Improvements to seasonal and longerterm forecasting for the Rio Grande Basin in Colorado and New Mexico

Transcription

1 Improvements to seasonal and longerterm forecasting for the Rio Grande Basin in Colorado and New Mexico Upper Colorado River Basin Forum, Colorado Mesa University, Grand Junction, Colorado November 7-8, 2018 Dagmar Llewellyn, Reclamation Albuquerque Area Office

2 The Upper Rio Grande System Map

3 Reclamation s San Juan-Chama Project Linkage between the Colorado and the Rio Grande. 3

4 Differences in forecast information in different regions of the country NOAA River Forecast Centers

5 Information available from NOAA River Forecast Centers West Gulf River Forecast Center

6 Forecast-Related Decisions Allocations of water from the Colorado Basin from Reclamation s San Juan-Chama Trans-mountain Diversion Project Endangered species requirements Water needs for Native American Pueblos Rio Grande Compact delivery requirements and timing of reservoir storage restrictions Balance between water storage and flood-control space needs in reservoirs. Irrigation district supplies, availability for water bank, cropping choices. Carriage water for municipal supplies (if insufficient, Albuquerque and Santa Fe need to rely on other sources) Staffing needs for river monitoring, endangered fish rescue, recreational guiding (fishing, rafting.).

7 Changes are underway that affect the accuracy of statistically based forecasts.

8 New Mexico s Disappearing Snowpack

9 Implications The Rio Grande near Socorro, 2012 (drought conditions)

10 Ways that improved seasonal forecasting can improve Rio Grande basin water operations in Colorado and New Mexico Step 1: Evaluate impact of temperature on runoff efficiency, and incorporate temperature projections into statistical streamflow forecasts. Flow in cfs Mar 15-Mar 29-Mar 12-Apr 26-Apr 10-May 24-May 7-Jun 21-Jun 5-Jul 19-Jul Date Historical Forecasted

11 Improving the Robustness of Southwestern Water Supply Forecasts Flavio Lehner (NCAR), Andy Wood (NCAR) Dagmar Llewellyn (Reclamation).

12 Including temperature into streamflow forecasting Statistical seasonal streamflow forecasting Water Supply Forecasts Meteorological predictability Snow+Rain Hydrological predictability Streamflow Q Lehner et al. (2017b) 12

13 Hydrological Predictability: The concept of runoff efficiency Runoff efficiency = water out/water in = streamflow/precipitation 13

14 Hydrological Predictability: The concept of runoff efficiency Runoff efficiency = water out/water in = streamflow/precipitation RE = f(watering can, plastic bottle) 14

15 The concept of runoff efficiency Runoff efficiency = water out/water in = streamflow/precipitation RE = f(precipitation, temperature, dust-on-snow, vegetation, groundwater, ) 15

16 Hydrological Predictability: The concept of runoff efficiency Runoff efficiency = water out/water in = streamflow/precipitation RE = f(precipitation, temperature, dust-on-snow, vegetation, groundwater, ) 16

17 Hydrological Predictability: The concept of runoff efficiency Upper Colorado River Woodhouse et al. (2016) Upper Rio Grande Lehner et al. (2017a) Upper Colorado River McCabe et al. (2017) 17

18 Growing evidence for temperature influence on streamflow econstructions CE % % % % % % 37% 28% 10% 2% 2% 6% 51% 68% Precipitation anomaly (KAF) % 36% 0% 0% 13% 0% 0% 13% Observations CE 25% 57% 14% 0% 0% 7% 86% 88% Precipitation anomaly (KAF) CESM picontrol (1,800 years) 52% 38% 19% 11% 21% 21% 20% 17% Temperature anomaly (C) Temperature anomaly (C) Temperature anomaly (C) Lehner et al. (2017a) 18

19 Growing evidence for temperature influence on streamflow When Precipitation is low and Temperature is high low Runoff Efficiency econstructions CE % % % % % % 37% 28% 10% 2% 2% 6% 51% 68% Precipitation anomaly (KAF) % 36% 0% 0% Observations CE 25% 57% 14% 0% % 0% 0% 7% 0% 86% 13% 88% CESM picontrol (1,800 years) Other 6000 studies 52% with similar 38% conclusions: % 11% Precipitation anomaly (KAF) Woodhouse et al. (2016) Udall & Overpeck (2017) McCabe 0 et al. (2017) Woodhouse et al. (2018) Chavarria & Gutzler (2018) % 21% 20% 17% etc Temperature anomaly (C) Temperature anomaly (C) Temperature anomaly (C) Lehner et al. (2017a) 19

20 Including temperature into streamflow forecasting Tendency to underforecast Tendency to overforecast Lehner et al. (2017b) 20

21 Including temperature into streamflow forecasting Statistical seasonal streamflow forecasting Water Supply Forecasts Snow+Rain Q ~ a SWE + b Rain + ε Streamflow Q Lehner et al. (2017b) 21

22 Including temperature into streamflow forecasting Colorado/Rio Grande headwaters Snow+Rain Q ~ a SWE + b Rain + c Temperature + ε Streamflow Q Lehner et al. (2017b) 22

23 Including temperature into streamflow forecasting without temperature with temperature Snow+Rain blue = reduced error ~1-10% improvement in forecast skill Streamflow Q Lehner et al. (2017b) 23

24 Step 2: Evaluate the chance of a good monsoon and incorporate that information into water supply planning.

25 Detecting, Interpreting, and Modeling Hydrologic Extremes to Support Flexible Water Management and Planning (S&T 1782) Erin Towler (C3WE/NCAR), Dagmar Llewellyn (BOR), Andreas Prein (C3WE/NCAR), Ariane Pinson (USACE), Lucas Barrett (BOR), Rick Young (BOR)

26 Goal: identify intersections between changing hydrology and water management. Changing atmospheric conditions, precipitation, hydrology Vulnerability: decreasing snowpack. Potential opportunity: summer monsoon, including extremes. Opportunities? Water management context

27 27 Weather Types in New Mexico s warm season Dry Wet in North Weak Monsoon Main Monsoon Main Monsoonal contributes ~60% of warm season rainfall in NM Weak Monsoon contributes ~30% Dry Wet in North Weak Monsoon Main Monsoon

28 28 Changes in Weather Type Frequencies Changes in WT frequencies 30-year moving average filtered data Dry Wet North Main Monsoon Weak Monsson Weak Monsoon Main Monsoon Wet North Dry Main Monsoon frequencies have a minimum at ~2030 and rapidly increase afterwards Main Monsoon increases on the cost of the Wet North weather type

29 Monsoon Increase is Robust Across Models and Scenarios Monsoonal Flow Weather Type 12 WT frequency changes: minus Monsoonal Flow Precipitation Anomaly Monsoon Increases in Frequency Robust and significant increases in NAM 10 out of 11 models show increase 8 models show significant increases up to +30% Prein et al. (in preparation) Simulating North American Weather Types with Regional Climate Models 29

30 Incorporating Weather Type information into statistical models to support Reclamation water management and planning. Statistical Modeling Physically-based covariates from Weather Types Input into management models (e.g., Upper Rio Grande Water Operations Model, a Riverware model)

31 Questions?