{"id":17222,"date":"2026-04-30T08:27:06","date_gmt":"2026-04-30T07:27:06","guid":{"rendered":"https:\/\/www.cimafoundation.org\/?p=17222"},"modified":"2026-05-04T15:26:47","modified_gmt":"2026-05-04T14:26:47","slug":"egu26-observing-predicting-acting-an-integrated-reading-of-risk-systems","status":"publish","type":"post","link":"https:\/\/www.cimafoundation.org\/en\/news\/egu26-observing-predicting-acting-an-integrated-reading-of-risk-systems\/","title":{"rendered":"EGU26, observing, predicting, acting: an integrated reading of risk systems"},"content":{"rendered":"\n

The images in this article were generated using the official tool provided by NASA, available at the following link: https:\/\/science.nasa.gov\/mission\/landsat\/outreach\/your-name-in-landsat\/<\/a> .<\/p>\n\n

<\/div>\n\n

Understanding environmental systems today means confronting increasing complexity, in which climatic, hydrological, and ecosystem processes interact across different spatial and temporal scales, generating effects that propagate and amplify over time. In this context, scientific research is called not only to observe and describe these phenomena, but also to develop tools capable of interpreting and anticipating them, making them actionable for decision-making. <\/p>\n\n

The EGU General Assembly 2026<\/a> fits within this framework as one of the leading international forums for Earth sciences, providing a space where disciplines, approaches, and applications converge to address the challenges posed by climate change and risk management.<\/p>\n\n

The contributions presented by CIMA Research Foundation are positioned within this perspective, following a pathway that ranges from the observation of environmental processes to their modelling, and ultimately to the translation of knowledge into operational tools and concrete actions. Taken together, they provide an integrated vision of risk systems, in which observing, predicting, and acting become interdependent components of a single process. <\/p>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

Special mention: the megafire threshold at the EGU 2026 press conference Among the contributions presented at the EGU General Assembly 2026, the study \u201cWhy some wildfires become megafires: compound short-term fire weather and antecedent drought controls in Mediterranean Europe<\/a><\/em>\u201d, developed by Farzad Ghasemiazma, PhD candidate at the University of Genoa at CIMA Research Foundation, has been selected for the official press conference<\/strong> of the event.<\/p>\n\n

The study analyses more than 11,000 wildfires across Mediterranean Europe, highlighting how the most extreme events are not simply the result of prolonged drought conditions, but emerge from the interaction between accumulated water stress over time and exceptional short-term atmospheric conditions, particularly elevated night-time temperatures and strong winds. This approach makes it possible to distinguish the factors driving the transition toward megafires, providing relevant insights for the development of increasingly impact-oriented early warning systems and the management of extreme events. <\/p>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

Observing complex systems: water, energy, and the cryosphere <\/h2>\n\n
<\/div>\n\n
\"\"
Water<\/figcaption><\/figure>\n\n
<\/div>\n\n

The continuous interaction between hydrological, energetic, and ecosystem processes implies that the water cycle can no longer be interpreted as a linear sequence, but rather as a dynamic system<\/strong> in which atmosphere, soil, and vegetation exchange water and energy fluxes over very short time scales.<\/p>\n\n

To find out more about these points, click on the section below.<\/mark><\/summary>\n

In this direction, new potential satellite missions<\/strong>, including Hydroterra+<\/strong> as one of the four ESA Earth Explorer 12 candidate missions<\/a>, aim to fill gaps in the observation of the fastest hydrological processes, enabling continuous monitoring of variables such as soil moisture, snow water content, and atmospheric water vapour. The ability to observe these phenomena at sub-daily scales<\/strong> represents a decisive step toward improving the understanding and prediction of hydrometeorological extreme events, particularly in vulnerable regions such as the Mediterranean and sub-Saharan Africa (Hydroterra+: an EO mission concept to reveal sub-daily water processes<\/a><\/em>). <\/p>\n\n\n\n

At more local scales, understanding snow dynamics is a key element for interpreting hydrological variability in mountain environments. In regions such as the Apennines, where the snowpack is highly variable from year to year, the integration of physically based models and satellite data enables the reconstruction of consistent multidecadal time series of snow water equivalent<\/strong>, improving the understanding of the role of snow in regional hydrological systems (Multidecadal snow water equivalent reconstruction in Central Italy using the Multiple Snow Data Assimilation System<\/a><\/em>). <\/p>\n\n\n\n

This complexity also emerges through localized atmospheric phenomena such as lake-effect snowfall<\/strong>. A comparative analysis between the central Apennines and the Japanese region of Niigata<\/strong> shows how similar configurations – cold air masses flowing over relatively warm water surfaces – can generate intense and analogous events, with significant impacts on infrastructure and local systems. In both cases, these snowfall events represent both a water resource and a hazard, and are highly sensitive to climatic variability (Cold on the water: mechanisms, impacts, and future scenarios of lake-effect snowfall in Italy and Japan<\/a><\/em>). <\/p>\n\n\n\n

At the same time, long-term observations of carbon fluxes allow for the analysis of ecosystem functioning and its evolution over time. The comparison between abandoned grasslands and mature forests in the Alps shows how land-use changes<\/strong> significantly affect CO\u2082 exchanges between the surface and the atmosphere<\/strong>, highlighting the role of ecological dynamics in carbon cycling (Tracing Carbon Flux Dynamics and Ecosystem Functioning Along a Land-Use Gradient: Long-Term Eddy Covariance Observations in the western Italian Alps<\/a><\/em>). <\/p>\n\n\n\n

In this context, climate change is also profoundly altering the functioning of forest ecosystems. In many European regions, beech forests are shifting from energy-limited to water-limited conditions, with direct implications for both carbon and water cycles. Integrated analyses along climatic gradients highlight how these systems are increasingly characterized by trade-offs between water use and productivity<\/strong>, making it necessary to adopt approaches that jointly consider carbon\u2013water cycles (From energy to water limitation: projecting carbon and water cycling in mediterranean beech forests under climate change<\/a><\/em>). <\/p>\n<\/details>\n\n

<\/div>\n\n

Special mention: the cryosphere and the future of water<\/strong><\/p>\n\n

Our researcher Francesco Avanzi is co-convener of the EGU session \u201cSnow and glacier hydrology<\/a><\/em>\u201d, dedicated to the challenges of understanding and modelling cryospheric processes and their role in hydrological system dynamics, in contexts characterized by high variability and increasing climatic pressure.<\/p>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

From system dynamics to impacts: drought as a systemic risk<\/h2>\n\n
<\/div>\n\n
\"\"
Drought<\/figcaption><\/figure>\n\n
<\/div>\n\n

Understanding environmental systems is not sufficient without analysing how their dynamics translate into concrete impacts. Among these, drought represents one of the most complex risks, capable of propagating across different sectors and affecting ecosystems, water resources, and societies. <\/p>\n\n

To find out more about these points, click on the section below. <\/mark><\/summary>\n

At the global scale, information on drought events<\/strong> is often fragmented across heterogeneous databases, making consistent risk assessment challenging. The integration of these sources, combined with the use of artificial intelligence<\/strong> techniques for extracting data from textual reports, enables the development of more comprehensive and structured datasets<\/strong>, improving the representation of impacts on populations and territories and supporting the derivation of vulnerability functions (Building a Comprehensive Drought Impact Dataset by Integrating Disaster Databases and Reports with the use of Large Language Models<\/a><\/em>). <\/p>\n\n\n\n

This complexity is also evident in mountain environments, where drought is emerging with increasing intensity. In glacierized catchments of the Italian Alps, water availability<\/strong> depends on a balance between precipitation, snow, ice, and hydrological processes interacting across multiple temporal scales. Over recent decades, drought conditions have progressively propagated across these compartments, influencing the duration and intensity of events and challenging the role of mountains as stable water reservoirs<\/strong> (Droughts in glacierized <\/a>catchments <\/a>of the Italian Alps: evolution and emerging high-elevation variabilities (2000\u20132024)<\/a><\/em>). <\/p>\n\n\n\n

These effects are directly reflected in public water supply systems, where reduced availability and deteriorating water quality can compromise essential services. The analysis of European databases reveals a high variability of impacts<\/strong>, ranging from usage restrictions to service interruptions, highlighting the combined role of climatic, infrastructural, and socio-economic factors, and the need for harmonized approaches to severity assessment (Drought <\/a>Impacts <\/a>on Public Water Supply in Europe: a challenge of diversity and severity assessment<\/a><\/em>). <\/p>\n\n\n\n

This dimension is closely linked to water-use management, which becomes particularly critical in Alpine contexts, where different sectors compete for increasingly variable water availability. The use of high-resolution satellite data<\/strong>, integrated with meteorological variables, enables the estimation of irrigation water requirements and their spatio-temporal variability<\/strong>, supporting more efficient allocation strategies and the planning of infrastructures such as storage reservoirs (Satellite-derived irrigation water requirement as a support tool for climate-resilient water management in the Alps<\/a><\/em>). <\/p>\n\n\n\n

At the same time, direct measurement of evapotranspiration allows for a more realistic observation of ecosystem water consumption, capturing dynamics that are often poorly represented by models, especially under stress conditions. The development of monitoring networks distributed along altitudinal gradients<\/strong> helps improve irrigation demand estimates and supports more adaptive water management strategies, strengthening the link between observation and decision-making (A regional evapotranspiration network for climate-resilient water management in mountain agro-ecosystems<\/a><\/em>). <\/p>\n<\/details>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

Predicting to anticipate: models and artificial intelligence<\/h2>\n\n
<\/div>\n\n
\"\"
Models<\/figcaption><\/figure>\n\n

Building on the understanding of impacts, prediction becomes a key tool for anticipating them. Environmental systems require models capable of handling high variability, non-linearity, and interactions among multiple processes. <\/p>\n\n

To find out more about these points, click on the section below.<\/mark><\/summary>\n

In this context, the integration of physically based models and artificial intelligence<\/strong> is transforming hydrological modelling, enabling efficient simulation of river dynamics and the identification of threshold exceedances. The use of heterogeneous real-time data allows these models to be applied at national scale and in near-real time, strengthening operational support for civil protection<\/strong> (A deep learning model of river water levels<\/a>). <\/p>\n\n\n\n

At the same time, the evolution toward seasonal drought impact forecasting models<\/strong> enables the translation of climatic indicators into information that can be directly used in decision-making processes. The application of machine learning techniques<\/strong>, combined with explainability tools, makes it possible to identify key drivers and temporal dynamics of impacts, improving the transparency and reliability of predictions and reinforcing the link between predictive capacity and action (Towards the Operational Implementation of Seasonal Drought Impact-based Forecasting with Explainable Machine Learning<\/a>). <\/p>\n<\/details>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

From prediction to action: scenarios, early warning systems, and risk governance<\/h2>\n\n
<\/div>\n\n
\"\"
Action<\/figcaption><\/figure>\n\n
<\/div>\n\n

If prediction allows for anticipating the possible evolution of environmental systems, the challenge becomes translating this information into actionable tools for decision-makers. In this transition, scenarios, early warning systems, and governance models represent key elements for making scientific knowledge operational. <\/p>\n\n

To find out more about these points, click on the section below.<\/mark><\/summary>\n

At the national level, the development of water resource scenarios represents an innovative step toward bridging the gap between climate projections and operational resource management<\/strong>. In Italy, within the IT-WATER project<\/a>, the development of national-scale water resource scenarios introduces a structured approach that integrates climatic and hydrological indicators into tools directly usable in decision-making processes. Through a co-design process involving institutional actors and water managers, these tools are developed based on users\u2019 actual needs, improving the effectiveness of information supporting adaptation strategies (Decision-relevant rendering of water-supply climate-change scenarios: a co-produced portfolio<\/a>). <\/p>\n\n\n\n

In the Alpine context, the integration of observational data, models, and local knowledge<\/strong> enables the development of operational solutions for drought risk management. The Interreg Alpine Space A-DROP project<\/a> demonstrates how the combination of climate data, in situ observations, and machine learning techniques can improve water availability forecasting and support more sustainable resource management practices (From data to decisions: co-developing drought risk management solutions for the European Alps<\/a>). <\/p>\n\n\n\n

In contexts characterized by high vulnerability, where natural risks intersect with socio-political and infrastructural factors, a multi-risk approach<\/strong> becomes essential to understand and manage events that do not occur in isolation, but as combinations of interacting phenomena.<\/p>\n\n\n\n

In Sudan<\/a>, the integration of high-resolution meteorological modelling<\/strong>, hydrological monitoring<\/strong>, and impact-based forecasting systems<\/strong> has enabled the development of an early warning system capable of operating even under conditions of limited data availability. Information on the expected impacts of forecasted events is translated into concrete indications of potential effects on populations, infrastructure, and territories, supporting operational decisions by authorities and humanitarian actors (Advancing Multi-Risk Early Warning in Fragile Contexts: Methodological Insights from Sudan<\/a>). <\/p>\n\n\n\n

These experiences are part of a broader framework in which challenges related to the implementation of early warning systems are widespread at the global scale, and where their effectiveness depends on the ability to translate forecasts into concrete actions. In the Global South, persistent challenges include data availability, institutional coordination, and the translation of information into operational decisions. Innovative approaches, based on knowledge co-production<\/strong>, integration of scientific<\/strong> and local knowledge<\/strong>, and transboundary collaboration, are contributing to making these systems more inclusive, effective, and action-oriented (Early Warning Systems in the Global South: challenges and innovative approaches<\/a>). <\/p>\n\n\n\n

In this direction, the evolution toward impact-based early warning systems represents a further step forward. Through co-development processes with local experts and institutions, the integration of hazard, exposure, and vulnerability information enables the production of quantitative impact forecasts<\/strong>, supporting the generation of more targeted, contextualized, and actionable warnings<\/strong>. This is demonstrated by applications in Eastern Africa, where drought and flood forecasts are translated into useful information for farmers, institutions, and local communities, taking into account crop seasonality and the specific conditions of the most vulnerable groups (From risk knowledge to effective early actions: a novel framework and application for impact-based early warning with a pilot study in Eastern Africa<\/a>). <\/p>\n\n\n\n

Beyond technical aspects, risk governance also requires participatory processes capable of actively involving communities and stakeholders, a key component of people-centred approaches to disaster preparedness and response<\/strong>. The MedEWSa project<\/a> shows how participatory planning can improve the effectiveness of risk reduction strategies, strengthening trust between institutions and citizens and fostering the integration of scientific and local knowledge. This approach has been translated into a structured operational tool<\/strong>, designed to support administrations and communities in implementing participatory and sustainable planning processes over time (Participatory Emergency Planning: a practical framework from the MedEWSa project<\/a>). <\/p>\n\n\n\n

Finally, the functioning of science\u2013policy interfaces (SPI)<\/strong> represents a crucial element to ensure that scientific knowledge is effectively integrated into decision-making processes. A comparative analysis of SPI initiatives<\/strong> across 16 African countries <\/strong>shows how social, institutional, and technical-operational factors must operate in an integrated manner to ensure their effectiveness and long-term sustainability. From stakeholder engagement to mediation capacity, from alignment with institutional frameworks to the presence of continuity mechanisms, key indicators have been identified to support integrated and sustainable science\u2013policy interactions (Assessing Science – Policy Interfaces for Climate Adaptation and Disaster Risk Reduction in Africa: a comparative indicator-based analysis<\/a>). <\/p>\n<\/details>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

From single hazards to multi-risk: wildfires, degradation, and interactions<\/h2>\n\n
<\/div>\n\n
\"EGU26
Multi-risk<\/figcaption><\/figure>\n\n
<\/div>\n\n

The analysis of natural hazards is increasingly shifting from a single-hazard perspective toward an integrated understanding of their interactions. In this context, the concept of multi-risk becomes central to understanding how different conditions can combine and amplify one another, generating more intense and complex events. <\/p>\n\n

To find out more about these points, click on the section below.<\/mark><\/summary>\n

In the case of wildfires, fire behaviour<\/strong> is increasingly driven by the combination of multiple meteorological variables – such as atmospheric dryness and wind – rather than by isolated extremes considered independently. For this reason, modelling these phenomena requires multivariate probabilistic approaches<\/strong> capable of representing dependencies among different drivers and simulating compound events. This enables improved wildfire risk estimation at the regional scale (Event-Based Copula Modeling of Compound Fire-Weather Extremes for Wildfire Risk Assessment<\/a>). <\/p>\n\n\n\n

In parallel, the identification of climatic drivers<\/strong> helps to understand the conditions that favour wildfire ignition and spread<\/strong>. In northern Italy, analyses of summer wildfire variability show that the dominant factor is often accumulated water stress over preceding months, rather than high temperatures alone, highlighting the temporal and cumulative nature of wildfire risk (Defining climatic drivers for the prediction of summer wildfires in northern Italy<\/a>). <\/p>\n\n\n\n

However, the most extreme events are not simply an extension of these dynamics, but respond to more specific conditions. National-scale analyses show that high-intensity wildfires are associated with critical combinations of drought, temperature, and wind<\/strong> acting over short timescales, generating conditions that are particularly difficult to control. The use of models based on extreme value theory allows these rare but high-impact events to be isolated, improving both prediction and management capabilities (Short-Term Hydrometeorological Drivers of Wildfires in Italy: Insights from Extreme Value Modeling<\/a>). <\/p>\n\n\n\n

These dynamics are further influenced by vegetation structure, which plays a key role in fire propagation. The integration of high-resolution data – <\/strong>such as those derived from UAV-based LiDAR remote sensing – with simulation models enables a more realistic representation of the transition from surface fires to crown fires, which are characterized by higher intensity, faster spread rates, and greater suppression difficulty. This improves both preventive planning and real-time operational management (Integrating UAV\u2013LiDAR Fuel Data into Stochastic Cellular Automata PROPAGATOR for Crown Fire Modelling<\/a>). <\/p>\n\n\n\n

In this context, the representation of vegetation fuel becomes a key element linking scientific knowledge with operational application. The development of dynamic fuel maps, based on the integration of environmental data and machine learning techniques<\/strong>, allows the complexity of vegetation flammability processes to be translated into synthetic and actionable information. By capturing seasonal variability in vegetation conditions, these approaches improve wildfire danger forecasting systems and enhance decision support (An Operational Framework for Dynamic ML-informed Fuel Mapping for Wildfire Risk Management<\/a>). <\/p>\n<\/details>\n\n

<\/div>\n\n

Special mention: shaping the scientific dialogue on natural hazards<\/strong><\/p>\n\n

Among the scientific contributions, the active role in shaping the event\u2019s scientific programme is also noteworthy. Andrea Trucchia, researcher at CIMA Research Foundation, serves as convener of the session \u201cLand Degradation and Natural Hazards: Entering the Critical Zone<\/a>\u201d, dedicated to the interactions between land degradation and natural hazards, and as co-convener of the session \u201cSpatial and Temporal Dynamics of Wildfires: Models, Theory, and Reality<\/a>\u201d, focused on processes and models describing wildfire dynamics in space and time. <\/p>\n\n

This is complemented by his role as Science Officer of the Natural Hazards Division<\/a>, contributing to the coordination and development of the division\u2019s scientific activities.<\/p>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

Building awareness: communication and new generations<\/h2>\n\n
<\/div>\n\n
\"\"
Awareness<\/figcaption><\/figure>\n\n
<\/div>\n\n

The management of natural hazards and climate change does not depend solely on the availability of data and models, but also on the ability to build awareness and shared understanding. In this context, science communication plays a central role, not only as a means of knowledge transfer, but as a process of active engagement capable of shaping perceptions, behaviours, and trust in science. <\/p>\n\n

To find out more about these points, click on the section below.<\/mark><\/summary>\n

In the case of climate change, narratives based exclusively on catastrophic scenarios or individual responsibility may produce counterproductive effects, particularly among younger generations, fostering anxiety and a sense of helplessness. Overcoming these dynamics requires a shift in approach. Participatory strategies<\/strong>, based on interactive and solution-oriented activities<\/strong>, have proven more effective in strengthening understanding and motivation to act, contributing to the development of trust in scientific knowledge. Communicating climate change effectively therefore means maintaining scientific rigour and accuracy while avoiding paralysing narratives and promoting a conscious and collective response to global challenges<\/strong> (Engaging young audiences in climate change: moving beyond fear through active science communication<\/a>). <\/p>\n\n\n\n

Within this perspective, educational experiences based on active participation represent a particularly effective tool. Initiatives such as the \u201cNext-Gen COP<\/em><\/a>\u201d simulate global decision-making processes, engaging students in negotiation, analysis, and co-design of solutions. Through pathways that integrate scientific training, interaction with experts, and the development of concrete proposals through dialogue and continuous exchange, these experiences foster key competences and strengthen awareness among younger generations. They demonstrate how knowledge can be translated into active participation in addressing climate challenges<\/strong>, encouraging young people to become active actors<\/strong> in building adaptation and resilience strategies (The \u201cNext-Gen COP\u201d as a tool for communicating climate change and catalyze solutions from high school students<\/a>). <\/p>\n<\/details>\n\n

<\/div>\n\n
\n\n
<\/div>\n\n

Observation, prediction, and action can no longer be considered separate domains, but rather components of a single knowledge system<\/strong>. From the dynamics of the water cycle to ecosystem processes, from the modelling of extreme events to the development of decision-relevant scenarios, and from early warning systems to participatory and science\u2013policy tools, it becomes clear that understanding risk today requires an integrated and multidimensional approach. <\/p>\n\n

Within this framework, science no longer simply describes the complexity of environmental systems, but increasingly contributes to making it interpretable and usable, transforming data and models into concrete tools for territorial management and the development of adaptation strategies<\/strong>. At the same time, the ability to communicate this complexity and actively engage communities becomes a necessary condition for translating knowledge into action. <\/p>\n\n

The participation of CIMA Research Foundation in the EGU General Assembly 2026 thus portrays research as an open and interconnected process, in which different disciplines, tools, and actors contribute to building a shared understanding of risks and ongoing transformations<\/strong>. A perspective that not only enhances predictive capacity, but also strengthens the link between science and decision-making, enabling more informed, coordinated, and sustainable management of environmental systems. <\/p>\n","protected":false},"excerpt":{"rendered":"

The images in this article were generated using the official tool provided by NASA, available at the following link: https:\/\/science.nasa.gov\/mission\/landsat\/outreach\/your-name-in-landsat\/ . Understanding environmental systems today means confronting increasing complexity, in which climatic, hydrological, and ecosystem processes interact across different spatial and temporal scales, generating effects that propagate and amplify over time. In this context, scientific […]<\/p>\n","protected":false},"author":4,"featured_media":17221,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[12],"tags":[179],"class_list":["post-17222","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-insights"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/posts\/17222","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/comments?post=17222"}],"version-history":[{"count":5,"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/posts\/17222\/revisions"}],"predecessor-version":[{"id":17293,"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/posts\/17222\/revisions\/17293"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/media\/17221"}],"wp:attachment":[{"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/media?parent=17222"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/categories?post=17222"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cimafoundation.org\/en\/wp-json\/wp\/v2\/tags?post=17222"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}