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

Abstract

The potential of user-centric climate services to facilitate proactive drought management approaches is leading to increased efforts to develop and test climate services that include hydro-meteorological forecast products. In Spain, there is an interest to incorporate seasonal forecasts into drought management, including by integrating the information these provide into the system of drought indicators that are used in current operations. However, despite seasonal forecast information being available to users, their actual use to support operational decisions is limited. One aspect that fosters uptake is how credible users consider seasonal forecasts, including the quality of the forecasts. Additionally, the salience of the information provided is important, with information being credible at the spatial and temporal scales commensurate to those of the indicators used to support decisions. Bias-correction of seasonal forecasts has a crucial role in enhancing forecast quality, though there are several aspects intrinsic to bias-correction processes that may influence the degree of quality enhancement from the perspective of the users’ interests. An aspect that has so far received little attention is the spatial resolution of the forecast and the reference precipitation datasets that are applied. Here, we examine the influence of spatial resolution of both the forecast and the reference precipitation datasets used in the bias-correction process on the skill of seasonal forecasting, from the perspective of the indicators used in operational drought management decisions. We apply bias-corrected daily rainfall forecasts (ECMWF System 5) for a region within the Spanish Douro River Basin. We use a Bayesian Joint Probability (BJP) modelling approach for bias correction and the Schaake Shuffle method to conserve the temporal and spatial correlations across lead times and sub-catchments, respectively (Schepen et al., 2018). We consider two resolutions of the forecast product (1° and 0.4°), and apply the bias correction at three nested spatial scales, namely independent sub-catchments, aggregated sub-catchments and the entire catchment. These spatial aggregations have been selected to match those of the indicators used in the drought management plan implemented by the Douro Basin Authority. Forecast quality is evaluated through a set of skill scores and metrics at the daily scale, as well as at the aggregated temporal scales of the indicators used. Our results show that the bias correction applied to the seasonal forecasts does improve the skill of the forecast information at the spatial and temporal scales that are relevant to the indicators used operationally. We also show the influence on the improvement of skill of choices made in selecting the spatial resolution of the forecasts themselves and at which bias correction is applied, and discuss how this can help inform the design of climate services to support operational drought management decisions.