Rainfall Intensity–Duration–Frequency (IDF) curves are among the most essential datasets used in water resources management across the globe. Traditionally, they are derived from observations of historical rainfall, under the assumption of stationarity. Change of climatic conditions makes use of historical data for development of IDFs for the future unreliable, and in some cases, may lead to underestimated infrastructure designs. The IDF_CC tool is designed to assist water professionals and engineers in producing IDF estimates under changing climatic conditions. The latest version of the tool (Version 4) provides updated IDF curve estimates for gauged locations (rainfall monitoring stations) and ungauged sites using a new gridded dataset of IDF curves for the land mass of Canada. The tool has been developed using web-based technologies and takes the form of a decision support system (DSS). The main modifications and improvements between version 1 and the latest version of the IDF_CC tool include: (i) introduction of the Generalized Extreme value (GEV) distribution; (ii) updated equidistant matching algorithm (QM); (iii) gridded IDF curves dataset for ungauged location and (iv) updated Climate Models.

Damages to urban systems as a result of water-related natural disasters have escalated in recent years. The observed trend is expected to increase in the future as the impacts of population growth, rapid urbanization and climate change persist. To alleviate the damages associated with these impacts, it is recommended to integrate disaster management methods into planning, design and operational policies under all levels of government. This manuscript proposes the concept of ResilSIM: A decision support tool that rapidly estimates the resilience (a modern disaster management measure that is dynamic in time and space) of an urban system to the consequences of natural disasters. The web-based tool (with mobile access) operates in near real-time. It is designed to assist decision makers in selecting the best options for integrating adaptive capacity into their communities to protect against the negative impacts of a hazard. ResilSIM is developed for application in Toronto and London, Ontario, Canada; however, it is only demonstrated for use in the city of London, which is susceptible to riverine flooding. It is observed how the incorporation of different combinations of adaptation options maintain or strengthen London’s basic structures and functions in the event of a flood.

Intensity-duration-frequency (IDF) relationships are traditional tools in water infrastructure planning and design. IDFs are developed under a stationarity assumption which may not be realistic, neither in the present nor in the future, under a changing climatic condition. This paper introduces a framework for generating non-stationary IDFs under climate change, assuming that probability of occurrence of quantiles changes over time. Using Extreme Value Theory, eight trend combinations in Generalized Extreme Value (GEV) parameters using time as covariate are compared with a stationary GEV, to identify the best alternative. Additionally, a modified Equidistance Quantile Matching (EQM_NS) method is implemented to develop IDFs for future conditions, introducing non-stationarity where justified, based on the Global Climate Models (GCM). The methodology is applied for Moncton and Shearwater gauges in Northeast Canada. From the results, it is observed that EQM_NS is able to capture the trends in the present and to translate them to estimated future rainfall intensities. Comparison of present and future IDFs strongly suggest that return period can be reduced by more than 50 years in the estimates of future rainfall intensities (e.g., historical 100-yr return period extreme rainfall may have frequency smaller than 50-yr under future conditions), raising attention to emerging risks to water infrastructure systems.

Flooding and overflow are recurring problems in several Brazilian cities, which usually face disorderly development. The causes vary, and include increased impervious surface areas, deficiency/inefficiency of drainage structures and lack of maintenance, siltation of rivers, channel obstructions, and climatic factors. In this paper, we present an analysis of mitigation measures to minimize flooding in a watershed located in the core of the city of São Paulo, the biggest city with the highest gross domestic product (GDP) in Brazil. Observed rainfall records and existing intensity duration frequency (IDF) curves for the region are used to obtain design storms. To account for climate change, the equidistance quantile matching method for updating IDF curves under climate change, a well-known procedure, was applied to the existing historical data. Several different global climate models (GCMs) and one regional model were applied to obtain and update rainfall design storms. The GCMs and future scenarios used were from Intergovernmental Panel on Climate Change—IPCC Assessment Report 5 (AR5) and two future projections—representative concentration pathway (RCP) 4.5 and 8.5. Spatially distributed reservoirs combined with low-impact development (LID) measures were used to evaluate different design storm scenarios combined with return periods of 25 and 100 years as well as the updated IDF under climate change for RCP 4.5 and RCP 8.5. Results show that the proposed changes to the drainage system can help reduce the risk and damage of flooding. The climate change scenarios, however, impose a significant threat and need immediate attention from city planners and stakeholders.

Changes in climatic conditions are expected to affect the hydrological cycle with intensification of extreme rainfall events caused by the disturbance in temperature and other atmospheric variables linked to precipitation. Extreme rainfall change will affect the intensity-duration-frequency (IDF) relationship, used in the design, maintenance, and operation of water infrastructure in Canada. This study presents a comparative analysis of the results from two IDF updating methods: (1) the IDF_CC tool, which applies an equidistance quantile-matching precipitation downscaling algorithm, and (2) the Clausius-Clapeyron (C-C) precipitation-temperature relationship, used with a proposed constant temperature scaling rate. The analyses were conducted using 358 selected Environment Canada hydro-meteorological stations from the IDF_CC tool database with record length longer than 20 years. Results for the future period (2061–2100), are based on the multimodel ensemble of 24 global climate models (GCMs). The difference in (1) projected precipitation and (2) uncertainty range for both IDF updating methods are presented and analyzed. The uncertainty range is defined in this work as the difference between IDF relationships obtained using various GCMs. The C-C temperature scaling method resulted, overall, in higher extreme precipitation projections than the IDF_CC tool for the stations located in the Canadian Prairies (i.e., the provinces of Alberta, Saskatchewan, and Manitoba). Stations located at the east and west coasts of Canada show smaller difference in the projected extremes. A similar pattern is observed for the multimodel ensemble median and the all individual GCMs. The difference in projected uncertainty range for both methods was analyzed for the multimodel ensemble and for representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5 emission scenarios. The C-C scaling shows a smaller uncertainty range for RCP 2.6 and RCP 4.5, and the IDF_CC tool shows a smaller uncertainty range for the RCP 8.5 scenario (especially for stations located in the Canadian Prairies). The difference in percent uncertainty ranges from $-75\%$ to about 100%, considering all stations across Canada. Both methods show comparable uncertainty range in the future. One significant conclusion is that the high level of uncertainty cannot be avoided, regardless of the method selected for updating IDF curves for future conditions. Use of the precipitation-based IDF_CC tool is recommended because of serious issues in using a constant scaling rate with C-C temperature scaling.

This paper presents a Decision Support System (DSS) to assist in the issuing of wastewater discharge and water abstraction rights, including the evaluation of alternative pollution control strategies used to facilitate the analysis and implementation of the instrument. The DSS substantiates its analysis with the use of evolutionary algorithms for the optimization of water demand and wastewater discharge allocation. It intends to maximize the uses and minimize the costs of wastewater treatment measures, according to the limits imposed by the water quality standards. Among the strategies considered for the issuing of permits were the compliance with environmental legislation for wastewater discharge, the equality between water users, the water quality standards set by the water bodies’ classification, and the restrictions imposed by the responsible controlling water agency. The DSS’s results were satisfactory to the strategies analyzed, as they complied with the restrictions and penalties imposed to the objective function. The main objective of the proposed strategies is to evaluate the performance of the DSS in getting the results, as well as to analyze the flexibility of the algorithms when new restrictions and penalties are introduced in the decision making process.