ALBERT

Description: The reduction of chromate ions by Fe(OH)2 and the iron (II)-iron (III) hydroxysulphate green rust, GR(SO42-), was studied to evaluate whether such synthetic layered hydroxides and the corresponding natural green rust mineral could be involved in the natural attenuation of contaminated environments. The resulting Cr (III) bearing phases, which would govern the subsequent behaviour of chromium, were clearly characterised. Both compounds proved to be very reactive and oxidised instantaneously while chromate ions were reduced to Cr (III) as evidenced by X-ray photoelectron spectroscopy. Mass balance (ICP-AES) demonstrated that the Fe/Cr ratio inside the solid end product was equal to the initial Fe/Cr ratio. The solid phases, analysed by X-ray diffraction, Raman and Mossbauer spectroscopies were identified as Cr-substituted poorly crystallised iron (III) oxyhydroxides in both cases, more precisely δ-FeOOH when starting with Fe(OH)2 and ferrihydrite when starting with GR(SO42-).

Description: Time domain reflectometry [TDR] was used to investigate the spatial and temporal variation in surface soil water dynamics under a number of types of vegetation, including both trees and crops grown in isolation, and grown together as an agroforestry system. The installation and operation of this technique are presented, and discussed in terms of its suitability to monitor rapid fluctuations in soil-water content in a spatially heterogeneous system such as that described in this experiment. The relatively small sampling volume of each of the TDR waveguides permitted discrete measurements to be made of soil water content (θv). In the tree-only and tree+crop treatments, this revealed considerable variation in θv resulting from spatial redistribution of rainfall under the tree canopies, with a significant input to soil close to the base of the trees being made by stemflow, i.e. water intercepted by the tree canopy and channelled down the stem. Over the experimental period (one rainy season) the TDR data suggested that net recharge to the soil profile in the sole crop system was 53 mm, almost 75% more than occurred in either of the two treatments containing trees, reflecting greater rainfall interception by the tree canopies.

Description: A Bayesian approach is described for dealing with the problem of infilling and generating stochastic flow sequences using rainfall data to guide the flow generation process, and including bounded (censored) observed flow and rainfall data to provide additional information. Solutions are obtained using a Gibbs sampling procedure. Particular problems discussed include developing new procedures for fitting transformations when bounded values are available, coping with additional information in the form of values, or bounds, for totals of flows across several sites, and developing relationships between annual flow and rainfall data. Examples are shown of both infilled values of unknown past river flows, with assessment of uncertainty, and realisations of flows representative of what might occur in the future. Several procedures for validating the model output are described and the central estimates of flows, taken as a surrogate for historical observed flows, are compared with long term regional flow and rainfall data.

Description: The spatial distribution of stream water composition, as determined by the Geochemical Baseline Survey of the Environment (G-BASE) conducted by the British Geological Survey (BGS) can be successfully related under baseflow conditions to bedrock geochemistry. Further consideration of results in conjunction with site-specific monitoring data enables factors controlling both spatial and temporal variability in major element composition to be highlighted and allows the value of the survey to be enhanced. Hence, chemical data (i) from streams located on Lower Silurian (Llandovery) bedrock at 1 km2 resolution collected as part of the G-BASE survey of Wales and the West Midlands and (ii) from catchment monitoring studies located in upland mid-Wales (conducted by Institute of Terrestrial Ecology), have been considered together as an example. Classification of the spatial survey data set in terms of potentially controlling factors was carried out so as to illustrate the level of explanation they could give in terms of observed spatial chemical variability. It was therefore hypothesised that on a geological lithostratigraphic series of limited geochemical contrast, altitude and land-use factors provide better explanation of this variability than others such as lithology at sampling site and stream order. At an individual site, temporal variability was also found to be of considerable significance and, at a monthly time-step, is explicable in terms of factors such as antecedent conditions and seasonality. Data suggest that the degree of this variability may show some relationship with stream order and land-use. Monitoring data from the region also reveal that relationships between stream chemistry and land-use may prove to be strong not only at base flow but also in storm flow conditions. In a wider context, predictions of the sensitivity of stream water to acidification based on classifications of soil and geology are successful on a regional scale. However, the study undertaken here has shown that use of such classification schemes on a catchment scale results in considerable uncertainty associated with prediction. Uncertainties are due to the large degree of variability in stream chemistry encountered both spatially within geological units and temporally at individual sampling sites.

Description: The land-surface parameters required as input to a GCM grid box (typically a few degrees) are often set to be those of the dominant vegetation type within the grid box. This paper discusses the use and effect of aggregation rules for specifying effective values of these land cover parameters by taking into account the relative proportion of each land-cover type within each individual grid box. Global land-cover classification data at 1 km resolution were used to define Biosphere Atmosphere Transfer Scheme (BATS) specific aggregate (using aggregation rules) land-cover parameters. Comparison of the values of the aggregate parameters and those defined using the single dominant vegetation type (default parameters) shows significant differences in some regions, particularly in the semi-desert and in forested regions, e.g. the Sahara Desert and the tropical forest of South America. These two different sets of parameters were used as input data for two 10-year simulations of the NCAR CCM3 model coupled to the BATS land-surface scheme. Statistical analyses comparing the results of the two model runs showed that the resulting effects on the land-surface diagnostics are significant only in specific regions. For example, the sensible heat flux in the Sahara Desert calculated for the aggregate parameter run increased due to the marked increase in the minimum stomatal resistance and the decrease in fractional vegetation cover in the aggregate parameters over the default parameters. The modelled global precipitation and surface air temperature fields were compared to observations: there is a general improvement in the performance of the aggregate parameter run over the default parameter run in areas where the differences between the aggregate and default parameter run are significant. However, most of the difference between the modelled and observed fields is attributable to other model deficiencies. It can be concluded that the use of aggregation rules to derive land-surface parameters results in significant changes in modelled climate and in some improvements in the land-surface diagnostics in selected regions. There is also some evidence that there is a response in the global circulation pattern, which is a focus of further work.

Description: In order to investigate the aggregation effects of surface heterogeneity in land surface processes we have adapted a theory of aggregation. Two strategies have been adopted: 1) Aggregation of radiative fluxes. The aggregated radiative fluxes are used to derive input parameters that are then used to calculate the aerodynamic fluxes at different aggregation levels. This is equivalent to observing the same area at different resolutions using a certain remote sensor, and then calculating the aerodynamic fluxes correspondingly. 2) Aggregation of aerodynamic fluxes calculated at the original observation scale to different aggregation levels. A case study has been conducted to identify the effects of aggregation on areal estimates of sensible and latent heat fluxes. The length scales of surface variables in heterogeneous landscapes are estimated by means of wavelet analysis.

Description: A Bayesian approach is described for dealing with the problem of infilling and generating stochastic flow sequences using rainfall data to guide the flow generation process, and including bounded (censored) observed flow and rainfall data to provide additional information. Solutions are obtained using a Gibbs sampling procedure. Particular problems discussed include developing new procedures for fitting transformations when bounded values are available, coping with additional information in the form of values, or bounds, for totals of flows across several sites, and developing relationships between annual flow and rainfall data. Examples are shown of both infilled values of unknown past river flows, with assessment of uncertainty, and realisations of flows representative of what might occur in the future. Several procedures for validating the model output are described and the central estimates of flows, taken as a surrogate for historical observed flows, are compared with long term regional flow and rainfall data.

Description: This article proposes a methodology for synthesising the rating curve in one or more cross-sections of a watercourse provided with stage data, when a reliable rating curve and stage data are also available in the upstream cross-section; the synthesised rating curves are consistent with each other. The proposed methodology uses a variable parameter Muskingum-Cunge model whose parameters take express account of travel times and attenuation of the flood wave, and are expressed in such a way that allows for an integration in the time-space domain even when a topographic survey of the river is not available. Furthermore, the methodology proposed implicitly provides a ready-calibrated simulation model whose ease of application suggests that it could also be useful in real time stage forecasting. The paper includes a description of a numerical application to a reach of the Po River (Italy).

Description: It has previously been argued that current Soil Vegetation Atmosphere Transfer (SVAT) models are over-parameterised given the calibration data typically available. Using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology, multiple feasible model parameter sets are here conditioned on latent heat fluxes and then additionally on the sensible and ground heat fluxes at a single site in Amazonia. The model conditioning schemes were then evaluated with a further data set collected at the same site according to their ability to reproduce the latent, sensible and ground heat fluxes. The results indicate that conditioning the model on only the latent heat flux component of the energy balance does not constrain satisfactorily the predictions of the other components of the energy balance. When conditioning on all heat flux objectives, significant additional constraint of the feasible parameter space is achieved with a consequent reduction in the predictive uncertainty. There are still, however, many parameter sets that adequately reproduce the calibration/validation data, leading to significant predictive uncertainty. Surface temperature measurements, whilst also subject to uncertainty, may be employed usefully in a multi-objective calibration of SWAT models.

Description: This paper compares the performance of two artificial neural network (ANN) models – the multi layer perceptron (MLP) and the radial basis function network (RBF) – with a stepwise multiple linear regression model (SWMLR) and zero order forecasts (ZOF) of river flow. All models were trained using 15 minute rainfall-runoff data for the River Mole, a flood-prone tributary of the River Thames, UK. The models were then used to forecast river flows with a 6 hour lead time and 15 minute resolution, given only antecedent rainfall and discharge measurements. Two seasons (winter and spring) were selected for model testing using a cross-validation technique and a range of diagnostic statistics. Overall, the MLP was more skillful than the RBF, SWMLR and ZOF models. However, the RBF flow forecasts were only marginally better than those of the simpler SWMLR and ZOF models. The results compare favourably with a review of previous studies and further endorse claims that ANNs are well suited to rainfall-runoff modelling and (potentially) real-time flood forecasting.