『Abstract
The validity of load estimates from intermittent, instantaneous
grab sampling is dependent on adequate spatial coverage by monitoring
networks and a sampling frequency that reflects the variability
in the system under study. Catchments with a flashy hydrology
due to surface runoff pose a particular challenge as intense short
duration rainfall events may account for a significant portion
of the total diffuse transfer of pollution from soil to water
in any hydrological year. This can also be exacerbated by the
presence of strong background pollution signals from point sources
during low flows.
In this paper, a range of sampling methodologies and load estimation
techniques are applied to phosphorus data from such a surface
water dominated river system, instrumented at three sub-catchments
(ranging from 3 to 5 km2 in area) with near-continuous
monitoring stations. Systematic and Monte Carlo approaches were
applied to simulate grab sampling using multiple strategies and
to calculate an estimated load, Le based
on established load estimation methods. Comparison with the actual
load, Lt, revealed significant average underestimation,
of up to 60％, and high variability for all feasible sampling approaches.
Further analysis of the time series provides an insight into
these observations; revealing peak frequencies and power-law scaling
in the distributions of P concentration, discharge and load associated
with surface runoff and background transfers. Results indicate
that only near-continuous monitoring that reflects the rapid temporal
changes in these river systems is adequate for comparative monitoring
and evaluation purposes. While the implications of this analysis
may be more tenable to small scale flashy systems, this represents
an appropriate scale in terms of evaluating catchment mitigation
strategies such as agri-environmental policies for managing diffuse
P transfers in complex landscapes.
Keywords: high resolution monitoring; Phosphorus; Load estimation』
1. Introduction
2. Methodology
2.1 Catchment characteristics
2.2. Instrumentation
2.3. Data processing and quality control
2.4. Completeness
2.5. Actual load and comparison with standard load estimation
methods
2.6. Sampling
2.7. Frequency analysis
3. Results and discussion
3.1. Patterns and variability in load estimates
3.2. Discrete frequency distributions
3.3. Sampling probabilities
3.4. Implications for sampling regimes
4. Conclusions
Acknowledgements
References