An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity

Abstract

Seasonal streamflow prediction skill can derive from catchment initial hydrological conditions (IHCs) and from the future seasonal climate forecasts (SCFs) used to produce the hydrological forecasts. Although much effort has gone into producing state-of-the-art seasonal streamflow forecasts from improving IHCs and SCFs, these developments are expensive and time consuming and the forecasting skill is still limited in most parts of the world. Hence, sensitivity analyses are crucial to funnel the resources into useful modeling and forecasting developments. It is in this context that a sensitivity analysis technique, the variational ensemble streamflow prediction assessment (VESPA) approach, was recently introduced. VESPA can be used to quantify the expected improvements in seasonal streamflow forecast skill as a result of realistic improvements in its predictability sources (i.e., the IHCs and the SCFs)—termed “skill elasticity”—and to indicate where efforts should be targeted. The VESPA approach is, however, computationally expensive, relying on multiple hindcasts having varying levels of skill in IHCs and SCFs. This paper presents two approximations of the approach that are computationally inexpensive alternatives. These new methods were tested against the original VESPA results using 30 years of ensemble hindcasts for 18 catchments of the contiguous United States. The results suggest that one of the methods, end point blending, is an effective alternative for estimating the forecast skill elasticities yielded by the VESPA approach. The results also highlight the importance of the choice of verification score for a goal-oriented sensitivity analysis.

Skilful seasonal forecasts of streamflow over Europe?

“…In this context, additional work to further disentangle and quantify the contribution of both predictability sources (seasonal climate forecasts versus IHC) to seasonal streamflow forecasting quality over Europe could be carried out by using the EPB (end point blending) method (Arnal et al, 2017).…”

Skilful seasonal forecasts of streamflow over Europe?

“…10 This paper assesses the added predictability from using seasonal climate forecasts, additionally to the IHC. Additional work to further disentangle and quantify the contribution of both predictability sources (seasonal climate forecasts versus IHC) to seasonal streamflow forecasting quality over Europe could be carried out by using the EPB (end point blending) method (Arnal et al, 2017).…”

The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers

“…This can be expected as the further away in time from the spring flood that the hindcast is initialised, increasing lead time, the less the initial hydrological conditions contribute to predictability and the more uncertain the forcing data become (e.g. Wood et al, 2016;Arnal et al, 2017).…”