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 – 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 root-mean-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

Citizens observatories for effective Earth observations: the WeSenseIt approach

The WeSenseIt project defines citizen observatories as “A 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 events” . 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 project’s 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)

Albedo effect on radiative errors in air temperature measurements

Most standard air temperature measurements are subject to significant errors mainly due to sensor heating by solar radiation, even when the measurement principle is accurate and precise. We present various air temperature measurements together with other measurements of meteorological parameters using different sensor systems at a snow-covered and a vegetated site. Measurements from naturally ventilated air temperature sensors in multiplate shields are compared to temperatures measured using sonic anemometers which are unaffected by solar radiation. Over snow, 30 min mean temperature differences can be as large as 10°C. Unshielded thermocouples were also tested and are generally less affected by shortwave radiation. Temperature errors decrease with decreasing solar radiation and increasing wind speed but do not completely disappear at a given solar radiation even in the presence of effective ventilation. We show that temperature errors grow faster for reflected than for incident solar radiation, demonstrating the influence of the surface properties on radiative errors, and we detect the albedo as a variable with major influence on the magnitude of the error as well as a key quantity in possible error correction schemes. An extension is proposed for an existing similarity regression model to correct for radiative errors; thus, surface-reflected shortwave radiation is identified as a principal source of error and the key variable for obtaining a unique nondimensional scaling of radiative errors

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 (Jm⁻² as opposed to Jm⁻³), 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 Wm⁻¹ K⁻¹ (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 Wm⁻¹ K⁻¹ is calculated. This is significantly higher than values generally used in climate models (0.31 Wm⁻¹ K⁻¹) or derived from in situ measurements (0.14 Wm⁻¹ K⁻¹) 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 Wm⁻², with a mean energy deficit of 3.5 Wm⁻² during winter and a mean surplus of 6.4 Wm⁻² during summer