<ol id="_0e867c64-4472-4aa7-987a-0a22ddfd4447" type="arabic"> <li> <p id="_f2a5fb6f-eebc-459b-8030-47d77d9dc05c">the applicability of open standards and specifications in support of data
+
<ol id="_d6741301-2f62-4363-a7ef-2481878da5b4" type="arabic"> <li> <p id="_062375a5-8745-4c6c-a44b-2e564b23307f">the applicability of open standards and specifications in support of data
discovery, data integration, data transformation, data processing, data
dissemination, and data visualization;</p>
</li>
-<li> <p id="_def4d486-a230-4b6f-b72f-f5c0287528f5">transparency of metadata, data quality, and provenance;</p>
2
+<li> <p id="_7c895262-77f3-4517-97c3-7297e85ab5fa">transparency of metadata, data quality, and provenance;</p>
The OGC Climate Resilience Pilot marked the beginning of a series of enduring climate initiatives with the primary goal of evaluating the value chain encompassing raw data to climate information processes within Climate Resilience Information Systems. This includes the transformation of geospatial data into meaningful knowledge for various stakeholders, including decision-makers, scientists, policymakers, data providers, software developers, service providers, and emergency managers. The results of the OGC Climate Resilience Pilot support the location community to develop more powerful visualization and communication tools to accurately address ongoing climate threats such as heat, drought, floods, and wild-fires as well as supporting governments in meeting commitments for their climate strategies. This will be accomplished through evolving geospatial data, technologies, and other capabilities into valuable information for decision-makers, scientists, policymakers, data providers, software developers, and service providers so they can make valuable, informed decisions to improve climate action.
+
I. Executive Summary
The OGC Climate Resilience Pilot marked the beginning of a series of enduring climate initiatives with the primary goal of evaluating the value chain encompassing raw data to climate information processes within Climate Resilience Information Systems. This includes the transformation of geospatial data into meaningful knowledge for various stakeholders, including decision-makers, scientists, policymakers, data providers, software developers, service providers, and emergency managers. The results of the OGC Climate Resilience Pilot support the location community to develop more powerful visualization and communication tools to accurately address ongoing climate threats such as heat, drought, floods, and wild-fires as well as supporting governments in meeting commitments for their climate strategies. This will be accomplished through evolving geospatial data, technologies, and other capabilities into valuable information for decision-makers, scientists, policymakers, data providers, software developers, and service providers so they can make valuable, informed decisions to improve climate action.
One of the most significant challenges so far has been converting the outputs of global and regional climate models into specific impacts and risks at the local level. The climate science community has adopted standards and there are now numerous climate resilience information systems available online, allowing experts to exchange and compare data effectively. However, professionals outside the weather and climate domain, such as planners and GIS analysts working for agencies dealing with climate change impacts, have limited familiarity with and capacity to utilize climate data.
Stakeholders depend on meaningful information to make decisions or advance their science. In the context of climate change, this meaningful information is delivered through climate services as a combination of technical applications and human consultation. The technical infrastructures underpinning climate services, named here as Climate Resilience Information Systems, require the processing of vast amounts of data from diverse providers across various scientific ecosystems as follows.
First, the raw data must be transformed into Data Cubes and delivered as Analysis-Ready Data.
This data must then be transformed into Decision Ready Indicators to become more useful for supporting decisions related to climate action.
Finally, the data for visualization and simulation on demand are delivered to stakeholders with guidance on how best to communicate the information.
-
This report assesses the value chain from raw data to climate information and the onward delivery to stakeholders. It explains good practices on how to design climate resilience information systems, identifies gaps, and gives recommendations on future work.
The OGC pilot demonstrated the capability of creating data pipelines to convert vast amounts of raw data through various steps into decision-ready information and 3D visualizations while embedding good practice approaches for communicating this knowledge to non-specialized individuals. In other words, in order to obtain decision-ready information, the data must first be collected from multiple sources and organized, then transformed into analysis-ready formats.
To address the value chain from raw data to decision-ready indicators, one focus of this pilot was to explore methods for extracting climate variables from climate model output scenarios and delivering them in formats that are more easily usable for post-processing experts, alongside being applicable to local situations and specific use-cases. Climate variable Data Cubes were extracted or aggregated into temporal and spatial ranges specific to the use cases. Then, the data structure was transformed from multidimensional gridded cubes into forms that can be readily utilized by geospatial applications. These pilot data flows serve as excellent examples of how climate data records can be translated into estimates of impacts and risk at the local level in a way that seamlessly integrates into existing planning workflows and is made available to a broad user community via open standards.
In addition, the pilot explored various parts of the processing pipelines that were examined using climate-impact case studies related to heat, droughts, floods, and wildfires, highlighting assessment tools and the complexities of climate indices. It also recognized the existence of solar radiation databases and web map services, emphasizing the need to enhance their accessibility and applicability at a national level to combat the effects of climate change by utilizing solar energy resources more efficiently. Ultimately, this Climate Resilience Pilot serves as a crucial asset for making well-informed decisions that bolster climate action. It particularly aids the location community in developing enhanced 3D visualization, simulation, and communication tools to effectively address prevalent climate change impacts or hazards caused by meteorological extreme events.
This report also demonstrates the workflow from data to 3D visualization, specifically for non-technical individuals. A chapter is dedicated to the options and challenges of applying artificial intelligence to establish a climate scenario digital twin where various scenarios of efficiencies of climate action can be simulated. These simulations can encompass the reduction of disaster risks through technical engineering. The concept of climate resilience is explored, not only considering the shift of meteorological phenomena but also accounting for land degradation and biodiversity loss. More specifically, the scenarios focus on understanding the effects of climate change on vegetation in the Los Angeles area. 3D landscape vegetation simulations are presented, demonstrating how different tree species adapt under changing climate conditions represented by a range of climate and policy scenarios over time.
The pilot acknowledges the significant challenges of effectively conveying information to decision-makers. This necessitates a thorough examination of communication methods. Consequently, a dedicated chapter emphasizes unique approaches to facilitate effective communication with non-technical individuals, who frequently hold responsibility for local climate resilience action strategies. The development and implementation of a stakeholder survey provides insight into the strengths and weaknesses of past adaptation processes and allows for the derivation of opportunities for improvement. By prioritizing communication, the pilot aims to bridge the gap between technical and non-technical stakeholders, ensuring accurate and comprehensive information transmission for the benefit of both sides. The addition of this chapter demonstrates the pilot’s aim to enhance communication strategies to foster improved decision-making in the realm of climate resilience.
Overall, this engineering report presents various workflow processes which illustrate the seamless exchange of data, models, and components, such as climate application packages, that emphasize the potential for optimization using OGC Standards.
In the context of climate and disaster resilience, this document greatly contributes to a comprehensive understanding of flood, drought, heat, and wildfire assessments offering insights into decision-making for climate actions, specifically addressing the enhancement of Climate Resilience Information Systems in line with FAIR Climate Services principles.
II. Keywords
The following are keywords to be used by search engines and document catalogues.
The various organizations and institutes that contribute to the Climate Resilience Pilot are described below.
Table — Contributors of this Climate Resilience Pilot
Name
Organization
Role or Summary of contribution
Guy Schumann
RSS-Hydro
Lead ER Editor
Albert Kettner
RSS-Hydro/DFO
Lead ER Editor
Sacha Lepretre
CAE, Presagis (CAE Subsidiary)
Use of AI DigitalTwin and Simulation for climate (5D Meta World demo with Laubwerk).
Timm Dapper
Laubwerk GmbH
Peng Yue
Wuhan University
Datacube component
Zhe Fang
Wuhan University
Climate ARD component
Hanwen Xu
Wuhan University
Drought impact use cases
Dean Hintz
Safe Software, Inc.
Climate Analysis Ready Data and Drought Indicator
Kailin Opaleychuk
Safe Software, Inc.
Climate Analysis Ready Data and Drought Indicator
Samantha Lavender
Pixalytics Ltd
Development of drought indicator
Andrew Lavender
Pixalytics Ltd
Development of drought indicator
Jenny Cocks
Pixalytics Ltd
Development of drought indicator
Jakub P. Walawender
Freelance climate scientist and EO/GIS expert
Climate ARD and solar radiation use case
Daniela Hohenwallner-Ries
alpS GmbH
Communication with stakeholders
Hanna Krimm
alpS GmbH
Communication with stakeholders
Hinnerk Ries
alpS GmbH
Communication with stakeholders
Paul Schattan
alpS GmbH
Communication with stakeholders
Jérôme Jacovella-St-Louis
Ecere Corporation
Datacube API client and server
Patrick Dion
Ecere Corporation
Datacube API client and server
Eugene Yu
GMU
Gil Heo
GMU
Glenn Laughlin
Pelagis Data Solutions
Coastal Resilience & Climate Adaptation
Tom Landry
Intact Financial Corporation
Steve Kopp
Esri
Climate services & web interface
Lain Graham
Esri
Climate services & web interface
Nils Hempelmann
OGC
Climate resilience Pilot Coordinator
III.A. About alpS
+
This report assesses the value chain from raw data to climate information and the onward delivery to stakeholders. It explains good practices on how to design climate resilience information systems, identifies gaps, and gives recommendations on future work.
The OGC pilot demonstrated the capability of creating data pipelines to convert vast amounts of raw data through various steps into decision-ready information and 3D visualizations while embedding good practice approaches for communicating this knowledge to non-specialized individuals. In other words, in order to obtain decision-ready information, the data must first be collected from multiple sources and organized, then transformed into analysis-ready formats.
To address the value chain from raw data to decision-ready indicators, one focus of this pilot was to explore methods for extracting climate variables from climate model output scenarios and delivering them in formats that are more easily usable for post-processing experts, alongside being applicable to local situations and specific use-cases. Climate variable Data Cubes were extracted or aggregated into temporal and spatial ranges specific to the use cases. Then, the data structure was transformed from multidimensional gridded cubes into forms that can be readily utilized by geospatial applications. These pilot data flows serve as excellent examples of how climate data records can be translated into estimates of impacts and risk at the local level in a way that seamlessly integrates into existing planning workflows and is made available to a broad user community via open standards.
In addition, the pilot explored various parts of the processing pipelines that were examined using climate-impact case studies related to heat, droughts, floods, and wildfires, highlighting assessment tools and the complexities of climate indices. It also recognized the existence of solar radiation databases and web map services, emphasizing the need to enhance their accessibility and applicability at a national level to combat the effects of climate change by utilizing solar energy resources more efficiently. Ultimately, this Climate Resilience Pilot serves as a crucial asset for making well-informed decisions that bolster climate action. It particularly aids the location community in developing enhanced 3D visualization, simulation, and communication tools to effectively address prevalent climate change impacts or hazards caused by meteorological extreme events.
This report also demonstrates the workflow from data to 3D visualization, specifically for non-technical individuals. A chapter is dedicated to the options and challenges of applying artificial intelligence to establish a climate scenario digital twin where various scenarios of efficiencies of climate action can be simulated. These simulations can encompass the reduction of disaster risks through technical engineering. The concept of climate resilience is explored, not only considering the shift of meteorological phenomena but also accounting for land degradation and biodiversity loss. More specifically, the scenarios focus on understanding the effects of climate change on vegetation in the Los Angeles area. 3D landscape vegetation simulations are presented, demonstrating how different tree species adapt under changing climate conditions represented by a range of climate and policy scenarios over time.
The pilot acknowledges the significant challenges of effectively conveying information to decision-makers. This necessitates a thorough examination of communication methods. Consequently, a dedicated chapter emphasizes unique approaches to facilitate effective communication with non-technical individuals, who frequently hold responsibility for local climate resilience action strategies. The development and implementation of a stakeholder survey provides insight into the strengths and weaknesses of past adaptation processes and allows for the derivation of opportunities for improvement. By prioritizing communication, the pilot aims to bridge the gap between technical and non-technical stakeholders, ensuring accurate and comprehensive information transmission for the benefit of both sides. The addition of this chapter demonstrates the pilot’s aim to enhance communication strategies to foster improved decision-making in the realm of climate resilience.
Overall, this engineering report presents various workflow processes which illustrate the seamless exchange of data, models, and components, such as climate application packages, that emphasize the potential for optimization using OGC Standards.
In the context of climate and disaster resilience, this document greatly contributes to a comprehensive understanding of flood, drought, heat, and wildfire assessments offering insights into decision-making for climate actions, specifically addressing the enhancement of Climate Resilience Information Systems in line with FAIR Climate Services principles.
II. Keywords
The following are keywords to be used by search engines and document catalogues.
The various organizations and institutes that contribute to the Climate Resilience Pilot are described below.
Table — Contributors of this Climate Resilience Pilot
Name
Organization
Role or Summary of contribution
Guy Schumann
RSS-Hydro
Lead ER Editor
Albert Kettner
RSS-Hydro/DFO
Lead ER Editor
Sacha Lepretre
CAE, Presagis (CAE Subsidiary)
Use of AI DigitalTwin and Simulation for climate (5D Meta World demo with Laubwerk).
Timm Dapper
Laubwerk GmbH
Peng Yue
Wuhan University
Datacube component
Zhe Fang
Wuhan University
Climate ARD component
Hanwen Xu
Wuhan University
Drought impact use cases
Dean Hintz
Safe Software, Inc.
Climate Analysis Ready Data and Drought Indicator
Kailin Opaleychuk
Safe Software, Inc.
Climate Analysis Ready Data and Drought Indicator
Samantha Lavender
Pixalytics Ltd
Development of drought indicator
Andrew Lavender
Pixalytics Ltd
Development of drought indicator
Jenny Cocks
Pixalytics Ltd
Development of drought indicator
Jakub P. Walawender
Freelance climate scientist and EO/GIS expert
Climate ARD and solar radiation use case
Daniela Hohenwallner-Ries
alpS GmbH
Communication with stakeholders
Hanna Krimm
alpS GmbH
Communication with stakeholders
Hinnerk Ries
alpS GmbH
Communication with stakeholders
Paul Schattan
alpS GmbH
Communication with stakeholders
Jérôme Jacovella-St-Louis
Ecere Corporation
Datacube API client and server
Patrick Dion
Ecere Corporation
Datacube API client and server
Eugene Yu
GMU
Gil Heo
GMU
Glenn Laughlin
Pelagis Data Solutions
Coastal Resilience & Climate Adaptation
Tom Landry
Intact Financial Corporation
Steve Kopp
Esri
Climate services & web interface
Lain Graham
Esri
Climate services & web interface
Nils Hempelmann
OGC
Climate resilience Pilot Coordinator
III.A. About alpS
alpS GmbH is an international engineering and consulting firm that supports companies, municipalities, and governments in sustainable development and in dealing with the consequences, opportunities, and risks of climate change. Over the past 20 years, alpS has worked with more than 250 municipalities and industrial partners on climate-related projects. alpS accompanied a large number of adaptation cycles from risk assessments to the implementation and evaluation of adaptation measures.
-
III.B. CAE
+
III.B. CAE
CAE is a high-tech company with a mission and vision focused on safety, efficiency, and readiness.
As a technology company, CAE digitalizes the physical world, deploying simulation training and critical operations support solutions.
@@ -1515,7 +1533,7 @@
CAE invests time and resources into building the next generation of cutting-edge, digitally immersive training and critical operations solutions while keeping positive environmental, social, and governance (ESG) impact at the core of its mission.
Presagis is part of CAE and is specialized in developing 3D Modeling & Simulation Software.
Presagis has developed VELOCITY 5D (V5D), a Next Generation 3D Digital Twins Creation and Simulation geospatial platform leveraging artificial intelligence.
-
III.C. About Ecere
+
III.C. About Ecere
Ecere is a small software company located in Gatineau, Québec, Canada.
Ecere develops the GNOSIS cross-platform suite of geospatial software, including a map server, a Software Development Kit and a 3D visualization client.
@@ -1524,29 +1542,29 @@
As a member of OGC, Ecere is an active contributor in several Standard Working Groups as co-chair and editor, and participated in several testbeds, pilots and code sprints.
In particular, Ecere has been a regular contributor and an early implementer for several OGC API standards in its GNOSIS Map Server and GNOSIS Cartographer client, and is also active in the efforts to modernize
the OGC CDB data store and OGC Styles & Symbology standard.
-
III.D. About Esri
+
III.D. About Esri
Esri is a leading provider of geographic information system (GIS) software, location intelligence, and mapping. Since 1969, Esri has supported customers (more than a half million organizations in over 200 countries) with geographic science and geospatial analytics, taking a geographic approach to problem-solving, brought to life by modern GIS technology. The ArcGIS platform includes an integrated system of desktop, web, and mobile software products and data committed to open science.
Within the context of this OGC engagement, Esri provides the full range of capabilities from CMIP climate data processing and publishing, spatial analysis for risk assessment, climate adaption and resilience, to web application development and science communication tools.
-
III.E. About George Mason University (GMU)
+
III.E. About George Mason University (GMU)
George Mason University (GMU) is a public research university that conducts research and provides training to postdoctoral fellows, PhD candidates, and master’s students in Geospatial information science, remote sensing, satellite image analysis, geospatial data processing, Earth system science, geospatial interoperability and standards, geographic information systems, and other related subjects. GMU will contribute an ARD use-case.
-
III.F. About Intact
+
III.F. About Intact
Intact Financial Corporation (IFC) is the largest provider of Property & Casualty (P&C) insurance in Canada. IFC’s purpose is to help people, businesses, and society prosper in good times and be resilient in bad1. The company has been on the front lines of climate change for more than a decade – getting its customers back on track and adapted to change. As extreme weather is predicted to get worse over the next decade, Intact intends to double down on adjusting to this changing environment to become more well prepared for floods, wildfire, and extreme heat2.
With close to 500 experts in data, artificial intelligence, machine learning, and pricing, the Intact Data Lab has deployed almost 300 AI models in production to date, focussing on improving risk selection and making operations as efficient as possible while creating outstanding interactions with customers. Within Intact’s Data Lab, the Centre for Climate and Geospatial Analytics (CCGA) uses weather, climate, and geospatial data along with machine learning models and claims data to develop risk maps and other specialized products.
-
III.G. About Laubwerk
+
III.G. About Laubwerk
Laubwerk is a software development company whose mission is to combine accurate, broadly applicable visualizations of vegetation with deeper information and utility that goes far beyond their visual appearance. Laubwerk achieves this through building a database that combines ultra-realistic 3D representations of plants with extensive metadata that represents plant properties. This unique combination makes Laubwerk a prime partner to bridge the gap from data-driven simulation to eye-catching visualizations.
-
III.H. About Pixalytics Ltd
+
III.H. About Pixalytics Ltd
Pixalytics Ltd is an independent consultancy company specializing in Earth Observation (EO) combining cutting-edge scientific knowledge with satellite and airborne data to provide answers to questions about EArth’s resources and behavior. The underlying work includes developing algorithms and software, with activities including a focus on EO quality control and end-user focused applications.
-
III.I. About Pelagis
+
III.I. About Pelagis
Pelagis is an OceanTech venture located in Nova Scotia, Canada focusing on the application of open geospatial technology and standards designed to promote the sustainable use of ocean resources. As a member of the Open Geospatial Consortium, Pelagis co-chairs the Marine Domain Working Group responsible for developing a spatially-aware federated service model of marine and coastal ecosystems.
-
III.J. About RSS-Hydro
+
III.J. About RSS-Hydro
RSS-Hydro is a geospatial solutions and service company focusing its R&D and commercial products in the area of water risks, with a particular emphasis on the SDGs. RSS-Hydro has been part of several successful OGC testbeds, including the DP 21 to which this pilot is linked, not only in terms of ARD and IRD but also in terms of use cases. In this pilot, RSS-Hydro’s main contribution is the lead of the Engineering report. In terms of technical contributions to various other OGC testbeds and pilots, RSS-Hydro is creating digestible OGC data types and formats for specific partner use cases, in particular producing ARD from publicly available EO and model data, including hydrological model output as well as climate projections. These ARD will feed into all use cases for all participants, especially use cases proposed for floods, heat, drought and health impacts by other participants in the pilot. The created ARD in various OGC interoperable formats will create digestible dataflows for the proposed OGC Use Cases.
@@ -1565,18 +1583,18 @@
Hydrological model simulation outputs at (sub)basin scale
-
III.K. About Safe Software
+
III.K. About Safe Software
Safe Software is a leader in supporting geospatial interoperability and automation for more than 25 years as creators of the FME platform. FME was created to promote FAIR principles, including data sharing across barriers and silos, with unparalleled support for a wide array of both vendor specific formats and open standards. Within this platform, Safe Software provides a range of tools to support interoperability workflows. FME Form is a graphical authoring environment that allows users to rapidly prototype transformation workflows in a no-code environment. FME Flow then allows users to publish data transforms to enterprise oriented service architectures. FME Hosted offers a low cost, easy to deploy, and scalable environment for deploying transformation and integration services to the cloud.
Open standards have always been a core strategy for Safe Software to better support data sharing. The FME platform can be seen as a bridge between the many supported vendor protocols and open standards such as XML, JSON, and OGC standards such as GML, KML, WMS, WFS, and OGC APIs. Safe Software has collaborated extensively over the years with the open standards community. Safe Software actively participates in the CityGML and INSPIRE communities in Europe and is also active within the OGC community and participated in many initiatives including test beds, pilots such as Maritime Limits and Boundaries and IndoorGML, and most recently the 2021 Disaster Pilot and 2023 Climate Resilience Pilot. Safe Software also actively participates in a number of Domain and Standards working groups.
-
III.L. About Jakub P. Walawender
+
III.L. About Jakub P. Walawender
Jakub P. Walawender is a freelance climate scientist and EO/GIS expert carrying out his PhD research on the solar radiation climatology of Poland at the Laboratory for Climatology and Remote Sensing (LCRS), Faculty of Geography, Philipps University in Marburg, Germany. Jakub specializes in the application of satellite remote sensing, GIS, and geostatistics in the monitoring and analysis of climate variability and extremes and supports users in the application of different climate data records to tackle the effects of climate change.
-
III.M. About Wuhan University (WHU)
+
III.M. About Wuhan University (WHU)
Wuhan University (WHU) is a university that plays a significant role in researching and teaching all aspects of surveying and mapping, remote sensing, photogrammetry, and geospatial information sciences in China. In this Climate Resilience Pilot, WHU will contribute three components (ARD, Drought Indicator, and Data Cube) and one use-case (Drought Impact Use-cases).
-
1. Terms, definitions and abbreviated terms
+
1. Terms, definitions and abbreviated terms
No terms and definitions are listed in this document.
Carrying Capacity
an area both suitable and available for human activity based on the state of the ecosystem and competitive pressures for shared resources
@@ -1588,7 +1606,7 @@
Sentinel (satellite mission)
a series of next-generation Earth observation missions developed by the European Space Agency (ESA) on behalf of the joint ESA/European Commission initiative Copernicus
-
1.1. Abbreviated terms
+
1.1. Abbreviated terms
ACDC
Atmospheric Composition Data Cube
ACDD
Attribute Convention for Data Discovery
@@ -1713,7 +1731,7 @@
WUI
Wildland-Urban Interface
XML
Extensible Markup Language
-
2. Introduction
The OGC Climate Resilience Pilot represents the first phase of multiple long term climate activities aiming to combine geospatial data, technologies, and other capabilities into valuable information for decision makers, scientists, policy makers, data providers, software developers, and service providers to assist in making valuable, informed decisions to improve climate action.
2.1. The goal of the pilot
+
2. Introduction
The OGC Climate Resilience Pilot represents the first phase of multiple long term climate activities aiming to combine geospatial data, technologies, and other capabilities into valuable information for decision makers, scientists, policy makers, data providers, software developers, and service providers to assist in making valuable, informed decisions to improve climate action.
2.1. The goal of the pilot
The goal of this pilot was to enable decision makers (scientists, city managers, politicians, etc.) in taking the relevant actions to address climate change and make well informed decisions for climate change adaptation. Since no single organization has all the data needed to understand the consequences of climate change, this pilot shows how to use data from multiple organizations—available at different scales for large and small areas—in scientific processes, analytical models, and simulation environments. The aim was to demonstrate visualization and communication tools used to craft the message in the best way for any client. Many challenges can be met through resources that adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The OGC Climate Resilience Pilot identifies, discusses, and develops these resources.
@@ -1726,7 +1744,7 @@
As illustrated, large sets of raw data from multiple sources require further processing in order to be used for analysis and climate change impact assessments. Applying data enhancement steps, such as bias adjustments, re-gridding, or calculation of climate indicators and essential variables creates “Decision Ready Indicators.” The spatial data infrastructures required for this integration should be designed with interoperable application packages following FAIR data principles. Heterogeneous data from multiple sources can be enhanced, adjusted, refined, or quality controlled to provide Science Services data products for Climate Resilience. The OGC Climate resilience pilot also illustrates the graphical exploration of the Decision Ready Indicators and effectively demonstrates how to design FAIR climate resilience information systems underpinning FAIR Climate Services. The OGC Pilot participants illustrate the necessary tools and the visualizations to address climate actions moving towards climate resilience.
The vision of the OGC Climate Resilience Community is to support efforts on climate actions, enable international partnerships (SDG 17), and move towards global interoperable open digital infrastructures providing climate resilience information on demand by users. This pilot contributes to establishing an OGC climate resilience concept store for the community where all appropriate climate information to build climate resilience information systems as open infrastructures can be found in one place, be it information about data services, tools, software, or handbooks, or a place to discuss experiences and needs. It covers all phases of Climate Resilience from initial hazards identification and mapping, vulnerability and risk analysis, options assessments, prioritization, and planning, to implementation planning and monitoring capabilities. These major challenges can only be met through the combined efforts of many OGC members across government, industry, and academia.
-
2.2. Objectives
+
2.2. Objectives
This Pilot set the stage for a series of follow up activities and focuses on use-case development, implementation, and exploration. It also answers the following questions.
@@ -1747,7 +1765,7 @@
to capture Best Practices and allow the Climate Community to copy and transform as many use-cases as possible to other locations or framework conditions.
-
2.3. Background
+
2.3. Background
With growing local communities, an increase in climate-driven disasters, and an increasing risk of future natural hazards, the demand for National Resilience Frameworks and Climate Resilience Information Systems (CRISs) cannot be overstated. CRISs are enabling data-search, -fetch, -fusion, -processing, and -visualization enabling access, understanding, and use of federal data, facilitating integration of federal and state data with local data, and serving as local information hubs for climate resilience knowledge sharing.
@@ -1774,7 +1792,7 @@
Figure 4 — Schematic Architecture of a Climate Resilience Information System. By respecting FAIR principles for the climate application packages the architecture enables open infrastructures to produce and deliver information on demand of the users needs
-
2.4. Technical Challenges
+
2.4. Technical Challenges
Realizing the delivery of Decision Ready Data on demand to achieve Climate Resilience involves a number of technical challenges that have already been identified by the community. A subset will be selected and embedded in use-cases that will be defined jointly by Pilot Sponsors and the OGC team. The goal is to ensure a clear value-enhancement pipeline as illustrated in Figure 1, above. This includes, among other elements, a baseline of standardized operators for data reduction and analytics. These need to fit into an overall workflow that provides translation services between upstream model data and downstream output — basically from raw data to analysis-ready data to decision-ready data.
@@ -1787,7 +1805,7 @@
Application packages for processing pipelines: Machine Learning and Artificial Intelligence plays an increasing role in the context of data science and data integration. This focus area evaluates the applicability of machine learning models in the context of the value-enhancing processing pipeline. What information needs to be provided to describe machine learning models and corresponding training data sufficiently to ensure proper usage at various steps of the pipeline? Upcoming options to deploy ML/AI within processing APIs to enhance climate services are rising challenges, e.g., on how to initiate or ingest training models and the appropriate learning extensions for the production phase of ML/AI. Heterogeneity in data spaces can be bridged with Linked Data and Data Semantics. Proper and common use of shared semantics is essential to guarantee solid value-enhancement processes. At the same time, resolvable links to procedures, sampling and data process protocols, and used applications will ensure transparency and traceability of decisions and actions based on data products. What level is currently supported? What infrastructure is required to support shared semantics? What governance mechanisms need to be put in place?
-
2.5. Relevance to the Climate Resilience Domain Working Group
+
2.5. Relevance to the Climate Resilience Domain Working Group
The Climate Resilience DWG will concern itself with technology and technology policy issues, focusing on geospatial information and technology interests as related to climate mitigation and adaptation, as well as the means by which those issues can be appropriately factored into the OGC standards development process.
@@ -1810,20 +1828,20 @@
provide software tool kits to facilitate the deployment of climate change services platforms.
-
2.6. Value Chain from raw data to Information
+
2.6. Value Chain from raw data to Information
During this pilot, participants have worked on a number of workflows and architectures focusing on several use cases of floods, droughts, heatwaves, and fires. It required the use of Climate Resilience Information Systems where interoperability played a vital role in producing climate information by enabling seamless integration and exchange of information between data, models, and various components.
The value chain from raw data to climate information (Figure 1) can be clustered in sections according to the value quality. This value chain, often also compared to a conveyor belt, can be designed with different component workflows which are developed, analyzed, and described in this pilot. The order of the chapters of the document reflects value chain organizing and processing starting from Raw data to Datacubes (Chapter 3). The following Chapter 4 describes the data refinement from Raw Data and Datacubes to Analysis Ready Data (ARD). Various data pipelines are considered and evaluated on how best to move raw data, first to data cubes for efficient handling, and then how to process them to ARD, or derive the ARD directly from the raw data. This guides the discussion on the standardization of Data Cubes and ARD. Subsequently, Chapter 5, illustrates how to transform ARD to Decision Ready Indicator (DRI) by including an example set of climate indices. The pilot also demonstrates the value added of high-end 3D visualization combined with artificial-intelligence-enriched simulations for increasing climate resilience and for facilitating the decision-making process. The use cases driven value chain from Data to Visualization is described in Chapter 6. To close an important gap, a strong emphasis has been made to Climate Information and Communication with Stakeholders in Chapter 7 lining out the importance of consultation work to non-technical users to identify their requirements and optimize the information delivery use-case specific on demand.
Some of the value chain elements from raw data to visualization are illustrated by Use cases in Chapter 8. And Lessons Learned (Chapter 9) showcase the pilot’s work and include challenges with the value chain from raw data to climate information. The final chapter, chapter 10Recommendations for future climate resilience pilots describes future work.
-
3. Raw data to Datacubes
Raw data and Datacubes are two different forms for organizing and structuring data in the context of data analysis and data warehousing.
Raw Data refers to the unprocessed, unorganized, and unstructured data that is collected or generated directly from various sources. It can include a variety of forms such as text, numbers, (geo) images, audio, video, or any other form of data. Raw data often lacks formatting or context and requires further processing or manipulation before it can be effectively analyzed or used for decision-making purposes. Raw data is typically stored in databases or data storage systems.
+
3. Raw data to Datacubes
Raw data and Datacubes are two different forms for organizing and structuring data in the context of data analysis and data warehousing.
Raw Data refers to the unprocessed, unorganized, and unstructured data that is collected or generated directly from various sources. It can include a variety of forms such as text, numbers, (geo) images, audio, video, or any other form of data. Raw data often lacks formatting or context and requires further processing or manipulation before it can be effectively analyzed or used for decision-making purposes. Raw data is typically stored in databases or data storage systems.
-
Datacubes, also known as multidimensional cubes, are a structured form of data representation that organizes and aggregates raw data into a multi-dimensional format. Datacubes are designed to facilitate efficient and fast analysis of data from different dimensions or perspectives. They are commonly used in data warehousing.
+
Datacubes, also known as multidimensional cubes, are a structured form of data representation that organizes and aggregates raw data into a multi-dimensional format. Datacubes are designed to facilitate efficient and fast analysis of data from different dimensions or perspectives. They are commonly used in data warehousing.
Datacubes organize data into a multi-dimensional structure typically comprising dimensions, hierarchies, and cells. Dimensions represent various attributes or factors that define the data, such as time, geography, or products. Hierarchies represent the levels of detail within each dimension. Cells typically store the aggregated data values at the intersection of dimensions.
Datacubes enable users to perform complex analytical operations like slicing, dicing, drilling down, or rolling up data across different dimensions.
They provide a summarized and pre-aggregated view of data that can significantly speed query processing and analysis compared to working directly with raw data,
which is very valuable for the climate resilience community. Therefore, Datacubes are often used to support decision-making processes.
-The example below highlights a climate resilience related example of how to create and make available Datacubes for wildfire risk analysis.
3.1. Analysis Ready Data Cubes — user-friendly sharing of climate data records
+The example below highlights a climate resilience related example of how to create and make available Datacubes for wildfire risk analysis.
3.1. Analysis Ready Data Cubes — user-friendly sharing of climate data records
Climate Data Record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine potential climate variability and change (US National Research Council). These measurements can be obtained through ground based stations or derived from a long time series of satellite data.
@@ -1858,7 +1876,7 @@
Other: natural disasters, air quality, land cover, terrain, soil, forest, and vegetation
WFS for vector data, Cloud Optimized GeoTIFFs for raster datasets (allowing import, subset, crop, and overlay parts of data for the local area.)
2000 — 2020 and Predictions based on Ensemble Machine Learning
Analysis Ready Data Cubes (ARDCs) play an important role in handling large volumes of data (such as satellite-based CDRs). They are often deployed on different spatial scales and consist of datasets dedicated for particular application. This makes them more accessible, easier to use, and less costly for the users.
-
3.2. Data cubes to support wildfire risk analysis
+
3.2. Data cubes to support wildfire risk analysis
To support the pilot activities, Ecere provided, as an in-kind contribution, a deployment of its GNOSIS Map Server implementing several OGC API standards enabling efficient access to data cubes.
The API and backend functionality for these data cubes, improved throughout this pilot, also support a Wildland Fire Fuel indicator workflow for the OGC Disaster Pilot taking place until the end of September 2023.
@@ -2371,7 +2389,7 @@
Work is ongoing to enhance the data integration capabilities and cross-collection queries to achieve the full potential of Part 3 bringing together local and remote OGC API data and processing capabilities.
-
4. Raw Data and Datacubes to Analysis Ready Data (ARD)
CEOS defines Analysis Ready Data (ARD) as satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort. ARD incorporates interoperability both through time and with other datasets. See https://ceos.org/ard/, and especially the information for data producers: https://ceos.org/ard/files/CARD4L_Info_Note_Producers_v1.0.pdf.
4.1. Transforming climate relevant raw data to ARD
+
4. Raw Data and Datacubes to Analysis Ready Data (ARD)
CEOS defines Analysis Ready Data (ARD) as satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort. ARD incorporates interoperability both through time and with other datasets. See https://ceos.org/ard/, and especially the information for data producers: https://ceos.org/ard/files/CARD4L_Info_Note_Producers_v1.0.pdf.
4.1. Transforming climate relevant raw data to ARD
Several successful OGC testbeds, including DP 21—which is linked to this pilot—have looked at ARD and IRD in terms of use cases. In this pilot, some main technical contributions have created digestible OGC data types and formats for specific partner use cases and have produced ARD from publicly available EO and model data, including hydrological and other types of model outputs, as well as climate projections.
@@ -2418,7 +2436,7 @@
Participants, particularly GMU CSISS, have demonstrated the use of ECV record information as input with OpenSearch service endpoints (currently CMR(CWIC) and FedEO), and for downloading URLs for accessing NetCDF or HDF files.
Outputs in this case include WCS service endpoints for accessing selected granule level product images (GeoTIFF, PNG, JPEG, etc.) focusing on the WCS for downloading images and WMS for showing layers on a base map.
-
4.2. Use of API services for climate related use cases
+
4.2. Use of API services for climate related use cases
Pixalytics has developed an OGC-compliant Application Programming Interface (API) service, see Figure 24, which will provide global information on droughts. The approach is to take global open data/datasets from organizations such as ESA/Copernicus, NASA/NOAA, and WMO and combine meteorology, hydrology, and remote sensing data to produce ARD data based on a composite of different indicators. Where globally calculated drought indicators already exist, these are used in preference to their re-calculation, although consistency and the presence of uncertainties are also considered.
@@ -2507,7 +2525,7 @@
-
4.3. From Raw Data and Data Cubes to ARD with the FME Platform
+
4.3. From Raw Data and Data Cubes to ARD with the FME Platform
4.3.1. Component Descriptions
@@ -2543,17 +2561,17 @@
Original Data workflow:
-
Split data cube
+
Split data cube
-
Set timestep parameters
+
Set timestep parameters
-
Compute timestep stats by band
+
Compute timestep stats by band
-
Compute time range stats by cell
+
Compute time range stats by cell
-
Classify by cell value range
+
Classify by cell value range
-
Convert grids to vector contours
+
Convert grids to vector contours
@@ -2588,15 +2606,15 @@
Modified ARD Data workflow
-
Split data cube
+
Split data cube
-
Set timestep parameters
+
Set timestep parameters
-
Compute timestep stats by band
+
Compute timestep stats by band
-
Compute time range stats by cell
+
Compute time range stats by cell
-
Convert grids to vector points
+
Convert grids to vector points
@@ -2612,21 +2630,21 @@
ARD Climate Variable Delta Data workflow
-
Split data cubes from historic and future netcdf inputs
+
Split data cubes from historic and future netcdf inputs
-
Set timestep parameters
+
Set timestep parameters
-
Compute historic mean for 1950 — 1980 per month based on historic time series input
+
Compute historic mean for 1950 — 1980 per month based on historic time series input
-
Multiply historic mean by -1
+
Multiply historic mean by -1
-
Use RasterMosaiker to sum all future grids with -1 * historic mean grid for that month
+
Use RasterMosaiker to sum all future grids with -1 * historic mean grid for that month
-
Normalize environmental variable difference by dividing by historic average for that month
+
Normalize environmental variable difference by dividing by historic average for that month
-
Convert grids to vector points
+
Convert grids to vector points
-
Define monthly environment variables from band and range values
+
Define monthly environment variables from band and range values
@@ -2711,7 +2729,7 @@
RCP4.5: ‘Business as usual’
-
4.4. A framework example for climate ARD generation
+
4.4. A framework example for climate ARD generation
4.4.1. Component: Surface Reflectance ARD
@@ -2856,7 +2874,7 @@
-
4.5. Climate Resilience Data
+
4.5. Climate Resilience Data
4.5.1. Climate Projection Data
@@ -3043,7 +3061,7 @@
Version 2 data development is underway and will include more indices, both imperial and metric units, and min/max/mean for statistics instead of only areal mean. All modeling will be updated to CMIP6 and expanded from US to global. The release is anticipated in Q4 2023.
-
5. ARD to Decision Ready Indicator (DRI)
A decision Ready Indicator (DRI) is information and knowledge that provides specific support for actions and decisions. These indicators are pre-determined, using a set recipe which pulls together one or more ARDs to create an indicator of action and/or decision. DRIs hold significant importance as they serve as benchmarks to determine when a decision-making process is adequately prepared and can proceed efficiently. Their importance lies in several aspects. Firstly, DRIs facilitate efficient decision-making by signaling that all necessary information, analysis, and resources are available, minimizing delays and preventing hasty or uninformed decisions. Secondly, they provide quality assurance by setting standards for the decision-making process, ensuring thorough consideration of relevant factors, accurate analysis, and reliable information. DRIs also promote accountability and transparency by defining expectations and providing a framework for evaluation, enabling stakeholders to understand the reasoning behind decisions and hold decision-makers accountable. Additionally, DRIs aid in effective resource allocation by identifying the point at which resources can be allocated, preventing wastage on under prepared decisions. They also assist in managing risks associated with decision-making by encouraging thorough analysis and consideration of potential risks. Furthermore, DRIs promote consistency and standardization, reducing subjectivity and increasing fairness across different decisions. In summary, DRIs play a crucial role in ensuring well-prepared, informed, and accountable decision-making processes, enhancing efficiency, quality, transparency, and resource management.
Analyze Ready Data (ARD) that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets, form the building blocks for DRIs. The transition from ARDs to DRIs encompasses a series of steps designed to extract meaningful insights and facilitate informed decision-making commencing with the collection and preparation of data, where relevant information is gathered from diverse sources and formatted appropriately for analysis. This involves data cleaning, standardization, and transformation to ensure consistency and reliability. Following data preparation, the integration stage merges multiple data sources, which are aligned based on common variables or identifiers, thereby creating a comprehensive dataset.
Subsequently, data exploration and analysis techniques are employed to delve into the dataset’s intricacies. Through statistical analysis, data visualization, and data mining, analysts uncover patterns, relationships, and trends that enable a deeper understanding of the underlying information. Feature engineering plays a crucial role in enhancing the analytical model’s performance. By selecting pertinent features, transforming existing variables, handling missing data, and encoding categorical variables, analysts optimize the model’s ability to extract insights from the data.
Once the data is prepared and features are engineered, model development ensues. Depending on the nature of the problem and the data at hand, analysts choose appropriate algorithms, such as regression, classification, clustering, or machine learning, to build predictive or analytical models. These models are then trained using a portion of the data, often referred to as the training set. Validation is performed using a separate portion of the data, the validation set, to assess the model’s performance. The model can then be fine-tuned for optimal results.
With the validated model in place, the focus shifts to generating DRIs. These indicators are specific metrics, scores, or predictions derived from the model’s outputs, providing actionable insights relevant to the decision-making process. The DRIs serve as valuable tools that support decision-makers in interpreting the analyzed data and, therefore, making well-informed choices.
The generated DRIs become pivotal components in the decision-making process. Decision-makers leverage these indicators to assess different scenarios, evaluate risks, and identify opportunities. By incorporating the insights gained from the analyzed data and model outputs, decision-makers can make more informed and data-driven decisions to achieve desired outcomes.
It is worth noting that while the outlined steps provide a general framework, the specific implementation of the process may vary based on the unique context, data characteristics, and analytical techniques employed. Nonetheless, the overarching objective remains constant: to transform Analyze Ready Data into Decision Ready Indicators that facilitate effective decision-making. Below we provide examples on what DRIs can be developed in relation to Climate Resilience.
5.1. Wildfire hazard component
+
5. ARD to Decision Ready Indicator (DRI)
A decision Ready Indicator (DRI) is information and knowledge that provides specific support for actions and decisions. These indicators are pre-determined, using a set recipe which pulls together one or more ARDs to create an indicator of action and/or decision. DRIs hold significant importance as they serve as benchmarks to determine when a decision-making process is adequately prepared and can proceed efficiently. Their importance lies in several aspects. Firstly, DRIs facilitate efficient decision-making by signaling that all necessary information, analysis, and resources are available, minimizing delays and preventing hasty or uninformed decisions. Secondly, they provide quality assurance by setting standards for the decision-making process, ensuring thorough consideration of relevant factors, accurate analysis, and reliable information. DRIs also promote accountability and transparency by defining expectations and providing a framework for evaluation, enabling stakeholders to understand the reasoning behind decisions and hold decision-makers accountable. Additionally, DRIs aid in effective resource allocation by identifying the point at which resources can be allocated, preventing wastage on under prepared decisions. They also assist in managing risks associated with decision-making by encouraging thorough analysis and consideration of potential risks. Furthermore, DRIs promote consistency and standardization, reducing subjectivity and increasing fairness across different decisions. In summary, DRIs play a crucial role in ensuring well-prepared, informed, and accountable decision-making processes, enhancing efficiency, quality, transparency, and resource management.
Analyze Ready Data (ARD) that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets, form the building blocks for DRIs. The transition from ARDs to DRIs encompasses a series of steps designed to extract meaningful insights and facilitate informed decision-making commencing with the collection and preparation of data, where relevant information is gathered from diverse sources and formatted appropriately for analysis. This involves data cleaning, standardization, and transformation to ensure consistency and reliability. Following data preparation, the integration stage merges multiple data sources, which are aligned based on common variables or identifiers, thereby creating a comprehensive dataset.
Subsequently, data exploration and analysis techniques are employed to delve into the dataset’s intricacies. Through statistical analysis, data visualization, and data mining, analysts uncover patterns, relationships, and trends that enable a deeper understanding of the underlying information. Feature engineering plays a crucial role in enhancing the analytical model’s performance. By selecting pertinent features, transforming existing variables, handling missing data, and encoding categorical variables, analysts optimize the model’s ability to extract insights from the data.
Once the data is prepared and features are engineered, model development ensues. Depending on the nature of the problem and the data at hand, analysts choose appropriate algorithms, such as regression, classification, clustering, or machine learning, to build predictive or analytical models. These models are then trained using a portion of the data, often referred to as the training set. Validation is performed using a separate portion of the data, the validation set, to assess the model’s performance. The model can then be fine-tuned for optimal results.
With the validated model in place, the focus shifts to generating DRIs. These indicators are specific metrics, scores, or predictions derived from the model’s outputs, providing actionable insights relevant to the decision-making process. The DRIs serve as valuable tools that support decision-makers in interpreting the analyzed data and, therefore, making well-informed choices.
The generated DRIs become pivotal components in the decision-making process. Decision-makers leverage these indicators to assess different scenarios, evaluate risks, and identify opportunities. By incorporating the insights gained from the analyzed data and model outputs, decision-makers can make more informed and data-driven decisions to achieve desired outcomes.
It is worth noting that while the outlined steps provide a general framework, the specific implementation of the process may vary based on the unique context, data characteristics, and analytical techniques employed. Nonetheless, the overarching objective remains constant: to transform Analyze Ready Data into Decision Ready Indicators that facilitate effective decision-making. Below we provide examples on what DRIs can be developed in relation to Climate Resilience.
5.1. Wildfire hazard component
To develop its component, Intact migrated its previous proprietary wildfire hazard model to a private on-premise data science environment. For key inputs to the model, external connections to several open data repositories were established. To facilitate these access tests, several public open source datasets, such as climate model outputs, Earth observations, weather, and geospatial, were vetted by the appropriate cybersecurity boards. The tests also informed experts of changes in platforms offerings, of new data products specifications, applicable licenses, and of current authoritative scientific references.
@@ -3080,7 +3098,7 @@
Figure 49 — IFC’s exposure synthetic dataset, with Montreal – Ottawa corridor on the left, and close-up of Montreal on the right. Color scale represent relative risk density in each cell, while points are representative individual risks
-
5.2. The Blue Economy
+
5.2. The Blue Economy
Pelagis’ participation in the Climate Resilience pilot focuses on enhancing the view of a global oceans observation system by combining real-world ground observations with analysis ready datasets. Monitoring aspects of the oceans through both a temporal and spatial continuum while providing traceability through the observations process allows stakeholders to better understand the stressors affecting ocean health and investigate opportunities to mitigate the longer term implications related to climate change.
@@ -3122,7 +3140,7 @@
Figure 50 — Architecture
-
5.3. DRI: Heat Impact and Drought Impact FME Components
+
5.3. DRI: Heat Impact and Drought Impact FME Components
The following subsections cover the heat and drought impact components developed by Safe Software using the FME platform. For more information on the ARD component these depend on see the 4.3 section on Data Cube to ARD with FME.
@@ -3175,7 +3193,7 @@
It should be stressed that the field of drought modeling is not new and there are many drought modeling tools available that are far more sophisticated than anything described above. As such, subsequent Climate and Disaster pilots should explore how future climate projections can be funneled into these more mature climate and impact models in an automated fashion to produce more refined estimates of projected drought risk. That said, it is hoped that this basic demonstration of the raw data to ARD to DRI value chain for drought can provide some insights into what type of indicators should be generated to help better understand future drought risks, and where improvements on this process can be made.
-
6. Data to Visualization
Advances in data representation and visualization have revolutionized the way we understand and analyze information. The ability to transform raw data into meaningful visual representations has become increasingly important across various fields, including climate change. The exponential growth of data generated by various sources such as in-situ sensors, EO sensors, and social media has all led to the emergence of big data. Data visualization techniques help in extracting insights, identifying patterns, and making data-driven decisions in the face of vast and complex datasets. Visualization plays a crucial role in exploring, summarizing, and communicating the results of data analysis, making it easier for decision-makers to comprehend complex information. Data visualization enhances storytelling by presenting information in a visually engaging and intuitive manner. It helps convey complex ideas more effectively, enabling clearer communication of data-driven narratives to both technical and non-technical audiences.
Above all, the general need for data visualization arises from the complexity and volume of data that is involved with climate change adaptation. Data visualizations are stimulated by the desire for actionable insights, and the importance of clear communication in various domains.
Below are some examples of how big data can be visualized in such a way that it captures the impact of climate change on, for example, vegetation in urban areas, or the impact of climate change on climate hazards and how to overcome challenges to realize these visualizations.
6.1. Visualizing the Impact of Climate Change and Mitigation on Vegetation
+
6. Data to Visualization
Advances in data representation and visualization have revolutionized the way we understand and analyze information. The ability to transform raw data into meaningful visual representations has become increasingly important across various fields, including climate change. The exponential growth of data generated by various sources such as in-situ sensors, EO sensors, and social media has all led to the emergence of big data. Data visualization techniques help in extracting insights, identifying patterns, and making data-driven decisions in the face of vast and complex datasets. Visualization plays a crucial role in exploring, summarizing, and communicating the results of data analysis, making it easier for decision-makers to comprehend complex information. Data visualization enhances storytelling by presenting information in a visually engaging and intuitive manner. It helps convey complex ideas more effectively, enabling clearer communication of data-driven narratives to both technical and non-technical audiences.
Above all, the general need for data visualization arises from the complexity and volume of data that is involved with climate change adaptation. Data visualizations are stimulated by the desire for actionable insights, and the importance of clear communication in various domains.
Below are some examples of how big data can be visualized in such a way that it captures the impact of climate change on, for example, vegetation in urban areas, or the impact of climate change on climate hazards and how to overcome challenges to realize these visualizations.
6.1. Visualizing the Impact of Climate Change and Mitigation on Vegetation
One of the biggest challenges in communicating climate change is to tie global changes to the local impact they will have. Photorealistic visualization is a critical component for assessing and communicating the impact of environmental changes and possibilities for mitigation. For this to work, it is crucial for visualizations to reflect the underlying data accurately and allow for quick iteration. In this regard, manual visualization processes are inferior. As much as possible, visualizations of real-life scenarios should be driven directly by available data of present states and simulations of possible scenarios. This is a first attempt at determining what already works and what doesn’t with existing data and technology.
@@ -3220,13 +3238,13 @@
The aforementioned data sources were combined to create a detailed visualization of the area in question. The pairs of images below show a visualization of the status quo first as an image and then a composite of the four scenarios that were visualized (one scenario per vertical stripe). The scenarios are projections of possible climate scenarios with and without mitigation measures in place and are in the following order.
-
Year 2045 without any mitigation measures. Plants that were likely to die off due to adverse climate events were just removed based on a probability.
+
Year 2045 without any mitigation measures. Plants that were likely to die off due to adverse climate events were just removed based on a probability.
-
Year 2070 without any mitigation measures with plants removed like in the scenario before.
+
Year 2070 without any mitigation measures with plants removed like in the scenario before.
-
Year 2045 with mitigation measures. Plants that were just removed in the two scenarios before have been replaced by plants that are more resilient and are part of the aforementioned initiatives for better climate resilience.
+
Year 2045 with mitigation measures. Plants that were just removed in the two scenarios before have been replaced by plants that are more resilient and are part of the aforementioned initiatives for better climate resilience.
-
Year 2070 with mitigation measures with the same replacement logic as in the scenario before.
+
Year 2070 with mitigation measures with the same replacement logic as in the scenario before.
@@ -3287,7 +3305,7 @@
As was expected, the data-driven visualization of very local phenomena and changes is a challenging problem which reveals many issues in terms of data availability as well as standardization and compatibility of storage formats.
-
6.2. 5D Meta World
+
6.2. 5D Meta World
Presagis offered the V5D rapid 3D (trial) Digital Twin generation capability to Laubwerk. Presagis gathered an open source GIS dataset for the Hollywood region in order to match the location of the tree dataset from Laubwerk. Using V5D, Presagis created a representative 3D digital twin of the buildinsg and terrain.
Presagis imported the Laubwerk tree point dataset providing vegetation type information inside V5 Presagis provided V5D Unreal plugin to Laubwerk in order to allow the insertion of the Laubwerk 3D tree (as Unreal assets) into the scene. Using V5D, Laubwerk is capable of adapting the tree model in order to demonstrate the impact of climate change on the city vegetation
@@ -3297,7 +3315,7 @@
Figure 65 — image of the Presagis deliverable to Laubwerk. At this stage, all trees are using the same 3D model (palm tree). Laubwerk will use V5D to assign a representative 3D model based the on point feature attribution accessible in V5D. With V5D, this operation takes seconds to do and visualize the result in 3D.
-
6.3. CRMA Web Application
+
6.3. CRMA Web Application
Decision makers, public authorities, and citizens will primarily access data via a custom Esri web application, providing a simple dashboard interface for viewing interactive maps and graphs of the indices, and output formatted reports. The indices are grouped by 5 climate hazard types (Wildfire, Heat, Drought, Inland Flooding, and Coastal Inundation). The current US project (https://livingatlas.arcgis.com/assessment-tool/explore/details) can be explored to gain context of what the global project will be.
6.4. Ecere’s Client for NOAA’s Environmental Data Retrieval API
+
6.4. Ecere’s Client for NOAA’s Environmental Data Retrieval API
For the D100 Client Instance deliverable, Ecere enhanced its GNOSIS Cartographer geospatial client to better support visualizing and accessing multi-dimensional datasets, both from local sources and remote sources such as through OGC API standards. Support for the OGC API — Environmental Data Retrieval (EDR) standard as well as for OGC netCDF was implemented in the GNOSIS Software Development Kit.
The GNOSIS implementation of the GNOSIS Map Tiles specification was also enhanced as an efficient format to store and exchange n-dimensional coverage tiles, including support for
@@ -3388,7 +3406,7 @@
Figure 81 — Ecere’s GNOSIS Cartographer client accessing NOAA’s EDR API (NCAR Livneh gridded precipitations for January 15, 2013)
-
7. Climate Information and Communication with Stakeholders
Climate change is happening: mitigation efforts will simply not be enough to tackle its impacts. Thus, climate action at the local level, mitigation as well as adaptation, is needed. The alpS GmbH supports communities, regions, and industrial partners in sustainable development and in dealing with the consequences, opportunities, and risks of climate change.
In the understanding of alpS, climate change consultancy services are successful if they trigger the implementation of proactive measures to enhance climate resilience that are supported by a large number of participants. However, the degree of effectiveness of the consultancy services of alpS as a function of various communication methods (e. g. presentations including processed local climate data, information processing, moderation techniques, discussion tools) and scientific know-how has never been systematically investigated. In the pilot project, alpS therefore evaluated methods used in climate change adaptation workshops and started with the improvement of the workshop setup and aspects of communication.
In addition, during this Climate Resilience Pilot, the importance of stakeholder participation became apparent. At the final workshop in Huntsville, all participants agreed that there needs to be more focus on stakeholder engagement and that questions should come from the stakeholders rather than being predefined by the availability of data. This would put communication with stakeholders at the center of upcoming phases of the project.
7.1. Climate adaptation processes
+
7. Climate Information and Communication with Stakeholders
Climate change is happening: mitigation efforts will simply not be enough to tackle its impacts. Thus, climate action at the local level, mitigation as well as adaptation, is needed. The alpS GmbH supports communities, regions, and industrial partners in sustainable development and in dealing with the consequences, opportunities, and risks of climate change.
In the understanding of alpS, climate change consultancy services are successful if they trigger the implementation of proactive measures to enhance climate resilience that are supported by a large number of participants. However, the degree of effectiveness of the consultancy services of alpS as a function of various communication methods (e. g. presentations including processed local climate data, information processing, moderation techniques, discussion tools) and scientific know-how has never been systematically investigated. In the pilot project, alpS therefore evaluated methods used in climate change adaptation workshops and started with the improvement of the workshop setup and aspects of communication.
In addition, during this Climate Resilience Pilot, the importance of stakeholder participation became apparent. At the final workshop in Huntsville, all participants agreed that there needs to be more focus on stakeholder engagement and that questions should come from the stakeholders rather than being predefined by the availability of data. This would put communication with stakeholders at the center of upcoming phases of the project.
7.1. Climate adaptation processes
One way to introduce adaptation processes is to frame them as a cycle (Figure 1), starting with the evaluation of past, present, and future climatic conditions to define the exposure of a system to the impacts of climate change. The second step is to assess the sensitivity of a system towards the impacts of climate change with local experts. Thus, the risk of a system results from its exposure and sensitivity, based on which targeted adaptation measures can be implemented. Finally, the fifth stage is monitoring and evaluation. At this point the cycle starts over again.
@@ -3399,7 +3417,7 @@
In the entire program, the focus is on supporting communities to secure the living and economic space at the local level, which requires a well-founded assessment of the climate risks supported by local experts. The aim is to minimize risks, take necessary measures, and raise awareness of precautionary planning, especially regarding the consequences of climate change.
The conducted evaluation focuses on one participatory element of adaptation cycles, the impact analysis workshops. The workshops aim to initiate stakeholder participation, raise local awareness of climate change impacts, gather expert input on sensitivity, and implement an adaptation process that is widely accepted.
-
7.2. Approach
+
7.2. Approach
alpS conducted a structured evaluation of available datasets of participatory processes with the goal to improve the level of information about climate change impacts and to identify the broadest accepted way of presenting user-related scientific statements. The assessment of adaptation cycles at different spatial levels allowed the further development and improvement of suitable interoperable solutions.
@@ -3410,14 +3428,14 @@
Figure 83 — Three-part questionnaire
-
7.3. Main results of interviews
+
7.3. Main results of interviews
In all surveyed municipalities or regions, it could be shown that the assessment of climate impacts must be done on the local level. Regional adaptation strategies and climate information provide a good overview and starting point for the municipal level. In topographically heterogeneous areas, such as mountainous areas, there is a need for assessments at the local level. It is therefore necessary to reassess climate impacts from a community perspective, considering the local risk landscape. A detailed consideration of the risks and the subsequent intersection of risks with the consequences of climate change is suitable to promote awareness, clarify the community’s concern, and facilitate the implementation of measures due to safety aspects. For this, climate information must be prepared accordingly. Climate data must not be too complicated, but should also not leave anything out. Climate impacts for which the community’s sensitivity is assessed in the vulnerability workshop by local actors must especially be accurate, consistent, and not duplicative. To achieve this, the climate impact chain will be introduced in the next section.
The abundance of content when initiating adaptation measures often leads to the community being overwhelmed. The limitation to selected climate impacts is achieved through the identification of adaptation needs. This leads to the necessary focus on a few urgent adaptation measures. The elaboration of measures must be done individually and in consideration of the communities’ ideas. Showing good examples of adaptation is useful and provides inspiration, however, in surveyed communities and regions mostly new measures were developed, which are exactly tailored to local conditions and needs. Necessary measures can partly be implemented directly by the municipality or only in cooperation with other actors (landowners, other municipalities, etc.). Both the development of measures and the process support must be carried out against this background.
Supporting communities throughout the process is essential. Equally important is the cooperation between local organizations and scientifically sound external support that conveys seriousness and builds stakeholder’s confidence in the adaptation process. In addition, an active contact person with sufficient time resources is needed in each community to bring together the relevant actors and to follow up the topic beyond the events. Only in this way can successful adaptation take place.
-
7.4. Improvement of the workshop setup and aspects of communication
+
7.4. Improvement of the workshop setup and aspects of communication
As part of the Climate Resilience Pilot, alpS was able to optimize two aspects in the adaptation process. First, the creation of climate impact chains for different sectors was initiated. The climate impact chain improves the consistency and understanding of climate impacts. Second, the guideline to deal with external factors was developed. The pre-test conducted before the workshop, which specifically asks about these external factors, enables a direct response and preparation for dealing with uncontrollable factors in the process.
@@ -3442,55 +3460,55 @@
Catalog of external factors
-
Natural space that the municipality/company is located in
+
Natural space that the municipality/company is located in
-
Number of inhabitants/number of employees
+
Number of inhabitants/number of employees
-
Vulnerabilities that are known to be affected by climate change
-
strong dependence on a few infrastructures
+
Vulnerabilities that are known to be affected by climate change
+
strong dependence on a few infrastructures
-
strong dependence on a few companies/sectors of the economy
+
strong dependence on a few companies/sectors of the economy
-
demographic characteristics
+
demographic characteristics
-
shortages in emergency responses
+
shortages in emergency responses
-
The municipality/company depends on its neighbors to carry out its adaptation measures (e.g., upstream/downstream riparian community set of problems).
+
The municipality/company depends on its neighbors to carry out its adaptation measures (e.g., upstream/downstream riparian community set of problems).
-
In case of a suffered catastrophe (here or elsewhere): Have neglected precautions led to legal or political consequences?
+
In case of a suffered catastrophe (here or elsewhere): Have neglected precautions led to legal or political consequences?
-
The municipality/company has experience with weather extremes or unusual seasonal conditions.
+
The municipality/company has experience with weather extremes or unusual seasonal conditions.
-
The municipality/company is affected by other geophysical, geopolitical, social, or economic crises.
+
The municipality/company is affected by other geophysical, geopolitical, social, or economic crises.
-
The handling of climate change in the media is present.
+
The handling of climate change in the media is present.
-
Political backing is given.
+
Political backing is given.
-
Provided human resources are sufficient.
+
Provided human resources are sufficient.
-
Monetary commitment for climate adaptation is sufficient.
+
Monetary commitment for climate adaptation is sufficient.
-
Participants are legally obligated to take precautions.
+
Participants are legally obligated to take precautions.
-
Risks of increased devaluation of real estate, equity investments, property, plant, and equipment as well as increased depreciation, interest, and insurance costs exist.
+
Risks of increased devaluation of real estate, equity investments, property, plant, and equipment as well as increased depreciation, interest, and insurance costs exist.
-
Participants recognize different needs, advantages, and benefits.
+
Participants recognize different needs, advantages, and benefits.
-
Individuals are willing to take responsibility.
+
Individuals are willing to take responsibility.
-
Different perception of the environment: outdoor professionals (e.g., farmers, foresters) as well as indoor professionals are participating.
+
Different perception of the environment: outdoor professionals (e.g., farmers, foresters) as well as indoor professionals are participating.
-
Different levels of knowledge: accepted experts for individual topics (e.g., infrastructure, public health) are participating.
+
Different levels of knowledge: accepted experts for individual topics (e.g., infrastructure, public health) are participating.
-
7.5. Outlook: Stakeholders as a starting point for processing climate information
+
7.5. Outlook: Stakeholders as a starting point for processing climate information
Overall, the consensus at the Closing Workshop in Huntsville was to focus more on stakeholder participation and to start from the stakeholders’ questions instead of the raw data. alpS is experienced in implementing and guiding participatory processes. In the coming project phase, alpS could offer a concept that enables data providers to identify their stakeholders, jointly define questions, and collect targeted feedback.
-
7.6. Summary
+
7.6. Summary
Component: Climate communication and support for adaptation.
@@ -3503,7 +3521,7 @@
What OGC standards or formats does the component use and produce: Processed local climate data, NetCDF.
-
8. Use cases
In a pilot study on interoperability, a use case represents a specific scenario or application that demonstrates how different components, such as data, models, and systems, interact and exchange information to address a particular challenge or problem. In the context of droughts and fires, use cases showcase how interoperability enables seamless integration and analysis of diverse geospatial data sources, coupled with specialized models, to enhance understanding, prediction, and mitigation of drought and fire risks. These use cases provide practical demonstrations of how interoperability workflows and techniques can be applied to foster effective collaboration, decision-making, and climate resilience in the face of drought and fire-related challenges.
8.1. Drought Impact Use Cases
+
8. Use cases
In a pilot study on interoperability, a use case represents a specific scenario or application that demonstrates how different components, such as data, models, and systems, interact and exchange information to address a particular challenge or problem. In the context of droughts and fires, use cases showcase how interoperability enables seamless integration and analysis of diverse geospatial data sources, coupled with specialized models, to enhance understanding, prediction, and mitigation of drought and fire risks. These use cases provide practical demonstrations of how interoperability workflows and techniques can be applied to foster effective collaboration, decision-making, and climate resilience in the face of drought and fire-related challenges.
8.1. Drought Impact Use Cases
Based on the ARD, drought indicator, and data cube components, WHU developed three use-cases based on a self-developed Open Geospatial Engine (OGE) for drought impact for rapid response to drought occurrences. Figure 85 shows the technical architecture of the OGE. It has the following features: 1) For data discovery, a catalog service from the OGE data center following OGC API is provided, allowing users to search geospatial data both available from WHU data stores and remote data stores; 2) For data integration, data can be integrated into the WHU software in the form of data cubes with three efforts: formalizing cube dimensions for multi-source geospatial data, processing geospatial data query along cube dimensions, and organizing cube data for high-performance geoprocessing; 3) For data processing, a processing chain is enabled in OGE using a code editor and model builder; and 4) For data visualization, a Web-based client for visualization of spatial data and statistics is provided using a virtual globe and charts.
@@ -3546,7 +3564,7 @@
Figure 89 — The changes in Poyang Lake before and during the drought period.
-
8.2. Analysis Ready Data (ARD) Use Case
+
8.2. Analysis Ready Data (ARD) Use Case
8.2.1. Background
@@ -3680,15 +3698,15 @@
The drought impact ARD case will demonstrate:
-
the applicability of open standards and specifications in support of data
+
the applicability of open standards and specifications in support of data
discovery, data integration, data transformation, data processing, data
dissemination, and data visualization;
-
transparency of metadata, data quality, and provenance;
+
transparency of metadata, data quality, and provenance;
-
efficiency of using ARD in modeling and analysis; and
+
efficiency of using ARD in modeling and analysis; and
-
interoperable dissemination of ARD abiding by FAIR principles.
+
interoperable dissemination of ARD abiding by FAIR principles.
The project aims at creating analysis ready solar radiation data cube and web map services for Poland to advance development of the solar-smart society and economy and to provide know-how and tools which are easily reusable in other geographical regions worldwide, in accordance with the FAIR principles.
@@ -3792,7 +3810,7 @@
The identified solar radiation services are currently being analyzed in terms of overall functional scope, usability of the interface, innovative tools, and possible shortcomings.
The results of this analysis will be verified against a detailed recognition of the potential user needs.
-
8.4. Wildfire resilience in insurance
+
8.4. Wildfire resilience in insurance
The main focus of IFC’s participation to this project is to better understand end-to-end hazard and risk modeling workflows, in turn supporting the climate services required for decision-making in the business. This participation is also intended to further open up Intact Lab to the outside world, by exchanging information on wildfire risks and climate resiliency in the context of the insurance industry.
@@ -3835,7 +3853,7 @@
As cities will keep sprawling as population increases, the WUI is also expected to grow. This is an issue since increased fire activity due to climate change is to be expected. Furthermore, this increased exposure will reach more vulnerable communities. It was shown that WUI is significantly related to socioeconomic variables such as GDP per capita, population density, road density, and the proportion of the population above 65 years old (https://docs.ogc.org/is/14-083r2/14-083r2.html).
The Canadian WUI dataset (https://doi.org/10.5220/0006681102050210) is unfortunately not available for download but could be replicated with open data sources, for instance through Natural Resources Canada (NRCAN) spatial infrastructures. When developing a WUI dataset, an important parameter for users to fine tune is the ember transport distance. Values can vary between the median value of maximum travel distances, which is 600 m (https://doi.org/10.3390/fire3020010), and the maximum travel distance of 2400 m which is the official standard in the United States. Novel wildfire risk models can also dynamically adapt fuel classes within the WUI to represent propagation more accurately. Producing, hosting and integrating WUI datasets can therefore support creation of better risk indices and also help identify vulnerable areas to support further adaptation.
-
8.5. Climate Resilience for Coastal Ecosystems
+
8.5. Climate Resilience for Coastal Ecosystems
The following use case(s) examine various scenarios designed to qualify the risks and pending impacts of climate change to coastal ecosystems. The scenarios are designed to leverage Analysis Ready Datasets combined with in-situ observations to draw direct relationships between a changing environment and dependent human activities.
@@ -3896,7 +3914,7 @@
Scalability
Considering the volume of data to describe climate trends specific to an area of interest, the methodology of how raw data through to ARD is loaded into a client environment needs to be addressed. The integration framework in support of the above use case tends to instantiate local copies of raw data and ARD datasets into the compute environment for processing and analysis. The OGC GeoDatacube initiative is well positioned to play a role in addressing the scalability requirements, although it’s unclear whether this approach addresses loosely coupled, distributed data pipelines or requires local caching of datasets within the GDC processing workflow.
-
9. Lessons Learned
In this first OGC Climate Resilience Pilot study, several valuable lessons have been learned regarding the effective integration and exchange of information between different components. These lessons highlight the importance of harmonizing extractions from diverse data sources, selecting or developing suitable models, and establishing robust workflows. Additionally, the pilot study has shed light on the significance of stakeholder engagement, iterative refinement, and continuous evaluation to enhance the interoperability of systems and components. By identifying and addressing challenges and leveraging these lessons learned, future climate resilience efforts can benefit from improved interoperability, enabling more informed decision-making and proactive strategies to mitigate the impacts of climate-related hazards.
Participants of the various organizations and institutes that contribute to the Climate Resilience Pilot noted the following gaps or challenges that still exist and require additional work (in future) to overcome.
The Pixalytics Drought indicator utilizes data from sources such as the Copernicus Climate Data Store (CDS), Global Drought Observatory, and NOAA Climate Environmental Data Retrieval (EDR) API. This included testing the various sources and datasets to assess the speed, reliability, and cost of accessing input data from different providers with a goal of enabling on-demand data processing.
As an example, the input precipitation data obtained from the ERA5 dataset within the Registry of Open Data on AWS was compared to the CDS API. It was found that accessing the data stored on Amazon Web Service (AWS) Simple Storage Service (S3) was faster once virtual Zarrs were set up. However, there are concerns regarding the data’s provenance, as it was uploaded to AWS by an organization other than the original data provider. Additionally, the Zarr approach faced challenges when dealing with more recent years’ data, as the NetCDFs stored on S3 had inconsistent chunking. To address this issue, a request has been submitted to enhance the Python kerchunk library’s ability to handle variable chunking. This is pointed out as it is not specific to this datasource; these challenges can happen to any large datasource that needs to transform into Zarrs to operate faster.
Also, through testing the ECMWF, CDS, and NOAA APIs it was seen that having an OGC API interface to datasets provided a more streamlined interface than directly accessing files as once code had been written it was easier to amend when an additional API was incorporated. Feedback was provided to ECMWF and NOAA on their API usage by Pixalytics, including collaborative discussions on potential improvements. In terms of the Pixalytics drought indicator output, QGIS modules have been identified to allow non-programmers to access and visualize the API outputs.
For Esri’s contribution, the following lessons were learned in building CMRA version 1 and the last 6 months since its release.
Survey responses and conversations with users of the CRMA app and the underlying data have confirmed that providing open and usable data is equally important to providing a well designed web application. Both are needed, for different audiences.
+
9. Lessons Learned
In this first OGC Climate Resilience Pilot study, several valuable lessons have been learned regarding the effective integration and exchange of information between different components. These lessons highlight the importance of harmonizing extractions from diverse data sources, selecting or developing suitable models, and establishing robust workflows. Additionally, the pilot study has shed light on the significance of stakeholder engagement, iterative refinement, and continuous evaluation to enhance the interoperability of systems and components. By identifying and addressing challenges and leveraging these lessons learned, future climate resilience efforts can benefit from improved interoperability, enabling more informed decision-making and proactive strategies to mitigate the impacts of climate-related hazards.
Participants of the various organizations and institutes that contribute to the Climate Resilience Pilot noted the following gaps or challenges that still exist and require additional work (in future) to overcome.
The Pixalytics Drought indicator utilizes data from sources such as the Copernicus Climate Data Store (CDS), Global Drought Observatory, and NOAA Climate Environmental Data Retrieval (EDR) API. This included testing the various sources and datasets to assess the speed, reliability, and cost of accessing input data from different providers with a goal of enabling on-demand data processing.
As an example, the input precipitation data obtained from the ERA5 dataset within the Registry of Open Data on AWS was compared to the CDS API. It was found that accessing the data stored on Amazon Web Service (AWS) Simple Storage Service (S3) was faster once virtual Zarrs were set up. However, there are concerns regarding the data’s provenance, as it was uploaded to AWS by an organization other than the original data provider. Additionally, the Zarr approach faced challenges when dealing with more recent years’ data, as the NetCDFs stored on S3 had inconsistent chunking. To address this issue, a request has been submitted to enhance the Python kerchunk library’s ability to handle variable chunking. This is pointed out as it is not specific to this datasource; these challenges can happen to any large datasource that needs to transform into Zarrs to operate faster.
Also, through testing the ECMWF, CDS, and NOAA APIs it was seen that having an OGC API interface to datasets provided a more streamlined interface than directly accessing files as once code had been written it was easier to amend when an additional API was incorporated. Feedback was provided to ECMWF and NOAA on their API usage by Pixalytics, including collaborative discussions on potential improvements. In terms of the Pixalytics drought indicator output, QGIS modules have been identified to allow non-programmers to access and visualize the API outputs.
For Esri’s contribution, the following lessons were learned in building CMRA version 1 and the last 6 months since its release.
Survey responses and conversations with users of the CRMA app and the underlying data have confirmed that providing open and usable data is equally important to providing a well designed web application. Both are needed, for different audiences.
There will always be groups that want their own application with their branding, even if its only slightly different than original.
@@ -3922,7 +3940,7 @@
More analysis with higher resolution time steps — weekly and daily. At the outset monthly time steps have been used to make it easier to prototype workflows. Daily time step computations will take significantly more processing time.
-
In addition, during the presentation of the outcomes at the OGC Member Meeting in Huntsville (June 2023) it was emphasized that for the next Pilot the logic needs to be changed. Instead of starting with the raw data and generating the information to support decisions, the work should start understanding the stakeholders interests and problems, and then the work should proceed backwards to find the raw data inputs that would help answer the stakeholders questions. There needs to be a focus on how to position knowledge in order to have an impact on decision makers. Questions include, what is the market need, benefit to communities, and how are we helping people.
10. Recommendations for future climate resilience pilots
Based on the experiences of this first Climate Resilience Pilot, the lessons learned could result in a refined design of the upcoming future pilot focusing on climate resilience to address the most relevant challenges. Within these efforts, the OGC Climate Resilience Community has been growing and is bringing together decision-makers, scientists, policymakers, data providers, software developers, and service providers. This includes scientists, decision-makers, city managers, politicians, and last but not least, it includes every one of us.
10.1. Thematic aspects: Climate change resilience to the triple crisis
+
In addition, during the presentation of the outcomes at the OGC Member Meeting in Huntsville (June 2023) it was emphasized that for the next Pilot the logic needs to be changed. Instead of starting with the raw data and generating the information to support decisions, the work should start understanding the stakeholders interests and problems, and then the work should proceed backwards to find the raw data inputs that would help answer the stakeholders questions. There needs to be a focus on how to position knowledge in order to have an impact on decision makers. Questions include, what is the market need, benefit to communities, and how are we helping people.
10. Recommendations for future climate resilience pilots
Based on the experiences of this first Climate Resilience Pilot, the lessons learned could result in a refined design of the upcoming future pilot focusing on climate resilience to address the most relevant challenges. Within these efforts, the OGC Climate Resilience Community has been growing and is bringing together decision-makers, scientists, policymakers, data providers, software developers, and service providers. This includes scientists, decision-makers, city managers, politicians, and last but not least, it includes every one of us.
10.1. Thematic aspects: Climate change resilience to the triple crisis
The current climate resilience pilot had focused on climate change-related phenomena, while the ongoing international discussion is moving toward the holistic perspective of the triple crisis, where the triple is targeting Climate Change, the loss of Biodiversity, and Pollution. Therefore, it is recommended to address these within the upcoming climate resilience pilot, in line with the technical challenges of this triple crisis and selecting doable aspects according. As a result of the closing workshop at the OGC Member Meeting in Huntsville, the thematic areas of upcoming work should focus on precipitation with extreme aspects extremes causing disasters: floods in case of extremely high amounts of precipitation and droughts and desertification in case of dryness anomalies. Both cases can lead to land degradation, which should be considered inline with climate resilience and loss of biodiversity.
@@ -3933,23 +3951,23 @@
The climate resilience pilot ran in parallel with other pilots and had many touch points with the disaster pilot. Important lessons learned from the growth of the OGC climate resilience community, and the common understanding of phenomena inline with climate resilience versus disaster resilience are guiding the recommendation to merge both pilot lines.
A future climate resilience pilot with disaster aspects will be respected since the technical Climate Resilience Information Systems (CRIS) and the aspects of FAIR Climate services establish modular climate application packages which are interoperable with each other and are guided by the same technical principles and tools.
-
10.2. Technical aspects: Climate resilience information systems towards FAIR Climate services
+
10.2. Technical aspects: Climate resilience information systems towards FAIR Climate services
Issues around the delivery of climate information to support adaptation decisions to facilitate the difficult and time-consuming work of climate service centers. These centers may have a local, regional, or international scope, but typically act as boundary organizations, connecting clients to climate science data and expertise. As demand increases for climate products, climate service centers are pressured to develop and deploy IT systems to access and process climate data more efficiently and expand the range and complexity of services delivered. Although climate adaptation challenges vary across regions, data processing workflows are very similar and could benefit from shared information systems. Land Degradation Neutrality (LDN) and climate resilience are strongly related to the scientific phenomenon as well the technical applications regarding the value chain from raw data to information and knowledge. In both cases, similar approaches have been developed concerning data handling in datacubes and analysis-ready data (ARD) up to the decision-ready indicators (DRI). Agreeing on standards regarding DataCube, ARD, and DRI would enable a better linkage between the information exchange within the UN climate policy frame and beyond.
-
10.3. Interoperability studies and gap analysis of data sources and infrastructures
+
10.3. Interoperability studies and gap analysis of data sources and infrastructures
Here, especially, the Copernicus Climate Change Service (C3S) with its underpinned Climate Data Store (CDS) is an example that has been renewed and moved into higher interoperability. A dedicated gap analysis and interoperability experiment concerning the usability of the C3S technical services inline with existing other climate resilience information systems would be a useful step towards the vision of global collaborative solutions. In this aspect, the concept of FAIR Climate services can be refined, extended, and properly documented.
It is further recommended to continue lowering the barriers for experts who want to spin off climate resilience information systems for their specific use cases and needs. As demonstrated in the pilot, the modular chaining of components is the recommended approach to design the architecture, with climate application packages being interoperable with each other and following the FAIR Climate Services principles. There are existing utilities (birdhouse approach demonstrated in this pilot) helping developers to establish climate application packages that need to be further developed and improved for better usability. Equality to climate application packages, the concept of LDN value chain, is following the same approach of modular interoperable components where interoperability can be enhanced.
It is further recommended, that aspects of data visualization and the use of case-specific simulations need to be emphasized. Especially the small-scale 3D visualizations, including realistic digital twins of vegetation, and respective trees in digital twins of urban areas are recommended for enhancement in the future. The pilot has shown the power of artificial intelligence to establish realistic simulations of use cases under different climatic scenarios. Enhancing the technology behind, and establishing the data visualization and simulation for, specific use-cases, political decisions or socioeconomic scenarios with respect to future climate projections would be a step forward in closing the gap between existing climate information and implemented climate action.
-
10.4. Climate Service Consultation aspects: Communication to Stakeholders
+
10.4. Climate Service Consultation aspects: Communication to Stakeholders
The upcoming CRP24 should have a user-centric approach, to tailor the data products and application packages for optimization to the user needs and requirements. The existing OGC stakeholder community should be more strongly included during the pilot execution to tailor the value chain from raw data to information according to the stakeholder requirements. It is recommended to design the OGC pilots with activities addressing potential stakeholders to grow the OGC Stakeholder community as well as to understand their requirements and address data products and tools related to their needs, further bridging the gap of scientific knowledge to policy-driven climate action. The huge amount of knowledge about climate change and its potential impact and the relatively low socio-economic change, called the knowing-doing-gap, can be addressed in future work. The improvement and incorporation of the communication aspects explored in the current pilot should be emphasized and enhanced with existing technologies, especially simulations and data visualization. Also, the good practice guidance of the UNCCD is proposing ‘decision trees’ for end users to identify the most reliable data that exist for a region.
OGC needs to continue to move towards non-technical communication; breaking down the very technical engineering reports into non-technical content, such as animation videos. Especially for the domain of climate and disaster resilience, the understanding of the principle importance for the work concerning FAIR Climate services is essential to be presented in other formats than only engineering reports. Besides explanation videos, capacity building can be done with modules in e-learning platforms that are currently being established in OGC. Future pilots should output tutorials and training materials to lower the barriers for developers by spinning off their applications based on good practice guidelines, tutorials, and e-learning modules. In this context, established running applications can be promoted over the upcoming open science persistent demonstrator. It is recommended that OGC tailor capacity-building material in formats that are exchangeable with other open knowledge platforms like the GEO Knowledge hub.
A further aspect of capacity building and reaching out is to move the upcoming work into multiple languages. The animation video of this pilot has already been produced in four languages; in addition to English, it is available in French, Spanish, and Chinese. Upcoming future work should not be restricted to only English, but should target to other languages as well.
