Figure 8: PGRB spring and summer DA and CTRL albedos at the snow and glacier ice HRUs highlighted in Figure 1. The 2018 and 2021 years are shown to represent wildfire and heatwave conditions, respectively.
4. Discussions
4.1. Albedo DA During Wildfire and Heatwave Conditions
The results presented in the previous sections have demonstrated that albedo DA can improve streamflow simulations during wildfires but not during heatwaves. The streamflow improvement response to albedo DA in the soot-feeding algae year was only considerable in PGRB. These results reveal somewhat contrasting processes happening in different zones of these basins. Over glacier ice, DA decreased albedo considerably for AGRB due to wildfire soot deposition, a process that was expected and confirmed in previous studies (Aubry-Wake et al. , 2022a; Bertoncini et al. , 2022). In PGRB, the decrease in ice albedo due to DA was not as pronounced because of prolonged spring and summer snowcover over ice. SWE is another state that is updated proportionally to albedo. Because these states are the mean of 20 ensembles, the likelihood of all ensembles converging in the absence of a snowpack becomes lower when several ensembles present elevated SWE values. Figures 5 and 6 show that the SWE ensemble spread in PGRB was wider than in AGRB, contributing to a shorter period of exposed ice in PGRB. This mechanism could have been caused by deeper snowpacks observed in terminal sections of PGRB and more frequent spring and summer snowfall events. The effect of prolonged snowcover when compared to control simulations in snow DA has been reported before, usually leading to snow depletion simulations closer to observations (Smyth et al. , 2020; Alonso-González et al. , 2022). It is worth noting that once the snow is depleted and firn and ice are exposed, temperature-driven albedo decrease ceases. This mechanism should be captured by the albedo decay algorithm that uses constant albedo values for exposed firn and ice. The latter can potentially explain why streamflow predictions were not sensitive to albedo DA during the heatwave year.
Unlike ice, snow has a different response to albedo DA. Albedo DA has shown to be larger than modelled by CTRL in AGRB high-elevation snow-dominated regions. The introduction of remotely sensed albedo through DA has revealed that snow was not completely melted in the AGRB high-elevation HRU examples displayed in Figure 7, i.e., albedo did not reach the 0.55 firn value. The low CTRL snow albedos can be a limitation of albedo algorithms based on decay functions, such as the one used hereby, which were developed for seasonal snowpacks at much lower elevations. A comparison of three empirical models with a full physically based model (closer to observations) has shown that empirical decay albedo models, indeed, underestimate snow albedo (Gardner and Sharp, 2010). On the other hand, DA snow albedo is often below CTRL in PGRB, but rarely reaches the firn value of 0.55 (Figure 8). This result suggests that snow-dominated PGRB surfaces would have a lower DA albedo than CTRL, since they are more heterogeneous due to greater firn and ice exposure than in AGRB. CTRL seems to miss processes well described at lower elevations but not at glacier accumulation zones in both basins. This finding calls for a better representation of glaciological albedo processes capable of accounting for the peculiarities of localized effects (Marshall and Miller, 2020).
This study tested two main assumptions by introducing remotely sensed albedo in a cold regions DA framework. First, soot deposition would decrease snow and ice albedo during extreme wildfire activity, such as in 2018. This assumption was confirmed by previous studies (Aubry-Wakeet al. , 2022a; Bertoncini et al. , 2022) and hereby for ice in AGRB and snow in PGRB. A greater albedo in the AGRB snow HRU example can be explained by the larger elevation range observed between snow and ice regions in AGRB (Pradhananga and Pomeroy, 2022). This larger elevation range creates a scenario in which snow-covered plateaux are at elevations more prone to new snowfall, suppressing the effect of soot deposition over snow at a faster pace. Rapid recovery from soot deposition over high-elevation snow in AGRB has been previously described in Bertoncini et al. (2022). Second, heatwaves would accelerate melt and expose ice or firn unseasonally. This process does not seem to substantially affect AGRB ice since exposure in 2021 is similar to other years. However, albedo is lower for snow during heatwaves when compared to wildfire years. Substantial snowmelt and decreased albedo in glacier snow due to extended periods of above-average temperature have been reported before in Greenland (Boxet al. , 2022) and the Austrian Alps (Koboltschnig et al. , 2009). This mechanism suggests that heatwaves have a greater influence on high-elevation snow-covered plateaux albedo decreases than does wildfire soot in AGRB. On the other hand, there is a larger sensitivity to wildfires than heatwaves for PGRB snow, perhaps because of more firn and ice exposure than in AGRB. PGRB ice shows greater albedo decrease during heatwaves due to longer ice exposure. In addition, the albedo algorithm is expected to be more robust during heatwaves than wildfires because it considers daily mean temperatures, limiting most albedo DA benefits to wildfire years.
4.2. Implications for Streamflow Prediction in a Changing Climate
Unprecedented wildfires and heatwaves are expected to increase with climate change (Jolly et al. , 2015; Kirchmeier-Young et al. , 2019; Al-Yaari et al. , 2023; Parisien et al. , 2023) and, hence, are likely to affect glacier contribution to streamflow. Although this is a somewhat intuitive assumption, this study has demonstrated inter- and intra-basin peculiarities in how the effects of wildfires and heatwaves will contribute to snow and ice melt. The findings show that wildfires and heatwaves can decrease glacier ice albedo, but the period that ice is covered by snow is the primary governing factor controlling ice melt. Likewise, the amount of summer precipitation falling as snowfall is another factor governing albedo dynamics in snow-dominated regions. This mechanism creates a scenario where the elevation difference from the terminal glacier to high-elevation snow plateaus dictates whether these basins would be affected by either wildfires or heatwaves. For instance, in AGRB where this elevation difference was higher than in PGRB, DA generated an increase in snow albedo. This finding alone reveals something that would not be possible using modelling alone; remotely sensed albedos were needed in a DA modelling framework to understand this process in a virtually inaccessible region. There lies the power of observational tools in aiding hydrological models beyond the conditions in which they were developed.
There are many implications of remotely sensed albedo DA for streamflow predictions under climate change. The most important one is that this study showed that even though DA was not beneficial for streamflow prediction during heatwaves, it was beneficial in the overall four-year evaluation in PGRB and favourable during wildfires and similar to CTRL in other years in AGRB. From this perspective, remotely sensed albedo DA is recommended under unprecedented hydrological extremes imposed by climate change; however, caution should be taken when interpreting albedo DA results during heatwaves. The latter calls for further investigation into other processes that might have contributed to albedo DA degradation of streamflow predictions under heatwaves beyond what has been discussed here. Remotely sensed albedo DA can also better inform hydrological modelling during wildfires and heatwaves. For instance, events of decreased albedo can happen in the future in high-elevation snow-dominated regions but be buffered subsequently by fresh snowfall, which can only be confirmed with certainty via remotely sensed albedos since precipitation measurements are usually taken at much lower rainfall-dominated elevations. Users of hydrological predictions can then understand de facto whether these extreme events will affect downstream streamflows.
4.3. Uncertainty within DA Modelling Framework
The DA modelling framework has worked satisfactorily in most simulated years; however, a few factors contributed to the uncertainty in modelling streamflow and other model states. First, although snow and ice remotely sensed albedo estimates were satisfactory here and elsewhere (Wang et al. , 2016; Li et al. , 2018; Bertonciniet al. , 2022), there is still a 5% uncertainty (\(\sigma_{o}\) = 0.046) in albedo estimation. This 5% uncertainty cannot be neglected in glacier ice albedo values. Therefore, even if modelling uncertainty is larger than 5%, this value would be, on average, the smallest uncertainty possible of an optimal albedo estimate for a particular assimilation date. Second, assimilation frequency can contribute to the influence of DA in modelling albedo and other states. AGRB (68 estimates) had more than double the number of assimilation dates of PGRB (33 estimates). The lower number of assimilation dates in PGRB could have contributed to longer periods of snowcover over ice, since albedo correction from a new remotely sensed update would take longer to occur. Finally, uncertainty in hydrological modelling can also affect streamflow prediction within a DA framework. This study presented DA NSE values of 0.74 and 0.78 for AGRB and PGRB, respectively. These NSE values are higher than the mean of maximum values (0.64) found in 20 studies that conducted uncalibrated streamflow predictions using the CRHM model (Pomeroy et al. , 2022). Nonetheless, these NSE values are not perfect, i.e., equal to 1, representing that there are still uncertainties in streamflow prediction that can be attributed to model structure, forcing errors, parameter errors, algorithm deficiencies, and uncertainty caused by the DA EnKF implementation.
5. Conclusions
This research implemented and tested a remotely sensed albedo DA framework to predict streamflow in two highly glacierized Canadian Rockies’s basins during environmental conditions ranging from normal, wildfire, and heatwave dominated. Glacier ice remotely sensed albedos presented satisfactory evaluation results (r = 0.96, bias = 0.026, RMSE = 0.060, and \(\sigma_{o}\) = 0.046) that were needed for assimilation. Albedo DA improved streamflow predictions in the heavily wildfire-impacted year of 2018 for both basins – a KGE improvement of 0.18 and 0.20 for AGRB and PGRB, respectively. DA in PGRB was beneficial for all years but 2021. In the soot-feeding algae year, streamflow improvement due to albedo DA was only considerable in PGRB. DA substantially enhanced overall four-year streamflow prediction in PGRB but just slightly in AGRB. DA’s streamflow prediction improvements were caused by a balance between changes in albedo of high-elevation snow and glacier ice. In AGRB, snow albedo was increased by DA due to frequent summer snowfall events that buffered the streamflow generated from decreased glacier ice albedo in lower elevations. In PGRB, snow albedo was decreased by DA, especially during wildfires. However, glacier ice DA albedo was only decreased during short periods of ice exposure caused by a prolonged spring and summer snowpack. The latter mechanism results from several ensembles generating elevated SWE values during spring and summer in PGRB glacier ice. These findings reveal that wildfires and heatwaves are capable of decreasing glacier ice albedo, but the resultant melt contribution to streamflow within a DA framework will depend on snowpack albedo and SWE dynamics.
The cloud-computing remotely sensed snow and ice albedo retrieval framework developed in this study could generate results with comparable accuracy to previous studies, while providing global reproducibility at high spatial and temporal resolutions. Before this study, albedo in snow-dominated glacier accumulation zones was based solely on albedo modelling developed at relatively lower elevations. This albedo representation could not account for the rapid recovery of albedo with fresh snowfall during wildfire and heatwave seasons. This finding was only possible utilizing high-resolution remotely sensed albedo estimates that could reach virtually inaccessible regions. The assimilation of these remotely sensed albedo estimates into the physically based CRHM model improved streamflow predictions for most of the analyzed years. Moreover, using albedo DA revealed contrasting processes happening in poorly observed glacier zones that resulted in different streamflow responses to wildfires and heatwaves. Considering that the environmental conditions observed during the study are expected to increase in a future of climate change, it can be advantageous to use remotely sensed high-resolution snow and ice albedo DA continuously for better streamflow predictions in glacierized basins during wildfires and heatwaves. This study’s findings also indicate that the response of glacierized basin streamflow to wildfires and heatwaves is not always as expected due to the interplay of different factors such as fresh snowfall, soot deposition, and unseasonal melt with the albedo algorithm. Using observational tools such as DA can help narrow water managers’ uncertainty when making decisions based on hydrological predictions under a warmer and more wildfire prone future.
Acknowledgements
We wish to thank NASA for the MODIS data and the European Space Agency (ESA) for the Sentinel-2 and ERA5-Land data used to estimate remotely sensed high-resolution albedos. We also acknowledge the support of the GEE platform for hosting the above data and providing cloud-computing resources, and the developers of the 6S atmospheric correction model and its Python implementation (Py6S). The help from Xing Fang to QC meteorological forcings and to set up the CRHM model is much appreciated. We thank Caroline Aubry-Wake and Dhiraj Pradhananga for the discussions about glacio-hydrological modelling and the many Centre for Hydrology field technicians who contributed to maintaining the AWS. We also would like to acknowledge the permission of Parks Canada for allowing our research to take place in the Banff and Jasper National Parks and Pursuit for assistance in logistics on Athabasca Glacier.