RIO GRANDE HEADWATERS RESPONSE TO CLIMATE AND FOREST CHANGE

Transcription

1 RIO GRANDE HEADWATERS RESPONSE TO CLIMATE AND FOREST CHANGE Melissa Valentin, PE, PhD Student and Terri Hogue, PhD, Professor Department of Civil and Environmental Engineering Colorado School of Mines, Golden, Colorado

2 SCIENCE QUESTIONS Have climate changes and forest disturbances already affected discharge in the Rio Grande Headwaters? How will future climate change impact flow in the Rio Grande Headwaters? 1

3 FOREST DISTURBANCE USGS (2013) Open File Report

4 FOREST DISTURBANCE Spruce beetle infestation Rio Grande National Forest, West Fork Complex Fire 2013 USFS (2013) stelprd jpg Indident Commant West Fork Complex (2013) inciweb.nwcg.gov 3

5 Study Area Rio Grande Headwaters Outlet: Del Norte, CO Area 3419 km 2 Mean slope 31% HUC Elevation 2432 to 4327 m Land cover Forest and Range 4

6 Historical Trends Discharge is decreasing at Del Norte ( ) Temperature is increasing (Snotel ) No significant Precipitation or SWE trends No significant flow trends at 3 upstream gages Del Norte Annual Mean Discharge (Mann Kendall p value 0.01, α=.05) 5 (Reference 7 )

7 Response to Forest Disturbance Methods Results Three time periods Control prior to 2002 Beetle after 2002 Burn after June 2013 Annual and monthly analyses of peak and mean discharge Wilcoxon rank sum α=.05 No significant change in flow after forest disturbance Del Norte 10% burned Wagon Wheel 10% burned South Fork 13% burned 30 Mile unburned 6

8 Monitoring of Tributaries Ongoing CSM monitoring in 3 extensively-burned and 3 unburned tributaries 7

9 8 Snow Dominated Watershed

10 Snow Modeling Methods Use SNOW17 snow accumulation and ablation model to simulate daily snow conditions Calibration Simulated vs. observed SWE at six Snotel stations Validation Simulate different time periods using calibrated parameters 2 sources of daily temperature and precipitation inputs 6 Snotel Stations, km PRISM grids, PRISM climate group, months NSE 0.79 Basin-wide SNOW17 simulations used 4km gridded PRISM data Anderson, 1973, NWS SNOW17 9

11 10 April 2005 SWE: SNOW17 & PRISM

12 11 June 2005 SWE: SNOW17 & PRISM

13 SNOW17 Calibration & Validation Calibrated to daily SWE at 6 SNOTEL stations Moriasi et al. (2009) 12

14 Sensitivity to temperature increase Use SNOW17 model to assess sensitivity of snow accumulation and snow melt to temperature increase Increase daily timeseries of Snotel observed temperature to +3 degrees C in increments of +0.1 per simulation (Lukas et al., 2014) 13

15 Sensitivity to temperature increase Earlier Spring peak Reduction in peak SWE Delayed onset Melts one month earlier 14

16 Future Snow Modeling Methods Use SNOW17 model to simulate future snow conditions using gridded climate projections from 17 CMIP5 models and RCP 4.5 (stabilized radiative forcing by 2100) MACAv2-METDATA data description 20 CMIP5 models downscaled to 4km resolution daily time series RCP 4.5 and RCP 8.5 scenarios temperature, precipitation, relative humidity, solar radiation and wind speed (Abatzoglou et al., 2012) 15

17 16 May 1, 2025 SWE: SNOW17- MACAv2

18 17 May 1, 2099 SWE: SNOW17- MACAv2

19 18 Snow conditions 2050 RCP4.5

20 Streamflow Modeling Methods Use SAC-SMA conceptual lumped rainfall-runoff model Lumped models at 4 long term stream gages 30 Mile, Wagon Wheel, South Fork and Del Norte Daily time series input Potential Evapotranspiration Penman-Monteith (Valiantzas, 2006) Precipitation Use output rain+melt from SNOW17 model Average of the 4km grids in each catchment area Calibration Separate calibration for periods before and after 2002 (forest disturbance) Sensitivity analysis, 14 parameters Shuffled Complex Evolution (Duan et al., 1994) Multi-Step Calibration (Hogue et al., 2006) 8 calibration periods, 16 validation periods 19

21 SAC-SMA Calibration & Validation 2 calibrations and 4 validations at each gage Calibrated to separate time periods, before and after forest disturbance 20

22 21 SAC-SMA Calibration

23 Future Runoff Modeling Methods Use SAC-SMA to simulate future runoff, 2016 to 2099, using multiple CMIP5 models at Del Norte MACAv2-METDATA, 17 CMIP5 models, RCP 4.5 Top 3 models selected based on statistical comparison to historical flow, (Annual flow NSE ranged from 0.83 to 0.95) Daily simulations for 2016 to 2099 Comparison to alternative sources of flow projections 22

24 23 Projected Streamflow

25 BSCD vs Simulated, HadGem2-cc Monthly Discharge at Del Norte HadGem2-cc RPC 4.5 Observations BSCD > MACAv2 Control parameters < Beetle parameters Low flows mid century Models converge and flows approach current mean by

26 Conclusions Del Norte mean discharge decreased , but no change was observed following forest destruction due to beetle kill and fire (only 10-13% burned area). Projected climate change (RCP 4.5) by mid century is projected dramatically reduce snowpack and change the discharge hydrograph at Del Norte. The developed framework, utilizing the SAC-SMA model with gridded SNOW17 simulations to evaluate potential hydrologic changes in the coming decades, can be applied to other snow dominated headwaters in Colorado. 25

27 Acknowledgments Terri S Hogue, PhD, Professor Civil & Environmental Engineering, Colorado School of Mines Edna B. Sussman Foundation Rio Grande Watershed Emergency Action Coordination Team (RWEACT) Rio Grande Headwaters Restoration Project Katherine Hegewisch, University of Idaho (MACAV2 data) Hogue Research Group Students and Staff 26