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Forest above‐ground biomass (AGB) is often estimated by converting the observed tree size using allometric scaling between the dry weight and size of an organism. However, the variations in biomass allocation and scaling between tree crowns and stems due to survival competition during a tree's lifecycle remain unclear. This knowledge gap can improve the understanding of modelling tree biomass allometry because traditional allometries ignore the dynamics of allocation.Herein, we characterised allometric scaling using the dynamic ratio (r) of the stem biomass (SB) to AGB and a dynamic exponent. The allometric models were biologically parameterised by the r values for initial, intermediate and final ages rather than only a regression result. The scaling was tested using field measurements of 421 species and 2213 different‐sized trees in pantropical regions worldwide.We found that the scaling fluctuated with tree size, and this fluctuation was driven by the trade‐off relationship of biomass allocation between the tree crown and stem depending on the dynamic crown trait. The allometric scaling between SB and AGB varied from 0.8 to 1.0 for a tree during its entire lifecycle. The fluctuations presented a general law for the allometric scaling of the pantropical tree biomass and size. Our model quantified the trade‐off and explained 94.1% of the allometric relationship between the SB and AGB (93.8% of which between D2H and AGB) for pantropical forests, which resulted in a better fit than that of the traditional model. Considering the effects of the trade‐off on modelling, the actual biomass of large trees could be substantially greater than conventional estimates.These results highlight the importance of coupling growth mechanisms in modelling allometry and provide a theoretical foundation for better describing and predicting forest carbon accumulation. [ABSTRACT FROM AUTHOR]

Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM2.5) due to their low cost, wide coverage, and all-weather capability. Estimation of PM2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons (p < 0.01) and different spatial resolutions (p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM2.5 monitoring stations and historical PM2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions. [ABSTRACT FROM AUTHOR]

Seasonal variation and influencing factors of river water isotopes were investigated in the Xiangjiang River basin located in the East Asian monsoon region. This investigation involved comprehensive sampling of daily precipitation and river water with a 5 d interval as well as observing hydrometeorological factors spanning 13 hydrological years from January 2010 to December 2022, combined with the temporal and spatial correlation analyses based on linear regression and the isotopic Atmospheric Water Balance Model. Key findings are as follows: river water δ2H (δ2H R) exhibited significant seasonal variation, with the most positive and negative values occurring in the spring flood period and summer drought period, respectively, in alignment with those observed in precipitation. The correlations of the δ2H R with corresponding hydrometeorological factors with a 5 d interval were commonly weak due to the seasonality of precipitation isotopes and mixing of various water bodies within the basin, but the changes in the runoff (ΔR) and δ2H R (Δδ2HR) between two contiguous samplings with 5 d or higher intervals showed significant responses to the corresponding accumulated precipitation and evaporation. Prolonged rainless intervals with high evaporation rates in 2013 and 2022 as well as significant precipitation events in major flood periods in 2011 and 2017 had a significant impact on the δ2H R and runoff discharge. However, the most positive δ2H R values were primarily influenced by precipitation input with the most enriched isotopes in the spring flood period, while the moderately isotope-depleted precipitation during limited wetness conditions led to the most negative δ2H R. The spatial correlation analysis between water isotopes and hydrometeorological factors at the observing site and in the surrounding regions supported the representation of the Changsha site in the Xiangjiang River basin. These results underscore the potential of Δδ2HR as a proxy that reflects the seasonal variations in local environments, while caution is advised when interpreting extreme isotopic signals in river water. Overall, this study provides insights into the seasonal variation, extreme signal interpreting, and controlling factors of δ2H R in the study area, which was valuable for paleoclimate reconstruction and establishment of isotope hydrologic models. [ABSTRACT FROM AUTHOR]

The responses of photosynthesis to change of temperature and CO2 concentration are divergent among various alpine plant species at high altitude regions; however, very few direct in situ measurements have been conducted to compare photosynthetic capacity among different functional plants along altitude gradients in the Qinghai‐Tibet Plateau (QTP). This study measured the net photosynthetic assimilation rate (An), maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), stomatal conductance (gs) and mesophyll conductance (gm) for CO2 of three plant functional types (PFTs, sedge, grass and shrub) and functional traits at different altitudes in the Qinghai Lake watershed during growing season. Meanwhile we simulated An of the PFTs under potential future scenario. Results indicate that grass maintain a relatively stable An by decreasing Vcmax, Jmax, ratio of Jmax to Vcmax (J/V) and gs, while slightly increasing gm with increasing altitudes. In contrast, the An of sedge and shrubs increased with rising Vcmax, Jmax, gs and gm values, resulting in a large increment in the An at low altitudes. Grass was less sensitive to temperature by reducing the supply of CO2, while sedge and shrub increased. Ca was a more dominant factor than Ta in affecting the An of grass. The order of rising An in PFTs was shrub > sedge > grass, and the An of alpine meadow was found to increase more under the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Our results indicate that grass may be more resistant to future warming and suggested that considering PFTs is of great significance for improving the simulating of the response of vegetation physiological and ecological processes to future climate change in alpine regions. [ABSTRACT FROM AUTHOR]

Urban landscape forms can be effective in reducing increasing PM2.5 concentrations due to urbanization in China, making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM2.5 variations. Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020, using a spatiotemporal geographically weighted regression model, random forest models and partial dependence plots. The results show that there are spatiotemporal differences in the impacts of landscape indices on PM2.5. the proportion of urban green infrastructure (PLAND-UGI) and the fractal dimension of urban green infrastructure (FRACT-UGI) exacerbate PM2.5 concentrations in the northwest, the proportion of impervious surfaces (PLAND-Impervious) mitigates air pollution in northwest and southwest China, and shannon's diversity index (SHDI) has seasonal differences in the northwest. PLAND-UGI is the landscape index with the largest contribution (30%) and interpretable range. The relationship between FRACT and PM2.5 was more complex than for other landscape indices. The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM2.5, contributing to clean urban development and sustainable development. [ABSTRACT FROM AUTHOR]

Long-term continuous monitoring of precipitation isotopes has great potential to advance our understanding of hydrometeorological processes that determine stable isotope variability in the monsoon regions. This study presents 4-yr daily precipitation isotopes from Yungui Plateau in southwestern China that are influenced by Indian summer monsoon and East Asian monsoon. The local meteoric water line (LMWL; δ2H = 8.12δ18O + 11.2) was first established at the Tengchong (TC) site, which was close to the global meteoric water line (GMWL; δ2H = 8δ18O + 10), indicating little secondary subcloud evaporation in the falling rain. Precipitation δ18O values exhibited significant inverse relationships with precipitation amount (r = −0.42), air temperature (r = −0.43), and relative humidity (r = −0.41) with lower correlation coefficients throughout the entire period, which indicated that precipitation isotopic variability in TC could not be well explained by the local meteorological factors but influenced by other combined factors of regional precipitation amount and upstream rainout. Precipitation δ18O values showed a clear V-shaped trend throughout the observation period, characterized by higher δ18O values during the premonsoon period whereas lower values during the postmonsoon period. This seasonal variation of precipitation δ18O values was associated with the seasonal movement of the intertropical convergence zone and seasonal changes in moisture transport. Combined with backward trajectory analysis, precipitation δ18O values were estimated by a Rayleigh distillation model showing that upstream rainout processes from the Bay of Bengal (BoB) toward land (Myanmar) and recycling moisture over land were key factors affecting the isotopic compositions of the TC precipitation. These findings could enhance our understanding of atmospheric dynamics and moisture source in the monsoon regions and will potentially facilitate the interpretation of numerous isotopic proxy records from this region. SIGNIFICANCE STATEMENT: The variability of the summer monsoon and its onset, duration, and failure directly determine the strong rainfall and drought in a given region and have great impacts on regional societies and agriculture. To better understand this variability, this study presented a 4-yr daily dataset of precipitation isotopes on the Yungui Plateau of southwestern China to explore atmospheric processes and moisture sources that drive isotopic variability in this region. Precipitation δ18O exhibited remarkably seasonal variability, with higher values in premonsoon period and lower values in the postmonsoon period. During the Indian summer monsoon period, moisture sources primarily originated from the BoB toward the TC site, experiencing rainout processes and local moisture recycling over land using a Rayleigh fractionation model. These findings shed new light on the temporal variations of precipitation stable isotopes and facilitate our understanding of hydrological cycle in the monsoon regions. [ABSTRACT FROM AUTHOR]