Spatiotemporal estimation of snow depth using point data from snow stakes, digital terrain models, and satellite data

Abstract

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical-based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis.Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross-validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

“…In-situ data is limited to lower and accessible altitudes and may not truly represent the snow and glacier changes [4] at higher altitudes like those of UIB. Remote sensing data and various modeling approaches such as physical-based models [13], cellular automata (CA) models [16,17], and artificial neural Water 2019, 11,761 3 of 19 networks [18,19] were used previously to analyze the snow cover in different regions of the world. Remote sensing data offers the quantitative examination of physical properties of snow and glaciers in remote areas where accessibility of data is expensive and dangerous [20].…”

Optimal design of snow stake networks to estimate snow depth in an alpine mountain range

“…Nevertheless, the proposed methodology can be applied to any case study to obtain an optimal snow depth monitoring network, being easy to adapt the cited code to make it applicable to other case studies. The methodology is divided into two main activities: definition of an RK model to estimate snow thickness, which was developed by Collados‐Lara et al (; summarized in Section 2.1), and optimization procedure, which is the main objective of this paper (Section 2.2).…”

“…Snowpack distribution is also traditionally approached by using in situ data and applying interpolation procedures or hydrological models that include estimates of snow processes (e.g., accumulation and fusion; Collados‐Lara, Pardo‐Iguzquiza, & Pulido‐Velazquez, ; Zeinivand & De Smedt, ). SWE is normally measured using snow pillows based on the hydrostatic pressure created by overlying snow (e.g., SNOTEL system, Fassnacht, Dressler, & Bales, ; Fassnacht, Venable, McGrath, & Patterson, ).…”

A Preliminary Assessment of the “Undercatching” and the Precipitation Pattern in an Alpine Basin

“…Nevertheless, we should also try to perform the best possible estimations in these cases with limited data. In the literature, we may find many research works focused on the best possible assessment of case studies with limited information [34].…”

“…In this study, we consider only the hydrological processes related to snow in a distributed way and the rest of the processes in a lumped way. With regard to snow modeling, there are different approaches in the scientific literature: interpolation methods (e.g., References [34,41,42]), evolutionary algorithms (e.g., References [43,44]), and conceptual (e.g., HBV [45]; SRM [46,47]) or more physically based models (e.g., CROCUS [48]).…”