Mountain View Storm Drain Master Plan Climate Change and Rainfall Statistics

Transcription

1 Floodplain Management Association 2017 Mountain View Storm Drain Master Plan Climate Change and Rainfall Statistics James Gregory, PE, ESA, 9/7/2017

2 Project overview The City is working to establish a prioritized CIP to reduce flood risk in Mountain View The CIP includes evaluating future climate scenarios for a Storm Drain Master Plan ESA developed a climate tool linking MATLAB and GIS to estimate changes in rainfall intensity-duration-frequency under effects of climate change

3 Climate change background General circulation models Emissions scenarios SRES (replaced) RCPs (IPCC AR5) Downscaling Dynamic Statistical wikipedia.org Adapted from IPCC, 2014 Cal-adapt.org

4 Climate change trends in California More frequent, and stronger, tropical cyclones Uncertain effect on winter extratropical cyclones More frequent, and heavier, atmospheric river events Accelerating sea level rise (WAIS) Emanuel 2013 Lehmann 2014 Jeon 2015

5 Climate Data Sources Existing data New Data WCRP CMIP5/CMIP3 Cal-Adapt California Climate Commons CMIP5 GCM data Scripps LOCA Resolution 12km 12km 270m 100km 4km Spatial coverage CONUS California and Nevada California Global CONUS Temporal coverage Available variables Number of GCMs Data access Raw data - temperature, rainfall, hydrologic Raw and postprocessed - temperature, rainfall, hydrologic, wind, fire Post-processed - temperature, rainfall, hydrologic Raw data - temperature, rainfall Raw data temperature, rainfall, hydrologic ns.org/ ojects/cmip5- dkrz/

6 Problem statement The need for floodplain managers to plan for climate change impacts is increasing Guidance is lacking with respect to quantifying the impact of climate change on rainfall Data resources exist to quantify change in climate conditions How to leverage this data to inform resource planning?

7 General approach EC 100yr depth 2100 high 100yr depth Future IDF curves Future IDF Spatial Data Hydrologic Model Hydraulic model Floodplains

8 Intensity-duration-frequency curves Typically available at County Level Also available digitally from NOAA (Atlas 14) Santa Clara County (2006) NOAA Atlas 14

9 Data Daily rainfall gridded data from ~60,000 grids/model 30 GCM model runs and 2 climate scenarios ~3.6M grids of rainfall data Historic period used to train models from Data downscaled to 12km resolution for CONUS BCCA downscaled data used Daily total rainfall, BCCA access1-0 GCM, July 07, 1957

10 Climate data frequency analysis 10.0 Historic frequency 10.0 Future frequency, 2067 RCP4.5 Peak annual rainfall (in) Probability Annual Maxima GEV fit Peak annual rainfall (in) Probability Annual Maxima GEV fit 0.010

11 Model distribution

12 Data output Emissions scenario Recurrence interval Year year 2100 RCP Climate model distribution statistic Percent change in rainfall depth Mean 17% Mean + 1SD 30% Mean + 2SD 43% Mean 25% Mean + 1SD 42% Mean + 2SD 58% Mean 20% Mean + 1SD 46% 100-year 2100 Mean + 2SD 71% Mean 26% Mean + 1SD 53% Mean + 2SD 80%

13 Further Applications Sizing new stormwater infrastructure to accommodate future climate conditions Understand future level of service for existing infrastructure Estimate timing over which existing infrastructure will need to be upgraded Develop future climate conditions IDF curves and spatial data for hydrology guidelines

14 Questions - Contact James Gregory, PE jgregory@ Carlos Diaz, PE cdiaz@