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Assessing the effect of fire severity on sediment connectivity in central Chile
Lorenzo Martini1, Lorenzo Faes2, Lorenzo Picco3
1University of Padova, Department of Land, Environment, Agriculture and Forestry, Legnaro, Italy..
Abstract:
Chilean territory is recurrently affected by severe wildfires, which drastically reduce the forest cover and promote runoff, soil erosion and slope instabilities. To understand how the geomorphic system responds to wildfires in terms of sediment dynamics, the assessment of sediment connectivity, i.e. the property describing the relationships between compartments of a geomorphic system, is crucial. This study aims to quantify the spatial linkages between fire severity and sediment connectivity to identify common patterns and driving factors. The compound use of field data and open-source satellite imagery helped to apply the Relative differenced Normalized Burn Ratio (RdNBR) and the Index of Connectivity (IC) in the context of two consecutive wildfires, occurred in 2002 and 2015, in the Rio Toro catchment (Chile). The fire severity assessment showed that the 2002 event affected 90% of the catchment, with high severity areas representing around 70%. The 2015 wildfire instead, affected 76% of the catchment with moderate severity around 42%. Accordingly, as result of the sudden reduction in forest cover in severely affected areas, the IC changed after both wildfires with an overall increase of 1.07 and 0.54, respectively. However, only for the second disturbance, it was possible to observe a clear relationship between the RdNBR and the IC variations. The different degree of vegetation cover heterogeneity between the two pre-wildfire scenarios contributed to different fire severity and IC variability between the two disturbances. The use of open-source satellite data and the development of a weighting factor (W), to be used in IC and able to capture the land cover change driven by the wildfires, could make the application of this approach straightforward, promoting its reproducibility in other catchments for land management and risk mitigation purposes.
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Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...

