Recent Advances in Coupled Hydrology - Vegetation-Atmosphere Modelling

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 (8 January 2021) | Viewed by 2478

Special Issue Editor


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Guest Editor
Faculty of Engineering, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
Interests: ecohydrological modelling; uncertainty quantification; physically-based modelling; earth system modelling

Special Issue Information

Dear Colleagues,

Atmosphere dedicates this Special Issue to recent advances of coupled hydrological–vegetation–atmosphere modelling. In recent years, land surface modules of Earth system models have increased their complexity by introducing a detailed description of hydrological and vegetation dynamics, and their feedbacks with the atmosphere. Simlteneously, global or contintental scale hyperresolution hydrological models can be run operationally for the first time. Such advances have provided unprecedented knowledge on the global scale interactions between the land and the atmophere, and the terrestrial water and carbon cycles enhancing our prediction skill with respect to climate change projections, and natural hazard risk management, including floods and droughts. However, in spite of such advences, coupled hydrological–vegetation–atmosphere modelling is a highly challenging task, including large computational demand and limited data contraining model parameters. Advances in computer science and remote sensing provide the capabilty of overcoming such issues. The continuously increasing computational power enables, for the first time, the exporation of uncertainty in coupled Earth system dynamics. Remote sensing provides global scale data for hydrological, meteorological, and vegetation dynamics at fine spatial and temporal scales. The full potential of integrating the acievements of computer science and remote sensing with coupled models, in order to understand Earth system dynamics and their uncertainty in depth is yet to be achieved.

For this Special Issue, we invite you to contribute your research on new developments and applications of coupled hydrological–vegetation–atmosphere models. Contributions include but are not limited to: hyper-resolution models investigating the importance of the coupled water and carbon cycles on weather and climate and flood/drought forecasting, model-data fusion of new streams of data, such as satellite remote sensing and novel plant trait databases, and model uncertainty quantification.

Dr. Athanasios Paschalis
Guest Editor

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Keywords

  • Hydrology
  • Vegetation dynamics
  • Modelling
  • Uncertainty
  • Global scale
  • Climate change
  • Climate modelling
  • Earth system modelling

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Published Papers (1 paper)

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Research

11 pages, 1961 KiB  
Article
Indication of the Two Linear Correlation Methods Between Vegetation Index and Climatic Factors: An Example in the Three River-Headwater Region of China During 2000–2016
by Jiaxin Xu, Shibo Fang, Xuan Li and Zichun Jiang
Atmosphere 2020, 11(6), 606; https://doi.org/10.3390/atmos11060606 - 9 Jun 2020
Cited by 5 | Viewed by 2047
Abstract
The within-growing-season correlations (WGSC) and the inter-growing-season correlations (IGSC) are widely used linear correlation analysis methods between vegetation index and climatic factors (such as temperature, precipitation, and so on). The WGSC method usually calculates the linear correlation coefficient between vegetation index and climatic [...] Read more.
The within-growing-season correlations (WGSC) and the inter-growing-season correlations (IGSC) are widely used linear correlation analysis methods between vegetation index and climatic factors (such as temperature, precipitation, and so on). The WGSC method usually calculates the linear correlation coefficient between vegetation index and climatic factors of each month in all the growing seasons, for instance, whether vegetation index or temperature had data of 204 months (12 months × 17 years) during 2000–2016 to get the WGSC. The IGSC calculates the linear correlation coefficient between the vegetation index and climatic factors in the same month of each growing season among all the years, for example, only 17 couples’ data of vegetation index and temperature during 2000–2016 were used to get the linear correlation of IGSC. What is the difference between the results of the two methods and why do the results show that difference? Which is the more suitable method for the analysis of the relationship between the vegetation index and climatic conditions? To clarify the difference of the two methods and to explore more about the relationship between the vegetation index and climatic factors, we collected the data of 2000–2016 moderate resolution imaging spectroradiometer (MODIS) 13A1 normalized difference vegetation index (NDVI) and the meteorological data-temperature and precipitation, then calculated WGSC and IGSC between NDVI and the climatic factor in three river-headwater regions of China. The results showed that: (1) As for WGSC, the more of the years included, the higher the correlation coefficient between NDVI and the temperature/precipitation. The correlation coefficient of WGSC is dependent on how many years’ the data were included, and it was increased with the more year’s data included, while the correlation coefficients of IGSC are relatively independent on the amount of the data; (2) the WGSC showed a pseudo linear correlation between NDVI and climatic conditions caused by the accumulation of data amount, while the IGSC can more accurately indicate the impact of climatic factors on vegetation since it did not rely on the data amount. Full article
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