Case study of the use of remotely sensed data for modeling flood inundation on the river Severn, UK.

A methodology for using remotely sensed data to both generate and evaluate a hydraulic model of floodplain inundation is presented for a rural case study in the United Kingdom: Upton-upon-Severn. Remotely sensed data have been processed and assembled to provide an excellent test data set for both model construction and validation. In order to assess the usefulness of the data and the issues encountered in their use, two models for floodplain inundation were constructed: one based on an industry standard one-dimensional approach and the other based on a simple two-dimensional approach. The results and their implications for the future use of remotely sensed data for predicting flood inundation are discussed. Key conclusions for the use of remotely sensed data are that care must be taken to integrate different data sources for both model construction and validation and that improvements in ground height data shift the focus in terms of model uncertainties to other sources such as boundary conditions. The differences between the two models are found to be of minor significance.

Modeling distributions of immediate memory effects: No strategies needed?

Many models of immediate memory predict the presence or absence of various effects, but none have been tested to see whether they predict an appropriate distribution of effect sizes. The authors show that the feature model (J. S. Nairne, 1990) produces appropriate distributions of effect sizes for both the phonological confusion effect and the word-length effect. The model produces the appropriate number of reversals, when participants are more accurate with similar items or long items, and also correctly predicts that participants performing less well overall demonstrate smaller and less reliable phonological similarity and word-length effects and are more likely to show reversals. These patterns appear within the model without the need to assume a change in encoding or rehearsal strategy or the deployment of a different storage buffer. The implications of these results and the wider applicability of the distributionmodeling approach are discussed.

Observations and modeling of banded orographic convection

Radar images and numerical simulations of three shallow convective precipitation events over the Coastal Range in western Oregon are presented. In one of these events, unusually well-defined quasi-stationary banded formations produced large precipitation enhancements in favored locations, while varying degrees of band organization and lighter precipitation accumulations occurred in the other two cases. The difference between the more banded and cellular cases appeared to depend on the vertical shear within the orographic cap cloud and the susceptibility of the flow to convection upstream of the mountain. Numerical simulations showed that the rainbands, which appeared to be shear-parallel convective roll circulations that formed within the unstable orographic cap cloud, developed even over smooth mountains. However, these banded structures were better organized, more stationary, and produced greater precipitation enhancement over mountains with small-scale topographic obstacles. Low-amplitude random topographic roughness elements were found to be just as effective as more prominent subrange-scale peaks at organizing and fixing the location of the orographic rainbands.

Dynamic causal modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan–rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.

Decadal-scale rainfall variability in Ethiopia recorded in an annually laminated, Holocene-age, stalagmite

An annually laminated, uranium-series dated, Holocene stalagmite from southeast Ethiopia has been analysed for growth rate and δ13C and δ18O variations at annual to biennial resolution, in order to provide the first long duration proxy record of decadal-scale rainfall variability in this climatically sensitive region. Our study site (10°N) is climatically influenced by both summer (June—August) and spring (March—May) rainfall caused by the annual movement of the Inter-Tropical Convergence Zone (ITCZ) and modulated by large-scale anomalies in the atmospheric circulation and in ocean temperatures. Here we show that stalagmite growth, episodic throughout the last 7800 years, demonstrates decadal-scale (8—25 yr) variability in both growth rate and δ 18O. A hydrological model was employed and indicates that this decadal variability is due to variations in the relative amounts of rainfall in the two rain seasons. Our record, unique in its combination of length (a total of ~1000 years), annual chronology and high resolution δ18O, shows for the first time that such decadal-scale variability in rainfall in this region has occurred through the Holocene, which implies persistent decadal-scale variability for the large-scale atmospheric and oceanic driving factors.

Modeling chemotaxis reveals the role of reversed phosphotransfer and a bi-functional kinase-phosphatase

Understanding how multiple signals are integrated in living cells to produce a balanced response is a major challenge in biology. Two-component signal transduction pathways, such as bacterial chemotaxis, comprise histidine protein kinases (HPKs) and response regulators (RRs). These are used to sense and respond to changes in the environment. Rhodobacter sphaeroides has a complex chemosensory network with two signaling clusters, each containing a HPK, CheA. Here we demonstrate, using a mathematical model, how the outputs of the two signaling clusters may be integrated. We use our mathematical model supported by experimental data to predict that: (1) the main RR controlling flagellar rotation, CheY6, aided by its specific phosphatase, the bifunctional kinase CheA3, acts as a phosphate sink for the other RRs; and (2) a phosphorelay pathway involving CheB2 connects the cytoplasmic cluster kinase CheA3 with the polar localised kinase CheA2, and allows CheA3-P to phosphorylate non-cognate chemotaxis RRs. These two mechanisms enable the bifunctional kinase/phosphatase activity of CheA3 to integrate and tune the sensory output of each signaling cluster to produce a balanced response. The signal integration mechanisms identified here may be widely used by other bacteria, since like R. sphaeroides, over 50% of chemotactic bacteria have multiple cheA homologues and need to integrate signals from different sources.

Modeling based on the structure of vicilins predicts a histidine cluster in the active site of oxalate oxidase

It is known that germin, which is a marker of the onset of growth in germinating wheat, is an oxalate oxidase, and also that germins possess sequence similarity with legumin and vicilin seed storage proteins. These two pieces of information have been combined in order to generate a 3D model of germin based on the structure of vicilin and to examine the model with regard to a potential oxalate oxidase active site. A cluster of three histidine residues has been located within the conserved beta-barrel structure. While there is a relatively low level of overall sequence similarity between the model and the vicilin structures, the conservation of amino acids important in maintaining the scaffold of the beta-barrel lends confidence to the juxtaposition of the histidine residues. The cluster is similar structurally to those found in copper amine oxidase and other proteins, leading to the suggestion that it defines a metal-binding location within the oxalate oxidase active site. It is also proposed that the structural elements involved in intermolecular interactions in vicilins may play a role in oligomer formation in germin/oxalate oxidase.

Determining the influence of rainfall patterns and carbendazim on the surface activity of the earthworm Lumbricus terrestris

Carbendazim is highly toxic to earthworms and is used as a standard control substance when running field-based trials of pesticides, but results using carbendazim are highly variable. In the present study, impacts of timing of rainfall events following carbendazim application on earthworms were investigated. Lumbricus terrestris were maintained in soil columns to which carbendazim and then deionized water (a rainfall substitute) were applied. Carbendazim was applied at 4 kg/ha, the rate recommended in pesticide field trials. Three rainfall regimes were investigated: initial and delayed heavy rainfall 24 h and 6 d after carbendazim application, and frequent rainfall every 48 h. Earthworm mortality and movement of carbendazim through the soil was assessed 14 d after carbendazim application. No detectable movement of carbendazim occurred through the soil in any of the treatments or controls. Mortality in the initial heavy and frequent rainfall was significantly higher (approximately 55%) than in the delayed rainfall treatment (approximately 25%). This was due to reduced bioavailability of carbendazim in the latter treatment due to a prolonged period of sorption of carbendazim to soil particles before rainfall events. The impact of carbendazim application on earthworm surface activity was assessed using video cameras. Carbendazim applications significantly reduced surface activity due to avoidance behavior of the earthworms. Surface activity reductions were least in the delayed rainfall treatment due to the reduced bioavailability of the carbendazim. The nature of rainfall events' impacts on the response of earthworms to carbendazim applications, and details of rainfall events preceding and following applications during field trials should be made at a higher level of resolution than is currently practiced according to standard International Organization for Standardization protocols.

Requirements for building information modeling based lean production management systems for construction

Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face.

Maize yield and rainfall on different spatial and temporal scales in Southern Brazil

This study aimed to establish relationships between maize yield and rainfall on different temporal and spatial scales, in order to provide a basis for crop monitoring and modelling. A 16-year series of maize yield and daily rainfall from 11 municipalities and micro-regions of Rio Grande do Sul State was used. Correlation and regression analyses were used to determine associations between crop yield and rainfall for the entire crop cycle, from tasseling to 30 days after, and from 5 days before tasseling to 40 days after. Close relationships between maize yield and rainfall were found, particularly during the reproductive period (45-day period comprising the flowering and grain filling). Relationships were closer on a regional scale than at smaller scales. Implications of the crop-rainfall relationships for crop modelling are discussed.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather-yield relationships

Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Exploring options for managing strategies for pea-barley intercropping using a modeling approach

A modeling Study was carried out into pea-barley intercropping in northern Europe. The two objectives were (a) to compare pea-barley intercropping to sole cropping in terms of grain and nitrogen yield amounts and stability, and (b) to explore options for managing pea-barley intercropping systems in order to maximize the biomass produced and the grain and nitrogen yields according to the available resources, such as light, water and nitrogen. The study consisted of simulations taking into account soil and weather variability among three sites located in northern European Countries (Denmark, United Kingdom and France), and using 10 years of weather records. A preliminary stage evaluated the STICS intercrop model's ability to predict grain and nitrogen yields of the two species, using a 2-year dataset from trials conducted at the three sites. The work was carried out in two phases, (a) the model was run to investigate the potentialities of intercrops as compared to sole crops, and (b) the model was run to explore options for managing pea-barley intercropping, asking the following three questions: (i) in order to increase light capture, Would it be worth delaying the sowing dates of one species? (ii) How to manage sowing density and seed proportion of each species in the intercrop to improve total grain yield and N use efficiency? (iii) How to optimize the use of nitrogen resources by choosing the most suitable preceding crop and/or the most appropriate soil? It was found that (1) intercropping made better use of environmental resources as regards yield amount and stability than sole cropping, with a noticeable site effect, (2) pea growth in intercrops was strongly linked to soil moisture, and barley yield was determined by nitrogen uptake and light interception due to its height relative to pea, (3) sowing barley before pea led to a relative grain yield reduction averaged over all three sites, but sowing strategy must be adapted to the location, being dependent on temperature and thus latitude, (4) density and species proportions had a small effect on total grain yield, underlining the interspecific offset in the use of environmental growth resources which led to similar total grain yields whatever the pea-barley design, and (5) long-term strategies including mineralization management through organic residue supply and rotation management were very valuable, always favoring intercrop total grain yield and N accumulation. (C) 2009 Elsevier B.V. All rights reserved.

Effects of summer rainfall manipulations on the abundance and vertical distribution of herbivorous soil macro-invertebrates

Soil invertebrate communities are likely to be highly vulnerable to low soil moisture, caused by a reduction in summer rainfall which is predicted for some regions under current climate change scenarios. However, the effects of changes in summer rainfall on soil invertebrate assemblages have rarely been tested experimentally. In this study, samples were taken in 2003 and 2004 from a long-running field experiment, to investigate the impact of 10 years of experimental summer drought and increased summer rainfall manipulations on the soil fauna of a calcareous grassland. Summer drought altered the soil invertebrate assemblage in the autumn, immediately following treatment application, but by the following spring treatment effects were no longer apparent. The two most common root herbivore species responded differently to the summer rainfall manipulations. Larvae of the dominant root-chewing species, Agriotes lineatus, were more numerous under enhanced rainfall in both the spring and autumn. In contrast, abundance of the Coccoidea Lecanopsis formicarum was unaffected by the rainfall manipulations. The responses of root herbivores to an increased incidence of summer droughts are therefore likely to vary, depending on their feeding strategy and life history. (c) 2007 Elsevier Masson SAS. All rights reserved.