Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

An Investigation into the Effects of Variable Lake Ice Properties on Passive and Active Microwave Measurements Over Tundra Lakes Near Inuvik, N.W.T.

The accurate estimation of snow water equivalent (SWE) in the Canadian sub-arctic is integral to climate variability studies and water availability forecasts for economic considerations (drinking water, hydroelectric power generation). Common passive microwave (PM) snow water equivalent (SWE) algorithms that utilize the differences in brightness temperature (Tb) at 37 GHz – 19 GHz falter in lake-rich tundra environments because of the inclusion of lakes within PM pixels. The overarching goal of this research was to investigate the use of multiple platforms and methodologies to observe and quantify the effects of lake ice and sub-ice water on passive microwave emission for the purpose of improving snow water equivalent (SWE) retrieval algorithms. Using in situ snow and ice measurements as input, the Helsinki University of Technology (HUT) multi-layer snow emission model was modified to include an ice layer below the snow layer. Emission for 6.9, 19, 37 and 89 GHz were simulated at horizontal and vertical polarizations, and were validated by high resolution airborne passive microwave measurements coincident with in situ sampling sites over two lakes near Inuvik, Northwest Territories (NWT). Overall, the general magnitude of brightness temperatures were estimated by the HUT model for 6.9 and 19 GHz H/V, however the variability was not. Simulations produced at 37 GHz exhibited the best agreement relative to observed temperatures. However, emission at 37 GHz does not interact with the radiometrically cold water, indicating that ice properties controlling microwave emission are not fully captured by the HUT model. Alternatively, active microwave synthetic aperture radar (SAR) measurements can be used to identify ice properties that affect passive microwave emission. Dual polarized X-band SAR backscatter was utilized to identify ice types by the segmentation program MAGIC (MAp Guided Ice Classification). Airborne passive microwave transects were grouped by ice type classes and compared to backscatter measurements. In freshwater, where there were few areas of high bubble concentration at the ice/water interface Tbs exhibited positive correlations with cross-polarized backscatter, corresponding to ice types (from low to high emission/backscatter: clear ice, transition zone between clear and grey ice, grey ice and rafted ice). SWE algorithms were applied to emission within each ice type producing negative or near zero values in areas of low 19 GHz Tbs (clear ice, transition zone), but also produced positive values that were closer to the range of in situ measurements in areas of high 19 GHz Tbs (grey and rafted ice). Therefore, cross-polarized X-band SAR measurements can be used as a priori ice type information for spaceborne PM algorithms, providing information on ice types and ice characteristics (floating, frozen to bed), integral to future tundra-specific SWE retrieval algorithms

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

A Review of Global Satellite-Derived Snow Products

Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation. Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow

Passive microwave derived snowmelt timing: significance, spatial and temporal variability, and potential applications

Snow accumulation and melt are dynamic features of the cryosphere indicative of a changing climate. Spring melt and refreeze timing are of particular importance due to the influence on subsequent hydrological and ecological processes, including peak runoff and green-up. To investigate the spatial and temporal variability of melt timing across a sub-arctic region (the Yukon River Basin (YRB), Alaska/Canada) dominated by snow and lacking substantial ground instrumentation, passive microwave remote sensing was utilized to provide daily brightness temperatures (Tb) regardless of clouds and darkness. Algorithms to derive the timing of melt onset and the end of melt-refreeze, a critical transition period where the snowpack melts during the day and refreezes at night, were based on thresholds for Tb and diurnal amplitude variations (day and night difference). Tb data from the Special Sensor Microwave Imager (1988 to 2011) was used for analyzing YRB terrestrial snowmelt timing and for characterizing melt regime patterns for icefields in Alaska and Patagonia. Tb data from the Advanced Microwave Scanning Radiometer for EOS (2003 to 2010) was used for determining the occurrence of early melt events (before melt onset) associated with fog or rain on snow, for investigating the correlation between melt timing and forest fires, and for driving a flux-based snowmelt runoff model. From the SSM/I analysis: the melt-refreeze period lengthened for the majority of the YRB with later end of melt-refreeze and earlier melt onset; and positive Tb anomalies were found in recent years from glacier melt dynamics. From the AMSR-E analysis: early melt events throughout the YRB were most often associated with warm air intrusions and reflect a consistent spatial distribution; years and areas of earlier melt onset and refreeze had more forest fire occurrences suggesting melt timing\u27s effects extend to later seasons; and satellite derived melt timing served as an effective input for model simulation of discharge in remote, ungauged snow-dominated basins. The melt detection methodology and results present a new perspective on the changing cryosphere, provide an understanding of melt\u27s influence on other earth system processes, and develop a baseline from which to assess and evaluate future change. The temporal and spatial variability conveyed through the regional context of this research may be useful to communities in climate change adaptation planning

Sea-ice surface properties and their impact on the under-ice light field from remote sensing data and in-situ measurements

The surface properties of sea ice dominate many key processes and drive important feedback mechanisms in the polar oceans of both hemispheres. Examining Arctic and Antarctic sea ice, the distinctly different dominant sea-ice and snow properties in spring and summer are apparent. While Arctic sea ice features a seasonal snow cover with widespread surface ponding in summer, a year-round snow cover and strong surface flooding at the snow/ice interface is observed on Antarctic sea ice. However, substantial knowledge gaps exist about the spatial distribution and temporal evolution of these properties, and their impacts on exchange processes across the atmosphere/ocean interface. This thesis aims to overcome these limitations by quantifying the influence of surface properties on the energy and mass budgets in the ice-covered oceans. Remote sensing data and in-situ observations are combined to derive the seasonal cycle of dominant sea-ice surface characteristics, and their relation to the transfer of solar radiation from the atmosphere through snow and sea ice into the upper ocean. This thesis shows that characteristics of the solar radiation under Arctic sea ice can be described directly as a function of sea-ice surface properties as, e.g., sea-ice type and melt pond coverage. Using this parameterization, an Arctic-wide calculation of solar radiation through sea ice identifies the surface melt onset as the main driver of the annual sea-ice mass and energy budgets. In contrast, an analysis of the spring-summer transition of Antarctic sea ice using passive microwave satellite observations indicates widespread diurnal freeze-thaw cycles in the top snow layers. While the associated temporary thawing is identified as the predominant melt process, subsequent continuous melt in deeper snow layers is rarely found on Antarctic sea ice. Instead of directly influencing the snow depth on Antarctic sea ice, these melt processes rather modify the internal stratigraphy and vertical density structure of the snowpack. An additional analysis of satellite scatterometer observations reveals that snow volume loss on Antarctic sea ice is mainly driven by changes in the lower snowpack, due to the widespread presence of sea-ice surface flooding and snow-ice formation prior to changes in the upper snowpack. As a consequence, the largely heterogeneous and metamorphous Antarctic snowpack prevents a direct correlation between surface properties and the respective characteristics of the penetrating solar radiation under the sea ice. However, surface flooding is identified as the key process governing the variability of the under-ice light regime on small scales. Overall, this thesis highlights that the mass and energy budgets of Antarctic sea ice are determined by processes at the snow/ice interface as well as the temporal evolution of physical snowpack properties. These results are in great contrast to presented studies on Arctic sea ice, where seasonally alternating interactions at the atmosphere/snow- or atmosphere/sea-ice interface control both the energy and mass budgets. An improved understanding of the seasonal cycle of dominant sea-ice and snow surface characteristics in the Arctic and Antarctic is crucial for future investigations retrieving sea-ice variables, such as sea-ice thickness and snow depth, from recent microwave satellite observations

Forward Modelling of Multifrequency SAR Backscatter of Snow-Covered Lake Ice: Investigating Varying Snow and Ice Properties Within a Radiative Transfer Framework

Lakes are a key feature in the Northern Hemisphere landscape. The coverage of lakes by ice cover has important implications for local weather conditions and can influence energy balance. The presence of lake ice is also crucial for local economies, providing transportation routes, and acting as a source of recreation/tourism and local customs. Both lake ice cover, from which ice dates and duration can be derived (i.e., ice phenology), and ice thickness are considered as thematic variables of lakes as an essential climate variable by the Global Climate Observing System (GCOS) for understanding how climate is changing. However, the number of lake ice phenology ground observations has declined over the past three decades. Remote sensing provides a method of addressing this paucity in observations. Active microwave remote sensing, in particular synthetic aperture radar (SAR), is popular for monitoring ice cover as it does not rely on sunlight and the resolution allows for the monitoring of small and medium-sized lakes. In recent years, our understanding of the interaction between active microwave signals and lake ice has changed, shifting from a double bounce mechanism to single bounce at the ice-water interface. The single bounce, or surface scattering, at the ice-water interface is due to a rough surface and high dielectric contrast between ice and water. However, further work is needed to fully understand how changes in different lake ice properties impact active microwave signals. Radiative transfer modelling has been used to explore these interactions, but there are a variety of limitations associated with past experiments. This thesis aimed to faithfully represent lake ice using a radiative transfer framework and investigate how changes in lake ice properties impact active microwave backscatter. This knowledge was used to model backscatter throughout ice seasons under both dry and wet conditions. The radiative transfer framework used in this thesis was the Snow Microwave Radiative Transfer (SMRT) model. To investigate how broad changes in ice properties impact microwave backscatter, SMRT was used to conduct experiments on ice columns representing a shallow lake with tubular bubbles and a deep lake without tubular bubbles at L/C/X-band frequencies. The Canadian Lake Ice Model (CLIMo) was used to parameterize SMRT. Ice properties investigated included ice thickness, snow ice bubble radius and porosity, root mean square (RMS) height of the ice-water interface, correlation length of the ice-water interface, and tubular bubble radius and porosity. Modelled backscatter indicated that changes in ice thickness, snow ice porosity, and tubular bubble radius and porosity had little impact on microwave backscatter. The property that had the largest impact on backscatter was RMS height at the ice-water interface, confirming the results of other recent studies. L and C-band frequencies were found to be most sensitive to changes in RMS height. Bubble radius had a smaller impact on backscatter, but X-band was found to be most sensitive to changes in this property and would be a valuable frequency for studying surface ice conditions. From the results of these initial experiments, SMRT was then used to simulate the backscatter from lake ice for two lakes during different winter seasons. Malcolm Ramsay Lake near Churchill, Manitoba, represented a shallow lake with dense tubular bubbles and Noell lake near Inuvik, Northwest Territories, represented a deep lake with no tubular bubbles. Both field data and CLIMo simulations for the two lakes were used to parameterize SMRT. Because RMS height was determined to be the ice property that had the largest impact on backscatter, simulations focused on optimizing this value for both lakes. Modelled backscatter was validated using C-band satellite imagery for Noell Lake and L/C/X-band imagery for Malcolm Ramsay Lake. The root mean square error values for both lakes ranged from 0.38 to 2.33 dB and Spearman’s correlation coefficient (ρ) values >0.86. Modelled backscatter for Noell Lake was closer to observed values compared to Malcolm Ramsay Lake. Optimized values of RMS height provided a better fit compared to a stationary value and indicated that roughness likely increases rapidly at the start of the ice season but plateaus as ice growth slows. SMRT was found to model backscatter from ice cover well under dry conditions, however, modelling backscatter under wet conditions is equally important. Detailed field observations for Lake Oulujärvi in Finland were used to parameterize SMRT during three different conditions. The first was lake ice with a dry snow cover, the second with an overlying layer of wet snow, and the third was when a slush layer was present on the ice surface. Experiments conducted under dry conditions continued to support the dominance of scattering from the ice-water interface. However, when a layer of wet snow or slush layer was introduced the dominant scattering interface shifted to the new wet layer. Increased roughness at the boundary of these wet layers resulted in an increase in backscatter. The increase in backscatter is attributed to the higher dielectric constant value of these layers. The modelled backscatter was found to be representative of observed backscatter from Sentinel-1. The body of work of this thesis indicated that the SMRT framework can be used to faithfully represent lake ice and model backscatter from ice covers and improved understanding of the interaction between microwave backscatter and ice properties. With this improved understanding inversion models can be developed to retrieve roughness of the ice-water interface, this could be used to build other models to estimate ice thickness based on other remote sensing data. Additionally, insights into the impact of wet conditions on radar backscatter could prove useful in identifying unsafe ice locations

Community Development of the Snow Microwave Radiative Transfer Model for Passive, Active and Altimetry Observations of the Cryosphere

The Snow Microwave Radiative Transfer (SMRT) model was initially developed to explore the sensitivity of microwave scattering to snow microstructure for active and passive remote sensing applications. Here, we discuss the modular design of SMRT that has enabled its rapid extension by the community. SMRT can now represent a layered medium consisting of snow, land ice, lake ice and/or sea ice overlying a substrate of soil, water or parameterized by reflectivity. A time-dependent radiative transfer solution method has also been added to allow for low resolution mode altimetry applications. We illustrate the use of SMRT to simulate brightness temperature for snow on lake ice, backscatter for snow on soil and altimeter waveforms for snow on sea ice

Characterizing a Multi-Sensor System for Terrestrial Freshwater Remote Sensing via an Observing System Simulation Experiment (OSSE)

Terrestrial freshwater storage (TWS) is the vertically-integrated sum of snow, ice, soilmoisture, vegetation water content, surface water impoundments, and groundwater. Among these components, snow, soil moisture, and vegetation are the most dynamic (i.e., shortest residence time) as well as the most variable across space. However, accurately retrieving estimates of snow, soil moisture, or vegetation using space-borne sensors often requires simultaneous knowledge of one or more of the other components. In other words, reasonably characterizing terrestrial freshwater requires careful consideration of the coupled snow-soil moisture-vegetation response that is implicit in both TWS and the hydrologic cycle. One challenge is to optimally determine the multi-variate, multi-sensor remote sensing observations needed to best characterize the coupled snow-soil moisture-vegetation system. Different types of sensors each have their own unique strengths and limitations. Meanwhile, remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations will dictate much of the efficacy in capturing the dynamics of the coupled snow-soil moisture-vegetation response. This study investigates different snow sensors and simulates the sensor coverage as a function of different orbital configurations and sensor properties in order to quantify the discontinuous nature of remotely-sensed observations across space and time. The information gleaned from this analysis, coupled with a time-varying snow binary map, is used to evaluate the efficacy of a single sensor (or constellation of sensors) to estimate terrestrial snow on a global scale. A suite of different combinations, and permutations, of different sensors, including different orbital characteristics, is explored with respect to 1-day, 3-day, and 30-day repeat intervals. The results show what can, and what cannot, be observed by different sensors. The results suggest that no single sensor can accurately measure all types of snow, but that a constellation composed of different types of sensors could better compensate for the limitations of a single type of sensor. Even though only snow is studied here, a similar procedure could be conducted for soil moisture or vegetation. To better investigate the coupled snow-soil moisture-vegetation system, an observing system simulation experiment (OSSE) is designed in order to explore the value of coordinated observations of these three separate, yet mutually dependent, state variables. In the experiment, a “synthetic truth” of snow water equivalent, surface soil moisture, and/or vegetation biomass is generated using the NoahMP-4.0.1 land surface model within the NASA Land Information System (LIS). Afterwards, a series of hypothetical sensors with different orbital configurations is prescribed in order to retrieve snow, soil moisture, and vegetation. The ground track and footprint of each sensor is approximated using the Trade-space Analysis Tool for Constellations (TAT-C) simulator. A space-time subsampler predicated on the output from TAT-C is then applied to the synthetic truth. Furthermore, a hypothesized amount of observation error is injected into the synthetic truth in order to yield a realistic synthetic retrieval for each of the hypothetical sensor configurations considered as part of this dissertation. The synthetic retrievals are then assimilated into the NoahMP-4.0.1 land surface model using different boundary conditions from those used to generate the synthetic truth such that the differences between the two sets of boundary conditions serve as a realistic proxy for real-world boundary condition errors. A baseline Open Loop simulation where no retrievals are assimilated is conducted in order to evaluate the added utility associated with assimilation of one (or more) of the synthetic retrievals. The impact of the assimilation of a given suite of one or more retrievals on land surface model estimates of snow, soil moisture, vegetation, and runoff serve as a numeric laboratory in order to assess which sensor(s), either separate or in a coordinated fashion, yield the most utility in terms of improved model performance. The results from this OSSE show that the assimilation of a single type of retrieval (i.e., snow or soil moisture or vegetation) may only improve the estimation of a small part of the snow-soil moisture-vegetation system, but may also degrade of other parts of that same system. Alternatively, the assimilation of more than one type of retrieval may yield greater benefits to all the components of the snow-soil moisture-vegetation system, because it yields a more complete, holistic view of the coupled system. This OSSE framework could potentially serve as an aid to mission planners in determining how to get the most observational “bang for the buck” based on the myriad of different sensor types, orbital configurations, and error characteristics available in the selection of a future terrestrial freshwater mission