Large scale evaluation of QPE, QPN and QPF improvements in a flash flood nowcasting framework with data assimilation

河流流量和山洪的临近预报构成了主要的挑战，部分原因是运行的NWP还不能有效地预测亚公里和小时尺度的对流降水事件。这将导致无法预料的山洪暴发，对公共财产和基础设施造成巨大损失，并可能造成生命损失。Geoverbund ABC/J地区的突出例子是2010年7月3日和2016年6月6日在Wachtberg发生的破坏性山洪暴发。该项目将为小流域（<500平方公里）的河流流量和山洪暴发开发一个新的概率临近预报框架。该项目侧重于Wachtberg、Ammer和Bode流域三个设备良好的小型水源集水区，这些地区容易发生山洪暴发。我们将使用P1、P2和P3开发的QPE、QPN和QPF（分别为定量降水估计、临近预报和预报）在河流流量的临近预报框架中，以评估改进后的产品对山洪预测的影响。该项目的原始特征之一将是应用不同的水文模型（概念和物理基础）与数据同化（流量和土壤湿度）进行山洪预报。我们将为本练习确定每个模型（和数据同化方法）的附加价值和局限性。虽然概念模型可以根据应用（例如高流量）校准其参数以最佳地拟合感兴趣的主要变量（流量），并且可以快速运行更大的集合，但基于物理的模型可以从越来越多的观测数据和更详细的过程和土地覆盖描述中受益，这使得这些模型易于转移到其他流域。我们将最终研究这些不同的方法是否可以为预测山洪暴发提供补充信息。

Nowcasting of river discharge and flash floods constitutes a major challenge, partly because operational NWP is not yet capable of predicting convective precipitation events at the sub-km and hourly scale in a useful quality. This leads to unforeseen flash floods resulting in large damages to public property and infrastructure, and potentially loss of life. Prominent examples in the area of the Geoverbund ABC/J are the destructive flash floods in Wachtberg on 3 July 2010 and on 6 June 2016. The project will develop a novel probabilistic nowcasting framework for river discharge and flash floods in small watersheds (<500km2). The project focuses on three well instrumented small headwater catchments of the Wachtberg, Ammer and Bode watersheds, prone to flash floods. We will employ QPE, QPN and QPF (Quantitative Precipitation Estimation, Nowcasting and Forecasting, respectively) developed by P1, P2 and P3 in a nowcasting framework for river discharge in order to assess the impact of the improved products on flash flood prediction. One of the original features of the project will be the application of different hydrological models (conceptual and physically-based) with data assimilation (discharge and soil moisture) for flash flood forecasting. We will identify the added value and limitation of each model (and data assimilation method) for this exercise. While conceptual models can benefit from the calibration of their parameters to best fit the main variable of interest (discharge) according to the application (e.g. high flows) and the possibility of quickly running larger ensembles, physically-based models can benefit from the increasing availability of observations and more detailed process and land cover descriptions, which make these models easily transferable to other catchments. We will ultimately investigate if these divergent approaches could provide complementary information for forecasting flash floods.