Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster Maps (Richard Koehler)
-
Upload
gis-colorado -
Category
Technology
-
view
36 -
download
3
Transcript of Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster Maps (Richard Koehler)
Richard Koehler, PhD, PHNOAA/National Weather Service
National Hydrologic and Geospatial Sciences Training Coordinator
Using GIS to Visualize and Analyze Environmental Time-Series Data
as Raster Maps
GIS Colorado Fall Meeting October 21, 2016
Source: nrcs.gov Source: noaa.govSource: usbr.gov
Quote
The application of GIS
is limited only by the imagination
of those who use it.
Jack Dangermond
Co-founder,
Time series in GIS
Two common approaches
1. Animate / time slider
Source: cuahsi.org
2. Line graphs
Source: esri
t5t4t3t2t1
Base map
• Most statistics are simpleMean, median, variance, standard deviation, min, max
• Multiple streamflow metrics exist (170+)
• Need time-scale analysis to find patterns
• Visualization overlooked as an analysis method
• GIS - technology for data analysis, configuration and visualization for spatial data
Magnitude 55%Frequency 8%
Duration 26%Timing 6%Flow change 5%
Composition attributesData order not a factor
Configuration attributesData order is fundamental
Time series data analysis
– why not for temporal data?
Data source: NOAA
Data source: USGS
Time series data displays
Spaghetti plotKettle River near Laurier, WA
Data source: USGSDay of Water Year
Dis
char
ge (
ft3/s
)
Assumption:Lines lay within a single plane
Water Year: Oct 1 – Sept 30
Display evolution
New assumption:Profiles stacked in multiple planes
!
Tilt and rotate display
New perspective,“aerial”
Hidden axis
Spaghetti plot perspective,“ground”
Wire diagram
Temporal map
• Dual timescale as X, Y
• Common framework
• Visualization options
• Allows data layering
Time-based coordinate system
X = Short-term coordinate
Y = Long-term coordinate
2016
2015
2014
Y (year)
293 294 295 X (day)
Time grid
Framework
Visualization and analysis
X = Short-term coordinate
Y = Long-term coordinate
Z = Value (raster cell color)
2016
2015
2014
Y (year)
293 294 295 X (day)
Z (value)
Time as raster
Streamflow exampleTraditional hydrograph
** Glen Canyon Dam
operational
What date is this event?
2
2 = Drought
3
3 = Low winter flow
4
4 = Storm flow
5
5 = Higher autumn flow
6
6 = Diversion tunnels closed
7
7 = El Niño runoff
8
8 = Artificial floods
9
9 = Sunday flow
10
10 = Christmas
11
11 = Monthly change
1
Pattern key1 = Snowmelt runoff
Raster hydrograph
Colorado River at Lees Ferry, AZOct – Sept (water year), 1921 to 2014
*
• First day of month
Glen Canyon Dam online
*
12
12 = Policy change
What date is this event?
‘96
Data source: USGS
Adopted by USGS
Annual peak streamflow (ft3/s)
An outlier is an observation point that is distant from other observations.
Outliers
Outlier detectionIDOR
MTWA
Outlier detection
Temporal outliers
+ Dworshakoperational
An outlier is an observation point that is distant from other observations.
IDOR
MTWA
Data source: USGS
Annual peak streamflow (ft3/s)
“War time”Year-round DST
1942 to 1945
1973 Energy CrisisEarly DST
1974 and 1975
Congress changes when DST begins
Switch to DST
CAN
MT
ID WY
Data Source: USACE
Fort Peck Reservoir computed daily inflow
Days with no data can provide information
Data quality
Missing
Lookout Creek near Blue River, ORElev = 1,378 ftDrainage area =24.10 mi2
Western Cascade geologyLow soil permeability
McKenzie River at Outlet of Clear Lake, ORElev = 3,015 ft, Drainage area = 92.40 mi2
High Cascade geologyModerate/High soil permeability
Source: Grant et al., 2010. Streamflow response to climate warming in mountain regions: Integrating the effects of snowpack and groundwater dynamics.http://www.fs.fed.us/psw/cirmount/meetings/mtnclim/2010/talks/pdf/Grant_Talk2010.pdf
Flow regime and geology (Oregon)
USFS - OSU study
Background map source: Google
McKenzie River Winter
Longer durationSummer
Higher baseflow
Lookout CreekWinter
Shorter durationSummer
Lower baseflow
Flow regime and geology
USFS - OSU study
Background map source: Google
Data source: USGS
Ocean tides (1 minute values)
Data source: NOAA
Traditional
1 day (1,440 pts) 1 week (10,080 pts)
1 month (43,200 pts) 3 months (129,600 pts)
Hawk Inlet, AK Apr-Jun 2016 predicted tides
Sunrise,Sunset
…
Raster
Units: FeetTime Zone: Alaska DSTDatum: Mean Lower Low Water (MLLW)
3 months (129,600 pts)
Bonneville Daily Count (2010 – 2014, 5 years)
Salmon migration
Source: Fish Passage Center
Bonneville Daily Count (1938 – 2014, 76 years)
Salmon migration
Data source: USACE
Traditional plots:
Puget Sound paralytic shellfish toxinsPuget Sound
“Red tide” analysis
Source: Moore, S.K., et al., 2009. Recent trends in paralytic shellfish toxins in Puget Sound, relationships to climate, and capacity for prediction of toxic events. Harmful Algae 8, 463–477 doi:410.1016/ j.hal.2008.1010.1003.
Time series datasets:
Environmental factors
1. Streamflow (m3s-1)
2. Air temp (C)
3. Precipitation (cm)
4. Wind speed (ms-1)
5. Tidal height difference (m)
6. Upwelling (m3s-1100 m-1)
7. Sea surface salinity (psu)
8. Sea surface temp (C)
Observed streamflow
1,716 days or 36% of days were in“criterion windows”
Criterion: Flow ≤ 350 m3s-1
Met = 1 Not met = 0
Missing
Apply a binary filter
Identify threshold days
8. Sea surface temp1,840 days
7. Salinity 2,513 days
6. Upwelling2,424 days
5. Tide range2,546 days
4. Wind2,662 days
3. Precipitation4,116 days
2. Air temp2,062 days
1. Streamflow1,716 days
Multi-layer analysis
Only 126 days meet all 8 criteria
1 - Apply criterion to each layer 2 – Produce a summary layer
J F M A M J J A S O N D
Summary
• Greater GIS versatilityPowerful “timescape” visualization
New opportunities for GIS
• Improve communicationEngage clients, funding sources, public
Enhance decision support information
• Increase ROI from GIS Leverage existing software
Expand products and servicesCompetitive advantage
Acknowledgements
• Golden Software, LLCSupport and feedback for this innovative use of Surfer®
• USGSIncorporated raster hydrographs into the Water Watch website
• NOAAProvided data and feedback
• Northwest Power and Conservation Council (NWPCC)Sponsored data visualization workshop - May 2015
Selected workshop graphics used in this talk