Parameter sensitivity and predictive uncertainty in a new water quality model, Q(2)

A new dynamic model of water quality, Q(2), has recently been developed, capable of simulating large branched river systems. This paper describes the application of a generalized sensitivity analysis (GSA) to Q(2) for single reaches of the River Thames in southern England. Focusing on the simulation of dissolved oxygen (DO) (since this may be regarded as a proxy for the overall health of a river); the GSA is used to identify key parameters controlling model behavior and provide a probabilistic procedure for model calibration. It is shown that, in the River Thames at least, it is more important to obtain high quality forcing functions than to obtain improved parameter estimates once approximate values have been estimated. Furthermore, there is a need to ensure reasonable simulation of a range of water quality determinands, since a focus only on DO increases predictive uncertainty in the DO simulations. The Q(2) model has been applied here to the River Thames, but it has a broad utility for evaluating other systems in Europe and around the world.

Analysis of water-level response to rainfall and implications for recharge pathways in the Chalk aquifer, SE England

Water table response to rainfall was investigated at six sites in the Upper, Middle and Lower Chalk of southern England. Daily time series of rainfall and borehole water level were cross-corretated to investigate seasonal variations in groundwater-level response times, based on periods of 3-month duration. The time tags (in days) yielding significant correlations were compared with the average unsaturated zone thickness during each 3-month period. In general, for cases when the unsaturated zone was greater than 18 m thick, the time tag for a significant water-level response increased rapidly once the depth to the water table exceeded a critical value, which varied from site to site. For shallower water tables, a linear relationship between the depth to the water table and the water-level response time was evident. The observed variations in response time can only be partially accounted for using a diffusive model for propagation through the unsaturated matrix, suggesting that some fissure flow was occurring. The majority of rapid responses were observed during the winter/spring recharge period, when the unsaturated zone is thinnest and the unsaturated zone moisture content is highest, and were more likely to occur when the rainfall intensity exceeded 5 mm/day. At some sites, a very rapid response within 24 h of rainfall was observed in addition to the longer term responses even when the unsaturated zone was up to 64 m thick. This response was generally associated with the autumn period. The results of the cross-correlation analysis provide statistical support for the presence of fissure flow and for the contribution of multiple pathways through the unsaturated zone to groundwater recharge. (c) 2006 Elsevier B.V. All rights reserved.

Rainfall and runoff water quality of the Pang and Lambourn, tributaries of the River Thames, south-eastern England

The water quality of rainfall and runoff is described for two catchments of two tributaries of the River Thames, the Pang and Lambourn. Rainfall chemistry is variable and concentrations of most determinands decrease with increasing volume of catch probably due to 'wash out' processes. Two rainfall sites have been monitored, one for each catchment. The rainfall site on the Lambourn shows higher chemical concentrations than the one for the Pang which probably reflects higher amounts of local inputs from agricultural activity, Rainfall quality data at a long-term rainfall site on the Pang (UK National Air Quality Archive) shows chemistries similar to that for the Lambourn site. but with some clear differences. Rainfall chemistries show considerable variation on an event-to-event basis. Average water quality concentrations and flow-weighted concentrations as well as fluxes vary across the sites, typically by about 30%. Stream chemistry is much less variable due to the main Source of water coming from aquifer sources of high storage. The relationship between rainfall and runoff chemistry at the catchment outlet is described in terms of the relative proportions of atmospheric and within-catchment sources. Remarkably, in view of the quantity of agricultural and sewage inputs to the streams, the catchments appear to be retaining both P and N.

Evaluation of two hybrid metric-conceptual models, for simulating phosphorus transfer from agricultural land in the river enborne, a lowland UK catchment

Across Europe, elevated phosphorus (P) concentrations in lowland rivers have made them particularly susceptible to eutrophication. This is compounded in southern and central UK by increasing pressures on water resources, which may be further enhanced by the potential effects of climate change. The EU Water Framework Directive requires an integrated approach to water resources management at the catchment scale and highlights the need for modelling tools that can distinguish relative contributions from multiple nutrient sources and are consistent with the information content of the available data. Two such models are introduced and evaluated within a stochastic framework using daily flow and total phosphorus concentrations recorded in a clay catchment typical of many areas of the lowland UK. Both models disaggregate empirical annual load estimates, derived from land use data, as a function of surface/near surface runoff, generated using a simple conceptual rainfall-runoff model. Estimates of the daily load from agricultural land, together with those from baseflow and point sources, feed into an in-stream routing algorithm. The first model assumes constant concentrations in runoff via surface/near surface pathways and incorporates an additional P store in the river-bed sediments, depleted above a critical discharge, to explicitly simulate resuspension. The second model, which is simpler, simulates P concentrations as a function of surface/near surface runoff, thus emphasising the influence of non-point source loads during flow peaks and mixing of baseflow and point sources during low flows. The temporal consistency of parameter estimates and thus the suitability of each approach is assessed dynamically following a new approach based on Monte-Carlo analysis. (c) 2004 Elsevier B.V. All rights reserved.

Teleconnections between Ethiopian summer rainfall and sea surface temperature: part I. Observation and modelling

In this study, the oceanic regions that are associated with anomalous Ethiopian summer rains were identified and the teleconnection mechanisms that give rise to these associations have been investigated. Because of the complexities of rainfall climate in the horn of Africa, Ethiopia has been subdivided into six homogeneous rainfall zones and the influence of SST anomalies was analysed separately for each zone. The investigation made use of composite analysis and modelling experiments. Two sets of composites of atmospheric fields were generated, one based on excess/deficit rainfall anomalies and the other based on warm/cold SST anomalies in specific oceanic regions. The aim of the composite analysis was to determine the link between SST and rainfall in terms of large scale features. The modelling experiments were intended to explore the causality of these linkage. The results show that the equatorial Pacific, the midlatitude northwest Pacific and the Gulf of Guinea all exert an influence on the summer rainfall in various part of the country. The results demonstrate that different mechanisms linked to sea surface temperature control variations in rainfall in different parts of Ethiopia. This has important consequences for seasonal forecasting models which are based on statistical correlations between SST and seasonal rainfall totals. It is clear that such statistical models should take account of the local variations in teleconnections.

Teleconnections between Ethiopian summer rainfall and sea surface temperature: part II. Seasonal forecasting

A seasonal forecasting system that is capable of skilfully predicting rainfall totals on a regional scale would be of great value to Ethiopia. Here, we describe how a statistical model can exploit the teleconnections described in part 1 of this pair of papers to develop such a system. We show that, in most cases, the predictors selected objectively by the statistical model can be interpreted in the light of physical teleconnections with Ethiopian rainfall, and discuss why, in some cases, unexpected regions are chosen as predictors. We show that the forecast has skill in all parts of Ethiopia, and argue that this method could provide the basis of an operational seasonal forecasting system for Ethiopia.

Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms-a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.

A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility Of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Parana river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.