A Bayesian approach to estimate sensible and latent heat over vegetated land surface

Sensible and latent heat fluxes are often calculated from bulk transfer equations combined with the energy balance. For spatial estimates of these fluxes, a combination of remotely sensed and standard meteorological data from weather stations is used. The success of this approach depends on the accuracy of the input data and on the accuracy of two variables in particular: aerodynamic and surface conductance. This paper presents a Bayesian approach to improve estimates of sensible and latent heat fluxes by using a priori estimates of aerodynamic and surface conductance alongside remote measurements of surface temperature. The method is validated for time series of half-hourly measurements in a fully grown maize field, a vineyard and a forest. It is shown that the Bayesian approach yields more accurate estimates of sensible and latent heat flux than traditional methods.

An estimate of the global impact of multiple scattering by clouds on outgoing long-wave radiation

Although the potential importance of scattering of long-wave radiation by clouds has been recognised, most studies have concentrated on the impact of high clouds and few estimates of the global impact of scattering have been presented. This study shows that scattering in low clouds has a significant impact on outgoing long-wave radiation (OLR) in regions of marine stratocumulus (-3.5 W m(-2) for overcast conditions) where the column water vapour is relatively low. This corresponds to an enhancement of the greenhouse effect of such clouds by 10%. The near-global impact of scattering on OLR is estimated to be -3.0 W m(-2), with low clouds contributing -0.9 W m(-2), mid-level cloud -0.7 W m(-2) and high clouds -1.4 W m(-2). Although this effect appears small compared to the global mean OLR of 240 W m(-2), it indicates that neglect of scattering will lead to an error in cloud long-wave forcing of about 10% and an error in net cloud forcing of about 20%.

Evaluation of NRC, UC Davis and ADAS approaches to estimate the metabolizable energy values of feeds at maintenance energy intake from equations utilizing chemical assays and in vitro determinations

Feed samples received by commercial analytical laboratories are often undefined or mixed varieties of forages, originate from various agronomic or geographical areas of the world, are mixtures (e.g., total mixed rations) and are often described incompletely or not at all. Six unified single equation approaches to predict the metabolizable energy (ME) value of feeds determined in sheep fed at maintenance ME intake were evaluated utilizing 78 individual feeds representing 17 different forages, grains, protein meals and by-product feedstuffs. The predictive approaches evaluated were two each from National Research Council [National Research Council (NRC), Nutrient Requirements of Dairy Cattle, seventh revised ed. National Academy Press, Washington, DC, USA, 2001], University of California at Davis (UC Davis) and ADAS (Stratford, UK). Slopes and intercepts for the two ADAS approaches that utilized in vitro digestibility of organic matter and either measured gross energy (GE), or a prediction of GE from component assays, and one UC Davis approach, based upon in vitro gas production and some component assays, differed from both unity and zero, respectively, while this was not the case for the two NRC and one UC Davis approach. However, within these latter three approaches, the goodness of fit (r(2)) increased from the NRC approach utilizing lignin (0.61) to the NRC approach utilizing 48 h in vitro digestion of neutral detergent fibre (NDF:0.72) and to the UC Davis approach utilizing a 30 h in vitro digestion of NDF (0.84). The reason for the difference between the precision of the NRC procedures was the failure of assayed lignin values to accurately predict 48 h in vitro digestion of NDF. However, differences among the six predictive approaches in the number of supporting assays, and their costs, as well as that the NRC approach is actually three related equations requiring categorical description of feeds (making them unsuitable for mixed feeds) while the ADAS and UC Davis approaches are single equations, suggests that the procedure of choice will vary dependent Upon local conditions, specific objectives and the feedstuffs to be evaluated. In contrast to the evaluation of the procedures among feedstuffs, no procedure was able to consistently discriminate the ME values of individual feeds within feedstuffs determined in vivo, suggesting that the quest for an accurate and precise ME predictive approach among and within feeds, may remain to be identified. (C) 2004 Elsevier B.V. All rights reserved.

Validation of a FFQ for estimating whole-grain cereal food intake

Estimation of whole-grain (WG) food intake in epidemiological and nutritional studies is normally based on general diet FFQ, which are not designed to specifically capture WG intake. To estimate WG cereal intake, we developed a forty-three-item FFQ focused on cereal product intake over the past month. We validated this questionnaire against a 3-d-weighed food record (3DWFR) in thirty-one subjects living in the French-speaking part of Switzerland (nineteen female and twelve male). Subjects completed the FFQ on day 1 (FFQ1), the 3DWFR between days 2 and 13 and the FFQ again on day 14 (FFQ2). The subjects provided a fasting blood sample within 1 week of FFQ2. Total cereal intake, total WG intake, intake of individual cereals, intake of different groups of cereal products and alkylresorcinol (AR) intake were calculated from both FFQ and the 3DWFR. Plasma AR, possible biomarkers for WG wheat and rye intake were also analysed. The total WG intake for the 3DWFR, FFQ1, FFQ2 was 26 (sd 22), 28 (sd 25) and 21 (sd 16) g/d, respectively. Mean plasma AR concentration was 55.8 (sd 26.8) nmol/l. FFQ1, FFQ2 and plasma AR were correlated with the 3DWFR (r 0.72, 0.81 and 0.57, respectively). Adjustment for age, sex, BMI and total energy intake did not affect the results. This FFQ appears to give a rapid and adequate estimate of WG cereal intake in free-living subjects.

ONeSAMP: a program to estimate effective population size using approximate Bayesian computation

The estimation of effective population size from one sample of genotypes has been problematic because most estimators have been proven imprecise or biased. We developed a web-based program, ONeSAMP that uses approximate Bayesian computation to estimate effective population size from a sample of microsatellite genotypes. ONeSAMP requires an input file of sampled individuals' microsatellite genotypes along with information about several sampling and biological parameters. ONeSAMP provides an estimate of effective population size, along with 95% credible limits. We illustrate the use of ONeSAMP with an example data set from a re-introduced population of ibex Capra ibex.

The use of image analysis to estimate population growth rate in Daphnia magna

1. Population growth rate (PGR) is central to the theory of population ecology and is crucial for projecting population trends in conservation biology, pest management and wildlife harvesting. Furthermore, PGR is increasingly used to assess the effects of stressors. Image analysis that can automatically count and measure photographed individuals offers a potential methodology for estimating PGR. 2. This study evaluated two ways in which the PGR of Daphnia magna, exposed to different stressors, can be estimated using an image analysis system. The first method estimated PGR as the ratio of counts of individuals obtained at two different times, while the second method estimated PGR as the ratio of population sizes at two different times, where size is measured by the sum of the individuals' surface areas, i.e. total population surface area. This method is attractive if surface area is correlated with reproductive value (RV), as it is for D. magna, because of the theoretical result that PGR is the rate at which the population RV increases. 3. The image analysis system proved reliable and reproducible in counting populations of up to 440 individuals in 5 L of water. Image counts correlated well with manual counts but with a systematic underestimate of about 30%. This does not affect accuracy when estimating PGR as the ratio of two counts. Area estimates of PGR correlated well with count estimates, but were systematically higher, possibly reflecting their greater accuracy in the study situation. 4. Analysis of relevant scenarios suggested the correlation between RV and body size will generally be good for organisms in which fecundity correlates with body size. In these circumstances, area estimation of PGR is theoretically better than count estimation. 5. Synthesis and applications. There are both theoretical and practical advantages to area estimation of population growth rate when individuals' reproductive values are consistently well correlated with their surface areas. Because stressors may affect both the number and quality of individuals, area estimation of population growth rate should improve the accuracy of predicting stress impacts at the population level.

Revised QUICKI provides a strong surrogate estimate of insulin sensitivity when compared with the minimal model

OBJECTIVE: To compare insulin sensitivity (Si) from a frequently sampled intravenous glucose tolerance test (FSIGT) and subsequent minimal model analyses with surrogate measures of insulin sensitivity and resistance and to compare features of the metabolic syndrome between Caucasians and Indian Asians living in the UK. SUBJECTS: In all, 27 healthy male volunteers (14 UK Caucasians and 13 UK Indian Asians), with a mean age of 51.2 +/- 1.5 y, BMI of 25.8 +/- 0.6 kg/m(2) and Si of 2.85 +/- 0.37. MEASUREMENTS: Si was determined from an FSIGT with subsequent minimal model analysis. The concentrations of insulin, glucose and nonesterified fatty acids (NEFA) were analysed in fasting plasma and used to calculate surrogate measure of insulin sensitivity (quantitative insulin sensitivity check index (QUICKI), revised QUICKI) and resistance (homeostasis for insulin resistance (HOMA IR), fasting insulin resistance index (FIRI), Bennetts index, fasting insulin, insulin-to-glucose ratio). Plasma concentrations of triacylglycerol (TAG), total cholesterol, high density cholesterol, (HDL-C) and low density cholesterol, (LDL-C) were also measured in the fasted state. Anthropometric measurements were conducted to determine body-fat distribution. RESULTS: Correlation analysis identified the strongest relationship between Si and the revised QUICKI (r = 0.67; P = 0.000). Significant associations were also observed between Si and QUICKI (r = 0.51; P = 0.007), HOMA IR (r = -0.50; P = 0.009), FIRI and fasting insulin. The Indian Asian group had lower HDL-C (P = 0.001), a higher waist-hip ratio (P = 0.01) and were significantly less insulin sensitive (Si) than the Caucasian group (P = 0.02). CONCLUSION: The revised QUICKI demonstrated a statistically strong relationship with the minimal model. However, it was unable to differentiate between insulin-sensitive and -resistant groups in this study. Future larger studies in population groups with varying degrees of insulin sensitivity are recommended to investigate the general applicability of the revised QUICKI surrogate technique.

Can shells of freshwater mussels (Unionidae) be used to estimate low summer discharge of rivers and associated droughts?

This paper examines if shell oxygen isotope ratios (d18Oar) of Unio sp. can be used as a proxy of past discharge of the river Meuse. The proxy was developed from a modern dataset for the reference time interval 1997–2007, which showed a logarithmic relationship between discharge and measured water oxygen isotope ratios(d18Ow). To test this relationship for past time intervals,d18Oar values were measured in the aragonite of the growth increments of four Unio sp. shells; two from a relatively wet period and two from a very dry time interval (1910–1918 and 1969–1977, respectively). Shell d18Oar records were converted into d18Ow values using existing water temperature records. Summer d18Ow values, reconstructed from d18Oar of 1910–1918, showed a similar range as the summer d18Ow values for the reference time interval 1997–2007, whilst summer reconstructed d18Ow values for the time interval 1969–1977 were anomalously high. These high d18Ow values suggest that the river Meuse experienced severe summer droughts during the latter time interval. d18Ow values were then applied to calculate discharge values. It was attempted to estimate discharge from the reconstructed d18Ow values using the logarithmic relationship between d18Ow and discharge. A comparison of the calculated summer discharge results with observed discharge data showed that Meuse low-discharge events below a threshold value of 6 m3/s can be detected in the reconstructed d18Ow records, but true quantification remains problematic.

Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere

Satellite measurements and numerical forecast model reanalysis data are used to compute an updated estimate of the cloud radiative effect on the global multi-annual mean radiative energy budget of the atmosphere and surface. The cloud radiative cooling effect through reflection of shortwave radiation dominates over the longwave heating effect, resulting in a net cooling of the climate system of –21 Wm-2. The shortwave radiative effect of cloud is primarily manifest as a reduction in the solar radiation absorbed at the surface of -53 Wm-2. Clouds impact longwave radiation by heating the moist tropical atmosphere (up to around 40 Wm-2 for global annual means) while enhancing the radiative cooling of the atmosphere over other regions, in particular higher latitudes and sub-tropical marine stratocumulus regimes. While clouds act to cool the climate system during the daytime, the cloud greenhouse effect heats the climate system at night. The influence of cloud radiative effect on determining cloud feedbacks and changes in the water cycle are discussed.

Multimodel estimate of the global terrestrial water balance: setup and first results

Six land surface models and five global hydrological models participate in a model intercomparison project (WaterMIP), which for the first time compares simulation results of these different classes of models in a consistent way. In this paper the simulation setup is described and aspects of the multi-model global terrestrial water balance are presented. All models were run at 0.5 degree spatial resolution for the global land areas for a 15-year period (1985-1999) using a newly-developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm year-1 (60,000 to 85,000 km3 year-1) and simulated runoff ranges from 290 to 457 mm year-1 (42,000 to 66,000 km3 year-1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically-based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between model are major sources of uncertainty. Climate change impact studies thus need to use not only multiple climate models, but also some other measure of uncertainty, (e.g. multiple impact models).

A method for the economic valuation of animal welfare benefits using a single welfare score

Unless the benefits to society of measures to protect and improve the welfare of animals are made transparent by means of their valuation they are likely to go unrecognised and cannot easily be weighed against the costs of such measures as required, for example, by policy-makers. A simple single measure scoring system, based on the Welfare Quality® index, is used, together with a choice experiment economic valuation method, to estimate the value that people place on improvements to the welfare of different farm animal species measured on a continuous (0-100) scale. Results from using the method on a survey sample of some 300 people show that it is able to elicit apparently credible values. The survey found that 96% of respondents thought that we have a moral obligation to safeguard the welfare of animals and that over 72% were concerned about the way farm animals are treated. Estimated mean annual willingness to pay for meat from animals with improved welfare of just one point on the scale was £5.24 for beef cattle, £4.57 for pigs and £5.10 for meat chickens. Further development of the method is required to capture the total economic value of animal welfare benefits. Despite this, the method is considered a practical means for obtaining economic values that can be used in the cost-benefit appraisal of policy measures intended to improve the welfare of animals.