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Effects of grid spacing on habitat hydraulic complexity metricsThe sensitivity of the habitat hydraulic complexity metrics to Δs was examined by calculating the metrics for Δs = 0.06, 0.12, 0.18, and 0.24 m (for M4, Δs = Δx = Δy). Figure 3 shows the variation of the metrics with grid spacing for scenarios with boulders. A preliminary assessment of no-boulder scenarios (S1-L and S1-H) showed that all the metrics decreased by increasing the grid spacing. However, because the metrics are mostly used in complex rather than non-obstructed and 1-D flows, the plots only include scenarios with boulder placement to highlight the effects of grid spacing on the metrics in complex flows. All the metrics generally decreased as Δs increased. At the low flow rate, by changing the Δs from the smallest to largest, i.e., 0.06 m to 0.024, the mean decreases in the M1, M2, and M4 metrics (averaged over all the scenarios with boulders) were 45.1, 9.9, and 74.7%, respectively. At the high flow rate, these reductions were 34.8, 14.7, and 82.5% for M1, M2, and M4, respectively. Table 2 shows the p-values associated with the changes in the metrics due to increasing Δs from 0.06 to 0.24 m for all scenarios. The table indicates that changes in M1 and M4 were statistically significant while for M2 they were not (p-values  > 0.05 for all scenarios except for S2-H). This result indicated the considerable influence of grid spacing on M1 and M4 metrics in the reaches with boulder placement. Additionally, the differences in the reported average reductions due to changing the flow rate were less than 10%, indicating an insubstantial effect of flow rate on the habitat hydraulic complexity metrics’ sensitivity to the grid spacing. The significant sensitivity of the metrics M1 and M4 to the grid spacing in this study is contrary to the findings of a previous study in which an insignificant correlation was found between the habitat hydraulic complexity metrics and Δs29. This difference can be attributed to different topographic features in the studied reaches. In the previous findings, measurements were mainly taken around the bends and reaches with no significant obstruction29, in which a more uniform flow with smaller velocity gradients is expected. However, in this study, the systematic boulder placement generated more complex flow patterns with noticeable velocity gradients. Therefore, due to the variations of flow velocities in the zone studied, substantially different values for the metrics are anticipated by computing the metrics over different spatial scales.Figure 3Variation of the habitat hydraulic complexity metrics with grid spacing (Δs) for scenarios with boulder placement. (a) kinetic energy gradient metric, M1, (b) normalized kinetic energy gradient metric, M2, (c) modified recirculation metric M4.Full size imageTable 2 p-values associated with changing the grid spacing from 0.06 to 0.24 m.Full size tableStatistical distribution of habitat hydraulic complexity metricsTable 3 lists the mean, minimum, maximum, and standard deviations of the habitat hydraulic complexity metrics (Δs = 0.06 m) for all the scenarios. To complement the results from Table 3 and assess whether the influences of solely changing the boulder concentration or flow rate on the metrics were statistically significant, Table 4 shows p-values associated with changing flow rate from low to high for a given boulder concentration, and Table 5 shows p-values associated with gradually increasing the boulder concentration for a given flow rate.Table 3 The statistical parameters of the habitat hydraulic complexity metrics in the detailed measurement zone.Full size tableTable 4 p-values from a t-test associated with changes in flow rate for a given boulder concentration.Full size tableTable 5 p-values from a t-test associated with changes in boulder concertation for a given flow rate.Full size tableFor metric M1, the mean M1 values for scenarios incorporating boulders showed the same order of magnitude as values from previous studies for reaches with single and multiple boulders27 but they were about one order of magnitude larger than calculated values in the confluence of two rivers11. Using a larger grid spacing in the study in the confluence of two rivers11 can be the reason for this difference. For a scenario at the higher flow rate, the mean M1 was on average (averaged for all the scenarios) 36% greater than its counterpart at the lower flow rate and this change in M1 values was statistically significant with p  More

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Global fire activity is changing in many areas as temperatures increase and land use intensifies1,2,3,4,5. This is sparking an increase in attention given to fire activity and fire ecology. However, the availability of data for spatially delineated fire events is limited or non-existent in many countries6, with most global fire data coming from satellite-based active fire detections7,8 and gridded burned area products9,10. The lack of products containing delineated events has led to many global studies about fire ecology that are computationally-intensive, coarse-scale trend analyses1,4.A key advantage of datasets like Monitoring Trends in Burn Severity (MTBS)11 or the Fire Occurrence Dataset12 lies in their ease of use. Since its inception in 2007 MTBS has been cited 947 times in peer-reviewed studies according to a Google Scholar search at the time of this writing, despite documented limitations for scientific use of some facets of the product13. The MTBS dataset is regularly updated, easy to find on the internet, and it is free, fast and easy to download and use. Many environmental scientists and resource managers do not have the computational budget or expertise in big data or remote sensing to deal with the challenges one must overcome to process large fire datasets. This is especially true for cases when all that is needed is a shapefile of fire perimeters that can be used to map fire history. Other global fire perimeter datasets have been produced from satellite-derived burned area products14,15, but these are only available in yearly or monthly global shapefiles. Often field-based studies of fire effects require an entire time series over study areas that are only a few hundred km in diameter16 or a single ecoregion17. The end user who wants to understand the fire history for their region would have to download yearly shapefiles with a global extent, clip all of those shapefiles to their area of interest, and then combine them into one shapefile, just to get started. We suspect that the lack of accessible fire perimeter datasets that are easy to download and use contributes to a disparity in research, where fire ecology studies are conducted mostly in developed countries that have either research infrastructure capable of handling big data or longer-term government records, or temperate forested regions that have substantial tree-ring records18.There are two existing global perimeter products, the Global Fire Atlas (GFA) (Andela et al.14) and the Global Wildfire Information System (GWIS) (Artes et al.15). Both were created by applying spatiotemporal flooding algorithms to the MODIS MCD64 Burned Area Product. These algorithms assign burned pixels from the MCD64 products using a moving window whose size is defined by spatial and temporal parameters. They are created as monthly or yearly slices of the entire globe, and they can be subsetted. These products are extremely valuable for global scale studies. But when we look at how those products delineate known fire events we see a consistent problem in that they both seem to over-segment events in ways that appear unrealistic. This inconsistent event delineation is not problematic for coarse-scale or regional estimates of burned area or fire seasonality, but can lead to unrealistic estimates for number of fire events and event-level characteristics like fire size and spread rate. In Fig. 1 we illustrate this with an example of the 2013 Rim Fire in California, United States, which was unmistakably a single event that burned about 90,000 ha over the course of three months. Figure 2 illustrates how the day-to-day progression of the Rim Fire was a steady progression from a single ignition in late August. Table 1 shows how the differences in event delineation propagate to calculations of burned area and number of events. In the GFA, the Rim Fire is delineated as one large event of 804.5 km2, and 13 additional events totaling 88.7 km2. in GWIS it is delineated as one event of 878 km2 and 47 additional events totalling 20 km2. With FIRED, there is one event of 892 km2 and 2 single pixel events totalling less than one km2. One cause for potential differences is how one defines a “fire event”. Large fires often have multiple ignition sources. The Global Fire Atlas algorithm and others19, for example, search for local minima to identify various ignition locations that may begin as small patches, only to later form a large complex and in the end described with a single fire perimeter. The choice of outside sources for optimizing the spatial-temporal parameters, the method of optimization, and the intent of the final product’s meaning (defining events as single ignition patches vs contiguous burned area) all lead to different outcomes in the final events that are delineated. Another likely source of this discrepancy is that GWIS and GFA are calibrated to create a single global product. Because different geographical areas have different types of fire regimes, they have fires that grow at different rates and to different sizes, and occur in greater or fewer frequencies, and so the spatial and temporal parameters that work well for defining a fire event in one area may result in over- or under-segmentation in other areas. Here, we decided upon an approach of creating many regional products across the globe, rather than one product for everywhere on earth.Fig. 1Comparison of global fire event products performance for the 2013 Rim Fire (a). In the FIRED product (b), the Rim fire was classified as one very large event with two single pixel events. The Global Fire Atlas (GFA, c) and Global Wildfire Information System (GWIS, d) each delineated a very large event, with 13 and 47 smaller events, respectively.Full size imageFig. 2The two primary outputs FIREDpy provides are a daily- and event-level product. Panel a shows the default single event polygon. In b, each day has a separate polygon, with associated statistics generated, within each event. Panel c shows the daily perimeters derived from the airborne infrared by the incident management team for comparison.Full size imageTable 1 Rim fire comparison.Full size tableBesides the ease of access and use, the advantage of the FIRED product lies in the user’s ability to use the open-source software, FIREDpy, to tailor the spatial and temporal parameters of the moving window algorithm in order to realistically delineate events for their region of interest. In Fig. 3, we illustrate this by comparing the three products for a pair of small fires in Florida. In this case, the FIRED product that was created with a larger moving window (5 pixels and 11 days) over-aggregated the events, but it only required one line of code at command line to recreate the product with a smaller moving window (1 pixel and 5 days) to get more realistic results.Fig. 3Product comparison for two small events in Florida, the Moonshine Bay and Sour Orange fires (outlined) that both ignited in February of 2007 and were delineated by MTBS. In b the firedpy product that was optimized for the entire United States with a moving window of 5 pixels, 11 days resulted in aggregation of the two fires delineated by MTBS, but also several smaller fires nearby. In b, it was re-ran with a window of one pixel and five days, for a more realistic result. Delineations by the Global Fire Atlas (c) and the Global Wildfire Information System (d) are shown for comparison.Full size imageHere, we present a collection of regionally-tailored fire perimeter datasets for every country in the world with significant fire activity20, which we created with the open source algorithm, FIREDpy21. Each dataset is either a single country or a broader region, depending on the data volume. These datasets differ from other similar efforts14,15 in that each dataset created by FIREDpy is a single file containing a collection of polygons that is generated for the entire time series, rather than monthly or yearly aggregations with a global extent. Furthermore, we have generated the data products at a spatial extent land managers and ecologists would typically use to do regional-scale research, and we adjusted the spatial and temporal parameters for each country to yield realistic event delineations. We also made every effort to ensure that download sizes are reasonable (  More

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