Data Basin Climate Center: sharing and manipulating spatial information on the web

Monitoring datasets is essential to detect changes that are occurring and identify thresholds that cause them, but scientists around the world are now generating large volumes of data that vary in quality, format, supporting documentation, and accessibility. Moreover, diverse models are being run at various spatial and temporal scales to try and understand past climate variability and its impacts, generate future climate and land use scenarios, and project potential future impacts to the planet. Conservation practitioners and land managers are struggling to synthesize this wealth of information, identify relevant and usable datasets, and translate evolving science results into on-the-ground climate-aware strategies.
In partnership with ESRI and Mambo media, the Conservation Biology Institute (CBI) is developing a versatile web-based resource that centralizes usable climate change-relevant datasets and provides analytical tools to visualize, analyze, and communicate findings for practical applications. To illustrate its capability to store, manipulate, and derive relevant conclusions to users, we present three examples of projects involving scientists and managers that are part of the Climate Center of Data Basin (http://www.databasin.org): a conservation project in the Puget Sound area of Washington State, a climate change impacts project in California, a North American project looking at climate change impacts on Canada lynx. We conclude by showing the use of this new web tool in workshops that bring together scientists and practitioners, allowing all to access the data and develop more effective management strategies

Data Basin Climate Center: sharing and manipulating spatial information on the web

Monitoring datasets is essential to detect changes that are occurring and identify thresholds that cause them, but scientists around the world are now generating large volumes of data that vary in quality, format, supporting documentation, and accessibility. Moreover, diverse models are being run at various spatial and temporal scales to try and understand past climate variability and its impacts, generate future climate and land use scenarios, and project potential future impacts to the planet. Conservation practitioners and land managers are struggling to synthesize this wealth of information, identify relevant and usable datasets, and translate evolving science results into on-the-ground climate-aware strategies.
In partnership with ESRI and Mambo media, the Conservation Biology Institute (CBI) is developing a versatile web-based resource that centralizes usable climate change-relevant datasets and provides analytical tools to visualize, analyze, and communicate findings for practical applications. To illustrate its capability to store, manipulate, and derive relevant conclusions to users, we present three examples of projects involving scientists and managers that are part of the Climate Center of Data Basin (http://www.databasin.org): a conservation project in the Puget Sound area of Washington State, a climate change impacts project in California, a North American project looking at climate change impacts on Canada lynx. We conclude by showing the use of this new web tool in workshops that bring together scientists and practitioners, allowing all to access the data and develop more effective management strategies

MESURER L'ESPRIT D'ENTREPRENDRE DES ELEVES INGENIEURS

La création d'entreprises innovantes est un enjeu économique vital pour l'Europe. Mais comment la développer chez les jeunes ? Comment favoriser l'éclosion d'un esprit d'entreprendre qui impulserait à la fois une dynamique d'innovation dans les grandes entreprises et la création d'entreprises nouvelles ? Si un consensus existe quant à ces nécessités, les moyens de développement et de stimulation de cet esprit ne sont pas complètement connus. Notamment parce que la notion « d'esprit d'entreprise » ou celle « d'esprit d'entreprendre » restent encore difficile à définir, à quantifier et à relier à la prise effective d'initiatives, ce qui rend délicate la mesure de l'impact des stratégies de sensibilisation ou de formation.Le propos de notre recherche est de définir ce qu'est l'esprit d'entreprendre chez les élèves ingénieur, et la manière avec laquelle il se manifeste. Il s'agit de formuler un cadre d'analyse d'une définition et de mesure du développement de l'esprit d'entreprendre chez les élèves ingénieurs au fil de leur cursus. Nous nous appuyons pour ce faire sur un modèle exploratoire de l'esprit d'entreprendre que nous avons bâti sur la base d'une étude extensive de la littérature en gestion, sociologie, psychologie et sciences de l'éducation appliquées ou non au champ de l'entrepreneuriat (Verzat, Bachelet, Hannachi et Frugier 03). La démarche que nous proposons s'appuie sur une étude quantitative longitudinale auprès de l'ensemble des élèves de l'école pendant trois années.Nous présenterons notre modèle de recherche, puis nous détaillerons la fabrication du questionnaire et son mode d'administration par web. Enfin nous proposerons quelques premiers résultats obtenus lors de notre première campagne d'enquête auprès d'élèves ingénieurs de première année.entrepreneuriat, esprit d'entreprendre, esprit d'entreprise, grande école, université

Estimation of potential evapotranspiration from extraterrestrial radiation, air temperature and humidity to assess future climate change effects on the vegetation of the Northern Great Plains, USA

The potential evapotranspiration (PET) that would occur with unlimited plant access to water is a central driver of simulated plant growth in many ecological models. PET is influenced by solar and long wave radiation, temperature, wind speed, and humidity, but it is often modeled as a function of temperature alone. This approach can cause biases in projections of future climate impacts in part because it confounds the effects of warming due to increased greenhouse gases with that which would be caused by increased radiation from the sun. We developed an algorithm for linking PET to extraterrestrial solar radiation (incoming top-of atmosphere solar radiation), as well as temperature and atmospheric water vapor pressure, and incorporated this algorithm into the dynamic global vegetation model MC1. We tested the new algorithm for the Northern Great Plains, USA, whose remaining grasslands are threatened by continuing woody encroachment. Both the new and the standard temperature-dependent MC1 algorithm adequately simulated current PET, as compared to the more rigorous PenPan model of Rotstayn et al. (2006). However, compared to the standard algorithm, the new algorithm projected a much more gradual increase in PET over the 21st century for three contrasting future climates. This difference led to lower simulated drought effects and hence greater woody encroachment with the new algorithm, illustrating the importance of more rigorous calculations of PET in ecological models dealing with climate change

Estimation of potential evapotranspiration from extraterrestrial radiation, air temperature and humidity to assess future climate change effects on the vegetation of the Northern Great Plains, USA

The potential evapotranspiration (PET) that would occur with unlimited plant access to water is a central driver of simulated plant growth in many ecological models. PET is influenced by solar and long wave radiation, temperature, wind speed, and humidity, but it is often modeled as a function of temperature alone. This approach can cause biases in projections of future climate impacts in part because it confounds the effects of warming due to increased greenhouse gases with that which would be caused by increased radiation from the sun. We developed an algorithm for linking PET to extraterrestrial solar radiation (incoming top-of atmosphere solar radiation), as well as temperature and atmospheric water vapor pressure, and incorporated this algorithm into the dynamic global vegetation model MC1. We tested the new algorithm for the Northern Great Plains, USA, whose remaining grasslands are threatened by continuing woody encroachment. Both the new and the standard temperature-dependent MC1 algorithm adequately simulated current PET, as compared to the more rigorous PenPan model of Rotstayn et al. (2006). However, compared to the standard algorithm, the new algorithm projected a much more gradual increase in PET over the 21st century for three contrasting future climates. This difference led to lower simulated drought effects and hence greater woody encroachment with the new algorithm, illustrating the importance of more rigorous calculations of PET in ecological models dealing with climate change

Soil depth affects simulated carbon and water in the MC2 dynamic global vegetation model

A B S T R A C T Climate change has significant effects on critical ecosystem functions such as carbon and water cycling. Vegetation and especially forest ecosystems play an important role in the carbon and hydrological cycles. Vegetation models that include detailed belowground processes require accurate soil data to decrease uncertainty and increase realism in their simulations. The MC2 DGVM uses three modules to simulate biogeography, biogeochemistry and fire effects, all three of which use soil data either directly or indirectly. This study includes a correlation analysis of the MC2 model to soil depth by comparing a subset of the model's carbon and hydrological outputs using soil depth data of different scales and qualities. The results show that the model is very sensitive to soil depth in simulations of carbon and hydrological variables, but competing algorithms make the fire module less sensitive to changes in soil depth. Simulated historic evapotranspiration and net primary productivity show the strongest positive correlations (both have correlation coefficients of 0.82). The strongest negative correlation is streamflow (À0.82). Ecosystem carbon, vegetation carbon and forest carbon show the next strongest correlations (0.78, 0.74 and 0.74, respectively). Carbon consumed by forest fires and the part of each grid cell burned show only weak negative correlations (À0.24 and À0.0013 respectively). In the model, when the water demand is met (deep soil with good water availability), production increases and fuels build up as more litter gets generated, thus increasing the overall fire risk during upcoming dry periods. However, when soil moisture is low, fuels dry and fire risk increases. In conclusion, it is clear climate change impact models need accurate soil depth data to simulate the resilience or vulnerability of ecosystems to future conditions

Estimation of potential evapotranspiration from extraterrestrial radiation, air temperature and humidity to assess future climate change effects on the vegetation of the Northern Great Plains, USA

The potential evapotranspiration (PET) that would occur with unlimited plant access to water is a central driver of simulated plant growth in many ecological models. PET is influenced by solar and longwave radiation, temperature, wind speed, and humidity, but it is often modeled as a function of temperature alone. This approach can cause biases in projections of future climate impacts in part because it confounds the effects of warming due to increased greenhouse gases with that which would be caused by increased radiation from the sun. We developed an algorithm for linking PET to extraterrestrial solar radiation (incoming top-of atmosphere solar radiation), as well as temperature and atmospheric water vapor pressure, and incorporated this algorithm into the dynamic global vegetation model MC1. We tested the new algorithm for the Northern Great Plains, USA, whose remaining grasslands are threatened by continuing woody encroachment. Both the new and the standard temperature-dependent MC1 algorithm adequately simulated current PET, as compared to the more rigorous PenPan model of Rotstayn et al. (2006). However, compared to the standard algorithm, the new algorithm projected a much more gradual increase in PET over the 21st century for three contrasting future climates. This difference led to lower simulated drought effects and hence greater woody encroachment with the new algorithm, illustrating the importance of more rigorous calculations of PET in ecological models dealing with climate change.Keywords: Potential evapotranspiration, Great Plains USA, Climate change, Vegetation dynamics, MC

Global ecosystems and fire: multi-model assessment of fire-induced tree cover and carbon storage reduction

In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001‐2012. Fire reduces the tree covered area and vegetation carbon storage by 10%. Regionally the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to zero. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems

A new model to simulate climate-change impacts on forest succession for local land management

We developed a new climate-sensitive vegetation state-and-transition simulation model (CV-STSM) to simulate future vegetation at a fine spatial grain commensurate with the scales of human land-use decisions, and under the joint influences of changing climate, site productivity, and disturbance. CV-STSM integrates outputs from four different modeling systems. Successional changes in tree species composition and stand structure were represented as transition probabilities and organized into a state-and-transition simulation model. States were characterized based on assessments of both current vegetation and of projected future vegetation from a dynamic global vegetation model (DGVM). State definitions included sufficient detail to support the integration of CV-STSM with an agent-based model of land-use decisions and a mechanistic model of fire behavior and spread. Transition probabilities were parameterized using output from a stand biometric model run across a wide range of site productivities. Biogeographic and biogeochemical projections from the DGVM were used to adjust the transition probabilities to account for the impacts of climate change on site productivity and potential vegetation type. We conducted experimental simulations in the Willamette Valley, Oregon, USA. Our simulation landscape incorporated detailed new assessments of critically imperiled Oregon white oak (Quercus garryana) savanna and prairie habitats among the suite of existing and future vegetation types. The experimental design fully crossed four future climate scenarios with three disturbance scenarios. CV-STSM showed strong interactions between climate and disturbance scenarios. All disturbance scenarios increased the abundance of oak savanna habitat, but an interaction between the most intense disturbance and climate-change scenarios also increased the abundance of subtropical tree species. Even so, subtropical tree species were far less abundant at the end of simulations in CV-STSM than in the dynamic global vegetation model simulations. Our results indicate that dynamic global vegetation models may overestimate future rates of vegetation change, especially in the absence of stand-replacing disturbances. Modeling tools such as CV-STSM that simulate rates and direction of vegetation change affected by interactions and feedbacks between climate and land-use change can help policy makers, land managers, and society as a whole develop effective plans to adapt to rapidly changing climate.This is the publisher’s final pdf. The published article is copyrighted by the Ecological Society of America and can be found at: http://www.esajournals.org/loi/ecapKeywords: Envision, Agent-based model, Disturbance, Dynamic global vegetation model, MC1, State-and-transition simulation model, Oregon, Fire, Willamette Valle

The status and challenge of global fire modelling

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP