Showing papers by "David Doxaran published in 2015"

A 50 % increase in the mass of terrestrial particles delivered by the Mackenzie River into the Beaufort Sea (Canadian Arctic Ocean) over the last 10 years

Abstract: . Global warming has a significant impact on the regional scale on the Arctic Ocean and surrounding coastal zones (i.e., Alaska, Canada, Greenland, Norway and Russia). The recent increase in air temperature has resulted in increased precipitation along the drainage basins of Arctic rivers. It has also directly impacted land and seawater temperatures with the consequence of melting permafrost and sea ice. An increase in freshwater discharge by main Arctic rivers has been clearly identified in time series of field observations. The freshwater discharge of the Mackenzie River has increased by 25% since 2003. This may have increased the mobilization and transport of various dissolved and particulate substances, including organic carbon, as well as their export to the ocean. The release from land to the ocean of such organic material, which has been sequestered in a frozen state since the Last Glacial Maximum, may significantly impact the Arctic Ocean carbon cycle as well as marine ecosystems. In this study we use 11 years of ocean color satellite data and field observations collected in 2009 to estimate the mass of terrestrial suspended solids and particulate organic carbon delivered by the Mackenzie River into the Beaufort Sea (Arctic Ocean). Our results show that during the summer period, the concentration of suspended solids at the river mouth, in the delta zone and in the river plume has increased by 46, 71 and 33%, respectively, since 2003. Combined with the variations observed in the freshwater discharge, this corresponds to a more than 50% increase in the particulate (terrestrial suspended particles and organic carbon) export from the Mackenzie River into the Beaufort Sea.

Characterization of Time-Varying Regimes in Remote Sensing Time Series: Application to the Forecasting of Satellite-Derived Suspended Matter Concentrations

Abstract: The spatial and temporal coverage of satellites provides data that are particularly well suited for the analysis and characterization of space–time-varying geophysical relationships. The latent-class models aim here to identify time-varying regimes within a dataset. This is of particular interest for geophysical processes driven by the seasonal variability. As a case example, we study the daily concentration of mineral suspended particulate matters estimated from satellite-derived datasets, in coastal waters adjacent to the French Gironde river mouth. We forecast this high-resolution dataset using environmental data (wave height, wind strength and direction, tides, and river outflow) and four latent-regime models: homogeneous and nonhomogeneous Markov-switching models, with and without an autoregressive term (i.e., the mineral suspended matter concentration observed the day before). Using a validation dataset, significant improvements are observed with the multiregime models compared to a classical multiregression and a state-of-the-art nonlinear model [support vector regression (SVR) model]. The best results are reported for a mixture of three regimes for the autoregressive model using nonhomogeneous transitions. With the autoregressive models, we obtain at day+1 for the mixture model forecasting performances of 93% of the explained variance, compared to 83% for a standard linear model and 85% using an SVR. These improvements are more important for the nonautoregressive models. These results stress the potential of the identification of geophysical regimes to improve the forecasting. We also show that nonhomogeneous transition probabilities and estimated autoregressive terms improve forecasting performances when observation data is lacking for short-time period of 1–15 days.