Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California’s population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.

The severity and frequency of wildfires have risen dramatically in recent years, drawing attention to the term ‘wildland-urban interface’ (WUI), the region where man-made constructions meet flammable vegetation. Herein, we mapped a finer-scale, novel linear WUI for California (CA) based on the intersection of boundaries of wildland vegetation and building footprint. The direct intersection is referred to as a direct WUI, whereas the intersection at 100-m is known as an indirect WUI. More fires were ignited closer to direct WUI than indirect WUI due to their proximity to communities. However, the overlap of past fire perimeters with indirect WUI is greater than that with direct WUI which shows that more areas were burned in the indirect WUI due to embers transported by strong wind gusts during large wildfires. The study’s findings will help land managers and policymakers in controlling fire dangers, land-use planning, and reducing threats to fire-prone communities.

The Nile river basin is one of the global hotspots vulnerable to climate change impacts due to fast growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from the 6 Coupled Model Intercomparison Project (CMIP6) benchmarked against a high spatial resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR). Next, a Bayesian Model Averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is under-dispersive and a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change varies considerably across the Nile basin. Specifically, it is found that projected change in the two headwaters basins, namely Blue Nile and upper White Nile is 0.03% and -1.65% respectively; both statistically insignificant at 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

Past studies reported a drastic growth in the wildland-urban interfaces (WUI), the locations where man-made structures meet or overlap wildland vegetation. There is a perception that damages due to wildfires are mainly located at the WUI. However, there is no clear evidence that wildfire intensity and frequency are highest in these regions. In this work, we have reported the actual occurrences of wildfires with respect to WUI and how much of the WUI are on complex topography in California (CA), the state with the highest burned area and risk of wildfires. We calculated the overlap of the burned area from previous wildfire events in California in the last ten years with the WUI perimeters. Two currently existing WUI definitions are used for this purpose. Furthermore, we also calculated the number of fire ignition points that lie within the WUI perimeters. We found that a very small percentage of wildfire ignitions actually occurred in the WUI areas. Moreover, the overlap between the wildfire burned area and WUI areas was also found to be small. To find out if the wildfires burned in the vicinity of WUI areas, we created buffers around both the WUI areas and the wildfire perimeters separately and computed the impact of buffer distance on the overlap. This behavior has been connected to the importance of firebrand ignition from spot fires in the WUI. Moreover, a majority of WUI areas in CA was found to be situated on complex topography. Therefore, we conclude that in CA, wildfires are not limited to WUI regions only, but their main fire fronts burn farther away from the WUI and are mostly located on complex topography, where controlling large wildfires is more difficult and fire behavior is more complex. Results from this study will give direction for remapping the existing WUI definitions, will be helpful for wildfire management and will benefit policymakers and land managers at the state and local level to focus on the factors that determine the high-risk prone areas for future wildfires.