Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

Abstract

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

Water and energy transfer modeling in a permafrost‐dominated, forested catchment of Central Siberia: The key role of rooting depth

“…Indeed, hydraulic conductivity of the frozen organic layer is very low while that of the saturated moss is very high, so the lateral drainage of snowmelt water through the thick moss layer is likely to be fast compared to the infiltration rate within the still frozen organic layer. However, the snowpack distribution and its rate of melting are controlled by complex phenomena that depend on, for example, land cover, insolation and snow depth, are of which all variable between NAS and SAS. Moreover, there are field observations of snowmelt waters that infiltrate the soil of some permafrost‐affected areas at the beginning of the spring flood, due for example to frost cracks .…”

“…However, the snowpack distribution and its rate of melting are controlled by complex phenomena that depend on, for example, land cover, insolation and snow depth, 68,69 are of which all variable between NAS and SAS. Moreover, there are field observations of snowmelt waters that infiltrate the soil of some permafrost-affected areas at the beginning of the spring flood, due for example to frost cracks.…”

The Utility of Infrequent Snow Depth Images for Deriving Continuous Space‐Time Estimates of Seasonal Snow Water Equivalent

“…Such approaches simultaneously account for uncertainty involved in the a priori modeling of SD, density, and SWE, while optimally extracting information from infrequent SD measurements, without the need for stand-alone density estimates from in situ measurements (e.g., Elder et al, 1991;Jonas et al, 2009) or deterministic off-line modeling (e.g., Hedrick et al, 2018;Painter et al, 2016) for transforming SD to SWE. The demonstration provided herein differs from previous work where assimilated SD has been either from in situ measurements (e.g., Charrois et al, 2016, Magnusson et al, 2017Stigter et al, 2017) or from SD estimates derived from microwave sensors (e.g., Andreadis & Lettenmaier, 2006), which are typically at coarse spatial resolution and prone to significant biases.…”

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

“…From MODIS and S2 spectral Top Of Atmosphere (TOA) radiance products, it is possible to retrieve the snowpack extent as a Snow Cover Fraction by pixel (SCF) and Bottom of Atmosphere (BOA) reflectances which requires to account for the complexity of the radiative transfer in mountainous area (Richter, 1998;Sirguey, 2009). Many studies focus on the assimilation of SCF, showing a strong impact of assimilation in hydrological models (De Lannoy et al, 2012;Thirel et al, 2013;Stigter et al, 2017;Aalstad et al, 2018;Baba et al, 2018). However, SCF is expected to be of less interest for detailed snowpack modelling in alpine terrain, because the information content is limited to the snow line (Andreadis and Lettenmaier, 2006;Toure et al, 2018).…”