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A measure of soil P status in agricultural soils is generally required for assisting with prediction of potential P loss from agricultural catchments and assessing risk for water quality. The objectives of this paper are twofold: (i) investigating the soil P status, distribution, and variability, both spatially and with soil depth, of two different first-order catchments; and (ii) determining variation in soil P concentration in relation to catchment topography (quantified as the "topographic index") and critical source areas (CSAs). The soil P measurements showed large spatial variability, not only between fields and land uses, but also within individual fields and in part was thought to be strongly influenced by areas where cattle tended to congregate and areas where manure was most commonly spread. Topographic index alone was not related to the distribution of soil P, and does not seem to provide an adequate indicator for CSAs in the study catchments. However, CSAs may be used in conjunction with soil P data for help in determining a more "effective" catchment soil P status. The difficulties in defining CSAs a priori, particularly for modeling and prediction purposes, however, suggest that other more "integrated" measures of catchment soil P status, such as baseflow P concentrations or streambed sediment P concentrations, might be more useful. Since observed soil P distribution is variable and is also difficult to relate to nationally available soil P data, any assessment of soil P status for determining risk of P loss is uncertain and problematic, given other catchment physicochemical characteristics and the sampling strategy employed.

Abstract:The application of a modified version of dynamic TOPMODEL for two subcatchments at Plynlimon, Wales is described. Conservative chemical mixing within mobile and immobile stores has been added to the hydrological model in an attempt to simulate observed stream chloride concentrations. The model was not fully able to simulate the observed behaviour, in particular the short-to medium-term dynamics. One of the primary problems highlighted by the study was the representation of dry deposition and cloud-droplet-deposited chloride, which formed a significant part of the long-term chloride mass budget. Equifinality of parameter sets inhibited the ability to determine the effective catchment mixing volumes and coefficients or the most likely partition between occult mass inputs and chloride mass inputs determined by catchment immobile-store antecedent conditions. Some success was achieved, in as much as some aspects of the dynamic behaviour of the signal were satisfactorily simulated, although spectral analysis showed that the model could not fully reproduce the 1/f power spectra of observed stream chloride concentrations with its implications of a wide distribution of residence times for water in the catchment.

The importance of temporal variability in relationships between phosphorus (P) concentration (C p ) and discharge (Q) is linked to a simple means of classifying the circumstances of C p Q relationships in terms of functional types of response. New experimental data at the upstream interface of grassland soil and catchment systems at a range of scales (lysimeters to headwaters) in England and Australia are used to demonstrate the potential of such an approach. Three types of event are defined as Types 13, depending on whether the relative change in Q exceeds the relative change in C p (Type 1), whether C p and Q are positively inter-related (Type 2) and whether C p varies yet Q is unchanged (Type 3). The classification helps to characterise circumstances that can be explained mechanistically in relation to (i) the scale of the study (with a tendency towards Type 1 in small scale lysimeters), (ii) the form of P with a tendency for Type 1 for soluble (i.e., <0.45 mm P forms) and (iii) the sources of P with Type 3 dominant where P availability overrides transport controls. This simple framework provides a basis for development of a more complex and quantitative classification of C p Q relationships that can be developed further to contribute to future models of P transfer and delivery from slope to stream. Studies that evaluate the temporal dynamics of the transfer of P are currently grossly under-represented in comparison with models based on static/spatial factors.

The global proliferation of harmful algal blooms poses an increasing threat to water resources, recreation and ecosystems. Predicting the occurrence of these blooms is therefore needed to assist water managers in making management decisions to mitigate their impact. Evaluation of the potential for forecasting of algal blooms using the phytoplankton community model PROTECH was undertaken in pseudo-real-time. This was achieved within a data assimilation scheme using the Ensemble Kalman Filter to allow uncertainties and model nonlinearities to be propagated to forecast outputs. Tests were made on two mesotrophic lakes in the English Lake District, which differ in depth and nutrient regime. Some forecasting success was shown for chlorophyll a, but not all forecasts were able to perform better than a persistence forecast. There was a general reduction in forecast skill with increasing forecasting period but forecasts for up to four or five days showed noticeably greater promise than those for longer periods. Associated forecasts of phytoplankton community structure were broadly consistent with observations but their translation to cyanobacteria forecasts was challenging owing to the interchangeability of simulated functional species.

1. Phosphorus (P) transfer from agricultural land to freshwater systems has been studied across many scales and environmental compartments that range from understanding biogeochemical processes in soils and fields, to assessment of localised in-stream biotic and ecological impacts. 2. This study tackles the challenges of scale when moving from soil hillslope to headwater catchment scale. The focus is on 'process rules' derived from reductionist approaches at the relatively fine scale, and exploring the signal and evidence thereof at the headwater catchment scale. 3. The methodology uses new data of P dynamics in agricultural grassland headwater catchments in south-west England. 4. We found the following: (i) it was not possible to disaggregate an influence of soil (Olsen) P concentration on P export at the larger scale; (ii) there was no clear temporally dynamic relationship between P additions of fertiliser and recycled manure and the resulting P transferred to the headwater scale; however, (iii) ploughing, digging of stream channel and leakage from farm storage all affected the temporal concentration dynamics; and (iv) overall P loss was influenced by higher long-term history of P inputs, livestock and the domination of hydrologic processes. 5. It is concluded that process rules derived at the finer soil or plot scale cannot always produce a clearly discernable signal when studied at the larger headwater catchment scale.

1. Rivers and their catchments are complex, dynamic and non-equilibrium systems. Although the general functioning of these ecosystems is familiar, the characteristics of particular sites are often poorly known and accurate prediction of future behaviour to inform management decisions is extremely difficult. 2. Simple relationships between river nutrient concentrations and the health of river ecosystems do not exist. Natural variability and practical and technical constraints reduce our ability to set nutrient targets to protect river ecosystems. Particularly challenging is the use of simple doseresponse relationships as regulatory threshold targets, whereby sections of river are classified as either passing or failing to meet good ecological status. 3. Ecologically meaningful frameworks are needed that take account of the epistemic uncertainty associated with our predictions. Such frameworks should have clearly defined goals; be holistic; allow for natural variability; help to define ecologically acceptable environmental regimes; recognise that significant uncertainties mean that we will often be using indices rather than absolute measures; and use measures that explicitly include uncertainty estimates. Such approaches allow those outside the decision-making process to understand the level of environmental precaution included in the management of complex systems.

Abstract. Part 1 of this paper has discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. Such deficits may include uncertainties about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalized on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions made, say, for risk management, so it is important to examine the sensitivity of such decisions to different feasible sets of assumptions, to communicate the meaning of associated uncertainty estimates, and to provide an audit trail for the analysis. A conceptual framework for good practice in dealing with epistemic uncertainties is outlined and the implications of applying the principles to natural hazard assessments are discussed. Six stages are recognized, with recommendations at each stage as follows: (1) framing the analysis, preferably with input from potential users; (2) evaluating the available data for epistemic uncertainties, especially when they might lead to inconsistencies; (3) eliciting information on sources of uncertainty from experts; (4) defining a workflow that will give reliable and accurate results; (5) assessing robustness to uncertainty, including the impact on any decisions that are dependent on the analysis; and (6) communicating the findings and meaning of the analysis to potential users, stakeholders, and decision makers. Visualizations are helpful in conveying the nature of the uncertainty outputs, while recognizing that the deeper epistemic uncertainties might not be readily amenable to visualizations.