Role of Edge Dynamics in Controllability of Flooding Networks
Abstract
Despite local and federal mitigation plans, damages due to inland flooding have been on the rise in the past few decades. In order to prepare for disastrous flooding events, it is essential to understand the control measures needed to contain or limit the extent of damages. From a systems analysis point of view, a system is called controllable if, with a proper choice of inputs, it can be driven from any initial state to any desired state. Viewing flooding networks as a system, controllability can then readily be interpreted as the ability to control flow levels a major goal in flood mitigation and management. Hydrologic networks are typically represented as a collection of nodes symbolizing junctions, inlets and outlets, and edges (links) symbolizing stream paths. Typically, controllability is measured through nodal dynamics. However, due to the importance of dynamical processes that occur along the flow conduits (i.e. edges), edge dynamics can highly affect our ability to control such networks. In our earlier study (Riasi & Yeghiazarian, 2017), we studied controllability of hydrological networks by focusing on nodal dynamics where we proposed a set of four metrics (Full controllability, Target control efficiency, control Centrality, and control Profile FTCP). These metrics collectively determine the structural boundaries of the systems control space and answer questions such as: How does the structure of a network affect its controllability? How to efficiently control a preselected subset of the network? Which nodes have the highest control power? What types of topological structures dominate controllability? In this study we implement both nodal- and edge-based controllability analysis on flooding networks. The objective of this study is twofold: i) understand the difference between node-controllability and edge-controllability for flooding; and ii) develop a framework to understand the impact of individual edges on the overall controllability of the system.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.H13B..11R