Lecture 15: Flood Mitigation and Forecast Modeling Key Questions 1. What is a 100-year flood inundation map? 2. What is a levee and a setback levee? 3. How are land acquisition, insurance, emergency response used to mitigate a flood 4. How is streamflow forecasting used to mitigate a flood? 5. What is the difference between weather and climate? 6. What has caused the climate to change in the last 100 years? 7. How will future climate impact snow and streamflow in the Nooksack basin? Niigata Japan, 1964 liquefaction Nooksack River
100-year Floodplain Map
Mitigation: 100 Year Flood Map
Mitigation: Structural Techniques 1. Levees are engineered embankments designed to contain the river
Mitigation: Structural Techniques Levees are designed to contain floods along most of the lower Nooksack (floods that range from 5 to 100 year return periods) levee
Mitigation: Setbacks Levees The Soldiers Home Setback Levee on the Puyallup River near Orting.
Mitigation: Structural Techniques 2. Dams can store and slowly release water storage capacity Δ = monitored release
Mitigation: Land Acquisition
Mitigation: Nooksack River
Mitigation: Insurance
Mitigation: Emergency Response
Mitigation: Forecast Modeling
Modeling the effects of climate change forecasts on streamflow in the Nooksack River basin
Climate versus Weather Weather: short-term, local variations in atmospheric conditions (one monthly average represents weather) Average January temperatures at the Clearbrook weather station.
Climate versus Weather Climate: long-term average weather conditions (30 years or longer) (collection of monthly values represents climate) What does the trend line indicate? Average January temperatures at the Clearbrook weather station.
Reconstructed temperatures for the past 2000 years http://www.globalwarmingart.com/wiki/file:1000_year_temperature_comparison_png#description
Measured temperatures for the past 200 years http://www.globalwarmingart.com/wiki/file:1000_year_temperature_comparison_png#description
97% of active, publishing climatologists think human activities have changed the climate n = 3146
Greenhouse Gases
Greenhouse Effect: CO 2 and other greenhouse gases trap radiated heat from the Earth and increase the temperature http://www.esrl.noaa.gov/gmd/outreach/carbon_toolkit/basics.html
Anthropogenic (human) causes for increased CO 2 Burning of fossil fuels Deforestation CO 2 source trees are a CO 2 sink
http://www.epa.gov/climatechange/
South Cascade Glacier One of three benchmark Glaciers that have been established in a USGS glacier monitoring program 1960 1928 2000
South Cascade Glacier One of three benchmark Glaciers that have been established in a USGS glacier monitoring program
Mote et al. (2008) Averaged sea level at stable tide gauge sites
Futuristic CO 2 Emission Scenarios
General Circulation Models (GCM) Emission Scenarios About 40 institutions world wide run GMCs
Modeled Temperature Predictions based on different CO 2 emission scenarios
GCM: NASA s GISS model
Modeling the effects of climate change forecasts on streamflow in the Nooksack River basin
Goal of Research To predict impact of climate change on snowpack and streamflow in the Nooksack River basin
The Nooksack River is a snow dominated basin that is sensitive to temperature changes spring peak due to snowmelt Discharge at North Cedarville, WA Water Year 2009 (Oct 2008 Sept 2009)
no snowpack so rain falls on exposed bedrock and thin, wet soils and produces a high peak snow pack Hydrograph Hydrograph Hydrograph Hydrograph more volume but less peaked Time Time
Approach Spatial characteristics of the Nooksack River basin Predicted climate data DHSVM Hydrology model Predict Future: Snow Water Equivalent (SWE) Streamflow Peak Flows Data processing
Methods 1. Hydrologic Model Set-up, Calibration, & Validation 2. Downscaling & Validation of Climate Change Forecasts 3. Hydrologic Modeling
Methods: DHSVM Distributed Hydrology Soil Vegetation Model DHSVM calculates a water and energy budget on each grid cell for each time step inputs - outputs = change in storage
The DHSVM also predicts snow accumulation and melt
Methods: DHSVM Spatial Input DEM Watershed Boundary Stream Network Soil Thickness Soil Type Landcover
Methods: DHSVM Meteorological Input Temperature Precipitation Wind Speed Relative Humidity Shortwave Radiation Longwave Radiation North Shore Weather Station, Lake Whatcom
DHSVM: Streamflow Calibration Calibration adjustment of model parameters to mimic an observed dataset Photo: USGS USGS Stream gauge at Cedarville
DHSVM: Calibration Nooksack River, WY 06-07 Initial Simulation Daily Mean Streamflow (cfs) 0 10000 30000 Cedarville - observed Cedarville - simulated 1Jan2006 2Jul2006 1Jan2007 2Jul2007 Date Nooksack River, WY 06-07 After Calibration Daily Mean Streamflow (cfs) 0 10000 30000 Cedarville - observed Cedarville - simulated 1Jan2006 2Jul2006 1Jan2007 2Jul2007 Date
DHSVM: Calibration & Validation Validation comparison of simulated data with observed data for a time period not included in the calibration Nooksack River, WY 06-09 Daily Mean Streamflow (cfs) 0 10000 20000 30000 40000 50000 Cedarville - observed Cedarville - simulated calibration validation 1Jan2006 1Jan2007 1Jan2008 1Jan2009 Date
DHSVM: SWE Calibration Calibration adjustment of model parameters to mimic an observed dataset Snotel Stations Photo: NRCS
DHSVM: Calibration & Validation Wells Creek Snotel (NF), WY 06-09 Daily Mean SWE (m) 0 1 2 3 4 observed simulated 1Jan2006 1Jan2007 1Jan2008 1Jan2009 Date Middle Fork Snotel, WY 06-09 Daily Mean SWE (m) 0 1 2 3 4 observed simulated 1Jan2006 1Jan2007 1Jan2008 1Jan2009 Date
Methods 1. Hydrologic Model Set-up, Calibration, & Validation 2. Downscaling & Validation of Climate Change Forecasts 3. Hydrologic Modeling
Emissions Scenarios IPCC 2001
Methods: Climate Change Forecasts Three General Circulation Models (GCMs) : 1. IPSL_CM4_A2 Institut Pierre Simon Laplace (with A2) 2. Echam5_A2 Max Planck Institute for Meteorology (with A2) 3. GISS_ER_B1 Goddard Institute for Space Studies (with B1) 2040s Changes in Temperature and Precipitation Mote and others, 2005
Methods: GCM Downscaling GCM scale of 100s km regional scale of 10s km local station CIG, 2010 Monthly time scale
Methods: GCM Downscaling Empirical Cumulative Distribution Functions (ecdf) April Mean Temperature April ecdf Frequency 0 2 4 6 8 10 Non-Exceedance Probability 0.0 0.2 0.4 0.6 0.8 1.0 Abbotsford, 1950-1999 7 8 9 10 11 12 Mean Temperature (C) 6 7 8 9 10 11 12 Mean Temperature (C)
Methods: GCM Downscaling April ecdfs Shift 50-year historical time series based on 31-year forecast period Non-Exceedance Probability 0.0 0.2 0.4 0.6 0.8 1.0 Abbotsford, 1950-1999 2050s GISS Forecast, 2035-2065 6 8 10 12 14 Mean Temperature (C) Result: 50-year forecast Non-Exceedance Probability 0.0 0.2 0.4 0.6 0.8 1.0 Abbotsford, 1950-1999 GISS Forecast, 2035-2065 Combined 2050s Forecast April ecdfs 6 8 10 12 14 Mean Temperature (C)
Methods: GCM Downscaling Each forecast is based on the Abbotsford time series Monthly Mean Temperature, 1950-1999 Temperature (C) -10 0 10 20 30 Abbotsford GISS_B1 2050 Forecast 0 100 200 300 400 500 600 Month
Methods: GCM Downscaling Each forecast is based on the Abbotsford time series Monthly Mean Temperature, 1950-1999 Temperature (C) -10 0 10 20 30 Abbotsford GISS_B1 2050 Forecast 0 100 200 300 400 500 600 Month
Methods: Validation of Downscaling 1 2 3 4 5 6 7 outlier 25 th 75 th percentiles median Monthly Mean Temperature, 1950-1999 minimum Temperature (C) -10 0 10 20 Abbotsford GISS_B1 Echam_A2 IPSL_A2 1 2 3 4 5 6 7 8 9 10 11 12 Month
Methods: Local Forecasts Monthly Mean Temperature - 2050 Abbotsford GISS_B1 ECHAM_A2 IPSL_A2 1 2 3 4 5 6 7 8 9 10 11 12 Month Temperature (C) -10 0 10 20 30
Methods: Local Forecasts Total Monthly Precipitation - 2050 Abbotsford GISS_B1 ECHAM_A2 IPSL_A2 1 2 3 4 5 6 7 8 9 10 11 12 Month Precipitation (mm) 0 100 200 300 400 500 600
Methods: Processing of Forecasts 1. Apply monthly ΔT to daily Abbotsford data 2. Disaggregate daily data to a 3-hour time step 3. Derive other 3-hour meteorological input from temperature and precipitation Shortwave Radiation Longwave Radiation Windspeed Relative Humidity
Methods 1. Hydrologic Model Set-up, Calibration, & Validation 2. Downscaling & Validation of Climate Change Forecasts 3. Hydrologic Modeling
Approach Spatial characteristics of the Nooksack River basin Predicted climate data DHSVM Hydrology model Predict Future: Snow Water Equivalent (SWE) Streamflow Peak Flows Data processing
Results: SWE Monthly Mean SWE at MF Snotel - GISS_B1 SWE(m) 0 1 2 3 4 5 1950-1999 2000 2025 2050 2075 1 2 3 4 5 6 7 8 9 10 11 12 Month Monthly Mean SWE at MF Snotel - IPSL_A2 SWE(m) 0 1 2 3 4 5 1950-1999 2000 2025 2050 2075 1 2 3 4 5 6 7 8 9 10 11 12 Month
Results: SWE Monthly Mean SWE at MF Snotel - GISS_B1 SWE (m) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 1950-1999 2000 2025 2050 2075 2 4 6 8 10 12 Month Monthly Mean SWE at MF Snotel - IPSL_A2 SWE (m) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 1950-1999 2000 2025 2050 2075 2 4 6 8 10 12 Month
Results: Streamflow Monthly Median Streamflow - GISS_B1 Streamflow (cfs) 0 5000 10000 15000 20000 1950-1999 2000 2025 2050 2075 1 2 3 4 5 6 7 8 9 10 11 12 Month Monthly Median Streamflow - IPSL_A2 Streamflow (cfs) 0 5000 10000 15000 20000 1950-1999 2000 2025 2050 2075 1 2 3 4 5 6 7 8 9 10 11 12 Month
Results: Streamflow Monthly Median Streamflow - GISS_B1 Streamflow (cfs) 0 2000 6000 10000 1950-1999 2000 2025 2050 2075 2 4 6 8 10 12 Month Monthly Median Streamflow - IPSL_A2 Streamflow (cfs) 0 2000 6000 10000 1950-1999 2000 2025 2050 2075 2 4 6 8 10 12 Month
Results: Peak Flow Events Annual Peak Flows (WY 1951-1999) - GISS_B1 Streamflow (cfs) 0e+00 4e+04 8e+04 Ferndale-observed Cedarville-simulated 2000 2025 2050 2075 Annual Peak Flows (WY 1951-1999) - IPSL_A2 Streamflow (cfs) 0e+00 4e+04 8e+04 Ferndale-observed Cedarville-simulated 2000 2025 2050 2075
Results: Peak Flow Events Simulated Peaks Above 30,000 cfs Frequency 0 20 40 60 80 100 GISS_B1 Echam_A2 IPSL_A2 2000 2025 2050 2075 Forecast Period IPSL_A2 2000 IPSL_A2 2025 IPSL_A2 2050 IPSL_A2 2075 Frequency 0 10 20 30 40 Frequency 0 10 20 30 40 Frequency 0 10 20 30 40 Frequency 0 10 20 30 40 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 Month Month Month Month
Temperature or Precipitation? Monthly Mean Temperature - 2075 Predicted increases in temperature and precipitation More agreement on temperature trends Temperature (C) -10 0 10 20 30 Abbotsford GISS_B1 ECHAM_A2 IPSL_A2 Previous regional studies indicate that temperature is the driving factor in changes to SWE (Hamlet et al., 2005, Mote et al., 2005, Mote et al., 2008) Precipitation (mm) 0 100 200 300 400 500 600 1 2 3 4 5 6 7 8 9 10 11 12 Month Total Monthly Precipitation - 2075 Abbotsford GISS_B1 ECHAM_A2 IPSL_A2 1 2 3 4 5 6 7 8 9 10 11 12 Month
Conclusions SWE will decrease Timing of peak SWE and of the spring melt peak in the hydrograph will move earlier in the year Winter streamflow will increase, summer streamflow will decrease Peak flow events will increase in magnitude and frequency Extent of change depends on temperature change Photo: John Scurlock