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The potential of data assimilation for hydrologic predictions has\udbeen demonstrated in many research studies. Watersheds over which multiple\udobservation types are available can potentially further benefit from data\udassimilation by having multiple updated states from which hydrologic\udpredictions can be generated. However, the magnitude and time span of the\udimpact of the assimilation of an observation varies according not only to its\udtype, but also to the variables included in the state vector. This study\udexamines the impact of multivariate synthetic data assimilation using the\udensemble Kalman filter (EnKF) into the spatially distributed hydrologic model\udCEQUEAU for the mountainous Nechako River located in British Columbia,\udCanada. Synthetic data include daily snow cover area (SCA), daily\udmeasurements of snow water equivalent (SWE) at three different locations and\uddaily streamflow data at the watershed outlet. Results show a large\udvariability of the continuous rank probability skill score over a wide range\udof prediction horizons (days to weeks) depending on the state vector\udconfiguration and the type of observations assimilated. Overall, the\udvariables most closely linearly linked to the observations are the ones worth\udconsidering adding to the state vector due to the limitations imposed by the\udEnKF. The performance of the assimilation of basin-wide SCA, which does not\udhave a decent proxy among potential state variables, does not surpass the\udopen loop for any of the simulated variables. However, the assimilation of\udstreamflow offers major improvements steadily throughout the year, but mainly\udover the short-term (up to 5 days) forecast horizons, while the impact of the\udassimilation of SWE gains more importance during the snowmelt period over the\udmid-term (up to 50 days) forecast horizon compared with open loop. The\udcombined assimilation of streamflow and SWE performs better than their\udindividual counterparts, offering improvements over all forecast horizons\udconsidered and throughout the whole year, including the critical period of\udsnowmelt. This highlights the potential benefit of using multivariate data\udassimilation for streamflow predictions in snow-dominated regions

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions