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

2007年夏天，英格兰发生的洪灾是二战以来该国和平时期最大的紧急事件，造成13人死亡，超过55,000户家庭和企业被淹，相关保险费用超过30亿英镑。在2007年洪水之前，英国在最近的过去经历了许多重大的洪水事件，其中包括：1)1998年复活节北安普顿及中部城镇的洪水，当时1：50的事件淹没了4,200户家庭；2)2005年冬天的卡莱尔洪水，1：200年一次，3人丧生，1,800处财产被淹。在2007年洪灾之后，政府委托皮特审查，以发现管理未来洪灾风险需要吸取的经验教训。PIT审查报告中报告的与这一申请有关的主要观察结果是，为管理河流走廊而采取的做法，即疏浚、清除碎屑，特别是清除植被，不再那么频繁地进行，以按照《水框架指令》维持河流的生态多样性。这大大降低了河道的通行能力，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(UKCIP09)预测，随着极端降雨事件的增加，冬季将更加潮湿约25%。英国的防洪工程由环境局管理。为了管理这些资源，我们需要了解河道和相关泛滥平原的能力。英国许多河流中都存在水生植被，这会降低河道的容量，导致水流速度降低，进而导致单位流量的水位更高，从而增加洪水的风险。因此，有必要加深我们对植被隔断在流速和深度变化之间如何泄流的理解，以及这反过来如何影响河道的泄流能力，即输送，以更好地管理英国境内的洪水预测和预防。这项提议认为，我们现在可以以高分辨率和精确度测量地形，并明确地将其纳入洪水模型。但植被的情况并非如此，人们仍然缺乏对如何表示植被对河流系统功能的影响的理解。事实上，洪水模型预测中的绝大多数不确定性源于植被对输送的影响。为了摆脱基于经验的植被阻力参数化方法，有必要对水流和湍流产生有一个新的理解，以便能够重新制定洪水模型的动态植被粗糙度处理方法，从而减少洪水预报的不确定性。这将通过在实验室进行高分辨率实验并开发一种新的三维模型来实现，该模型能够预测流动和植物运动。该模型将使用实验数据进行验证，然后将两个数据集结合在一起，以实现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一个行业标准的洪水预报模型。一个现有的洪水实例将被用来开发和测试模型，因为这将使我们能够：1)评估这种新的建模方法改善模型预测的程度；2)理清洪水模型中的参数化和数据误差，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.