Climate change affects us all. It is an urgent issue that requires practical solutions to mitigate its impacts. Data is at the center of understanding this challenge. In this informative webinar, we will explore how data can be leveraged to translate climate change projections into tangible hazard and risk assessments at the local level. The webinar will cover a range of topics: including flood, fire, heat, drought, population health, and critical infrastructure, among others. We will also highlight our partner and customer experiences in this field and present key results from our participation in recent OGC pilots on Climate Resilience and Disaster Response. We will also be joined by special guests sharing their experience in the AgriTech sector, where gathering metrics and data from sensors is helping to reduce the demand from farming on precious resources like water for irrigation. Through live demos, attendees will gain practical knowledge in accessing climate services from USGS & Environment Canada and how to convert climate model NetCDF outputs into more GIS-friendly formats like geodatabase & GeoJSON. Finally, we will address the significant gaps and challenges that remain in assessing climate-related hazards and risks, and explore how FME can play a critical role in addressing these gaps. Join us for this important discussion on how you can use FME to build resilience and mitigate the impacts of climate change.

1. Translating Climate Models
into Risk Estimates: Data
Integration for Climate Change

2. Dean
Hintz
Strategic Solutions
Manager
Don
Murray
Co-Founder
Oliver
Morris
Data Integration
specialist,
Tensing UK
Panagis
Tzivras
GIS Software
Engineer,
Agri-EPI Centre

3. Agenda
1 Introduction
2 Understanding Climate Change Data
3 Who is this important to
4 Story 1: Agri-EPI Centre - Precision Farming
5 Story 2: Climate Projections to Risk Estimates - OGC
6 Key Results: Climate Resilience Pilot - Data pipeline
7 Gaps & Challenges in Assessing Hazards
8 Conclusion and Q&A
Agenda

4. Welcome to Livestorm.
A few ways to engage with us during the webinar:
Audio issues? Click this for 4 simple
troubleshooting steps.

5. How to download slides
1. Hover over the
slide deck in the
webinar room
2. Click this button

6. Chat Storm:
What is your biggest data
challenge related to climate
change impacts?

7. 1
Introduction

8. Data is key to understanding
our Changing Climate
In particular the Data Cube and its importance will be
made clear during this webinar.

9. Unrivalled Data Support
, ,...

10. 29+
27K+
128
190
20K+
years of solving data
challenges
FME Community
members
countries with
FME customers
organizations worldwide
global partners with
FME services
29+
29K+
128
140+
25K+
years of solving data
challenges
FME Community
members
countries with
FME customers
organizations worldwide
global partners with
FME services
200K+
users worldwide

11. FME Form
(was FME Desktop)
FME Flow
(was FME Server)
FME Flow Hosted
(was FME Cloud)
FME Enterprise Integration Platform

12. Largest
Ecosystem
(Only with
Geospatial)
Unrivalled
Productivity
(No-Code Environment)
Deploy
Anywhere
(performance,
security)
We Care
You Get
It All
(Every component,
every capability)
(Restaurant Model)

13. Flash
Demo

14. Source: Open OGC API Features connection to:
https://disasterpilot-dean.fmecloud.com/fmedatastre
aming/OGCAPI/collections.fmw/collections
Parameters:
Collection: Precipitation-by-Mon-points
StartYear: 2030
EndYear: 2031
bbox: -98.0,49.0,-96.0,50.0
Limit: 20000
Flash Demo: Climate Feature Service - OGC API

15. Flash Demo: Climate Feature Service - OGC API

16. Challenges around Climate Data
● Understanding climate model terminology
● Finding and knowing how to use the data to support
different use cases
● Techniques to simplify & make the data easy to use.

17. 2
Understanding
Climate Change
Data

18. Poll:
What is your experience with climate
model services & data?

19. Disaster and Climate Data
Sources to Impact Risk
How can data for climate impact and disaster
indicators be provided to a wider audience?
● Past & present data: Situational awareness -
base map, hazards, imagery, sensors.
● Future data: Change awareness - risk scenarios
due to climate change - climate variables: e.g.
precipitation, temperature.
● Challenge: Climate services not well known or
utilized within the communities likely to be affected
by impacts.
Environment Canada NetCDF GCM time series
downscaled to Vancouver area, 1950-2042
https://climate-change.canada.ca/climate-data/#/
downscaled-data

20. Climate Model Results: Time Series Data Cube
Predictive weather selection for impact analysis:
● Emissions scenario: RCP 2.6, 4.5, …
● Model type: Regional (RCM), Global (GCM)
● Extent: Spatial and temporal
● Resolution: spatial, temporal
● Climate variable: temperature, precipitation,
soil moisture
● Statistics: mean, min, max
● Target data structure: geometry, values
Goal: Extract climate variables to assess
population and critical infrastructure impacts
Data cube - example data structure
Data cube - example data formats

21. Climate Data Services
Sources:
● Environment Canada
Climate Scenarios
portal
● Climate Data Canada
● Copernicus Climate
Data Store
● USGS THREDDS
Data Service
● NOAA and others

22. Climate Data Services
Sources:
● Environment Canada
Climate Scenarios
portal
● Climate Data Canada
● Copernicus Climate
Data Store
● USGS THREDDS
Data Service
● NOAA and others
USGS THREDDS Data Service

23. Manitoba Heat & Drought: From Climate Model
https://en.wikipedia.org/wiki/Representative_Concentration_Pathway
Key Climate Model Terms:
needed to get the same results
● RCP - Representative
Concentration Pathway -
emissions scenario (2.6,
4.5, 6.0, 8.5)
● CMIP - Coupled Model
Intercomparison Project -
model generation (CMIP5)
● BCSD - Bias corrected
statistically downscaled
RCP4.5: ‘Business as usual’
"the most probable baseline scenario
(no climate policies) taking into
account the exhaustible character of
non-renewable fuels."

24. Manitoba Heat & Drought: Climate Model Data Cube
NetCDF in FME

25. Heat Impact Component: Manitoba
● Max temperature
● Heat wave: consecutive
days above threshold temp
● Integrate land use &
building effects (OSM,
CityGML)
● Integrate population &
medical infrastructure
● Multi-scenario evaluation
● Proxy indicators as needed
(% change)
https://www.cbc.ca/news/canada/edmonton/alberta-saskatchewan-britis
h-columbia-facing-heat-wave-climate-change-1.6551675

26. Drought Impact Component: Manitoba
● Aggregate
precipitation by catch
basin over time
● Hydrology, geology,
soils, vegetation land
use, surface types &
withdrawals
● Gauges, IoT
● Identify key drought
indicators from
stakeholder
feedback
● Trends vs historical
Manitoba Drought Monitor - Drought
Indicator Map - Groundwater
Canadian Drought Monitor
https://agriculture.canada.ca/en/agricultural-p
roduction/weather/canadian-drought-monitor

27. 3
Who is this
important to?

28. Who is this important to?
● Planners, managers: Local & Regional
Gov’t
● Engineers: Utilities, transportation,
facilities
● Government: Environment, public
safety, health, energy, agriculture, etc
● Insurance: Interested in quantifying
future risks
● Citizens: We are all impacted by climate
change.

29. 4
Story 1:
Panagis Tzivras - AEC
Oliver Morris - Tensing

30. ● Introductions
● Who we are and what we support
● How is agriculture affected by Climate Change
● How can Agri-tech help mitigate the Climate
Change effects
Agri-EPI Centre

31. Agri-EPI Centre
Our purpose
Our vision
Our mission

32. Our key collaborators

33. ● Changing Precipitation and
Temperature
● Extreme Weather
● Pest and Disease
● Socio Economic Impact
Agriculture and Climate Change

34. ● Efficient Resource Management
● Digital Tools & Data Analytics
● Soil Monitoring
● Precision Farming
● Harvest Management
How can Agri-tech help mitigate the
Climate Change effects?

35. ● Climate impacts herd health and crop
growth
● FME is used to integrate data from a range
of platforms including:
○ Beef Monitor, Fullwood, UNIFORM
Agri
“Fitbit” for Cows!
Sensors and systems to make
Agriculture ‘smarter’

36. ● Network of high precision soil sensors
● 70 IoT sensors across 20 farms, gathering data since January 2021
● Datasets include soil moisture, salinity, and temperature
● Allows real-time monitoring of water use for precision irrigation
● IrriMAX Live exposes the sensor data via an API
A focus on IrriMAX

37. ● Helping Agri-EPI centralise datasets from a range of
platforms including farm computer systems, webpages and
third-party APIs
● Created a repository of data in a PostgreSQL database
● All powered by FME: a number of FME data integration
workflows, built with FME Form, automated using
scheduled triggers in FME Flow
Tensing

38. Demo

40. ● Smart Sensors in Agri-Tech: The future of agri-tech
● FME Integration: Integrates IoT datasets to drive real-time insights
● Data Centralisation: Access latest data
● Wide Accessibility: Data is made available to wide audience
Mitigating Climate Risk in Agriculture

41. 5
Story 2: Climate
Projections to
Risk Assessments

42. OGC Climate Resilience Pilot 2023
Pilot Goals:
● Build climate resilience
● Expand audience for climate services
● Demonstrate the value of OGC
standards and SDI’s (FAIR)
● Show how OGC can support international
climate change goals
● Build a community of stakeholders
better understand the range of possible
impacts - allows us to better prepare and
compensate for them
https://www.ogc.org/initiatives/crp/

43. Climate Model Results: Time Series Data Cube
Model results selection for predictive weather for
use in impact analysis:
● Emissions scenario: RCP 4.5
● Model type: Regional (RCM)
● Extent: Spatial and temporal: MB, LA
● Resolution: spatial, temporal: 10km, month
● Climate variable: temperature, precipitation
● Statistics: mean, min, max
● Target data structure: geometry, values
Goal: Extract climate variables to assess
population and critical infrastructure impacts
Time series data cube - example structure

44. Data Cube to GIS: NetCDF to GeoPackage
How to extract and transform climate results
(NetCDF data cubes) in order to load a
database for use in impact analysis?
1. Split data cube into individual grids
2. Set grid timestep parameters
3. Compute timestep stats by band
4. Compute time range stats by cell
5. Classify by cell value range
6. Convert grids to vector areas by class
7. Aggregation by month, year
Goal: Assess impact: population and critical
infrastructure Input NetCDF from ECCC climate model

45. Data Cube to GIS: NetCDF to GeoPackage
1. Split data cube
2. Set timestep
parameters
3. Compute
timestep stats by
band
4. Compute time
range stats by cell
5. Classify by cell
value range
6. Convert grids to
vector contour
areas by class
Split: RasterBandSeparator
Stats: RasterStatisticsCalculator
Classify: RasterCellValueReplacer
Convert: RasterCellCoercer

46. Data Cube to ARD: Key Raster Filters
Classification: Temp Min / Max Range
Band
Statistic
s
Cell
Statistics
Aggregated
By Time
Range
(month,
year)

47. Geopackage Time Series: MaxYearly Temp

48. Query: Monthly Max Temp Contours

49. Query: Average Daily Max Temp > 25C
Time: July 2039

50. Query: Average Daily Max Temp > 25C
Highlighted area on previous slide close
to location where the town of Lytton, BC
burned to the ground during the Western
Canadian heat dome of summer 2021.
https://www.bbc.com/news/world-us-cana
da-57678054
https://www.cbc.ca/news/canada/british-c
olumbia/bc-wildfires-lytton-july-1-2021-1.6
087311
Source BBC
Source CBC
Source google maps

51. Precipitation Stats by Month: Area Time Series
Manitoba precipitation areas / contours

52. Time Series: NetCDF to Geopackage Points
Convert data cube to grids,
then extract monthly and yearly points for
both temperature and precipitation
(based on end user feedback)

53. Data Cube to GIS: NetCDF to GeoPackage

54. FME Cloud: OGC API Service Test
https://disasterpilot-dean.fmecloud.com/fmedatastreaming/OGCAPI/collections.fmw/collections

55. OGC API Querier: Geopackage to GeoJSON

56. Temperature Stats byYear – Point Time Series

57. Temperature Stats byYear – Area Time Series
Query:
Temp Class = 25:
(Max period temp >
20)
Band max > 23
Band min > 18.5

58. Temperature Stats byYear – Point Time Series

59. Indicator Queries to Support
Heat waves:
● Max period temp > TH
(e.g. 26C)
● Min period temp > TM
(e.g. 20C)
● Difference from historical (max, min, mean)
Drought:
● Total precipitation (total, max, min, mean)
● Soil moisture period stats (max, min, mean)
● Difference from historical (max, min, mean)
Fire & Health
● Temperature, Soil Moisture, Precipitation, but likely with different business rules
● Wind speed, direction, vegetation, fuel

60. OGC API Feature Layers & Parameters
Allows user to explore climate scenario values by type, value range, time & extent
Heat waves:
● Temperature Stats per month
● Temperature delta from historical per month
Drought:
● Precipitation, soil moisture per month
● Precipitation delta from historical
Parameters:
● Bbox, StartTime, EndTime, ClimateVarMax, ClimateVarMin

61. OGC API Querier: Geopackage to GeoJSON
Variable range, temporal, spatial extents and feature limit passed to database reader

62. Precipitation Stats by Month – Point Time Series

63. FME Climate Service Component Integration with
Pixalytics Drought Indicator Pilot Component
● FME Analysis Ready Data
(ARD) Component extracts
NetCDF to Precipitation
totals by month time series
GeoJSON
● Pixalytics Drought Decision
Ready Indicator (DRI)
component integrates future
projections with historical
and present data
Samantha Lavender, pixalytics.com

64. Los Angeles Drought Scenario: Landscape Visualization
Goal:
Use climate projections to visualize
the impact of climate change on the
Los Angeles landscape over time.
Process:
Apply input climate variables to
vegetation model to determine which
species survive per time step.
Result:
Sequence of landscape visualizations
over time showing the effects of
climate change.
Getty images
Dean Hintz

65. LA Drought: NetCDF Source from RCM
https://en.wikipedia.org/wiki/Representative_Concentration_Pathway
Regional Climate Model (RCM)
• Future total monthly
precipitation and mean temp
from RCP45 CMIP5
• for 2020-2100
• Statistically downscaled climate
scenarios (BCSD)
• from USGS THREDDS
RCP4.5: ‘Business as usual’
"the most probable baseline
scenario (no climate policies) taking
into account the exhaustible
character of non-renewable fuels."

66. LA: Precipitation Delta
1. Calculate historical mean precipitation per month
across 30 years of time series using grid algebra (by
cell, 1950 -1980)
2. Read future precipitation time series per month
3. PrecipDelta_ts = PrecipFuture_ts –
PrecipHistoricalMean_ts
4. PrecipIndex = PrecipDelta / PrecipHistoricalMean
5. Raster to vector convert delta grid to points
6. Apply PrecipDelta properties to points, and write to
Geopackage, GeoJSON
7. Provided as input to vegetation model within
visualization component
RasterMosaicker:
grid calculations - historical
average monthly precipitation

67. LA Precipitation: Historical Mean
Calculate historical mean precipitation per month per cell
across 30 years of time series (1950 -1980)
Jan 1950
Jan 1951
Jan 1952
Jan 1953
Jan 1954
…
Jan 1980
Feb 1950
Feb 1951
Feb 1952
Feb 1953
Feb 1954
…
Feb 1980
https://www.linkedin.com/pulse/explore-spatial-data-space-time-pattern-mining-emrah-dirmit/

68. LA Precipitation: Future Delta
PrecipDelta = PrecipFuture – PrecipHistoricalMean
_
=

69. LA: Precipitation Delta: FME Workflow
1. Read and split data cubes
2. Set time step properties and dates
3. Compute historical average per month
4. Compute delta by subtracting historical
average from future time steps values
5. Merge in record level metadata
6. Write to geopackage

70. LA: Precipitation Delta

71. Laubwerk: Vegetation Visualization
Laubwerk.com

72. LA: Precipitation Delta > Visualization
1. Compute Precipitation
Delta (Future – Historical)
2. Export to format suitable for
Laubwerk’s visualization
platform: GeoJSON
3. Vegetation is modelled
based on combination of
growth model and
environmental conditions
over time
4. First visualization based on
current climate
5. Second visualization
incorporates climate
variables from FME
Visualizations
provided
courtesy of
Timm Dapper,
Laubwerk

73. LA Visualization: Landscape Impact

74. LA Visualization: Landscape Impact

75. 2020

76. 2040a

77. 2060a

78. 2040b

79. 2060b

80. Climate / Disaster Pilot Progress
● Stakeholder feedback on desired outputs: e.g., point vs. classified contours
● Climate variable time series via OGC Features API - client and test service
● Layers from climate model outputs: Manitoba Temp, Precip (Monthly and Yearly)
● Additional query parameters: Extents, date, min/max climate variable value, limit
● Rate limiting with default max features cap
● LA: Computed historical mean precipitation, estimated future delta per timestep.

81. To Do
● Gather feedback from stakeholders & users
● Generate metadata, register service with catalog
● Publish US NW data
● Add context layers, new climate variables for MB
● Experiment with other statistical approaches
● Provide additional data format options, cloud native (ZARR)
● Test direct connection to climate services using URL, APIs + cloud native (USGS, NOAA)
● Investigate pan sharpening techniques to improve resolution of climate-related impacts.

82. Lessons Learned
● Improve access to climate model results for geospatial industry and climate resilience
● Retain climate variable information sufficient to support decision making
● Point data instead of classified contours for data cube translation
● Point data with statistics enables advanced query capabilities
● Empower domain expert users, collaborate on indicator business rules for services
● The goal is not to make climate predictions. Rather this process serves as a pipeline to help
users consume projection scenarios from climate services and distill potential risks from
them.

83. Outstanding Questions: Data Cube / Time Series
● What climate variables would be most useful for you to track related to your local context?
● What impacts are you most concerned about?
● What are best methods for summarizing time series data to support impact analysis?
● How can we effectively aggregate data from single to multi-timesteps? Should we use
maximum, mean, clustering, or other techniques?
● Which data structures (raster, vector, geometry) are most useful for output or analytics?
● What questions can be answered using existing analysis ready data such as temperature,
precipitation, or soil moisture time series?
● Which indicators are best served by comparing between historical and future periods?
These questions may seed QnA discussion, or contact us any time to follow-up.

84. Disaster Pilot 2021:
Red River Flood Routing Recipe

85. Flood Routing Recipe:
Flood Contours Published to Pilot GeoNode/GeoServer
DP21 Pilot GeoNode
● Layer search
● Metadata
● Styling
● Interactive web map
interface
● Time Series
● Data download
● OGC web services:
WMS / WFS

86. 20
22
FME
User
Conference
Routing Client for Exploring Decision Ready Indicators
Optimal route
Select flood levels for
Road restrictions New optimal route

87. Assiniboine River Flood Risk: Winnipeg, MB

88. Assiniboine River Flood Risk: Winnipeg, MB

89. FireSmoke.ca
https://firesmoke.ca/forecasts/current/

90. CUSTOMER STORY
“We love FME.
We’ve been using it for about 20 years.”
- Piet Nooij, Fortis BC
PROJECT
Assess the current wildfire threat to
assets.
SOLUTION
Integrate active wildfire data from
provincial government with their GIS.
RESULTS
● Workflow automatically runs at
same interval as source dataset
updates.
● Notifications & reports are
immediately sent to Operations
Managers who can coordinate
with Emergency Services.
FORTIS BC >

91. 6
Conclusion

92. Summary
● Combine past, current and future environment data: to better assess climate change risks
● Stakeholder feedback: relevant data, services, indicators for climate impact management
● Optimal detail through data simplification: Goldilocks principle
● Geometry trade-offs: vector vs raster, client vs data flexibility
● Agile rapid prototyping of data transform models with FME
● Prioritize open standards, OGC APIs, cloud-native: for availability, scalability & collaboration
● By publishing and evaluating a range of climate impact scenarios we can explore mitigation
options to help better prepare for improved resilience

93. 7
Next Steps

94. We’d love to help you get
started.
Get in touch with us at
info@safe.com
Experience the FME Accelerator
Contact Us
Unlock the power of your
data in only 90 minutes
Register for free at
fme.safe.com/accelerator

95. 8
Resources

96. Get our Ebook
Spatial Data for the
Enterprise
fme.ly/gzc
Guided learning experiences
at your fingertips
community.safe.com
/s/academy
FME Academy

97. Check out how-to’s & demos
in the knowledge base
community.safe.com
/s/knowledge-base
Knowledge Base Webinars
Upcoming & on-demand
webinars
safe.com/webinars

98. ● OGC Disaster Pilot - Safe Contribution
● Flood and Landslide Impact Components for the OGC 2021
Disaster Pilot using FME
● Using Data Integration to Deliver Intelligence to Anyone,
Anywhere (Disaster Focus)
Implementation Examples:
● Weather Network: Real time lightning
● Wildfire Threat Assessment
● Manitoba Hydro: Fire Proximity Awareness
Climate Data Services:
● Climate Change Canada - Data Extraction Tool
● USGS THREDDS Data Service
Climate Change Resources

99. Our largest
FME user
conference
yet.
Sept 5-7 | 100+ sessions
co-hosted by Safe Software & con terra

100. ClaimYour Community Badge
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Join the Community today!

101. 9
Q&A

102. ThankYou
dean.hintz@safe.com
omorris@tensing.com
panagis.tzivras@agri-epicentre.com
Fill out our
webinar survey

103. Bridge the gap between
climate models and GIS
Using extraction, transforms and automations with FME!