报告题目：Quantifying soil spatial variability for sustainable resource management
报 告 人：Asim Biswas, assistant professor, McGill University
报告时间： 6月4号上午9:00-11:00 （Presentation & Questions）
报告地点：中国旱区节水农业研究院报告厅
报告人简介：
Asim Biswas博士现为加拿大McGill大学自然资源科学系助理教授。主要研究领域包括多尺度土壤水分运移、土壤空间变异与空间统计、渗流区水文学等。目前已在《Journal of Hydrology》、《Hydrological Processes》、《Soil Science Society of America》、《European Journal of Soil Science》、《Geoderma》、《Catena》等土壤学和水文学主流期刊发表学术30余篇，取得了较高的国际知名度和成就。
报告内容简介：
Soil properties vary from location to location within a soil landscape, and information on this spatial variability is necessary for optimal and sustainable management of agricultural and natural resources. Systematic studies to quantify the variability identified various characteristics including scale dependence, nonstationarity, nonlinearity, and anisotropy. The focus of my work is to develop, modify and utilize various methods to better quantify spatial variability of soil properties and to determine how this information can be used to further investigate underlying soil processes, such as soil development, and to improve predictive relationships (e.g. pedotransfer functions). In characterizing and quantifying soil spatial variability, few questions are commonly asked: What is the dominant scale of soil spatial variation and how do we assess soil functions at multiple scales? Where do we sample or monitor in the field and how can we meet the user’s data demand? How do we untangle the complexity in the relationship between soil properties to predict better? What do we know about the underlying soil processes and the development of soil? In order to answer these known and known-unknown questions, we need explicit information or methods to quantify soil spatial variability. Here I summarize how different methods can be used to quantify soil spatial variability and how this information can be used in different applications. Untangling the scale-specific variability can reveal hidden correlations between soil properties which will help improve the prediction and to identify the dominant control of the variability of a soil property. Separation of the overall variability at multiple scales can be used to meet the user’s data demand and the scale- and direction-specific variations can be used to infer the underlying soil and landscape developmental and modification processes. Better understanding and quantification of soil spatial variability provide new avenues for improved management of natural resources.
中国旱区节水农业研究院
2014年5月26日