Hydrological forecasting can benefit from a better understanding of urban floods and of the thresholds values of the hydrological variables that are crucial for making decisions. This session addresses these two aspects.
Urban flooding is becoming a major issue in many megacities around the world due to a lack of adequate storm water management, hydrologic design, and failure of aging hydrologic infrastructure. To model such extreme flood events, it is of utmost importance to develop state-of-the-art disaster mitigation and damage reduction measures, as well as one and two-dimensional hydrologic and coupled hydrodynamic modelling approaches. Innovative methods are needed to address the modelling and management of urban floods and their spatial and temporal complexity. The session discusses urban floods analysis and measures to mitigate the effects of these events, emerging (e.g., Internet-of-Things (IoT)-based) flood monitoring systems, street-level flood forecasting, dissemination of flood warnings and measures to evacuate people, case studies that provide a better understanding of urban flood management, and innovative methods of floodwater conservation, including strategies and practices to control surface runoff at its sources in a sustainable way.
In hydrological forecasting, where the stochastic nature of the processes makes impossible a deterministic forecast of both the magnitude of the processes and their effects, threshold values can be of great importance and usefulness. Thresholds can be simple (e.g., the threshold of rainfall intensity that might separate stratiform from convective rainfall) or complex and multi-variate (e.g., the threshold for damaging snow-melt flooding, or the threshold for intense hillslope erosion in an agricultural field). They can be useful for real-time forecasts based on simple thresholds on rainfall data (e.g., activation of mass movements such as landslides, debris flow, rill and inter-rill erosion, etc.), for the adoption of satellite data in the management of ground actions (e.g., values of the satellite indexes to be used in irrigation management), for distinguishing among water flow regimes, among other applications.
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