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Modelling macronutrient dynamics in the Hampshire Avon river: a Bayesian approach to estimate seasonal variability and total flux

Modelling macronutrient dynamics in the Hampshire Avon river: a Bayesian approach to estimate seasonal variability and total flux
Modelling macronutrient dynamics in the Hampshire Avon river: a Bayesian approach to estimate seasonal variability and total flux
The macronutrients nitrate and phosphate are aquatic pollutants that arise naturally, however, in excess concentrations they can be harmful to human health and ecosystems. These pollutants are driven by river currents and show dynamics that are affected by weather pattern and extreme rainfall events. As a result, the nutrient budget in the receiving estuaries and coasts can change suddenly and seasonally, causing ecological damage to resident wildlife and fish populations. In this paper, we propose a statistical change-point model with interactions between time and river flow, to capture the macronutrient dynamics and their responses to river flow threshold behaviour. It also accounts for the nonlinear effect of water quality properties via nonparametric penalised splines. This model enables us to estimate the daily levels of riverine macronutrient fluxes and their seasonal and annual totals. In particular, we present a study on macronutrient dynamics on the Hampshire Avon River, which flows to the southern coast of the UK through the Christchurch Harbour estuary. We model daily data for more than a year during 2013-14 in which period there were multiple severe meteorological conditions leading to localised flooding. Adopting a Bayesian inference framework, we have quantified riverine macronutrient fluxes based on input river flow values. Out of sample empirical validation methods justify our approach, which captures also the dependencies of macronutrient concentrations with water body characteristics.
Change-point analysis, Bayesian inference, Macronutrients, Fluxes, River flows, Water quality properties
0048-9697
1449-1460
Pirani, Monica
655b535b-5117-4a63-84e7-0588fbe0acc1
Panton, Anouska
9fff77ed-1abb-4322-abb3-ffb0d17c9601
Purdie, Duncan
18820b32-185a-467a-8019-01f245191cd8
Sahu, Sujit
33f1386d-6d73-4b60-a796-d626721f72bf
Pirani, Monica
655b535b-5117-4a63-84e7-0588fbe0acc1
Panton, Anouska
9fff77ed-1abb-4322-abb3-ffb0d17c9601
Purdie, Duncan
18820b32-185a-467a-8019-01f245191cd8
Sahu, Sujit
33f1386d-6d73-4b60-a796-d626721f72bf

Pirani, Monica, Panton, Anouska, Purdie, Duncan and Sahu, Sujit (2016) Modelling macronutrient dynamics in the Hampshire Avon river: a Bayesian approach to estimate seasonal variability and total flux. Science of the Total Environment, 572, 1449-1460. (doi:10.1016/j.scitotenv.2016.04.129).

Record type: Article

Abstract

The macronutrients nitrate and phosphate are aquatic pollutants that arise naturally, however, in excess concentrations they can be harmful to human health and ecosystems. These pollutants are driven by river currents and show dynamics that are affected by weather pattern and extreme rainfall events. As a result, the nutrient budget in the receiving estuaries and coasts can change suddenly and seasonally, causing ecological damage to resident wildlife and fish populations. In this paper, we propose a statistical change-point model with interactions between time and river flow, to capture the macronutrient dynamics and their responses to river flow threshold behaviour. It also accounts for the nonlinear effect of water quality properties via nonparametric penalised splines. This model enables us to estimate the daily levels of riverine macronutrient fluxes and their seasonal and annual totals. In particular, we present a study on macronutrient dynamics on the Hampshire Avon River, which flows to the southern coast of the UK through the Christchurch Harbour estuary. We model daily data for more than a year during 2013-14 in which period there were multiple severe meteorological conditions leading to localised flooding. Adopting a Bayesian inference framework, we have quantified riverine macronutrient fluxes based on input river flow values. Out of sample empirical validation methods justify our approach, which captures also the dependencies of macronutrient concentrations with water body characteristics.

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More information

Accepted/In Press date: 22 April 2016
e-pub ahead of print date: 11 May 2016
Published date: 1 December 2016
Keywords: Change-point analysis, Bayesian inference, Macronutrients, Fluxes, River flows, Water quality properties
Organisations: Ocean and Earth Science, Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 393510
URI: http://eprints.soton.ac.uk/id/eprint/393510
ISSN: 0048-9697
PURE UUID: daed3c7b-a098-48bf-a496-9a13598935d5
ORCID for Anouska Panton: ORCID iD orcid.org/0000-0003-3834-1532
ORCID for Duncan Purdie: ORCID iD orcid.org/0000-0001-6672-1722
ORCID for Sujit Sahu: ORCID iD orcid.org/0000-0003-2315-3598

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Date deposited: 27 Apr 2016 13:11
Last modified: 28 Apr 2022 04:21

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Contributors

Author: Monica Pirani
Author: Anouska Panton ORCID iD
Author: Duncan Purdie ORCID iD
Author: Sujit Sahu ORCID iD

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