivo EM38®. Os semivariogramas cruzados foram avaliados e a dependência espacial foi verificada por procedimentos geoestatísticos. Verificou-se procedimentos geoestatísticos, uma baixa variação da umidade do solo e variação intermediária para CEa. O uso da geoestatística permitiu identificar a covariância entre a umidade do solo e o CEa, bem como a dependência espacial para ambas as variáveis, para as áreas agrícolas. Verificou-se que a umidade do solo, mesmo em níveis próximos ao residual, constitui um componente secundário relevante para o aumento da precisão do mapeamento da salinidade do solo e, consequentemente, para a agricultura de precisão.   Palavras-chave: geoestatística, semiárido, agricultura de precisão


2007 ◽  
Vol 7 (3) ◽  
pp. 8455-8524
Author(s):  
B. Hennemuth ◽  
A. Weiss ◽  
J. Bösenberg ◽  
D. Jacob ◽  
H. Linné ◽  
...  

Abstract. A comparison study of water cycle parameters derived from ground-based remote-sensing instruments and from the regional model REMO is presented. Observational data sets were collected during three measuring campaigns in summer/autumn 2003 and 2004 at Richard Aßmann Observatory, Lindenberg, Germany. The remote sensing instruments which were used are differential absorption lidar, Doppler lidar, ceilometer, cloud radar, and micro rain radar for the derivation of humidity profiles, ABL height, water vapour flux profiles, cloud parameters, and rain rate. Additionally, surface latent and sensible heat flux and soil moisture were measured. Error ranges and representativity of the data are discussed. For comparisons the regional model REMO was run for all measuring periods with a horizontal resolution of 18 km and 33 vertical levels. Parameter output was every hour. The measured data were transformed to the vertical model grid and averaged in time in order to better fit with gridbox model values. The comparisons show that the atmospheric boundary layer is not adequately simulated, on most days it is too shallow and too moist. This is found to be caused by a wrong partitioning of energy at the surface, particularly a too large latent heat flux. The reason is obviously an overestimation of soil moisture during drying periods by the one-layer scheme in the model. The profiles of water vapour transport within the ABL appear to be realistically simulated. The comparison of cloud cover reveals an underestimation of low-level and mid-level clouds by the model, whereas the comparison of high-level clouds is hampered by the inability of the cloud radar to see cirrus clouds above 10 km. Simulated ABL clouds apparently have a too low cloud base, and the vertical extent is underestimated. The ice water content of clouds agree in model and observation whereas the liquid water content is unsufficiently derived from cloud radar reflectivity in the present study. Rain rates are similar, but the representativeness of both observations and grid box values is low.


2021 ◽  
pp. 1-34
Author(s):  
Douglas E. Miller ◽  
Zhuo Wang ◽  
Bo Li ◽  
Daniel S. Harnos ◽  
Trent Ford

AbstractSkillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States (US). We use a leading principal component of US soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP’s Climate Forecast System version 2 (CFSv2) at weeks 3-4 in the eastern US. It is found that the North Pacific SST anomalies persist several weeks and are associated with a persistent wave train pattern (WTZ500), which leads to increased occurrences of blocking and extreme temperature over the eastern US. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and decrease in latent heat flux, which may help maintain the overlying anticyclone. The clear sky conditions associated with blocking anticyclones further decrease soil moisture conditions and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, reservoir operation, and provide mitigation of impacts from extreme heat events.


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