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

Jiang, Kang; Pan, Zhihua; Pan, Feifei; Teuling, Adriaan J.; Han, Guolin; An, Ping; Xiao, Chen; Wang, Jialin; Song, Yu; Liu, Cheng; Zhang, Ziyuan; Huang, Na; Ma, Shen; Gao, Riping; Zhang, Zhenzhen; Men, Jingyu; Lv, Xudong; Dong, Zhiqiang

doi:10.1016/j.isci.2023.106837

Cited by 11 publications

(7 citation statements)

References 67 publications

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“…This pattern aligned with the result that evapotranspiration correlated with specific humidity across the sites at a monthly scale rather than yearly. At a yearly scale, wind, radiation, and other factors may affect this relationship [41].…”

Section: Specific Humidity Soil Water Content and Precipitationmentioning

confidence: 99%

Patterns of Water Consumption in Longleaf Pine Restoration Areas and the Relationship with Cone Production

Chen,

Willis,

Guo

2023

Forests

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Monitoring water consumption dynamics across the geographic range of an ecosystem may indicate the possible variation and stress in a biome. Here, model output data based on remote sensing (1979–2022) were used to examine the water consumption dynamics and effects on cone production in three geographic margins in the longleaf pine’s range (i.e., Bladen Lake State Forest, Escambia Experimental Forest, and Kisatchie National Forest) under varying climatic conditions. Results indicated that the mean annual transpiration at Escambia was approximately 431 mm and that at Bladen and Kisatchie was 500 mm. Mean monthly transpiration peaked twice (June and October) at Escambia but only once (August) at Bladen and Kisatchie. The mean annual evapotranspiration ranged from approximately 900 mm at Kisatchie to about 791 mm at Escambia and Bladen. The mean annual transpiration/evapotranspiration ratio was about 0.65 at Bladen and 0.55 at Escambia and Kisatchie. A significant correlation existed between evapotranspiration and specific humidity across the sites on a monthly scale but not on a yearly scale. Significant negative relationships existed between precipitation and the ratios of transpiration/precipitation and evapotranspiration/precipitation on the yearly scale across the sites. Negative power relationships were observed between precipitation and the specific humidity/precipitation ratio on monthly and yearly scales. Cone production was generally highest in years with moderate water consumption. These results provide baseline information on how hydrological and ecological processes of longleaf pine forests interact with climate across broad spatial and temporal scales.

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Section: Specific Humidity Soil Water Content and Precipitationmentioning

confidence: 99%

Patterns of Water Consumption in Longleaf Pine Restoration Areas and the Relationship with Cone Production

Chen,

Willis,

Guo

2023

Forests

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“…Climate warming would increase evaporation rates and atmospheric humidity (Song et al., 2022). Higher atmospheric humidity strengthens the greenhouse effect of water vapor, contributing to further warming (Jiang et al., 2023; Sherwood et al., 2018). Moreover, General Circulation Models (GCMs) in CMIP5 and observational data suggest that the warming climate will alter convective precipitation patterns, resulting in a phenomenon known as “wet‐get‐wetter” and “dry‐get‐drier” (Dittus et al., 2016; Donat et al., 2016; Du et al., 2022; IPCC et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Generally, higher soil moisture can contribute to temperature reduction due to the energy consumption involved in leaf/soil evaporation processes (Xu et al., 2004). However, it is essential to note the influence of soil moisture on climate warming varies depending on different background temperature thresholds (Jiang et al., 2023). These interactive climate changes are expected to have a significant impact on crop yield and soil GHG emissions, particularly in regions with stronger temperature‐moisture couplings (Griffis et al., 2017; Gupta et al., 2021; IPCC et al., 2021; Lesk et al., 2021; Tian, Lu, et al., 2016; Tian, Ren, et al., 2016; Tian et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

A Warmer and Wetter World Would Aggravate GHG Emissions Intensity in China's Cropland

Zhang,

Tian,

et al. 2024

Earth's Future

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Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world.

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“…As a crucial variable governing energy exchange between the atmosphere and land surface, land surface temperature (LST) plays an important role in assessing climate change, hydrological cycles, vegetation growth, and ecosystem dynamics. Due to its intricate spatiotemporal characteristics, LST has emerged as a prominent research focus in the past decades [1][2][3][4]. Given the profound impact of LST on agricultural productivity, industrial operations, and societal well-being, it is of great importance to comprehend its spatiotemporal dynamics and analyze the underlying driving forces.…”

Section: Introductionmentioning

confidence: 99%

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

Zhang,

Yang,

Zhang

et al. 2024

Land

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The identification of influencing factors (IFs) of land surface temperature (LST) is crucial for developing effective strategies to mitigate global warming and conducting other relevant studies. However, most previous studies ignored the potential impact of interactions between IFs, which might lead to biased conclusions. Generalized additivity models (GAMs) can provide more explanatory results compared to traditional machine learning models. Therefore, this study employs GAMs to investigate the impact of IFs and their interactions on LST, aiming to accurately detect significant factors that drive the changes in LST. The results of this case study conducted in Nanjing, China, showed that the GAMs incorporating the interactions between factors could improve the fitness of LST and enhance the explanatory power of the model. The autumn model exhibited the most significant improvement in performance, with an increase of 0.19 in adjusted-R2 and a 17.9% increase in deviance explained. In the seasonal model without interaction, vegetation, impervious surface, water body, precipitation, sunshine hours, and relative humidity showed significant effects on LST. However, when considering the interaction, the previously observed significant influence of the water body in spring and impervious surface in summer on LST became insignificant. In addition, under the interaction of precipitation, relative humidity, and sunshine hours, as well as the cooling effect of NDVI, there was no statistically significant upward trend in the seasonal mean LST during 2000–2020. Our study suggests that taking into account the interactions between IFs can identify the driving factors that affect LST more accurately.

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

Cited by 11 publications

References 67 publications

Patterns of Water Consumption in Longleaf Pine Restoration Areas and the Relationship with Cone Production

Patterns of Water Consumption in Longleaf Pine Restoration Areas and the Relationship with Cone Production

A Warmer and Wetter World Would Aggravate GHG Emissions Intensity in China's Cropland

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

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