2015 GSA Annual Meeting in Baltimore, Maryland, USA (1-4 November 2015)
Paper No. 63-9
Presentation Time: 4:00 PM
A NEW FRAMEWORK FOR FORWARD AND INVERSE MODELING OF STORMWATER LIDS
MASSOUDIEH, Arash and MAGHREBI, Mahdi, Civil Engineering, The Catholic University of America, 620 Michigan Ave. N.E., Washington, DC 20064, firstname.lastname@example.org
Design of Low Impact Developments LIDs to control stormwater runoff and contaminants has received considerable attentions within the last years. Despite their popularity, LID models are simplistic and there are uncertainties associated with their predictions of water quantity and quality. It is therefore essential to have tools that are able to 1) represent the LID systems and processes more accurately and with a higher level of flexibly and to 2) quantify the uncertainty associated with model structure, model parameters, and model prediction. The present research demonstrates development of a stand-alone mathematical modelling framework that can be used to assess the performance of different LIDS in terms of both water quantity and quality. In this framework, the LIDs are constructed using a set of blocks and connectors representing different components of an urban LID including soil, storage, pond, and overland sheet flow. The blocks and connectors can be made to accommodate different governing equations for flow and transport including but not limited to Richard’s equation for flow in unsaturated soil, kinematic wave equation, and transport of dissolved and particle-bond contaminants as well as the biochemical transformation of contaminants. An adaptive time-step Newton-Raphson method is used to solve the system. The framework is also equipped with parameter estimation and uncertainty assessment tools respectively using genetic algorithm and Markov-Chain Monte-Carlo.
The modeling framework, as a demonstration, is applied to perform parameter estimation and performance evaluation on runoff and water quality for two serial rain-gardens at Cincinnati that were subject to extensive runoff and water quality monitoring. The modeling results are used to evaluate the impact of various model parameters on rain-garden performance. The results can be used to guide the practitioners in designing more efficient rain-gardens.