On the Ability of LIDAR Snow Depth Measurements to Determine or Evaluate the HRU Discretization in a Land Surface Model

Abstract

To find the adequate spatial model discretization scheme, which balances the models capabilities and the demand for representing key features in reality, is a challenging task. It becomes even more challenging in high alpine catchments, where the variability of topography and meteorology over short distances strongly influences the distribution of snow cover, the dominant component in the alpine water cycle. For the high alpine Research Catchment Zugspitze (RCZ) a new method for objective delineation of hydrological response units (HRUs) using a time series of high resolution LIDAR derived snow depth maps and the physiographic properties of the RCZ is introduced. Via principle component analysis (PCA) of these maps, a dominant snow depth pattern, that turned out to be largely defined during the (winter) accumulation period was identified. This dominant pattern serves as a reference for HRU delineations on the basis of cluster analyses of the catchment’s physiographic properties. The method guarantees for an appropriate, objective, spatial discretization scheme, which allows for a reliable and meaningful reproduction of snow cover variability with the Cold Regions Hydrological Model — at the same time avoiding significant increase of computational demands. Different HRU schemes were evaluated with measured snow depth and the comparison of their model results identified significant differences in model output and best performance of the scheme which best represents measured snow depth distribution.

Reply to RC3

“…Since 2014, a snow scale was installed at the LWD station, which enabled us to use these measured SWE for more recent years as a good possibility to investigate the precipitation under catch. Similar to the literature reported under catch of snow precipitation of up to 50% (WMO, 2011; Grossi et al, 2017), we also found out in our former study (Weber et al, 2020) that we can expect an under catch of 50% for the RCZ. Assuming that under catch variations in precipitation over the entire catchment are rather negligible, the DWD snow precipitation has been corrected with the factor derived at the LWD station.…”

“…ever, as we found out in our former study for the RCZ (Weber et al, 2020) and which is also known from other studies (e.g. Grünewald et al 2013), the spatial distribution in various years and also during the most of the season does not change much (except at the very beginning and the very end of the season).…”

“…Grünewald et al 2013), the spatial distribution in various years and also during the most of the season does not change much (except at the very beginning and the very end of the season). In the study of Weber et al (2020), we were able to include information of several LiDAR snow depth data sets derived in the years 2015 and 2016 which were conducted for the RCZ to delineate HRUs that guarantee for optimal representation of the snow cover distribution in RCZ. This HRU delineation was, as mentioned in the manuscript, also used for this study.…”

Technical note: Introduction of a superconducting gravimeter as novel hydrological sensor for the Alpine research catchment Zugspitze

“…Detailed water balance and karst water discharge studies at the Partnach spring investigated runoff responses to rainfall and snowmelt dynamics and characterised the karst groundwater aquifer (Hürkamp et al, 2019;Morche and Schmidt, 2012;Rappl et al, 2010;Wetzel, 2004). Numerous snow-hydrological studies investigated the spatiotemporal dynamics of snow cover and the snow water equivalent (SWE; Bernhardt et al, 2018), combining monitoring techniques, such as terrestrial photogrammetry (Härer et al, 2013(Härer et al, , 2016, remote sensing (Härer et al, 2018) or lidar observations (Weber et al, 2016(Weber et al, , 2020(Weber et al, , 2021 with different complex snow-hydrological modelling. Some limitations arising from these studies were the small number of cloudfree remote sensing scenes in the visible and near-infrared spectrum to derive spatially distributed snow cover maps at high temporal resolutions and the limited spatial extent of the terrestrial photogrammetry and lidar observations in the RCZ (Härer et al, 2016;Weber et al, 2016Weber et al, , 2020.…”

“…Both photogrammetry and lidar observation techniques are only capable of measuring snow heights, but not hydrologically relevant SWE values, directly and thus rely on additional snow density data from local snow pit or snow weight measurements. While snow cover and snow height data are able to condition the snow-hydrological model behaviour to some degree (Weber et al, 2021), it was recently shown that integral data from satellite (Bahrami et al, 2020) and terrestrial gravimetry (Güntner et al, 2017) providing a footprintaveraged time series of the terrestrial water storage anomaly (TWSA) can greatly improve the identification of water balance components and relevant hydrological processes on catchment scale.…”