Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

Defining climate-change victims

This article introduces the concept of “climate-change victims” and classifies categories of threats and groups of people who would be vulnerable to and victimized by human-induced climate change. (The full, correct wording is “human-induced climate-change victims”, but we will use just “climate-change victims” in the rest of the article.) It offers a definition with three levels of climate-change victimization and differentiates “climate-change victims” from “natural-disasters victims” and from “climate-change migrants”. The article sets an agenda for a new type of victimhood and could lead to further research on possible prevention, accountability measures, environmental tribunals, and compensation mechanisms to recompense climate-change victims.