Comparing the Penman-Monteith equation and a modified Jarvis-Stewart model with an artificial neural network to estimate stand-scale transpiration and canopy conductance

The responses of canopy conductance to variation in solar radiation, vapour pressure deficit and soil moisture have been extensively modelled using a Jarvis-Stewart (JS) model. Modelled canopy conductance has then often been used to predict transpiration using the Penman-Monteith (PM) model. We previously suggested an alternative approach in which the JS model is modified to directly estimate transpiration rather than canopy conductance. In the present study we used this alternative approach to model tree water fluxes from an Australian native forest over an annual cycle. For comparative purposes we also modelled canopy conductance and estimated transpiration via the PM model. Finally we applied an artificial neural network as a statistical benchmark to compare the performance of both models. Both the PM and modified JS models were parameterised using solar radiation, vapour pressure deficit and soil moisture as inputs with results that compare well with previous studies. Both models performed comparably well during the summer period. However, during winter the PM model was found to fail during periods of high rates of transpiration. In contrast, the modified JS model was able to replicate observed sapflow measurements throughout the year although it too tended to underestimate rates of transpiration in winter under conditions of high rates of transpiration. Both approaches to modelling transpiration gave good agreement with hourly, daily and total sums of sapflow measurements with the modified JS and PM models explaining 87% and 86% of the variance, respectively. We conclude that these three approaches have merit at different time-scales. © 2009 Elsevier B.V. All rights reserved

Partitioning of turbulent flux reveals contrasting cooling potential for woody vegetation and grassland during heat waves

© 2015 Royal Meteorological Society. We compared the capacity of woody versus grassy vegetation covers to buffer high temperatures during heat waves by partitioning turbulent heat between latent (λE) and sensible (H) fluxes, and quantifying advection using the Priestley-Taylor coefficient (α), for a16-year old grassland and an adjoining 6-year old plantation. We found that because λE dominated (>65%) the turbulent flux in the plantation and was at least twice as large as on the grassland (λE35°). Annual evapotranspiration was 54% lower from the grassland (384 mm) than from the plantation (834 mm). Woody vegetation covers dominated by broadleaved species are therefore preferred for buffering extreme high temperatures during heat waves and recommended for rehabilitating degraded landscapes in urban areas. We also present functions for approximating α for soil water limited conditions

Innovation as a Nonlinear Process, the Scientometric Perspective, and the Specification of an "Innovation Opportunities Explorer"

The process of innovation follows non-linear patterns across the domains of science, technology, and the economy. Novel bibliometric mapping techniques can be used to investigate and represent distinctive, but complementary perspectives on the innovation process (e.g., "demand" and "supply") as well as the interactions among these perspectives. The perspectives can be represented as "continents" of data related to varying extents over time. For example, the different branches of Medical Subject Headings (MeSH) in the Medline database provide sources of such perspectives (e.g., "Diseases" versus "Drugs and Chemicals"). The multiple-perspective approach enables us to reconstruct facets of the dynamics of innovation, in terms of selection mechanisms shaping localizable trajectories and/or resulting in more globalized regimes. By expanding the data with patents and scholarly publications, we demonstrate the use of this multi-perspective approach in the case of RNA Interference (RNAi). The possibility to develop an "Innovation Opportunities Explorer" is specified.Comment: Technology Analysis and Strategic Management (forthcoming in 2013

A modified Jarvis-Stewart model for predicting stand-scale transpiration of an Australian native forest

Rates of water uptake by individual trees in a native Australian forest were measured on the Liverpool Plains, New South Wales, Australia, using sapflow sensors. These rates were up-scaled to stand transpiration rate (expressed per unit ground area) using sapwood area as the scalar, and these estimates were compared with modelled stand transpiration. A modified Jarvis-Stewart modelling approach (Jarvis 1976), previously used to calculate canopy conductance, was used to calculate stand transpiration rate. Three environmental variables, namely solar radiation, vapour pressure deficit and soil moisture content, plus leaf area index, were used to calculate stand transpiration, using measured rates of tree water use to parameterise the model. Functional forms for the model were derived by use of a weighted non-linear least squares fitting procedure. The model was able to give comparable estimates of stand transpiration to those derived from a second set of sapflow measurements. It is suggested that short-term, intensive field campaigns where sapflow, weather and soil water content variables are measured could be used to estimate annual patterns of stand transpiration using daily variation in these three environmental variables. Such a methodology will find application in the forestry, mining and water resource management industries where long-term intensive data sets are frequently unavailable. © 2007 Springer Science+Business Media B.V

Onboard Evolution of Understandable Swarm Behaviors

Designing the individual robot rules that give rise to desired emergent swarm behaviors is difficult. The common method of running evolutionary algorithms off‐line to automatically discover controllers in simulation suffers from two disadvantages: the generation of controllers is not situated in the swarm and so cannot be performed in the wild, and the evolved controllers are often opaque and hard to understand. A swarm of robots with considerable on‐board processing power is used to move the evolutionary process into the swarm, providing a potential route to continuously generating swarm behaviors adapted to the environments and tasks at hand. By making the evolved controllers human‐understandable using behavior trees, the controllers can be queried, explained, and even improved by a human user. A swarm system capable of evolving and executing fit controllers entirely onboard physical robots in less than 15 min is demonstrated. One of the evolved controllers is then analyzed to explain its functionality. With the insights gained, a significant performance improvement in the evolved controller is engineered

Turning the tables : regions shaping university performance

[EN] This paper straddles the systems of innovation and the economic geography theories that conceptualize universities as engines of regional development and drivers of growth. However, these approaches overlook the heterogeneity of universities in the process of engagement, assuming their equal capacity to contribute to their region. In the view proposed here, not only does the university influence the surrounding region, but also regional characteristics shape university performance. The paper puts into perspective differences between university profiles in Spain based on their strategies and performance, and the scale and scope of the capabilities to contribute to their regionsThis research was supported by the Spanish Ministry of Education through the Formación de Profesorado Universitario (FPU) programme.Sánchez-Barrioluengo, M. (2014). Turning the tables : regions shaping university performance. Regional Studies Regional Science. 1(1):276-285. doi:10.1080/21681376.2014.964299S2762851

How energy and water availability constrain vegetation water-use along the North Australian Tropical Transect

© 2016, Gorgan Univ Agricultural Sciences and Natural Resources. All rights reserved. Energy and water availability were identified as the first order controls of evapotranspiration (ET) in ecohyrodrology. With a ~1,000 km precipitation gradient and distinct wet-dry climate, the North Australian Tropical Transect (NATT) was well suited for evaluating how energy and water availabilities constrain water use by vegetation, but has not been done yet. In this study, we addressed this question using Budyko framework that quantifies the evapotranspiration as a function of energy-limited rate and precipitation. Path analysis was adopted to evaluate the dependencies of water and carbon fluxes on ecohydrological variables. Results showed that the major drivers of water and carbon fluxes varied between wet and dry savannas: down-welling solar radiation was the primary driver of the wet season ET in mesic savanna ecosystems, while soil water availability was the primary driver in inland dryland ecosystems. Vegetation can significantly regulate water and carbon fluxes of savanna ecosystems, as supported by the strong link of LAI with ET and GPP from path analysis. Vegetation structure (i.e. the tree:grass ratio) at each site can regulate the impact of climatic constraint on ET and GPP. Sites with a low tree:grass ratio had ET and GPP that exceeded sites with high a tree:grass ratio when the grassy understory was active. Identifying the relative importance of these climate drivers and vegetation structure on seasonal patterns of water use by these ecosystems will help us decide our priorities when improving the estimates of ET and GPP

Kinetic and structural mechanism for DNA unwinding by a non-hexameric helicase

UvrD, a model for non-hexameric Superfamily 1 helicases, utilizes ATP hydrolysis to translocate stepwise along single-stranded DNA and unwind the duplex. Previous estimates of its step size have been indirect, and a consensus on its stepping mechanism is lacking. To dissect the mechanism underlying DNA unwinding, we use optical tweezers to measure directly the stepping behavior of UvrD as it processes a DNA hairpin and show that UvrD exhibits a variable step size averaging ~3 base pairs. Analyzing stepping kinetics across ATP reveals the type and number of catalytic events that occur with different step sizes. These single-molecule data reveal a mechanism in which UvrD moves one base pair at a time but sequesters the nascent single strands, releasing them non-uniformly after a variable number of catalytic cycles. Molecular dynamics simulations point to a structural basis for this behavior, identifying the protein-DNA interactions responsible for strand sequestration. Based on structural and sequence alignment data, we propose that this stepping mechanism may be conserved among other non-hexameric helicases