UNIVERSITY OF COLORADO CEAE DEPARTMENT FACULTY MEETING MINUTES

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1 UNIVERSITY OF COLORADO CEAE DEPARTMENT FACULTY MEETING MINUTES Date February 15, 2012 Time 12:00 PM 1:00 PM Facilitator Keith Molenaar Scribe Keith Molenaar Location ECCE 1B41 Subject 2012 Spring Semester Meeting Hearn, Beamer, Zagona, Rosario Ortiz, Regueiro, Corotis, Halek, McCartney, Attendees Chase, Hallowell, Linden, Henze, Hernandez, Pak, Vernerey, Rajaram, Znidarcic, Halstead Williamson, Jerick, Warren Key Points discussed No. Topic Highlights 1 Family Campaign Our college will undertake a Family Campaign as part of the overall CU Fundraising Campaign: Creating Futures. A family campaign involves gifts from employees and retirees. Our department has been asked to identify one purpose for its enrichment gifts. The executive committee has selected to construct a collaborative graduate student research space. College Matching Employee and retiree gifts to this particular purpose will receive a 100% match (1:1) from the dean s fund, up to a maximum of $3000 from any one person. Department Matching The chair will personally match (1:1) the first $1,000 in gifts. The department will also match (1:1) the first $10,000 in gifts from the chair s excellence fund. Therefore, if we can raise $10,000 in faculty gifts, this will be matched by $10,000 from the dean s fund, $10,000 from the chair s fund and $1,000 from the chair for a total of $31,000. The primary round of the family campaign for enrichment gifts will take place between Spring Break and the end of this semester. However, individuals may make pledges to be paid over FY12, FY13 and FY 14 (i.e., until 6/30/2014). Finally, we need a faculty volunteer to help with the family campaign. 2 Boase Lecture Nominations Keith has asked for nominations by February 29, If no applications are received, he will make a nomination on behalf of the department. 3 Faculty Presentation Rajagopalan Balaji gave a presentation on his research. Please see the presentation attached. 4 Scholarship Announcement Milan Halek asked the faculty to encourage CEAE sophomores, juniors, seniors and fifth year seniors to announce the college s merit scholarship program in their classes. There is currently a surplus of scholarship funds for the CEAE students. Students below a 3.0 may in fact be considered for a merit scholarship. 1 P age

2 (Water) Resources Management Under Nonstationary Climate Balaji Rajagopalan Department of Civil, Environmental and Architectural Engineering & Fellow CIRES University of Colorado, Boulder, CO Research Faculty Meeting Feb 15, 2012

3 Collaborators E. Zagona K. Nowak, J. Prairie (& USBR) Researchers at CIRES-WWA and NOAA NCAR R. Katz, M. Clark S. Summers

4 A Water Resources Management Perspective T i m e H o r i z o n Inter-decadal Decision Analysis: Risk + Values Facility Planning Hours Reservoir, Treatment Plant Size Policy + Regulatory Framework Flood Frequency, Water Rights, 7Q10 flow Operational Analysis Reservoir Operation, Flood/Drought Preparation Emergency Management Flood Warning, Drought Response Data: Historical, Paleo, Scale, Models Climate Weather

5 Research Framework What Drives Year to Year Variability in regional Hydrology? (Floods, Droughts etc.) Diagnosis Hydroclimate Predictions Scenario Generation (Nonlinear Time Series Tools, Watershed Modeling) Application Forecast/ Projection Total Colorado River Use 9-year moving average. NF Lees Ferry 9-year moving average Decision Support System (Evaluate decision strategies Under uncertainty) 14 Annual Flow (MAF) Calnder Year

6 Motivation Efficient management of resources Skillful projections of resource supply short & long time scales Quantification of uncertainty Projections of relevant extreme events Suite of Stochastic Methods for water resources management (quantity and quality) Ensemble streamflow projection Modeling hydrologic and water quality extremes Water management under climate change Age of Big Data NY Times Sunday (Feb 12) Article GOOD with numbers? Fascinated by data? The sound you hear is opportunity knocking.

7 Collaborations with CEAE Colleagues Henze Rule extraction from model predictive control for building systems management Summers Modeling threshold exceedances of TOC in source water (extreme value analysis) Silverstein Understanding and modelling relationship between wastewater plant capacity, inflow and regulatory violations Pfeffer Combining sea level information from multiple sources and multiple data sets with uncertainty Proposals Pending Hallowell/Molenaar; Neupauer; Linden/Summers/Silverstein/Zagona Past Papers/proposals Rajaram; Chase; Corotis/Frangopol; Saouma; Molenaar; Xi; Strzepek

8 Some Current Research Activities Improving short term hydrologic forecasting Colorado Basin - (NOAA) Multisite stochastic weather generators Couple it with hydrologic models Combine ensemble forecasts from multiple sources Bayesian Model Averaging Stream water temperature management for improving aquatic health Central Valley, CA - (NASA) Develop statistical models for attributes of water temperature (threshold exceedance, hours of exposure etc.) Combine with a decision system to manage reservoir releases Reconstruction of past streamflows using tree ring for water supply risk assessment (CWCB/River District/NOAA) Gila River; Implications for Colorado River supply risk Water supply demand modeling for drinking water utilities (AWWA/NOAA, Summers) Develop tools for modeling attributes of water supply demand variability mean and extremes - using climate information

9 Research Activities.. Modelling spatial climate variability and extremes in the Pampas region of Argentina for improved agriculture management (NSF) Spatial weather generator Epochal climate behaviour Combine with agriculture/socio-economic models Indian Monsoon Variability Variability and Predictability Future variability Socio-economic-health impacts Past variability (paleo) and their insights for future

10 Examples Colorado River Basin Water Resources Management Short term Forecasting Mid-term (1-2 yr) ensemble streamflow projection (Bracken, 2011) Multidecadal projection (Nowak, 2011) Under climate change (Rajagopalan et al., 2009) Water Quality Modeling under hydroclimate extremes (Towler et al., 2010, 2011)

11 Water Supply Forecast Overview (River Forecast Centers) (Water and Climate Center) SWS (Statistical Prediction) ESP (Hydrologic Model Prediction) VIPER (Statistical Prediction) Forecast Coordination Official Coordinate d Forecast Decisions Multimodel Ensemble Forecasting Water Managers and Users Other Inputs....

12 Ensemble Streamflow Prediction

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16 Bracken et al. 2010, WRR Using Climate Information for long-lead Streamflow Forecast

17 Water Resources Management (Interannual time scale) Reservoir Operations 12 major reservoirs (9 Upper, 3 Lower Basin) Seasonal/Monthly forecasts are input to the systems model In RiverWare contains all the operating rules and constraints Obtain mid-term reservoir conditions (storage, elevation, release, hydropower, etc.) Close coordination between USBR and NOAA-CBRFC

18 Seasonal to Two Year Simulation and Forecasting Hidden Markov model for simulation and forecasting Seasonal Forecast Historical Data Mo. Traces HMM Forecast Midterm Operations Model Probabilistic Reservoir Outlook Pool Elevation [feet] Western Water Assessment Month

19 Application (Portland Water Utility Turbidity) Schoharie Reservoir, NY, after Hurricane Floyd in 1999 (Miller and Yates 2006) Towler, E., B. Rajagopalan, E. Gilleland, R. Summers, D. Yates and R. W. Katz, Modeling hydrologic and water quality extremes in changing climate, Water Resources Research, 46, W11504, doi: /2009wr008876, 2010 Towler, E., B. Rajagopalan, S. Summers and D. Yates, A framework for probabilistic forecasting of seasonal water quality threshold exceedance, Water Resources Research, 46, W06511, doi: /2009wr00783, 2010

20 High runoff events have caused source water turbidity exceedances Precipitation High Flows Turbidity SWTR Exceedances Back-up groundwater source (Pumping $$) Turbidity Exceedances Under Future Climate?

21 Maximum Flow Turbidity Relationship P( E) = 0 P( E S) P( S) Conditional P(E) Probability of a turbidity spike given a certain maximum flow Maximum Flow (cfs) Local Logistic Regression, Loader (1999)

22 GEV P( E) = 0 P( E S) P( S) Maximum flow probability distribution GEV Distribution is fit to historic maximum flow values (S) (Coles 2001)

23 GEV with Covariates - nonstationarity Apply to location parameter: µ = β + βx 0 1 Precipitation (P) Temperature (T) Time Palmer Drought Severity Index (PDSI) Test covariate(s) to find best-fit Likelihood Ratio Test i PxT combination resulted in the best-fit µ = β + β 1( PxT i 0 i )

24 Conditional quantiles correspond well to observed record (Cross validated) Maximum Streamflow (cfs) Q99 Uncond (100 Year Flood) Year (Towler et al., 2010)

25 Climate change projections indicate that Pacific Northwest will become wetter Avg. Precip Response (%) (Solomon et al 2007) Directly use projections to quantify extreme flow and turbidity exceedances

26 Maximum Streamflow Anomaly (%) Results indicate increasing maximum streamflow anomalies Historic (USGS) Year 36 model runs 36 model average (Towler et al., 2010)

27 Likelihood of a turbidity exceedance increases under climate change log(p(e)*100) Climate Change Seasonal Scenario P(E) Very Wet 14% Wet 6.7% Historic 5.8% Dry 4.1% Very Dry 1.5% (Towler, E., et al. 2009).

28 Turbidity exceedance projections are useful for future planning Expected Loss (% Change) = Cost ) FutureP( E Cost ) HistoricP( E Future Historic

29 Small shifts in risk can result in large percent increases Change in Expected Loss Relative to Historic Period (%) Q50 Q Change in risk is high, especially for the risk averse (i.e., Q95)

30 Water Resources Management (Rajagopalan et al., 2009 Prairie et al., 2007)

31 Colorado River Basin Overview Source:Reclamation 1 acre-foot = 325,000 gals, 1 maf = 325 * 10 9 gals 1 maf = 1.23 km 3 = 1.23*10 9 m 3 7 States, 2 Nations Upper Basin: CO, UT, WY, NM Lower Basin: AZ, CA, NV Fastest Growing Part of the U.S. Over 1,450 miles in length Basin makes up about 8% of total U.S. lands Highly variable Natural Flow which averages 15 MAF 60 MAF of total storage 4x Annual Flow 50 MAF in Powell + Mead Irrigates 3.5 million acres Serves 30 million people Very Complicated Legal Environment Denver, Albuquerque, Phoenix, Tucson, Las Vegas, Los Angeles, San Diego all use CRB water DOI Reclamation Operates Mead/Powell

32 Colorado River Demand - Supply Total Colorado River Use 9-year moving average. NF Lees Ferry 9-year moving average UC CRSS stream gauges LC CRSS stream gauges Annual Flow (MAF) Calnder Year What is the Colorado River System-wide Water supply risk profile under climate change? Need to consider the entire syste (~60AF Storage) Need to generate streamflow scenarios consistent with climate projections and combining (Paleo?) Is there flexibility within the existing management framework? Can Management Mitigate the future risk?

33 Below normal flows into Lake Powell %, 59%, 25%, 51%, 51%, respectively 2002 at 25% lowest inflow recorded since completion of Glen Canyon Dam Woodhouse et al., WRR, 2007 Motivation Recent conditions in the Paleo Context Some relief in % of normal inflows Not in 2006! 73% of normal inflows 2007 at 68% of Normal inflows 2008 at 111% of Normal inflows 2009 at 88% and 2010 at 72.5% 5 year running average Decadal Variability!

34 Most runoff comes from small part of the basin > 9000 feet Very Little of the Runoff Comes from Below 9000 (16% Runoff, 87% of Area) 84% of Total Runoff Comes from 13% of the Basin Area all above % 18% 16% 14% 12% 10% Elevation % Total Runoff % Total Area "Productivity" ,000 25% 6.3% ,000-11,000 27% 4.3% , % % Total 2.1% Runoff ,000-13,000 11% 0.5% 20.4 Sums % 13.2% Below % 87% 0.2 Basin Area and Runoff By Elevation Runoff 8% 6% Basin Area 4% 2% 0% Runoff as % of Total Area as % of Upper Basin Total

35 Water Balance Model: Our version Climate Change -20% LF flows over 50 years Lees Ferry Natural Flow (15.0) + Intervening flows (0.8) - Upper Basin Consumptive Use (4.5+) Evaporation (varies with stage; 1.4 avg declining to 1.1) LB Consumptive Use + MX Delivery + losses (9.6) Bank Storage is near long-term equilibrium Initial Net Inflow = +0.4

36 Natural Climate Variability Climate Change 10% reduction Climate Change 20% reduction

37 Shortage Volume Under Climate Change 10% Reduction 20% Reduction

38 Sensitivity to Initial Demand - 20% reduction Initial Demand 13.5MaF Initial Demand 12.7MaF Actual Average Consumption In the recent decade

39 What do we do?