Experimental and numerical assessment of initial abstraction values in Eastern Ontario

低影响开发（LID）是指通过创建额外的现场存储和/或通过促进渗透来减少建筑工地径流的技术组合。LID的使用已经成为新的雨水管理系统的标准设计实践。已知有几种LID技术可以 ** 在降雨事件开始时使用各种机制增加场地上储存的水量。在风暴事件开始时捕获的水量称为初始抽取量（Ia）。它的大小由几个因素控制，如土地使用，土壤类型和在 ** 流路中是否存在LID基础设施。在估计给定LID的性能时的一个挑战是在给定站点的特定配置的情况下准确估计 ** 初始提取。在文献中可以找到一些典型的值（和分歧），但显然需要一种方法来更好地估计Ia。文献综述表明，有空间的非线性方法来预测新的方法，可以导致更精确的估计Ia。因此，本研究计划的目的是收集实验数据，以更准确地估计不同类型树木下的Ia。结果将在水文 ** 建模软件（SWMMHYMO）中实施，以更好地模拟LID狭窄情况下的径流。更好的Ia估计将导致更经济有效的设计，可以更好地复制开发后的条件 **，而不需要过多的校准和各种数据系列。

Low Impact Developments (LID's) refers to an ensemble of techniques that reduce runoff from construction**sites by creating additional onsite storage and/or by promoting infiltration. the use of LIDs has become the**standard design practice for new storm-water management systems. Several LID techniques are known to**increase the amount of water stored on the site at the beginning of rainfall events using various mechanism.**The amount of water captured at the beginning of storm events is called initial abstraction (Ia). Its magnitude is**controlled by several factors such as land use, soil type and the presence or not of LID infrastructures in the**flow path. A challenge in the estimation of the performance of a given LID is the accurate estimation of the**initial abstraction given the specific configuration of the site. A number of typical values (and diverging) can**be found in the literature but there is a clear need for a methodology a better estimation of Ia.A literature**review suggests that there is room for non-linear approach to predict for new approaches that can lead to more**precise estimations of Ia. The objective of thus research project is to collect experimental data for a more**accurate estimation of Ia under different types of trees. The results will be implemented in a hydrological**modelling software (SWMMHYMO) to better simulate runoff in presence of LID strictures. A better**estimation of Ia will lead to more cost-efficient designs that could better replicate post-development conditions**without the need for excess calibration and various data series.