Dataset Information

Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background.

ABSTRACT: Soil moisture (SM) and atmospheric humidity (AH) are crucial climatic variables that significantly affect the climate system. However, the combined influencing mechanisms of SM and AH on the land surface temperature (LST) under global warming are still unclear. Here, we systematically analyzed the interrelationships among annual mean values of SM, AH, and LST using ERA5-Land reanalysis data and revealed the role of SM and AH on the spatiotemporal variations of LST through mechanism analysis and regression methods. The results showed that net radiation, SM, and AH could well model the long-term variability of LST well and explain 92% of the variability. Moreover, SM played an essential and different role under the different LST backgrounds. The AH always displayed a greenhouse effect on the LST. This study provides essential insights into the global climate change mechanism from the surface hydrothermal processes perspective.

SUBMITTER: Jiang K

PROVIDER: S-EPMC10209534 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background.

Jiang Kang K Pan Zhihua Z Pan Feifei F Teuling Adriaan J AJ Han Guolin G An Pingli P Chen Xiao X Wang Jialin J Song Yu Y Cheng Lu L Zhang Ziyuan Z Huang Na N Ma Shangqian S Gao Riping R Zhang Zhenzhen Z Men Jingyu J Lv Xiaoqin X Dong Zhiqiang Z

iScience 20230509 6

Soil moisture (SM) and atmospheric humidity (AH) are crucial climatic variables that significantly affect the climate system. However, the combined influencing mechanisms of SM and AH on the land surface temperature (LST) under global warming are still unclear. Here, we systematically analyzed the interrelationships among annual mean values of SM, AH, and LST using ERA5-Land reanalysis data and revealed the role of SM and AH on the spatiotemporal variations of LST through mechanism analysis and ...[more]

PMID: 37250783

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Project description:Changes in soil moisture and soil temperature result from the combined effects of several environmental factors. Scientific determination of the response characteristics of soil moisture and soil temperature to environmental factors is critical for adjusting the sloping land use structure and improving the ecological environment in China's loess hilly region. Soybean sloping fields, maize terraced fields, jujube orchards, and grasslands in the loess hilly region were selected as the research areas. The change in characteristics of soil moisture and soil temperature, as well as their interactions and statistical relationships with meteorological factors, were analyzed using continuously measured soil moisture, soil temperature, and meteorological factors. The results revealed that air temperature and humidity were the main controlling factors affecting soil moisture changes in the 0-60 cm soil layer of soybean sloping fields and grasslands in the normal precipitation year (2014) and the dry year (2015). Humidity and wind speed were the main meteorological factors affecting soil moisture changes in the maize terraced field. Air temperature had a significant negative effect on soil moisture in the jujube orchard. Soil moisture and soil temperature were all negatively correlated under the four sloping land use types. In normal precipitation years, atmospheric humidity had the greatest direct and comprehensive effect on soil moisture in soybean sloping fields, maize terraced fields, and grasslands; soil temperature had a relatively large impact on soil moisture in jujube orchards. The direct and comprehensive effects of soil temperature on soil moisture under all sloping land use types were the largest and most negative in the dry year. Air temperature had a high correlation with soil temperature in the 0-60 cm soil layer under the four sloping land use types, and the grey relational grade decreased as the soil layer deepened. The coefficient of determination between the 0-20 cm soil temperature and air temperature in the maize terraced field was low, indicating a weak response to air temperature. The above findings can serve as a scientific foundation for optimizing sloping land use structures and maximizing the efficient and sustainable utilization of sloping land resources in China's loess hilly region.

Project description:The coupling between carbon uptake and water loss through stomata implies that gross primary production (GPP) can be limited by soil water availability through reduced leaf area and/or stomatal conductance. Ecosystem and land-surface models commonly assume that GPP is highest under well-watered conditions and apply a stress function to reduce GPP as soil moisture declines. Optimality considerations, however, suggest that the stress function should depend on climatic aridity: ecosystems adapted to more arid climates should use water more conservatively when soil moisture is high, but maintain unchanged GPP down to a lower critical soil-moisture threshold. We use eddy-covariance flux data to test this hypothesis. We investigate how the light-use efficiency (LUE) of GPP depends on soil moisture across ecosystems representing a wide range of climatic aridity. 'Well-watered' GPP is estimated using the sub-daily P model, a first-principles LUE model driven by atmospheric data and remotely sensed vegetation cover. Breakpoint regression is used to relate daily β(θ) (the ratio of flux data-derived GPP to modelled well-watered GPP) to soil moisture estimated via a generic water balance model. The resulting piecewise function describing β(θ) varies with aridity, as hypothesised. Unstressed LUE, even when soil moisture is high, declines with increasing aridity index (AI). So does the critical soil-moisture threshold. Moreover, for any AI value, there exists a soil moisture level at which β(θ) is maximised. This level declines as AI increases. This behaviour is captured by universal non-linear functions relating both unstressed LUE and the critical soil-moisture threshold to AI. Applying these aridity-based functions to predict the site-level response of LUE to soil moisture substantially improves GPP simulation under both water-stressed and unstressed conditions, suggesting a route towards a robust, universal model representation of the effects of low soil moisture on leaf-level photosynthesis.

Dataset Information

Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background.

Publications

Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background.

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