E. M. Scott

Benchmarking of numerical models describing the dispersion of radionuclides in the Arctic Seas

As part of the International Arctic Seas Assessment Project (IASAP) of the International Atomic Energy Agency (IAEA), a working group was created to model the dispersal and transfer of radionuclides released from radioactive waste disposed of in the Kara Sea. The objectives of this group are: (1) development of realistic and reliable assessment models for the dispersal of radioactive contaminants both within, and from, the Arctic ocean; and (2) evaluation of the contributions of different transfer mechanisms to contaminant dispersal and hence, ultimately, to the risks to human health and environment. With regard to the first objective, the modelling work has been directed towards assessment of model reliability and asone aspect of this, a benchmarking exercise has been carried out. This paper briefly describes the benchmark scenario, the models developed and used, and discusses some of the benchmarking results. The role of the exercise within the modelling programme of IASAP will be discussed and future work described.

Spatiotemporal statistical modelling of long-term change in river nutrient concentrations in England & Wales.

Concentrations of nutrient nitrogen (N) and phosphorus (P) are elevated in rivers across large areas of Europe (European Nitrogen Assessment (ENA), Sutton et al., 2011). Environmental policies have been implemented over the past 20 years with the aim of reducing nitrogen inputs to surface waters. However, environmental and ecological status is still below set targets (ENA, Sutton et al., 2011). Identification of patterns in long-term change for nutrient trends in hydrological catchments in England & Wales is required to assess impacts of nutrient management policy and provide better evidence for future policy. Such information could provide essential evidence for supporting policy by combining information from the wider catchment, rather than relying on the analysis of data from individual sites. Surface water quality is subject to considerable spatial and short-period temporal variability, reflecting variability in loading and dilution. This makes it difficult to determine temporal trends at individual monitoring sites with relatively sparse sampling. Here we apply spatiotemporal statistical additive models for both nitrogen and phosphorus in river networks across England & Wales to investigate the overall pattern of nutrient concentrations in these river surface waters over the past 20-40 years. Concentrations of Orthophosphate (OP) have generally decreased over time for many of the Large Hydrological Areas with a seasonal pattern highlighting one peak in the summer months. Over the past ten years, Total Oxidised Nitrogen (Nitrate+Nitrite, TON) concentrations have generally been slowly decreasing or fairly constant. However, prior to 2000, concentrations were generally on an upward trend. The seasonal pattern highlights one trough in the summer months. The highest levels for OP and TON broadly occur in the same general areas across England & Wales. On average, over time, the lowest values are evident in the north-west and south-west (particularly for OP) and highest values are evident in the Midlands, Anglian and Southern regions.

Spatially weighted functional clustering of river network data

Incorporating spatial covariance into clustering has previously been considered for functional data to identify groups of functions which are similar across space. However, in the majority of situations that have been considered until now the most appropriate metric has been Euclidean distance. Directed networks present additional challenges in terms of estimating spatial covariance due to their complex structure. Although suitable river network covariance models have been proposed for use with stream distance, where distance is computed along the stream network, these models have not been extended for contexts where the data are functional, as is often the case with environmental data. The paper develops a method of calculating spatial covariance between functions from sites along a river network and applies the measure as a weight within functional hierarchical clustering. Levels of nitrate pollution on the River Tweed in Scotland are considered with the aim of identifying groups of monitoring stations which display similar spatiotemporal characteristics.

Extreme value theory applied to the definition of bathing water quality discounting limits

The European Community Bathing Water Directive (European Parliament, 2006) set compliance standards for bathing waters across Europe, with minimum standards for microbiological indicators to be attained at all locations by 2015. The Directive allows up to 15% of samples affected by short-term pollution episodes to be disregarded from the figures used to classify bathing waters, provided certain management criteria have been met, including informing the public of short-term water pollution episodes. Therefore, a scientifically justifiable discounting limit is required which could be used as a management tool to determine the samples that should be removed. This paper investigates different methods of obtaining discounting limits, focusing in particular on extreme value methodology applied to data from Scottish bathing waters. Return level based limits derived from threshold models applied at a site-specific level improved the percentage of sites which met at least the minimum required standard. This approach provides a method of obtaining limits which identify the samples that should be removed from compliance calculations, although care has to be taken in terms of the quantity of data which is removed.

Flow-directed PCA for monitoring networks

Measurements recorded over monitoring networks often possess spatial and temporal correlation inducing redundancies in the information provided. For river water quality monitoring in particular, flow‐connected sites may likely provide similar information. This paper proposes a novel approach to principal components analysis to investigate reducing dimensionality for spatiotemporal flow‐connected network data in order to identify common spatiotemporal patterns. The method is illustrated using monthly observations of total oxidized nitrogen for the Trent catchment area in England. Common patterns are revealed that are hidden when the river network structure and temporal correlation are not accounted for. Such patterns provide valuable information for the design of future sampling strategies.