H2020Индивидуална стипендия2017–2019

SPFireSD · Seasonal Prediction of Fire danger using Statistical and Dynamical models

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2017-09-06 → 2019-11-04
Финансиране от ЕС: 170 122 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Сезонният риск от горски пожари се анализира чрез статистически и динамични модели за климата. По-точните прогнози помагат за опазването на човешкия живот, здравето и околната среда.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Seasonal Prediction of Fire danger using Statistical and Dynamical models

Wildfires are the largest source of biomass burning and a great source of pollutants and atmospheric CO2. In addition to having a great impact on the environment, wildfires can also pose a threat to property and human lives and health. The occurrence of fire in natural vegetation is dependent on several factors: human activities, accumulation of fine dead fuels (grass, leaves and twigs) and climatic variability. The Earth's climate undergoes natural variability at seasonal-to-decadal timescales. Informing public sectors that are vulnerable to its variations is a key societal and economical challenge. Operational seasonal climate predictions are now routinely performed around the world and multi-model ensemble forecasts systems provide more realistic forecasts than those provided by a single model. This climate information is used for many applications in fields such as agriculture, health, water management and energy. In light of this, seasonal prediction of wildfire danger appears as a priority for health, safety and economic welfare. Climate is partially predictable on seasonal timescales and operational seasonal climate forecasts show significant skill. Opportunities therefore exist of relying on this climate skill to develop potentially skillful seasonal wildfire forecasting systems. This project proposes to develop and assess seasonal fire prediction capability through a variety of complementary and innovative methods using statistical and dynamical models.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Wildfires have a great impact on the environment and can pose a threat to property and human lives and health. The occurrence of fire in natural vegetation is dependent on human activities and climate variability. In tropical areas such as the Amazon basin and Indonesia, wildfires are greatly affected by inter-annual fluctuations in tropical Sea Surface Temperatures (SSTs). During the El Niño events of 1997-1998 and 2015-2016, uncontrolled wildfires caused record impacts on health, transportation and the economy. The European countries of the Mediterranean basin are frequently plagued by drought episodes (e.g. during the summer of 2016), causing dangerous wildfires which result in deaths, health problems and economic losses.Seasonal climate prediction is a field which typically forecasts seasonal average precipitation and temperature anomalies with a few months lead time. The main sources of predictability are SSTs, soil moisture, snow cover and teleconnections with the tropics. Seasonal climate predictions are performed operationally in Europe and globally, and are used in fields such as agriculture, health, water management and energy. While some effort has been put into short-term forecasts of fire danger in Europe, there is currently no operational seasonal wildfire forecasting system for Europe and only a few for other continents. The goal of this project is to develop and assess seasonal fire prediction capability through a variety of complementary and innovative methods, with a focus on Europe, the Amazonian basin and Indonesia.

Оригинален текст от CORDIS (на английски).

Участници

BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACION · BARCELONAКоординаторИспания