Abstract: In boundary layer flows, it is now recognized that the net momentum and mass exchange rates are dominated by the statistical properties of ejecting and sweeping motion often linked to the presence of coherent turbulent structures. Over vineyards, three main factors impact the transport properties of such coherent motion: presence of sloping terrain, variations in leaf area index (LAI) during the growing season, and thermal stratification. The effect of these factors on momentum and heat transport is explored for three vineyard sites situated on different slopes. All three sites experience similar seasonal variation in LAI and mean wind conditions. The analysis is carried out using a conventional quadrant analysis technique and is tested against two models approximating the joint probability density function (JPDF) of the flow variables. It is demonstrated that a Gaussian JPDF explains much of the updraft and downdraft statistical contributions to heat and momentum transport efficiencies for all three sites. An incomplete or truncated third-order cumulant expansion method (ICEM) of the JPDF that retains only the mixed moments and ignores the skewness contributions describes well all the key properties of ejections and sweeps for all slopes, LAI, and stability classes. The implication of these findings for diagnosing potential failures of gradient-diffusion theory over complex terrain is discussed. Because only lower order moments are needed to describe the main characteristics of the JPDF, the use of the Moving Equilibrium Hypothesis (MEH) to predict these moments from the locally measured sensible heat flux and friction velocity is explored. Provided the planar fit coordinate transformation is applied to the data, the MEH can describe these statistical moments at all three sites regardless of terrain slopes and LAI values. (C) 2012 Elsevier B.V. All rights reserved.

Abstract: Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.