Evaluation of summer drought ensemble prediction over the Yellow River basin

摘要

TAccurately predicting drought a few months in advance is important for drought mitigation and agricultural and water resources management, especially for a river basin like that of the Yellow River in North China. However, summer drought predictability over the Yellow River basin is limited because of the low influence from ENSO and the large interannual variations of the East Asian summer monsoon. To explore the drought predictability from an ensemble prediction perspective, 29-year seasonal hindcasts of soil moisture drought, taken directly from several North American multimodel ensemble (NMME) models with different ensemble sizes, were compared with those produced by combining bias-corrected NMME model predictions and variable infiltration capacity (VIC) land surface hydrological model simulations. It was found that the NMME/VIC approach reduced the root-mean-square error from the best NMME raw products by 48% for summer soil moisture drought prediction at the lead-1 season, and increased the correlation significantly. Within the NMME/VIC framework, the multimodel ensemble mean further reduced the error from the best single model by 6%. Compared with the NMME raw forecasts, NMME/VIC had a higher probabilistic drought forecasting skill in terms of a higher Brier skill score and better reliability and resolution of the ensemble. However, the performance of the multimodel grand ensemble was not necessarily better than any single model ensemble, suggesting the need to optimize the ensemble for a more skillful probabilistic drought forecast.摘要准确的干旱季节预测对农业和水资源管理非常重要。受东亚夏季风年际变率以及不同纬度多尺度相互作用的影响，黄河流域夏季干旱的可预报性有限。本文利用北美多模式集合（NMME）的不同海-陆-气耦合模式的29年土壤湿度季节回报数据计算干旱指数，与偏差校正NMME气候预测后驱动陆面水文模型VIC的土壤干旱季节预测结果比较，发现：NMME/VIC方法预测夏季土壤干旱均方根误差比NMME中最优模型的误差降低了48%，相关性显著提高。多模式集合相对于最优单个模型预测土壤干旱，其均方根误差进一步减小了6%。与NMME原始土壤干旱预测相比，NMME/VIC具有更高的干旱概率预报技巧，表现为更高的布莱尔技巧评分以及更高的可靠性和分辨率。