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

2007年夏天英格兰的洪水是自第二次世界大战以来该国最大的和平时期紧急情况，造成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，英国在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处财产被淹没。在2007年的洪水之后，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此应用相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，碎片清除和特别是植被清除，不再经常进行，以保持河流的生态多样性遵循水框架指令。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，极端气候将增加。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。英国的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的变化之间分配流量的理解，以及如何反过来影响河道的流量承载能力，即输送，以更好地管理英国的洪水预测和预防。该提案认为，我们现在可以测量地形，以高分辨率和精度，并将其明确纳入洪水模型。植被的情况并非如此，人们仍然缺乏对如何代表植被对河流系统功能的影响的理解。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来实现，同时开发一种新的三维模型，该模型能够预测水流和植物运动。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。一个现有的洪水实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 sinceWorld War II, with 13 deaths, over 55,000 homes & businesses flooded & an associatedinsurance cost of over £3 billion. Prior to 2007 floods, the UK had experienced a numberof 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 4,200homes were flooded in a 1:50 year event &; 2) the winter 2005 floods of Carlisle, a 1:200year event, when 3 people lost their lives & 1,800 properties were flooded. Following the2007 floods the Government commissioned the Pitt Review to discover the lessons thatneeded to be learnt to manage future flood risk. The key observation reported within thePitt Review relevant to this application is that practices which were undertaken to managethe river corridor; namely dredging, debris removal & notably vegetation clearance, were nolonger being performed as frequently, in order to maintain the ecological diversity of the riverfollowing 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 contextof 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 rainfallevents. Flood defences in the UK are managed by the Environment Agency. In order to managethese resources we require knowledge of the capacity of river channels & associatedfloodplains. Aquatic vegetation is present in many UK rivers & this reduces the capacity ofthe channel that causes a reduction in flow velocity, which in turn produces higher waterlevels per unit discharge, thus increasing the risk of flooding. Therefore, there is a needto develop our understanding of how vegetation partitions discharge between changesin velocity & depth & how, in turn, this impacts upon the discharge carrying capacity of achannel, 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, & thereremains a lack of understanding of how to represent the influence of vegetation on fluvialsystem function. Indeed, the vast majority of uncertainty in flood model predictions stem fromthe influence of vegetation on conveyance. In order to move away from an empirical basedapproach to the parameterisation of vegetation resistance, a new understanding of theflow & turbulence production is necessary to be able to re-formulated a dynamic vegetationroughness treatment for flood models & thus reduce the uncertainty in flood predictions. Thiswill be achieved by undertaking high resolution experiments in the laboratory in conjunctionwith the development of a new three dimensional model that is capable of predicting boththe 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 causedby the vegetation. This new understanding of the influence of vegetation of drag will beincorporated into an industry standard flood prediction model. An existing flood examplewill be used to develop & test the model as this will allow us to; 1) assess how well this newmodeling approach improves model predictions &; 2) disentangle parameterization & dataerror in flood models & enable us to assess what uncertainty needs to be addressed nextgeneration of predictive flood models.