Coupling WRF and NRCS-CN Models for Flood Forecasting in Paraíba do Meio River Basin in Alagoas, Brazil

Abstract Coupling the WRF and NRCS-CN models was assessed as a tool for a flood forecast system. The models were applied to the Paraíba do Meio River basin, located in Alagoas, Brazil. FNL (Final Analysis GFS) data provided by the Global Forecast System model were used as initial conditions for WRF. Precipitations and observed discharges were collected in data collection platforms. Nine microphysics configurations were used to optimize WRF forecast. For hydrological, the automatic calibrations, available in HMS was used to get the optimum CN model parameters. Optimized precipitations Model performance was assessed with the indicators: bias, root-mean-square error, Pearson’s linear correlation coefficient, Nash-Sutcliffe coefficient, Heidke skill score, hit rate and false alarm rate. WRF´s predictive ability for the optimum configuration was satisfactory. The NRCS-CN yielded good results. The predictive ability of the hydrological model was ranked between satisfactory and acceptable. In a flood forecasting step, the coupled model yielded Nash-Sutcliffe of 0.749 and 0.572 for Atalaia and Viçosa basins. Overall, the method showed potential for the development of a flood alert system.摘要：本研究对WRF（Weather Research and Forecasting，天气研究与预报）模型与NRCS-CN模型的耦合方案作为洪水预报系统工具的效能进行了评估。上述模型被应用于巴西阿拉戈斯州的帕拉伊巴杜梅乌河流域。采用全球预报系统（Global Forecast System, GFS）生成的FNL（GFS最终分析）数据作为WRF模型的初始条件。通过数据采集平台获取了研究区域的降水与实测径流数据。设置9种微物理参数化方案对WRF的预报结果进行优化。针对水文模拟环节，借助HMS内置的自动率定工具获取了NRCS-CN模型的最优参数。基于优化后的降水输入，采用偏差（bias）、均方根误差（root-mean-square error）、皮尔逊线性相关系数（Pearson’s linear correlation coefficient）、纳什-苏特克利夫效率系数（Nash-Sutcliffe coefficient）、海德克技能评分（Heidke skill score）、命中率（hit rate）与虚警率（false alarm rate）共7项指标对模型性能进行评估。结果表明，最优配置下WRF模型的预报能力令人满意，NRCS-CN模型亦取得了良好的模拟效果，该水文模型的预报能力评级处于良好至可接受区间。在洪水预报环节中，耦合模型在阿塔拉亚与维索萨流域的纳什-苏特克利夫效率系数分别为0.749与0.572。总体而言，该耦合方法具备开发实用型洪水预警系统的应用潜力。