Improving soil organic carbon parameterization of land surface model for cold regions in the Northeastern Tibetan Plateau, China

Vegetation Changing Patterns and Its Sensitivity to Climate Variability across Seven Major Watersheds in China

Climate changes have profound impacts on vegetation and further alter hydrological processes through transpiration, interception, and evaporation. This study investigated vegetation's changing patterns and its sensitivity to climate variability across seven major watersheds in China based on a hybrid regionalization approach and a novel, empirical index-Vegetation Sensitivity Index (VSI). Vegetation showed linearly increasing trends in most of the seven watersheds, while decreases in vegetation were mostly found in the source regions of the Yangtze River Basin (YZRB) and Yellow River Basin (YRB), the forest and grassland areas of the Songhua River Basin (SHRB) and Liao River Basin (LRB), the Yangtze River Delta, and the Pearl River Delta during the growing season. The selected watersheds can be categorized into 11 sub-regions, and the regionalization result was consistent with the topography and vegetation types; the characteristics of vegetation dynamics were more homogeneous among sub-regions. Vegetation types such as forests and shrubland in the central parts of the YZRB were relatively more vulnerable to climate variations than the grasslands and alpine meadows and tundra (AMT) in the source regions of the YZRB and YRB and the Loess Plateau of the YRB. In arid and semi-arid regions, precipitation had a profound impact on vegetation, while, at low latitudes, solar radiation was the main controlling factor. Such comprehensive investigations of the vegetation-climate relationship patterns across various watersheds are expected to provide a foundation for the exploration of future climate change impacts on ecosystems at the watershed scale.

Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel; The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate that the proposed method can accurately detect the spatial distribution of grassland utilization intensity which is appliable in the similar regions.

Long-Term Lake Area Change and Its Relationship with Climate in the Endorheic Basins of the Tibetan Plateau

Lakes are sensitive indicators of climate change in the Tibetan Plateau (TP), which have shown high temporal and spatial variability in recent decades. The driving forces for the change are still not entirely clear. This study examined the area change of the lakes greater than 1 km2 in the endorheic basins of the Tibetan Plateau (EBTP) using Landsat images from 1990 to 2019, and analysed the relationships between lake area and local and large-scale climate variables at different geographic scales. The results show that lake area in the EBTP has increased significantly from 1990 to 2019 at a rate of 432.52 km2·year−1. In the past 30 years, lake area changes in the EBTP have mainly been affected by local climate variables such as precipitation and temperature. At a large scale, Indian Summer Monsoon (ISM) has correlations with lake area in western sub-regions in the Inner Basin (IB). While Atlantic Multidecadal Oscillation (AMO) has a significant connection with lake area, the North Atlantic Oscillation (NAO) does not. We also found that abnormal drought (rainfall) brought by the El Niño/La Niña events are significantly correlated with the lake area change in most sub-regions in the IB.

The role of climate change and vegetation greening on the variation of terrestrial evapotranspiration in northwest China's Qilian Mountains

Terrestrial evapotranspiration (ET_a) reflects the complex interactions of climate, vegetation, soil and terrain and is a critical component in water and energy cycles. However, the manner in which climate change and vegetation greening influence ET_a remains poorly understood, especially in alpine regions. Drawing on the Global Land Evaporation Amsterdam Model (GLEAM) ET_a data, the interannual variability of ET_a and its ties to precipitation (P), potential evaporation (ET_p) and vegetation (NDVI) were analysed. The Budyko framework was implemented over the period of 1982 to 2015 to quantify the response of ET_a to climate change's direct (P and ET_p) and indirect (NDVI) impacts. The ET_a, P, ET_p and NDVI all showed significant increasing trends from 1981 to 2015 with rates of 1.52 mm yr^-1, 3.18 mm yr^-1, 0.89 mm yr^-1 and 4.0 × 10^-4 yr^-1, respectively. At the regional level, the positive contribution of increases in P and NDVI offset the negative contribution of ET_p to the change in ET_a (∆ET_a). The positive ∆ET_a between 1982 and 2001 was strongly linked with the concomitant increase in NDVI. Increases in vegetation contributing to a positive ∆ET_a differed among landscape types: for shrub, meadow and steppe they occurred during both periods, for alpine vegetation between 1982 and 2001, and for desert between 2002 and 2015. Climate change directly contributed to a rise in ET_a, with P as the dominant factor affecting forested lands during both periods, and alpine vegetation between 2002 and 2015. Moreover, ET_p was a dominant factor for the desert between 1982 and 2001, where the variation of P was not significant. The contributions of factors having an impact on ∆ET_a are modulated by both the sensitivity of impact factors acting on ET_a as well as the magnitudes of factor changes. The greening of vegetation can influence ET_a by increasing vegetation transpiration and rainfall interception in forest, brush and meadow landscapes. These findings can help in developing a better understanding of the interaction of ecosystems and hydrology in alpine regions.

An analytical reductionist framework to separate the effects of climate change and human activities on variation in water use efficiency

Ecosystem water use efficiency (WUE) is a key indicator that depicts the carbon-water coupling relationship in terrestrial ecosystems. Separating the effects of climate change and human activities to the variation in WUE are essential for water resources and ecosystem management, especially for fragile ecosystems such as the Tibetan Plateau (TP). In this study, we introduced an analytical framework that combined the attribution approach with the elastic coefficient separation method to assess the impact of climate change and human activities on WUE variation in the TP from 1982 to 2015. The results are the following: (1) the multiyear mean annual WUE over the TP was 0.65 g C·kg^-1 H₂O and had a slightly increasing trend with a slope of 0.004 g C·kg^-1 H₂O yr^-1 with about 87% of the vegetated area showed increasing trend. (2) WUE was positively correlated with temperature, precipitation and air pressure. The northwest TP tends to be a water-limited condition, while the thermal stress is spatially universal in the TP, climate warming and wetting promoted the gross primary productivity (GPP) and WUE enhancement in the TP. (3) WUE was more sensitive to GPP, and variation in WUE was mainly contributed by GPP. Climate change and human activities tend to cause more variations in GPP rather than evapotranspiration (ET), but great differences exist for different regions and vegetation types. (4) There was a good consistency between the WUE variation calculated by the framework and the actual WUE variation (R² = 0.95). Climate change dominated the increase of WUE in the TP with a contribution rate of 79.8%, while human activities tend to reduce WUE (-20.2%). Ecological projects played a positive role in the ecological restoration of the TP, but there may be other human activities, which caused ecological degradation, that may need more attention in future ecological protections.

Atmosphere-surface fluxes modeling for the high Andes: The case of páramo catchments of Ecuador

Interest in atmosphere-surface flux modeling over the mountainous regions of the globe has increased recently, with a major focus on the prediction of water, carbon and other functional indicators in natural and disturbed conditions. However, less research has been centered on exploring energy fluxes (net radiation; sensible, latent and soil heat) and actual evapotranspiration (ETa) over the Neotropical Andean biome of the páramo. The present study assesses the implementation and parameterization of a state-of-art Land-Surface Model (LSM) for simulation of these fluxes over two representative páramo catchments of southern Ecuador. We evaluated the outputs of the LSM Community Land Model (CLM ver. 4.0) with (i) ground-level flux observations from the first (and highest) Eddy Covariance (EC) tower of the Northern Andean páramos; (ii) spatial ETa estimates from the energy balance-based model METRIC (based on Landsat imagery); and (iii) derived ETa from the closure of the water balance (WB). CLM's energy predictions revealed a significant underestimation on net radiation, which impacts the sensible and soil heat fluxes (underestimation), and delivers a slight overestimation on latent heat flux. Modeled CLM ETa showed acceptable goodness-of-fit (Pearson R = 0.82) comparable to ETa from METRIC (R = 0.83). Contrarily, a poor performance of ETa WB was observed (R = 0.46). These findings provide solid evidence on the CLM's accuracy for the ETa modeling, and give insights in the selection of other ETa methods. The study contributes to a better understanding of ecosystem functioning in terms of water loss through evaporative processes, and might help in the development of future LSMs' implementations focused on climate / land use change scenarios for the páramo.

Spatial variation of human influences on grassland biomass on the Qinghai-Tibetan plateau

An improved understanding of increased human influence on ecosystems is needed for predicting ecosystem processes and sustainable ecosystem management. We studied spatial variation of human influence on grassland ecosystems at two scales across the Qinghai-Tibetan Plateau (QTP), where increased human activities may have led to ecosystem degradation. At the 10 km scale, we mapped human-influenced spatial patterns based on a hypothesis that spatial patterns of biomass that could not be attributed to environmental variables were likely correlated to human activities. In part this hypothesis could be supported via a positive correlation between biomass unexplained by environmental variables and livestock density. At the 500 m scale, using distance to settlements within a radius of 8 km as a proxy of human-influence intensity, we found both negatively human-influenced areas where biomass decreased closer to settlements (regions with higher livestock density) and positively human-influenced areas where biomass increased closer to settlements (regions with lower livestock density). These results suggest complex relationships between livestock grazing and biomass, varying between spatial scales and regions. Grazing may boost biomass production across the whole QTP at the 10 km scale. However, overgrazing may reduce it near settlements at the 500 m scale. Our approach of mapping and understanding human influence on ecosystems at different scales could guide pasture management to protect grassland in vulnerable regions on the QTP and beyond.

Reactive molecular dynamics simulations on the thermal decomposition of poly alpha-methyl styrene

Using molecular dynamics simulations with ReaxFF reactive force field, the thermal decomposition mechanism of poly alpha-methyl styrene (PAMS) materials and the effects of heating rate and impurity fluorobenzene on PAMS thermal decompositions are studied. The results show that: 1) Pyrolysis mechanism of PAMS consists of initiation and propagation processes. In the initiation stage, random scissions of C-C backbone produce fragments, and in the propagation stage, depolymerizing reactions generate monomers and other products. 2) Higher decomposition temperature is needed for greater heating rate. 3) The presence of impurity fluorobenzene retards thermal decomposition of PAMS.