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Advances in Evaporation and Evaporative Demand

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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 40266

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Dr. Aristoteles Tegos

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Guest Editor

1. Laboratory of Hydrology and Water Resources Development, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 9, 157 80 Zographou, Greece
2. Ryan Hanley Ltd. Ireland, 170/173 Ivy Exchange, Granby Pl, Parnell Square W, D01 N938 Dublin, Ireland
Interests: hydrology; floods; remote sensing
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Dr. Nikolaos Malamos

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Guest Editor

Department of Agriculture, University of Patras, 27200 Amaliada, Greece
Interests: hydrology; hydroinformatics; irrigation; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The importance of evapotranspiration is well-established in different disciplines such as hydrology, agronomy, climatology, and other geosciences. Reliable estimates of evapotranspiration are also vital to develop criteria for in-season irrigation management, water resource allocation, long-term estimates of water supply, demand and use, design and management of water resources infrastructure, and evaluation of the effect of land use and management changes on the water balance. The objective of this Special Issue is to define and discuss several ET terms, including potential, reference, and actual (crop) ET, and present a wide spectrum of innovative research papers and case studies. We, therefore, encourage researchers and experts to present their innovative contributions in the following areas:

New techniques for estimating evapotranspiration and comparative analysis of different evapotranspiration models
New methodologies for estimating evapotranspiration and evaporation in temporal time scales from hourly to monthly
Global and local calibration of parsimonious PET model in data scarce areas using limited climate data
Advanced techniques for quantifying evapotranspiration spatial variability
Calibration of large-scale hydrological model using evapotranspiration spatial products
Micrometeorological evapotranspiration modeling focusing on smart farming
Modeling Evapotranspiration for precision irrigation purposes
Weather forecasting model associated with the hydrological modelling and optimal irrigation scheduling
Remote Sensing application for evapotranspiration assessment
Public available hydrological and agronomical software incorporating evapotranspiration modeling
Effect of evapotranspiration in drought modeling
Irrigation management and evapotranspiration assessment
Crop coefficients and Eddy measurements
Use of evapotranspiration in climatic studies and long-term climatic trend analysis

Dr. Aristoteles Tegos
Dr. Nikolaos Malamos
Guest Editors

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Keywords

Evapotranspiration
Hydrology
Agronomy
Remote Sensing

Published Papers (12 papers)

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Editorial

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6 pages, 195 KiB

Open AccessEditorial

Advances in Evaporation and Evaporative Demand

by Nikolaos Malamos and Aristoteles Tegos

Hydrology 2022, 9(5), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology9050078 - 06 May 2022

Cited by 4 | Viewed by 1575

Abstract

The importance of evapotranspiration is well-established in various disciplines such as hydrology, agronomy, climatology, and other geosciences [...] Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

Research

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26 pages, 8285 KiB

Open AccessEditor’s ChoiceArticle

Determinants of Evapotranspiration in Urban Rain Gardens: A Case Study with Lysimeters under Temperate Climate

by Ahmeda Assann Ouédraogo, Emmanuel Berthier, Brigitte Durand and Marie-Christine Gromaire

Hydrology 2022, 9(3), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology9030042 - 23 Feb 2022

Cited by 5 | Viewed by 3274

Abstract

Accurate evaluation of evapotranspiration (ET) flux is an important issue in sustainable urban drainage systems that target not only flow rate limitations, but also aim at the restoration of natural water balances. This is especially true in context where infiltration possibilities are limited. However, its assessment suffers from insufficient understanding. In this study, ET in 1 m³ pilot rain gardens were studied from eight lysimeters monitored for three years in Paris (France). Daily ET was calculated for each lysimeter based on a mass balance approach and the related uncertainties were assessed at ±0.42 to 0.58 mm. Results showed that for these lysimeters, ET is the major term in water budget (61 to 90% of the precipitations) with maximum values reaching 8–12 mm. Furthermore, the major determinants of ET are the existence or not of an internal water storage and the atmospheric factors. The vegetation type is a secondary determinant, with little difference between herbaceous and shrub configurations, maximum ET for spontaneous vegetation, and minimal values when vegetation was regularly removed. Shading of lysimeters by surroundings buildings is also important, leading to lower values. Finally, ET of lysimeters is higher than tested reference values (evaporimeter, FAO-56, and local Météo-France equations). Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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14 pages, 3128 KiB

Open AccessEditor’s ChoiceArticle

Stochastic Analysis of Hourly to Monthly Potential Evapotranspiration with a Focus on the Long-Range Dependence and Application with Reanalysis and Ground-Station Data

by Panayiotis Dimitriadis, Aristoteles Tegos and Demetris Koutsoyiannis

Hydrology 2021, 8(4), 177; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8040177 - 01 Dec 2021

Cited by 5 | Viewed by 2577

Abstract

The stochastic structures of potential evaporation and evapotranspiration (PEV and PET or ETo) are analyzed using the ERA5 hourly reanalysis data and the Penman–Monteith model applied to the well-known CIMIS network. The latter includes high-quality ground meteorological samples with long lengths and simultaneous measurements of monthly incoming shortwave radiation, temperature, relative humidity, and wind speed. It is found that both the PEV and PET processes exhibit a moderate long-range dependence structure with a Hurst parameter of 0.64 and 0.69, respectively. Additionally, it is noted that their marginal structures are found to be light-tailed when estimated through the Pareto–Burr–Feller distribution function. Both results are consistent with the global-scale hydrological-cycle path, determined by all the above variables and rainfall, in terms of the marginal and dependence structures. Finally, it is discussed how the existence of, even moderate, long-range dependence can increase the variability and uncertainty of both processes and, thus, limit their predictability. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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13 pages, 30060 KiB

Open AccessArticle

Precipitation and Potential Evapotranspiration Temporal Variability and Their Relationship in Two Forest Ecosystems in Greece

by Stefanos Stefanidis and Vasileios Alexandridis

Hydrology 2021, 8(4), 160; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8040160 - 18 Oct 2021

Cited by 26 | Viewed by 3586

Abstract

The assessment of drought conditions is important in forestry because it affects forest growth and species diversity. In this study, temporal variability and trends of precipitation (P), potential evapotranspiration (PET), and their relationship (P/PET) were examined in two selected forest ecosystems that present different climatic conditions and vegetation types due to their location and hypsometric zone. The study area includes the forests of Pertouli and Taxiarchis, which are managed by the Aristotle University Forest Administration and Management Fund. The Pertouli is a coniferous forest in Central Greece with a maximum elevation of 2073 m a.s.l, and Taxiarchis is a broadleaved forest in Northern Greece with a maximum elevation of 1200 m a.s.l. To accomplish the goals of the current research, long–term (1974–2016) monthly precipitation and air temperature data from two mountainous meteorological were collected and processed. The PET was estimated using a parametric model based on simplified formulation of the Penman–Monteith equation rather than the commonly used Thornthwaite approach. Seasonal and annual precipitation, potential evapotranspiration (PET), and their ratio (P/PET) values were subjected to Mann–Kendall tests to assess the possible upward or downward trends, and Sen’s slope method was used to estimate the trends magnitude. The results indicated that the examined climatic variables vary greatly between seasons. In general, negative trends were detected for the precipitation time series of Pertouli, whereas positive trends were found in Taxiarchis; both were statistically insignificant. In contrast, statistically significant positive trends were reported for PET in both forest ecosystems. These circumstances led to different drought conditions between the two forests due to the differences of their elevation. Regarding Pertouli, drought trend analysis indicated downward trends for annual, winter, spring, and summer values, whereas autumn showed a slight upward trend. In addition, the average magnitude trend per decade was approximately −2.5%, −3.5%, +4.8%, −0.8%, and +3.3% for annual, winter, autumn, spring, and summer seasons, respectively. On the contrary, the drought trend and the associated magnitude per decade for the Taxiarchis forest were found to be as follows: annual (+2.2%), winter (+6.2%), autumn (+9.2%), spring (+1.0%), and summer (−5.0%). The performed statistical test showed that the reported trend was statistically insignificant at a 5% significance level. These results may be a useful tool as a forest management practice and can enhance the adaptation and resilience of forest ecosystems to climate change. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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17 pages, 5825 KiB

Open AccessArticle

Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

by Eva Melišová, Adam Vizina, Martin Hanel, Petr Pavlík and Petra Šuhájková

Hydrology 2021, 8(4), 153; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8040153 - 12 Oct 2021

Cited by 3 | Viewed by 2502

Abstract

Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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26 pages, 11703 KiB

Open AccessEditor’s ChoiceArticle

Integrating Drone Technology into an Innovative Agrometeorological Methodology for the Precise and Real-Time Estimation of Crop Water Requirements

by Stavros Alexandris, Emmanouil Psomiadis, Nikolaos Proutsos, Panos Philippopoulos, Ioannis Charalampopoulos, George Kakaletris, Eleni-Magda Papoutsi, Stylianos Vassilakis and Antoniοs Paraskevopoulos

Hydrology 2021, 8(3), 131; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8030131 - 01 Sep 2021

Cited by 18 | Viewed by 5291

Abstract

Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The proposed methodology was based on the crop water stress index (CWSI) and was applied in Greece within the ongoing research project GreenWaterDrone. The innovative approach combines real spatial data, such as infrared canopy temperature, air temperature, air relative humidity, and thermal infrared image data, taken above the crop field using an aerial micrometeorological station (AMMS) and a thermal (IR) camera installed on an unmanned aerial vehicle (UAV). Following an initial calibration phase, where the ground micrometeorological station (GMMS) was installed in the crop, no equipment needed to be maintained in the field. Aerial and ground measurements were transferred in real time to sophisticated databases and applications over existing mobile networks for further processing and estimation of the actual water requirements of a specific crop at the field level, dynamically alerting/informing local farmers/agronomists of the irrigation necessity and additionally for potential risks concerning their fields. The supported services address farmers’, agricultural scientists’, and local stakeholders’ needs to conform to regional water management and sustainable agriculture policies. As preliminary results of this study, we present indicative original illustrations and data from applying the methodology to assess UAV functionality while aiming to evaluate and standardize all system processes. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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22 pages, 4957 KiB

Open AccessArticle

Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

by Jae-Cheol Jang, Eun-Ha Sohn, Ki-Hong Park and Soobong Lee

Hydrology 2021, 8(3), 129; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8030129 - 27 Aug 2021

Cited by 9 | Viewed by 2261

Abstract

Evapotranspiration (ET) is a fundamental factor in energy and hydrologic cycles. Although highly precise in-situ ET monitoring is possible, such data are not always available due to the high spatiotemporal variability in ET. This study estimates daily potential ET (PET) in real-time for the Korean Peninsula, via an artificial neural network (ANN), using data from the GEO-KOMPSAT 2A satellite, which is equipped with an Advanced Meteorological Imager (GK2A/AMI). We also used passive microwave data, numerical weather prediction (NWP) model data, and static data. The ANN-based PET model was trained using data for the period 25 July 2019 to 24 July 2020, and was tested by comparing with in-situ PET for the period 25 July 2020 to 31 July 2021. In terms of accuracy, the PET model performed well, with root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (R) of 0.649 mm day⁻¹, −0.134 mm day⁻¹, and 0.954, respectively. To examine the efficiency of the GK2A/AMI-derived PET data, we compared it with in-situ ET measured at flux towers and with MODIS PET data. The accuracy of the GK2A/AMI-derived PET, in comparison with the flux tower-measured ET, showed RMSE, bias, and Pearson’s R of 1.730 mm day⁻¹, 1.212 mm day⁻¹, and 0.809, respectively. In comparison with the in-situ PET, the ANN model produced more accurate estimates than the MODIS data, indicating that it is more locally optimized for the Korean Peninsula than MODIS. This study advances the field by applying an ANN approach using GK2A/AMI data and could play an important role in examining hydrologic energy for air-land interactions. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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16 pages, 2347 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Simplified Interception/Evaporation Model

by Giorgio Baiamonte

Hydrology 2021, 8(3), 99; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8030099 - 02 Jul 2021

Cited by 8 | Viewed by 4190

Abstract

It is known that at the event scale, evaporation losses of rainfall intercepted by canopy are a few millimeters, which is often not much in comparison to other stocks in the water balance. Nevertheless, at yearly scale, the number of times that the canopy is filled by rainfall and then depleted can be so large that the interception flux may become an important fraction of rainfall. Many accurate interception models and models that describe evaporation by wet canopy have been proposed. However, they often require parameters that are difficult to obtain, especially for large-scale applications. In this paper, a simplified interception/evaporation model is proposed, which considers a modified Merrian model to compute interception during wet spells, and a simple power-law equation to model evaporation by wet canopy during dry spells. Thus, the model can be applied for continuous simulation, according to the sub hourly rainfall data that is appropriate to study both processes. It is shown that the Merrian model can be derived according to a simple linear storage model, also accounting for the antecedent intercepted stored volume, which is useful to consider for the suggested simplified approach. For faba bean cover crop, an application of the suggested procedure, providing reasonable results, is performed and discussed. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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14 pages, 4228 KiB

Open AccessArticle

Sensitivity of the Evapotranspiration Deficit Index to Its Parameters and Different Temporal Scales

by Frank Joseph Wambura

Hydrology 2021, 8(1), 26; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8010026 - 02 Feb 2021

Cited by 3 | Viewed by 1828

Abstract

Sound estimates of drought characteristics are very important for planning intervention measures in drought-prone areas. Due to data scarcity, many studies are increasingly using less data-intensive approaches, such as the evapotranspiration deficit index (ETDI), in estimations of agricultural droughts. However, little is known about the sensitivity of this specific ETDI formula to its parameters, and to data at different temporal scales. In this study, a general ETDI formula, homologous to the specific ETDI formula, was introduced and used to test the sensitivity of the ETDI to its parameters and to data at different temporal scales. The tests used time series of remotely sensed evapotranspiration data in the Ruvu River basin in Tanzania. The parameter sensitivity tests revealed that ETDI is sensitive to its parameters, and different parameter combinations resulted in different drought characteristics. The temporal scale sensitivity test showed that drought characteristics, such as the number of drought events and the total drought durations, decreased as the temporal scale increased. Thus, an inappropriate temporal scale may lead to the misrepresentation of drought characteristics. To reduce uncertainty and increase the accuracy of ETDI-based agricultural drought characteristics, ETDI requires parameter calibration and the use of data with small temporal scales, respectively. These findings are useful for improving estimations of ETDI-based agricultural droughts. Full article

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15 pages, 1975 KiB

Open AccessArticle

Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

by Ali Rashid Niaghi, Oveis Hassanijalilian and Jalal Shiri

Hydrology 2021, 8(1), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8010025 - 02 Feb 2021

Cited by 24 | Viewed by 2668

Abstract

Evapotranspiration (ET) is widely employed to measure amounts of total water loss between land and atmosphere due to its major contribution to water balance on both regional and global scales. Considering challenges to quantifying nonlinear ET processes, machine learning (ML) techniques have been increasingly utilized to estimate ET due to their powerful advantage of capturing complex nonlinear structures and characteristics. However, limited studies have been conducted in subhumid climates to simulate local and spatial ET_o using common ML methods. The current study aims to present a methodology that exempts local data in ET_o simulation. The present study, therefore, seeks to estimate and compare reference ET (ET_o) using four common ML methods with local and spatial approaches based on continuous 17-year daily climate data from six weather stations across the Red River Valley with subhumid climate. The four ML models have included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF) with three input combinations of maximum and minimum air temperature-based (Tmax, Tmin), mass transfer-based (Tmax, Tmin, U: wind speed), and radiation-based (Rs: solar radiation, Tmax, Tmin) measurements. The estimates yielded by the four ML models were compared against each other by considering spatial and local approaches and four statistical indicators; namely, the root means square error (RMSE), the mean absolute error (MAE), correlation coefficient (r²), and scatter index (SI), which were used to assess the ML model’s performance. The comparison between combinations showed the lowest SI and RMSE values for the RF model with the radiation-based combination. Furthermore, the RF model showed the best performance for all combinations among the four defined models either spatially or locally. In general, the LR, GEP, and SVM models were improved when a local approach was used. The results showed the best performance for the radiation-based combination and the RF model with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among the models and approaches. Full article

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Review

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22 pages, 1572 KiB

Open AccessEditor’s ChoiceReview

Evapotranspiration Trends and Interactions in Light of the Anthropogenic Footprint and the Climate Crisis: A Review

by Stavroula Dimitriadou and Konstantinos G. Nikolakopoulos

Hydrology 2021, 8(4), 163; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology8040163 - 01 Nov 2021

Cited by 26 | Viewed by 4984

Abstract

Evapotranspiration (ET) is a parameter of major importance participating in both hydrological cycle and surface energy balance. Trends of ET are discussed along with the dependence of evaporation to key environmental variables. The evaporation paradox can be approached via natural phenomena aggravated by anthropogenic impact. ET appears as one of the most affected parameters by human activities. Complex hydrological processes are governed by local environmental conditions thus generalizations are difficult. However, in some settings, common hydrological interactions could be detected. Mediterranean climate regions (MCRs) appear vulnerability to the foreseen increase in ET, aggravated by precipitation shifting and air temperature warming, whereas in tropical forests its role is rather beneficial. ET determines groundwater level and quality. Groundwater level appeared to be a robust predictor of annual ET for peatlands in Southeast Asia. In semi-arid to arid areas, increases in ET have implications on water availability and soil salinization. ET-changes after a wildfire can be substantial for groundwater recharge if a canopy-loss threshold is surpassed. Those consequences are site-specific. Post-fire ET rebound seems climate and fire-severity-dependent. Overall, this qualitative structured review sets the foundations for interdisciplinary researchers and water managers to deploy ET as a means to address challenging environmental issues such as water availability. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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Other

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9 pages, 4177 KiB

Open AccessEssay

RASPOTION—A New Global PET Dataset by Means of Remote Monthly Temperature Data and Parametric Modelling

by Aristoteles Tegos, Nikolaos Malamos and Demetris Koutsoyiannis

Hydrology 2022, 9(2), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/hydrology9020032 - 10 Feb 2022

Cited by 6 | Viewed by 2757

Abstract

Regional estimations of Potential Evapotranspiration (PET) are of key interest for a number of geosciences, particularly those that are water-related (hydrology, agrometeorology). Therefore, several models have been developed for the consistent quantification of different time scales (hourly, daily, monthly, annual). During the last few decades, remote sensing techniques have continued to grow rapidly with the simultaneous development of new local and regional evapotranspiration datasets. Here, we develop a novel set T maps over the globe, namely RASPOTION, for the period 2003 to 2016, by integrating: (a) mean climatic data at 4088 stations, extracted by the FAO-CLIMWAT database; (b) mean monthly PET estimates by the Penman–Monteith method, at the aforementioned locations; (c) mean monthly PET estimates by a recently proposed parametric model, calibrated against local Penman–Monteith data; (d) spatially interpolated parameters of the Parametric PET model over the globe, using the Inverse Distance Weighting technique; and (e) remote sensing mean monthly air temperature data. The RASPOTION dataset was validated with in situ samples (USA, Germany, Spain, Ireland, Greece, Australia, China) and by using a spatial Penman–Monteith estimates in England. The results in both cases are satisfactory. The main objective is to demonstrate the practical usefulness of these PET map products across different research disciplines and spatiotemporal scales, towards assisting decision making for both short- and long-term hydro-climatic policy actions. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand)

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