Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology.

Soil moisture deficits and water table dynamics are major biophysical controls on peat and non-peat fires in Indonesia. Development of modern fire forecasting models in Indonesia is hampered by the lack of scalable hydrologic datasets or scalable hydrology models that can inform the fire forecasting models on soil hydrologic behaviour. Existing fire forecasting models in Indonesia use weather data-derived fire probability indices, which often do not adequately proxy the sub-surface hydrologic dynamics. Here we demonstrate that soil moisture and water table dynamics can be simulated successfully across tropical peatlands and non-peatland areas by using a process-based eco-hydrology model (ecosys) and publicly available data for weather, soil, and management. Inclusion of these modelled water table depth and soil moisture contents significantly improves the accuracy of a neural network model in predicting active fires at two-weekly time scale. This constitutes an important step towards devising an operational fire early warning system for Indonesia.

Extreme fire weather is the major driver of severe bushfires in southeast Australia.

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.

Daily mapping of Australian Plague Locust abundance.

Locust population outbreaks have been a longstanding problem for Australian agriculture. Since its inception in the mid-1970s, The Australian Plague Locust Commission (APLC) is responsible for monitoring, forecasting and controlling populations of several locust pest species across inland eastern Australia (ca. two million km2). Ground surveys are typically targeted according to prevailing environmental conditions. However, due to the sheer size of the region and limited resources, such surveys remain sparse. Here we develop daily time-step statistical models of populations of Chortoicetes terminifera (Australian plague locust) that can used to predict abundances when observations are lacking, plus uncertainties. We firstly identified key environmental covariates of locust abundance, then examined their relationship with C. terminifera populations by interpreting the responses of Generalized Additive Models (GAM). We also illustrate how estimates of C. terminifera abundance plus uncertainties can be visualized across the region. Our results support earlier studies, specifically, populations peak in grasslands with high productivity, and decline rapidly under very hot and dry conditions. We also identified new relationships, specifically, a strong positive effect of vapour pressure and sunlight, and a negative effect of soil sand content on C. terminifera abundance. Our modelling tool may assist future APLC management and surveillance effort.

Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks.

Climate is a major limiting factor for insect distributions and it is expected that a changing climate will likely alter spatial patterns of pest outbreaks. The Australian plague locust (APL) Chortoicetes terminifera, is the most economically important locust species in Australia. Invasions cause large scale economic damage to agricultural crops and pastures. Understanding the regional-scale and long-term dynamics is a prerequisite to develop effective control and preventive management strategies. In this study, we used a 32-year locust survey database to uncover the relationship between historical bioclimatic variables and spatial seasonal outbreaks by developing two machine learning species distribution models (SDMs), random forest and boosted regression trees. The explanatory variables were ranked by contribution to the generated models. The bio-climate models were then projected into a future climate change scenario (RCP8.5) using downscaled 34 global climate models (GCMs) to assess how climate change may alter APL seasonal distribution patterns in eastern Australia. Our results show that the model for the distribution of spring outbreaks performed better than those for summer and autumn, based on statistical evaluation criteria. The spatial models of seasonal outbreaks indicate that the areas subject to APL outbreaks were likely to decrease in all seasons. Multi-GCM ensemble means show the largest decrease in area was for spring outbreaks, reduced by 93-94% by 2071-2090, while the area of summer outbreaks decreased by 78-90%, and 67-74% for autumn outbreaks. The bioclimatic variables could explain 78-98% outbreak areas change. This study represents an important step toward the assessment of the effects of the changing climate on locust outbreaks and can help inform future priorities for regional mitigation efforts in the context of global climate change in eastern Australia.