Experimental investigation of the Marangoni effect on the stability of a double-diffusive layer

Stability experiments were carried out in 4-cm-thick, salt-stratified fluid layer by heating from below and cooling from above. The bottom boundary was rigid while the top was either free or rigid. The initial solute Rayleigh number varied from 2.5 x 10(exp 6) to 4.6 x 10(exp 7). For the rigid-free case, at initial solute Rayleigh numbers R(sub s) greater than 5.4 x 10(exp 6), thermal Marangoni instabilities were observed to onset along the free surface at a relatively low thermal Rayleigh number, R(sub t). The convection was very weak, and it had almost no effect on the concentration and temperature distributions. Double-diffusive instabilities along the top free surface were observed to onset at a higher R(sub t), with much stronger convection causing changes in the concentration and temperature distributions near the top. At a yet higher R(sub t), double-diffusive convection was observed to onset along the bottom boundary. Fluid motion in the layer then evolved into fully developed thermal convection of a homogeneous fluid without any further increase in the imposed Delta T. For layers with R(sub s) less than 5.4 x 10(exp 6), Marangoni and double-diffusive instabilities onset simultaneously along the free surface first, while double-diffusive instabilities along the bottom wall onset at a higher R(sub t)

Experimental study on the structure and stability of a double-diffusive interface in a laterally heated enclosure

Experiments are carried out to investigate the structure of a double diffusive interface separating two layers in a laterally heated enclosure. The main goal of this work is to study the structure of the interface and some of its instability characteristics. The velocity field at the vicinity of the interface is measured by a PIV system. Vertical concentration and temperature profiles are measured using a micro-scale conductivity/temperature instrument and the flow is visualized using the schlieren technique. Analysis of mean horizontal velocity profiles, obtained at different times during the experiment, illustrates the increasing tilt of the interface with time. Spectral analyses of velocity perturbations under unstable and stable conditions confirm the existence of the coherent vortices observed by the schlieren technique. The vortices above and below the interface are associated with different dominant frequencies due to the asymmetric character of the flow. Measurements show that the vortices are generated outside the region of the stabilizing concentration profile by a mechanism, which is essentially thermal and similar to Rayleigh-Bénard instability with weak shear

Evapotranspiration Measurements and Modeling

Evaporation is the conversion process of liquid water into vapor and the consequent transport of that vapor into the atmosphere [...

Application of the Flux-Variance Technique for Evapotranspiration Estimates in Three Types of Agricultural Structures

Irrigation of protected crops requires sound knowledge of evapotranspiration. Previous studies have established that the eddy-covariance (EC) technique is suitable for whole canopy evapotranspiration measurements in large agricultural screenhouses. Nevertheless, the eddy-covariance technique remains difficult to apply in the farm due to costs, operational complexity, and postprocessing of data, thereby inviting alternative techniques to be developed. The subject of this paper is the evaluation of a turbulent transport technique, the flux variance (FV), whose instrumentation needs and operational demands are not as elaborate as the EC, to estimate evapotranspiration within large agricultural structures. Measurements were carried out in three types of agricultural structures: (i) a banana plantation in a light-shading (8%) screenhouse (S1), (ii) a pepper crop in an insect-proof (50-mesh) screenhouse (S2), and (iii) a tomato crop in a naturally ventilated greenhouse with a plastic roof and 50-mesh screened sidewalls (S3). Quality control analysis of the EC data showed that turbulence development and flow stationarity conditions in the three structures were suitable for flux measurements. However, within the insect-proof screenhouse (below the screen) and the plastic-covered greenhouse, R2 of the energy balance closure was poor; hence, the alternative simple method could not be used. Results showed that the FV technique was suitable for reliable estimates of ET in shading and insect-proof screenhouses with R2 of the regressions between FV latent heat flux and latent heat flux deduced from energy balance closure of 0.99 and 0.92 during validation for S1 and S2, respectively

Fetch Effect on Flux-Variance Estimations of Sensible and Latent Heat Fluxes of Camellia Sinensis

Precise estimation of surface-atmosphere exchange is a major challenge in micrometeorology. Previous literature presented the eddy covariance (EC) as the most reliable method for the measurements of such fluxes. Nevertheless, the EC technique is quite expensive and complex, hence other simpler methods are sought. One of these methods is Flux-Variance (FV). The FV method estimates sensible heat flux (H) using high frequency (~10Hz) air temperature measurements by a fine wire thermocouple. Additional measurements of net radiation (Rn) and soil heat flux (G) allow the derivation of latent heat flux (LE) as the residual of the energy balance equation. In this study, the Flux Variance method was investigated, and the results were compared against eddy covariance measurements. The specific goal of the present study was to assess the performance of the FV method for the estimation of surface fluxes along a variable fetch. Experiment was carried out in a tea garden; an EC system measured latent and sensible heat fluxes and five fine-wire thermocouples were installed towards the wind dominant direction at different distances (fetch) of TC1 = 170 m, TC2 = 165 m, TC3 = 160 m, TC4 = 155 m and TC5 = 150 m from the field edge. Footprint analysis was employed to examine the effect of temperature measurement position on the ratio between 90% footprint and measurement height. Results showed a good agreement between FV and EC measurements of sensible heat flux, with all regression coefficients (R2) larger than 0.6; the sensor at 170 m (TC1), nearest to the EC system, had highest R2 = 0.86 and lowest root mean square error (RMSE = 25 Wm−2). The estimation of LE at TC1 was also in best agreement with eddy covariance, with the highest R2 = 0.90. The FV similarity constant varied along the fetch within the range 2.2–2.4

Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS), which is based on lookup tables. Furthermore, a predictor importance analysis was performed to evaluate the importance of the different meteorological inputs in estimating ET. On the half-hourly time scale, the deep learning method showed a significant 13.5% decrease in nRMSE (normalized root mean square error) throughout all datasets and gap durations. A substantially smaller standard deviation of mean nRMSE, compared with marginal distribution sampling, was also observed. On the whole-gap time scale (half a day to six days), average nMBE (normalized mean bias error) was similar to that of MDS, whereas standard deviation was improved. Using only air temperature and relative humidity as input variables provided an RMSE that was significantly smaller than that resulting from the MDS method. These results suggest that the deep learning method developed here is reliable and more consistent than the standard gap-filling method and thereby demonstrates the potential of advanced deep learning techniques for improving dynamic time series modeling

Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS), which is based on lookup tables. Furthermore, a predictor importance analysis was performed to evaluate the importance of the different meteorological inputs in estimating ET. On the half-hourly time scale, the deep learning method showed a significant 13.5% decrease in nRMSE (normalized root mean square error) throughout all datasets and gap durations. A substantially smaller standard deviation of mean nRMSE, compared with marginal distribution sampling, was also observed. On the whole-gap time scale (half a day to six days), average nMBE (normalized mean bias error) was similar to that of MDS, whereas standard deviation was improved. Using only air temperature and relative humidity as input variables provided an RMSE that was significantly smaller than that resulting from the MDS method. These results suggest that the deep learning method developed here is reliable and more consistent than the standard gap-filling method and thereby demonstrates the potential of advanced deep learning techniques for improving dynamic time series modeling

Estimating Evapotranspiration of Screenhouse Banana Plantations Using Artificial Neural Network and Multiple Linear Regression Models

Measured evapotranspiration (LE) of screenhouse banana plantations was utilized to derive and compare two types of machine-learning models: artificial neural network (ANN) and multiple linear regression (MLR). The measurements were conducted by eddy-covariance systems and meteorological sensors in two similar screenhouse banana plantations during two consecutive seasons, 2016 and 2017. Most of the study focused on the season of 2017, which includes a more extended data set (141 days) than 2016 (52 days). The results show that in most cases, the ANN model was superior to MLR. When trained and validated over the whole data set of 2017, the ANN and MLR models provided R2 of 0.92 and 0.89, RMSE of 37.5 and 45.1 W m−2 and MAE of 21 and 27.2 W m−2, respectively. Models could be derived using a training dataset as short as one month and still provide reliable estimations. Depending on the chosen calendar month for training, R2 of the ANN model varied in the range 0.81–0.89, while for the MLR model, it ranged 0.73–0.88. When trained using a data set as short as one week, there was some deterioration in model performance; the corresponding ranges of R2 for the ANN and MLR models were 0.37–0.89 and 0.37–0.71, respectively. As expected for a screenhouse decoupled environment, solar radiation (Rg) was the variable that most influenced LE; using Rg as the sole input variable, the ANN model resulted in R2, RMSE and MAE of 0.88 and 47 W m−2 and 25.6 W m−2, respectively, values that are not much worse than using all input variables (solar radiation, air temperature, air relative humidity and wind speed). Using Rg alone as the input to the MLR model only slightly deteriorated R2 (=0.88); however, RMSE (=124 W m−2) and MAE (=75.7 W m−2) were significantly larger compared to a model based on all input variables. To examine model performance in different seasons, models were trained using the data set of 2017 and validated in 2016, and vice versa. Results showed that training on the data of 2017 and validation in 2016 provided superior results than the opposite, presumably since the 2017 measurement season was longer and weather conditions were more diverse than in the 2016 data set. It is concluded that the ANN and MLR models are reasonable options for estimating evapotranspiration in a banana screenhouse