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Assessing the contribution of monitoring and modelling data to water quality
mapping estimation error
Joseph V. McGovern, Dr. Michael Hartnett
Civil Engineering Department, Environmental Informatics, Ryan Institute
j.mcgovern1@nuigalway.ie; michael.hartnett@nuigalway.ie
Abstract
The Water Framework Directive (WFD) (EC 2000)
sets the primary objective that all water bodies will
achieve a minimum of “good” status by 2015. Limited
guidance is available at present for assembly of
monitoring programmes for the WFD. Bayesian
Maximum Entropy incorporates monitoring data and
model data, enabling accurate maps to be generated
without the requirement for intensive monitoring
campaigns. The author intends to utilise a number of
approaches with BME to assist in optimisation of
monitoring programmes. Here, an assessment is made
of the relative contribution of monitoring and model
output to the lowering of mapping estimation error.
1. Introduction
Monitoring programme guidance provided under the
Common Implementation Strategy for the WFD lacks a
rigorous methodology. Advice relating to WFD
monitoring programmes is provided in the areas of
monitoring point location and quantity and sampling
frequency. Suggestions include grouping of waterbodies
with similar natural conditions/anthropogenic pressures,
increasing the density of sampling in space & time to
counteract high heterogeneity and locating monitoring
points at the most sensitive locations. Statistical
assistance is limited to approximating the number of
monitoring points for a desired confidence level &
precision. (Commission 2003)
2. Sparse data and Bayesian Maximum
Entropy
Sparse data availability limits the understanding of
water quality processes and causations. Normal
mapping interpolation of monitoring data via
simple/ordinary kriging would result in maps of limited
use unless a high number of monitoring points were
sampled for mapping, which would prove costly. BME
incorporates information such as measured field data
and model output to generate grid mean estimates. BME
allows the determination of the probability that the
variable under examination will be above an
environmental quality standard. BME presents an
opportunity to improve uncertainty in water quality
estimates and assess the efficacy of existing & proposed
monitoring programmes, both in terms of cost &
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sufficiency of estimation uncertainty(Joseph N.
LoBuglio 2007). BME involves two main stages: the
prior and posterior. The prior stage assembles a general
knowledge based pdf fg which describes the likely range
of values throughout the domain covered by
monitoring/model results. The posterior stage integrates
the domain pdf with adjacent data to give a pdf of the
estimation points in question (Christakos 2000).
4. Results
Maps were generated using BME with varying numbers
monitoring and modelling results for Ammoniacal
nitrogen concentrations in Cork harbour. Maps of
estimation error were then created. A significant
reduction in estimation error is observed with the use of
numerical model results. A larger area of low estimation
error is observed with the inclusion of uncertain model
data. Research will continue into the influence of
uncertain model outputs in BME mapping.
5. References
Christakos, G. (2000). Modern Spatiotemporal Geostatistics,
Oxford
University Press.
Commission, E. (2003). Monitoring under the Water Framework
Directive. . E. Commission.
EC (2000). Directive of the European Parliament and of the Council
2000/60/EC, establishing a framework for community action in the
field of water policy. . Official Journal of the European Communities.
Joseph N. LoBuglio, G. W. C., Marc L. Serre (2007). "Cost-effective
water quality assessment through the integration of monitoring data
and modelling results." Water Resources Research 43(3): W03435.1-
W03435.16.