Optical Characterisation of Suspended Particles in the Mackenzie River Plume (Canadian Arctic Ocean) and Implications for Ocean Colour Remote Sensing

Climate change significantly impacts Arctic shelf regions in terms of air temperature, ultraviolet radiation, melting of sea ice, precipitation, thawing of permafrost and coastal erosion. Direct consequences have been observed on the increasing Arctic river flow and a large amount of organic carbon sequestered in soils at high latitudes since the last glacial maximum can be expected to be delivered to the Arctic Ocean during the coming decade. Monitoring the fluxes and fate of this terrigenous organic carbon is problematic in such sparsely populated regions unless remote sensing techniques can be developed and proved to be operational. The main objective of this study is to develop an ocean colour algorithm to operationally monitor dynamics of suspended particulate matter (SPM) on the Mackenzie River continental shelf (Canadian Arctic Ocean) using satellite imagery. The water optical properties are documented across the study area and related to concentrations of SPM and particulate organic carbon (POC). Robust SPM and POC : SPM proxies are identified, such as the light backscattering and attenuation coefficients, and relationships are established between these optical and biogeochemical parameters. Following a semi-analytical approach, a regional SPM quantification relationship is obtained for the inversion of the water reflectance signal into SPM concentration. This relationship is reproduced based on independent field optical measurements. It is successfully applied to a selection of MODIS satellite data which allow estimating fluxes at the river mouth and monitoring the extension and dynamics of the Mackenzie River surface plume in 2009, 2010 and 2011. Good agreement is obtained with field observations representative of the whole water column in the river delta zone where terrigenous SPM is mainly constrained (out of short periods of maximum river outflow). Most of the seaward export of SPM is observed to occur within the west side of the river mouth. Future work will require the validation of the developed SPM regional algorithm based on match-ups with field measurements, then the routine application to ocean colour satellite data in order to better estimate the fluxes and fate of SPM and POC delivered by the Mackenzie River to the Arctic Ocean

Monitoring Ice Break-Up on the Mackenzie River Using Remote Sensing

The Mackenzie Basin is composed of eight sub-basins (North Mountains, Liard, Peace, Athabasca, Great Bear Low Plains, Great Slave and Arctic Red) and includes three large lakes (Great Bear Lake, Great Slave Lake, Lake Athabasca) and three deltas (Peace-Athabasca Delta, Slave Delta, Mackenzie Delta), one of which is the world’s largest inland delta (Peace-Athabasca Delta). Annually, the Mackenzie River experiences freeze-up during the fall season and ice break-up in the spring, having an important influence on the basin hydrology Furthermore, the type of ice break-up event is dependent on the magnitude of hydrological and meteorological conditions present. In light of the decreasing network of ground-based stations operated by the Water Survey of Canada on the Mackenzie River, this study explored the use of satellite remote sensing data to improve monitoring capabilities during the ice break-up period. MODIS Level 3 snow products (MOD/MYD10A1) and MODIS Level 1B radiance products (MOD/MYD02QKM) are used to monitor ice cover during the break-up period on the Mackenzie River, Canada, for 13 ice seasons (2001-2013). The initiation of the break-up period was observed to occur between days of year (DOY) 115-125 and end DOY 145-155, resulting in average melt durations of 30-40 days. Floating ice running northbound could therefore generate multiple periods of ice-on and ice-off observations at the same geographic location. At the headwaters of the Mackenzie River, ice break-up was thermodynamically driven as opposed to dynamically, as observed downstream near the Mackenzie Delta. MODIS observations also revealed that ice runs were largely influenced by channel morphology (islands and bars, confluences and channel constriction). MODIS was found to be a powerful tool for monitoring ice break-up processes at multiple geographical locations simultaneously along the Mackenzie River. Finally, MODIS was found to be a viable tool for estimating river ice velocity where channel morphology least affected river flow. Ice run velocities north of 66° N ranged from 1.21-1.84 ms-1

Spatial and temporal patterns in Arctic river ice breakup revealed by automated ice detection from MODIS imagery

The annual spring breakup of river ice has important consequences for northern ecosystems and significant economic implications for Arctic industry and transportation. River ice breakup research is restricted by the sparse distribution of hydrological stations in the Arctic, where limited available data suggests a trend towards earlier ice breakup. The specific climatic mechanisms driving this trend, however, are complex and can vary both regionally and within river systems. Consequently, understanding the response of river ice processes to a warming Arctic requires simultaneous examination of spatial and temporal patterns in breakup timing. In this paper, we describe an automated algorithm for river ice breakup detection using MODIS satellite imagery that enables identification of spatial and temporal breakup patterns at large scales. We examine breakup timing on the Mackenzie, Lena, Ob' and Yenisey rivers for the period 2000-2014. By dividing the rivers into 10 km segments and classifying each river pixel in each segment as snow/ice, mixed ice/water or open water based on MODIS reflectance, we determine breakup dates with a mean uncertainty of ±. 1.3 days. All statistically significant temporal trends are negative, indicating an overall shift towards earlier breakup. Considerable variability in the statistical significance and magnitude of trends along each river suggests that different climatic and physiographic drivers are impacting spatial patterns in breakup. Trends detected on the lower Mackenzie corroborate recent studies indicating weakening ice resistance and earlier breakup timing near the Mackenzie Delta. In Siberia, the increased magnitude of trends upstream and strong correlation between breakup initiation and whole-river breakup patterns suggest that earlier onset of upstream discharge may play the dominant role in determining breakup timing. Exploratory analysis demonstrates that MODIS imagery may also be used to differentiate thermal and mechanical breakup events

Seasonal dynamics of dissolved organic matter in the Mackenzie Delta, Canadian Arctic waters

Increasing air temperatures and associated permafrost thaw in Arctic river watersheds, such as the Mackenzie River catchment, are directly affecting the aquatic environment. As a consequence, the quantity and the quality of dissolved organic carbon (DOC) that is transported via the Mackenzie River into the Arctic Ocean is expected to change. Particularly in these remote permafrost regions of the Arctic, monitoring of terrigenous organic carbon fluxes is insufficient and knowledge of distribution and fate of organic carbon when released to the coastal waters is remarkably lacking. Despite its poorly evaluated performance in Arctic coastal waters, Satellite Ocean Colour Remote Sensing (SOCRS) remains a powerful tool to complement monitoring of land-ocean DOC fluxes, detect their trends, and help in understanding their propagation in the Arctic Ocean. In this study, we use in situ and SOCRS data to show the strong seasonal dynamics of the Mackenzie River plume and the spatial distribution of associated terrigenous DOC on the Beaufort Sea Shelf for the first time. Using a dataset collected during an extensive field campaign in 2019, the performance of three commonly-used atmospheric correction (AC) algorithms and two available colored dissolved organic matter (CDOM) retrieval algorithms were evaluated using the Ocean and Land Colour Instrument (OLCI). Our results showed that in optically-complex Arctic coastal waters the Polymer AC algorithm performed the best. For the retrieval of CDOM, the gsmA algorithm (Mean Percentage Error (MPE) = 35.7%) showed slightly more consistent results compared to the ONNS algorithm (MPE = 37.9%). By merging our measurements with published datasets, the newly-established DOC-CDOM relationship for the Mackenzie-Beaufort Sea region allowed estimations of DOC concentrations from SOCRS across the entire fluvial-marine transition zone with an MPE of 20.5%. Finally, we applied SOCRS with data from the Sentinel-3 OLCI sensor to illustrate the seasonal variation of DOC concentrations in the surface waters of the Beaufort Sea on a large spatial scales and high frequency throughout the entire open water period. Highest DOC concentrations and largest lateral extent of the plume were observed in spring right after the Mackenzie River ice break-up indicating that the freshet was the main driver of plume propagation and DOC distribution on the shelf. Satellite-derived images of surface water DOC concentration placed the in situ observations into a larger temporal and spatial context and revealed a strong seasonal variability in transport pathways of DOC in the Mackenzie- Beaufort Sea region

On the Use of MODIS for Lake and Land Surface Temperature Investigations in the Regions of Great Bear Lake and Great Slave Lake, N.W.T.

Lake surface temperature (LSTlake) can be obtained and studied in different ways: using in situ measurements, satellite imagery and modeling. Collecting spatially representative in situ data over lakes, especially for large and deep ones, is a real challenge. Satellite data products provide the opportunity to collect continuous data over very large geographic areas even in remote regions. Numerical modeling is also an approach to study the response and the role of lakes in the climate system. Satellite instruments provide spatial information unlike in situ measurements and one-dimensional (1-D) lake models that give vertical information at a single point or a few points in lakes. The advantage of remote sensing also applies to land where temperature measurements are usually taken at meteorological stations whose network is extremely sparse in northern regions. This thesis therefore examined the value of land/lake surface (skin) temperature (LSTland/lake) measurements from satellites as a complement to in situ point measurements and numerical modeling. The thesis is organized into two parts. The first part tested, two 1-D numerical models against in situ and satellite-derived LST measurements. LSTlake and ice phenology were simulated for various points at different depths on Great Slave Lake (GSL) and Great Bear Lake (GBL), two large lakes located in the Mackenzie River Basin in Canada’s Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002-2010 period. Input data from three weather stations (Yellowknife, Hay River and Deline) were used for model simulations. LSTlake model results are compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. The main goal was to examine the performance of the FLake and CLIMo models in simulating LSTlake and ice-cover under different conditions against satellite data products. Both models reveal a good agreement with daily average MODIS LSTlake from GSL and GBL on an annual basis. CLIMo showed a generally better performance than FLake for both lakes, particularly during the ice-cover season. Secondly, MODIS-derived lake and land surface temperature (LSTland/lake) products are used to analyze land and lake surface temperature patterns during the open-water and snow/ice growth seasons for the same period of time in the regions of both GBL and GSL. Land and lake temperatures from MODIS were compared with near-surface air temperature measurements obtained from nearby weather stations and with in situ temperature moorings in GBL. Results show a good agreement between satellite and in situ observations. MODIS data were found to be very useful for investigating both the spatial and temporal (seasonal) evolution of LSTland/lake over lakes and land, and for improving our understanding of thermodynamic processes (heat gains and heat loses) of the lake/land systems. Among other findings, the MODIS satellite imagery showed that the surface temperature of lakes is colder in comparison to the surrounding land from April-August and warmer from September until spring thaw

The Arctic Nearshore Turbidity Algorithm (ANTA) - A multi sensor turbidity algorithm for Arctic nearshore environments

The Arctic is greatly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems by reducing light transmission through the water column and altering the biogeochemistry, but also the subsistence economy of local people, and changes in climate because of the transformation of organic matter into greenhouse gases. Yet, the quantification of suspended sediment in Arctic coastal and nearshore waters remains unsatisfactory due to the absence of dedicated algorithms to resolve the high loads occurring in the close vicinity of the shoreline. In this study we present the Arctic Nearshore Turbidity Algorithm (ANTA), the first reflectance-turbidity relationship specifically targeted towards Arctic nearshore waters that is tuned with in-situ measurements from the nearshore waters of Herschel Island Qikiqtaruk in the western Canadian Arctic. A semi-empirical model was calibrated for several relevant sensors in ocean color remote sensing, including MODIS, Sentinel 3 (OLCI), Landsat 8 (OLI), and Sentinel 2 (MSI), as well as the older Landsat sensors TM and ETM+. The ANTA performed better with Landsat 8 than with Sentinel 2 and Sentinel 3. The application of the ANTA to Sentinel 2 imagery that matches in-situ turbidity samples taken in Adventfjorden, Svalbard, shows transferability to nearshore areas beyond Herschel Island Qikiqtaruk

DETECTING PATTERNS AND DRIVERS OF ICE ON AND ICE OFF TIMING IN ALASKAN RIVERS WIDER THAN 150 m USING MODIS

Annual river ice freeze-up and breakup have major implications for northern ecosystems and infrastructure and are particularly responsive to climate change. However, a lack of ground-based observations hampers understanding of large-scale patterns in ice timing. Here I detect freeze-up and breakup dates on Alaskan rivers wider than 150 m using MODIS satellite imagery from 2000-2017, the first large-scale detection of ice freeze-up using remote sensing and an expansion of breakup detection to rivers narrower than 500 m. I find statistically significant trends in breakup dates in the North Slope (-0.67 days/year, p<0.05) and West Central regions (-0.63 days/year, p<0.10). I find no long-term regional freeze-up trends. Regional timeseries of ice timing are instead dominated by teleconnections. Pacific Decadal Oscillation and Southern Oscillation Indices in the preceding fall and concurrent spring correlate highly to breakup dates, suggesting regional predictability. Methods described here can detect freeze-up and breakup timing on pan-Arctic rivers

Passive Microwave Remote Sensing of Ice Cover on Large Northern Lakes: Great Bear Lake and Great Slave Lake, Northwest Territories, Canada

Time series of brightness temperature (TB) measurement obtained at various frequencies by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) are investigated to determine ice phenology parameters and ice thickness on Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada. TB measurements from the 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz channels (H- and V- polarization) are compared to assess their potential for detecting freeze-onset (FO)/melt-onset (MO), ice-on/ice-off dates, and ice thickness on both lakes. The sensitivity of TB measurements at 6.9, 10.7, and 18.7 GHz to ice thickness is also examined using a previously validated thermodynamic lake ice model and the most recent version of the Helsinki University of Technology (HUT) model, which accounts for the presence of a lake-ice layer under snow. This study shows that 18.7 GHz H-pol is the most suitable AMSR-E channel for detecting ice phenology events, while 18.7 GHz V-pol is preferred for estimating lake ice thickness on the two large northern lakes. These two channels therefore form the basis of new ice cover retrieval algorithms. The algorithms were applied to map monthly ice thickness products and all ice phenology parameters on GBL and GSL over seven ice seasons (2002-2009). Through application of the algorithms much was learned about the spatio-temporal dynamics of ice formation, decay and growth rate/thickness on the two lakes. Key results reveal that: 1) both FO and ice-on dates occur on average 10 days earlier on GBL than on GSL; 2) the freeze-up process or freeze duration (FO to ice-on) takes a comparable amount of time on both lakes (two to three weeks); 3) MO and ice-off dates occur on average one week and approximately four weeks later, respectively, on GBL; 4) the break-up process or melt duration (MO to ice-off) lasts for an equivalent period of time on both lakes (six to eight weeks); 5) ice cover duration is about three to four weeks longer on GBL compared to its more southern counterpart (GSL); and 6) end-of-winter ice thickness (April) on GBL tends to be on average 5-15 cm thicker than on GSL, but with both spatial variations across lakes and differences between years