Web-Based Tool for the Development of Intensity Duration Frequency Curves under Changing Climate at Gauged and Ungauged Locations

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.

[1]  Demetris Koutsoyiannis,et al.  Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation of long rainfall records / Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: II. Recherche empirique sur de longues séries de précipitations , 2004 .

[2]  Alain Mailhot,et al.  Future changes in intensity and seasonal pattern of occurrence of daily and multi-day annual maximum precipitation over Canada , 2010 .

[3]  M. Robin,et al.  Institutional Adaptation of Water Resource Infrastructures to Climate Change in Eastern Ontario , 2006 .

[4]  P. Coulibaly,et al.  Supplement of Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves? , 2017 .

[5]  Shigetoshi Sugahara,et al.  Non‐stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil , 2009 .

[6]  Alex J. Cannon,et al.  Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? , 2015 .

[7]  Slobodan P. Simonovic,et al.  Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change , 2014, Water Resources Management.

[8]  A. Aghakouchak,et al.  Nonstationary Precipitation Intensity-Duration-Frequency Curves for Infrastructure Design in a Changing Climate , 2014, Scientific Reports.

[9]  Alex J. Cannon,et al.  Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods , 2016 .

[10]  Comparison of the Theoretical Clausius–Clapeyron Scaling and IDF_CC Tool for Updating Intensity-Duration-Frequency Curves under Changing Climatic Conditions in Canada , 2018, Journal of Hydrologic Engineering.

[11]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[12]  T. Andrews,et al.  PDRMIP: A Precipitation Driver and Response Model Intercomparison Project, Protocol and preliminary results. , 2017, Bulletin of the American Meteorological Society.

[13]  G. Schmidt The contribution of greenhouse gases to the recent slowdown in global-mean temperature trends , 2016 .

[14]  Alex J. Cannon,et al.  Downscaling Extremes: An Intercomparison of Multiple Methods for Future Climate , 2013 .

[15]  Slobodan P. Simonovic,et al.  A decision support system for updating and incorporating climate change impacts into rainfall intensity-duration-frequency curves: Review of the stakeholder involvement process , 2016, Environ. Model. Softw..

[16]  S. Simonovic,et al.  Gridded Extreme Precipitation Intensity–Duration–Frequency Estimates for the Canadian Landmass , 2020 .

[17]  E. Maurer,et al.  Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods , 2007 .

[18]  Aart Overeem,et al.  Rainfall depth-duration-frequency curves and their uncertainties , 2008 .

[19]  J. Olsson,et al.  Applying climate model precipitation scenarios for urban hydrological assessment: a case study in Kalmar City, Sweden. , 2009 .

[20]  Dawei Han,et al.  Regional Frequency Analysis , 2011 .

[21]  Yuzhi Cai,et al.  Minimum Sample Size Determination for Generalized Extreme Value Distribution , 2010, Commun. Stat. Simul. Comput..

[22]  D. Sandink,et al.  Urban flooding and ground‐related homes in Canada: an overview , 2016 .

[23]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[24]  J. R. Wallis,et al.  Estimation of the generalized extreme-value distribution by the method of probability-weighted moments , 1985 .

[25]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[26]  H. V. Vyver,et al.  Spatial regression models for extreme precipitation in Belgium , 2012 .

[27]  Slobodan P. Simonovic,et al.  Rainfall Intensity Duration Frequency Curves Under Climate Change: City of London, Ontario, Canada , 2012 .

[28]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[29]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[30]  Slobodan P. Simonovic,et al.  A web-based tool for the development of Intensity Duration Frequency curves under changing climate , 2016, Environ. Model. Softw..

[31]  T. Andrews,et al.  Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models , 2013 .

[32]  Slobodan P. Simonovic,et al.  Assessment on variability of extreme climate events for the Upper Thames River basin in Canada , 2012 .

[33]  Amin Elshorbagy,et al.  Quantile-Based Downscaling of Precipitation Using Genetic Programming: Application to IDF Curves in Saskatoon , 2014 .

[34]  S. Simonovic,et al.  The Comparison of GEV, Log-Pearson Type 3 and Gumbel Distributions in the Upper Thames River Watershed under Global Climate Models , 2011 .

[35]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[36]  V. Nguyen,et al.  A statistical approach to downscaling of sub-daily extreme rainfall processes for climate-related impact studies in urban areas , 2007 .

[37]  P. Claps,et al.  MultiRain: a GIS-based tool for multi-model estimation of regional design rainfall for scientists and practitioners , 2019, Journal of Hydroinformatics.

[38]  D. Caya,et al.  Assessment of future change in intensity–duration–frequency (IDF) curves for Southern Quebec using the Canadian Regional Climate Model (CRCM) , 2007 .

[39]  R. Meentemeyer,et al.  Climatologically Aided Mapping of Daily Precipitation and Temperature , 2005 .

[40]  A. Ganguly,et al.  Intensity, duration, and frequency of precipitation extremes under 21st-century warming scenarios , 2011 .

[41]  Richard W. Katz,et al.  Statistics of extremes in climate change , 2010 .

[42]  H. Auld,et al.  Regionalization of heavy rainfall to improve climatic design values for infrastructure: case study in Southern Ontario, Canada , 2011 .

[43]  M. Dettinger,et al.  The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California , 2010 .

[44]  J. Hosking L‐Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics , 1990 .

[45]  T. Andrews,et al.  Efficacy of Climate Forcings in PDRMIP Models , 2019, Journal of geophysical research. Atmospheres : JGR.

[46]  Lukas Gudmundsson,et al.  Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods , 2012 .

[47]  R. Stouffer,et al.  Stationarity Is Dead: Whither Water Management? , 2008, Science.

[48]  S. Simonovic,et al.  Mapping Extreme Rainfall Statistics for Canada under Climate Change Using Updated Intensity-Duration-Frequency Curves , 2017 .

[49]  T. Reitan,et al.  Generalized extreme value shape parameter and its nature for extreme precipitation using long time series and the Bayesian approach , 2016 .

[50]  T. A. Solaiman,et al.  Extreme precipitation vulnerability in the Upper Thames River basin: uncertainty in climate model projections , 2011 .

[51]  S. Lai,et al.  RainIDF: automated derivation of rainfall intensity–duration–frequency relationship from annual maxima and partial duration series , 2013 .

[52]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[53]  C. Prudhomme,et al.  Mapping an index of extreme rainfall across the UK , 1998 .

[54]  J. Platt Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[55]  D. Shindell,et al.  Anthropogenic and Natural Radiative Forcing , 2014 .

[56]  Masson-Delmotte,et al.  The Physical Science Basis , 2007 .

[57]  M. Khaliq,et al.  Canadian RCM Projected Changes to Extreme Precipitation Characteristics over Canada , 2011 .

[58]  David D. Parrish,et al.  NORTH AMERICAN REGIONAL REANALYSIS , 2006 .

[59]  K. Trenberth Changes in precipitation with climate change , 2011 .