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The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own strengths and limitations. For example, remote sensing has the strength of maintaining the spatial variability of near-surface soil moisture, while in-situ measurements are accurate and preserve the dynamics range of soil moisture at both surface and larger depths. Hence, this study is aimed at (1) merging the strength of SMAP with in-situ measurements and (2) exploring the effectiveness of merged SMAP/in-situ soil moisture in improving ensemble streamflow forecasts. The conditional merging technique was adopted to merge the SMAP-enhanced soil moisture (9 km) and its downscaled version (1 km) separately with the in-situ soil moisture collected over the au Saumon watershed, a 1025 km2 watershed located in Eastern Canada. The random forest machine learning technique was used for downscaling of the near-surface SMAP-enhanced soil moisture to 1 km resolution, whereas the exponential filter was used for vertical extrapolation of the SMAP near-surface soil moisture. A simple data assimilation technique known as direct insertion was used to update the topsoil layer of a physically-based distributed hydrological model with four soil moisture products: (1) the merged SMAP/in-situ soil moisture at 9 and 1 km resolutions; (2) the original SMAP-enhanced (9 km), (3) downscaled SMAP-enhanced (1 km), and (4) interpolated in-situ surface soil moisture. In addition, the vertically extrapolated merged SMAP/in-situ soil moisture and subsurface (rootzone) in-situ soil moisture were used to update the intermediate layer of the model. Results indicate that downscaling of the SMAP-enhanced soil moisture to 1 km resolution improved the spatial variability of soil moisture while maintaining the spatial pattern of its original counterpart. Similarly, merging of the SMAP with in- situ soil moisture preserved the dynamic range of in-situ soil moisture and maintained the spatial heterogeneity of SMAP soil moisture. Updating of the top layer of the model with the 1 km merged SMAP/in-situ soil moisture improved the ensemble streamflow forecast compared to the model updated with either the SMAP-enhanced or in-situ soil moisture alone. On the other hand, updating the top and intermediate layers of the model with surface and vertically extrapolated SMAP/in-situ soil moisture, respectively, did not further improve the accuracy of the ensemble streamflow forecast. Overall, this study demonstrated the potential of merging the SMAP and in-situ soil moisture for streamflow forecast. [ABSTRACT FROM AUTHOR]

Lakes are important sources of freshwater and provide essential ecosystem services. Monitoring their spatial and temporal variability, and their functions, is an important task within the development of sustainable water management strategies. The Surface Water and Ocean Topography (SWOT) mission will provide continuous information on the dynamics of continental (rivers, lakes, wetlands and reservoirs) and ocean water bodies. This work aims to contribute to the international effort evaluating the SWOT satellite (2022 launch) performance for water balance assessment over large lakes (e.g., >100 km 2). For this purpose, a hydrodynamic model was set up over Mamawi Lake, Canada, and different wind scenarios on lake hydrodynamics were simulated. The derived water surface elevations (WSEs) were compared to synthetic elevations produced by the Jet Propulsion Laboratory (JPL) SWOT high resolution (SWOT-HR) simulator. Moreover, water storages and net flows were retrieved from different possible SWOT orbital configurations and synthetic gauge measurements. In general, a good agreement was found between the WSE simulated from the model and those mimicked by the SWOT-HR simulator. Depending on the wind scenario, errors ranged between approximately -2 and 5 cm for mean error and from 30 to 70 cm root mean square error. Low spatial coverage of the lake was found to generate important biases in the retrievals of water volume or net flow between two satellite passes in the presence of local heterogeneities in WSE. However, the precision of retrievals was found to increase as spatial coverage increases, becoming more reliable than the retrievals from three synthetic gauges when spatial coverage approaches 100 %, demonstrating the capabilities of the future SWOT mission in monitoring dynamic WSE for large lakes across Canada. [ABSTRACT FROM AUTHOR]

The moisture maximization approach has a simple technique for controlling the risk of overestimating the Probable Maximum Precipitation (PMP): the maximization ratio is limited by an upper bound. The upper bound limit depends on storm records and watershed characteristics. However, it is not readily available in many watersheds. A robust scientific justification for limiting the maximization ratio is missing. In this paper, a novel approach is proposed to estimate the maximization ratio which does not impose an upper limit. The new approach, which uses regional climate model data, is based on constructing annual maximum precipitable water time series with precipitable water values for which atmospheric variables are similar to the original event to be maximized. These time series are then used to estimate the 100 year return period of the precipitable water value required to calculate the ratio. The new approach was tested in three watersheds in the province of Québec, Canada. Results showed that maximization ratio values were lower than the proposed upper bound value for these watersheds. In comparison to the approach using an upper bound, the proposed approach reduced the PMPs in these watersheds by 11%. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze relationships between the variability of MODIS derived snow covered area (SCA) and flood dynamics for the management of reservoirs. The study was conducted in the Nechako River Basin in British Columbia (Canada). Relationships were analyzed between daily SCA calculated from filtered MODIS images and the fraction of the flood volume that had already entered the reservoir during the snowmelt period. Results indicate a robust (R = 0.87) and significant ( p < 0.001) correlation over a 14-years (2000-2013) period. A similar analysis was conducted for each sub-basin, producing an even more robust relationship (R = 0.90) for the Tahtsa Lake sub-basin. Two distinct relationships corresponding to years with either large or small spring runoff were identified by classifying years according to their maximum snow water equivalents (SWE), peak inflows and flood volume, thus improving the correlations. These empirical relationships are a simple forecasting tool that does not require any additional data other than MODIS SCA and SWE measurements. This straightforward approach can be applied to other mountainous watersheds dominated by snow accumulation and melt. [ABSTRACT FROM AUTHOR]

Climate change will have a significant impact on the hydrological cycle. This paper presents the results of a pilot project for the Yamaska River in Québec. The objective of this project is to evaluate the river's vulnerability to low flows attributable to climate change and to analyze a no-regrets adaptation strategy at locations identified as vulnerable. The vulnerability was evaluated using statistical indicators (low flow indices) based on long-term observations at four locations in the basin. A distributed physically-based hydrological model in use in Québec was calibrated and validated against observed streamflow data to properly represent low flows. Hydrological simulations used seven climate projections provided by the north american regional climate change assessment program (NARCCAP) s project. Also, five members of the canadian regional climate model (CRCM), nested with the coupled global climate model (CGCM) under the special report on emission scenarios (SRES) A2 emission scenario, were run for a reference (1971-2000) period and a future (2041-2070) period. Streamflow simulations indicate a degradation of future low flow conditions, particularly in June and August, when compared to the reference period. In addition, the 7-day low flow value with a 2-year return period (7Q2) and the 7-day low flow value with a 10-year return period (7Q10) decrease by 16-64% and 18-45% respectively. A no-regrets adaptation strategy allowing stakeholders to reduce withdrawal according to alert levels was implemented. Simulations of the application of the no-regrets adaptation strategy reduced the number of days where streamflows are below the Crisis level in the future period by at least 20%. [ABSTRACT FROM AUTHOR]

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[1] This study is focused on assessing a summer-fall probable maximum precipitation (PMP) under recent climate conditions and then applying it under a future projected climate using output information from a regional climate model. The estimated PMPs are forced into a hydrological model to investigate potential changes in probable maximum flood (PMF) values. The PMP method is based on the moisture maximization method developed by the World Meteorological Organization. Precipitable water amounts are evaluated using data from the Canadian Regional Climate Model. The approach was tested on the Manic-5 River basin in Canada. Results show the PMP intensity could increase by 0.5-6% for 48 h and 72 h PMP values by the 2071-2100 horizon. These PMP values were used to assess projected PMF values. A lumped conceptual hydrological model was calibrated under recent climate values and model parameters were kept unchanged for modeling future hydrological regimes. Hydrological modeling results indicate modest changes of the PMF values for future climate projections, with no clearly identified upward or downward trends. The study also highlighted the need for a coherent approach to estimate PMF values under future climate conditions using projected PMP estimates, since the current practice of simulating the PMF value by inserting half of a PMP six days prior to the PMP event in a meteorological time series did not produce consistent results. An approach based on a random insertion of the PMP into a meteorological time series is a promising avenue to explore. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081-2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study. [ABSTRACT FROM AUTHOR]

The behaviour of the water resource system of the Peribonka River (Quebec, Canada) exploited for hydropower is evaluated under various hydrological regimes, using different climate change scenarios. The hydrological regime of the recent past and the regimes of 30 climate projections are considered. The potential hydrological regimes are simulated for climate projections from five general circulation models (GCM) for two greenhouse gas emission scenarios and three temporal horizons (2020, 2050 and 2080). For each hydrological regime, weekly reservoir operating rules are calculated with a dynamic and stochastic optimization model. Simulations of the water resource system with adapted operating rules in these climate change contexts are compared with the management of the water resource system at the control period (1961–1990). For the majority of climate projections, the analysis of simulations in the context of climate change shows an increase in hydropower and in annual unproductive spills. These increases reach 22% and 300%, respectively, compared to the control period. Also, the reliability of a reservoir is compromised for half of the climate projections, with annual probabilities reaching above the maximum operating levels, up to 0.3%, whereas these probabilities were null for the control period. Despite the rise in production, the annual efficiency of the power plants would fluctuate between −5 to +8%, depending on the power plant, the climate projection and the horizon. [ABSTRACT FROM AUTHOR]

This study aims at evaluating the hydrologic impacts of climate change on the Châteauguay River basin in the province of Quebec, Canada. Three global climate models (GCMs) covering a range of climate sensitivities were selected, and their output was employed to adjust the parameters of a stochastic weather generator using simple transformation rules for precipitation and temperature. Values of monthly precipitation and temperature were extracted from the GCMs for the current (1960–1990) and future (2040–2060) climate. The International Panel on Climate Change emission scenario known as B2 was selected. It represents an average scenario and corresponds approximately to a doubling of the atmospheric CO2 concentration. Resorting to stochastically generated climate scenarios allowed assessing whether the modelled effects of climate change on flows were statistically significant. Results indicate that spring and summer–fall peak flows were reduced on average by 30% and 12%, respectively, using the Echam4 model derived scenarios. The Hadcm3 model produced a weaker signal that was not statistically significant. The CGCM2 model produced a statistically significant reduction in spring peak flows of 8% on average, whereas the simulated reduction in summer flows was not statistically significant for many of the return periods considered. Many sources of uncertainties were partially considered in this study. One is the downscaling of the GCM climatology at the watershed scale. The approach employed to generate the future climate scenarios changed the precipitation variability through an adjustment of the parameters of the Gamma distribution function used to model precipitation amounts. Whether this approach is truly typical of climate change effect remains to be ascertained. Using more physically based hydrological models would help reduce uncertainties in climate change impacts studies. [ABSTRACT FROM AUTHOR]

This study utilized the hydrodynamic model, ONE-D, coupled to the distributed hydrological model WATFLOOD, to evaluate the potential effects of a shift in climate on the hydrological regimes of three large lakes (Athabasca, Claire, and Mamawi), and two important sources of inflow (the Peace and Athabasca rivers) in the Peace-Athabasca Delta (PAD). The coupled WATFLOOD/ONE-D system was forced by current climatology and downscaled climate change scenarios from five selected general circulation models (GCMs). Under the selected climate change scenarios, water levels in Lakes Athabasca. Claire, and Mamawi peaked, on average, 40-50 days earlier than at present. Some GCM scenarios predicted an increase in peak lake levels while others predicted suppressed lake levels. Inter-annual water level variability was also sensitive to predicted changes in precipitation and temperature, increasing in winter and decreasing in summer, Water level fluctuations in the major input rivers of the PAD were found to be more variable under the climate change simulations. Moreover, spring freshet peaks were estimated to occur earlier (20-30 days) and to be considerably reduced (up to 1-0 m reductions). Although the simulations converged towards the same general results in seasonality shift, flow level amplitude was GCM-dependent. Simple downscaling methods may well be too coarse to adequately address the important spatial variations that can occur in the long-term climate signal. It is therefore important to understand the sensitivity of this regime to local climatic influences to produce more reliable, quantitative results. An ensemble of approaches that provide meaningfully downscaled results should be considered to confirm the results presented. [ABSTRACT FROM AUTHOR]

This study conducted a systematic examination of the flood hydrology of the Peace-Athabasca Delta (PAD), a complex and internationally important freshwater ecosystem located in northwestern Canada. Three distinct zones of floodwater origin within the PAD were discerned on the basis of the dominant hydrology and the topography represented by a digital-elevation model (DEM): (1) perimeter Peace Delta, (2) perimeter Athabasca Delta, and (3) central delta lakes. Analysis of the satellite image and DEM-derived flood maps of historically significant events, combined with field data of open-water and ice-influenced flood stages, revealed that ice-jamming was the most effective and likely the only mechanism capable of recharging the highly elevated perimeter areas in the Athabasca and Peace Deltas. This supports the conclusion of previous studies that used hydrometric data external to the delta to inter inter-delta flood conditions. By contrast, even the record open water flow to the Peace Delta could not generate overbank flow. Hydraulic simulation of the naturalized system (no dam & no weirs) suggests that overbanking of the lower Peace River would have been a rare occurrence without the effects of regulation. Both ice-jam induced and open-water high waters were found to recharge perched basins in the Athabasca Delta. Enlargement of the central delta lakes beyond the shoreline was determined to be an effective flood mechanism for filling low to mid-elevation wetlands. Comparison of the observed and naturalized lake levels indicates that flow regulation of the Peace River and addition of the weirs on delta outflow channels has, depending on the year, led to an increase or a reduction in the annual maximum potential of inland flooding via lake expansion. [ABSTRACT FROM AUTHOR]

Many watersheds in Canada and in the northern United States see most of their precipitation in the form of snow. Many of these watersheds are the sites of important hydropower development projects. During snowmelt, watershed managers require information on snowpack depletion in order to optimize power production while minimizing flooding risk. In many cases, management techniques are based on simple correlations extracted from data collected in previous years or on inappropriate tools for data interpretation. Both of these factors can affect the reliability of forecasts and result in production losses or increased risk in downstream areas. This paper presents an approach to improve snowmelt forecasts. A simple distributed snowmelt model based on the degree-days approach is used to predict snowmelt based on weather forecasts. To improve forecasts, a feedback algorithm is presented that allows for real-time model adjustment using the integration of NOAA-AVHRR remote sensing data. A case study is presented based on a central Quebec watershed for the 1999 snowmelt season. This watershed is mostly under the management of Alcan Inc., which uses hydropower for the production of aluminum in its Jonquière, Que., Canada plant. A real-time simulation was carried out that resulted in a significant improvement of the timing of the flood peak forecast. With only one satellite image, the forecasting error of the flood peak was decreased by 5 days (from 7 to 2) and reduced to less than 1 day with the use of a second image, acquired 2 days later. For this watershed, each one-day improvement in the timing of the peak flood forecast is worth tens of thousands of dollars in hydropower. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of the use of an externally linked (MOBIDIC-MODFLOW) and a physically based (MIKE SHE) surface water-groundwater model to capture the integrated hydrologic responses of the Thomas Brook catchment, in Canada. The main objective of the study is to investigate the effect of simplification in representation of the hydrological processes in MOBIDIC-MODFLOW on its simulation accuracy. To this aim, MOBIDIC and MODFLOW were coupled in order to sequentially exchange the groundwater recharge and baseflow discharges within each computation time step. Using identical sets of hydrogeological properties for the two models, the coefficients of the gravity and capillary reservoirs in MOBIDIC were calibrated so as to closely predict the hydrological budget of the catchment simulated with MIKE SHE. The simulated results show that the two models can closely replicate the observed water table responses at two monitoring wells. However, in very shallow water table locations, the instantaneous response of the water table was not precisely captured in MOBIDIC-MODFLOW. Additionally, the simplified conceptualization of the unsaturated flow in MOBIDIC-MODFLOW resulted in overestimated groundwater recharge during spring and underestimation during summer. Moreover, the computational efficiency of MOBIDIC-MODFLOW, as compared to MIKE SHE, along with less required input data, confirms its potential for regional scale groundwater-surface water interaction modelling applications. [ABSTRACT FROM AUTHOR]

Examines the impact of climate change on seasonal floods of a southern Quebec River Basin. Generation of 24-hour rainfall hyetographs using region-specific cumulative distributions provided by the Canadian Atmospheric Environment Service; Use of the hyetographs as inputs to study hydrology-hydraulics model to simulate hydrographs; Possibility of increase in the volume of runoff with climatic change.

The potential of remote sensing for providing information to hydrologists and water resources practitioners has been recognized since the1970s. The variety of satellite and airborne platforms and the greater ease of access to imagery now make it possible to evaluate and quantify an increasingly large number of watershed physical characteristics and state variables. Canadian scientists have been very active over the last 5 years creating algorithms to extract hydrological information from remotely sensed data and to develop new, or adapt existing, hydrological methods capable of making efficient use of this new information. Over the years, research and applications of remote sensing in Canadian hydrology have embraced a variety of topics and recentresearch has placed significant emphasis on radar remote sensing as the Canadian RADARSAT satellite was launched successfully on 4 November 1995. This paper reviews recent (1995-99) remote sensing contributions in hydrology by Canadians, specifically focusing on the usefulness and applicability of current remote sensing technology for water management purposes. A very brief description of the theory underlyingeach application as well as relevant sensors is presented. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the effects of model structure and parameter equifinality on the uncertainty related to hydrological modelling in climate change impact studies. The study is conducted on a snow-dominated watershed located in the southern part of the province of Quebec (Canada). Hydrological model structure uncertainty is examined through the use of two very different simulation tools, one lumped conceptual model and one spatially-distributed physically-based model. Parameter equifinality is examined by performing multiple automatic calibrations with both hydrological models. The analysis is first carried out under recent past climate and then under modified climate conditions following two contrasted projections that are analysed separately. The delta change approach is used to build the two climate projections. Overall, this study reveals that the impact of hydrological model structure uncertainty is more significant than the effect of parameter uncertainty, under recent past climate as well as future climate conditions. Ultimately, the use of hydrological models with different levels of complexity should be considered as part of the global uncertainty related to hydrological model structure. [Copyright &y& Elsevier]

Summary: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution. [Copyright &y& Elsevier]

Summary: The impact of climate change on the hydrology of the Chute-du-Diable watershed (Quebec, Canada) is studied by comparing statistics on current and projected future discharge resulting from a wide range of climate change scenarios. The use of 10 equally weighted climate projections from a combination of 5 general circulation models (GCMs) and 2 greenhouse gas emission scenarios (GHGES) allows for the definition of an uncertainty envelope of future hydrologic variables. GCM data is downscaled using the change factor approach for 30-year time slices centered around years 2020, 2050 and 2080. To estimate natural variability, synthetic time series are then computed for each horizon and for each climate change scenario, using a stochastic weather generator (30 series of 30 years), and are entered into a hydrology model. Future hydrological regimes are then compared to the control period (1961–1990) using the annual and seasonal mean discharge, peak discharge and timing of peak discharge criteria. Results indicate a 1–14°C increase in seasonal temperature and a −9 to +55% change in seasonal precipitation. The largest increases in both temperature and precipitation are observed in the winter and spring seasons. The main hydrologic impact observed is a spring flood appearing 1–5 weeks earlier than usual and a modification of its amplitude from −40 to +25%. Most scenarios suggest increases in the winter, spring and fall discharge, whereas summer is expected to see a decrease in discharge. While there is still a large scatter in projected values, the uncertainty analysis projects a better view of the most probable future hydrologic behaviour of the watershed. Of all sources of uncertainty considered in this study, the largest comes from the choice of a GCM. Accordingly, all impact studies based on results from only one GCM should be interpreted with caution. [Copyright &y& Elsevier]

Abstract: The physical characteristics of air inclusions embedded in the ice covers of the Saint François River (Quebec, Canada) and the Athabasca River (Alberta, Canada) are studied because of the importance of such inclusions to the analysis and interpretation of Synthetic Aperture Radar satellite images used to characterize river ice. Studies of ice cores sampled from these two rivers show that the concentration of air inclusions in the ice cover is highly dependent on both the ice type as well as the rate of freezing. When this rate is slow, the ice cover will have few air inclusions. However, when it is rapid and sustained in duration, the amount and cross sectional diameter of these inclusions increase. Air inclusions in the snow ice were found to be spherical and ranging in size from just a few millimetres up to about a centimetre. Generally, air inclusions formed in the sampled columnar ice were sparse and either spherical or tubular in shape. Air inclusions in the frazil ice were found to present a different structure from those formed in the other ice types. In most cases, their shape were irregular and their distribution was inhomogeneous. The study of these inclusions under a microscope showed them to have angular boundaries with a whitish appearance. These results will be valuable to the development of radar backscatter analysis algorithms for river ice characterization. [Copyright &y& Elsevier]