Assessing the robustness of spring snowpack as a drought indicator in the Upper Colorado River Basin under future climate change

Transcription

1 Assessing the robustness of spring snowpack as a drought indicator in the Upper Colorado River Basin under future climate change Ben Livneh, Andrew Badger, Jeffrey Lukas, and Lisa Dilling Cooperative Institute for Research in Environmental Sciences UNIVERSITY OF COLORADO, BOULDER College of Engineering & Applied Science

2 Acknowledgements Dr. Andrew Badger (University of Colorado) NOAA Sectoral Applications Research Program Grant, NA16OAR , PI-Livneh: Advancing the Use of Drought Early Warning Systems in the Upper Colorado River Basin 2

3 Overview Managing for Drought in the Upper Colorado River Basin Snowpack as a Predictor of Streamflow Future Snow Projections and Changes in Streamflow Predictability 3

4 Problem: Supply vs. Demand Colorado River Water Supply: 7 states (2 countries), 40 million people, 5.5 million acres of farmland Seasonal forecasting is important, especially in dry years USBR: Colorado River Basin Water Supply And Demand Study, Study Report, 2012 : 4

5 Managing for drought Phase-1: Interview stakeholders in W. Colorado about what information they currently use to manage for drought. (Rebecca 10:45 session) Snow-based drought information (Snow Water Equivalent: SWE) was identified as the most reliable for water supply forecasting by 112 UCRB stakeholders (McNie, 2014) McNie, E Evaluation of the NIDIS Upper Colorado River Basin Drought Early Warning System. Available at: 5

6 Predictive power of historical snowpack SWE has been used to predict warm-season streamflow in two ways: Initializing numerical models As a predictor in regression equations that relate historical April 1 SWE* with April-May- June-July (AMJJ) streamflow *Other forecast dates include 1 Jan, Feb, Mar, May Accumulated precipitation is commonly used together with SWE Courtesy: Stacie Bender 6

7 How will the predictive value of snow information change under a warmer climate? Research Plan for this Presentation: Step 1 Baseline: Evaluate the predictive power of historical snow observations to forecast warm season streamflow, through simple correlation Step 2 Model validation: Repeat Step 1, using hydrologic model portrayals of snow and streamflow SNOTEL stations UCRB Step 3 Future Change: Quantify how Stream Gauge predictive power will change using CMIP5 downscaled hydrologic simulations for Courtesy: Jenna Stewart 7 mid-century

8 UCRB Streamflow at Lees Ferry (MAF) Baseline: Predictive power of historical snowpack Simple comparison of April 1 SWE with UCRB streamflow for April-May-June-July (AMJJ) Strong correlation, R=0.82, denotes predictive power R=0.82 Mean Apr 1 SWE (133 SNOTEL sites) 8

9 UCRB Streamflow at Lees Ferry (MAF) Can simulated hydrology be used to make similar predictions? Historical and future hydrologic simulations from the VIC model 1 (CMIP5, downscaled, bias-corrected) Nash-Sutcliffe Efficiency = VIC captures the overall water balance well month 1. Brekke, L., Thrasher, B. L., Maurer, E. P., and Pruitt, T. (2013). Downscaled CMIP3 and CMIP5 climate projections: Release of downscaled CMIP5 climate projections, 9 comparison with preceding information, and summary of user needs. Technical Service Center, Bureau of Reclamation, US Department of the Interior, Denver, CO.

10 Simulated SWE has strong correspondence to SNOTEL observations, despite persistent bias Historical SWE simulations 1 VIC grid box 1/8 ~ 12 km Negative bias is partly due to spatial scale and representativeness issue SNOTEL SWE (133 SNOTEL sites) 1. Brekke, L., Thrasher, B. L., Maurer, E. P., and Pruitt, T. (2013). Downscaled CMIP3 and CMIP5 climate projections: Release of downscaled CMIP5 climate projections, 10 comparison with preceding information, and summary of user needs. Technical Service Center, Bureau of Reclamation, US Department of the Interior, Denver, CO. VIC simulated SWE, grid-box nearest SNOTEL sites SWE Comparison Bias = -31%, R=

11 UCRB VIC Streamflow at Lees Ferry (MAF) Model has similar predictive power for historical period Comparison of simulated April 1 SWE with UCRB streamflow Correlation, R=0.84, denotes comparable predictive power to observed (R=0.82) R= R=0.88 VIC April 1 SWE (133 SNOTEL sites) 11

12 UCRB VIC Streamflow at Lees Ferry (MAF) Future predictive power is only slightly diminished relative to historical Downscaled hydrology from 31 CMIP5 models: Reduced SWE Diminished correlation, R=0.81, relative to the historical, R=0.84 (R=0.88) not statistically significant R=0.81 VIC April 1 SWE (133 SNOTEL sites) Brekke, L., Thrasher, B. L., Maurer, E. P., and Pruitt, T. (2013). Downscaled CMIP3 and CMIP5 climate projections: Release of downscaled CMIP5 climate projections, comparison with preceding information, and summary of user needs. Technical Service Center, Bureau of Reclamation, US Department of the Interior, Denver, CO.

13 Correlation Correlation Apr 1 SWE vs AMJJ streamflow UCRB Streamflow (MAF) Drought years have less predictive power during both time periods Drought year Non-Drought year All years Also, streamflow decreases (6.9%) and tends to peak a month earlier Historical Future Drought defined as streamflow below the 20 th percentile of historical flows. Month In future this threshold becomes the 24 th percentile, e.g. more future droughts 13

14 Returning to project goals: we are interested in specific stakeholders and operational predictions Official regression equations based on VIPER and they relate multiple SWE and precipitation stations to streamflow: n Q = i=1 m a i SWE i + j=1 b j P j + ε AMJJ streamflow Station data Coefficients VIC simulated data at station locations, to support future projections NRCS Visual Interactive Prediction and Estimation Routines (VIPER) supports linear, Z-score, and principal components regression 14

15 RCP4.5 VIC SWE Future snow is reduced Example: Eagle R. near Gypsum Snow Water Equivalent (SWE): RCP4.5 versus historical percentiles >80 th percentile n m Q = a i SWE i + b j P j + ε i=1 j=1 Future SWE generally falls below the oneto-one line, e.g. less future SWE One-to-one line lowest 20 th percentile Historical VIC April SWE (mm) 15

16 RCP4.5 VIC Precipitation Future Oct-Apr precipitation is increased Example: Eagle R. near Gypsum Accumulated Precipitation Oct-Apr: RCP4.5 versus historical percentiles >80 th percentile n m Q = a i SWE i + b j P j + ε i=1 j=1 Future precipitation generally falls above the one-to-one line, e.g. more future precipitation One-to-one line lowest 20 th percentile Historical Precipitation Oct-Apr (mm) 16

17 RCP4.5 VIPER Inflows Future SWE and precipitation, counteracting effect on inflows Example: Eagle R. near Gypsum Apr-Jul VIPER Inflows: RCP4.5 versus historical percentiles >80 th percentile n Q = a i SWE i + b j P j + ε i=1 m j=1 Future inflows close to the one-to-one line, with the exception that highflows seem to get higher One-to-one line lowest 20 th percentile Historical VIPER Inflows Oct-Apr (mm) 17

18 Conclusions Drought prediction Snow information is widely used to forecast water supply Drought years appear marginally less predictable relative to nondrought years Future hydrology Snow and total precipitation appear to counteract inflow volumes Future hydrology is likely to be less predictable on the basis of snowpack Future efforts should be directed towards exploring more physicallybased forecast techniques and also to include other predictors such as from satellite data as record lengths expand 18

19 Thank you 19

20 Exploring the impact of changing snowpack *Phase-2: How the value of snowpack will change as a drought indicator in a warming climate. UCRB has snowmelt dominated hydrology Extensive SWE monitoring through SNOTEL Courtesy: Jenna Stewart 20

21 Ratio of future SD to historic SD Projected streamflow standard deviations (SD) increase Future ( ) and historical ( ) inflows calculated using VIPER Median VIPER-calculated changes for downscaled hydrology from 31 models Using VIC simulations for 31 GCMs Normalized mean flow change 21