Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2024
Authors
Erico Kutchartt José Ramón González-Olabarria Núria Aquilué Jordi Garcia-Gonzalo Antoni Trasobares Brigite Botequim Marius Hauglin Palaiologos Palaiologou Vassil Vassilev Adrian Cardil Miguel Ángel Navarrete Christophe Orazio Francesco PirottiAbstract
No abstract has been registered
Authors
Erico Kutchartt José Ramón González-Olabarria Antoni Trasobares Núria Aquilué Juan Guerra-Hernández Leónia Nunes Ana Catarina Sequeira Brigite Botequim Marius Hauglin Palaiologos Palaiologou Adrian Cardil Martino Rogai Vassil Vassilev Francois Pimont Olivier Martin-Ducup Francesco PirottiAbstract
Canopy base height (CBH) and canopy bulk density (CBD) are forest canopy fuel parameters that are key for modeling the behavior of crown wildfires. In this work, we map them at a pan-European scale for the year 2020, producing a new dataset consisting of two raster layers containing both variables at an approximate resolution of 100 m. Spatial data from Earth observation missions and derived down-stream products were retrieved and processed using artificial intelligence to first estimate a map of aboveground biomass (AGB). Allometric models were then used to estimate the spatial distribution of CBH using the canopy height values as explanatory variables and CBD using AGB values. Ad-hoc allometric models were defined for this study. Data provided by FIRE-RES project partners and acquired through field inventories was used for validating the final products using an independent dataset of 804 ground-truth sample plots. The CBH and CBD raster maps have, respectively, the following accuracy regarding specific metrics reported from the modeling procedures: (i) coefficient of correlation (R) of 0.445 and 0.330 (p-value < 0.001); (ii) root mean square of error (RMSE) of 3.9 m and 0.099 kg m−3; and (iii) a mean absolute percentage error (MAPE) of 61% and 76%. Regarding CBD, the accuracy metrics improved in closed canopies (canopy cover > 80%) to R = 0.457, RMSE = 0.085, and MAPE = 59%. In short, we believe that the degree of accuracy is reasonable in the resulting maps, producing CBH and CBD datasets at the pan-European scale to support fire mitigation and crown fire simulations.
Authors
Knut ØistadAbstract
No abstract has been registered
Authors
Knut ØistadAbstract
No abstract has been registered
Authors
Knut ØistadAbstract
No abstract has been registered
Authors
Knut ØistadAbstract
No abstract has been registered
Authors
Johanna Lykke Sörensen Stephanie Eisner Jonas Olsson Stein Beldring Vanessa S.B. Carvalho Maria Elenius Carlos Ruberto Fragoso Jr Anna Hansen Trine Jahr Hegdahl Benedito C. Silva Michelle S. Reboita Daniela R.T. Riondet-Costa Nívea A.D. Pons Cintia B. UvoAbstract
Decision Support Indicators (DSIs) are metrics designed to inform local and regional stakeholders about the characteristics of a predicted (or ongoing) event to facilitate decision-making. In this paper, the DSI concept was developed to clarify the different aims of different kinds of indicators by naming them, and a framework was developed to describe and support the usage of such DSIs. The framework includes three kinds of DSI: hydroclimatic DSIs which are easy to calculate but hard to understand by non-experts; impact-based DSIs which are often difficult to calculate but easy to understand by non-experts; and event-based DSIs, which compare a current or projected state to a locally well-known historical event, where hydroclimatic and impact-based DSIs are currently mainly used. Tables and figures were developed to support the DSI development in collaboration with stakeholders. To develop and test the framework, seven case studies, representing different hydrological pressures on three continents (South America, Asia, and Europe), were carried out. The case studies span several temporal and spatial scales (hours-decades; 70–6,000 km2) as well as hydrological pressures (pluvial and riverine floods, drought, and water scarcity), representing different climate zones. Based on stakeholder workshops, DSIs were developed for these cases, which are used as examples of the conceptual framework. The adaptability of the DSI framework to this wide range of cases shows that the framework and related concepts are useful in many contexts.
Abstract
The decision support indicators (DSIs) are specifically designed to inform local and regional stakeholders on the characteristics of a predicted event to facilitate decision-making. They can be classified as conventional, impact-based and event-based DSIs. This study aims to develop methodologies for calculating event-based DSIs and to evaluate the usefulness of different classes of DSIs for climate impact assessment and climate actions by learning about users' perceptions. The DSIs are calculated based on an ensemble of hydrological projections in western Norway under two representative concentration pathway (RCP) scenarios. The definitions, methodologies and results of the indicators are summarized in questionnaires and evaluated by key stakeholders in terms of understandability, importance, plausibility and applicability. Based on the feedback, we conclude that the conventional DSIs are still preferred by stakeholders and an appropriate selection of conventional DSIs may overcome the understanding problems between the scientists and stakeholders. The DSIs based on well-known historical events are easy to understand and can be a useful tool to convey climate information to the public. However, they are not readily implemented by stakeholders in the decision-making process. The impact-based DSI is generally easy to understand and important but it can be restricted to specific impact sectors.
Authors
Stefano PulitiAbstract
No abstract has been registered
Abstract
No abstract has been registered