GIS-based methodologies for assessing nitrate, nitrite and ammonium distributions across a major UK basin, the Humber

Abstract

The distributions of nitrate, nitrite and ammonium at various monitoring sites across the Humber basin (area 24 000 km 2 ) were examined within a Geographical Information System (GIS) framework. This basin contains diverse characteristics, from areas of high population and industry to rural and arable regions. The Humber River is a major provider of nutrient fluxes to the North Sea from the UK. Within the GIS analysis, the distributions of mean and mean flow weighted concentrations, flux and flux per unit area, were investigated. Empirical relationships between land characteristics and water quality for the whole catchment draining to each water quality monitoring site were established. Thirty-eight catchments were chosen for this analysis, with areas ranging from 46 km 2 to 8225 km 2 . These catchments are distributed across the Humber, encompassing the different conditions across the basin, thus allowing relationships between water quality and catchment characteristics to be used to estimate the nitrogen concentrations and flux throughout the basin river network. The main water quality data source was the Land Ocean Interaction Study (LOIS) dataset. The Environment Agency of England and Wales water quality datasets were used to infill areas of sparse LOIS monitoring network density within the Humber. The work shows the feasibility of estimating nitrate and, to a lesser extent, nitrite and ammonium concentrations and fluxes across the river network based on land characteristics, using a GIS methodology. The estimations work particularly well for the main river channels. However, there are local anomalies which are more difficult to predict. Maps showing concentration variations at 500 m intervals along the Humber basin river networks are presented; these are of particular value for environmental managers and socio-economists.

Issues of diffuse pollution model complexity arising from performance benchmarking

“…REGRESSION MODEL FOR N CONCENTRATION Davies and Neal (2004) have shown that mean N concentration at a given location can be simulated using information on the proportions of constituent land cover classes within the catchment. Therefore, a model was set up, based on data throughout England and Wales, against which data from the case study catchments could be tested.…”

“…Classes in LCM2000 were aggregated as described by Davies and Neal (2004) into three land cover classes, arable (A), urban (U) and upland (UP); these were the only classes that showed a relationship between their percentage cover in a catchment and N concentration.…”

Estimating nutrient concentrations from catchment characteristics across the UK

“…1. To obtain a sufficient number of test sites within the Humber study, Davies and Neal (2004) used many nested catchments, which may not have been independent of each other. As this study is UK-wide, greater catchment independence was achieved.…”

“…In this paper, the relationships between catchment characteristics and mean nitrate and orthophosphate concentrations in UK rivers have been examined using the GIS approach as in Davies and Neal (2004). Although only the dissolved forms of nitrogen and phosphorus were considered, there are also potential issues in respect of ammonium and particulate phosphorus.…”

“…Although only the dissolved forms of nitrogen and phosphorus were considered, there are also potential issues in respect of ammonium and particulate phosphorus. In the study of Davies and Neal (2004), only nitrogen (nitrate, nitrite and ammonium) and land-use catchment characteristics were considered and the regional assessment was confined to the Humber River where areas of urban/industrial cover are disproportionately large relative to the UK. In the present study, the analysis has been extended significantly using national datasets and including information on hydrology in the form of a baseflow index (BFI) and rainfall coupled with evaporation to provide a measure of effective rainfall (ER; i.e.…”