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631.
Relationship between soil and water conservation practices and soil conditions in low mountain and hilly region of Northeast China 总被引:1,自引:0,他引:1
Yubin Zhang Ning Cao Xiaohong Xu Feng Zhang Fei Yan Xinsheng Zhang Xinlong Tang 《中国地理科学(英文版)》2014,24(2):147-162
The soil and water conservation practices of ecological restoration(ER),fish scale pit(FP),furrow and ridge tillage across the slope(FR),shrub strips(SS),and vegetation-covered ridge(VR) are characteristic of the Jixing small watershed of the low mountain and hilly region of Jilin Province,Northeast China. This study aims to elucidate the effects of soil and water conservation practices on soil conditions after the short-term implementation of practices. Soil samples were collected from five soil and water conservation sites(ER,FP,FR,SS,and VR) and two controls(BL and CT) to investigate their properties. To evaluate the influence of soil and water conservation practices on soil quality,an integrated quantitative index,soil quality index(QI),was developed to compare the soil quality under the different soil and water conservation practices. The results show that not all soil and water conservation practices can improve the soil conditions and not all soil properties,especially soil organic carbon(SOC),can be recovered under soil and water conservation practice in short-term. Moreover,the QI in the five soil and water conservation practices and two controls was in the following order: ER VR BL FR CT SS FP. ER exhibited a higher soil quality value on a slope scale. In the low mountain and hilly region of Northeast China,ER is a better choice than the conversion of farmlands to planted grasslands and woodlands early in the soil and water conservation program. 相似文献
632.
An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes. 相似文献
633.