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3 pages, 189 KiB

Open AccessEditorial

Evapotranspiration Measurements and Modeling

by Josef Tanny

Water 2022, 14(16), 2474; https://doi.org/10.3390/w14162474 - 11 Aug 2022

Cited by 2 | Viewed by 1854

Abstract

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

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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21 pages, 4635 KiB

Open AccessFeature PaperArticle

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

by Efi Yohanani, Amit Frisch, Victor Lukyanov, Shabtai Cohen, Meir Teitel and Josef Tanny

Water 2022, 14(7), 1130; https://doi.org/10.3390/w14071130 - 1 Apr 2022

Cited by 5 | Viewed by 2194

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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17 pages, 4553 KiB

Open AccessArticle

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

by Lior Fine, Antoine Richard, Josef Tanny, Cedric Pradalier, Rafael Rosa and Offer Rozenstein

Water 2022, 14(5), 763; https://doi.org/10.3390/w14050763 - 28 Feb 2022

Cited by 8 | Viewed by 4796

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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28 pages, 4899 KiB

Open AccessArticle

Refinements and Analysis of the Optical-Microwave Scintillometry Method Applied to Measurements over a Vineyard in Chile

by Francisca Aguirre, Oscar Hartogensis, Francisco Meza and Francisco Suárez

Water 2022, 14(3), 474; https://doi.org/10.3390/w14030474 - 5 Feb 2022

Cited by 5 | Viewed by 2237

Abstract

Evapotranspiration (ET) is a critical component of the hydrological cycle, and it links water and energy budgets in the form of latent heat (L_vE) released into the atmosphere. However, ET is difficult to measure and is not always well [...] Read more.

Evapotranspiration (ET) is a critical component of the hydrological cycle, and it links water and energy budgets in the form of latent heat (L_vE) released into the atmosphere. However, ET is difficult to measure and is not always well described in arid regions. Thus, novel techniques are required for its accurate measurement. Scintillometers are an interesting alternative for traditional methods, such as Eddy Covariance systems (EC). Scintillometer studies have reported good results, but their signals can present unwanted contributions that result in incorrect heat fluxes. In this study, scintillometer data showed unrealistic heat flux values, and thus, the data were reprocessed through spectral analysis to eliminate unwanted contributions from electronic noise, absorption, and tripod vibrations using a new proposed data cleaning method. After performing the spectral cleaning method, scintillometer-based heat fluxes were calculated using several methods: (i) the standalone LAS method, (ii) Hill model, (iii) Lüdi et al. model, and (iv) a hybrid model between Hill and Lüdi et al. Furthermore, a Monin–Obukhov similarity theory (MOST) analysis was performed to evaluate the fluxes’ sensitivity to the choice of the similarity functions. Corrected sensible heat flux (H) estimations agreed well with those obtained with an EC system. However, considerable differences were found for L_vE (and, consequently, ET). The Lüdi et al. model L_vE estimates were closer to those obtained with the EC system, overestimating it by 14%, with a correlation slope of 1.07, R² = 0.91, and a Nash-Sutcliffe efficiency of 0.90. Furthermore, it was found that using different Monin–Obukhov similarity functions resulted in more than ±12% of difference on the estimated L_vE. For future works, it is strongly recommended to apply the proposed spectral cleaning method as it greatly improves scintillometer data. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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23 pages, 4718 KiB

Open AccessArticle

Energy Exchange and Evapotranspiration over the Ejina Oasis Riparian Forest Ecosystem with Different Land-Cover Types

by Weizhen Wang, Feinan Xu and Jiemin Wang

Water 2021, 13(23), 3424; https://doi.org/10.3390/w13233424 - 3 Dec 2021

Cited by 6 | Viewed by 2100

Abstract

Investigating the energy and water vapor exchange in oasis riparian forest ecosystems is of significant importance to improve scientific understanding of land surface processes in extreme arid regions. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) provided many observations of water vapor and [...] Read more.

Investigating the energy and water vapor exchange in oasis riparian forest ecosystems is of significant importance to improve scientific understanding of land surface processes in extreme arid regions. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) provided many observations of water vapor and heat fluxes from riparian forest ecosystem by using a network of eddy-covariance (EC) systems installed over representative surfaces in the Ejina Oasis, which is located in the downstream areas of the Heihe River Basin, northwestern China. Based on EC flux measurements and meteorological data performed at five stations and covering representative surface types of Populus euphratica tree with associated Tamarix chinensis shrub, Tamarix chinensis shrubland, cantaloupe cropland, and barren-land, this study explored the spatio-temporal patterns of heat and water vapor fluxes over the Ejina Oasis riparian forest ecosystem with five different surface types over the course of a growing season in 2014. Energy balance closure of the flux data was evaluated; footprint analysis for each EC site was also performed. Results showed that energy balance closure for the flux data was reasonably good, with average energy balance ratio (EBR) of 1.03. The seasonal variations in net radiation (R_n), latent (LE), and sensible heat flux (H) over the five contrasting surfaces were similar, and a reverse seasonal change was observed in energy partitioning into LE and H. Remarkable differences in R_n, LE, and H between the five surfaces were explored preliminarily, associated closely with the soil properties and foliage phenology. Over the growing season (May–October) in 2014, the total ET ranged 622–731 mm for mixed forest of P. euphratica trees with associated T. chinensis shrubs with average daily ET of 3.6–4.2 mm; ET from T. chinensis shrubland was about 541 mm, with average daily ET of 3.6 mm. ET for barren-land was 195 mm. The total ET in irrigated cantaloupe cropland with plastic mulch was 431 mm for its four-month growing period with a total average of 3.8 mm d⁻¹. Determination of ET over riparian forest ecosystem helps to improve reasonable use of limited water resource in the Ejina Oasis. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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18 pages, 7823 KiB

Open AccessArticle

Prediction of Grape Sap Flow in a Greenhouse Based on Random Forest and Partial Least Squares Models

by Xuelian Peng, Xiaotao Hu, Dianyu Chen, Zhenjiang Zhou, Yinyin Guo, Xin Deng, Xingguo Zhang and Tinggao Yu

Water 2021, 13(21), 3078; https://doi.org/10.3390/w13213078 - 2 Nov 2021

Cited by 10 | Viewed by 2477

Abstract

Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) [...] Read more.

Understanding variations in sap flow rates and the environmental factors that influence sap flow is important for exploring grape water consumption patterns and developing reasonable greenhouse irrigation schedules. Three irrigation levels were established in this study: adequate irrigation (W1), moderate deficit irrigation (W2) and deficit irrigation (W3). Grape sap flow estimation models were constructed using partial least squares (PLS) and random forest (RF) algorithms, and the simulation accuracy and stability of these models were evaluated. The results showed that the daily mean sap flow rates in the W2 and W3 treatments were 14.65 and 46.94% lower, respectively, than those in the W1 treatment, indicating that the average daily sap flow rate increased gradually with an increase in the irrigation amount within a certain range. Based on model error and uncertainty analyses, the RF model had better simulation results in the different grape growth stages than the PLS model did. The coefficient of determination and Willmott’s index of agreement for RF model exceeded 0.78 and 0.90, respectively, and this model had smaller root mean square error and d-factor (evaluation index of model uncertainty) values than the PLS model did, indicating that the RF model had higher prediction accuracy and was more stable. The relative importance of the model predictors was determined. Moreover, the RF model more comprehensively reflected the influence of meteorological factors and the moisture content in different soil layers on the sap flow rate than the PLS model did. In summary, the RF model accurately simulated sap flow rates, which is important for greenhouse grape irrigation. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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14 pages, 3508 KiB

Open AccessArticle

Evaluation of Evapotranspiration in Brazilian Cerrado Biome Simulated with the SWAT Model

by Amanda do Nascimento Ferreira, Andréia de Almeida, Sergio Koide, Ricardo Tezini Minoti and Mario Benjamim Baptista de Siqueira

Water 2021, 13(15), 2037; https://doi.org/10.3390/w13152037 - 26 Jul 2021

Cited by 13 | Viewed by 3296

Abstract

Evapotranspiration represents a significant part on the water balance and, thus, the correct evaluation of this hydrological parcel is relevant when modeling a watershed. The objective of this work is to evaluate the Soil and Water Assessment Tool (SWAT) model’s capability in adequately [...] Read more.

Evapotranspiration represents a significant part on the water balance and, thus, the correct evaluation of this hydrological parcel is relevant when modeling a watershed. The objective of this work is to evaluate the Soil and Water Assessment Tool (SWAT) model’s capability in adequately simulating evapotranspiration in a watershed with predominance of the Brazilian Cerrado biome. Hydrological modeling of the Gama watershed located in the Federal District, which has 57.5% of its total area covered by pristine Cerrado, was conducted. Hydrometeorological and turbulent flow variables have been monitored in weather station and Eddy Covariance (EC) tower, respectively. SWAT simulations were performed for potential evapotranspiration methods: Hargreaves (H), Priestley–Taylor (PT) and Penman–Monteith (PM). Modified versions of SWAT for estimating actual (ET) by Strauch and Volk (2013) (SV) and Arroio Junior (2016) (AR) were also tested. The calibration and verification of the SWAT model, in terms of daily flow, were carried out using a Particle Swarm Optimization algorithm, and fair results were obtained with all the methods evaluated. The actual evapotranspiration (ET) simulated with SWAT (ETsim) using the PM, PT, H, SV and AR methods for a Cerrado hydrological response unit (HRU) were evaluated and compared with the ET obtained using the turbulent flow (Eddy Covariance) method (ETobs). Comparing ETobs and ETsim results, the PM method showed the best fitness and the H and PT methods showed better fit for the dry and the rainy periods, respectively. Although representing an advance on ET modeling, the SV and AR modifications did not improve the response in terms of simulation of the studied area. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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25 pages, 7274 KiB

Open AccessArticle

Hydrological Functioning of Maize Crops in Southwest France Using Eddy Covariance Measurements and a Land Surface Model

by Oluwakemi Dare-Idowu, Lionel Jarlan, Valerie Le-Dantec, Vincent Rivalland, Eric Ceschia, Aaron Boone and Aurore Brut

Water 2021, 13(11), 1481; https://doi.org/10.3390/w13111481 - 25 May 2021

Cited by 4 | Viewed by 2809

Abstract

The primary objective of this study is to evaluate the representation of the energy budget for irrigated maize crops in soil–vegetation–atmosphere transfer (SVAT) models. To this end, a comparison between the original version of the interactions between the soil–biosphere–atmosphere (ISBA) model based on [...] Read more.

The primary objective of this study is to evaluate the representation of the energy budget for irrigated maize crops in soil–vegetation–atmosphere transfer (SVAT) models. To this end, a comparison between the original version of the interactions between the soil–biosphere–atmosphere (ISBA) model based on a single-surface energy balance and the new ISBA-multi-energy balance (ISBA-MEB) option was carried out. The second objective is to analyze the intra- and inter-seasonal variability of the crop water budget by implementing ISBA and ISBA-MEB over six irrigated maize seasons between 2008 and 2019 in Lamasquère, southwest France. Seasonal dynamics of the convective fluxes were properly reproduced by both models with R² ranging between 0.66 and 0.80 (RMSE less than 59 W m⁻²) for the sensible heat flux and between 0.77 and 0.88 (RMSE less than 59 W m⁻²) for the latent heat flux. Statistical metrics also showed that over the six crop seasons, for the turbulent fluxes, ISBA-MEB was consistently in better agreement with the in situ measurements with RMSE 8–30% lower than ISBA, particularly when the canopy was heterogeneous. The ability of both models to partition the evapotranspiration (ET) term between soil evaporation and plant transpiration was also acceptable as transpiration predictions compared very well with the available sap flow measurements during the summer of 2015; (ISBA-MEB had slightly better statistics than ISBA with R² of 0.91 and a RMSE value of 0.07 mm h⁻¹). Finally, the results from the analysis of the inter-annual variability of the crop water budget can be summarized as follows: (1) The partitioning of the ET revealed a strong year-to-year variability with transpiration ranging between 40% and 67% of total ET, while soil evaporation was dominant in 2008 and 2010 due to the late and poor canopy development; (2) drainage losses are close to null because of an impervious layer at 60 cm depth; and (3) this very specific condition limited the inter-annual variability of irrigation scheduling as crops can always extract water that is stored in the root zone. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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15 pages, 2998 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Quantifying the Impact of Evapotranspiration at the Aquifer Scale via Groundwater Modelling and MODIS Data

by Nicolò Colombani, Mattia Gaiolini, Gianluigi Busico and Matteo Postacchini

Water 2021, 13(7), 950; https://doi.org/10.3390/w13070950 - 31 Mar 2021

Cited by 4 | Viewed by 2835

Abstract

In shallow alluvial aquifers characterized by coarse sediments, the evapotranspiration rates from groundwater are often not accounted for due to their low capillarity. Nevertheless, this assumption can lead to errors in the hydrogeological balance estimation. To quantify such impacts, a numerical flow model [...] Read more.

In shallow alluvial aquifers characterized by coarse sediments, the evapotranspiration rates from groundwater are often not accounted for due to their low capillarity. Nevertheless, this assumption can lead to errors in the hydrogeological balance estimation. To quantify such impacts, a numerical flow model using MODFLOW was set up for the Tronto river alluvial aquifer (Italy). Different estimates of evapotranspiration rates were retrieved from the online Moderate Resolution Imaging Spectroradiometer (MODIS) database and used as input values. The numerical model was calibrated against piezometric heads collected in two snapshots (mid-January 2007 and mid-June 2007) in monitoring wells distributed along the whole alluvial aquifer. The model performance was excellent, with all the statistical parameters indicating very good agreement between calculated and observed heads. The model validation was performed using baseflow data of the Tronto river compared with the calculated aquifer–river exchanges in both of the simulated periods. Then, a series of numerical scenarios indicated that, although the model performance did not vary appreciably regardless of whether it included evapotranspiration from groundwater, the aquifer–river exchanges were influenced significantly. This study showed that evapotranspiration from shallow groundwater accounts for up to 21% of the hydrogeological balance at the aquifer scale and that baseflow observations are pivotal in quantifying the evapotranspiration impact. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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23 pages, 3393 KiB

Open AccessArticle

Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data

by Josefina Mosre and Francisco Suárez

Water 2021, 13(6), 870; https://doi.org/10.3390/w13060870 - 23 Mar 2021

Cited by 28 | Viewed by 4379

Abstract

Actual evapotranspiration (ET_a) estimations in arid regions are challenging because this process is highly dynamic over time and space. Nevertheless, several studies have shown good results when implementing empirical regression formulae that, despite their simplicity, are comparable in accuracy to more [...] Read more.

Actual evapotranspiration (ET_a) estimations in arid regions are challenging because this process is highly dynamic over time and space. Nevertheless, several studies have shown good results when implementing empirical regression formulae that, despite their simplicity, are comparable in accuracy to more complex models. Although many types of regression formulae to estimate ET_a exist, there is no consensus on what variables must be included in the analysis. In this research, we used machine learning algorithms—through implementation of empirical linear regression formulae—to find the main variables that control daily and monthly ET_a in arid cold regions, where there is a lack of available ET_a data. Meteorological data alone and then combined with remote sensing vegetation indices (VIs) were used as input in ET_a estimations. In situ ET_a and meteorological data were obtained from ten sites in Chile, Australia, and the United States. Our results indicate that the available energy is the main meteorological variable that controls ET_a in the assessed sites, despite the fact that these regions are typically described as water-limited environments. The VI that better represents the in situ ET_a is the Normalized Difference Water Index, which represents water availability in plants and soils. The best performance of the regression equations in the validation sites was obtained for monthly estimates with the incorporation of VIs (R² = 0.82), whereas the worst performance of these equations was obtained for monthly ET_a estimates when only meteorological data were considered. Incorporation of remote-sensing information results in better ET_a estimates compared to when only meteorological data are considered. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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13 pages, 2208 KiB

Open AccessEditor’s ChoiceArticle

A Canopy Transpiration Model Based on Scaling Up Stomatal Conductance and Radiation Interception as Affected by Leaf Area Index

by Muhammad Shahinur Alam, David William Lamb and Nigel W. M. Warwick

Water 2021, 13(3), 252; https://doi.org/10.3390/w13030252 - 20 Jan 2021

Cited by 10 | Viewed by 4526

Abstract

Estimating transpiration as an individual component of canopy evapotranspiration using a theoretical approach is extremely useful as it eliminates the complexity involved in partitioning evapotranspiration. A model to predict transpiration based on radiation intercepted at various levels of canopy leaf area index (LAI) [...] Read more.

Estimating transpiration as an individual component of canopy evapotranspiration using a theoretical approach is extremely useful as it eliminates the complexity involved in partitioning evapotranspiration. A model to predict transpiration based on radiation intercepted at various levels of canopy leaf area index (LAI) was developed in a controlled environment using a pasture species, tall fescue (Festuca arundinacea var. Demeter). The canopy was assumed to be a composite of two indistinct layers defined as sunlit and shaded; the proportion of which was calculated by utilizing a weighted model (W model). The radiation energy utilized by each layer was calculated from the PAR at the top of the canopy and the fraction of absorbed photosynthetically active radiation (fAPAR) corresponding to the LAI of the sunlit and shaded layers. A relationship between LAI and fAPAR was also established for this specific canopy to aid the calculation of energy interception. Canopy conductance was estimated from scaling up of stomatal conductance measured at the individual leaf level. Other environmental factors that drive transpiration were monitored accordingly for each individual layer. The Penman–Monteith and Jarvis evapotranspiration models were used as the basis to construct a modified transpiration model suitable for controlled environment conditions. Specially, constructed self-watering tubs were used to measure actual transpiration to validate the model output. The model provided good agreement of measured transpiration (actual transpiration = 0.96 × calculated transpiration, R² = 0.98; p < 0.001) with the predicted values. This was particularly so at lower LAIs. Probable reasons for the discrepancy at higher LAI are explained. Both the predicted and experimental transpiration varied from 0.21 to 0.56 mm h⁻¹ for the range of available LAIs. The physical proportion of the shaded layer exceeded that of the sunlit layer near LAI of 3.0, however, the contribution of the sunlit layer to the total transpiration remains higher throughout the entire growing season. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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27 pages, 4609 KiB

Open AccessEditor’s ChoiceArticle

Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method

by Marcos Ruiz-Aĺvarez, Francisco Gomariz-Castillo and Francisco Alonso-Sarría

Water 2021, 13(2), 222; https://doi.org/10.3390/w13020222 - 18 Jan 2021

Cited by 15 | Viewed by 3497

Abstract

Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate [...] Read more.

Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET

_{0}

) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET

_{0}

was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET

_{0}

. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE)

M = 0.903

,

S D = 0.034

for KGE and

M = 3.17

,

S D = 2.97

for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET

_{0}

increase in headwaters and a smaller increase in the coast. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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18 pages, 7254 KiB

Open AccessArticle

A Scheme to Estimate Diurnal Cycle of Evapotranspiration from Geostationary Meteorological Satellite Observations

by Jing Lu, Li Jia, Chaolei Zheng, Ronglin Tang and Yazhen Jiang

Water 2020, 12(9), 2369; https://doi.org/10.3390/w12092369 - 24 Aug 2020

Cited by 3 | Viewed by 2391

Abstract

The diurnal cycle of evapotranspiration (ET) is significant in studying the dynamics of land–atmosphere interactions. The diurnal ET cycle can be considered as an indicator of dry/wet surface conditions. However, the accuracy of current models in estimating the diurnal ET cycle is generally [...] Read more.

The diurnal cycle of evapotranspiration (ET) is significant in studying the dynamics of land–atmosphere interactions. The diurnal ET cycle can be considered as an indicator of dry/wet surface conditions. However, the accuracy of current models in estimating the diurnal ET cycle is generally low. This study developed an improved scheme to estimate the diurnal cycle of ET by solving the surface energy balance equation combined with simplified parameterization, with daily ET as the constraint. Meteosat Second Generation (MSG) land surface temperature, and longwave and shortwave radiation products were the primary inputs. Daily ET was from the remote sensing-based ETMonitor model. The estimated instantaneous (30 min) ET from the improved scheme outperformed the official MSG instantaneous ET product when compared with in situ half-hourly measurements at 35 flux sites from the FLUXNET2015 dataset, and was also comparable with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 ET data, with an R² of 0.617 and root mean square error (RMSE) of 65.8 W/m² for the improved scheme. Results were largely improved compared with those without daily ET as the constraint. The improved method was stable for the estimation of ET’s diurnal cycle at the similar atmospheric conditions and the accuracy was comparative at different land cover surfaces. Errors in the input variables and the simplification of surface heat flux parameterization affected surface energy balance closure, which can lead to instability of the solution of constants in the simplified parameterization and further to the uncertainty of ET’s diurnal cycle estimation. Measurement errors, different source areas in measured variables, and inconsistent spatial representativeness between remote sensing and site measurements also impacted the evaluation. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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27 pages, 11477 KiB

Open AccessArticle

Microclimate Characteristics and Evapotranspiration Estimates of Cucumber Plants in a Newly Developed Sunken Solar Greenhouse

by Haijun Liu, Congyan Yin, Xiaodong Hu, Josef Tanny and Xiaopei Tang

Water 2020, 12(8), 2275; https://doi.org/10.3390/w12082275 - 13 Aug 2020

Cited by 15 | Viewed by 3761

Abstract

In north China, vegetables are always cultivated in conventional solar greenhouses (SG), however, these structures cannot be used during most of the winter due to extremely low temperatures. In this study, a new type of a solar greenhouse named sunken solar greenhouse (SSG), [...] Read more.

In north China, vegetables are always cultivated in conventional solar greenhouses (SG), however, these structures cannot be used during most of the winter due to extremely low temperatures. In this study, a new type of a solar greenhouse named sunken solar greenhouse (SSG), where the inside soil surface is lowered 1–2 m below outside and the back wall is 5–8 m width at the bottom and 1.5–2 m on top, was investigated. Inside climatic variables were recorded and compared with those outside during seven cucumber cultivation seasons. Crop evapotranspiration (ET_c) was estimated using the Penman–Monteith method. Results show that inside solar radiation was reduced by approximately 40%, however days with a daily maximum inside temperature higher than 20 °C accounted for 80–90% of the days during the winter, which greatly enhanced cucumber fruit production compared to common SGs. The reference crop evapotranspiration (ET_o) inside the SSG was reduced by 27% compared to outside. The estimated ET_c was generally lower than 4 mm day⁻¹, which resulted in a basal crop coefficient of 0.83. In conclusion, the SSG is environmental-friendly, preferable for winter vegetable cultivation in north China, and can be useful for other regions of the world with cold winter conditions. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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19 pages, 9770 KiB

Open AccessArticle

Trend and Sensitivity Analysis of Reference Evapotranspiration in the Senegal River Basin Using NASA Meteorological Data

by Papa Malick Ndiaye, Ansoumana Bodian, Lamine Diop, Abdoulaye Deme, Alain Dezetter, Koffi Djaman and Andrew Ogilvie

Water 2020, 12(7), 1957; https://doi.org/10.3390/w12071957 - 10 Jul 2020

Cited by 31 | Viewed by 768341

Abstract

Understanding evapotranspiration and its long-term trends is essential for water cycle studies, modeling and for water uses. Spatial and temporal analysis of evapotranspiration is therefore important for the management of water resources, particularly in the context of climate change. The objective of this [...] Read more.

Understanding evapotranspiration and its long-term trends is essential for water cycle studies, modeling and for water uses. Spatial and temporal analysis of evapotranspiration is therefore important for the management of water resources, particularly in the context of climate change. The objective of this study is to analyze the trend of reference evapotranspiration (ET₀) as well as its sensitivity to climatic variables in the Senegal River basin. Mann-Kendall’s test and Sen’s slope were used to detect trends and amplitude changes in ET₀ and climatic variables that most influence ET₀. Results show a significant increase in annual ET₀ for 32% of the watershed area over the 1984–2017 period. A significant decrease in annual ET₀ is observed for less than 1% of the basin area, mainly in the Sahelian zone. On a seasonal scale, ET₀ increases significantly for 32% of the basin area during the dry season and decreases significantly for 4% of the basin during the rainy season. Annual maximum, minimum temperatures and relative humidity increase significantly for 68%, 81% and 37% of the basin, respectively. However, a significant decrease in wind speed is noted in the Sahelian part of the basin. The wind speed decrease and relative humidity increase lead to the decrease in ET₀ and highlight a “paradox of evaporation” in the Sahelian part of the Senegal River basin. Sensitivity analysis reveals that, in the Senegal River basin, ET₀ is more sensitive to relative humidity, maximum temperature and solar radiation. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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20 pages, 877 KiB

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A Review of Evapotranspiration Measurement Models, Techniques and Methods for Open and Closed Agricultural Field Applications

by Ikhlas Ghiat, Hamish R. Mackey and Tareq Al-Ansari

Water 2021, 13(18), 2523; https://doi.org/10.3390/w13182523 - 15 Sep 2021

Cited by 69 | Viewed by 15690

Abstract

Detailed knowledge of energy and mass fluxes between land and the atmosphere are necessary to monitor the climate of the land and effectively exploit it in growing agricultural commodities. One of the important surface land fluxes is evapotranspiration, which combines the process of [...] Read more.

Detailed knowledge of energy and mass fluxes between land and the atmosphere are necessary to monitor the climate of the land and effectively exploit it in growing agricultural commodities. One of the important surface land fluxes is evapotranspiration, which combines the process of evaporation from the soil and that of transpiration from plants, describing the movement of water vapour from the land to the atmosphere. Accurately estimating evapotranspiration in agricultural systems is of high importance for efficient use of water resources and precise irrigation scheduling operations that will lead to improved water use efficiency. This paper reviews the major mechanistic and empirical models for estimating evapotranspiration including the Penman–Monteith, Stanghellini, Priestly–Taylor, and Hargreaves and Samani models. Moreover, the major differences between the models and their underlined assumptions are discussed. The application of these models is also reviewed for both open and closed field mediums and limitations of each model are highlighted. The main parameters affecting evapotranspiration rates in greenhouse settings including aerodynamic resistance, stomatal resistance and intercepted radiation are thoroughly discussed for accurate measurement and consideration in evapotranspiration models. Moreover, this review discusses direct evapotranspiration measurements systems such as eddy covariance and gas exchange systems. Other direct measurements appertaining to specific parameters such as leaf area index and surface leaf temperature and indirect measurements such as remote sensing are also presented, which can be integrated into evapotranspiration models for adaptation depending on climate and physiological characteristics of the growing medium. This review offers important directions for the estimation of evapotranspiration rates depending on the agricultural setting and the available climatological and physiological data, in addition to experimentally based adaptation processes for ET models. It also discusses how accurate evapotranspiration measurements can optimise the energy, water and food nexus. Full article

(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)

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