Advances in Evaporation and Evaporative Demand: Part II

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: 22 May 2024 | Viewed by 5553

Special Issue Editors

Laboratory of Hydrology and Water Resources Development, School of Civil Engineering, National Technical University of Athens, GR-15780 Athens, Greece
Interests: hydrology; environmental; floods; remote sensing
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Special Issue Information

Dear Colleagues,

We are thrilled to introduce our new Special Issue entitled “Advance in Evaporation and Evaporation Demand—Part II” following the great success of Volume 1. 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 (MODIS, etc.);
  • 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 modelling;
  • Drought analysis and evapotranspiration modelling in the context of water resources management;
  • Irrigation management and evapotranspiration assessment;
  • Crop coefficients and Eddy measurements;
  • Use of evapotranspiration in climatic studies, long-term climatic trend analysis and stochastic modelling.

Dr. Aristoteles Tegos
Dr. Nikolaos Malamos
Guest Editors

Manuscript Submission Information

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Keywords

  • evapotranspiration
  • hydrology
  • agronomy
  • remote sensing

Published Papers (2 papers)

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Research

13 pages, 2613 KiB  
Article
On the Sensitivity of Standardized-Precipitation-Evapotranspiration and Aridity Indexes Using Alternative Potential Evapotranspiration Models
by Aristoteles Tegos, Stefanos Stefanidis, John Cody and Demetris Koutsoyiannis
Hydrology 2023, 10(3), 64; https://doi.org/10.3390/hydrology10030064 - 06 Mar 2023
Cited by 11 | Viewed by 2394
Abstract
This paper examines the impacts of three different potential evapotranspiration (PET) models on drought severity and frequencies indicated by the standardized precipitation index (SPEI). The standardized precipitation-evapotranspiration index is a recent approach to operational monitoring and analysis of drought severity. The standardized precipitation-evapotranspiration [...] Read more.
This paper examines the impacts of three different potential evapotranspiration (PET) models on drought severity and frequencies indicated by the standardized precipitation index (SPEI). The standardized precipitation-evapotranspiration index is a recent approach to operational monitoring and analysis of drought severity. The standardized precipitation-evapotranspiration index combines precipitation and temperature data, quantifying the severity of a drought as the difference in a timestep as the difference between precipitation and PET. The standardized precipitation-evapotranspiration index thus represents the hydrological processes that drive drought events more realistically than the standardized precipitation index at the expense of additional computational complexity and increased data demands. The additional computational complexity is principally due to the need to estimate PET within each time step. The standardized precipitation-evapotranspiration index was originally defined using the Thornthwaite PET model. However, numerous researchers have demonstrated the standardized precipitation-evapotranspiration index is sensitive to the PET model adopted. PET models requiring sparse meteorological inputs, such as the Thornthwaite model, have particular utility for drought monitoring in data scarce environments. The aridity index (AI) investigates the spatiotemporal changes in the hydroclimatic system. It is defined as the ratio between potential evapotranspiration and precipitation. It is used to characterize wet (humid) and dry (arid) regions. In this study, a sensitivity analysis for the standardized precipitation-evapotranspiration and aridity indexes was carried out using three different PET models; namely, the Penman–Monteith model, a temperature-based parametric model and the Thornthwaite model. The analysis was undertaken in six gauge stations in California region where long-term drought events have occurred. Having used the Penman–Monteith model as the PET model for estimating the standardized precipitation-evapotranspiration index, our findings highlight the presence of uncertainty in defining the severity of drought, especially for large timescales (12 months to 48 months), and that the PET parametric model is a preferable model to the Thornthwaite model for both the standardized precipitation-evapotranspiration index and the aridity indexes. The latter outcome is worth further consideration for when climatic studies are under development in data scarce areas where full required meteorological variables for Penman–Monteith assessment are not available. Full article
(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand: Part II)
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26 pages, 2541 KiB  
Article
A Comparative Study of Potential Evapotranspiration Estimation by Three Methods with FAO Penman–Monteith Method across Sri Lanka
by Himasha Dilshani Abeysiriwardana, Nitin Muttil and Upaka Rathnayake
Hydrology 2022, 9(11), 206; https://doi.org/10.3390/hydrology9110206 - 21 Nov 2022
Cited by 5 | Viewed by 2417
Abstract
Among numerous methods that have been developed to estimate potential evapotranspiration (PET), the Food and Agricultural Organization Penman–Monteith model (FAO P–M) is often recognized as a standard method to estimate PET. This study was conducted to evaluate the applicability of three other PET [...] Read more.
Among numerous methods that have been developed to estimate potential evapotranspiration (PET), the Food and Agricultural Organization Penman–Monteith model (FAO P–M) is often recognized as a standard method to estimate PET. This study was conducted to evaluate the applicability of three other PET estimation methods, i.e., Shuttleworth–Wallace (S–W) model, Thornthwaite (TW) and pan methods, to estimate PET across Sri Lanka with respect to the FAO P–M model. The meteorological data, i.e., temperature, relative humidity, wind speed, net solar radiation, and pan evaporation, recorded at 14 meteorologic stations, representing all climate and topographic zones of Sri Lanka, were obtained from 2009 to 2019. The models’ performances were assessed based on three statistical indicators: root mean squared error (RMSE), bias, and Pearson correlation coefficient (R). In comparison with the FAO P–M model estimates, the seasonal and annual estimates of all three models show great differences. The results suggested that pan and S–W methods perform better in the dry zone of the country. Both S–W and pan methods underestimated PET over the entire county in all seasons. TW does not show consistent results over the country, thus being found as the least reliable alternative. Although S–W is highly correlated with the FAO P–M model, the application of the model in a data-scarce region is more constrained, as it requires more parameters than the FAO P–M model. Thus, the study suggests employing alternative methods based on the region of the country instead of one single method across the entire country. Full article
(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand: Part II)
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