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

现在预报河流流量和山洪暴发是一项重大挑战，部分原因是业务数值预报尚不能有效地预报亚公里和小时尺度的对流降水事件。这导致不可预见的山洪暴发，对公共财产和基础设施造成巨大破坏，并可能造成生命损失。吉奥弗外滩ABC/J地区的突出例子是2010年7月3日和2016年6月6日在瓦赫特堡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种新的概率近距离预报框架。该项目的重点是瓦赫特贝格、安默和博德流域的三个仪表齐全的小型源头流域，这些流域容易发生山洪暴发。我们将把由P1、P2和P3开发的QPE、QPN和QPF(分别为定量降水估计、预报和预报)应用于河流流量的短期预报框架，以评估改进后的产品对山洪预报的影响。该项目的最初特点之一将是应用不同的水文模型(概念性的和基于物理的)和数据同化(流量和土壤湿度)来预报山洪暴发。我们将确定每个模式(和数据同化方法)在这次练习中的附加值和局限性。虽然概念性模型可以受益于根据应用(如大流量)对其参数进行校准以最好地拟合主要的相关变量(流量)，以及快速运行更大集合的可能性，但基于物理的模型可以受益于越来越多的观测资料以及更详细的过程和土地覆盖描述，这使得这些模型很容易转移到其他流域。我们最终将调查这些不同的方法是否可以为预测山洪提供补充信息。

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.