Abstract:
Accurately estimating riverine nutrient loads remains an imperative step towards
mitigating and managing eutrophication-related impairments. However, load estimation
is often affected by the infrequent monitoring of riverine nutrient concentrations. In this
work, two novel Spatio-temporal Bayesian Hierarchical models (BHM), the ratio
estimator approach and the regression approach, are proposed, developed and validated.
The models are assessed with regards to their performance in estimating total phosphorus
loads between 2005 and 2020 across eight intensively monitored watersheds in Ohio,
USA. The two proposed BHMs incorporate watershed-level predictors that allow for the
partial-pooling of data collected from multiple watersheds over time. The proposed
models include two modelling hierarchies, namely one spatial and another temporal. The
spatial hierarchy attempts to model inter-station variability in loading by accounting for
watershed-level differences in land cover/land use (LULC), while the temporal hierarchy
attempts to capture the inter-annual variabilities in loads due to changes in flows. To
assess the skill of the two proposed models, the model estimates are compared to the "true loads" and to loads estimated using typical load estimation methods. Moreover, the
performance of the models is tested as a function of two different sampling frequencies.
Overall, the results showed that the BHM ratio estimator approach outperformed all other
load estimation methods. Moreover, its results proved to be invariant with the changes to
the sampling frequency which will allow for more efficient monitoring of watersheds, as
it can predict loads with a high degree of accuracy using as few as one sample every 10
days. This significantly reduces the cost and labor associated with monitoring, as well as
the potential for errors or inaccuracies. Finally, the results highlighted that accounting for
the spatial variabilities in LULC that existed between watersheds within the model hierarchical was significantly more important than accounting for the temporal
differences in flow.
