2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities

Development of Groundwater Levels Dataset for Chile since 1970

Access to accurate spatio-temporal groundwater level data is crucial for sustainable water management in Chile. Despite this importance, a lack of unified, quality-controlled datasets have hindered large-scale groundwater studies. Our objective was to establish a comprehensive, reliable nationwide groundwater dataset. We curated over 120,000 records from 640 wells, spanning 1970-2021, provided by the General Water Resources Directorate. One notable enhancement to our dataset is the incorporation of elevation data. This addition allows for a more comprehensive estimation of groundwater elevation. Rigorous data quality analysis was executed through a classification scheme applied to raw groundwater level records. This resource is invaluable for researchers, decision-makers, and stakeholders, offering insights into groundwater trends to support informed, sustainable water management. Our study bridges a crucial gap by providing a dependable dataset for expansive studies, aiding water management strategies in Chile.

Data Filling of Micrometeorological Variables in Complex Terrain for High-Resolution Nowcasting

In this paper, two different computationally inexpensive methods for nowcasting/data filling spatially varying meteorological variables (wind velocity components, specific humidity, and virtual potential temperature) covering scales ranging from 100 m to 5 km in regions marked by complex terrain are compared. Multivariable linear regression and artificial neural networks are used to predict micrometeorological variables at eight locations using the measurements from three nearby weather stations. The models are trained using data gathered from a system of eleven low-cost automated weather stations that were deployed in the Cadarache Valley of southeastern France from December 2016 to June 2017. The models are tested on two held-out periods of measurements of thermally-driven flow and synoptically forced flow. It is found that the models have statistically significant performance differences for the wind components during the synoptically driven flow period (p = 6.6 × 10−3 and p = 2.0 × 10−2 for U and V, respectively), but perform the same otherwise. These methods can be used to spatially fill gaps in micrometeorological datasets. Recommended future work should include statistically interpreting the predictive models and testing their capabilities on meteorological datasets from different locations.

Australian fire nourishes ocean phytoplankton bloom

An unprecedented devastating forest fire occurred in Australia from September 2019 to March 2020. Satellite observations revealed that this rare fire event in Australia destroyed a record amount of more than 202,387 km² of forest, including 56,471 km² in eastern Australia, which is mostly composed of evergreen forest. The released aerosols contained essential nutrients for the growth of marine phytoplankton and were transported by westerly winds over the Southern Ocean, with rainfall-induced deposition to the ocean beneath. Here, we show that a prominent oceanic bloom, indicated by the rapid growth of phytoplankton, took place in the Southern Ocean along the trajectory of fire-born aerosols in response to atmospheric deposition. Calculations of carbon released during the fire versus carbon absorbed by the oceanic phytoplankton bloom suggest that they were nearly equal. This finding illustrates the critical role of the oceans in mitigating natural and anthropogenic carbon dioxide releases to the atmosphere, which are a primary driver of climate change.

Causes and Effects of Sand and Dust Storms: What Has Past Research Taught Us? A Survey

Barren ground and sites with low coverage by vegetation (e.g., dunes, soil surfaces, dry lakes, and riverbeds) are the main source areas of sand and dust storms (SDS). The understanding of causes, processes (abrasion, deflation, transport, deposition), and influencing factors of sandy and dusty particles moving by wind both in the boundary layer and in the atmosphere are basic prerequisites to distinguish between SDS. Dust transport in the atmosphere modulates radiation, ocean surface temperature, climate, as well as snow and ice cover. The effects of airborne particles on land are varied and can cause advantages and disadvantages, both in source areas and in sink or deposition areas, with disturbances of natural environments and anthropogenic infrastructure. Particulate matter in general and SDS specifically can cause severe health problems in human respiratory and other organs, especially in children. Economic impacts can be equally devastating, but the costs related to SDS are not thoroughly studied. The available data show huge economic damages caused by SDS and by the mitigation of their effects. Management of SDS-related hazards utilizes remote sensing techniques, on-site observations, and protective measures. Integrated strategies are necessary during both the planning and monitoring of these measures. Such integrated strategies can be successful when they are developed and implemented in close cooperation with the local and regional population and stakeholders.

Rainfall-Linked Megafires as Innate Fire Regime Elements in Arid Australian Spinifex (Triodia spp.) Grasslands

Large, high-severity wildfires, or “megafires,” occur periodically in arid Australian spinifex (Triodia spp.) grasslands after high rainfall periods that trigger fuel accumulation. Proponents of the patch-burn mosaic (PBM) hypothesis suggest that these fires are unprecedented in the modern era and were formerly constrained by Aboriginal patch burning that kept landscape fuel levels low. This assumption deserves scrutiny, as evidence from fire-prone systems globally indicates that weather factors are the primary determinant behind megafire incidence, and that fuel management does not mitigate such fires during periods of climatic extreme. We reviewed explorer’s diaries, anthropologist’s reports, and remotely sensed data from the Australian Western Desert for evidence of large rainfall-linked fires during the pre-contact period when traditional Aboriginal patch burning was still being practiced. We used only observations that contained empiric estimates of fire sizes. Concurrently, we employed remote rainfall data and the Oceanic Niño Index to relate fire size to likely seasonal conditions at the time the observations were made. Numerous records were found of small fires during periods of average and below-average rainfall conditions, but no evidence of large-scale fires during these times. By contrast, there was strong evidence of large-scale wildfires during a high-rainfall period in the early 1870s, some of which are estimated to have burnt areas up to 700,000 ha. Our literature review also identified several Western Desert Aboriginal mythologies that refer to large-scale conflagrations. As oral traditions sometimes corroborate historic events, these myths may add further evidence that large fires are an inherent feature of spinifex grassland fire regimes. Overall, the results suggest that, contrary to predictions of the PBM hypothesis, traditional Aboriginal burning did not modulate spinifex fire size during periods of extreme-high arid zone rainfall. The mechanism behind this is that plant assemblages in seral spinifex vegetation comprise highly flammable non-spinifex tussock grasses that rapidly accumulate high fuel loads under favorable precipitation conditions. Our finding that fuel management does not prevent megafires under extreme conditions in arid Australia has parallels with the primacy of climatic factors as drivers of megafires in the forests of temperate Australia.

Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained directly on this information. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems.

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.