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Holdover fires are usually associated with lightning-ignited wildfires (LIWs), which can experience a smoldering phase or go undetected for several hours, days or even weeks before being reported. Since the existence and duration of the smoldering combustion in LIWs is usually unknown, holdover time is conventionally defined as the time between the lightning event that ignited the fire and the time the fire is detected. Therefore, all LIWs have an associated holdover time, which may range from a few minutes to several days. However, we lack a comprehensive understanding of holdover times. Here, we introduce a global database on holdover times of LIWs. We have collected holdover time data from 29 different studies across the world through a literature review and datasets assembled by authors of the original studies. The database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). Censored data are the core of the database and consist of different frequency distributions reporting the number or relative frequency of LIWs per interval of holdover time. In addition, ancillary data provide further information to understand the methods and contexts in which the data were generated in the original studies. The first version of the database contains 42 frequency distributions of holdover time built with data on more than 152 375 LIWs from 13 countries in five continents covering a time span from 1921 to 2020. This database is the first freely available, harmonized and ready-to-use global source of holdover time data, which may be used in different ways to investigate LIWs and model the holdover phenomenon. The complete database can be downloaded at 10.5281/zenodo.7352172 (Moris et al., 2022). [ABSTRACT FROM AUTHOR]

Holdover fires are usually associated with lightning-ignited wildfires (LIWs), which can experience a smouldering phase or go undetected for several hours to days and weeks before being reported. Since the existence and duration of the smouldering combustion in LIWs is usually unknown, holdover time is conventionally defined as the time between the lightning event that ignited the fire and the time the fire is detected. Therefore, all LIWs have an associated holdover time, which may range from a few minutes to several days. However, we lack a comprehensive understanding of holdover times. Here, we introduce a global database on holdover times of LIWs. We have collected holdover time data from 29 different studies across the world through a literature review and datasets assembled by authors of the original studies. The database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). Censored data are the core of the database and consist of different frequency distributions reporting the number or relative frequency of LIWs per interval of holdover time. In addition, ancillary data provide further information to understand the methods and contexts in which the data were generated in the original studies. The first version of the database contains 42 frequency distributions of holdover time built with data on more than 152,375 LIWs from 13 countries in five continents covering a time span from 1921 to 2020. This database is the first freely available, harmonized, and ready-to-use global source of holdover time data, which may be used in different ways to investigate LIWs and model the holdover phenomenon. The complete database can be downloaded at https://doi.org/10.5281/zenodo.7352172 (Moris et al., 2022). [ABSTRACT FROM AUTHOR]

• Holdover time is the time between ignition and detection of lightning wildfires. • Gamma distributions fit well to holdover time data of lightning-ignited wildfires. • We estimated survival functions of holdover time based on gamma distributions. • Survival functions provide probabilities of reaching holdover times. • Survival functions can help assess probabilities of lightning causing wildfires. Lightning-ignited wildfires (LIWs) can go unreported for hours, days or even weeks before being reported. This is due to the fact that some LIWs present an intermediate phase between ignition and detection characterized by a smoldering combustion. Holdover time is generally defined as the time between lightning-induced ignition and fire detection. This study aims at obtaining survival functions to estimate the probability of a LIW reaching a certain holdover time. To this end, we fitted nine different probability distributions (exponential, chi-squared, log-normal, log-logistic, F, gamma, Weibull, Pareto, and Gompertz) to data from a database gathering 42 frequency distributions of holdover time obtained from more than 152,375 LIWs in 13 countries from 1921 to 2020. Gamma distributions provide the best fits to the observed holdover times. Accordingly, we estimated several survival functions derived from gamma distributions fitted to holdover time data. The survival functions are monotonically decreasing functions characterized by high probabilities for short holdover times and low probabilities for long holdover times. These survival functions can be used for holdover times of LIWs occurring globally as well as in boreal, Mediterranean and temperate coniferous forest biomes. Survival functions are intended to provide a more reliable way to assess holdover time-based probabilities of lightning causing wildfires. [ABSTRACT FROM AUTHOR]

Lightning is the main precursor of wildfires in Arizona, New Mexico, and Florida during the fire season. Forecasting the occurrence of Lightning-Ignited Wildfires (LIW) is an essential tool to reduce their impacts on the environment and society. Long-Continuing-Current (LCC) lightning is proposed to be the main precursor of LIW. The long-lasting continuing current phase of LCC lightning is that which is more likely to ignite vegetation. We investigated the meteorological conditions and vegetation type associated with LIW and LCC lightning flashes in Arizona, New Mexico, and Florida. We analyzed LIW between 2009 and 2013 and LCC lightning between 1998 and 2014 and combined lightning and meteorological data from a reanalysis data set. According to our results, LIW tend to occur during dry thunderstorms with a high surface temperature and a high temperature gradient between the 700 hPa and the 450 hPa vertical levels for high-based clouds. In turn, we obtained a high lightning-ignition efficiency in coniferous forests, such as the ponderosa pine in Arizona and New Mexico and the slash pine in Florida. We found that the meteorological conditions that favor fire ignition and spread are more significant in Florida than in Arizona and New Mexico, while the meteorological conditions that favor the occurrence of LIW in Arizona and New Mexico are closely related with the meteorological conditions that favor high lightning activity. In turn, our results indicate high atmospheric instability during the occurrence of LIW. Our findings suggest that LCC (>18 ms) lightning tends to occur in thunderstorms with high relative humidity and ice content in the clouds, and with low temperature in the entire troposphere. Additionally, a weak updraft in the lower troposphere and a strong one in the upper troposphere favor the occurrence of LCC (>18 ms) lightning. We found that the meteorological conditions that favor the occurrence of LCC (>18 ms) lightning are not necessarily the preferential meteorological conditions for LIW. [ABSTRACT FROM AUTHOR]

• An approach to find igniting strokes based on the confidence ellipse is described. • Holdover fires are relatively common in the Alps. • Positive lightning ignite forest fires more frequently than expected by chance. • The methodological choice is important for matching igniting lightning and fires. Lightning is the most important natural wildfire ignition source worldwide. However, identifying the specific lightning causing a forest fire is a challenging task. The goal of this study is to understand how different methodological approaches affect the association between igniting lightning and natural fires. To this purpose, we combined data on 267 lightning-caused forest fires from Switzerland with data on cloud-to-ground lightning strokes for the period 2000–2018. We searched for the most probable igniting lightning (candidate) among all lightning that occur during the days (up to two weeks) before fire detection in the vicinity (< 10 km) of the ignition point. We tested the suitability of 14 methods that combine different spatio-temporal approaches and selection criteria. Our results show that each method selected different candidate lightning for a subset of the fires, while for other fires identical candidate lightning were selected by different methods. Methods using criteria that combine simultaneously space and time, such as the index A, selected candidate lightning with short distances from the ignition points and short holdover times (i.e., the time between ignition and fire detection). On the contrary, methods that minimize the holdover time selected a great proportion of candidate lightning located at long distances. The majority of the candidate lighting were recorded within 1 km from the fire starting point and in less than 24 h before fire detection. The proportion of positive strokes was significantly higher among candidate lightning than in the rest of lightning, which supports the hypothesis that positive lightning are more likely to ignite a forest fire than negative ones. This study highlights the importance of the methodological choice when searching for the candidate igniting lightning of a wildfire. [ABSTRACT FROM AUTHOR]

In the context of the Kyoto Protocol, the mandatory accounting of Afforestation and Reforestation (AR) activities requires estimating the forest carbon (C) stock changes for any direct human-induced expansion of forest since 1990. We used the Carbon Budget Model (CBM) to estimate C stock changes and emissions from fires on AR lands at country level. Italy was chosen because it has one of the highest annual rates of AR in Europe and the same model was recently applied to Italy's forest management area. We considered the time period 1990-2020 with two case studies reflecting different average annual rates of AR: 78 kha yr-1, based on the 2013 Italian National Inventory Report (NIR, official estimates), and 28 kha yr-1, based on the Italian Land Use Inventory System (IUTI estimates). We compared these two different AR rates with eight regional forest inventories and three independent local studies. The average annual C stock change estimated by CBM, excluding harvest or natural disturbances, was equal to 1738 Gg C yr-1 (official estimates) and 630 Gg C yr-1 (IUTI estimates). Results for the official estimates are consistent with the estimates reported by Italy to the KP for the period 2008-2010; for 2011 our estimates are about 20% higher than the country's data, probably due to different assumptions on the fire disturbances, the AR rate and the dead wood and litter pools. Furthermore, our analysis suggests that: (i) the impact on the AR sink of different assumptions of species composition is small; (ii) the amount of harvest provided by AR has been negligible for the past (< 3%) and is expected to be small in the near future (up to 8% in 2020); (iii) forest fires up to 2011 had a small impact on the AR sink (on average, < 100 Gg C yr-1). Finally the comparison of the historical AR rates reported by NIR and IUTI with other independent sources gives mixed results: the regional inventories support the AR rates reported by the NIR, while some local studies suggest AR rates somehow intermediate between NIR and IUTI. In conclusion, this study suggests that the CBM can be applied at country level to estimate the C stock changes resulting from AR, including the effect of harvest and fires, though only a comparison with results based on direct field measurements could verify the model's capability to estimate the real C stock change. [ABSTRACT FROM AUTHOR]

Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long‐lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time‐series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide. These observations consist of 5971 population‐level time‐series from 974 species in 66 countries. The mean and median time‐series length is 12.4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (≥20 years of observations). For a subset of well‐studied species, MASTREE+ includes extensive replication of time‐series across geographical and climatic gradients. Here we describe the open‐access data set, available as a.csv file, and we introduce an associated web‐based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long‐lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics. [ABSTRACT FROM AUTHOR]