MeteoIO 2.4.2: a preprocessing library for meteorological data

Bavay, Mathias; Egger, Thomas

doi:10.5194/gmd-7-3135-2014

Cited by 111 publications

(130 citation statements)

References 44 publications

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“…in the town of Davos about 2 km away and has high-quality meteorological observations. The local effect of terrain shadowing to incoming short-wave radiation was corrected by applying a shade-filter available in the preprocessing library for meteorological data, MeteoIO (Bavay and Egger, 2014). This filter transfers measured radiation of a measurement station to another site taking into account local shading.…”

Section: Study Sites and Snow Depositsmentioning

confidence: 99%

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Snow farming: conserving snow over the summer season

2018

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Abstract. Summer storage of snow for tourism has seen an increasing interest in the last years. Covering large snow piles with materials such as sawdust enables more than two-thirds of the initial snow volume to be conserved. We present detailed mass balance measurements of two sawdust-covered snow piles obtained by terrestrial laser scanning during summer 2015. Results indicate that 74 and 63 % of the snow volume remained over the summer for piles in Davos, Switzerland and Martell, Italy. If snow mass is considered instead of volume, the values increase to 83 and 72 %. The difference is attributed to settling and densification of the snow. Additionally, we adapted the one-dimensional, physically based snow cover model SNOWPACK to perform simulations of the sawdust-covered snow piles. Model results and measurements agreed extremely well at the point scale. Moreover, we analysed the contribution of the different terms of the surface energy balance to snow ablation for a pile covered with a 40 cm thick sawdust layer and a pile without insulation. Short-wave radiation was the dominant source of energy for both scenarios, but the moist sawdust caused strong cooling by long-wave emission and negative sensible and latent heat fluxes. This cooling effect reduces the energy available for melt by up to a factor of 12. As a result only 9 % of the net short-wave energy remained available for melt. Finally, sensitivity studies of the parameters "thickness of the sawdust layer", "air temperature", "precipitation" and "wind speed" were performed. We show that sawdust thickness has a tremendous effect on snow loss. Higher air temperatures and wind speeds increase snow ablation but less significantly. No significant effect of additional precipitation could be found as the sawdust remained wet during the entire summer with the measured quantity of rain. Setting precipitation amounts to zero, however, strongly increased melt. Overall, the 40 cm sawdust provides sufficient protection for midelevation (approx. 1500 m a.s.l.) Alpine climates and can be managed with reasonable effort.

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Section: Study Sites and Snow Depositsmentioning

confidence: 99%

“…All input data were filtered, quality checked and resampled to the modelling time step of 15 min using the meteorological input-output library MeteoIO (Bavay and Egger, 2014). Model outputs are time series of snow profiles and fluxes reflecting the state of the snowpack at different points in time.…”

Section: Snowpack Modelmentioning

confidence: 99%

Snow farming: conserving snow over the summer season

2018

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“…The only external utility required by StreamFlow is the library MeteoIO (Bavay and Egger, 2014), which is used to read input files and interpolate meteorological data in space and time.…”

Section: Model Implementationmentioning

confidence: 99%

“…Incoming short-and long-wave radiation are directly obtained from meteorological measurements. They are spatially interpolated by StreamFlow over the stream network using library MeteoIO (Bavay and Egger, 2014), taking topographic shading into account. Riparian forest shading is currently not represented in the model, hereby restricting the application of StreamFlow to high-altitude catchments.…”

Section: Stream Energy-balance Computationmentioning

confidence: 99%

StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

Gallice

Bavay²,

Brauchli

et al. 2016

Geosci. Model Dev.

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Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as one of the promising approaches to reduce our uncertainty of future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface flow, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash-Sutcliffe efficiency (NSE) of 0.82 and hourly mean temperature with a NSE of 0.78.

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“…The Alpine3D simulations were run for a domain of 21.5 km×21.5 km with a grid cell size of 100 m×100 m, giving a total size of 215 × 215 grid cells. The model was run in hourly time steps, providing meteorological forcing data per time step for each pixel by interpolating from the meteorological stations in and just outside the Davos area using the MeteoIO library (Bavay and Egger, 2014). Per hourly time step, four SNOWPACK time steps are executed at 15 min resolution.…”

Section: Simulation Set-upmentioning

confidence: 99%

Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment

Wever

Comola

Bavay³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The assessment of flood risks in alpine, snowcovered catchments requires an understanding of the linkage between the snow cover, soil and discharge in the stream network. Here, we apply the comprehensive, distributed model Alpine3D to investigate the role of soil moisture in the predisposition of the Dischma catchment in Switzerland to high flows from rainfall and snowmelt. The recently updated soil module of the physics-based multilayer snow cover model SNOWPACK, which solves the surface energy and mass balance in Alpine3D, is verified against soil moisture measurements at seven sites and various depths inside and in close proximity to the Dischma catchment. Measurements and simulations in such terrain are difficult and consequently, soil moisture was simulated with varying degrees of success. Differences between simulated and measured soil moisture mainly arise from an overestimation of soil freezing and an absence of a groundwater description in the Alpine3D model. Both were found to have an influence in the soil moisture measurements. Using the Alpine3D simulation as the surface scheme for a spatially explicit hydrologic response model using a travel time distribution approach for interflow and baseflow, streamflow simulations were performed for the discharge from the catchment. The streamflow simulations provided a closer agreement with observed streamflow when driving the hydrologic response model with soil water fluxes at 30 cm depth in the Alpine3D model. Performance decreased when using the 2 cm soil water flux, thereby mostly ignoring soil processes. This illustrates that the role of soil moisture is important to take into account when understanding the relationship between both snowpack runoff and rainfall and catchment discharge in high alpine terrain. However, using the soil water flux at 60 cm depth to drive the hydrologic response model also decreased its performance, indicating that an optimal soil depth to include in surface simulations exists and that the runoff dynamics are controlled by only a shallow soil layer. Runoff coefficients (i.e. ratio of rainfall over discharge) based on measurements for high rainfall and snowmelt events were found to be dependent on the simulated initial soil moisture state at the onset of an event, further illustrating the important role of soil moisture for the hydrological processes in the catchment. The runoff coefficients using simulated discharge were found to reproduce this dependency, which shows that the Alpine3D model framework can be successfully applied to assess the predisposition of the catchment to flood risks from both snowmelt and rainfall events.

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MeteoIO 2.4.2: a preprocessing library for meteorological data

Cited by 111 publications

References 44 publications

Snow farming: conserving snow over the summer season

Snow farming: conserving snow over the summer season

StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment

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