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Atmosphere
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Evapotranspiration Observation and Prediction: Uncertainty Analysis
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Evapotranspiration Observation and Prediction: Uncertainty Analysis

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: closed (15 September 2019) | Viewed by 12662

Special Issue Editor

Dr. Sébastien Garrigues

E-Mail Website
Guest Editor
Centre for Ecology and Hydrology, Wallingford, UK
CESBIO, Toulouse, France
Interests: land surface modelling; uncertainty analysis; evapotranspiration; soil water stress; soil water transfer parametrization; climate change analysis

Special Issue Information

Dear Colleagues,

Evapotranspiration (ET) is a key component of the water balance and the energy budget of land surfaces. ET plays a key role in the dynamic of land surface–atmosphere interactions (Seneviratne et al. 2006) and the dynamic of soil water content (Desborough 1997). ET can be modeled from land surface models (LSMs), which describe the vertical exchange of energy and mass between soil, vegetation, and atmosphere. LSMs have been designed to be coupled to atmospheric or hydrology models for large-scale studies. Uncertainties in simulated ET can be attributed to the following three components: (1) model structure, (2) model parameters, and (3) errors in the climate and the surface variables used to drive the model and to integrate it spatially (Vrugt et al. 2009; Garrigues et al. 2015a). The identification of the most influential sources of uncertainty in the representation of the spatiotemporal dynamic of ET is a crucial aspect to reduce the uncertainty in the prediction of the long-term evolution of the terrestrial water cycle and the land–atmosphere interactions.

This Special Issue is focused on the uncertainty analysis in ET predictions from a local to a global scale. It offers an opportunity to publish articles on the quantification of uncertainties in the simulation of ET fluxes, to identify possible shortcomings in the representation of key processes (e.g, soil water transfer parametrization, stomatal conductance, soil water stress, and energy balance), and to analyze the impact of ET uncertainties on the prediction of atmospheric variables. We particularly encourage the following:

  • The use of state-of-the art methods in uncertainty quantification and sensitivity analysis (e.g., Bayesian framework, metamodeling, and variance-based sensitivity indices);
  • Multi-model comparison;
  • The use of Earth observation data to resolve the spatiotemporal structure in ET uncertainties at regional and continental scales;
  • Atmosphere–land simulation or off-line simulation analysis.

Dr. Sébastien Garrigues
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evapotranspiration
  • uncertainty analysis
  • model intercomparison
  • land surface models
  • earth system models
  • satellite evapotranspiration products
  • partitioning between plant transpiration
  • soil evaporation
  • plant interception
  • long-term prediction
  • impact of climate and land-use changes
  • soil moisture stress
  • soil water transfer parametrization
  • energy balance

Published Papers (4 papers)

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Research

35 pages, 3018 KiB  
Article
Modeling Land Surface Fluxes from Uncertain Rainfall: A Case Study in the Sahel with Field-Driven Stochastic Rainfields
by Bernard Cappelaere, Denis Feurer, Théo Vischel, Catherine Ottlé, Hassane Bil-Assanou Issoufou, Stéphane Saux-Picart, Ibrahim Maïnassara, Monique Oï, Jean-Philippe Chazarin, Hélène Barral, Benoit Coudert and Jérôme Demarty
Atmosphere 2020, 11(5), 465; https://doi.org/10.3390/atmos11050465 - 4 May 2020
Cited by 1 | Viewed by 2393
Abstract
In distributed land surface modeling (LSM) studies, uncertainty in the rainfields that are used to force models is a major source of error in predicted land surface response variables. This is particularly true for applications in the African Sahel region, where weak knowledge [...] Read more.
In distributed land surface modeling (LSM) studies, uncertainty in the rainfields that are used to force models is a major source of error in predicted land surface response variables. This is particularly true for applications in the African Sahel region, where weak knowledge of highly time/space-variable convective rainfall in a poorly monitored region is a considerable obstacle to such developments. In this study, we used a field-based stochastic rainfield generator to analyze the propagation of the rainfall uncertainty through a distributed land surface model simulating water and energy fluxes in Sahelian ecosystems. Ensemble time/space rainfields were generated from field observations of the local AMMA-CATCH-Niger recording raingauge network. The rainfields were then used to force the SEtHyS-Savannah LSM, yielding an ensemble of time/space simulated fluxes. Through informative graphical representations and innovative diagnosis metrics, these outputs were analyzed to separate the different components of flux variability, among which was the uncertainty represented by ensemble-wise variability. Scale dependence was analyzed for each flux type in the water and energy budgets, producing a comprehensive picture of uncertainty propagation for the various flux types, with its relationship to intrinsic space/time flux variability. The study was performed over a 2530 km2 domain over six months, covering an entire monsoon season and the subsequent dry-down, using a kilometer/daily base resolution of analysis. The newly introduced dimensionless uncertainty measure, called the uncertainty coefficient, proved to be more effective in describing uncertainty patterns and relationships than a more classical measure based on variance fractions. Results show a clear scaling relationship in uncertainty coefficients between rainfall and the dependent fluxes, specific to each flux type. These results suggest a higher sensitivity to rainfall uncertainty for hydrological than for agro-ecological or meteorological applications, even though eddy fluxes do receive a substantial part of that source uncertainty. Full article
(This article belongs to the Special Issue Evapotranspiration Observation and Prediction: Uncertainty Analysis)
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Graphical abstract

21 pages, 7088 KiB  
Article
Spatiotemporal Variability of Actual Evapotranspiration and the Dominant Climatic Factors in the Pearl River Basin, China
by Weizhi Gao, Zhaoli Wang and Guoru Huang
Atmosphere 2019, 10(6), 340; https://doi.org/10.3390/atmos10060340 - 22 Jun 2019
Cited by 9 | Viewed by 2988
Abstract
Evapotranspiration is a vital component of the land surface process, thus, a more accurate estimate of evapotranspiration is of great significance to agricultural production, research on climate change, and other activities. In order to explore the spatiotemporal variation of evapotranspiration under global climate [...] Read more.
Evapotranspiration is a vital component of the land surface process, thus, a more accurate estimate of evapotranspiration is of great significance to agricultural production, research on climate change, and other activities. In order to explore the spatiotemporal variation of evapotranspiration under global climate change in the Pearl River Basin (PRB), in China, this study conducted a simulation of actual evapotranspiration (ETa) during 1960–2014 based on the variable infiltration capacity (VIC) model with a high spatial resolution of 0.05°. The nonparametric Mann–Kendall (M–K) test and partial correlation analysis were used to examine the trends of ETa. The dominant climatic factors impacting on ETa were also examined. The results reveal that the annual ETa across the whole basin exhibited a slight but not significant increasing trend during the 1960–2014 period, whereas a significant decreasing trend was found during the 1960–1992 period. At the seasonal scale, the ETa showed a significant upward trend in summer and a significant downward trend in autumn. At the spatial scale, the ETa generally showed a decreasing, but not significant, trend in the middle and upper stream of the PRB, while in the downstream areas, especially in the Pearl River Delta and Dongjiang River Basin, it exhibited a significant increasing trend. The variation of the ETa was mainly associated with sunshine hours and average air pressure. The negative trend of the ETa in the PRB before 1992 may be due to the significant decrease in sunshine hours, while the increasing trend of the ETa after 1992 may be due to the recovery of sunshine hours and the significant decrease of air pressure. Additionally, we found that the “paradox” phenomenon detected by ETa mainly existed in the middle-upper area of the PRB during the period of 1960–1992. Full article
(This article belongs to the Special Issue Evapotranspiration Observation and Prediction: Uncertainty Analysis)
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15 pages, 8069 KiB  
Article
A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates
by Mohammad Valipour, Mohammad Ali Gholami Sefidkouhi, Mahmoud Raeini-Sarjaz and Sandra M. Guzman
Atmosphere 2019, 10(6), 311; https://doi.org/10.3390/atmos10060311 - 5 Jun 2019
Cited by 22 | Viewed by 3583
Abstract
In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature [...] Read more.
In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine (n), and wind speed (WS). The results indicated that a coupled model containing the Tmean and WS can predict ETo accurately (RMSE = 0.3263 mm day−1) for arid, semiarid, and Mediterranean climates. Therefore, this model was adjusted using the GEP for all 18 synoptic stations. Under very humid climates, it is recommended to use a temperature-based GEP model versus wind speed-based GEP model. The optimal and lowest performance of the GEP belonged to Shahrekord (SK), RMSE = 0.0650 mm day−1, and Kerman (KE), RMSE = 0.4177 mm day−1, respectively. This research shows that the GEP is a robust tool to model ETo in semiarid and Mediterranean climates (R2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R2 < 0.50) due to its poor performance under such extreme conditions. Full article
(This article belongs to the Special Issue Evapotranspiration Observation and Prediction: Uncertainty Analysis)
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13 pages, 1217 KiB  
Article
Evapotranspiration Estimation Using Surface Energy Balance System Model: A Case Study in the Nagqu River Basin
by Lei Zhong, Kepiao Xu, Yaoming Ma, Ziyu Huang, Xian Wang and Nan Ge
Atmosphere 2019, 10(5), 268; https://doi.org/10.3390/atmos10050268 - 13 May 2019
Cited by 5 | Viewed by 3273
Abstract
Calculation of actual evapotranspiration (AET) is of vital importance for the study of climate change, ecosystem carbon cycling, flooding, drought, and agricultural water demand. It is one of the more important components in the hydrological cycle and surface energy balance (SEB). How to [...] Read more.
Calculation of actual evapotranspiration (AET) is of vital importance for the study of climate change, ecosystem carbon cycling, flooding, drought, and agricultural water demand. It is one of the more important components in the hydrological cycle and surface energy balance (SEB). How to accurately estimate AET especially for the Tibetan Plateau (TP) with complex terrain remains a challenge for the scientific community. Using multi-sensor remote sensing data, meteorological forcing data, and field observations, AET was derived for the Nagqu river basin of the Northern Tibetan Plateau from a surface energy balance system (SEBS) model. As inputs for SEBS, improved algorithms and datasets for land surface albedo and a cloud-free normalized difference vegetation index (NDVI) were also constructed. The model-estimated AET were compared with results by using the combinatory method (CM). The validation indicated that the model estimates of AET agreed well with the correlation coefficient, the root mean square error, and the mean percentage error of 0.972, 0.052 mm/h, and −10.4%, respectively. The comparison between SEBS estimation and CM results also proved the feasibility of parameterization schemes for land surface parameters and AET. Full article
(This article belongs to the Special Issue Evapotranspiration Observation and Prediction: Uncertainty Analysis)
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