Iowa Manure Matters: Odor and Nutrient Management, Summer 2007, Vol. 10, no. 2

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Summer 2008, Vol. 11, no. 1

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Summer 2009, Vol. 12, no. 2

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2000, Vol. 3, no. 3

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2001, Vol. 4, no. 4

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2003, Vol. 6, no. 9

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2004, Vol. 7, no. 4

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2005, Vol. 8, no. 4

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2006, Vol. 9, no. 4

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2007, Vol. 10, no. 4

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2008, Vol. 11, no. 3

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Iowa Manure Matters: Odor and Nutrient Management, Winter 2002, Vol. 5, no. 4

Iowa Manure Matters: Odor and Nutrient Management is published by Iowa State University Extension, with funding support from the USDA Natural Resource Conservation Service.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

A subcritical instability of wave-driven alongshore currents

The development of shear instabilities of a wave-driven alongshore current is investigated. In particular, we use weakly nonlinear theory to investigate the possibility that such instabilities, which have been observed at various sites on the U.S. coast and in the laboratory, can grow in linearly stable flows as a subcritical bifurcation by resonant triad interaction, as first suggested by Shrira eta/. [1997]. We examine a realistic longshore current profile and include the effects of eddy viscosity and bottom friction. We show that according to the weakly nonlinear theory, resonance is possible and that these linearly stable flows may exhibit explosive instabilities. We show that this phenomenon may occur also when there is only approximate resonance, which is more likely in nature. Furthermore, the size of the perturbation that is required to trigger the instability is shown in some circumstances to be consistent with the size of naturally occurring perturbations. Finally, we consider the differences between the present case examined and the more idealized case of Shrira et a/. [ 1997]. It is shown that there is a possibility of coupling between triads, due to the richer modal structure in more realistic flows, which may act to stabilize the flow and act against the development of subcritical bifurcations. Extensive numerical tests are called for.