Wildfires increasingly impact western US fluvial networks

Ball, Grady P.; Regier, Peter; González‐Pinzón, Ricardo; Reale, Justin K.; Horn, David Van

doi:10.1038/s41467-021-22747-3

Cited by 47 publications

(37 citation statements)

References 57 publications

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“…Many other national datasets containing water-and forest-related information can also be leveraged to produce general yet important information on wildfires within freshwater environments. For instance, a recent study calculated temporal changes in river lengths impacted by fire in the Western US (Ball et al 2021), and such work could be easily reproduced an adapted to the Canadian context using the federal vector database, or CANVEC, as well as the National Hydro Network, or NHN. In particular, CANVEC and NHN provide information to several water elements, natural and manmade, that could even generate more ideas for scientific studies, or even simple risk assessments.…”

Section: Protecting Community Water Supplymentioning

confidence: 99%

Baseline geographic information on wildfire-watershed risk in Canada: needs, gaps, and opportunities

Robinne

Paquette

Hallema

et al. 2022

Canadian Water Resources Journal / Revue canadienne des ressour

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As the pressures on water supply from shifting forest disturbance regimes continue to escalate, researchers are being asked to answer increasingly complex questions. However, many questions in wildfire-watershed risk (WWR) research remained unaddressed due to a paucity of relevant datasets. There are, indeed, many fundamental processes we do not understand that require additional data collection to develop risk management frameworks.As such, WWR researchers and managers face a paradox in their need to address critical questions important for the sustainability of socio-hydrological systems while dealing with incomplete information. In many cases, this leads to valuable research ideas being discarded on the account of limited data availability. However, imperfect, incomplete, or limited data should not deter researchers and managers from performing analyses to assess risk. In fact, such analyses improve the research benefit-to-cost ratio of existing data, help unravel gaps in data sources, enable generation of new hypotheses, and highlight where data availability and openness can be improved. If we do not use what we have, how can we know what we need? This issue is of particular interest in Canada, where baseline WWR information for the entire country is generally missing, despite growing concerns about water security in the face of a shifting wildfire regimes. In this commentary, we (a)identify several relevant open geospatial datasets, (b) illustrate how these datasets can be leveraged to produce simple yet relevant risk information, (c) identify some high priority data gaps that require immediate attention, and (d) discuss future avenues towards the creation of baseline Pan-Canadian WWR information. RésuméAlors que les changements dans les régimes de perturbations forestières exercent une pression croissante sur l'approvisionnement en eau, les chercheurs doivent répondre à des questions de plus en plus complexes. Pourtant, de nombreuses questions relatives à la recherche sur les risques incendies dans les bassins-versants (RIBV) restent ignorées en raison du manque de données adéquates. Il reste, en effet, de nombreux processus

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Section: Protecting Community Water Supplymentioning

confidence: 99%

Baseline geographic information on wildfire-watershed risk in Canada: needs, gaps, and opportunities

Robinne

Paquette

Hallema

et al. 2022

Canadian Water Resources Journal / Revue canadienne des ressour

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“…The fires burnt through 97,000 km 2 of south‐eastern Australia across subtropical, Mediterranean, and temperate bioregions and lasted an astonishing 8 months (July 2019–March 2020) (Collins et al, 2021). Similarly, the five most recent wildfires that ripped through California, USA, were the most severe and intense in the state's recorded history (Ball et al, 2021 and references within). These events are mere examples of the increasing severity of wildfire events.…”

Section: Introductionmentioning

confidence: 99%

Fire and rain: A systematic review of the impacts of wildfire and associated runoff on aquatic fauna

Isaza

Cramp

Franklin

2022

Global Change Biology

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Climate and land‐use changes are expected to increase the future occurrence of wildfires, with potentially devastating consequences for freshwater species and ecosystems. Wildfires that burn in close proximity to freshwater systems can significantly alter the physicochemical properties of water. Following wildfires and heavy rain, freshwater species must contend with complex combinations of wildfire ash components (nutrients, polycyclic aromatic hydrocarbons, and metals), altered light and thermal regimes, and periods of low oxygen that together can lead to mass mortality events. However, the responses of aquatic fauna to wildfire disturbances are poorly understood. Here we provide a systematic review of available evidence on how aquatic animals respond to and recover from wildfire disturbance. Two databases (Web of Science and Scopus) were used to identify key literature. A total of 83 studies from across 11 countries were identified to have assessed the risk of wildfires on aquatic animals. We provide a summary of the main ecosystem‐level changes associated with wildfires and the main responses of aquatic fauna to such disturbances. We pay special focus to physiological tools and biomarkers used to assess how wildfires impact aquatic animals. We conclude by providing an overview of how physiological biomarkers can further our understanding of wildfire‐related impacts on aquatic fauna, and how different physiological tools can be incorporated into management and conservation plans and serve as early warning signs of wildfire disturbances.

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“…In 2018, a massive fire in California covering approximately 18,000 km 2 killed 100 people and caused financial damage worth nearly USD 2 billion [6]. The accident happened again in 2020 in the same state, and burned approximately 17,000 km 2 [7]. Such catastrophic and deadly fires frequently occur around the world.…”

Section: Introductionmentioning

confidence: 99%

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

et al. 2022

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Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.

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Wildfires increasingly impact western US fluvial networks

Cited by 47 publications

References 57 publications

Baseline geographic information on wildfire-watershed risk in Canada: needs, gaps, and opportunities

Baseline geographic information on wildfire-watershed risk in Canada: needs, gaps, and opportunities

Fire and rain: A systematic review of the impacts of wildfire and associated runoff on aquatic fauna

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

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