Effect of snow cover on water and heat transfer in alpine meadows in the source region of Yellow River.

Extreme snowfall events have been increasing in the Tibetan Plateau, causing greater variations in the snow cover conditions. However, the soil water-heat transfer under different snow conditions has rarely been characterized in detail. Here, by using the multi-source observation data of five years, we analyzed the influences of snow cover on water-heat transfer in alpine meadows of the source region of the Yellow River. The main findings are as follows: In the deep soil, the yearly warming rate from spring to summer was much faster than the cooling rate from autumn to winter, while in the shallow soil, conversely, the former was slower than the latter. Snow cover not only decreased the average soil temperature but also inhibited the occurrence of extremely low temperatures in the soil. The insulation effect of snow was mainly in the mid-frozen period. It was insufficient to balance out the heat lost by the high albedo during early and late frozen periods. In years with more snow, different depths of the soil featured similar thawing dates and plenty of soil voids due to small solid water content and high gravel content, together creating favorable conditions for the snowmelt infiltration, which passed through the frozen layer and infiltrated into the soil of 3.20 m or deeper. In years with less snow, the long-term freezing-thawing cycles aggravated the evaporation and loss of surface soil water in spring. Under different snow cover conditions, the difference in the sensible heat flux was much larger than the latent heat flux in winter and early spring. This study provides a refined physical image of soil water-heat transfer under extreme snow cover conditions in the Tibetan Plateau, which is expected to light the snow cover-frozen soil interaction in the mid-latitude and high-elevation areas.

Scaling up experimental stress responses of grass invasion to predictions of continental-level range suitability.

Understanding how the biological invasion is driven by environmental factors will improve model prediction and advance early detection, especially in the context of accelerating anthropogenic ecological changes. Although a large body of studies has examined how favorable environments promote biological invasions, a more comprehensive and mechanistic understanding of invasive species response to unfavorable/stressful conditions is still developing. Grass invasion has been problematic across the globe; in particular, C4 grass invaders, with high drought tolerance, adaptations to high temperatures, and high water use efficiency, could become more severe. Here, we conducted a rigorous microcosm experiment, with one of the most damaging invasive C4 grass, cogongrass (Imperata cylindrica), to explore how cogongrass responds to soil water and nutrient stress. We further integrated the results of the microcosm study with a species distribution model to (1) corroborate greenhouse results with field observations and (2) validate the robustness of our findings at subcontinental scales. Both the microcosm experiments and species distribution model agreed that soil water stress had a stronger impact on cogongrass than the nutrient one. New vegetative growth of cogongrass continued to be inhibited by the prior water stress. The significant water effect on cogongrass total biomass was supported by the finding that both allometric and biochemical traits of cogongrass did not show significant responses to the changes in water treatment. Different to the conventional wisdom that nutrient enrichment plays a bigger role in facilitating biological invasions, this study highlighted the possibility that water conditions may have a more substantial effect on some aggressive invaders. Therefore, an important implication of this study on biological conservation is that field managers might take advantage of the negative effect of global drought on some invasive species to increase the efficiency of their controlling efforts because invasive species may become more vulnerable under drought effect.

Integrating fast and slow processes is essential for simulating human-freshwater interactions.

Integrated modeling is a critical tool to evaluate the behavior of coupled human-freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human-freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human-freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.