| Content:(1)The experiment was carried out on leymus chinensis steppe, which enclosed from 2001, located on Bashang area of Hebei province China during 2005-2007 years. We altered growing season rainfall input by using rainfall shelter to decrease and irrigation to increase methods, altered clipping intensity by adjusting growing season clipping frequency. We study the change rule on soil water content, quantitative characters, canopy structure, above/below biomass under different combine rainfall gradients and clipping intensity treatments. Also, we research the biomass accumulation rules of dominant species L. chinensis and total biomass, and L.chinensis's and Cleistogenes squarrosa's leaf potential and L.chinensis's leaf photosynthetic performance change rules in soil water potential variation. (2)In 2007, we sample 115 plots along different elevation on an Artemisia frigida and Potentilla acaulis steppe in Taipusi country, inner Mongolia China, to analysis the reasons causing above biomass response to different elevation level. (3)We analyzed the relationship between rainfall amounts and above biomass using the weather and plant's data from Xilinguole meteorologic station during 1989 to 2002 (omitted 1999) years.1) Soil water content (SWC) was significantly different between rainfall gradient on month, 10-days and day time scale, SWC of R5 is higher on month scale, R1 is lower on 10-days scale. After one rainfall event, at the beginning, there was much difference between R2-R5, 8d later they were become approximative, R1 was always lower than other treatments. the influence of clipping intensity on SWC had contrary result on different time scale, on month scale more clipping frequency led SWC to increase (during 2005-2006), on days scale it decreased (in 2007), the main reason was that canopy structure could both change evapotranspiration and rainfall interception.2) Both rainfall gradient and clipping intensity can influence the quantitative characters, comparing to rainfall, clipping had the more performance to change species and community height, however there was less rule on density. With the rainfall increasing, the density of L.chinensis became decreasing. With rainfall gradients and clipping intensity increased, index of diversity increased and eveness became stable, but not for R1 treatment. Increasing rainfall amounts would decrease important value of L.chinensis, increase Artemisia eriopoda, no rule found on C. squarrosa.3) Logistic equation could well express the L.chinensis and total biomass growth progress, all 3 years relative growth rate (RGR) become negative around Aug.20, which was the best hay harvest periods. Low temperature causing by less radiation and more rainfall made much influence on RGR, heavy rainfall even made RGR negative in growing season.4) Canopy structure had no significant difference between rainfall gradient, the community canopy structure after clipping had significant change comparing to no-clipping treatment, Leaf area index (LAI) can well simulated the above biomass.5) When soil water potential decreased, leaf potential would decreased. However leaf potential will decrease the stomatal conductance (Gs) and photosynthetic performance. In wetness condition, the lower temperature on roots area caused leaf growth point temperature to decrease, which decreased the leaf photosynthetic performance during early morning and dusk.6) Compare to rainfall, clipping had much influence on above biomass. With clipping intensity increasing, the above biomass will increase. Comparing total rainfall, soil water content in the late of one rainfall event had more influence on above biomass accumulation.7) Both rainfall and clipping had no significantly effect on below biomass in short time, while rainfall increasing or decreasing would made below biomass had a decreasing tendency in the late growth season, the reason was different.8) We could use the soil water content (0-10cm depth), number of species and cover to model above biomass change.9) It is the standard deviation of rainfall on 10-days or month scale that can well prophesy the above biomass, it can explain 68.9% variation of above biomass change, higher than other statistics.
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