Can an ensemble of downscaled global hydrological model outputs improve the performance and spatially disaggregate the output of a catchment scale model in data scarce contexts?

Global climate and hydrological modelling have shown that human influence on the hydrosphere has been growing and is projected to continue increasing. Global models can inform us of the regional trends and events occurring in the stream network, however, operational water management and research often require tailored and detailed modelling to support decision making. Decisions on which kind of hydrological model (lumped, distributed) and at what scale can, however, impact on the usability of the model outputs for use cases which were not anticipated during the model set-up.

Here we conduct two experiments with an objective to determine whether an ensemble of a downscaled Global Hydrological Models (GHM) can be used 1) to improve the performance, and 2) to spatially disaggregate the output of a catchment scale model to its sub-basins. We use two existing distributed models set up for research purposes in the Sekong, Sesan, and Srepok Rivers (a major tributary of the Mekong), and in the Grijalva-Usumacinta catchments in Mexico. In the first experiment, we downscale off-the-shelf runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) using a recently developed areal interpolation method, route the downscaled runoff, and apply model averaging on an ensemble consisting of the downscaled GHM timeseries and the output of the distributed model at the observation stations. In the second experiment, we downscale and route runoff from the GHMs down the river network, as in the first experiment. During the routing step we record the sub-basin of origin and the timestep of runoff as it reaches an observation station. This record is then used to reconstruct a distributed estimate of discharge (back-traced from the existing model output) in all river reaches. We validate the reconstructed distributed estimates by comparing their spatial distribution to the outputs of the original distributed hydrological models, and against streamflow records.

Our initial experiments show that the downscaled estimates from GHMs have potential to increase the performance of the model outputs. We also show that the reconstruction of hydrographs in sub-basins of the modelled area is possible, however, the uncertainties related to the method are large and the estimates are sensitive to the routing solution used in the back-tracing, and to the performance of the ensemble of GHMs.

The methodology has potential for improving the usability of GHMs in local contexts. Owing to the promptly available GHM outputs, the method allows for swift exploration of hydrological questions before a proper modelling experiment is set up. Using GHMs as supplementary ensemble members can also aid in locations where calibration of the models is difficult due to scarce or ill-fitting data, or when the original choice of model fails to capture some aspects of the hydrograph.