Modeling the dispersion of precipitation in Hillsborough County, FL

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1 Modeling the dispersion of precipitation in Hillsborough County, FL Toni Panaou ESRI Water Conference Orlando, Florida Feb 8, 2017? Source: Azhagu Boomi (2010)

2 Outline Overview GIS Technique Results Conclusion Source: WordlessTech (2015)

3 Climate Change Impacts Water Supply Source: EPA 1 (N.A.)

4 EPA s Climate Projections for Tampa Bay Average annual temps in the SE US region will increase by 4 F to 8 F by 2100 with more days with temps over 95 F and fewer freezing events. Higher air temps, Higher ET More intense-stronger hurricane-related rainfall Source: EPA 2 (2016); EPA 3 (2016)

Timeframes 2020 to 2030 2060 to 2070 2090 to 2100

5 Overview Study Site: Hillsborough County, Florida General Circulation Models (GCMs) GFDL-ESM2G NorESM1-M Timeframes 2020 to to to 2100 Precipitation Annual Mean Summer Mean Source: Grid Point Model (N.A.)

6 Representative Concentration Pathway GCMs: GFDL-ESM2G & NorESM1-M, RCP 4.5, 6.0 & 8.5 RPC RCP2.6 RCP4.5 RCP6.0 RCP8.5 Definition Before 2100 the radiative forcing peaks at around 3 W/m 2 and then declines. After 2100 Extended Concentration Pathways (ECP) assumed emissions to be constant. After 2100 the radiative forcing are stabilized at around 4.5 W/m 2. ECPs concentrations are constant after After 2100 the radiative forcing are stabilized at around 6.0 W/m 2. ECPs concentrations are constant after By 2100 the radiative forcing reaches are greater than 8.5 W/m 2. ECP are assumed to have constant emissions after 2100 and constant concentrations after 2250.)

7 Downscaling & Bias-correction Bias-Correction & Stochastic Analog Method -temporal and spatial statistics

Kriging Geostatistical Analyst Tool Ordinary Prediction Assumes constant mean Z(s) = µ + ε(s) Z S 0 = N i=1 λ i Z S i Where S 0 is the prediction location; λ i = an unknown weight for the measured

8 Kriging Geostatistical Analyst Tool Ordinary Prediction Assumes constant mean Z(s) = µ + ε(s) Z S 0 = N i=1 λ i Z S i Where S 0 is the prediction location; λ i = an unknown weight for the measured value at the i th location; Z(S i ) is the measured value at the i th location; and N is the number of measured values. In this research, the unknown precipitation value Z(S 0 ) was a random variable located in (S 0 ), where the values of neighbor s samples (Z S i ), i = 1,..., N. Z(S i ) was also estimated as a random variable located in the interpolator S i Source: ESRI (2016)

9 Semivariogram y S i, S j = 1 2 var(z S i Z S j ) If S i and S j are located near each other, the difference in their values, Z(S i ) Z(S j ), will be small Example Semivariogram (GFDL-ESM2G RCP 4.5, Annual Mean )

10 GFDL-ESM2G Statistics Annual Annual Average of the Range Regression Mean Root Squared Mean Standardized RCP 4.5 RCP 6.0 RCP 8.5 RCP 4.5 RCP 6.0 RCP 8.5 RCP 4.5 RCP 6.0 RCP Summer Summer Average of the Range Regression Mean Root Squared Mean Standardized RCP 4.5 RCP 6.0 RCP 8.5 RCP 4.5 RCP 6.0 RCP 8.5 RCP 4.5 RCP 6.0 RCP

11 Results: GFDL-ESM2G Annual

12 Results: GFML-ESM2G Summer

13 Results: NorESM1-M Annual

14 Results: NorESM1-M Summer

15 Conclusions - GFDL-ESM2G Comparing RCP by time periods RCP 4.5 Minimal change in annual precipitation Slightly wetter summers RCP 6.0 Minimal change in annual precipitation Slightly drier summer RCP 8.5 Decrease annual precipitation Increased summer precipitation Drier winters Greatest Change by Comparing RCP to each other Annual For RCP 4.5 has highest and RCP 8.5 Lowest precipitation Summer For ALL have similar precipitation. For RCP 8.5 has highest precipitation Source: Clipart Kid (Feb 2015)

16 Conclusions NorESM1-M Comparing RCP by time periods RCP 4.5 Slightly Increased annual precipitation Slightly wetter summers RCP 6.0 Slightly Increased annual precipitation Minimal change in summer precipitation RCP 8.5 Decrease annual precipitation Slightly Drier Summers Comparing RCP to each other Annual For RCP 4.5 RCP 6.0 have highest precipitation with similar dispersion Summer For ALL have similar precipitation. For RCP 4.5 has highest precipitation Source: Clipart Kid (Feb 2015)

17 Conclusions Annual Comparison Similarities: RCP 4.5 ( ): Similar precipitation & dispersion RCP 8.5: Decreased annual precipitation GFDL-ESM2G NorESM1-M

18 Conclusions Summer Comparison Similarities: ALL ( ): Similar precipitation & dispersion RCP 4.5: Slightly wetter summer RCP 6.0: No change by 2100 RCP 8.5: No change by 2100 GFDL-ESM2G NorESM1-M

19 Questions? Contact Information: Toni Panaou

20 References Azhagu Boomi (2010). Jayam College of Engineering & Technology < (Nov, 23, 2016) Grid Point Models (N.A.). < gcm_grid_graphic.jpg> (Sept 21, 2016) Clipart Kid (Feb 2015). Did You Know That Raindrops Are Not Really Shaped Like < (Nov 28, 2016) Environmental Systems Research Institute, Inc. (ESRI) (2016). Understanding ordinary kriging < (Nov 30, 2016)

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