Modelling natural disturbances in forest ecosystems: a review

Natural disturbances play a key role in ecosystem dynamics and are important factors for sustainable forest ecosystem management. Quantitative models are frequently employed to tackle the complexities associated with disturbance processes. Here we review the wide variety of approaches to modelling natural disturbances in forest ecosystems, addressing the full spectrum of disturbance modelling from single events to integrated disturbance regimes. We applied a general, process-based framework founded in disturbance ecology to analyze modelling approaches for drought, wind, forest fires, insect pests and ungulate browsing. Modelling approaches were reviewed by disturbance agent and mechanism, and a set of general disturbance modelling concepts was deduced. We found that although the number of disturbance modelling approaches emerging over the last 15 years has increased strongly, statistical concepts for descriptive modelling are still largely prevalent over mechanistic concepts for explanatory and predictive applications. Yet, considering the increasing importance of disturbances for forest dynamics and ecosystem stewardship under anthropogenic climate change, the latter concepts are crucial tool for understanding and coping with change in forest ecosystems. Current challenges for disturbance modelling in forest ecosystems are thus (i) to overcome remaining limits in process understanding, (ii) to further a mechanistic foundation in disturbance modelling, (iii) to integrate multiple disturbance processes in dynamic ecosystem models for decision support in forest management, and (iv) to bring together scaling capabilities across several levels of organization with a representation of system complexity that captures the emergent behaviour of disturbance regimes. (C) 2010 Elsevier B.V. All rights reserved

A cautionary note regarding comparisons of fire danger indices

Over the past decade, several methods have been used to compare the performance of fire danger indices in an effort to find the most appropriate indices for particular regions or circumstances. Various authors have proposed comparators and demonstrated different responses of indices to their tests, but rarely has much effort been put into demonstrating the validity of the comparators themselves. We present a demonstration that many of the published comparators are sensitive to the different frequency distributions, that may be inherent in the performance of the different indices, and outline a non-parametric method that may be useful for future work. We compare four hypothetical fire danger indices, three of which are simple mathematical transformations of each other. The hypothesis tested is that the comparators often used in such studies may indicate spurious performance differences between these indices, which is found to be the case. Non-parametric methods are robust to differences in index value frequency distribution and may allow more valid comparisons of fire danger indices. The new comparison method is shown to have advantages over other non-parametric comparators

Wireless distributed environmental sensor networks for air pollution measurement-the promise and the current reality

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids ofWireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented

Selecting the best performing fire weather indices for Austrian ecoregions

The interpretation and communication of fire danger warning levels based on fire weather index values are critical for fire management activities. A number of different indices have been developed for various environmental conditions, and many of them are currently applied in operational warning systems. To select an appropriate combination of such indices to work in different ecoregions in mountainous, hilly and flat terrain is challenging. This study analyses the performance of a total of 22 fire weather indices and two raw meteorological variables to predict wildfire occurrence for different ecological regions of Austria with respect to the different characteristics in climate and fire regimes. A median-based linear model was built based on percentile results on fire days and non-fire days to get quantifiable measures of index performance using slope and intercept of an index on fire days. We highlight the finding that one single index is not optimal for all Austrian regions in both summer and winter fire seasons. The summer season (May-November) shows that the Canadian build-up index, the Keetch Byram Drought Index and the mean daily temperature have the best performance; in the winter season (December-April), the M68dwd is the best performing index. It is shown that the index performance on fire days where larger fires appeared is better and that the uncertainties related to the location of the meteorological station can influence the overall results. A proposal for the selection of the best performing fire weather indices for each Austrian ecoregion is made