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Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Marine science and numerical modeling (MASNUM) is widely used in forecasting ocean wave movement, through simulating the variation tendency of the ocean wave. Although efforts have been devoted to improve the performance of MASNUM from various aspects by existing work, there is still large space unexplored for further performance improvement. In this paper, we aim at improving the performance of propagation solver and data access during the simulation, in addition to the efficiency of output I/O and load balance. Our optimizations include several effective techniques such as the algorithm redesign, load distribution optimization, parallel I/O and data access optimization. The experimental results demonstrate that our approach achieves higher performance compared to the state-of-the-art work, about 3.5x speedup without degrading the prediction accuracy. In addition, the parameter sensitivity analysis shows our optimizations are effective under various topography resolutions and output frequencies. [ABSTRACT FROM AUTHOR]

Climate change can severely impact artisanal fisheries and affect the role they play in food security. We study climate change effects on the triple bottom line of ecological productivity, fishers’ incomes, and fish consumption for an artisanal open-access fishery. We develop and apply an empirical, stochastic bio-economic model for the Senegalese artisanal purse seine fishery on small pelagic fish and compare the simulated fishery’s development using four climate projections and two policy scenarios. We find that economic processes of adaptation may amplify the effects of climate variations. The regions’ catch potential increases with climate change, induced by stock distribution changes. However, this outcome escalates over-fishing, whose effects outpace the incipiently favorable climate change effects under three of the four climate projections. Without policy action, the fishery is estimated to collapse in 2030–2035 on average over 1000 runs. We propose an easily implementable and overall welfare-increasing intervention: reduction of fuel subsidies. If fuel subsidies were abolished, ecological sustainability as well as the fishery’s welfare contribution would increase regardless of the climate projection. [ABSTRACT FROM AUTHOR]

Recently available downscaled ocean climate models for the Bering Sea and Aleutian Arc offer the opportunity to assess climate vulnerability for upper trophic level consumers such as marine birds. We analyzed seasonal and annual spatial projections from three climate models for two physical climate variables (seawater temperature and sea ice) and three forage variables (large copepods, euphausiids, and benthic infauna), comparing projected conditions from a recent time period (2003–2012) to a future time period (2030–2039). We focused the analyses on core areas within globally significant Important Bird Areas, and developed indices of the magnitude of projected change and vulnerability agreement among models. All three climate models indicated a high degree of change for seawater temperature warming (highest in the central and eastern Aleutian Islands) and ice loss (most significant in the eastern Bering Sea) across scales, and we found those changes to be significant for every species and virtually every core area assessed. There was low model agreement for the forage variables; while the majority of core areas were identified as climate vulnerable by one or more models (72% for large copepods, 73% for euphausiids, and 94% for benthic infauna), very few were agreed upon by all three models (only 6% of euphausiid-forager core areas). Based on the magnitude-agreement score, euphausiid biomass decline affected core areas for fulmars, gulls, and auklets, especially along the outer shelf and Aleutian Islands. Benthic biomass decline affected eiders along the inner shelf, and large copepod decline was significant for storm-petrels and auklets in the western Aleutians. Overall, 12% of core areas indicated climate vulnerability for all variables assessed. Modeling and interpreting biological parameters to project future dynamics remains complex; the strong signal for projected physical changes raised concerns about lagged responses such as distribution shifts, breeding failures, mortality events, and population declines. [ABSTRACT FROM AUTHOR]

Dynamical malaria models can relate precipitation to the availability of vector breeding sites using simple models of surface hydrology. Here, a revised scheme is developed for the VECTRI malaria model, which is evaluated alongside the default scheme using a two year simulation by HYDREMATS, a 10 metre resolution, village-scale model that explicitly simulates individual ponds. Despite the simplicity of the two VECTRI surface hydrology parametrization schemes, they can reproduce the sub-seasonal evolution of fractional water coverage. Calibration of the model parameters is required to simulate the mean pond fraction correctly. The default VECTRI model tended to overestimate water fraction in periods subject to light rainfall events and underestimate it during periods of intense rainfall. This systematic error was improved in the revised scheme by including the a parametrization for surface run-off, such that light rainfall below the initial abstraction threshold does not contribute to ponds. After calibration of the pond model, the VECTRI model was able to simulate vector densities that compared well to the detailed agent based model contained in HYDREMATS without further parameter adjustment. Substituting local rain-gauge data with satellite-retrieved precipitation gave a reasonable approximation, raising the prospects for regional malaria simulations even in data sparse regions. However, further improvements could be made if a method can be derived to calibrate the key hydrology parameters of the pond model in each grid cell location, possibly also incorporating slope and soil texture. [ABSTRACT FROM AUTHOR]