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)

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 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 (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

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 Nash–Sutcliffe efficiency (NSE) of 0.82 and hourly mean temperature with a NSE of 0.78

Evolution of stream and lake water temperature under climate change

This report presents past observations and projects the future development of water temperature in Swiss lakes and rivers. Projections are made until the end of the 21 st century using the CH2018 climate scenarios. Besides climate change effects on temperature, we also discuss effects on discharge for rivers, and effects on the thermal structure, and specifically the seasonal mixing regime and ice cover of lakes. Observations over the past 40 years show a clear increase in river temperatures, with a mean trend of 0.33 ± 0.03 °C per decade, corresponding to ~80% of the observed air temperature trend. This warming has been continuous over the last four decades and impacts the health of stream ecosystems (e.g. by favouring the spread of fish diseases) and their services (e.g., the water usage for industrial cooling). The temperature rise is more pronounced in the Swiss Plateau than in the Alps, where snow and glacier melt partially mitigates (for now) the effects of increasing air temperature. Conversely, annual average discharge shows no significant trend. Similar trends have also been reported for Swiss lakes with mean summer lake surface temperature increasing by 0.40 ± 0.08 °C per decade since the 1950s. This warming trend affects lake stratification. Warm periods may for instance increase the occurrence of deep-water anoxic conditions, as observed during the 2003 heat wave in Lake Zurich. In mild winters, ice cover duration is reduced in alpine lakes, and winter deep mixing is less intense in large-peri-alpine lakes. The mild winter 2006/7 limited, for instance, the seasonal mixing of Lake Constance to about 60 m depths. Effects of warming on lake thermal structure vary within and between regions, due to both lake and watershed characteristics as well as regional climate change patterns. We simulated the future evolution of stream temperature for 10 catchments in Switzerland for a historical reference period (1990–2000) and two future periods: 2055–2065 (mid-century) and 2080–2090 (end of the century). Results show that the temperature will stabilize by the end of the century for the RCP2.6 scenario (strong CO2 emission reduction), whereas the warming will accelerate with time for the RCP8.5 scenario (business as usual scenario). This expected warming will have significant impacts on the stream ecosystems. Alpine and lowland catchments will experience a similar annual mean temperature increase but display different seasonal effects. While Swiss Plateau rivers will become warmer both in winter and summer (but more in summer), alpine rivers will experience almost no warming in winter but a strong warming exceeding that of air temperature in summer. This is explained by an abrupt decrease in discharge, and by the soil warming resulting from the absence of snow and thus a lower albedo. Lake temperature projections are based on one-dimensional, vertically resolved, hydrodynamic simulations for 29 lakes. The simulated lakes cover a wide range of sizes, depths and water quality, and an altitude range from 200 to 1800 m a.s.l. Simulations indicate substantial changes in lake thermal structure for RCP8.5 with surface temperatures increasing on average by 3.3 °C at the end of the 21 st century. This increase is limited to 0.9 °C in the mitigation scenario RCP2.6. We identified an altitude-dependent evolution of the durations of summer and winter stratification as well the ice-covered period. Larger changes in stratification duration are expected to occur at higher altitude lakes. Yet, these lakes will still maintain winter stratification and a shortened ice- covered period while lower altitude lakes (below ~1500 m a.s.l.) risk drastic changes in the mixing regime. e.g., a complete loss of the ice cover and winter stratification under the RCP8.5 scenarios. Such changes in the mixing regime may strongly impact lake ecosystems. These low to mid altitude lakes may therefore be considered as the most vulnerable to climate chang