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The Qinghai Tibet Plateau (QTP), known as the 'Third Pole' of the Earth' and the 'Water Tower of Asia,' plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there is very limited data on vegetation types and their geographical distributions on the QTP due to harsh natural environment. Currently, land cover/surface vegetation (LCSV) data are typically obtained using independent classification methods for each period's product, based on remote sensing information. These approaches do not consider the time continuity of vegetation to presence, and leads to a gradual increase in the number of misclassified pixels and the uncertainty of their locations, consequently decreasing the interpretability of the long-time series remote sensing products. To address this issue, this study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 80.9 % based on 733 samples from literature, with the reference annual 2020 reaching an accuracy of 86.5 % and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-term continuous annual vegetation. [ABSTRACT FROM AUTHOR]

The transport of sulfate-bearing brines is closely relevant to mineralization of sulfide deposits as metal-sulfate complexes exist in hydrothermal fluids. Liquid-liquid phase separation evidently occurs in various metal-sulfate systems with transport and precipitating different from homogeneous fluids. Previous studies have revealed a new species with a Raman peak at ~1020 cm-1 in rich concentration phase of liquid-liquid phase separated MgSO4 solution, and it was interpreted as chain structure polymers. Ab initio molecular dynamics simulations (AIMD) and autocorrelation functions for frequency calculation have been performed to disclose the speciation. The results show that more Mg2+ ions surrounding a SO42- anion lead to higher wavenumber of Raman peaks, which indicates the formation of complicate clusters with ion associations similar to kieserite. Besides, the splitting peaks of v-980 Raman bands at ~980, 990, and 1005 cm-1 in homogeneous solution represent more monodentate Mg-Os (Os: O of SO42-) associations instead of certain species, which favors the formation of prenucleation clusters. Furthermore, bidentate Mg-SO4 ligand is less stable than monodentate ligands at 543 K by applying free energy calculations. Our findings give atomic level recognition of concentrated phase in liquid-liquid phase separated MgSO4 fluids and theoretical explanation of the 980 cm-1 Raman peak shifting, which will further inspire understandings on nucleation processes of hydrated sulfate minerals and Raman spectra resolving of other sulfate systems. [ABSTRACT FROM AUTHOR]

The impact of global warming on extreme precipitation over China is projected based on CMIP5 simulations under three representative concentration pathway scenarios. When global warming is 1.5 °C above pre‐industrial (1861–1890), precipitation intensity and frequency increase, which leads to an increase relative to the period 1986–2005 in total wet daytime precipitation in northeast China, north China, and the Qinghai–Tibet Plateau. However, south China and southwest China experience fewer precipitation days and less total precipitation despite increasing simple daily intensity (SDII). Under 2 °C of warming, the number of wet days (R1mm) increases north of 30°N and decreases to the south, whereas consecutive dry days (CDD) displays the opposite pattern. The other eight extreme precipitation events increase during the simulation period nationwide, with varying intensity. An increase in global warming from 1.5 to 2 °C is projected to lead to an increase in precipitation intensity over China, except for some scattered regions in the northwest and southwest of the country. More frequent extreme precipitation days are also expected, although decreases in R1mm are projected in north China and extend to northwest China. An overall small decrease in CDD is predicted for China. All annual regional‐mean precipitation events have an apparent linear relationship with global mean temperature, except for CDD. The rate of increase of extreme precipitation with temperature in the future on an annual scale is much faster than for a reference period (1986–2005), whereas no noticeable difference exists on a daily scale. The relationships between daily precipitation extremes and temperature for the present day and for the future show a quadratic polynomial structure, increasing up to 19 °C but decreasing at higher temperatures. There is a significant positive influence on extreme precipitation when warming is limited to 1.5 °C, compared with a limit of 2 °C. An increase in global warming from 1.5 to 2 °C is projected to lead to an increase in precipitation intensity over China, except for some scattered regions in the northwest and southwest of the country. All annual regional‐mean precipitation events have an apparent linear relationship with global mean temperature, except for CDD. The relationships between daily precipitation extremes and temperature for the present day and for the future show a quadratic polynomial structure, increasing up to 19 °C but decreasing at higher temperatures. In the figure, difference in annual PRCPTOT (a), SDII (b), RX1day (c), RX5day (d), R1mm (e), R10mm (f), R20mm (g), R95p (h), R99p (i), and CDD (j) between 1.5 and 2 °C warming. The dots represent regions where at least 66% of all the models agree on the sign of change in the ensemble mean. [ABSTRACT FROM AUTHOR]

• Net radiation (Rn) dominated evapotranspiration (ET) of maize at daily scale. • Leaf area index (LAI) played an important mediating role in the relationship among climate, soil variables, and ET. • Rn dominated ET in an increasing order of dry, normal, and moist years. Conversely, the effects of LAI on ET followed a decreasing trend. As a central process in the hydrological system and the climate system, terrestrial evapotranspiration is a key factor furthering our understanding of the climate change processes. Knowledge of factors controlling the variability in evapotranspiration is crucial for the prediction of the fate of terrestrial ecosystems under environmental changes. Based on long-term (2005–2014) eddy covariance flux data observed at a rainfed maize site in northeast China, the purpose of this study was to clarify the environmental regulation of actual evapotranspiration (ET) and the extent to which the regulatory effects on ET are directly or indirectly mediated by changes in biotic factors, using the structural equation modeling (SEM) method. The results showed that annual total ET was 397 ± 35 mm for the rainfed maize site in comparison with 575 ± 169 mm of precipitation (Prec), with an ET/Prec ratio ranging from 0.43 (2012) to 1.14 (2014). It was revealed that net radiation (Rn) was the primary controlling factor of the maize ET, followed by leaf area index (LAI), vapor pressure deficit (VPD), air temperature (Ta), and soil water content (SWC). The adjusted SEM models explained 71%, 67%, and 67% of the variation in daily ET of the maize growing season (ET gs) for dry, normal, and moist years, respectively. Rn and VPD dominated ET gs in an increasing order of dry, normal, and moist years. Conversely, the effects of LAI and Ta on ET gs followed the opposite trend. This indicated that drought may increase the sensitivity of maize ET to temperature changes, and decrease the sensitivity of maize ET to radiation changes. In SEM analysis, LAI played an important mediating role in the relationship among climate, soil variables, and ET gs. Rn, VPD, Ta, and SWC all had significant indirect effects on ET gs mediated through LAI. At the annual scale, it was identified that most active days could be a robust predictor of annual ET. [ABSTRACT FROM AUTHOR]