Data for Lake Mendota Phosphorus Cycling Model

There is an opportunity to advance both prediction accuracy and
scientific discovery for phosphorus cycling in Lake Mendota
(Wisconsin, USA). Twenty years of phosphorus measurements show
patterns at seasonal to decadal scales, suggesting a variety of
drivers control lake phosphorus dynamics. Our objectives are to
produce a phosphorus budget for Lake Mendota and to accurately predict
summertime epilimnetic phosphorus using a simple and adaptable
modeling approach. We combined ecological knowledge with machine
learning in the emerging paradigm, theory-guided data science (TGDS).
A mass balance model (PROCESS) accounted for most of the observed
pattern in lake phosphorus. However, inclusion of machine learning
(RNN) and an ecological principle (PGRNN) to constrain its output
improved summertime phosphorus predictions and accounted for long term
changes missed by the mass balance model. TGDS indicated additional
processes related to water temperature, thermal stratification, and
long term changes in external loads are needed to improve our mass
balance modeling approach.