Estimation of Scalar Source/Sink Distributions in Plant Canopies Using Lagrangian Dispersion Analysis: Corrections for Atmospheric Stability and Comparison with a Multilayer Canopy Model

Source/sink distributions of heat, water vapour andCO₂ within a rice canopy were inferred using aninverse Lagrangian dispersion analysis and measuredmean profiles of temperature, specific humidity andCO₂ mixing ratio. Monin–Obukhov similarity theorywas used to account for the effects of atmosphericstability on _w(z), the standard deviation ofvertical velocity and _L(z), the Lagrangian timescale of the turbulence. Classical surface layer scaling was applied in the inertial sublayer (z > z_ruf)using the similarity parameter = (z - d)/L, where z is height above ground, d is the zero plane displacementheight for momentum, L is the Obukhov length,and z_ruf 2.3h_c, where h_c iscanopy height. A single length scale h_c, was usedfor the stability parameter 3 = h_c/L in the height range 0.25 < z/h_c < 2.5. This choice is justified by mixing layer theory, which shows that within the roughness sublayer there is one dominant turbulence length scaledetermined by the degree of inflection in the windprofile at the canopy top. In the absence of theoretical or experimental evidence for guidance,standard Monin–Obukhov similarity functions, with = h_c/L, were used to calculate the stabilitydependence of _w(z) and _L(z) in the roughness sublayer. For z/h_c < 0.25 the turbulence length and time scales are influenced by the presence of the lowersurface, and stability effects are minimal. With theseassumptions there was excellent agreement between eddycovariance flux measurements and deductions from theinverse Lagrangian analysis. Stability correctionswere particularly necessary for night time fluxes whenthe atmosphere was stably stratified.The inverse Lagrangian analysis provides a useful toolfor testing and refining multilayer canopy models usedto predict radiation absorption, energy partitioningand CO₂ exchanges within the canopy and at thesoil surface. Comparison of model predictions withsource strengths deduced from the inverse analysisgave good results. Observed discrepancies may be dueto incorrect specification of the turbulent timescales and vertical velocity fluctuations close to theground. Further investigation of turbulencecharacteristics within plant canopies is required toresolve these issues.     

Rangeland production: use of models incorporating aggregated knowledge and fuzzy construction

This work reports on a method using fuzzy membership functions to construct an aggregated interaction matrix in which the summation of variables is scaled according to the way rainfall and soil variables affect water availability to plants and hence influence rangeland productivity. Aggregation of the variables gives a comprehensive value which can be used to predict production. The model increases the predicability of production to 81% compared to models using rainfall alone and a multiplicative parametric one which give predictibilities of 61 and 76% respectively. The results showed that (1) the importance of rainfall in determining production was most important at lower rainfalls i.e. <350 mm; (2) soil texture and particularly slope were important through out the rainfall range (149–700 mm) investigated; and (3) soil depth was only important at the higher >350 mm rainfalls. The aggregated interaction matrix gives a measure of land productive capability.     

How to increase technology transfers to developing countries: a synthesis of the evidence

The existing United Nations Framework Convention on Climate Change (UNFCCC) has failed to deliver the rate of low-carbon technology transfer (TT) required to curb GHG emissions in developing countries. This failure has exposed the limitations of universalism and renewed interest in bilateral approaches to TT. Gaps are identified in the UNFCCC approach to climate change TT: missing links between international institutions and the national enabling environments that encourage private investment; a non-differentiated approach for (developing) country and technology characteristics; and a lack of clear measurements of the volume and effectiveness of TTs. Evidence from econometric literature and business experience on climate change TT is reviewed, so as to address the identified pitfalls of the UNFCCC process. Strengths and weaknesses of different methodological approaches are highlighted. International policy recommendations are offered aimed at improving the level of emission reductions achieved through TT.     

Rotation Of Trajectories In Lagrangian Stochastic Models Of Turbulent Dispersion

We present a new measure for the rotation of Lagrangian trajectories in turbulence that simplifies and generalises that suggested by Wilson and Flesch ( Boundary-Layer Meteorol. 84, 411–426). The new measure is the cross product of the velocity and acceleration and is directly related to the area, rather than the angle, swept out by the velocity vector. It makes it possible to derive a simple but exact kinematic expression for the mean rotation of the velocity vector and to partition this expression into terms that are closed in terms of Eulerian velocity moments up to second order and unclosed terms. The unclosed terms arise from the interaction of the fluctuating part of the velocity and the rate of change of the fluctuating velocity.We examine the mean rotation of a class of Lagrangian stochastic models that are quadratic in velocity for Gaussian inhomogeneous turbulence. For some of these models, including that of Thomson ( J. Fluid Mech. 180, 113–153), the unclosed part of the mean rotation vanishes identically, while for other models it is non-zero. Thus the mean rotation criterion clearly separates the class of models into two sets, but still does not provide a criterion for choosing a single model.We also show that models for which = 0 are independent of whether the model is derived on the assumption that total Lagrangian velocity is Markovian or whether the fluctuating part is Markovian.     

Stochastic model-based methods for handling uncertainty in areal interpolation

Handling of uncertainty in the estimation of values from source areas to target areas poses a challenge in areal interpolation research. Stochastic model-based methods offer a basis for incorporating such uncertainty, but to date they have not been widely adopted by the GIS community. In this article, we propose one use of such methods based in the problem of interpolating count data from a source set of zones (parishes) to a more widely used target zone geography (postcode sectors). The model developed also uses ancillary statistical count data for a third set of areas nested within both source and target zones. The interpolation procedure was implemented within a Bayesian statistical framework using Markov chain Monte Carlo methods, enabling us to take account of all sources of uncertainty included in the model. Distributions of estimated values at the target zone level are presented using both summary statistics and as individual realisations selected to illustrate the degree of uncertainty in the interpolation results. We aim to describe the use of such stochastic approaches in an accessible way and to highlight the need for quantifying estimation uncertainty arising in areal interpolation, especially given the implications arising when interpolated values are used in subsequent analyses of relationships.     

FREDERICK JACKSON TURNER AS A REGIONALIST∗

Basic contemporary portfolio models are applied to an interregional economic analysis of U.S. Census Regions during 1963-1978. The models are well suited the type of analysis that partitions a region's economic cycle into national and local components. Portfolio theory provides a logic for interregional cross investment; results of an empirical test indicate portfolio theory may be superior to simple neoclassical theory in explaining spatial patterns of capital investment.     

Exploring Regional Firm-Size Structure in Canadian Housebuilding: Ontario, 1991 and 1996

This paper is concerned with the regional firm-size structure of housebuilding in Canada based on a case study of the province of Ontario. Using an innovative database of builders in Ontario and geographic information systems, measures of regional firm-size structure are developed and are modeled with data from the Canadian census. The results corroborate established models in the city systems and industrial organization literatures in that industrial concentration is negatively associated with regional population size, economic diversity, and economic performance. Small housebuilding firms abound in large, diverse and economically vibrant regions, especially large urban regions, and thereby maintain industrial deconcentration.