A seasonal to decadal calibration of 1990-2100 eastern Canadian freshwater discharge simulations by observations, data models, and neural networks

Abstract

A configuration of the NCAR WRF-Hydro model was sought using well established data models to guide the initial hydrologic model setup, as well as a seasonal streamflow post-processing by neural networks. Discharge was simulated using an eastern Canadian river network at two-km resolution. The river network was taken from a digital elevation model that was made to conform to observed catchment boundaries. Perturbations of a subset of model parameters were examined with reference to streamflow from 25 gauged catchments during the 2019 warm season. A data model defines the similarity of modelled streamflow to observations, and improvements were found in about half the individual catchments. With reference to 183 gauged catchments (1990-2022), further improvements were obtained at monthly and annual scales by neural network post-processing that targets all catchments at once as well as individual catchments. This seasonal calibration was applied to uncoupled WRF-Hydro simulations for the 1990-2100 warming period. Historic and future forcing were provided, respectively, by a European Centre for Medium-Range Weather Forecasting reanalysis (ERA5), and by a WRF atmospheric model downscaling of a set of Coupled Model Intercomparison Project (CMIP) models, where the latter were also seasonally calibrated. Eastern Canadian freshwater discharge peaks at about 10$^5$ m$^3$ s$^{-1}$, and as previous studies have shown, there is a trend toward increasing low flows during the cold season and an earlier peak discharge in spring. By design, neural networks yield more precise estimates by compensating for different hydrologic process representations.