Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas*

Abstract

Good initial states can improve the skill of hydrological ensemble predictions. In mountainous regions such as Switzerland, snow is an important component of the hydrological system. Including estimates of snow cover in hydrological models is of great significance for the prediction of both flood and streamflow drought events. In this study, gridded snow water equivalent (SWE) maps, derived from daily snow depth measurements, are used within the gridded version of the conceptual hydrological model Precipitation Runoff Evapotranspiration Hydrotope (PREVAH) to replace the model SWE at initialization. The ECMWF Ensemble Prediction System (ENS) reforecast is used as meteorological input for 32-day forecasts of streamflow and SWE. Experiments were performed in several parts of the Alpine Rhine and the Thur River. Predictions where modeled SWE estimates were replaced with SWE maps could successfully enhance the predictability of SWE up to a lead time of 25 days, especially at the beginning and the end of the snow season. Additionally, the prediction of the runoff volume was improved, particularly in catchments where the snow accumulation, and thus the runoff volume, had been greatly overestimated. These improvements in predictions have been made without affecting the ability of the forecast system to discriminate between the different runoff volumes observed. A spatial similarity score was first used in the context of SWE forecast verification. This confirmed the findings of the time series analysis and yielded additional insight on regional patterns of extended range SWE predictability.

Importance of maximum snow accumulation for summer low flows in humid catchments

“…This procedure resulted in a homogenized data set that covers the period Number of days below specified runoff threshold (25 % quantile of runoff from May to October used) Sum of winter precipitation (November-April) Rate of snowfall vs. total winter precipitation (S/P ) Sum of positive SWE changes from November to April Sum of positive air temperatures from November to April Current precipitation index C PI (Smakhtin and Masse, 2000) Day of year (DOY) with maximum SWE 1971-2012. This same data set has already been adopted to update initial conditions of a hydrological model used for ensemble monthly predictions of SWE and runoff (Jörg-Hess et al, 2015). Further details on the methodology used to process the SWE data are available in Jörg-Hess et al (2014), which further assessed the accuracy of the homogenized maps.…”

Mapping dominant runoff processes: an evaluation of different approaches using similarity measures and synthetic runoff simulations

“…Therefore, for this study, SN03 maps were always set as reference maps. A detailed description of the two similarity measures is reported in Hagen-Zanker (2009) and Hangrove et al (2006), while applications in hydrology are described in Speich et al (2015) and Jörg-Hess et al (2015). To identify those landscapes where automatic approaches perform better, the comparison measures were applied to the single sub-catchments, at a high spatial resolution, to take into account the added value of the finest maps.…”

Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

“…The predictability for SWE detected in this study could be related both to some amount of skill in precipitation prediction and to previous findings on the persistence of SWE predictions with shorterterm forecast horizons. For instance, in the case of Alpine snow cover, Jörg-Hess et al (2015) underline the persistence of SWE predictions at least up to a lag of 2 weeks.…”

“…For water management demands such as efficient hydropower production, large efforts have been made to measure SWE in catchments of reservoirs (Painter et al, 2016;Krajči et al, 2017;Schattan et al, 2017), to simulate distributed SWE in basins of reservoirs and water intakes Hanzer et al, 2016), to improve flood forecasts with distributed SWE data (Schöber et al, 2014), and to model future runoff under climate change conditions in snow-and ice-melt-dominated catchments (Barnett et al, 2005;Finger et al, 2012;Hanzer et al, 2017). Gridded SWE data used for initialization of a process-based hydrological model improved predictions of SWE with lead times up to 1 month (Jörg-Hess et al, 2015). Seasonal streamflow and reservoir inflow predictions in snow-dominated basins were quite skilful during the snowmelt season and showed larger uncertainties during the rest of the year (Schick et al, 2015;Anghileri et al, 2016).…”