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Ice-jam floods are a common, but dangerous natural phenomena occurring in many northern rivers. These events are even more dangerous when only limited gauged river data is available to be used to predict the severity of events in ice-jam prone areas, especially when these areas are close to a community. This study examines an alternative method for gathering ungauged river data for ice-jam flood hazard assessments, specifically the use of dendrogeomorphological methodologies. The most accurate way to gather this information is through collection of cross-sectional samples from trees that have been scarred by past ice-jam events, rather than the collection of core samples. Therefore, cross-sectional sampling methods were chosen for this study. The years and heights of past ice-jam events were then compared to instantaneous maximum water levels collected from a nearby river gauge to evaluate whether the dendrogeomorphological findings were accurate. Our results show that there is a direct correlation between gauged river data and dendrogeomorphological findings, with signs of ice-jamming events identified in the trees from the five highest years of instantaneous maximum water levels. Additionally, a correlation between the peak events in the two data sources was also revealed. [ABSTRACT FROM AUTHOR]

Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences. [ABSTRACT FROM AUTHOR]

Identification of integrated models is still hindered by submodels' uncertainty propagation. In this article, a novel identifiability and identification framework is applied to screen and establish reasonable hypotheses of an integrated instream (WASP) and catchment water quality (VENSIM) model. Using the framework, the models were linked, and critical parameters and processes identified. First, an ensemble of catchment nutrient loads was simulated with randomized parameter settings of the catchment processes (e.g. nutrient decay rates). A second Monte Carlo analysis was then staged with randomized loadings and parameter values mimicking insteam processes (e.g. algae growth). The most significant parameters and their processes were identified. This coupling of models for a two-step global sensitivity analysis is a novel approach to integrated catchment-scale water quality model identification. Catchment processes were, overall, more significant to the river's water quality than the instream processes of this Prairie river system investigated (Qu'Appelle River). [ABSTRACT FROM AUTHOR]

This article addresses the question: What is expected from frazil ice activity in rivers, taking into account the changing climate? It begins with an overview of what frazil ice is and what is required for the occurrence of frazil ice events, namely a supercooled water column. Methodologies to anticipate frazil ice events in the short term are based on air temperature and water discharge, underlining the significance of these two parameters for any predictive methods. Longer-term approaches, calibrated against past events (hindcasting), are used to anticipate frazil ice activity into the future, with indicators such as frazil ice risk, water temperature and frazil volume. Any of these approaches could conceivably be applied to frazil-prone river stretches. To assess climate impact, each location should be treated separately. River ice dynamics can lead to the formation of a hanging dam, a frequent outcome of frazil ice generation in the early winter, causing flow restriction. Flood modeling and forecasting capabilities have been developed and implemented for operational use. More frequent mid-winter breakups are expected to extend the occurrence of frazil ice events into the winter months – the prediction of these will require climate model output to adequately capture month-to-month variability. [ABSTRACT FROM AUTHOR]

Satellites are designed to monitor geospatial data over large areas at a catchment scale. However, most of satellite validation works are conducted at local point scales with a lack of spatial representativeness. Although upscaling them with a spatial average of several point data collected in the field, it is almost impossible to reorganize backscattering responses at pixel scales. Considering the influence of soil storage on watershed streamflow, we thus suggested watershed-scale hydrological validation. In addition, to overcome the limitations of backscattering models that are widely used for C-band Synthetic Aperture Radar (SAR) soil moisture but applied to bare soils only, in this study, RADARSAT-2 soil moisture was stochastically retrieved to correct vegetation effects arising from agricultural lands. Roughness-corrected soil moisture retrievals were assessed at various spatial scales over the Brightwater Creek basin (land cover: crop lands, gross drainage area: 1540 km2) in Saskatchewan, Canada. At the point scale, local station data showed that the Root Mean Square Errors (RMSEs), Unbiased RMSEs (ubRMSEs) and biases of Radarsat-2 were 0.06~0.09 m3/m3, 0.04~0.08 m3/m3 and 0.01~0.05 m3/m3, respectively, while 1 km Soil Moisture Active Passive (SMAP) showed underestimation at RMSEs of 0.1~0.22 m3/m3 and biases of −0.036~−0.2080 m3/m3. Although SMAP soil moisture better distinguished the contributing area at the catchment scale, Radarsat-2 soil moisture showed a better discharge hysteresis. A reliable estimation of the soil storage dynamics is more important for discharge forecasting than a static classification of contributing and noncontributing areas. [ABSTRACT FROM AUTHOR]

In the spring of 2020, the town of Fort McMurray, which lies on the banks of the Athabasca River, experienced an ice-jam flood event that was the most severe in approximately 60 years. In order to capture the severity of the event, a stochastic modelling approach, previously developed by the author for ice-jam flood forecasting, has been refined for ice-jam flood hazard and risk assessments and ice-jam mitigation feasibility studies, which is the subject of this paper. Scenarios of artificial breakage demonstrate the applicability of the revised modelling framework. [ABSTRACT FROM AUTHOR]

The article focuses on ice-jam flooding can be more severe than open-water flooding, leading to human casualties, damages to property and infrastructure, and adverse impacts on the ecology. Topics include the ice-jam flood hazard delineation and risk analysis are important measures for flood preparation, mitigation, and management strategies, and the techniques for open-water flood hazard and risk assessment are well established, methodologies and techniques for ice-jam flood hazard.

• A novel, operational real-time ice-jam flood forecasting system is presented. • The framework captures stochastic nature of river ice processes. • Forecasts are placed in a probabilistic context in the form of flood hazard maps. • The volume of ice remains the most uncertain variable in the forecasting. Forecasting ice jams and their consequential flooding is more challenging than predicting open water flood conditions. This is due to the chaotic nature of ice jam formation since slight changes in water and ice flows, location of the ice jam toe along the river and initial water levels at the time of jam formation can lead to marked differences in the outcome of backwater level elevations and flood severity. In this paper, we introduce a novel, operational real-time flood forecasting system that captures this stochastic nature of ice-jam floods and places the forecasts in a probabilistic context in the form of flood hazard maps (probability of flood extents and depths). This novel system was tested successfully for the ice-cover breakup period in the spring of 2018 along the Athabasca River at the Town of Fort McMurray, Canada. [ABSTRACT FROM AUTHOR]

Evaluating driving factors of river ice freeze-up and break-up is critical for the understanding associated with river ice processes, as well as for providing a means to predict potential natural hazards of river ice formation or deformation. Spatiotemporal changes from fifteen years (2000–2015) of MODIS reflectance band 2 images were analyzed in pixel-wise linear regressions to be used as an indicator of the rates of ice freeze-up and break-up along the Slave River (Canada). The random forest algorithm was used to model the rates using river corridor slope, effective width, shape, and sinuosity as a set of predictor variables. The results reveal that the selected predictor variables can explain about 80% and 70% of the variations in ice freeze-up and break-up rates along the Slave River respectively. The slope was the most important factor in determining both freeze-up and break-up rates, followed by shape, effective width, and sinuosity. The spatiotemporal patterns of river freeze-up and ice-cover break-up obtained from the analysis of MODIS data and geomorphological variables show strong agreement with ground observations and hydrometeorological conditions along the Slave River. [ABSTRACT FROM AUTHOR]

Highlights • Seasonality, and not diffuse loading drives eutrophication of a river-lake system. • Nutrient and phytoplankton cycles predominantly govern the upper, eutrophic system. • P & N cycles, diffuse loading, and DO balance regulate the middle reach. • Phosphorus release from sediments plays a key role in nutrient dynamics. Abstract Located on the same river, the degree of eutrophication in the upper and middle reaches of the Qu'Appelle River in Saskatchewan are different. While the upper Qu'Appelle is eutrophic, the middle Qu'Appelle River is hypereutrophic. To manage the river sustainably, there is a need to understand key processes governing eutrophication in both systems. In this study, a comprehensive global sensitivity analysis technique, Variogram Analysis of Response Surface (VARS), was applied to gain insights to the functioning of the two systems. Eutrophication in both systems was modelled using the Water quality Analysis Simulation Program (WASP 7.52). The performance of the model to predict key variables of eutrophication was measured using relative root mean square error. The global sensitivity analyses showed that although diffuse loading has significant influence on the systems, prevailing processes governing eutrophic state in the upper Qu'Appelle River include: nutrient and, phytoplankton cycles. Meanwhile, in the middle Qu'Appelle River a number of processes including phytoplankton cycle, nutrient cycle, diffuse loading and DO balance together sustain its hypereutrophic state. [ABSTRACT FROM AUTHOR]

Delineation of flood hazard and risk on maps is useful as a means of public education and as a basis for measures aimed at lessening future flood damages. In many northern countries, rivers and streams are prone to ice-related flooding that often results in higher water levels and more extensive damages than open-water events. Procedures and standards for analysing ice-related flooding, however, are much less common than well-established standardized approaches for the open-water events. Nonetheless, the inherent flood hazard along many northern and mid-latitude rivers is not fully represented on flood-plain, flood-hazard, and flood-risk mapping if the possibility of ice-jam floods is ignored. Fortunately, the biophysical, past-flood, and flood-envelope approaches for flood hazard can be readily applied to ice-related floods, and hydrotechnical approaches based on an improved understanding of river-ice processes have been developed. In this paper, the nature and severity of ice-jam flooding, the present status of delineating ice-related flood events, and challenges to delineating ice-related floods are discussed. [ABSTRACT FROM AUTHOR]

Monitoring spatiotemporal variations of river ice covers is critical for selecting safe ice transportation routes. The coherence of synthetic aperture radar (SAR) interferometry (InSAR) conveys important information on variations of river ice characteristics. However, limited research has been conducted to determine the coherence of multi-frequency L-band ALOS-1 PALSAR, C-band Sentinel-1, and Radarsat-2 images, for such applications. This study was piloted using imagery acquired from the Slave River Delta, Canada, to fill this knowledge gap. The findings were validated using ice core data extracted near Fort Smith. The results indicate that the revisit interval of the satellite is a governing factor of InSAR coherence loss. The ALOS-1 PALSAR images have the smallest coherence mainly due to the 46-day revisit interval and are the least promising for monitoring spatiotemporal variations of river ice covers. Coherence of both Sentinel-1A with a 12-day revisit interval and Radarsat-2 images with a 24-day revisit interval is an important aid for determining safe ice crossings. [ABSTRACT FROM AUTHOR]

A novel approach is presented for model calibration and global sensitivity analyses in which stage frequency distributions (SFD) are used as objective functions, not single values or time series, for example water levels or flows, which are traditionally used in hydraulic and hydrological modelling. This allows the stochastic nature of the input parameter to be characterised which is an important feature for some hydraulic processes of northern rivers such as ice jam formation. The observation SFD can also be used as part of an objective function for global sensitivity analysis, also a novelty presented in this paper, to determine the sensitivity of input parameter and boundary condition frequency distributions on different flooding regimes. [ABSTRACT FROM AUTHOR]

Communities along the Slave River are concerned about changes in the river ice cover, such as the formation of air pockets and double layers of ice in the ice cover. These anomalies make it challenging to find suitable locations for river ice crossings in winter, necessary for the transportation of goods to and from remote regions and to access hunting, trapping and fishing areas. This study utilized satellite imagery and field observations to document these anomalies of the river ice during the winters from November 2013 to March 2016. Backscatter analysis of the satellite imagery identified low and high density areas of air pockets along the river. Satellite images also revealed cracks and flooding of the river ice, particularly along the downstream reach of the river. [ABSTRACT FROM AUTHOR]

Inaccuracies in topographic data can be a significant source of error and uncertainty for hydrologic, atmospheric-land exchange and climate change modeling. This study was conducted to examine how different data sources and techniques used to generate digital elevation models (DEM) influence DEM accuracy. Using the interferometric synthetic aperture radar (InSAR) technique, the system acquisition parameters and the topographic characteristics are important factors in determining DEM quality. ICESat elevation footprints and highly accurate ground control points (based on GPS measurements) are used as independent references to validate the accuracy of DEMs. The TanDEM-X DEM shows the highest accuracy among all studied DEM products, with the root mean square error (RMSE) of 2.9 m, followed by 3.3 m for Canadian digital elevation data, 5.2 m for ASTER-GDEM, and 5.5 m for ALOS-PALSAR DEM. The RMSEs of the RADARSAT-2 based InSAR DEM and global GTOPO30 are considerably higher than the other DEMs, 24 m and 36 m, respectively. The interferograms of RADARSAT-2 and ALOS-PALSAR data acquired in early winter resulted in higher DEM accuracy compared with the summer image pairs, even with their lower InSAR coherence. This is attributed to the higher importance of perpendicular baseline and height of ambiguity and potentially the significant influence of atmospheric phase delay in DEM generation. We also compared the accuracy of DEMs of different land cover types and their representation of the land-water surface. The results show that vegetated areas of coniferous and broadleaf trees (i.e., white and black spruce, trembling aspen, balsam poplar) seem to be the main sources of error in all DEMs. The river slopes derived from the TanDEM-X, ASTER, and ALOS-PALSAR DEMs show patterns consistent with the river bed slope from our river bathymetry survey. Further studies are necessary to evaluate the performance of those different DEMs in earth surface modeling as well as to improve the quality of high error DEMs such as RADARSAT-2 data in this study. The Slave River Delta in the Northwest Territories of Canada was used as a test case. [ABSTRACT FROM PUBLISHER]

Highlights from the article: The 39th Canadian Symposium on Remote Sensing was held on June 19-21, 2018 in Saskatoon, Canada. Ces thématiques incluaient les eaux de surface et la Mission topographique de l'océan (SWOT-C), la mission Constellation RADARSAT (MCR), la fusion de données et les techniques intelligentes d'extraction de l'information à partir de grandes quantités de données, ainsi que les toutes dernières applications des données multi-sources pour la surveillance des ressources en eau et des milieux humides, du pergélisol, de l'environnement, des feux de forêt, ainsi que de la glace de rivières et la glace de mer. Les thématiques des sessions du colloque étaient: (1) La Mission SWOT-C: une nouvelle vision pour le future de l'eau au Canada; (2) Suivi des caractéristiques de la couverture de glace des lacs; (3) Progrès dans les techniques de télédétection pour les modèles numériques d'altitude; (4) L'eau et les zones humides; (5) Le Lidar hydro-géomatique à l'appui de la classification et du suivi hydro-écologique; (6) Les applications de la polarimétrie compacte de la Constellation RADARSAT; (7) Les nouvelles technologies et la fusion de données pour quantifier l'évolution du pergélisol dans les régions froides; (8) L'apprentissage machine pour la classification des images de télédétection; (9) La photogrammétrie et la télédétection à partir des drones; (10) La surveillance environnementale; (11) Simulation d'images optiques pour les missions à venir; et (12) La télédétection de la glace de mer et des océans.

River ice plays an important role in the physical, biological, and human systems in northern regions. Due to the large extent and poor accessibility of many river systems, remote sensing techniques are a potential tool for collecting information and analyzing characteristics of river ice covers. Optical data with high temporal resolution (e.g. MODIS products) are suitable for monitoring river ice phenology and mapping river ice cover extent, while radar data (e.g. RADARSAT-2 imagery) offer an optimal approach to characterizing river ice thickness, ice types and seasonal changes of ice-cover characteristics. An integrated automated approach, utilizing remotely sensed data from both active and passive sensors, was developed and applied to characterize river ice processes along the Slave River, Northwest Territories, Canada. The results show that the analysis of MODIS band 2 (841–876 nm) time series can detect the date of river ice freeze onset (FO) and water clear of ice (WCI) with a deviation of up to 12 days and 3 days, respectively. The rate of river ice breakup is much faster than the rate of river freeze-up. The profiles of MODIS band 2 reflectance data along the river, however, showed insignificant differences between different breakup stages and ice types, making it challenging to map breakup ice types. In this case, RADARSAT-2 imagery can be used successfully to characterize and map four classes of water and ice covers during the breakup period, including intact ice, smooth rubble ice, rough rubble ice and open water. In association with hydrometeorological parameters, the thermal deterioration of the ice cover during breakup appears to be the primary breakup event in the Slave River. The results obtained from the analysis of MODIS and RADARSAT-2 data were compared with ground measurements of weather, flow and ice conditions (2000–2015), time-lapse photos and aerial surveys (2013–2015) along the Slave River, and show strong agreement. [ABSTRACT FROM AUTHOR]

Spring floods have become less frequent along the Slave River and Slave River Delta. As a result, prolonged dry periods have occurred leading to an influx of invasive vegetation more tolerant to dry conditions (e.g., willows). Ice cover breakup and ice jamming can be important mechanisms in river flooding. A clear understanding of spatial and temporal patterns of the spring ice cover breakup along the Slave River could identify possible causes of reduced flood magnitude and frequency in the Slave River Delta. Few attempts have been made to examine the patterns of ice cover breakup along this river. A geospatial model has been introduced in this study to pinpoint the most likely areas of breakup initiation and persistent ice and ice cover at the end of the breakup period along the river. Relatively narrow river sections are responsible for initiation of breakup and relatively wide sections of the river have a strong predisposition for persistent ice along the Slave River. Daily time series of MODIS satellite images acquired from different years were used to examine the spatial and temporal patterns of ice breakup along the Slave River. In addition to geomorphological influence, air temperature and flow conditions also have strong impacts on the spatial and temporal patterns of the ice cover breakup. [ABSTRACT FROM AUTHOR]

The main difference between an open-water (regular) flood and an ice jam flood is that it is normally the whole river length that is overtopped whereas an ice jam flood is localized to where the jam is located. Comparatively, the regular flood analysis can use the value of the extreme discharge as the main input parameter for a long river section, an ice jam flood needs to account for the probability of jams of various lengths and intensities occurring at specific locations under significantly variable discharges while having several mechanical ice parameters to be considered. Through the case study of the Chaudière River, the methodology presented in this paper demonstrates how to statistically characterize four significant inputs (jam location, jam length, jam properties and river discharge during jam event) into the widely used numerical river water model (HEC-RAS) and how Monte-Carlo simulations are generated to estimate probable ice jam floods along a whole river reach. The purpose of this article is to propose a robust methodology through a case study and asses the sensitivity that historical and mechanical parameters have as to why specific locations along the reach have higher 1:100 AEP ice-induced water levels as to 1:100 AEP open-water levels. [ABSTRACT FROM AUTHOR]

During the formation of a frazil-generated ice cover, ice bridging can occur along sections of rivers that are geomorphologically suited (e.g. very sinuous, narrowing channel width, low gradient bed slope) to arrest the flow of ice pans. Bridging may occur at several locations along the river independently from one another with separate ice cover formations occurring simultaneously. Juxtaposition of incoming frazil ice and ice pans extending from the ice bridgings can cause backwater levels to rise upstream and cause flooding. The separate ice covers will eventually merge to completely cover the river sooner than if no additional bridging had occurred. This makes it difficult to predict the time required for a river to be completely ice covered and the locations and timing of related potential flood events. A geospatial model is introduced in this paper that applies a principal component analysis to cluster geomorphological features - such as sinuosity, channel width and channel slope - into typologies. Certain combinations of these typologies reveal predisposition of certain geomorphological features to ice bridging or non-bridging. The Dauphin River in Manitoba is used as a test case for the development of the model. [ABSTRACT FROM AUTHOR]

In 2011, Manitoba was stricken by wide-scale flooding causing high flows along the Dauphin River. The unprecedented high discharges at freeze-up created the potential for excessive ice cover thickening and backwater staging exacerbating the flood risk already threatening the communities along the river and upstream-lying Lake St. Martin. Hence, the river ice model RIVICE was implemented to determine flood protection elevations to which existing dikes needed to be raised and extended. Two reaches of the river were modelled separately representing distinct geomorphological characteristics: mildly sloping, more sinuous upper reach and steeper, more channelized lower reach. A global sensitivity analysis based on a Monte Carlo analysis was carried out to determine how differences in these morphological features influence different processes of the ice cover formation. It was found that the sinuous and braided morphology of the upper reach has a marked impact on the sensitivity of the hydraulic roughness and strength parameters. The structure of the ice (porosity) and the discharge were most sensitive to the backwater level outcomes of the steeper and straighter lower reach. [ABSTRACT FROM AUTHOR]

Abstract: In 2011, Manitoba experienced extreme flooding on a wide extended scale that affected many large watersheds, in particular the Upper Assiniboine, Qu''Appelle and Souris River watersheds. In order to protect Winnipeg from excessive flooding, some of the flood waters were diverted via the Portage Diversion into Lake Manitoba and Lake St. Martin, causing extreme high water levels and flooding of these lakes. The outlet of this entire drainage basin is the Dauphin River flowing from Lake St. Martin to Lake Winnipeg. The river''s conveyance capacity was exceeded by the flood and the lakes could not be drained fast enough to avoid damage from high flood levels and shore line erosion and property damage from accentuated wind setup (seiche) and wave uprush (runup) of the lake water. An Emergency Channel was constructed to compensate for this constriction, which now conveys additional water from Lake St. Martin towards Lake Winnipeg. Due to the extreme flooding, the Dauphin River had unprecedented record flows in the autumn of 2011. Ice cover formation during winter freeze-up of these waters could potentially exacerbate the flooding situation for communities alongside Lake St. Martin and the Dauphin River. Hence, the river ice model RIVICE was implemented to determine flood protection elevations to which existing dikes needed to be raised and extended. The modelling takes into consideration the potential frazil ice generation that can raise water levels by up to 3m from open water levels. High discharge and water level data was available to calibrate the model both for an open-water and an ice-covered case. Three scenarios were then simulated to determine ice-covered water levels of the river for early, best-estimate and late freeze-up events to determine flood protection levels along the Dauphin River and Lake St. Martin. The model was successfully validated using data collected during a freeze-up event in December 2011. [Copyright &y& Elsevier]

Summary: Highly concentrated asset values are often protected by dikes stretching along the river course. During extreme floods, dikes may fail due to various breach mechanisms and cause considerable damage. Therefore detention basins are often additionally installed to reduce the flood risk for downstream communities. In such situations, however, the systemic performance of dikes and spatial redistribution of inundation patterns are often unknown. Intuitively expected effects such as more probable breaches downstream due to fewer breaches upstream and consequently higher conveyance of upstream reaches lack evidential proof. With a coupled probabilistic–deterministic 1D-channel – dike breach – 2D-inundation – flood damage model chain the impact of a detention basin on losses to residential buildings and agricultural crops is investigated. We demonstrate the changes in dike performance due to systemic load and relief along the river course on the Middle Elbe, Germany considering three breach mechanisms: overtopping, piping and slope micro-instability. The reduction of overtopping failures due to the detention basin resulted in the slightly increased breach probabilities due to piping and micro-instability farther downstream. Finally, the uncertainty in hazard and damage estimations are analysed using the Monte Carlo simulation and applying several damage models. Despite high uncertainties in flood hazard and damage estimations, we conclude that the risk reduction to residential buildings downstream of the detention basin exceeds the higher losses to agricultural crops within the filled detention area. [ABSTRACT FROM AUTHOR]

River polders are retention basins contained by levees alongside rivers into which water from the main river channel is diverted during extreme floods in order to cap the peak discharge of the flood hydrograph and to alleviate downstream flood risk by reducing the water levels. The retained water, however, is stagnant and the organic material in the water and the bottom sediments imposes a strong oxygen demand on the water. This paper presents a quasi two-dimensional computer-based methodology to assess the environmental risk exhibited by the operation of polders with which the concentration of dissolved oxygen in river and polder water can be simulated. A Monte-Carlo analysis allows the probability distribution of all the outcomes of the minimum dissolved oxygen levels in the water to be derived. From this analysis, the environmental risk of the dissolved oxygen concentrations in the polder water falling below 2 mg O2/L (the level considered critical for aquatic ecosystems) can be determined. The August 2002 extreme flood event on the Elbe River, Germany, with a proposed polder system variant was used to calibrate the model. A daily time step was used to for the simulations for a time frame 12–21 August 2008. The results show plausible spatial and temporal variations in the dissolved oxygen concentrations within the polders. The quasi-2D approach was successful in simulating the spatial distribution of water quality constituents in the polder system. There is up to approximately 20% risk that dissolved oxygen levels fall below 2 mg/L in the polders. This risk can potentially increase if sediment oxygen demand increases due to crop residue and water temperatures in polders increase. High nutrient transport in the river during flooding can cause a spurt of phytoplankton growth in the polders. [Copyright &y& Elsevier]

In flood modeling, many one-dimensional (1D) hydrodynamic and water quality models are too restricted in capturing the spatial differentiation of processes within a polder or system of polders and two-dimensional (2D) models are too demanding in data requirements and computational resources, especially if Monte-Carlo techniques are to be used for model uncertainty analyses. The first goal of this paper is to show the successful development of a quasi-2D modeling approach which still calculates the dynamic wave in 1D but the discretisation of the computational units is in 2D, allowing a better spatial representation of the flow and substance transport processes in the polders without a large additional expenditure on data pre-processing and simulation processing. The models DYNHYD (1D hydrodynamics) and TOXI (sediment and micro-pollutant transport) were used as a basis for the hydrodynamic and water quality simulations. An extreme flood event on the Elbe River, Germany, with a proposed polder system variant was used as a test case. The results show a plausible differentiation of suspended sediment and zinc concentrations within the polders both spatially and temporally. This fulfills the second goal of this research. The third goal of this work is to provide an example methodology of carrying out an environmental risk assessment in inundated areas by flood waters, as required by the European Union floods directive. The deposition of zinc in polders was used for this example, due to its high contamination potential in the Elbe River. The extended quasi-2D modeling system incorporates a Monte-Carlo uncertainty analysis to assess the environmental impact of heavy metal deposition in the polders during extreme flooding. The environmental risk computed gives a 48% chance of exceeding the inspection value of 500 mg zinc/kg sediment for a flood such as the August 2002 event. [Copyright &y& Elsevier]

In flood modeling, many one-dimensional (1D) hydrodynamic and water-quality models are too restricted in capturing the spatial differentiation of processes within the floodplain and two-dimensional (2D) models are too demanding in data requirements and computational resources. The latter is an important consideration when uncertainty analyses using the Monte Carlo techniques are to complement the modeling exercises. This paper describes the development of a quasi-2D modeling approach that still calculates the dynamic wave in 1D but the discretization of the computational units is in 2D, allowing a better spatial representation of the flow and substance transport processes in the floodplain without a large additional expenditure on data preprocessing and simulation processing. The DYNHYD (1D hydrodynamics) and TOXI (sediment and micropollutant transport) models from the WASP5 modeling package were used as a basis for the simulations. The models were extended to incorporate the quasi-2D approach and a Monte Carlo analysis was used to investigate the contribution of uncertainty from parameters and boundary conditions to the resulting substance concentrations. A flood event on the Saale River, Germany, was simulated as a test case. The results show a plausible differentiation of suspended sediment and zinc concentrations between the floodplain and the main channel. Based on the results it appears that for flood simulations, uncertainties in boundary conditions are higher and need to be given more attention that uncertainties in model parameters. [ABSTRACT FROM AUTHOR]

There are many sources of uncertainty in modelling systems including uncertainty due to parameter estimations, input data and structure of the system. In this paper, modelling system structure is understood to be the algorithms and equations used to describe and calculate processes. Specific attention is given to the uncertainty in the equations, which link models interactively in a modelling system and algorithms, which pass information between the models. This paper places emphasis on structural uncertainty in modelling systems since this topic has received very little attention in research compared to the wealth of literature on parameter and input data uncertainty. This imbalance is partly due to the difficulty in quantifying structural uncertainty and an example is given showing how this may be done. The modelling system used for this study incorporates the coupling of three models from the WASP5 package (Water quality Analysis Simulation Program) developed by the US Environmental Protection Agency: DYNHYD (hydrodynamics), EUTRO (dissolved oxygen, nutrient and phytoplankton dynamics) and TOXI (transport and transformation of sediments and micro-pollutants). The three models were coupled in the high level architecture (HLA) platform to enable interactions between the models during simulations. This platform allowed ease of implementing Monte Carlo simulation runs, which were used for the uncertainty analyses. The modelling system structure focuses on the exchange of data between EUTRO and TOXI in simulating two processes: (i) sorption of heavy metals to suspended solids as a function of the fraction of organic carbon constituting the solids and (ii) phytoplankton growth limitation by light extinction through the water column as a function of the concentration of suspended solids and chlorophyll a in the water. Both processes are described by empirical relationships using regression curves derived from field data. The variance in the y-axis interceptions of the curves (also called regression uncertainty) is taken to be the uncertainty bounds for the Monte Carlo simulations used for the uncertainty analyses. The results show that structural uncertainty can be more significant than parameter and input data uncertainty, especially if the processes are very sensitive to the state variables. [Copyright &y& Elsevier]

Abstract: Snowling and Kramer [2001. Evaluating modelling uncertainty for model selection. Ecol. Modell. 138(1), 17–30] proposed a hypothesis stating that as a model becomes more complex in terms of increased number of parameters and variable, the error between simulations and measurements decreases and the overall model sensitivity increases. In this paper, the hypothesis is tested using a river water quality model of the lower course of the Saale river, Germany. The eutrophication module of WASP5 (5th version of Water quality Analysis Simulation Program), which was developed by the U.S. EPA, was implemented. The model allows the complexity of the dissolved oxygen balance and dynamics in a water body to be easily varied from a simple Streeter–Phelps approach of dissolved oxygen–biological oxygen demand (DO–BOD) interaction to more complex phytoplankton–nutrient dynamics. Five complexities were modelled and plotted against a normalized root-mean-squared error and a normalized global sensitivity. The results verify the hypothesis. A utility function, which minimizes both error and sensitivity, shows that the most complex model is not necessarily the most “useful”. In the case of the lower Saale river, modelling only the phytoplankton–nutrient cycle has almost as much descriptive power as when the complexity is increased by adding the DO–BOD cycle. The low sensitivity of the parameters linking the two cycles also indicates their weak coupling in the Saale river system. This verifies the observations that the source of the organic loading in the Saale has shifted from primary (point load) to secondary (phytoplankton) origin. [Copyright &y& Elsevier]

Abstract A finite volume computer model (current flow and transport) was used to simulate the reciprocal water exchange between a river (Havel) and a lake (Tegeler See) appendaged to the river in Berlin, Germany. The discretization of the model control volume is 2-D along the plane of the water surface, which restricts the modelling to time periods of complete overturn. The finite volume method does, however, allow a depth to be given for each volume cell. A k-ε turbulence submodel was integrated into the model to calculate the distinct dispersion coefficients for each volume cell. As current flow measurements were unavailable, the model was verified indirectly by the transport simulation of dissolved chloride, a conservative substance. The results show that the Havel contributes up to 30% of the total inflow to Tegeler See when it is overturned. As the Havel is more heavily polluted with respect to phosphorus loading, this has negative implications to the water quality of Tegeler See. Suggestions are given for controlling the Havel inflow amount by increasing the output of the phosphate elimination facility, the second main inflow to Tegeler See. Its output has very low phosphorus concentrations and serves as a flushing function. [ABSTRACT FROM AUTHOR]

River ice-jams can create severe flooding along many rivers in cold regions. While ice-jams often form during the spring breakup, the midwinter breakup can cause ice-jamming and flooding. Although many studies have already been focused on forecasting spring ice-jam flooding, studies related to forecasting mid-winter breakup jamming and flooding severity are sparse. The main purpose of this research is to develop a stochastic framework to forecast the severity of mid-winter ice-jam flooding along the transborder (New Brunswick/Maine) Saint John River of North America. A combination of hydrological (MESH) and hydraulic model (RIVICE) simulations was applied to develop the stochastic framework. A mid-winter breakup along the river that occurred in 2018 has been hindcasted as a case study. The result shows that the modelling framework can capture the real-time ice-jam severity. The results of this research will help to improve the capacity of ice-jam flood management in cold regions. [ABSTRACT FROM AUTHOR]

SAR data provide the high-resolution images useful for monitoring environment, and natural resources. Nevertheless, it has been a great challenge to retrieve soil moisture over vegetated sites from SAR backscatter coefficients, as it is almost impossible to parameterize spatially heterogeneous and time-varying roughness, the effect of rainfall or canopy volume scattering with implicit equations. We suggest a Monte Carlo Method (MCM) as a strategy to mitigate non-linear errors in retrievals arising from rainfall, and vegetation growth. The Advanced Integral Equation Model (AIEM) is repeatedly run in a forward mode for establishing the Gaussian-distributed soil roughness and backscatter coefficients. The mean value of soil moisture ensembles inverted from those was taken as an optimal estimate. Local validations show that Root Mean Square Errors (RMSEs) were 0.05 ~ 0.07 m³/m³ at the stations in Saskatchewan, Canada. Biases were 0.01m³/m³. Spatial distribution illustrates that the retrieval biases were mitigated, resolving AIEM inversion errors. [ABSTRACT FROM AUTHOR]

Model parameters and boundary conditions characterizing flood domains in riverine flood modelling play an important role in the delineation of flood hazard along rivers. Since the digital elevation model (DEM) is an integral part of the delineation of flood hazard, it is necessary to determine the relative sensitivity of the DEM alongside the hydraulic model parameters and boundary conditions. This study provides a novel framework to examine the relative sensitivity of a river ice hydraulic model and various DEMs on ice-jam flood delineation. The Athabasca River at Fort McMurray in Canada is presented as a test site. The study found that ice-jam flood delineation is highly sensitive to DEMs. While flood hazard delineation is low to moderate sensitive to all the model parameters, it is highly sensitive to almost all the boundary conditions. • The relative sensitivity of a river ice hydraulic model parameters, boundary conditions and DEM on ice-jam flood delineation. • Ice-jam flood hazard delineation is highly sensitive to DEMs. • The flood hazard delineation is highly sensitive to almost all the boundary conditions. [ABSTRACT FROM AUTHOR]

Sediment transport is a complex, multi-dimensional process. With the advancement in computing power and sophistication of computer applications over recent decades, it has become possible to conduct detailed analysis and simulations of soil erosion and sediment transport. The primary objective of this study was to examine and predict the potential influence of human activities on sediment transport. This was achieved by analyzing sediment transport in the Saskatchewan River beneath the E.B. Campbell Dam and in the Saskatchewan River Delta. The Hydrologic Engineering Center's-River Analysis System (HEC-RAS) was deployed to ascertain the sediment transport capacity and estimate erosion, sedimentation, and riverbed changes. Cross-sectional data, flow data, and sediment data were used in conjunction with HEC-RAS. The simulation results reveal that sediment transport below the E.B. Campbell Dam is limited, leading to notable sediment erosion. The selected study area has witnessed significant erosion during high-flow periods, particularly in the event of floods. Between 2012 and 2019, the riverbed elevation at the selected survey site decreased by approximately 0.45 m. The study findings corroborate that the Saskatchewan River and its delta have been impacted by human activities. Potential erosion and deposition below the E.B. Campbell Dam have been simulated for the selected site. The aim is to provide decision-makers or related stakeholders with insight into how dam operations can be adjusted to decrease erosion while sustaining hydrological, ecological, and environmental outcomes from human activities. • The Saskatchewan River and its delta have been influenced by human activities, climate changes, and their combined effects. • The sediment transport model via HEC-RAS evaluates the potential influences on riverbed erosion in the Saskatchewan River. • Riverbed erosion intensifies downstream of the E.B. Campbell dam. [ABSTRACT FROM AUTHOR]

In the higher latitudes of the northern hemisphere, ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Since ice-jam formation mechanism is stochastic and depends on numerous unpredictable hydraulic and river ice factors, ice-jam associated flood forecasting is a very challenging task. A stochastic modelling framework was developed to forecast real-time ice jam flood severity along the transborder (New Brunswick/Maine) Saint John River of North America during the spring breakup 2021. Modélisation environnementale communautaire—surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast. A Monte-Carlo analysis (MOCA) framework was applied to simulate hundreds of possible ice-jam scenarios for the model domain from Fort Kent to Grand Falls using a hydrodynamic river ice model, RIVICE. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season. [ABSTRACT FROM AUTHOR]

Assessing the impact of future climate on the severity of ice jam floods (IJFs) is an essential component of a flood mitigation strategy for many ice jam-prone northern communities. The general circulation model (GCM) outputs are used to derive hydrological conditions under future climate scenarios. Although GCMs are often downscaled to the point of interest, there can still be significant differences between modelled climate scenarios and historically observed climate scenarios. Therefore, the changes between the model simulated baseline and future scenarios are applied to observed baseline values to derive projected future values. In IJF modelling, such projected values are provided as input (boundary conditions) to assess the frequency and severity of future IJF events. Different methods can be used to calculate the climate change signal (difference between model-simulated future vs model-simulated historical conditions), and depending upon the method employed, results can vary. In this study, we evaluate the impact of using different delta change methods (i.e., absolute vs relative) to directly bias-correct the hydrological model output on the assessment of frequency and severity of IJFs under future climate. The method was tested in the Athabasca River at Fort McMurray in western Canada, an ice jam-prone location, to assess the IJF probabilities and intensities in the 2041–2070 period. Our results indicate that there is a notable difference in the projected frequency and severity of IJFs between absolute and relative delta change approaches, suggesting the methods should be carefully selected and results cautiously interpreted. [ABSTRACT FROM AUTHOR]

Ice jams are impacted by several climatic factors that are likely to change under a future warming climate. Due to the complexity of river ice phenology, projection of future ice jams is challenging. However, it is important to be able to project future ice jam behavior. Additionally, ice jam research is limited by the shortage of long-term monitoring data. In this paper, a novel framework for projecting future ice jam behavior is developed and implemented for ice jams in a data-sparse region, the Slave River Delta, NWT, Canada, situated in the Mackenzie River Basin (MRB). This framework employs both historical records and future hydro-meteorological data, acquired from climate and hydrological models, to drive the river ice models and quantify climate-induced influences on ice jams. Ice jam behavior analysis is based on three outputs of the framework: potential of river ice jamming, ice jam initiation date, and the stage frequency distribution of backwater elevation induced by ice jams. Trends of later ice jam initiation and decreased possibility of ice jam formation are projected, but ice jamming events in the Slave River Delta are likely to be more severe and cause higher backwater levels. [ABSTRACT FROM AUTHOR]

One of the most prominent sources of error and uncertainty in water quality modelling results is the input data. In this study, data from three meteorological databases were used to test the performance of a water temperature model of Lake Diefenbaker: the data from Environment and Climate Change Canada (ECCC) had long-term quality control history (>20 years); the data from the AccuWeather had short-term quality control history (<10 years), and the data from the MeteoBlue database were modelled values. The CE-QUAL-W2 hydrodynamic and water quality model was used for this study. The model was calibrated by adjusting model coefficients controlling the amounts of measured solar radiation and wind that reach the surface of the water. The sensitivity results showed very similar performances, with slightly better performances (root mean square root difference of ± 0.1) with the ECCC data followed by the MeteoBlue data and thereafter by the AccuWeather data. [ABSTRACT FROM AUTHOR]

• A probabilistic modelling framework was proposed to better assess the effect of cascade reservoir regulation on ice-jam flooding. • Incorporating both regulated and naturalized flows, the modelling framework provides a credible assessment. • The modelling framework was successfully verified for the Sanhuhekou bend reach regulated by eight cascade reservoirs. In regulated rivers, dams and their reservoirs are the main means to change the flow conditions, and strategic reservoir operations can be an important measure for mitigating ice-jam flooding (IJF). Previous studies have shown the effect of single reservoir operation on IJF probabilities, but little is known about the effect of cascade reservoir operation on IJF occurrences and severity. This study presents a probabilistic modelling framework that couples machine learning (LSTM, Long Short-Term Memory) and river ice (RIVICE) models to assess the effects of regulated and naturalized flows on IJF backwater levels. The trained LTSM shows good performance in both validation (RMSE = 398.88, R = 0.86 and NSE = 0.67) and testing (RMSE = 366.57, R = 0.84 and NSE = 0.71) periods and the calibrated RIVICE can well simulate backwater levels caused by three IJFs from 2008 to 2010 with total volumes of incoming ice for 0.4 million m3, 0.55 million m3 and 0.32 million m3. The completed framework was used to assess IJF hazard along the Sanhuhekou bend, a reach of the upper Yellow River, whose hydraulic regime is impacted by eight upstream reservoirs. The effect of naturalized and regulated flows on IJF are compared from two aspects. Firstly, comparing the median of naturalized and regulated IJF backwater level ensembles, the results show that regulation increases the IJF backwater levels. Secondly, comparing the extreme values of naturalized and regulated IJF backwater level ensembles, results show that regulation can increase the hazard of IJF inundation when annual exceedance probability (AEP) is less severe than 1:100 and regulation can decrease the hazard of IJF when AEP is more severe than 1:125. [ABSTRACT FROM AUTHOR]

For ice-jam flood forecasting it is important to differentiate between intact ice covers and ice runs. Ice runs consist of long accumulations of rubble ice that stem from broken up ice covers or ice-jams that have released. A water wave generally travels ahead of the ice run at a faster celerity, arriving at the potentially high flood–risk area much sooner than the ice accumulation. Hence, a rapid detection of the ice run is necessary to lengthen response times for flood mitigation. Intact ice covers are stationary and hence are not an immediate threat to a downstream flood situation, allowing more time for flood preparedness. However, once ice accumulations are moving and potentially pose imminent impacts to flooding, flood response may have to switch from a mitigation to an evacuation mode of the flood management plan. Ice runs are generally observed, often by chance, through ground observations or airborne surveys. In this technical note, we introduce a novel method of differentiating ice runs from intact ice covers using imagery acquired from space-borne radar backscatter signals. The signals are decomposed into different scatter components—surface scattering, volume scattering and double-bounce—the ratios of one to another allow differentiation between intact and running ice. The method is demonstrated for the breakup season of spring 2018 along the Athabasca River, when an ice run shoved into an intact ice cover which led to some flooding in Fort McMurray, Alberta, Canada. [ABSTRACT FROM AUTHOR]

In cold-region environments, ice-jam floods (IJFs) can result in high water levels in rivers to overtop levees, leading to devastating floods. Since climatic conditions play an important role in ice-jam flooding, there is a growing concern among property developers, insurance companies, government agencies, and communities on future IJF probabilities, especially in the context of changing climate. This study presents a stochastic framework for future IJF risk assessment and hazard mapping. Future hydrological conditions derived from a physically based hydrological model (forced with meteorological inputs from the Canadian regional climate model driven by two atmospheric-ocean general circulation climate models) were coupled to a fully dynamic hydraulic river-ice model to evaluate ice-jam scenarios and subsequent backwater-level profiles for the 2041–2070 period along the Athabasca River at Fort McMurray in Alberta, Canada. The modeling results show that future IJF risk will be lower and flood inundations less severe due to projected changes in climate, hydrology, and ice phenology in the 2041–2070 period compared to the baseline period of 1971–2000. However, extreme IJF events are still probable and can flood a considerable area of the town. [ABSTRACT FROM AUTHOR]

Dynamic contributing areas, various fill-and-spill mechanisms and cold-region processes make the hydrological modelling of the Prairies very challenging. Several models (from simple conceptual to advanced process-based) are available, but the focus has been largely in reproducing streamflow. Few studies have assimilated soil moisture and other hydrological fluxes for improved simulation, but the emphasis has been predominately on simulating contributing areas. However, previous research has shown that the contributing areas are dynamic, and can vary from one year to the next, depending on hydro-meteorological conditions. Therefore, the areas deemed non-contributing can also occasionally contribute to streamflow. In this study, we introduce a progressive two-stage calibration strategy to constrain soil moisture in non-contributing areas. We demonstrate that constraining soil moisture in non-contributing areas can result in improved hydrological simulations and more realistic process representations. The Nash-Sutcliffe efficiency (NSE) values for simulated soil moisture in contributing areas increased by 68% at 20 cm and 25% at 50 cm soil depths during validation when noncontributing areas were constrained. This further led to increases in NSE values in streamflow simulation during calibration (6%) and validation (12%). Our findings suggest that soil moisture in non-contributing areas should be properly constrained for improved modelling of Prairie catchments. [ABSTRACT FROM AUTHOR]

Traditionally, hydrological models are only calibrated to reproduce streamflow regime without considering other hydrological state variables, such as soil moisture and evapotranspiration. Limited studies have been performed on constraining the model parameters, despite the fact that the presence of a large number of parameters may provide large degree of freedom, resulting in equifinality and poor model performance. In this study, a multi-objective optimization approach is adopted, and both streamflow and soil moisture data are calibrated simultaneously for an experimental study basin in the Saskatchewan Prairies in western Canada. The results of this study show that the multi-objective calibration improves model fidelity compared to the single objective calibration. Moreover, the study demonstrates that single objective calibration performed against only streamflow can fairly mimic the streamflow hydrograph but does not yield realistic estimation of other fluxes such as evapotranspiration and soil moisture (especially in deeper soil layers). [ABSTRACT FROM AUTHOR]

• Traditional ice-jam risk assessment lacks a consideration of evolving human behavior. • We built an agent-based model to assess the long-term ice-jam flood risk. • This study highlights the role of dynamic adaptive behavior in ice-jam flood risk. • Artificial breakage can lead to a regime shift in the ice-jam flood risk. • Artificial breakage can decrease the number of adapted residents to flood-proofing. Ice-jam flooding is a prevalent extreme event that impacts flood hazard and vulnerability. We introduce a conceptual model framework for Dynamic Ice-jam Flood Risk Assessment (DIFRA). DIFRA integrates ice-jam flood hazard, ice-jam flood risk, and human adaptation. Using agent-based modeling, we captured top-down (artificial breakup) and bottom-up (flood-proofing) adaptive behavior. Our study in Fort McMurray, Canada, shows the complex interaction between micro-level behaviors and macro-level phenomena over time. Our variance-based global sensitivity analysis shows the role of dynamic adaptive behavior in ice-jam flood risk, where the artificial breakage by the government can lead to a regime shift and a decrease in the ice-jam flood risk. However, it can also decrease the number of newly adapted residents to flood-proofing and the role of residents in ice-jam flood risk. DIFRA offers a comprehensive approach to understanding and managing ice-jam flood risk, with potential applications to similar riverine communities in cold regions. [ABSTRACT FROM AUTHOR]

Dam operations are known to have significant impacts on reservoir hydrodynamics and solute transport processes. The Gardiner Dam, one of the structures that forms the Lake Diefenbaker reservoir located in the Canadian Prairies, is managed for hydropower generation and agricultural irrigation and is known to have widely altering temperature regimes and nutrient circulations. This study applies the hydrodynamic and nutrient CE-QUAL-W2 model to explore how various withdrawal depths (5, 15, 25, 35, 45, and 55 m) influence the concentrations and distribution of nutrients, temperature, and dissolved oxygen (DO) within the Lake Diefenbaker reservoir. As expected, the highest dissolved nutrient (phosphate, P O 4 3 − - P and nitrate, N O 3 − - N ) concentrations were associated with hypoxic depth horizons in both studied years. During summer high flow period spillway operations impact the distribution of nutrients, water temperatures, and DO as increased epilimnion flow velocities route the incoming water through the surface of the reservoir and reduce mixing and surface warming. This reduces reservoir concentrations but can lead to increased outflow nitrogen (N) and phosphorus (P) concentrations. Lower withdrawal elevations pull warmer surface water deeper within the reservoir and decrease reservoir DO during summer stratification. During fall turnover low outflow elevations increase water column mixing and draws warmer water deeper, leading to slightly higher temperatures and nutrient concentrations than shallow withdrawal elevations. The 15 m depth (540 m above sea level) outflow generally provided the best compromise for overall reservoir and outflow nutrient reduction. [ABSTRACT FROM AUTHOR]

River ice plays an important role in high latitude regions, and ice thickness is an important consideration to local residents as well as hydrologists. Two approaches, measurement (both remotely sensed and direct) and numerical calculations, are common methods used for ice thickness quantification. Measurement of ice thickness using SAR remote sensing approaches is still very challenging due to the low signals in thermal ice and limited penetration capability of C-band on consolidated ice covers. The numerical calculation can provide reasonable ice thickness estimation at a specific location but not the spatial variation. To carry out ice thickness estimations, numerous equations were proposed for numerical calculation. The Ashton equation, incorporating properties of ice and snow cover, was selected to determine the ice thicknesses along the Slave River in Canada. A framework (IceThick-RS) is proposed in this study, which incorporates polarimetric parameters of RADARSAT-2 images (quad-pol mode and C-band) into the ice thickness model. This framework was applied to estimate ice thicknesses along the Slave River. Direct measurements, gathered during the winter of 2014–2015, were used to calibrate the model. The results show that the IceThick-RS framework can be used to carry out river ice thickness estimation, especially in poorly accessible regions. [ABSTRACT FROM AUTHOR]

Authorship of academic articles remains contentious, especially in the context of reporting from community-based participatory research. Policies and guidelines exist to help decide when authorship is warranted, but there are gaps in process and practice for inclusion of community-based groups as authors. We synthesize the experience of a collaborative group in submitting a manuscript derived from blending Traditional Knowledge and science-based approaches into the larger practice of the institution of peer-reviewed publications. We then provide survey results from 11 journal editors across natural resource and policy journals on their policies for authorship and copyright signatories for community-based groups. We create a spectrum of empowerment for publishing with community-based groups, and map our sample to that spectrum. Implications for journal editors, community-based participatory researchers, and community members are discussed. [ABSTRACT FROM AUTHOR]

• A real-time ice-jam risk assessment system was developed to better regulate reservoir discharges. • The modelling system improves ice-jam flood predictions considering the influence of reservoir regulation. • Machine learning with deterministic modelling provides more accurate ice-jam flood predictions of regulated rivers. • The modelling system was successfully verified for the Sanhuhekou bend reach regulated by the Sanshenggong reservoir. To effectively alleviate ice-jam flood disasters, it is necessary to carry out hazard assessments and predictions of ice-jam flooding influenced by the operational scheme of a reservoir. However, traditional hydrologic flood routing techniques cannot effectively address the huge uncertainties caused by the many factors that lead to ice-jam flooding. In this paper, a hazard assessment system for regulating flood discharge schemes is developed; it is composed of a machine learning (ML) model, Long Short-Term Memory (LSTM), and a river-ice dynamic model (RIVICE) within a probabilistic method. The modelling system is to aid in the challenge of predicting ice-jam flooding downstream of reservoirs. The LSTM model forecasts the downstream flow under the operational discharge scheme and, combined with the RIVICE model, the backwater level profile of ice jams can be forecasted. Furthermore, a set of backwater level profiles can be provided by probabilistic modelling, and the probability of ice-jam flood inundation can be calculated by comparing backwater levels with the elevation of the river bank; this information can be used to warn of the hazard induced by operational discharges to better aid in the preparedness and mitigation of ice-jam floods. This system was tested successfully for the ice-cover breakup period in the spring of 2008 and 2018 along the Sanhuhekou bend reach of the Yellow River in China. [ABSTRACT FROM AUTHOR]

The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013-2014 and 2014-2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013-2014 winter had the higher variation than the 2014-2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period. [ABSTRACT FROM AUTHOR]

[Display omitted] • Model benchmarking was performed using four different meteorological forcing data. • Calculation of water balance revealed the dominant hydrological processes. • Hydrological conditions under future climatic conditions were assessed. • Uncertainty in future flow projections were quantified. Climate change introduces substantial uncertainty in water resources planning and management. This is particularly the case for the river systems in the high latitudes of the Northern Hemisphere that are more vulnerable to global change. The situation becomes more challenging when there is a limited hydrological understanding of the basin. In this study, we assessed the impacts of future climate on the hydrology of the Saint John River Basin (SJRB), which is an important transboundary coastal river basin in northeastern North America. We also additionally performed model benchmarking for the SJRB using four different meteorological forcing datasets. Using the best performing forcing data and model parameters, we studied the water balance of the basin. Our results show that meteorological forcing data play a pivotal role in model performance and therefore can introduce a large degree of uncertainty in hydrological modelling. The analysis of the water balance highlights that runoff and evapotranspiration account for about 99% of the total basin precipitation, with each constituting approximately 50%. The simulation of future flows projects higher winter discharges, but summer flows are estimated to decrease in the 2041–2070 and 2071–2100 periods compared to the baseline period (1991–2020). However, the evaluation of model errors indicates higher confidence in the result that future winter flows will increase, but lower confidence in the results that future summer flows will decrease. [ABSTRACT FROM AUTHOR]