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.     

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.     

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.     

Forecasting ocean wave energy: The ECMWF wave model and time series methods

Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave energy flux. In the initial tests, the ECMWF model and the statistical models are compared directly. The statistical models do better at short horizons, producing more accurate forecasts in the 1-5 h range. The ECMWF model is superior at longer horizons. The convergence point, at which the two methods achieve comparable degrees of accuracy, is in the area of 6 h. By implication, the physics-based model captures the underlying signals at lower frequencies, while the statistical models capture relationships over shorter intervals. Further tests are run in which the forecasts from the ECMWF model are used as inputs in regressions and neural networks. The combined models yield more accurate forecasts than either one individually.     

Interpolation of wave heights

Remote sensing of waves often necessitates presentation of data in the form of wave height values grouped over large time intervals. This restricts their use to long-term applications only. This paper describes how such data can be made suitable for short-term usage in the field. Weekly mean significant wave heights were derived from their monthly mean observations with the help of different alternative techniques. These include model-free neural network schemes as well as model-based statistical and numerical methods. Superiority of neural networks was noted when the estimations were compared with corresponding observations. The network was trained using three different training algorithms, viz., error back propagation, conjugate gradient and cascade correlation. The technique of cascade correlation took minimum training time and showed better coefficient of correlation between observations and network output.     

Dynamics of ocean cables with local low-tension regions

A general set of 3-D dynamic field equations for a cable segment is derived based on the classical Euler-Kirchhoff theory of an elastica. The model includes flexural stiffness to remove the potential singularity when cable tension vanishes and can be reduced to the equations for a perfectly flexible cable. A hybrid model and a solution scheme by direct integration are then proposed to solve the oceanic cable/body system with a localized low-tension region. Numerical examples demonstrate the capability and validity of the formulation and the numerical algorithm.     

Significant load and green water on deck of offshore units/vessels

In this paper the probability of green water occurrence is investigated by taking into account the threshold of the vertical relative motion exceeding the freeboard. The number of wetting of the unit/vessel is predicted using probabilistic method. This paper compares the significant load, due to shipping of green water with the works of other researchers, and the loads are found to be close with the results presented in this paper. There is no direct relation between the velocities in the waves and the water velocity over the deck. The water velocities around the bow are heavily distributed by the presence of the bow. This scenario makes the flow very complex for schematic modelling.