Figure 3: Remotely sensed snow and ice albedos for both basins during mid-summer conditions. Light grey corresponds to masked areas due to snow- and ice-free pixels, obstruction by clouds and shadows, or if the BRDF retrieval was not possible for that landcover class.
3.2. Albedo DA Streamflow Evaluation
DA and CTRL streamflow evaluation metrics had accurate results for most analyzed years. In the heavily wildfire-impacted year of 2018, DA outperformed CTRL for both basins. The KGE difference between DA and CTRL was 0.18 and 0.20 for AGRB and PGRB, respectively. In 2019 (soot algae growth) and 2020 (normal year), DA was only beneficial for PGRB. In 2021, the year affected by heatwaves and a few light late summer wildfires, DA did not improve streamflow predictions for either basins, indicating that other mechanisms might have influenced streamflow predictions during heatwave conditions or that the operation of the albedo algorithm in CRHM could not be improved upon by assimilating observations. The four-year overall evaluation revealed that albedo DA substantially benefited streamflow predictions in PGRB (KGE improvement of 0.12), but only a slight advantage was found for streamflow predictions in AGRB (KGE improvement of 0.02) (Table 2). The four-year overall evaluation NSEs for AGRB (0.74) and PGRB (0.78) were above the mean of maximum values (0.64) found in 20 studies that predicted streamflow with the CRHM model (Pomeroy et al. , 2022).
Table 2: Streamflow evaluation metrics for AGRB and PGRB. Metrics were calculated for the combined four melt seasons and each melt season individually (May 1 to Sept. 30), since streamflow rarely occurs outside that period.