scholarly journals Analysis of rainfed alfalfa evapotranspiration measured by an eddy covariance system

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
A. Vinci ◽  
L. Vergni ◽  
F. Todisco ◽  
F. Mannocchi
Keyword(s):  
Soil Water ◽  
Eddy Covariance ◽  
Statistical Tests ◽  
Meteorological Data ◽  
Central Italy ◽  
Original Data ◽  
Water Model ◽  
Growth Stages ◽  
Concentration Data ◽  
Eddy Covariance System

The aim of this study was to quantify the evapotranspiration (ETec) of a rainfed alfalfa crop using the eddy covariance technique. The study was carried out during the alfalfa growing seasons (April- August 2009, April-August 2010) at the experimental farm of the University of Perugia. In central Italy alfalfa is grown for 3 to 4 years continuously, with at least 3 cutting cycles for year (usually between April and August) and a dormant period in winter. For the quantification of ETec an open-path eddy covariance system (EC) was used. The derivation of water and energy fluxes starting from raw wind, temperature and gas concentration data by means of the EC technique implies a remarkably long sequence of operations including calibration, corrections and statistical tests for assessing data quality. These operations were carried out by the EddyPro® software. After that, the output data were used for the flux-partitioning and all original data, flagged with a quality indicator with non-turbulent conditions, were dismissed. Then the gap-filling of the EC and meteorological data was performed to obtain reliable values. Furthermore the test of the energy balance closure gave satisfactory results. The ETec dynamics were consistent with the growth stages and the cuttings during both 2009 and 2010. Furthermore the comparison between the tabulated crop coefficients (Kc) and the ratio of ETec to reference evapotranspiration (ET0) was performed. This analysis showed a good agreement during the 2nd cutting cycle (May-June) for both 2009 and 2010, whilst during the 3rd cutting cycle (July-August) the ratio ETec/ET0 was considerably lower than Kc for both years. The reason of this behavior was found in the presence of water stress conditions during the last cutting cycle. This fact was confirmed by the application of a bucket soil water model, used as an exploratory, not confirmatory, tool to analyze the soil water availability dynamics during the growing season. Additional measurement campaigns will be carried out in order to deepen the knowledge about the Kc dynamics in rainfed crops and to assess the productivity of water under various meteorological and agricultural conditions.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences

2021 ◽  
Vol 13 (6) ◽  
pp. 1098
Author(s):  
Egor Prikaziuk ◽  
Peiqi Yang ◽  
Christiaan van der Tol
Keyword(s):  
Meteorological Data ◽  
Original Data ◽  
Google Earth ◽  
Target Area ◽  
Pixel Resolution ◽  
Point Of Interest ◽  
Homogeneity Assumption ◽  
Google Earth Engine ◽  
Level 1 ◽  
Monitoring Service

In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band. We compared GEE and DHUS single pixel time series for the period from April 2016 to September 2020 and identified two sources of this discrepancy: the ground pixel position and reprojection. The ground pixel position of OLCI product can be determined in two ways: from geo-coordinates (DHUS) or from tie-point coordinates (GEE). We recommend using geo-coordinates for pixel extraction from the original data. When the Sentinel Application Platform (SNAP) Pixel Extraction Tool is used, an additional distance check has to be conducted to exclude pixels that lay further than 212 m from the point of interest. Even geo-coordinates-based pixel extraction requires the homogeneity of the target area at a 700 m diameter (49 ha) footprint (double of the pixel resolution). The GEE OLCI dataset can be safely used if the homogeneity assumption holds at 2700 m diameter (9-by-9 OLCI pixels) or if the uncertainty in the radiance of 10% is not critical for the application. Further analysis showed that the scaling factors reported in the GEE dataset description must not be used. Finally, observation geometry and meteorological data are not present in the GEE OLCI dataset, but they are crucial for most applications. Therefore, we propose to calculate angles and extraterrestrial solar fluxes and to use an alternative data source—the Copernicus Atmosphere Monitoring Service (CAMS) dataset—for meteodata.

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

Parameter selection and testing the soil water model SOIL

1997 ◽  
Vol 195 (1-4) ◽  
pp. 312-334 ◽  
Author(s):  
M.B. McGechan ◽  
R. Graham ◽  
A.J.A. Vinten ◽  
J.T. Douglas ◽  
P.S. Hooda
Keyword(s):  
Soil Water ◽  
Parameter Selection ◽  
Water Model ◽  
Model Soil ◽  
Soil Water Model

scholarly journals Energy balance measurements over a banana orchard in the Semiarid region in the Northeast of Brazil

2009 ◽  
Vol 44 (11) ◽  
pp. 1365-1373 ◽  
Author(s):  
Carlos Antonio Costa dos Santos ◽  
Bernardo Barbosa da Silva ◽  
Tantravahi Venkata Ramana Rao ◽  
Christopher Michael Usher Neale
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Eddy Covariance ◽  
Heat Fluxes ◽  
Irrigation District ◽  
Semiarid Region ◽  
Energy Balance Closure ◽  
Wet Season ◽  
Growing Seasons ◽  
Eddy Covariance System

The objective of this work was to evaluate the reliability of eddy covariance measurements, analyzing the energy balance components, evapotranspiration and energy balance closure in dry and wet growing seasons, in a banana orchard. The experiment was carried out at a farm located within the irrigation district of Quixeré, in the Lower Jaguaribe basin, in Ceará state, Brazil. An eddy covariance system was used to measure the turbulent flux. An automatic weather station was installed in a grass field to obtain the reference evapotranspiration (ET0) from the combined FAO-Penman-Monteith method. Wind speed and vapor pressure deficit are the most important variables on the evaporative process in both growing seasons. In the dry season, the heat fluxes have a similar order of magnitude, and during the wet season the latent heat flux is the largest. The eddy covariance system had acceptable reliability in measuring heat flux, with actual evapotranspiration results comparing well with those obtained by using the water balance method. The energy balance closure had good results for the study area, with mean values of 0.93 and 0.86 for the dry and wet growing seasons respectively.

scholarly journals Emissions of carbon dioxide and methane from fields fertilized with digestate from an agricultural biogas plant

2018 ◽  
Vol 32 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Robert Czubaszek ◽  
Agnieszka Wysocka-Czubaszek
Keyword(s):  
Carbon Dioxide ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Eddy Covariance ◽  
Soil Structure ◽  
Mineral Fertilizers ◽  
Gas Emissions ◽  
Gas Fluxes ◽  
Biogas Plants ◽  
Eddy Covariance System

AbstractDigestate from biogas plants can play important role in agriculture by providing nutrients, improving soil structure and reducing the use of mineral fertilizers. Still, less is known about greenhouse gas emissions from soil during and after digestate application. The aim of the study was to estimate the emissions of carbon dioxide (CO2) and methane (CH4) from a field which was fertilized with digestate. The gas fluxes were measured with the eddy covariance system. Each day, the eddy covariance system was installed in various places of the field, depending on the dominant wind direction, so that each time the results were obtained from an area where the digestate was distributed. The results showed the relatively low impact of the studied gases emissions on total greenhouse gas emissions from agriculture. Maximum values of the CO2and CH4fluxes, 79.62 and 3.049 µmol s−1m−2, respectively, were observed during digestate spreading on the surface of the field. On the same day, the digestate was mixed with the topsoil layer using a disc harrow. This resulted in increased CO2emissions the following day. Intense mineralization of digestate, observed after fertilization may not give the expected effects in terms of protection and enrichment of soil organic matter.

scholarly journals Summer drought in Switzerland 1981‒2020: Events, trends and drivers

2021 ◽  
Author(s):  
Simon C. Scherrer ◽  
Christoph Spirig ◽  
Martin Hirschi ◽  
Felix Maurer ◽  
Sven Kotlarski
Keyword(s):  
Regional Climate Model ◽  
Soil Water ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Summer Drought ◽  
Temperature And Precipitation ◽  
Temperature Scaling

<p>The Alpine region has recently experienced several dry summers with negative impacts on the economy, society and ecology. Here, soil water, evapotranspiration and meteorological data from several observational and model-based data sources is used to assess events, trends and drivers of summer drought in Switzerland in the period 1981‒2020. 2003 and 2018 are identified as the driest summers followed by somewhat weaker drought conditions in 2020, 2015 and 2011. We find clear evidence for an increasing summer drying in Switzerland. The observed climatic water balance (-39.2 mm/decade) and 0-1 m soil water from reanalysis (ERA5-Land: -4.7 mm/decade; ERA5: -7.2 mm/decade) show a clear tendency towards summer drying with decreasing trends in most months. Increasing evapotranspiration (potential evapotranspiration: +21.0 mm/decade; ERA5-Land actual evapotranspiration: +15.1 mm/decade) is identified as important driver which scales excellently (+4 to +7%/K) with the observed strong warming of about 2°C. An insignificant decrease in precipitation further enhanced the tendency towards drier conditions. Most simulations of the EURO-CORDEX regional climate model ensemble underestimate the changes in summer drying. They underestimate both, the observed recent summer warming and the small decrease in precipitation. The changes in temperature and precipitation are negatively correlated, i.e. simulations with stronger warming tend to show (weak) decreases in precipitation. However, most simulations and the reanalysis overestimate the correlation between temperature and precipitation and the precipitation-temperature scaling on the interannual time scale. Our results emphasize that the analysis of the regional summer drought evolution and its drivers remains challenging especially with regional climate model data but considerable uncertainties also exist in reanalysis data sets.</p>

Response of wheat to single short-term waterlogging during and after stem elongation

1988 ◽  
Vol 39 (1) ◽  
pp. 11 ◽  
Author(s):  
WS Meyer ◽  
HD Barrs
Keyword(s):  
Grain Yield ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Root Length ◽  
Stem Elongation ◽  
Stem Growth ◽  
Yield Reduction ◽  
Growth Stages ◽  
Short Term

Transient waterlogging associated with spring irrigations on slowly draining soils causes yield reduction in irrigated wheat. Physiological responses to short-term flooding are not well understood. The aim of this experiment was to monitor above- and below-ground responses of wheat to single waterlogging events during and after stem elongation and to assess the sensitivity of the crop at these growth stages to flooding. Wheat (cv. Bindawarra) was grown in drainage lysimeters of undisturbed cores of Marah clay loam soil. A control treatment (F0) was well-watered throughout the season without surface flooding, while three others were flooded for 96 h at stem elongation (Fl), flag leaf emergence (F2) and anthesis (F3), respectively. Soil water content, soil O2, root length density, leaf and stem growth, apparent photosynthesis (APS), plant nutrient status and grain yield were measured. Soil water content increased and soil O2 levels decreased following flooding; the rate of soil O2 depletion increasing with crop age and root length. Leaf and stem growth and APS increased immediately following flooding, the magnitude of the increases was in the order F1 >F2>F3. A similar order existed in the effect of flooding which decreased the number of roots. Subsequently, leaf and stem growth decreased below that of F0 plants in F1, and briefly in F2. Decreases in APS of treated plants compared to F0 plants appeared to be due to their greater sensitivity to soil water deficit. There was no effect of flooding on grain yield. It is suggested that, while plant sensitivity to flooding decreased with age, flooding at stem elongation had no lasting detrimental effect on yield when post-flood watering was well controlled.

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