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

Questions?

Richard Koehler, PhD, PH

richard.koehler@noaa.gov