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Hydrological Cycle and Land-Atmosphere Interactions: From Evapotranspiration to Precipitation
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Hydrological Cycle and Land-Atmosphere Interactions: From Evapotranspiration to Precipitation

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Ecohydrology".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 6949

Special Issue Editors

Prof. Dr. Jiangfeng Wei

E-Mail Website
Guest Editor
School of Atmospheric Science, Nanjing University of Information Science & Technology, Nanjing, China
Interests: land–atmosphere interactions; water cycle; aerosol–climate interactions
Prof. Dr. Min-Hui Lo

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Interests: land–atmosphere interactions; terrestrial hydrology; anthropogenic effects on the water cycle

Special Issue Information

Dear Colleagues,

The moisture transport process from the surface to the atmosphere and then precipitate is one of the most uncertain segments of the hydrological cycle. It involves interactions among the land or ocean surface, atmospheric boundary layer, cloud physics, atmospheric circulation, and even atmospheric aerosols. There are many unsolved issues in the observation and modeling of these processes, and resolving them could significantly enhance our understanding of the hydrological cycle. In this Special Issue, we seek studies that investigate the facts and mechanisms related to the processes from surface evapotranspiration to precipitation. The topics covered by this Special Issue will include but not be limited to the following:

  • Land–atmosphere interactions and the hydrological cycle;
  • Atmospheric boundary layer processes, cloud physics, and precipitation;
  • Atmospheric moisture transport and tracking;
  • Impact of human activities on the hydrological cycle;
  • Aerosol–cloud–climate interactions.

Prof. Dr. Jiangfeng Wei
Prof. Dr. Min-Hui Lo
Guest Editors

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. Water is an international peer-reviewed open access semimonthly 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 2600 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

  • land–atmosphere interactions
  • hydrological cycle
  • water cycle
  • evapotranspiration
  • precipitation
  • atmospheric boundary layer
  • aerosols

Published Papers (2 papers)

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Research

16 pages, 6710 KiB  
Article
Calibration and Evaluation of Empirical Methods to Estimate Reference Crop Evapotranspiration in West Texas
by Ripendra Awal, Atikur Rahman, Ali Fares and Hamideh Habibi
Water 2022, 14(19), 3032; https://0-doi-org.brum.beds.ac.uk/10.3390/w14193032 - 27 Sep 2022
Cited by 4 | Viewed by 1707
Abstract
Evapotranspiration is an essential component of the hydrologic cycle, and its accurate quantification is crucial for managing crop water requirements and the operation of irrigation systems. Evapotranspiration data is key to hydrological and water management research investigations, including studying the impact of various [...] Read more.
Evapotranspiration is an essential component of the hydrologic cycle, and its accurate quantification is crucial for managing crop water requirements and the operation of irrigation systems. Evapotranspiration data is key to hydrological and water management research investigations, including studying the impact of various climatic factors on crop water requirements. It has been estimated as the product of the reference crop evapotranspiration and crop coefficient. Daily reference crop evapotranspiration (ETo) can be determined by several methods and equations. The Food and Agriculture Organization Penman-Monteith equation requires complete weather data, whereas empirical equations such as Hargreaves and Samani, Valiantzas, Priestley-Taylor, Makkink, and Stephens-Stewart require limited weather data. This work evaluated different empirical equations for West Texas using the standard FAO Penman-Monteith method and calibrated their parameters to improve ETo estimation. Detailed meteorological data from West Texas Mesonet and high resolution (800 m) Parameter-elevation Regressions on Independent Slopes Model (PRISM) datasets from 2007 to 2016 were used. Daily ETo calculated using the standard FAO Penman-Monteith equation was compared to ETo estimated based on different empirical methods. The results show that all original empirical equations underestimated ETo. Calibration improved the performance of tested equations; however, there seems to be underestimation of ETo in the 8–16 mm range. Overall, the monthly Hargreaves and Samani equation with either original or calibrated values of its parameters outperformed all tested models. This equation seems to be a reasonable estimator, especially under limited weather data conditions. Full article
(This article belongs to the Special Issue Hydrological Cycle and Land-Atmosphere Interactions: From Evapotranspiration to Precipitation)
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14 pages, 3420 KiB  
Article
Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions
by Chentao He, Jiangfeng Wei, Yuanyuan Song and Jing-Jia Luo
Water 2021, 13(22), 3294; https://0-doi-org.brum.beds.ac.uk/10.3390/w13223294 - 21 Nov 2021
Cited by 9 | Viewed by 3394
Abstract
The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five [...] Read more.
The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions. Full article
(This article belongs to the Special Issue Hydrological Cycle and Land-Atmosphere Interactions: From Evapotranspiration to Precipitation)
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