Estimating discharge from inundation imagery

Remote sensing from space-borne platforms is often seen as an appealing method of monitoring components of the hydrological cycle, including river discharge, due to its spatial coverage. However, data from these platforms is often less than ideal because the geophysical properties of interest are rarely measured directly and the measurements that are taken can be subject to significant errors. This study assimilated water levels derived from a TerraSAR-X synthetic aperture radar image and digital aerial photography with simulations from a two dimensional hydraulic model to estimate discharge, inundation extent, depths and velocities at the confluence of the rivers Severn and Avon, UK. An ensemble Kalman filter was used to assimilate spot heights water levels derived by intersecting shorelines from the imagery with a digital elevation model. Discharge was estimated from the ensemble of simulations using state augmentation and then compared with gauge data. Assimilating the real data reduced the error between analyzed mean water levels and levels from three gauging stations to less than 0.3 m, which is less than typically found in post event water marks data from the field at these scales. Measurement bias was evident, but the method still provided a means of improving estimates of discharge for high flows where gauge data are unavailable or of poor quality. Posterior estimates of discharge had standard deviations between 63.3 m3s-1 and 52.7 m3s-1, which were below 15% of the gauged flows along the reach. Therefore, assuming a roughness uncertainty of 0.03-0.05 and no model structural errors discharge could be estimated by the EnKF with accuracy similar to that arguably expected from gauging stations during flood events. Quality control prior to assimilation, where measurements were rejected for being in areas of high topographic slope or close to tall vegetation and trees, was found to be essential. The study demonstrates the potential, but also the significant limitations of currently available imagery to reduce discharge uncertainty in un-gauged or poorly gauged basins when combined with model simulations in a data assimilation framework.

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.

Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles

Process-based integrated modelling of weather and crop yield over large areas is becoming an important research topic. The production of the DEMETER ensemble hindcasts of weather allows this work to be carried out in a probabilistic framework. In this study, ensembles of crop yield (groundnut, Arachis hypogaea L.) were produced for 10 2.5 degrees x 2.5 degrees grid cells in western India using the DEMETER ensembles and the general large-area model (GLAM) for annual crops. Four key issues are addressed by this study. First, crop model calibration methods for use with weather ensemble data are assessed. Calibration using yield ensembles was more successful than calibration using reanalysis data (the European Centre for Medium-Range Weather Forecasts 40-yr reanalysis, ERA40). Secondly, the potential for probabilistic forecasting of crop failure is examined. The hindcasts show skill in the prediction of crop failure, with more severe failures being more predictable. Thirdly, the use of yield ensemble means to predict interannual variability in crop yield is examined and their skill assessed relative to baseline simulations using ERA40. The accuracy of multi-model yield ensemble means is equal to or greater than the accuracy using ERA40. Fourthly, the impact of two key uncertainties, sowing window and spatial scale, is briefly examined. The impact of uncertainty in the sowing window is greater with ERA40 than with the multi-model yield ensemble mean. Subgrid heterogeneity affects model accuracy: where correlations are low on the grid scale, they may be significantly positive on the subgrid scale. The implications of the results of this study for yield forecasting on seasonal time-scales are as follows. (i) There is the potential for probabilistic forecasting of crop failure (defined by a threshold yield value); forecasting of yield terciles shows less potential. (ii) Any improvement in the skill of climate models has the potential to translate into improved deterministic yield prediction. (iii) Whilst model input uncertainties are important, uncertainty in the sowing window may not require specific modelling. The implications of the results of this study for yield forecasting on multidecadal (climate change) time-scales are as follows. (i) The skill in the ensemble mean suggests that the perturbation, within uncertainty bounds, of crop and climate parameters, could potentially average out some of the errors associated with mean yield prediction. (ii) For a given technology trend, decadal fluctuations in the yield-gap parameter used by GLAM may be relatively small, implying some predictability on those time-scales.

The effect of paddock size on the response of seasonal anoestrous ewes to the ram effect

Many aspects of the conditions required to maximize the ewe's response to ram introduction in the late anoestrous season remain unclear. The aim of this research was to determine whether grazing space allowances could influence the efficacy of the ram effect. In August 1995, at Reading (latitude 51degrees27'N), following a 3-month isolation period from rams, two groups of nulliparous Mule ewes, aged 15 months, were introduced to four rains in a low (12 ewes/ha; treatment L, n = 124) or in a high stocking rate (84 ewes/ha; treatment H, n = 126). From the beginning of August until the end of August oestrous behaviour was recorded by daily checks of mating marks on ewes. Rams were removed and in October all ewes were scanned (day 50) for pregnancy. No significant differences were found in the parameters investigated. Eighty-two percent of the L and 75.4% of the H ewes exhibited oestrus, with a pronounced peak on day 23 following ram introduction and a compact concentration in the 21-25-day period. The oestrous synchronisation rate in this 5-day period was 69.4 and 68.3%, respectively for L and H. The mean interval from ram introduction to oestrus was 23.17+/-2.4 days in L and 23.0+/-2.2 days in the H group. Conception rates were 84.3 and 87.4% for L and H groups, respectively. These results suggest that the response of anoestrous ewes to the introduction of rams was not affected by grazing space allowances and that yearling Mule ewes respond well to the ram effect in the late anoestrus season. (C) 2003 Elsevier Science B.V. All rights reserved.

Adaptation to climate change and variability: farmer responses to intra-seasonal precipitation trends in South Africa

We describe the nature of recent (50 year) rainfall variability in the summer rainfall zone, South Africa, and how variability is recognised and responded to on the ground by farmers. Using daily rainfall data and self-organising mapping (SOM) we identify 12 internally homogeneous rainfall regions displaying differing parameters of precipitation change. Three regions, characterised by changing onset and timing of rains, rainfall frequencies and intensities, in Limpopo, North West and KwaZulu Natal provinces, were selected to investigate farmer perceptions of, and responses to, rainfall parameter changes. Village and household level analyses demonstrate that the trends and variabilities in precipitation parameters differentiated by the SOM analysis were clearly recognised by people living in the areas in which they occurred. A range of specific coping and adaptation strategies are employed by farmers to respond to climate shifts, some generic across regions and some facilitated by specific local factors. The study has begun to understand the complexity of coping and adaptation, and the factors that influence the decisions that are taken.

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

Seasonal variations in the developmental competence of bovine oocytes matured in vitro

Ovaries were collected over a period of two years from heifers slaughtered at under 30 months of age and used to harvest 1757 oocytes. After in vitro maturation, fertilisation and culture, the proportions of oocytes and cleaved embryos that developed to blastocysts were significantly higher (P < 0.01) in the autumn, from September to November, than in the spring, from March to May. In contrast, embryo development, as assessed by oocytes that developed to eight or more cells and blastocysts, was lowest (P < 0.01) in the spring. These results were consistent during the two-year study, indicating a seasonal fluctuation in oocyte competence.

The development of ISSR-derived SCAR markers around the Seasonal Flowering Locus (SFL) in fragaria vesca

Fragaria vesca is a short-lived perennial with a seasonal-flowering habit. Seasonality of flowering is widespread in the Rosaceae and is also found in the majority of temperate polycarpic perennials. Genetic analysis has shown that seasonal flowering is controlled by a single gene in F. vesca, the SEASONAL FLOWERING LOCUS (SFL). Here, we report progress towards the marker-assisted selection and positional cloning of SFL, in which three ISSR markers linked to SFL were converted to locus-specific sequence-characterized amplified region (SCAR1–SCAR3) markers to allow large-scale screening of mapping progenies. We believe this is the first study describing the development of SCAR markers from ISSR profiles. The work also provides useful insight into the nature of polymorphisms generated by the ISSR marker system. Our results indicate that the ISSR polymorphisms originally detected were probably caused by point mutations in the positions targeted by primer anchors (causing differential PCR failure), by indels within the amplicon (leading to variation in amplicon size) and by internal sequence differences (leading to variation in DNA folding and so in band mobility). The cause of the original ISSR polymorphism was important in the selection of appropriate strategies for SCAR-marker development. The SCAR markers produced were mapped using a F. vesca f. vesca × F. vesca f. semperflorens testcross population. Marker SCAR2 was inseparable from the SFL, whereas SCAR1 mapped 3.0 cM to the north of the gene and SCAR3 1.7 cM to its south.