Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human-and lightning-caused fires during the period 1961-2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009-2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact.Keywords: human-caused static fire danger map; lightning-caused static fire danger map; normalized difference vegetation index; normalized difference water index; precipitable water; surface temperature
The Elbow River watershed in Alberta covers an area of 1,238 km(2) and represents an important source of water for irrigation and municipal use. In addition to being located within the driest area of southern Canada, it is also subjected to considerable pressure for land development due to the rapid population growth in the City of Calgary. In this study, a comprehensive modeling system was developed to investigate the impact of past and future land-use changes on hydrological processes considering the complex surface-groundwater interactions existing in the watershed. Specifically, a spatially explicit land-use change model was coupled with MIKE SHE/MIKE 11, a distributed physically based catchment and channel flow model. Following a rigorous sensitivity analysis along with the calibration and validation of these models, four land-use change scenarios were simulated from 2010 to 2031: business as usual (BAU), new development concentrated within the Rocky View County (RV-LUC) and in Bragg Creek (BC-LUC), respectively, and development based on projected population growth (P-LUC). The simulation results reveal that the rapid urbanization and deforestation create an increase in overland flow, and a decrease in evapotranspiration (ET), baseflow, and infiltration mainly in the east sub-catchment of the watershed. The land-use scenarios affect the hydrology of the watershed differently. This study is the most comprehensive investigation of its nature done so far in the Elbow River watershed. The results obtained are in accordance with similar studies conducted in Canadian contexts. The proposed modeling system represents a unique and flexible framework for investigating a variety of water related sustainability issues.
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