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.

Seasonal and Inter-Annual Variations in Carbon Dioxide Exchange over an Alpine Grassland in the Eastern Qinghai-Tibetan Plateau.

This work analyzed carbon dioxide exchange and its controlling factors over an alpine grassland on the eastern Qinghai-Tibetan Plateau. The main results show that air temperature and photosynthetically active radiation are two dominant factors controlling daily gross primary production. Soil temperature and soil water content are the main factors controlling ecosystem respiration. Canopy photosynthetic activity is also responsible for the variation of daily ecosystem respiration other than environmental factors. No clear correlation between net ecosystem exchange and environmental factors was observed at daily scale. Temperature sensitive coefficient was observed to increase with larger soil water content. High values of temperature sensitive coefficient occurred during the periods when soil water content was high and grass was active. Annual integrated net ecosystem exchange, gross primary production and ecosystem respiration were -191, 1145 and 954 g C m-2 for 2010, and -250, 975 and 725 g C m-2 for 2011, respectively. Thus, this alpine grassland was a moderate carbon sink in both of the two years. Compared to alpine grasslands on the Qinghai-Tibetan Plateau, this alpine grassland demonstrated a much greater potential for carbon sequestration than others. Annual precipitation is a dominant factor controlling the variation of annual net ecosystem exchange over this grassland. The difference in gross primary production between the two years was not caused by the variation in annual precipitation. Instead, air temperature and the length of growing season had an important impact on annual gross primary production. Variation of annual ecosystem respiration was closely related to annual gross primary production and soil water content during the growing season.

Biophysical regulation of carbon fluxes over an alpine meadow ecosystem in the eastern Tibetan Plateau.

The eddy covariance method was used to measure net ecosystem CO2 exchange (NEE) between atmosphere and an alpine meadow ecosystem in the eastern Tibetan Plateau of China in 2010. Our results show that photosynthesis was reduced under low air temperature (T a), high vapor pressure deficit (VPD), and medium soil water content (SWC) conditions, when compared to that under other T a (i.e., medium and high), VPD (i.e., low and medium), and SWC (i.e., low and high) conditions. The apparent temperature sensitivity of ecosystem respiration (Q 10) declined with progressing phenology during the growing season and decreased with an increase of soil temperature (T s) during the non-growing season. Increased ecosystem respiration (R eco) was measured during spring soil thawing. By the path analysis, T a, T s, and VPD were the main control factors of CO2 exchange at 30-min scale in this alpine meadow. Integrated NEE, gross primary production (GPP), and R eco over the measured year were -156.4, 1164.3, and 1007.9 g C m(-2), respectively. Zoige alpine meadow was a medium carbon sink based on published data for grassland ecosystems.

Energy-based wavelet de-noising of hydrologic time series.

De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed.