Reducing uncertainty in flood prediction: the representation of vegetation in hydraulic models

2007年夏天的英格兰洪水是该国自第二次世界大战以来最大的和平时期紧急情况，有13人死亡，超过55，000所房屋和企业被淹没，相关保险费用超过30亿英镑。在2007年洪水之前，英国最近经历了许多重大的洪水事件，其中包括：1）1998年复活节北安普顿和中部地区周边城镇的洪水，4，200所房屋在1：50年的事件中被洪水淹没; 2）2005年冬季卡莱尔的洪水，1：200年的事件，3人丧生，1，800处财产被洪水淹没。在2007年洪水之后，政府委托皮特审查，以发现管理未来洪水风险需要吸取的教训。《皮特评论》中报告的与这一申请有关的关键观察结果是，为管理河流走廊而采取的做法，即疏浚、清除碎片和特别是清除植被，不再经常进行，以保持河流的生态多样性。这大大减少了河道的容量，从而增加了洪水的可能性。这是在英国未来洪水风险增加的背景下设定的，气候变化模型（UKCIP 09）预测冬季将增加约25%的湿度，极端降雨事件增加。英国的防洪设施由环境署管理。为了管理这些资源，我们需要了解河道和相关洪泛区的容量。英国许多河流中都有水生植被，这降低了河道的容量，导致流速降低，从而导致单位排放量的水位升高，从而增加了洪水的风险。因此，有必要发展我们对植被如何在速度和深度变化之间划分流量的理解，以及这反过来又如何影响通道的流量承载能力，即输送，以更好地管理英国境内的洪水预测和预防。该提案认为，我们现在可以测量地形的高分辨率和精度，并将其明确纳入洪水模型。植被的情况并非如此，人们仍然不了解如何代表植被对河流系统功能的影响。事实上，洪水模型预测的绝大多数不确定性源于植被对输水的影响。为了摆脱基于经验的植被阻力参数化方法，需要对水流和湍流产生有新的理解，以便能够重新制定洪水模型的动态植被粗糙度处理，从而减少洪水预测的不确定性。这将通过在实验室中进行高分辨率实验以及开发能够预测流量和植物运动的新三维模型来实现。该模型将使用实验数据进行验证，然后将两个数据集结合起来，使植被引起的阻力的新公式。这种对植被阻力影响的新认识将被纳入行业标准洪水预测模型。现有的洪水示例将用于开发和测试模型，因为这将使我们能够：1）评估这种新的建模方法如何改善模型预测; 2）解开洪水模型中的参数化和数据错误，使我们能够评估下一代预测洪水模型需要解决的不确定性。

The summer 2007 flooding in England was the country's largest peacetime emergency since World War II, with 13 deaths, over 55,000 homes & businesses flooded & an associated insurance cost of over £3 billion. Prior to 2007 floods, the UK had experienced a number of significant flood events over the recent past which have included amongst others; 1) the Easter 1998 floods of Northampton & surrounding towns in the Midlands when 4,200 homes were flooded in a 1:50 year event &; 2) the winter 2005 floods of Carlisle, a 1:200 year event, when 3 people lost their lives & 1,800 properties were flooded. Following the 2007 floods the Government commissioned the Pitt Review to discover the lessons that needed to be learnt to manage future flood risk. The key observation reported within the Pitt Review relevant to this application is that practices which were undertaken to manage the river corridor; namely dredging, debris removal & notably vegetation clearance, were no longer being performed as frequently, in order to maintain the ecological diversity of the river following the Water Framework Directive. This has substantially reduced the capacity of the river channel & has thus increased the potential of flooding. This is set within the context of the risk of flooding within the UK increasing into the future, with climate change models (UKCIP09) predicting that winters will be ~25% wetter, with an increase in extreme rainfall events. Flood defences in the UK are managed by the Environment Agency. In order to manage these resources we require knowledge of the capacity of river channels & associated floodplains. Aquatic vegetation is present in many UK rivers & this reduces the capacity of the channel that causes a reduction in flow velocity, which in turn produces higher water levels per unit discharge, thus increasing the risk of flooding. Therefore, there is a need to develop our understanding of how vegetation partitions discharge between changes in velocity & depth & how, in turn, this impacts upon the discharge carrying capacity of a channel, namely conveyance, to better manage flood prediction & prevention within the UK. This proposal argues that we can now measure topography to a high resolution & precision & incorporate it into flood models explicitly. This is not the case for vegetation, & there remains a lack of understanding of how to represent the influence of vegetation on fluvial system function. Indeed, the vast majority of uncertainty in flood model predictions stem from the influence of vegetation on conveyance. In order to move away from an empirical based approach to the parameterisation of vegetation resistance, a new understanding of the flow & turbulence production is necessary to be able to re-formulated a dynamic vegetation roughness treatment for flood models & thus reduce the uncertainty in flood predictions. This will be achieved by undertaking high resolution experiments in the laboratory in conjunction with the development of a new three dimensional model that is capable of predicting both the flow & the plant movement. The model will be validated using the experimental data & then the two data sets will be combined to enable a new formulation of the drag caused by the vegetation. This new understanding of the influence of vegetation of drag will be incorporated into an industry standard flood prediction model. An existing flood example will be used to develop & test the model as this will allow us to; 1) assess how well this new modeling approach improves model predictions &; 2) disentangle parameterization & data error in flood models & enable us to assess what uncertainty needs to be addressed next generation of predictive flood models.