35 research outputs found
Reconciling different observational data sets from Surface Heat Budget of the Arctic Ocean (SHEBA) for model validation purposes
Citizens observatories for effective Earth observations: the WeSenseIt approach
The WeSenseIt project defines citizen observatories as âA method, an environment and an infrastructure supporting an information ecosystem for communities and citizens, as well as emergency operators and policymakers, for discussion, monitoring and intervention on situations, places and eventsâ . A collaborative approach has been taken to develop solutions that involve an exchange of information and expertise from all participants and where the focus is on arriving at practical solutions with a clear vision and direction. This has created a shared ownership scheme, and shifts power to the process itself rather than remaining within authorities, developers or decision-makers. The projectâs emphasis is on delivering highly innovative technologies to support citizens, communities and authorities in developing a real-time situation awareness while ensuring all stakeholders play their part. Implementation has been through a combination of crowdsourcing, custom applications and dedicated web portals designed to foster collaboration, and which has created a shared knowledge base that facilitates decision-making processes and engages with communities. Data is captured via innovative sensors that are used directly by citizens, crowdsourcing from social networks (or by collective intelligence)
Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model
The development of stream temperature regression models at regional scales has regained some popularity over the past years. These models are used to predict stream temperature in ungauged catchments to assess the impact of human activities or climate change on riverine fauna over large spatial areas. A comprehensive literature review presented in this study shows that the temperature metrics predicted by the majority of models correspond to yearly aggregates, such as the popular annual maximum weekly mean temperature (MWMT). As a consequence, current models are often unable to predict the annual cycle of stream temperature, nor can the majority of them forecast the inter-annual variation of stream temperature. This study presents a new statistical model to estimate the monthly mean stream temperature of ungauged rivers over multiple years in an Alpine country (Switzerland). Contrary to similar models developed to date, which are mostly based on standard regression approaches, this one attempts to incorporate physical aspects into its structure. It is based on the analytical solution to a simplified version of the energy-balance equation over an entire stream network. Some terms of this solution cannot be readily evaluated at the regional scale due to the lack of appropriate data, and are therefore approximated using classical statistical techniques. This physics-inspired approach presents some advantages: (1) the main model structure is directly obtained from first principles, (2) the spatial extent over which the predictor variables are averaged naturally arises during model development, and (3) most of the regression coefficients can be interpreted from a physical point of view â their values can therefore be constrained to remain within plausible bounds. The evaluation of the model over a new freely available data set shows that the monthly mean stream temperature curve can be reproduced with a rootmean-square error (RMSE) of +/-1.3 °C, which is similar in precision to the predictions obtained with a multi-linear regression model. We illustrate through a simple example how the physical aspects contained in the model structure can be used to gain more insight into the stream temperature dynamics at regional scales
A multilayer sigma-coordinate thermodynamic sea ice model: Validation against Surface Heat Budget of the Arctic Ocean (SHEBA)/Sea Ice Model Intercomparison Project Part 2 (SIMIP2) data
A new multilayer sigma-coordinate thermodynamic sea ice model is presented. The model employs a coordinate transformation which maps the thickness of the snow and ice slabs onto unity intervals and thus enables automatic relayering when the snow or ice thickness changes. This is done through an advection term which naturally appears in the transformed energy equation. Unlike previous approaches, the model conserves the total energy per layer (Jmâ»ÂČ as opposed to Jmâ»Âł), which takes into account the changes in internal energy associated with thickness changes. This model was then tested against observational data from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment in the context of the Sea Ice Model Intercomparison Project, Part 2, Thermodynamics (SIMIP2). In general, the model reproduces the observed internal snow-ice temperature and the ice thickness evolution very well. Results show that the ice thickness evolution is very sensitive to the ocean heat flux (F/ocn) and the thickness of the snow cover in winter. Given that the spatial variability in snow depth at small scale is large, the specification of the snow depth temporal evolution is crucial for an intercomparison project. Since F/ocn in SIMIP2 is calculated as a residual of the observed basal growth rates and heat conduction, the salinity of newly formed ice used in the simulations must be consistent with that used to derive F/ocn. Simulated and observed snow surface and snow-ice interface temperatures suggest that not enough heat is conducted through the snow layer even when using a snow thermal conductivity as large as 0.50 Wmâ»Âč Kâ»Âč (value derived from observed snow and ice internal temperature profiles). A surface energy budget of simulated and observed energy fluxes confirms this finding
Reconciling different observational data sets from Surface Heat Budget of the Arctic Ocean (SHEBA) for model validation purposes
Observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) are analyzed to develop a consistent data set suitable for the validation of snow and sea ice components used in climate models. Since the snow depth is a crucial variable to properly determine the ice thickness evolution, several methods are tested to estimate the actual snow depth at the exact location of the measured internal temperatures. Snow and ice thickness gauge measurements show high spatial variability at small spatial scales. Consequently, individual measurements of snow/ice thickness are not representative of the thickness at the locations where temperature profiles were measured. Observed skin temperatures and snow internal temperature profiles suggest that the mean winter snow cover at the reference mass balance site was thicker by 11 cm when compared with gauge observations at a small distance from that reference site. The mean winter snow cover thickness measured at the SHEBA mass balance site, Pittsburgh, is larger by a factor of 2.3 when compared to the snow depth derived from precipitation measurements. Assuming continuity of heat fluxes at the snow-ice interface, an effective snow thermal conductivity of 0.50 Wmâ»Âč Kâ»Âč is calculated. This is significantly higher than values generally used in climate models (0.31 Wmâ»Âč Kâ»Âč) or derived from in situ measurements (0.14 Wmâ»Âč Kâ»Âč) at SHEBA. Ocean heat fluxes, inferred from ice thickness and internal temperature measurements at various sites, are very consistent and match reasonably well those derived from turbulence measurements and a bulk formulation. A heat budget of surface fluxes shows a mean annual net imbalance of 1.5 Wmâ»ÂČ, with a mean energy deficit of 3.5 Wmâ»ÂČ during winter and a mean surplus of 6.4 Wmâ»ÂČ during summer
StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction
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 NashâSutcliffe efficiency (NSE) of
0.82 and hourly mean temperature with a NSE of 0.78
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Thermal diffusivity of seasonal snow determined from temperature profiles
Thermal diffusivity of snow is an important thermodynamic property associated with key hydrological phenomena such as snow melt and heat and water vapor exchange with the atmosphere. Direct determination of snow thermal diffusivity requires coupled point measurements of thermal conductivity and density, which are nonstationary due to snow metamorphism. Traditional methods for determining these two quantities are generally limited by temporal resolution. In this study we present a method to determine the thermal diffusivity of snow with high temporal resolution using snow temperature profile measurements. High resolution (between 2.5 and 10 cm at 1 minute) temperature measurements from the seasonal snow pack at Plaine-Morte glacier in Switzerland are used as initial conditions and Neumann (heat flux) boundary conditions to numerically solve the one-dimensional heat equation and iteratively optimize for thermal diffusivity. The implementation of Neumann boundary conditions and a t-test, ensuring statistical significance between solutions of varied thermal diffusivity, are important to help constrain thermal diffusivity such that spurious high and low values as seen with Dirichlet (temperature) boundary conditions are reduced. The results show that time resolved thermal diffusivity can be determined from easily implemented and inexpensive temperature measurements of seasonal snow with good agreement to density-based empirical parameterizations for thermal conductivity.Keywords: thermal diffusivity, porous media, heat diffusion, snow temperature measurements, thermal conductivit