Influence of spatial resolution on forecast quality of bias-corrected seasonal hydro-meteorological forecasts from a drought management perspective

In Spain, there is an interest to incorporate seasonal forecasts into drought management, particularly by integrating the information these provide into the established system of drought indicators that are used to trigger drought management measures and to guide water infrastructure operations. So far, the use of such information to support operational decisions has been limited. Decision makers often quote poor forecast quality as well as information not being available at the scale commensurate to the drought indicators they use as a reason. Provision of forecast data that are credible, and at the spatial and temporal scales relevant to the decision-making process is key to improving uptake. Bias correction of seasonal forecasts plays a fundamental role in improving forecast quality. Several aspects inherent to the bias-correction processes may influence the degree of quality improvement from the perspective of user needs. An aspect that has, however, so far received little attention is the influence of the spatial resolution of both the forecast and the reference datasets that are applied on the quality of the decision-relevant indicators derived from the bias-corrected forecast. 

In this study, we investigate the influence on forecast quality of the spatial resolution of both the forecast and the reference precipitation datasets in the bias correction process. We evaluate quality from the perspective of the indicators used in established operational drought management decisions. A Bayesian Joint Probability approach is used to bias-correct daily precipitation forecasts (ECMWF System 5) for a region within the Spanish Douro River Basin. We consider two resolutions of the forecast product (1° and 0.4°) and apply the bias correction using a historical dataset as a reference at three levels of catchment aggregation, namely, from the finest to the coarsest: independent sub-catchments used for the hydrological modelling of the region in the Douro Basin; aggregated sub-catchments used by the Douro Authority to determine the drought indicators used; and the entire catchment area of the main river in the region. Corrected seasonal precipitation forecasts at these three catchment aggregation scales are used to force a semi-distributed hydrological model to provide drought indicators derived from streamflow. Forecast quality is evaluated through a set of skill scores and user-centred metrics. Results show that the skill and reliability of bias-corrected precipitation forecasts do improve when compared to the raw forecast, with more substantial improvements at the coarser catchment aggregations and temporal resolutions. Results also show little difference in skill between the two resolutions of the forecast product. Furthermore, we show the influence of spatial resolution of both the forecast and the reference dataset on the skill improvement of the hydrological forecasts.  

Results from this study contribute to the understanding and quantification of uncertainty in hydro-meteorological forecasts and in bias-correction and post-processing techniques. These findings are also useful in implementing seasonal forecasting and bias correction methods at scales appropriate to decision-making, thereby supporting operational drought management.