Seasonal prediction of climate-driven fire risk for decision-making and operational applications in a Mediterranean region

Abstract

In this paper, we assess and develop a climate service focused on the production of seasonal predictions for summer wildfires in a Mediterranean region through a participatory approach with end-users. We start by building a data-driven model that links a drought indicator (Standardised Precipitation Evapotranspiration Index; SPEI) with a series of burned areas in Catalonia (northeastern Spain). Afterwards, we feed this model with SPEI forecasts obtained through a combination of the antecedent observed conditions and climatology. Finally, we assess the forecasting skill of the system by using cross-validation to evaluate the predictions as if they had been made operationally. Our fire forecasting system reveals an untapped and useful burned area predictive ability. We argue that this source of predictability is mostly attributable to the effect of observed initial conditions on summer drought conditions. This system was conceived with the stakeholders, merging climate-driven predictions with information that is of interests to the users, including the identification of climate variables, thresholds and models. The co-production of this customized system allows fire-risk outlooks to be translated into usable information for fire management. This fire forecasting ability plays a crucial role in developing proactive fire management practices such as long-term fuel assessment and other fire-risk planning, thus minimising the impact of adverse climate conditions on summer burned area. © 2019. Licensed under the Creative Commons [CC-BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/]. Highlights• A climate service for seasonal predictions for summer wildfires is proposed • Logistic regression models linking drought and fire variables are defined • Persistence-based drought forecasts feed this data-driven fire model• Summer fires can be forecast months in advance with this strategy • The co-production of this system is key to providing usable information

Study on the Diurnal Dynamic Changes and Prediction Models of the Moisture Contents of Two Litters

“…With the litter moisture content as the dependent variable, the meteorological factors with significant influence were used as the independent variables in the stepwise regression method. The equation employed for this has the form of a multivariate linear equation, as shown in Equation (5).…”

“…The goal of forecasting forest fires is to calculate the probability of fire occurrence and potential damage [1,2]. With global warming and the increasing occurrence of forest fires, the study of forest fire forecasting is becoming increasingly important [3][4][5]. High-accuracy forest fire forecasting can reduce the losses caused by forest fires through the formulation of firefighting plans according to the possibility of forest fires and potential damage.…”

Current and Emerging Developments in Subseasonal to Decadal Prediction

“…Additional sectors for which S2D forecasts are being assessed for decision-making include agriculture (Klemm and McPherson 2017), energy (demand and wind power generation, Clark et al 2017;Lledó et al 2019), tropical cyclone (Bergman et al 2019) and coastal flooding (Widlansky et al 2017) preparedness, Arctic marine transportation (Stephenson and Pincus 2018), wildfire risk (Turco et al 2019), and food security (Funk et al 2019).…”

Barriers and Opportunities for Actionable Knowledge Production in Drought Risk Management: Embracing the Frontiers of Co-production

“…For example, while exploring drought impacts and MEWS development in Europe, North America, and Australia, the "DrIVER" project incorporated learnings from stakeholder workshops to develop collective insights into different perspectives of drought impacts and MEWS needs (Collins et al, 2016;Hannaford et al, 2018). In a Mediterranean context, Turco et al (2019) developed a fire forecasting system that merged user relevant information with seasonal climate forecasts to provide tailored outlooks for fire managers in Catalunya. Ferguson et al (2016) collaborated with a Native American community in the United States Southwest on a drought information system that combined local observations and knowledge with scientific monitoring data.…”