森林流域单元水文模型的构建与应用

目前全球缺水、水污染、洪涝灾害以及水土流失仍然非常严重，尤其在我国北方地区。流域水文模型可用来进行不同需水管理的情景分析，为解决我国水问题提供科学依据。分布式水文模型是流域水文模型的发展方向，具有显著特点：1）应用前景广泛，不仅可以模拟流域水文过程，还可以协助模拟泥沙或污染物的运移过程，为水利工程设计、水土保持、环境保护等领域提供技术支持；2）能够预测流域土地利用或气候变化下的流域水文响应过程变化，为管理部门提供决策支持；3）模型所需要的参数全部具有物理意义，可通过实际测量确定，适合模拟实测系列较短或是无观测流域的水文过程；4）对于目前国际水文界的前沿问题—水文尺度转换提供了一种有效的解决途径。 然而分布式水文模型还不完善，如1)真实性问题。对一些水文过程和边界条件还不确定。2)尺度转换问题。目前很少考虑尺度对参数有效性的影响。3)检验问题。还无法判断对有些难以测量的水文状态变量的模拟正确与否。4)计算时间和数据存储的问题。有些分布式水文模型虽然具有很强的水文物理基础和完善的模型结构，但是计算时间过长和(或)数据存储过大，难以应用。上述问题的核心就是对分布式水文模型的核心—单元水文模型的研究不够，需要为进一步完善单元水文模型进行研究。 本文采用饱和入渗理论、Saint-Venant方程、Richards方程、Penman-Monteith方程等等构建了以有限差分法求解的适用于森林流域的单元水文模型，并通过实验室模拟试验和坡地径流场资料进行了验证，主要结论为： 通过不同坡度和不同雨强下的室内坡面产汇流实验模拟，表明：该模型模拟的坡面流和壤中流过程与实测过程基本一致，峰现时间、径流历时、峰值流量、出流总量模拟值与实测值的相对误差均较小，基本小于10%。模型的模拟精度较高，实用性较强，为深入研究壤中流机制和改进流域降雨-径流模型提供了理论依据。 通过坡地径流观测场实测资料的验证，表明：该模型模拟的坡面流过程精度较高，累计流量的精度更高于小时过程的精度，离差系数、效率系数、确定系数均较理想，具有应用价值，有助于改善分布式水文模型在森林流域的模拟效果。

逆向物流流量不确定闭环供应链鲁棒运作策略设计

考虑一类同时具有再分销、再制造和再利用的闭环供应链在逆向物流流量不确定环境下的运作问题.采用具有已知概率的离散情景描述逆向物流流量的不确定性,利用基于情景分析的鲁棒线性优化方法建立该闭环供应链的多目标运作模型.设计了一个数值算例,其结果验证了运作策略的鲁棒性.在该算例基础上,分析了逆向物流流量的大小对闭环供应链系统运作性能的影响.

Sequence context analysis in the mouse genome: Single nucleotide polymorphisms and CpG island sequences

A genome-wide view of sequence mutability in mice is still limited, although biologists usually assume the same scenario for mice as for humans. In this study, we examined the sequence context in the local environment of 482,528 mouse single nucleotide po

Analysis of the sea-level variability along the Chinese coast and estimation of the impact of a CO2-perturbed atmospheric circulation

The relationship between monthly sea-level data measured at stations located along the Chinese coast and concurrent large-scale atmospheric forcing in the period 1960-1990 is examined. It is found that sea-level varies quite coherently along the whole coast, despite the geographical extension of the station set. A canonical correlation analysis between sea-level and sea-level pressure (SLP) indicates that a great part of the sea-level variability can be explained by the action of the wind stress on the ocean surface. The relationship between sea-level and sea-level pressure is analyzed separately for the summer and winter half-years. In winter, one factor affecting sea-level variability at all stations is the SLP contrast between the continent and the Pacific Ocean, hence the intensity of the winter Monsoon circulation. Another factor that affects coherently all stations is the intensity of the zonal circulation at mid-latitudes. In the summer half year, on the other hand, the influence of SLP on sea-level is spatially less coherent: the stations in the Yellow Sea are affected by a more localized circulation anomaly pattern, whereas the rest of the stations is more directly connected to the intensity of the zonal circulation. Based on this analysis, statistical models (different for summer and winter) to hindcast coastal sealevel anomalies from the large-scale SLP field are formulated. These models have been tested by fitting their internal parameters in a test period and reproducing reasonably the sea-level evolution in an independent period. These statistical models are also used to estimate the contribution of the changes of the atmospheric circulation on sea-level along the Chinese coast in an altered climate. For this purpose the ouput of 150 year-long experiment with the coupled ocean-atmosphere model ECHAM1-LSG has been analyzed, in which the atmospheric concentration of greenhouse gases was continuously increased from 1940 until 2090, according to the Scenario A projection of the Intergovermental Panel on Climate Change. In this experiment the meridional (zonal) circulation relevant for sea-level tends to become weaker (stronger) in the winter half year and stronger (weaker) in summer. The estimated contribution of this atmospheric circulation changes to coastal sea-level is of the order of a few centimeters at the end of the integration, being in winter negative in the Yellow Sea and positive in the China Sea with opposite signs in the summer half-year.