Lake Modelling

We scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. Forecasts were more accurate than a null model. Lake characteristics interact with weather to control the predictability of thermal structure.

Lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. We found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty.

Five lake models forced by four different climate model projections are used to simulate historic and future change in lake evaporation for Lake Kinneret (Israel). There were considerable differences in simulated evaporation rates among the models, with the annual average evaporation rates varying between 1232 mm year-1 and 2608 mm year-1 during the historic period (1901-2005). By the end of the 21st century (2070-2099), annual average evaporation rates would increase by 9-22% under RCPs 2.6-8.5. Our projections suggest that the water balance of Lake Kinneret could experience a deficit of 14-40% this century.

A novel method to inhibit stratification by reducing residence times is proposed as a way of controlling the length of the hypolimnetic anoxic period, thus reducing the loading of nutrients from the sediments into the water column. We found that reducing annual water residence times shortened and weakened summer stratification.

We scaled a near-term, iterative water temperature forecasting system to six conterminous NEON lakes. We generated forecasts using a hydrodynamic model. Forecasts were more accurate than a null model. Lake characteristics interact with weather to control the predictability of thermal structure.

Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported.

Develop an R package that is user-friendly, facilitates model running, sensitivity analysis, calibration and output processing all within the R environment.