Status Quo and Perspectives of Flood Runoff Analysis for Urban Watersheds

Abstract

This paper outlines the current status of flood runoff analysis for urban watersheds and presents its future prospects. First, I will describe the urban flood disaster, then briefly review the history of the runoff models before explaining the current state of application of flood runoff models for urban watersheds. Next, storage function models as the lumped conceptual model are outlined, and the urban storage function (USF) model is explained. Then, the necessity of a detailed distributed physical model in urban watershed is described by explaining the difference between mesh data and the urban landscape GIS delineation. I introduce TSR model which is a precise distributed physical model utilizing the urban landscape GIS delineation. Finally, I overlook the future issues to be solved for lumped conceptual and distributed physical models as well as making some recommendations for reduction of urban flood disaster.

Urban Flood Runoff Modeling in Japan: Recent Developments and Future Prospects

“…Since then, along with the rapid development of computer and IT technology, the analysis of flood runoff in urban watersheds has been improved in several respects. Data collection by, e.g., satellites and radar is a significant advancement, as is the advancement of GIS technology [69]. Furthermore, a range of new methods based on, e.g., optimization methods, network theory, and AI, have been applied to flood runoff analysis and modeling.…”

“…Baba et al [22] and Hoshi et al [84] developed an SF model that directly uses the observed rainfall and runoff to solve the problems associated with the estimation of direct runoff. The proposal of a model concept that does not require the separation of baseflow is groundbreaking [69].…”

“…Machine learning using AI technology has developed remarkably in recent years. In order to utilize machine learning for flood forecasting, many AI-based flood runoff models have been constructed using, e.g., multilayered artificial neural networks (ANN) or deep learning (DL) [69]. Many of these models have been constructed for large river watersheds [104,105] using hourly observation data as input, and some DL applications for small-to-medium-sized mountain river watersheds have used 10-minute data to construct the models [106].…”