Partitioning of Sensible and Latent Heat Fluxes in Different Vegetation Types and Their Spatiotemporal Variations Based on 203 FLUXNET Sites

Lin, Huiqing; Li, Yan; Zhao, Lei

doi:10.1029/2022jd037142

Cited by 15 publications

(3 citation statements)

References 77 publications

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“…Bowen ratio is defined as the ratio of sensible to latent heat fluxes at a certain interface, is often used to characterize their partitioning with broad implications for many fundamental physical processes of the Earth system (Bowen 1926). Bowen ratio plays a crucial role in measuring the energy and mass exchange between the land and the atmosphere (Lin et al 2022). A higher Bowen ratio suggests that more available energy is channeled into sensible heat for direct heating, increasing surface temperature.…”

Section: Exploring the Effect Of Land-atmosphere Coupling Associated ...mentioning

confidence: 99%

Land–atmosphere coupling exacerbates the moisture-associated heterogeneous impacts of compound extreme events on maize yield in China

Li,

Liu,

et al. 2024

Environ. Res.: Climate

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Compound climate events are major threats to crop production under climate change. However, the heterogeneity in the impact of compound events on crop yield and its drivers remain poorly understood. Herein, we used empirical approach to evaluate the impact of compound hot–dry and cold–wet events on maize yield in China at the county level from 1990 to 2016, with a special focus on the spatial heterogeneity. Our findings indicate comparable impact of extremely compound cold–wet events (−12.8% ± 3.6%) on maize yield loss to extremely compound hot–dry events (−11.3% ± 2.1%). The spatial pattern of compound hot–dry and cold–wet events impacts on maize yield was dominantly associated with moisture regime, followed by management practices and soil properties. Specifically, drier counties and counties with less fraction of clay soil and organic carbon tend to experience greater yield loss due to compound hot–dry events, and wet condition, excessive fertilizer, clay soil and rich organic carbon aggravate the maize yield loss due to compound cold–wet events. Moreover, the land–atmosphere coupling exacerbated the heterogeneous yield impact through divergent heat transfer. In drier regions, the greater proportion of sensible heat creates a positive feedback between drier land and hotter atmosphere. In contrast, the greater proportion of latent heat in wetter regions results in a positive feedback between wetter land and colder atmosphere. Our results highlighted a critical element to explore in further studies focused on the land-atmosphere coupling in agricultural risk under climate change.

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Section: Exploring the Effect Of Land-atmosphere Coupling Associated ...mentioning

confidence: 99%

Land–atmosphere coupling exacerbates the moisture-associated heterogeneous impacts of compound extreme events on maize yield in China

Li,

Liu,

et al. 2024

Environ. Res.: Climate

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“…In short, although the atmospheric evaporation demand of the MGP is high, the arid climate condition and vegetation characteristics result in the weak ability of the region to meet the atmospheric evaporative demand. Research has shown that when evaporation is limited, SSH is the preferred way of heat conversion [59], which means that more heat is converted to SSH in MGP (Supplementary Figure S12c). This explains why the contribution of SSH is higher than that of SLH in MGP.…”

Section: The Relative Contribution Of Lai and Heat Fluxesmentioning

confidence: 99%

Vegetation Influences on Cloud Cover in Typical Plain and Plateau Regions of Eurasia: 2001–2021

Lu,

Han,

Zhou

et al. 2024

Remote Sensing

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The feedback of vegetation on cloud cover is an important link in the global water cycle. However, the relative importance of vegetation and related factors (surface properties, heat fluxes, and environmental conditions) on cloud cover in the context of greening remains unclear. Combining the Global Land Surface Satellite (GLASS) leaf area index (LAI) product and the fifth-generation reanalysis data of the European Centre for Medium-Range Weather Forecasts (ERA5), we quantified the relative contribution of vegetation and related factors to total cloud cover (TCC) in typical regions (Eastern European Plain, Western Siberian Plain, Mongolian Plateau, and Northeastern China Plain) of Eurasia over 21 years, and investigated how vegetation moderated the contribution of the other factors. Here, we show that the relative contribution of different factors to TCC was closely related to the climate and vegetation characteristics. In energy-limited (moisture-limited) areas, temperature (relative humidity) was more likely to be the factor that strongly contributed to TCC variation. Except for sparsely vegetated ecosystems, the relative contribution of LAI to TCC was stable within a range of 8–13%. The case study also shows that vegetation significantly modulated the contribution of other factors on TCC, but the degree of the regulation varied among different ecosystems. Our results highlight the important influence of vegetation on cloud cover during greening, especially the moderating role of vegetation on the contribution of other factors.

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“…Surface water and heat fluxes consist of two main components: Latent heat flux (LE) and sensible heat flux (H). Changes in LE and H are generally influenced by a combination of various environmental factors, such as vegetation, meteorological elements, and human activities [8][9][10]. Of these, vegetation changes and aerosols are currently the two largest uncertainties in surface flux retrieval and prediction.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects

Chen,

et al. 2024

Remote Sensing

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The exchange of moisture and energy between the land and the atmosphere plays a crucial role in terrestrial hydrological cycle and climate change. However, existing studies on the retrieval of surface water and heat flux tend to overlook the dynamic changes in surface vegetation and atmospheric aerosols, which directly affect surface energy and indirectly alter various meteorological factors, including cloud, precipitation, and temperature. In this study, we assess the machine-learning retrieval method for surface fluxes that takes into account vegetation changes and aerosol effects, using FLUXNET observations and remote sensing data to retrieve latent heat flux (LE) and sensible heat flux (H). We constructed four sets of deep neural network models: (a) The first set considers only meteorological factors, (b) the second set considers meteorological factors and aerosols, (c) the third set considers meteorological factors and vegetation changes, and (d) the fourth set comprehensively considers meteorological factors, aerosols, and vegetation changes. All model performances were evaluated using statistical indicators. ERA5 reanalysis and remote sensing data were used to drive the models and retrieve daily H and LE. The retrieved results were validated against ground observation sites that were not involved in model training or the FLUXCOM product. The results show that the model that considers meteorological factors, aerosols, and vegetation changes has the smallest errors and highest correlation for retrieving H and LE (RH = 0.85, RMSEH = 24.88; RLE = 0.88, RMSELE = 22.25). The ability of the four models varies under different vegetation types. In terms of seasons, the models that consider meteorological factors and vegetation changes, as well as those that comprehensively consider meteorological factors, aerosols, and vegetation changes, perform well in retrieving the surface fluxes. As for spatial distribution, when atmospheric aerosols are present in the region, the model that considers both meteorological factors and aerosols retrieves higher values of H compared to the model that considers only meteorological factors, while the LE values are relatively lower. The model that considers meteorological factors and vegetation changes, as well as the model that comprehensively considers meteorological factors, aerosols, and vegetation changes, retrieves lower values in most regions. Through the validation of independent observation sites and FLUXCOM products, we found that the model, considering meteorological factors, aerosols, and vegetation changes, was generally more accurate in the retrieval of surface fluxes. This study contributes to improving the retrieval and future prediction accuracy of surface fluxes in a changing environment.

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Partitioning of Sensible and Latent Heat Fluxes in Different Vegetation Types and Their Spatiotemporal Variations Based on 203 FLUXNET Sites

Cited by 15 publications

References 77 publications

Land–atmosphere coupling exacerbates the moisture-associated heterogeneous impacts of compound extreme events on maize yield in China

Land–atmosphere coupling exacerbates the moisture-associated heterogeneous impacts of compound extreme events on maize yield in China

Vegetation Influences on Cloud Cover in Typical Plain and Plateau Regions of Eurasia: 2001–2021

Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects

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