Kristoffer Aalstad

• Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2020). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment.  ISSN 0034-4257.  239 . doi: 10.1016/j.rse.2019.111618 Full text in Research Archive. Show summary

  The seasonal snow-cover is one of the most rapidly varying natural surface features on Earth. It strongly modulates the terrestrial water, energy, and carbon balance. Fractional snow-covered area (fSCA) is an essential snow variable that can be retrieved from multispectral satellite imagery. In this study, we evaluate fSCA retrievals from multiple sensors that are currently in polar orbit: the operational land imager (OLI) on-board Landsat 8, the multispectral instrument (MSI) on-board the Sentinel-2 satellites, and the moderate resolution imaging spectroradiometer (MODIS) on-board Terra and Aqua. We consider several retrieval algorithms that fall into three classes: thresholding of the normalized difference snow index (NDSI), regression on the NDSI, and spectral unmixing. We conduct the evaluation at a high-Arctic site in Svalbard, Norway, by comparing satellite retrieved fSCA to coincident high-resolution snow-cover maps obtained from a terrestrial automatic camera system. For the lower resolution MODIS retrievals, the regression-based retrievals outperformed the unmixing-based retrievals for all metrics but the bias. For the higher resolution sensors (OLI and MSI), retrievals based on NDSI thresholding overestimated the fSCA due to the mixed pixel problem whereas spectral unmixing retrievals provided the most reliable estimates across the board. We therefore encourage the operationalization of spectral unmixing retrievals of fSCA from both OLI and MSI.

• Fiddes, Joel; Aalstad, Kristoffer & Westermann, Sebastian (2019). Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering. Hydrology and Earth System Sciences.  ISSN 1027-5606.  23(11), s 4717- 4736 . doi: 10.5194/hess-23-4717-2019 Full text in Research Archive. Show summary

  Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1–2 orders of magnitude of computational cost due to ensemble simulations, we save 4–5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions.

• Yilmaz, Yeliz; Aalstad, Kristoffer & Sen, Omer L. (2019). Multiple remotely sensed lines of evidence for a depleting seasonal snowpack in the near east. Remote Sensing.  ISSN 2072-4292.  11(5) . doi: 10.3390/rs11050483 Full text in Research Archive. Show summary

  The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set to understand the relationship between the terrestrial water storage anomalies and the mountain snowpack. The results from different satellite retrievals show a clear signal of a depletion of both water storage and the seasonal snowpack in four basins in the region. We find a strong reduction in terrestrial water storage over the Gravity Recovery and Climate Experiment (GRACE) observational period, particularly over the higher elevations. Snow-cover duration estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) products point towards negative and significant trends up to one month per decade in the current era. These numbers are a clear indicator of the partial disappearance of the seasonal snow-cover in the region which has been projected to occur by the end of the century. The spatial patterns of changes in the snow-cover duration are positively correlated with both GRACE terrestrial water storage decline and peak snow water equivalent (SWE) depletion from the ERA5 reanalysis. Possible drivers of the snowpack depletion are a significant reduction in the snowfall ratio and an earlier snowmelt. A continued depletion of the montane snowpack in the Near East paints a bleak picture for future water availability in this water-stressed region.

• Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere.  ISSN 1994-0416.  12(1), s 247- 270 . doi: 10.5194/tc-12-247-2018 Full text in Research Archive. Show summary

  With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain en- vironments. However, estimating the snow water equiva- lent (SWE) is challenging in remote-sensing applications al- ready at medium spatial resolutions of 1km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1km scale by assimilating fractional snow-covered area (fSCA) satel- lite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be log- normally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previ- ous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimi- lation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Reso- lution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79◦ N, Svalbard, Norway) where field measure- ments of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20%, re- spectively, when compared to the prior, yielding RMSEs of 0.01, 0.09m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

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• Aalstad, Kristoffer & Westermann, Sebastian (2020). ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway). Show summary

  The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestiral automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway.

• Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel; Karsten, Logan; McCreight, James & Bertino, Laurent (2020). Testing ensemble-based snow reanalysis in the Lakes basin using the ASO.
• Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel; McCreight, James & Bertino, Laurent (2020). Retrieving, validating, and assimilating fractional snow-covered area from emerging optical satellites for snow reanalysis. Show summary

  Accurately estimating the snow water equivalent (SWE) that is stored in the worlds mountains remains a challenging and important unsolved problem. The SWE reconstruction approach, where the remotely sensed seasonal depletion of fractional snow-covered area (fSCA) is used with a snow model to build up the snowpack in reverse, has been used for decades to help tackle this problem retrospectively. Despite some success, this deterministic approach ignores uncertainties in the snow model, the meteorological forcing, and the remotely sensed fSCA. A trade-off has also existed between the desired temporal and spatial resolution of the satellite-retrieved fSCA depletion. Recently, ensemble-based data assimilation techniques that can account for the uncertainties inherent in the reconstruction exercise have allowed for probabilistic snow reanalyses. In addition, new higher resolution optical satellite constellations such as Sentinel-2 and the PlanetScope cubesats have been launched into polar orbit, potentially eliminating the aforementioned trade-off. We combine these two developments, namely ensemble-based data assimilation and the emerging remotely sensed data streams, to see if snow reanalyses can be improved at the hillslope (100 m) scale in complex terrain. As a first step, we develop accurate high-resolution binary snow-cover maps using a terrestrial automatic camera system installed on a mountaintop near Ny-Ålesund (Svalbard, Norway). These maps are used to validate fSCA retrieved from various satellite sensors (MODIS, Sentinel-2 MSI, and Landsat 8 OLI) using algorithms ranging from simple thresholding of the normalized difference snow index to spectral unmixing. Through the validation, we demonstrate that the spectral unmixing technique can obtain unbiased fSCA retrievals at the hillslope scale. Next, we move to the Mammoth Lakes basin in the Californian Sierra Nevada, USA, where we have access to independent validation data retrieved from several Airborne Snow Observatory (ASO) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flights. Using these airborne retrievals as a reference, we show that fSCA can be retrieved at the hillslope scale with reasonable accuracy at an unprecedented near daily revisit period using a combination of the Landsat, Sentinel-2 MSI, RapidEye, and PlanetScope satellite constellations. In a series of data assimilation experiments we show how the combination of these constellations can lead to significant improvements in hillslope scale snow reanalyses as gauged by various evaluation metrics. Furthermore, it is suggested that an iterative ensemble smoother data assimilation scheme can provide more robust SWE estimates than other smoothers that have previously been proposed for snow reanalysis. We briefly conclude with thoughts as to the current impediments to conducting a global hillslope scale snow reanalysis and propose avenues for further research, such as how snow reanalyses can help in the prediction exercise.

• Aalstad, Kristoffer; Westermann, Sebastian; Pirk, Norbert; Fiddes, Joel; Karsten, Logan; McCreight, James; Alonso-Gonzalez, Esteban & Bertino, Laurent (2020). Constraining land surface models with emerging observations.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M. (2020). Fennoscandian snow cover phenology from MODIS, CLM5, and climate reanalyses.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M. (2020). Scandinavian snow cover phenology from MODIS, CLM, and reanalyses.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M. (2020). The Representation of the Fennoscandian Snow Cover Phenology in Reanalyses and CLM5 during the MODIS-era. Show summary

  Snow plays an important role in cold regions through its effect on the terrestrial exchange of energy, water, and carbon. Accurately simulating snow processes is therefore important in capturing various climate feedbacks in Earth system models. The representation of the subgrid heterogeneity of snow properties (e.g. coverage, depth, density, albedo) are, in addition to accumulation and snowmelt, major sources of uncertainty in the snow modules of land surface schemes. Using multiple data sources is essential to address these uncertainties and to evaluate overall model performance. Unlike in-situ observations, satellite remote sensing products provide unique representative information at the scale of Earth system models. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua and Terra satellites provide a continuous long-term climate record for the last two decades. In this study, two daily snow cover data sets from MODIS (MOD10A1 and MYD10A1) were used to retrieve fractional snow-covered area (fSCA) and several snow cover metrics (e.g. snow cover duration, first and last day of snow) over Fennoscandia for the 2001-2020 water years. We use these retrievals to evaluate the fSCA outputs from multiple reanalyses (ERA5-Land, ERA5, and MERRA-2) and the latest version of the Community Land Model (CLM5) which is the land component of the Community Earth System Model (CESM) and the Norwegian Earth System Model (NorESM). In order to test the accuracy of the MODIS data, we employed Sentinel-2 and Landsat 8 satellite retrievals as well as local‐scale measurements around the Finse Eco-Hydrological Observatory (Finse EcHO), a low-alpine site in central Norway. Lastly, we compared the trends in snow cover metrics with terrestrial water storage anomalies obtained from the Gravity Recovery and Climate Experiment (GRACE) to better understand the regional water cycle dynamics over this region. This study provides a useful starting point for integrating Earth observations into Earth system modeling in cold regions to help identify and constrain sources of uncertainty. Acknowledgement : This study is conducted under the LATICE strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo, and the EMERALD (project #294948) funded by the Research Council of Norway.

• Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2019). Ensemble-based retrospective analysis of the seasonal snowpack. Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo.. 2186. Show summary

  This thesis presents a satellite-based modeling framework that can estimate how much snow was stored in the terrain. These estimates can help guide climate analysis and prediction. Accurate quantification of Earth’s snow mass is a long-standing problem to which a solution is direly needed with ongoing climate change. Snow plays an essential role in the climate system and snowmelt is a vital source of freshwater for a quarter of the world’s population. The framework combines satellite imagery and historic weather data to remotely estimate snow mass by leveraging enhanced ensemble-based data assimilation algorithms. The result is a retrospective analysis (reanalysis) of the snow mass that can be obtained for any location on Earth. So far, this framework has been successfully implemented in three different environments: Svalbard, the Californian Sierra Nevada, and the Swiss Alps. In the future, snow reanalyses could be used to train algorithms to predict snow mass in near real time. They may also help validate and subsequently improve climate models. Ultimately this would allow us to make even more informed future projections of the possible fate of the environment that sustains us.

• Aalstad, Kristoffer; Westermann, Sebastian; Fiddes, Joel & Bertino, Laurent (2019). Retrieving the depletion of snow-covered area from multiple optical satellite sensors with applications for SWE reanalysis.
• Aalstad, Kristoffer; Westermann, Sebastian; Bertino, Laurent; Schuler, Thomas; Boike, Julia & Karsten, Logan (2018). Towards high-resolution Bayesian snow reconstruction in permafrost regions.
• Aalstad, Kristoffer; Westermann, Sebastian; Karsten, Logan; Fiddes, Joel & Bertino, Laurent (2018). Snow history matching in mountainous terrain.
• Aalstad, Kristoffer; Westermann, Sebastian; Karsten, Logan; Gutmann, Ethan; McCreight, James; Fiddes, Joel & Bertino, Laurent (2018). Ensemble-based reanalysis of the seasonal montane snowpack: Lessons from the ASO.
• Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2017). Ensemble-based subgrid snow data assimilation.
• Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2017). Towards High-Resolution SWE Mapping in Permafrost Regions.
• Aalstad, Kristoffer; Westermann, Sebastian; Schuler, Thomas; Boike, Julia & Bertino, Laurent (2017). Towards High-Resolution SWE Mapping in Permafrost Regions.
• Christakos, Konstantinos; Varlas, George; Cheliotis, Ioannis; Aalstad, Kristoffer; Papadopoulos, Anastasios; Katsafados, Petros & Steeneveld, Gert Jan (2017). Quantitative variability of renewable energy resources in Norway.
• Leclercq, Paul Willem; Aalstad, Kristoffer; Altena, Bas & Elvehøy, Hallgeir (2017). Modelling of glacier surface mass balance with assimilation of glacier mass balance and snow cover observations from remote sensing.
• Leclercq, Paul Willem; Aalstad, Kristoffer; Elvehøy, Hallgeir & Altena, Bas (2016). Assimilation of glacier mass balance and snow cover fraction observations in a glacier surface mass balance model.
• Aalstad, Kristoffer; Bertino, Laurent & Westermann, Sebastian (2016). An ensemble-based subgrid snow data assimilation framework.
• Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2016). An ensemble-based snow data assimilation framework.
• Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2016). An ensemble-based subgrid snow data assimilation framework.
• Aalstad, Kristoffer; Westermann, Sebastian & Bertino, Laurent (2016). An ensemble-based subgrid snow data assimilation framework applied to the southern Swiss alps.
• Aalstad, Kristoffer; Westermann, Sebastian; Boike, Julia; Bertino, Laurent & Aas, Kjetil Schanke (2016). An ensemble-based snow data assimilation framework with applications to permafrost modeling.
• Leclercq, Paul Willem; Aalstad, Kristoffer; Elvehøy, Hallgeir & Altena, Bas (2016). Assimilation of glacier mass balance and snow cover fraction observations in a glacier surface mass balance model.
• Schuler, Thomas; Aalstad, Kristoffer; Aas, Kjetil Schanke; Burkhart, John; Dunse, Thorben; Filhol, Simon; Hulth, John; Østby, Torbjørn Ims & Westermann, Sebastian (2016). Towards real-time snow products for Svalbard.
• Westermann, Sebastian; Langer, Moritz; Østby, Torbjørn Ims; Peter, Maria; Boike, Julia; Gisnås, Kjersti; Aalstad, Kristoffer; Schuler, Thomas; Etzelmüller, Bernd; Jaroslav, Obu; Georg, Schwamborn & Lutz, Schirrmeister (2016). Mapping the thermal state of permafrost through modeling and remote sensing.
• Aalstad, Kristoffer & Berntsen, Terje Koren (2015). Applying the Eddy Covariance Method Under Difficult Conditions. Show summary

  We assess how reliable the Eddy-Covariance (EC) method is in estimating surface fluxes under the difficult conditions that occur in the high Arctic. Emphasis is placed on stable stratification and the breakdown of EC assumptions that may occur in such a regime. To investigate these difficulties we developed an EC processing module from scratch, providing an extensive and transparent overview of the EC method. Raw data was obtained from an open path EC system located in the Bayelva catchment near Ny Ålesund (79 ◦ N), Svalbard, Norway. Our flux estimates are in reasonable agreement with those found from the standardized EC package TK2. Strong relative non-stationarity represents the greatest hindrance to data quality at Bayelva, occurring for 11% of the data period. Overall, average relative flux uncertainties were found to be 20% for both the sensible (SH) and latent heat (LH) flux. Under stable stratification these uncertainties were considerably higher, 27% on average. Through Ogive classification we found that the traditional 30 minute SH and LH fluxes converged (resolved the turbulent cospectrum) 70% of the time. Here too the stable regime stands out, with low convergence fractions of 41% and 48% for LH and SH, respectively. To our knowledge it is the first time such an analysis has been carried out in the Arctic. Concluding, while usually successful for neutral and unstable conditions, the traditional 30 minute flux averaging period is, more often than not, poorly suited for the stable regime. We attribute this to the observed and predicted shift in cospectral peaks towards lower periods under stable stratification, along with an erosion of the cospectral gap. An apparently simple fix of reducing the averaging period is not generally a valid solution. The required reduction could introduce unacceptable levels of flux uncertainty.

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