Evaluating Regional Watershed Sensitivity to Climate Change: Future Runoff and Sediment Variability in Southern California

Transcription

1 Evaluating Regional Watershed Sensitivity to Climate Change: Future Runoff and Sediment Variability in Southern California Dr. Terri Hogue Sonya Lopez, Ph.D. Candidate University of California Los Angeles Hydrology & Water Resources

2 Managing an Uncertain Future GCM change in temperature simulations from IPCC AR4 Synthesis Report (2007) How will this climate variability affect southern California?

3 Watershed Response How will regional watersheds respond to future climate variability? What level of cahnge can we expect for runoff, sediment, other water quality parameters? How will varying watershed characteristics (e.g. land use patterns) mitigate response to future climate? How will downstream ecosystems respond to altered inputs (flow and sediment)?

4 Climate Models Hydro model What are they? GCMs use CO 2 emission scenarios to general circulation variables How are they useful? Identify overall trends for large spatial regions Resolution: Issue? Fail to capture climatology at a resolution necessary for regional or watershed scale analysis Solution: Reduce simulation uncertainty (downscale) to make water quantity and quality predictions at resolutions appropriate for predictions and management decisions

5 Research Approach Phase I: Develop Framework for Regional Assessment of Climate Change (Lopez et al., 2011, in review) Phase II: Statistical Downscaling GCM Simulations for Southern California Phase III: Investigation of Climate Change Impacts on Southern California Watersheds using Hydrologic Models Phase IV: Water Quality and Quantity Comparison of the Quasi-Synthetic Framework and SD Approach

6 Phase I: Framework for Regional Assessment Goal: Develop quasi-synthetic framework to perform a quick regional assessment of flow and sediment changes due to climate variability. Hypothesis: By developing archetypal or representative that (1) emulate observed hydrologic behavior and (2) have observed physiological features we will ascertain impacts to water resources. Motivation: Efficient regional assessment for water resource managers Can be used to evaluate land-use influence Can help understand impact on downstream ecosystems. This work is performed in collaboration with Southern California Coastal Water Research Project (SCCWRP) Lopez et al. 2011

7 Coastal Watersheds Region I (n=4) Region II (n=2) Region III (n=5)

8 Percent Area [%] Percent Area [%] Regional Land Cover % % 75% RI (veg) RII (urban) RIII (mix) Region RI (veg) I RII Region (urban) II Region RIII (mix) III Los Angeles San Diego Ventura County County County Urbanized Mixed Vegetated Agriculture Agriculture Forest Grass Forest Grass Shrub/Sage/Chaparral Bare Rural Shrub/Sage/Chapa Bare Rural Urban Development Water Urban Development Water Wetland Wetland

9 Design 3 Regional Archetypal Watersheds

10 Annual Precip. [cm] Annual Precip. [cm] Annual Precip. [cm] Climate Scenarios Temperature - Regression 0.5 to 3 C using historical data 0.5 to 2 C in California within the first 30 years of the 21st century (California Action Team, 2009) GCM change in temperature simulations from IPCC AR4 Synthesis Report (2007) Precipitation - Increase variability: 5, 10, 25, 50% Region I: Santa Maria 80 (a) = Region II: Los Angeles 80 (b) = Region III: San Diego 80 (c) = = = =

11 Climate Matrix Temperature Precipitation [%] # [ C] Scenarios Regression 1 Total 21

12 Hydrologic Model: EPA HSPF Environmental Protection Agency Hydrologic Simulation Program Fortran Conceptual-based, lumped parameter model Hydrology parameters required 20 Pervious 6 Impervious Operates on watershed scale Required HSPF inputs: precipitation and potential evaporation (or inputs of Temperature for internal calculations of PE) Parameter feasibility obtained from previous studies and moderate adjustments to parameters

13 Model Verification: Annual Runoff Runoff ratio - RO Q P RO RO I 0.11 obs 0.12 RO RO II obs RO RO III obs Annual response best at lower flows for Archetypes Difficulty capturing high flows in vegetated and mixed archetypes Best model representation in urban system

14 Model Verification: Seasonal Patterns Seasonal Response generally captured by Archetypes Larger Variability in vegetated and mixed hydrographs Best model representation in urban system

15 Results: Storm Volume Changes Recurrence intervals for total storm volume Uncertainty bounds relative wide for all systems More for vegetated and mixed Change to dryer years (more frequent) Change to wetter years (less frequent) Liters RI 2x RII 1x RIII 3x yr Event -7% 1.9x % 2.1x % 9.5x % 9.7x10 13 Liters 35 yr Event RI 1x % 1.0x % 1.2x10 14 RII 4x % 4.0x % 4.6x10 14 RIII -1% 9.9x % 2.8x x % 1.32x % 3.3x10 13 Large deviations in the wetter years

16 Results: Peak Flow Changes Recurrence intervals for Peak Flow (Qpk) Uncertainty bounds wide for all systems during extreme storm events Infrequent storm events with a higher recurrence interval will be more extreme Change to dryer years (more frequent) Change to wetter years (less frequent) cms 2 yr Event RI -5% % 25.7 RII -8% % RIII -5% % cms 35 yr Event RI -4% % 88.3 RII +5% % RIII +6% % Large deviations in the wetter years

17 Annual Monthly Veg. Urban More vegetation reduced flow due to temperature increases Loss primarily occurs during dry periods Mixed

18 Results: Annual Storm Sediments Recurrence interval changes Precipitation Variability & Temperature Inc Enhanced Sediment Flux Wash-off during intense storms

19 Phase II: Statistical Downscaling of GCMs Step 1: Extract P & T obs Step 2: GCMs Observed Precipitation Temperature 4 GCMs identified 21 GCM variables Step 3: Statistical Downscaling methods Enhanced Canonical Correlation Analysis (Lopez and Hogue) Identifies spatial/temporal patterns Multiple Linear Regression Analysis (Zepeda, in progress) Integrates multiple variables into predicting Testing multiple predictor/predictand relationships & consider land use and topography

20 2.8 x 2.8 Grid Predictand Observations Mean Annual Precip [ ] 47 Precipitation sites 29 Temperature sites Historical trend analysis (Zepeda, in progress) Mean Annual Temp [ ] Data Period Historical: Counties: Santa Barbara, Ventura, Los Angeles, Orange, San Diego

21 Mean Daily T [K] Mean Daily T [K] Mean Daily T [K] Mean Daily T [K] Temperature Results: Los Angeles Daily Temperature ( ) Model Statistic GCM ECCA CNRM-CM3 RMSE % BIAS R GFDL-CM2.0 RMSE % BIAS R GFDL-CM2.1 RMSE Pre-ECCA % 298 BIAS R MRI-CGCM2-3.2a RMSE % 292 BIAS R Pre-ECCA 286 OBS CNRM-CM3 284 GFDL-CM2.0 GFDL-CM2.1 MRI-CGCM2-3.2A /00 04/09 07/18 10/ Post-ECCA / RMSE = Root Mean squared Error measure of the differences between predicted and observed 286 %BIAS = Percent Bias Oversimulation or undersimulation indicator 284 R 2 = Correlation measures strength 282 of linear relationship 01/00 04/09 07/18 10/ Hydrology /00 and 04/09 Water 07/18 Resources 10/26 at UCLA

22 Mean Daily Precip [cm] Mean Daily Precip [cm] Mean Daily Precip [cm] Pre-ECCA OBS CNRM-CM3 GFDL-CM2.0 GFDL-CM2.1 MRI-CGCM2-3.2A 0 01/00 04/09 07/18 10/ Precipitation Results: Los Angeles Post-ECCA OBS CNRM-CM3 GFDL-CM2.0 GFDL-CM2.1 MRI-CGCM2-3.2A Bias Post-ECCA Daily Precipitation ( ) OBS CNRM-CM3 GFDL-CM2.0 GFDL-CM2.1 MRI-CGCM2-3.2A Model Statistic GCM ECCA CNRM-CM3 RMSE /00 04/09 07/18 10/26 % BIAS R GFDL-CM2.0 RMSE % BIAS R GFDL-CM2.1 RMSE % BIAS R MRI-CGCM2-3.2a RMSE % BIAS R /00 04/09 07/18 10/26 RMSE = Root Mean squared Error measure of the differences between predicted and observed %BIAS = Percent Bias Oversimulation or undersimulation indicator R 2 = Correlation measures strength of linear relationship

23 Concluding Remarks Key Results Temperature increase: Greatest impact on vegetated systems during the low flow season Causes minimal change to storm volume and peak discharge Causes increase in sediments during low flow periods Precipitation variability: Causes increase in peak storm discharge in all systems More significant in urbanized systems Produces a shift in recurrence intervals Precipitation variability and temperature increase: Causes SIGNIFICANT increase in storm sediments in urban systems Enhanced scour and wash-off from pockets of pervious surfaces Benefits of Archetypal Method User-defined regional classification Minimal geographic and met data required Potential application to ungauged systems Ongoing Work Refinement of precipitation and temperature time-series using IPCC simulations and statistical downscaling Use to drive a diverse set of regional watersheds (specific systems) Compare two approaches

24 Questions?? Acknowledgements SCCWRP, NSF GRFP, NSF UCLA SEE-LA GK-12, NSF CAREER