Coupling wildfire spread and erosion models to quantify post-fire erosion before and after fuel treatments

2019 ◽  
Vol 28 (9) ◽  
pp. 687 ◽  
Author(s):  
Michele Salis ◽  
Liliana Del Giudice ◽  
Peter R. Robichaud ◽  
Alan A. Ager ◽  
Annalisa Canu ◽  
...  

Wildfires are known to change post-fire watershed conditions such that hillslopes can become prone to increased erosion and sediment delivery. In this work, we coupled wildfire spread and erosion prediction modelling to assess the benefits of fuel reduction treatments in preventing soil runoff. The study was conducted in a 68000-ha forest area located in Sardinia, Italy. We compared no-treatment conditions v. alternative strategic fuel treatments performed in 15% of the area. Fire behaviour before and after treatments was estimated by simulating 25000 wildfires for each condition using the minimum travel time fire-spread algorithm. The fire simulations replicated historic conditions associated with severe wildfires in the study area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within fuel treatment scenarios. The most efficient treatment alternative was that implemented near the road network. We also evaluated other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimise proactive risk management activities intended to reduce post-fire erosion.

2020 ◽  
Author(s):  
Liliana Del Giudice ◽  
Bachisio Arca ◽  
Peter Robichaud ◽  
Alan Ager ◽  
Annalisa Canu ◽  
...  

<p>High severity wildfires can have many negative impacts on ecosystems. In this work, we coupled wildfire spread and erosion prediction modelling to evaluate the effects of fuel reduction treatments in preventing soil runoff in Mediterranean ecosystems. The study was carried out in a 68,000-ha forest area located in Northern Sardinia, Italy. We treated 15% of the study area, and compared no-treatment conditions vs alternative strategic fuel treatments. We estimated pre- and post-treatment fire behaviour by using the Minimum Travel Time (MTT) fire spread algorithm. For each fuel treatment scenario, we simulated 25,000 wildfires replicating the historic weather conditions associated with severe wildfires in the area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within the fuel treatment scenarios tested. The treatments realized nearby roads were the most efficient. We also evaluated the effects of other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimize proactive risk management activities aimed at reducing post-fire erosion in Mediterranean areas.</p>


Author(s):  
Mei-Chin Su ◽  
Yu-Chun Chen ◽  
Mei-Shu Huang ◽  
Yen-Hsi Lin ◽  
Li-Hwa Lin ◽  
...  

Background: Effectively predicting and reducing readmission in long-term home care (LTHC) is challenging. We proposed, validated, and evaluated a risk management tool that stratifies LTHC patients by LACE predictive score for readmission risk, which can further help home care providers intervene with individualized preventive plans. Method: A before-and-after study was conducted by a LTHC unit in Taiwan. Patients with acute hospitalization within 30 days after discharge in the unit were enrolled as two cohorts (Pre-Implement cohort in 2017 and Post-Implement cohort in 2019). LACE score performance was evaluated by calibration and discrimination (AUC, area under receiver operator characteristic (ROC) curve). The clinical utility was evaluated by negative predictive value (NPV). Results: There were 48 patients with 87 acute hospitalizations in Pre-Implement cohort, and 132 patients with 179 hospitalizations in Post-Implement cohort. These LTHC patients were of older age, mostly intubated, and had more comorbidities. There was a significant reduction in readmission rate by 44.7% (readmission rate 25.3% vs. 14.0% in both cohorts). Although LACE score predictive model still has room for improvement (AUC = 0.598), it showed the potential as a useful screening tool (NPV, 87.9%; 95% C.I., 74.2–94.8). The reduction effect is more pronounced in infection-related readmission. Conclusion: As real-world evidence, LACE score-based risk management tool significantly reduced readmission by 44.7% in this LTHC unit. Larger scale studies involving multiple homecare units are needed to assess the generalizability of this study.


2007 ◽  
Vol 15 (2) ◽  
pp. 223-233 ◽  
Author(s):  
J. Engels ◽  
D. Dixon-Hardy ◽  
C. McDonald ◽  
K. Kreft-Burman

Author(s):  
Cristina Serra-Castelló ◽  
Sara Bover-Cid ◽  
Margarita Garriga ◽  
Tina Beck Hansen ◽  
Annemarie Gunvig ◽  
...  

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