Stochastic modelling of soil moisture dynamics in a grassland of Qilian Mountain at point scale |
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作者单位: | LIU Hu(Laboratory of Watershed Hydrology and Ecology, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China) ;
ZHAO WenZhi(Laboratory of Watershed Hydrology and Ecology, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China) ;
HE ZhiBin(Laboratory of Watershed Hydrology and Ecology, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China) ;
ZHANG LiJie(Laboratory of Watershed Hydrology and Ecology, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China) ; |
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摘 要: | Stochastic modeling of soil moisture dynamics is crucial to the quantitative understanding of plant responses to water stresses,hydrological control of nutrient cycling processes,water competition among plants,and some other ecological dynamics,and thus has become a hotspot in ecohydrology at present.In this paper,we based on the continuously monitored data of soil moisture during 2002―2005 and daily precipitation date of 1992―2006,and tried to make a probabilistic analysis of soil moisture dynamics at point scale in a grassland of Qilian Mountain by integrating the stochastic model improved by Laio and the Monte Carlo method.The results show that the inter-annual variations for the soil moisture patterns at different depths are very significant,and that the coefficient of variance(CV) of surface soil moisture(20 cm) is almost continually kept at about 0.23 whether in the rich or poor rainy years.Interestingly,it has been found that the maximal CV of soil moisture has not always appeared at the surface layer.Comparison of the analytically derived soil moisture probability density function(PDF) with the statistical distribution of the observed soil moisture data suggests that the stochastic model can reasonably describe and predict the soil moisture dynamics of the grassland in Qilian Mountain at point scale.By extracting the statistical information of the historical precipitation data in 1994―2006,and inputting them into the stochastic model,we analytically derived the long-term soil moisture PDF without considering the inter-annual climate fluctuations,and then numerically derived the one when considering the inter-annual fluctuation effects in combination with a Monte-Carlo procedure.It was found that,though the peak position of the probability density distribution significantly moved towards drought when considering the disturbance forces,and its width was narrowed,accordingly its peak value was increased,no significant bimodality was observed in the soil moisture dynamics under the given intensity of random fluctuation disturbance.
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收稿时间: | 5 April 2007 |
修稿时间: | 16 July 2007 |
Stochastic modelling of soil moisture dynamics in a grassland of Qilian Mountain at point scale |
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Authors: | Liu Hu Zhao WenZhi He ZhiBin Zhang LiJie |
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Institution: | Laboratory of Watershed Hydrology and Ecology, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China |
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Abstract: | Stochastic modeling of soil moisture dynamics is crucial to the quantitative understanding of plant responses to water stresses,
hydrological control of nutrient cycling processes, water competition among plants, and some other ecological dynamics, and
thus has become a hotspot in ecohydrology at present. In this paper, we based on the continuously monitored data of soil moisture
during 2002–2005 and daily precipitation date of 1992–2006, and tried to make a probabilistic analysis of soil moisture dynamics
at point scale in a grassland of Qilian Mountain by integrating the stochastic model improved by Laio and the Monte Carlo
method. The results show that the inter-annual variations for the soil moisture patterns at different depths are very significant,
and that the coefficient of variance (CV) of surface soil moisture (20 cm) is almost continually kept at about 0.23 whether
in the rich or poor rainy years. Interestingly, it has been found that the maximal CV of soil moisture has not always appeared
at the surface layer. Comparison of the analytically derived soil moisture probability density function (PDF) with the statistical
distribution of the observed soil moisture data suggests that the stochastic model can reasonably describe and predict the
soil moisture dynamics of the grassland in Qilian Mountain at point scale. By extracting the statistical information of the
historical precipitation data in 1994–2006, and inputting them into the stochastic model, we analytically derived the long-term
soil moisture PDF without considering the inter-annual climate fluctuations, and then numerically derived the one when considering
the inter-annual fluctuation effects in combination with a Monte-Carlo procedure. It was found that, though the peak position
of the probability density distribution significantly moved towards drought when considering the disturbance forces, and its
width was narrowed, accordingly its peak value was increased, no significant bimodality was observed in the soil moisture
dynamics under the given intensity of random fluctuation disturbance.
Supported by the National Natural Science Foundation of China (Grant No. 40601007) and Subsidy Funds of Personnel Training
of the National Fundamental Fund Project (Grant No. J0630966) |
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Keywords: | soil moisture dynamics stochastic modelling Monte Carlo method probability density function grassland ecosystem |
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