Floods are among the most frequent and damaging natural hazards worldwide, and reliable observations of water surface elevation (WSE) are essential for improving flood modelling and risk management. The Surface Water and Ocean Topography (SWOT) satellite, launched in 2022, offers new opportunities to monitor river hydrodynamics from space, but its performance in relatively narrow rivers (< 50 m width) remains poorly documented. This study evaluates the potential of SWOT WSEs for flood monitoring through a site-specific hydraulic application by comparing them with in situ observations as well as simulations from an existing large-scale hydraulic model (LISFLOOD-FP) on the Du Gouffre River (width ≈ 40 m), located in Quebec, Canada. The L2_HR_RiverSP (RiverSP) SWOT product Version D, derived from a priori database (SWORD-version 17b), was first compared with one-minute WSE measurements from a tidal gauge located downstream the Du Gouffre River in the St. Lawrence River. This comparison, based on in situ reference measurements, confirmed the overall quality of the SWOT data in this area, with a Root Mean Square Error (RMSE) of 0.25 m. Then, a major flood event (with a return period of about 60 years) which occurred on 1 May 2023, during the SWOT's calibration orbit, was used to conduct a daily analysis of the entire flood event. Eleven observation cycles, covering the period from 25 April to 7 May 2023, were analysed. Limited ground-based observations were available along the studied reach during the flood, highlighting the added value of SWOT observations in this data-scarce context. The 1D/2D hydraulic model LISFLOOD-FP was run for the discharges corresponding to eleven SWOT cycles. Comparisons between SWOT-derived WSEs and model-simulated WSEs yielded biases ranging from −0.30 to 0.43 m and RMSE values between 0.22 and 0.54 m, indicating generall consistent behavior between observed and simulated WSEs across the analyzed cycles. During the flood peak on 1 May, larger discrepancies were observed, reflecting uncertainties in upstream discharge estimates under extreme flow conditions. In this context, SWOT-derived WSEs provided complementary information that helped diagnose discharge underestimation during the peak event. These results are not intended as an independent validation of SWOT measurement accuracy but are specific to the river and flood event under study and demonstrate the practical usefulness of SWOT observations for hydraulic studies in narrow rivers. They highlight the potential contribution of SWOT data for flood monitoring and hydraulic modelling in similar data-scarce contexts, particularly under extreme hydrological conditions.

The Outdoor Experimental River Facility (OERF) is a new large-scale, semi-natural research facility designed to study river dynamics at scales that bridge small laboratory models and natural rivers. The facility comprises a 50 m long, 20 m wide floodplain corridor and is designed to sustain discharges up to 800 Ls-1, allowing subcritical, fully rough flow with field-like Reynolds numbers approaching 105 - beyond values typical of small-scale planform experiments constrained by Froude similarity. This paper reports the first of three planned experimental campaigns at the OERF, providing a foundational assessment of facility capabilities and operational constraints to guide subsequent sinuous and vegetated experiments with sediment recirculation. In an initial 338 h (similar to 14 d) straight-channel run without upstream sediment supply, a bi-modal gravel-sand bed (initial median diameter = 10 mm) progressively armoured to similar to 22 mm, and reach-scale planform change remained modest despite a width-to-depth ratio of 12 and near-threshold mobility. A three-phase, mathematically designed inlet bar-pool perturbation increased local velocities by 8 %-27 % and produced limited lateral bank erosion (similar to 2.5-7.5 cm). The results delineate a narrow operational window for sustained bar growth and migration, long adjustment times, practical constraints of outdoor operation, and the moderating role of bank-material strength and toe armouring. Together, these findings show that field-like hydraulics are achievable within the facility while clarifying what limits mobility at this scale, and they motivate future experiments that couple hydrodynamic similarity with controlled sediment recirculation/feed and refined boundary controls to advance understanding of controls on bank erosion and planform evolution.

Gravel-bed rivers widen and narrow as bar-push and bank-pull wax and wane through individual floods, yet over decades the channel often holds near constant width, evidence of coupling between inner-bank deposition and outer-bank erosion. Because large, event-scale data sets are scarce and most field rivers have mixed grain-size beds, the physics of this coupling remains uncertain. Here we use a unique Outdoor Experimental River Facility (OERF) with sediment recirculation to investigate this coupling in 50 m-long, 3 m-wide sine-generated gravel-bed channel using four identical seven-stage flood hydrographs (129-hr total duration). In an unvegetated experimental gravel-bed channel with erodible banks (50% gravel; median size of 16 mm), twenty-nine drone photogrammetry surveys (i.e., Digital Elevation Models) and bedload samples were collected to determine a novel thalweg-centered volumetric framework and analysis that partitions every survey into inner-bank and outer-bank contributions. Flood peaks, though only 3% of the experiment duration, produced 83% of the 52% increase in planform area relative to the initial condition: centroid migration reached 1.3 m (27% of width), with the widening varying from one bend to the other. At peaks, bank-pull dominated 50% of events and bar-push 40%, whereas during rising and falling limbs symmetric widening prevailed (41%) with bar-push still active (31%). Local context modulated these stage effects: under identical forcing, one bend damped toward a medium-stability balance that approached a steady but non-zero bar-push/bank-pull imbalance, a mid-reach bend reached high stability with near-balanced inner and outer bank volumetric changes, and an outlet-proximal bend diverged into low-stability widening. We conclude that in gravel channels the bar-push/bank-pull is both stage-dependent and bend-specific; short peaks set the morphodynamic trajectory, but sub-bankfull limbs are responsible for most of the in-channel geomorphic work.

The SWOT Level 2 Lake Single-Pass Vector Product, LakeSP, provides a standardized data set for tracking global lake dynamics. However, spurious measurements remain in LakeSP time series, making robust filtering for scientific applications a persistent challenge. While native LakeSP quality flags encode multiple error sources, fixed combinations of these flags can be overly stringent, creating temporal gaps, or overly permissive, retaining errors that distort seasonal signals. We introduce a Heuristic Adaptive Lake Filtering (HALF) framework, which balances error removal with preservation of hydrological variability using lake-specific rules. HALF filters LakeSP water surface elevation (WSE) time series in three steps: (1) calibrating lake-specific heuristic thresholds for key diagnostic variables to derive a physically constrained baseline; (2) iteratively removing outliers through low-pass filtering while enforcing temporal-coverage criteria; and (3) harmonizing intra-cycle WSE inconsistency through cross-pass bias correction. We evaluated HALF for both LakeSP Versions C and D using gauge observations for approximately 1,000 lakes worldwide and benchmarked its performance against native quality flags. Across validated lakes, HALF reduced WSE mean absolute errors to median values of 0.11–0.12 m and 68th-percentile (P68) values of 0.17–0.18 m, while retaining a median of at least 80% of raw observations. The retained time series yielded median errors in normalized seasonal WSE variability of 0.09–0.14 and P68 errors of 0.17–0.28, along with median LakeSP–gauge correlations of 0.92–0.93. Performance remained robust across lake sizes, regions, ice conditions, and product versions, suggesting that HALF improves the accuracy-coverage tradeoff and supports broad-scale LakeSP applications.

Accurate forecasting of spring flow is essential for mitigating flood damage and optimizing hydroelectric power production. In northern countries such as Canada, this flow is mainly driven by snowmelt processes. By integrating snowpack data from diverse sources (in situ, remote sensing, and reanalysis) with modelled snow-related state variables through data assimilation (DA), it is possible to leverage both modeling and observations for more accurate spring flow estimates. Accurate estimates of snow water equivalent (SWE) within a heterogeneous snowpack are crucial for optimizing the advantages of snow DA. Here we assess the potential effect of distributed SNOw Data Assimilation System (SNODAS) SWE data on improving spring flow in two hydrological models having distinct inner structures: HSAMI, a lumped model, and HYDROTEL, a distributed model. DA analyses used an ensemble Kalman filter scheme, which was run over a three-year period. The results were then compared with those from an open-loop experiment. We used Nash-Sutcliffe efficiency (NSE) and bias to assess the results and found that SNODAS SWE DA did not improve 1-day spring flow forecasts for the lumped model. In contrast, HYDROTEL produced more accurate 1-day spring flow estimates for all 3 years, improving NSE of the spring flow forecasts from 0.52 to 0.70, 0.32 to 0.68, and 0.39 to 0.67 for the 2014-2017 period. This study demonstrates that incorporating distributed snow data into a distributed hydrological model can improve spring flow forecasts. Given the availability of SNODAS dataset over most Canadian watersheds, this DA framework is aimed to serve as an asset to enhance operational flood forecasting systems across Canada.

The concentration of frazil ice, crucial to the development of river ice covers and the numerical modeling of ice cover development, is challenging to measure in situ. Remote sensing using deep neural networks on images of frazil drift ice taken from drone is promising but faces challenges due to limited annotated datasets and difficulty in visually distinguishing ice types and boundaries. In this work, a method for acquiring and processing optical drone river ice images was developed to estimate the concentration of frazil drift ice, mostly in frazil slush form. Drone images were acquired on four mesoscale rivers (widths of approximate to 30 to 100 m) situated in the south of the Province of Quebec, Canada during the 2022-2023 and the 2023-2024 winter. A first Convolutional Neural Network was trained to perform an initial classification. This Convolutional Neural Network, the static ice model, was trained to segment the images in four classes: water, static ice, trees above water and other. Despite a few minor classification errors, the model was used to estimate the extent of static ice cover. Once the initial classification was made, the frazil drift ice concentration was estimated by taking into account only the flow zone. To do so, two Convolutional Neural Networks were trained with the same dataset but annotated with two different techniques: semantic segmentation and regression. Following the analysis of the results, it was concluded that regression is highly promising for estimating frazil drift ice concentration, particularly when the ice is in slush form and at high concentrations. The differences between the concentrations obtained using this method and those obtained manually are quite small (between 0 % and 2.2 %). With the same annotation effort as regression, the segmentation technique shows higher deviations (between 0.1 % and 9.4 %). The segmentation trained model encounters challenges in accurately identifying water areas surrounded by frazil and tend to extend frazil boundaries beyond their actual limits, which lead to an overestimation of the frazil drift ice concentration. These results confirm the potential of using drone imagery to train a regression-annotated Convolutional Neural Network for estimating frazil surface concentration in mesoscale rivers.

Despite recent advances, streamflow ensemble forecasting systems often suffer from unreliability and under-dispersion. Improving flood forecasting quality benefits decision-makers and the public. Statistical processing methods can enhance raw forecasts. Therefore, identifying the most effective scenario among pre-processing, post-processing, or a combination of both is crucial. While most studies have focused on lumped models, more comprehensive analysis is needed for systems employing spatially distributed models. This study evaluates scenarios involving the pre-processing of both temperature and precipitation forecasts and the post-processing of streamflow forecasts in a streamflow ensemble forecasting system with a spatially distributed hydrological model. It also explores the impact of incorporating flood events and the length of the training data on the performnace of pre- and post-processing approaches through various training and validation strategies. Weighted Ensemble Dressing (WED) is coupled with two bias correction methods: Cumulative Distribution Function Matching (CDFM) and Event Bias Correction (EBC). The forecast assessment covers lead times of 1-5 days for the au Saumon watershed in southern Quebec, Canada. Although pre-processing alone marginally improves raw forecasts, it fails to fully compensate for bias in under-dispersed ensemble forecasts. The combined pre- and post-processing approach proves superior in forecast skill and reliability to other scenarios. These findings align with existing literature, reinforcing the validity of the applied methodologies within current hydrological research. Both bias correction methodsperform well with WED; CDFM is more reliable for short lead times, while EBC excels at longer lead times. Notably, incorporating flood events and optimizing data length further improve processing performance, highlighting the importance of effective data strategies in streamflow forecasting. Malgr & eacute; des progr & egrave;s r & eacute;cents, les syst & egrave;mes de pr & eacute;vision d'ensemble des d & eacute;bits fluviaux souffrent souvent de probl & egrave;mes de fiabilit & eacute; et de sous-dispersion. L'am & eacute;lioration de la qualit & eacute; des pr & eacute;visions des crues profite aux d & eacute;cideurs et au grand public. Les m & eacute;thodes statistiques de traitement peuvent am & eacute;liorer les pr & eacute;visions brutes, ce qui rend crucial l'identification du sc & eacute;nario le plus efficace parmi le pr & eacute;-traitement, le post-traitement, ou leur combinaison. Alors que la plupart des & eacute;tudes se concentrent sur des mod & egrave;les globalis & eacute;s, une analyse plus approfondie est n & eacute;cessaire pour les syst & egrave;mes utilisant des mod & egrave;les hydrologiques distribu & eacute;s spatialement. Cette & eacute;tude & eacute;value diff & eacute;rents sc & eacute;narios impliquant le pr & eacute;-traitement des pr & eacute;visions de temp & eacute;rature et de pr & eacute;cipitations, ainsi que le post-traitement des pr & eacute;visions de d & eacute;bits, dans un syst & egrave;me de pr & eacute;vision d'ensemble des d & eacute;bits reposant sur un mod & egrave;le hydrologique distribu & eacute; spatialement. Elle explore & eacute;galement l'impact de l'int & eacute;gration des & eacute;v & eacute;nements de crue et de la longueur des donn & eacute;es d'entra & icirc;nement sur la performance des approches de pr & eacute;- et post-traitement, & agrave; travers diverses strat & eacute;gies d'entra & icirc;nement et de validation. Le Weighted Ensemble Dressing (WED) est coupl & eacute; & agrave; deux m & eacute;thodes de correction des biais : le Cumulative Distribution Function Matching (CDFM) et la correction des biais d'& eacute;v & eacute;nement (Event Bias Correction, EBC). La performance des pr & eacute;visions est & eacute;valu & eacute;e pour des horizons de 1 & agrave; 5 jours dans le bassin versant de la rivi & egrave;re au Saumon, au sud du Qu & eacute;bec, Canada. Le pr & eacute;-traitement am & eacute;liore marginalement les pr & eacute;visions brutes, mais ne corrige pas enti & egrave;rement les biais dans les ensembles sous-dispers & eacute;s. L'approche combin & eacute;e de pr & eacute;- et post-traitement am & eacute;liore significativement la comp & eacute;tence et la fiabilit & eacute; des pr & eacute;visions, surpassant les autres sc & eacute;narios. Ces r & eacute;sultats concordent avec la litt & eacute;rature existante, renfor & ccedil;ant la robustesse des m & eacute;thodologies appliqu & eacute;es dans la recherche hydrologique actuelle. Les deux m & eacute;thodes de correction des biais fonctionnent bien avec WED ; le CDFM est plus fiable pour les horizons courts, tandis que l'EBC excelle pour les horizons plus longs. L'int & eacute;gration des & eacute;v & eacute;nements de crue et l'optimisation de la longueur des donn & eacute;es am & eacute;liorent en outre la performance du traitement, soulignant l'importance de strat & eacute;gies efficaces de gestion des donn & eacute;es dans la pr & eacute;vision des d & eacute;bits.

The SWOT satellite is a near-nadir Ka-band interferometric radar, capable of monitoring water bodies larger than 6 ha. Launched in December 2022, the satellite was in a calibration/validation orbit until July 2023, where it acquired measurements every day over certain regions. Canadian lakes were ice-covered at the start of the calibration period, offering the opportunity to study the Ka-band backscatter in the presence of ice and snow. The SWOT signal is also affected when the water surface is very smooth (e.g. in the absence of wind), or attenuated by heavy precipitation. These preliminary results demonstrate for the first time the impact of ice, snow, wind, and rain on the detection of water bodies by the SWOT satellite signal.

Topo-bathymetric LiDAR (TBL) can provide a continuous digital elevation model (DEM) for terrestrial and submerged portions of rivers. This very high horizontal spatial resolution and high vertical accuracy data can be promising for flood plain mapping using hydrodynamic models. Despite the increasing number of papers regarding the use of TBL in fluvial environments, its usefulness for flood mapping remains to be demonstrated. This review of real-world experiments focusses on three research questions related to the relevance of TBL in hydrodynamic modelling for flood mapping at local and regional scales: (i) Is the accuracy of TBL sufficient? (ii) What environmental and technical conditions can optimise the quality of acquisition? (iii) Is it possible to predict which rivers would be good candidates for TBL acquisition? With a root mean square error (RMSE) of 0.16 m, results from real-world experiments confirm that TBL provides the required vertical accuracy for hydrodynamic modelling. Our review highlighted that environmental conditions, such as turbidity, overhanging vegetation or riverbed morphology, may prove to be limiting factors in the signal's capacity to reach the riverbed. A few avenues have been identified for considering whether TBL acquisition would be appropriate for a specific river. Thresholds should be determined using geometric or morphological criteria, such as rivers with steep slopes, steep riverbanks, and rivers too narrow or with complex morphologies, to avoid compromising the quality or the extent of the coverage. Based on this review, it appears that TBL acquisition conditions for hydrodynamic modelling for flood mapping should optimise the signal's ability to reach the riverbed. However, further research is needed to determine the percentage of coverage required for the use of TBL as a source of bathymetry in a hydrodynamic model, and whether specific river sections must be covered to ensure model performance for flood mapping. When TBL signal reaches riverbed, elevation estimation is accurate. Conditions where depth, bed reflectivity and turbidity cause voids in TBL coverage still need to be understood. Relevance of TBL in critical portions of rivers as required in modelling is still to be demonstrated. image

In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.

Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which the modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal, dimensions. In this study, a set of synthetic experiments were designed to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM, as well as streamflow. The results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of the modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts the modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but to a lesser degree in the bottom soil layer. The updating of the model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments.

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.

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.

This study investigates the different performances between Compact-Polarimetric (CP) and Full-Polarimetric (FP) SAR to retrieve multiple crop growth parameters including Vegetation Water Content (VWC), Leaf Area Index (LAI), height, and dry biomass. The objective is to study at which conditions the CP SAR can obtain comparable retrieval accuracy as FP SAR for specific crop types and growth descriptors. Polarimetric decompositions were used to extract CP and FP explanatory variables to quantify crop growth status. Then, the sensitivities of CP and FP variables to different crop descriptors were analyzed, revealing higher sensitivity to height than LAI, VWC, and dry biomass. In order to reduce the information redundancy of sets of CP and FP SAR variables, Partial Least Square (PLS) approach was used to develop new orthogonal SAR parameters, while Step-Wise Regression (SWR) was used to determine the optimal SAR parameters, followed by retrievals of multiple crop growth descriptors using the developed estimators. Validated by the ground measurements, the retrieval performances are found to be highly dependent on polarimetry dimension (CP or FP), crop types, and targeted crop descriptors. With crop growth and enhanced depolarization effects, the CP SAR can capture the increasing volume power and decreasing surface power but at a lower rate than FP SAR. The m-δ and m-χ decompositions provide similar scattering components which differ from the Random Volume Over Ground (RVOG) decomposition. The third Stokes parameter g3 of the CP SAR data that indicates the strength of the circular polarization component is a common important parameter to infer the multiple crop parameters of interest. In agreement with the sensitivity analysis, better retrieval accuracies were obtained for height and dry biomass than VWC and LAI. For short vegetation such as soybean, full polarimetry is required to obtain the estimates of all four crop growth descriptors. On the contrary, for tall crop growth parameters, such as canola (height and dry biomass), corn (height, VWC, dry biomass), and wheat (LAI, VWC, dry biomass), the CP SAR features can provide comparable robust retrieval accuracy as the FP SAR features. This study deepens our insights into the CP SAR of the RADARSAT Constellation Mission (RCM) to timely monitor crop growth and health status.

Incorporation of observed streamflow into different hydrological models has resulted in improved streamflow forecasting in many studies. This approach is currently used by different hydropower companies to maximize hydroelectric production and also to predict and mitigate flood damages. In addition, snow-related model states, such as SWE, snow depth, and snow wetness, also carry important information regarding both timing and volume of spring flow in snow-dominated regions. Consequently, the main objective of this study is to combine assimilation of observed streamflow and reanalyzed SWE to enhance spring flow forecasting. The reanalyzed SWE product investigated is SNODAS. SNODAS is a snow data assimilation system that improves outputs of a snow model by assimilating observed snow data provided by airborne platforms, satellites, and ground stations and generates snow-related data, such as SWE and snow depth at 1 km resolution. SNODAS is run each day so that the data product is available in near real-time. In this study, SNODAS SWE data has been assimilated into HYDROTEL, a physically-based distributed hydrological model equipped with a snow module based on a mixed energy budget – degree-day approach, along with observed streamflow during the spring flow season in order to enhance spring flow forecasts. The study site is Au Saumon watershed, located in southern Quebec, Canada. The Au Saumon watershed has an area of 1022 and is predominantly forested. The simulation period is the 2014-2015 water year. Preliminary results show that combined assimilation of SNODAS SWE and observed streamflow improve spring flow predictions, while SWE assimilation has a more delayed impact than streamflow assimilation. Keywords: Data Assimilation, SNODAS, SWE, Spring Flow Prediction

Assessing trade-offs among ecosystem services (ESs) that are provided by forests is necessary to support decision-making and to minimize negative effects of timber harvesting. In this study, we examined how spatial data, forest operational rules, ESs, and probabilistic statistics can be combined into a practical tool for trade-off analysis that could guide decision-making towards sustainable forestry. Our main goal was to analyze trade-offs among the wood provisioning ES and other forest ESs at the landscape level using a Bayesian belief network (BBN). We used LiDAR data to derive four ES layers as inputs to a spatial BBN: (i) wood provisioning; (ii) erosion regulating; (iii) climate regulating; and (iv) habitat supporting. We quantified operational constraints with four forest operational rules (FOR) that were defined in terms of: (i) potential harvest block size; (ii) distance between a small potential harvest block and a larger harvest block; (iii) gross merchantable volume (GMV); and (iv) distance to an existing resource road. Maps of the most probable trade-off classes between the wood provisioning ES and other ESs enabled us to identify areas where timber harvesting should be avoided or where timber harvesting should have a very low negative effect on other ESs. Even with our most restrictive management scenario, the total GMV that could be harvested met the annual allowable cut (AAC) volume required to meet sustainable forestry objectives. Through our study, we demonstrated that high-resolution spatial data could be used to quantify trade-offs among wood provisioning ES and other forest-related ESs and to simulate small changes in ES indicators within the BBN. We also demonstrated the potential to evaluate management scenarios to reduce trade-offs by considering FOR as inputs to the BBN. Maps of the most probable trade-off classes among two or three ESs under operational constraints provide key information to guide forest management decision-making towards sustainable forestry.

Previous studies have shown that assimilating satellite soil moisture data in land surface models can improve the estimations of soil moisture. One of the limitations of these satellite soil moisture products is that there are often spatial gaps (in the horizontal direction) in data availability over certain areas due to issues such as dense vegetation or hilly terrain. These products are also limited in the vertical direction because, for the microwave-based products for example, the microwave radiation captured by the satellite sensors to estimate soil moisture is usually representative of a very thin top layer of soil (up to about 5 cm). Lastly, data over a specific watershed may not be available every day (i.e. temporal gaps) because of the orbital configuration of the satellite in question. From the existing literature, it is not clear what the benefits will be for soil moisture modeling, if these spatio-temporal gaps in satellite soil moisture datasets could somehow be minimized or eliminated. To answer this question, a synthetic assimilation study was carried out on the Noah-MP land surface model within the WRF-Hydro modeling system. The study was conducted with ERA5 forcing data on the Susquehanna River Basin and the Ensemble Kalman Filter was the chosen assimilation algorithm. Multiple scenarios were explored in which spatio-temporal gaps were introduced in the synthetic observations by mimicking the actual spatio-temporal gaps that are present in the SMAP soil moisture product. Results indicate that the model’s ability to accurately simulate soil moisture is much lower when assimilated observations have spatio-temporal gaps, compared to model simulations where there are no gaps in the assimilated observations. However, it was found that this lower model accuracy can be improved if the model grids with missing observations are updated based on the covariance between the soil moisture of those grids and their surrounding grids.

An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been shown to have a strong impact on the performance of the data assimilation method. Many metrics can be used to calibrate these hyper-parameters but may not all yield the same optimal set of values. The current study investigated the importance of the choice of metric used during the hyper-parameter calibration phase and its impact on discharge forecasts. The types of metrics used each focused on discharge accuracy, ensemble spread or observation-minus-background statistics. The calibration was performed for the ensemble square root Kalman filter over two catchments in Canada using two different hydrologic models per catchment. Results show that the optimal set of hyper-parameters depended heavily on the choice of metric used during the calibration phase, where data assimilation was applied. These sets of hyper-parameters in turn produced different hydrologic forecasts. This influence was reduced as the forecast lead time increased, because of not applying data assimilation in the forecast mode, and accordingly, convergence of model state ensembles produced in the calibration phase. However, the influence could remain considerable for a few days up to multiple weeks depending on the catchment and the model. As such, a preliminary analysis would be recommended for future studies to better understand the impact that metrics can have within and outside the bounds of hyper-parameter calibration.

The future surface water and ocean topography (SWOT) satellite mission will provide images of surface water topography for inland water bodies and oceans. Over land, water surface elevation (WSE) will be retrieved at 10 cm accuracy for water bodies with areas > 250 m × 250 m and rivers with widths > 100 m, when averaging over 1 km 2 . Studies have shown that the Ka-band used by SWOT's main payload can be affected by aquatic and emergent riparian vegetation, which in turn could influence SWOT capacity to correctly observe water extent. The current study investigates effects of aquatic and emergent riparian vegetation on SWOT water extent and WSE detection capabilities through the use of NASA/JPL's SWOT simulator (HR). Data from the AirSWOT airborne campaign over Mamawi Lake (163 km 2 ) in the Peace-Athabasca Delta (PAD, Alberta, Canada), are used to establish a land cover classification and backscattering values for simulation inputs. Simulation results have shown that aquatic vegetation has a negligible effect on the SWOT signal. Yet, simulations showed that water extent misclassification can occur for water with emergent riparian vegetation in the specific case of wetlands surrounding lakes (i.e., small differences in backscattering values between surrounding land and water with emergent riparian vegetation). Simulations featuring the smallest difference between emergent riparian vegetation and land (1.3 dB) showed a 32–35% lake extent reduction from true extent. As expected, this study reveals that estimating water extent from SWOT in very wet environments with emergent vegetation can be challenging.

Maps of ecosystem services are becoming increasingly useful for reporting on the potential impacts of human activity on the environment. However, interactions in watersheds are complex, and mapping hydrological ecosystem services (HES) requires indicators which accurately measure underlying processes. The main objective of this study was to take advantage of the Soil and Water Assessment Tool (SWAT) and Light detection and ranging (LiDAR) data to map the erosion regulation service for a managed boreal forest watershed. To do so, SWAT and partial least-squares (PLS) regression were used to select explanatory variables for sediment yield. Variables of importance in projection (VIP) with a score > 1 were selected to develop LiDAR-based ecological indicators. Four categories of variables were identified as VIP from the PLS: (i) climate: annual precipitation, (ii) land use: forest, cutovers; (iii) land use patterns: cutover patch cohesion index, and (iv) morphometric: main channel length, channel length and sub-watershed area. The height of the 95th percentile of LiDAR returns (p95) < 5 m provided the most accurate spatial representation of cutovers and the optimal cutover patch cohesion index. Other morphometrics were obtained from a LiDAR-based digital terrain model. Explanatory variables for sediment yield were combined in a sediment erosion control (SEC) index, except for yearly average precipitation because the SEC index is not actually used as a temporal index. As expected, a negative relationship was found between sediment yield and SEC index rankings for the 2006-2015 period (Spearman, rho = -0.6, p < 0.05). Moreover, the overall agreement between SWAT and SEC index classes was 87% for 31 sub-watersheds. The study provides a list of relevant explanatory variables for modelling sediment yield in a boreal forest watershed where timber harvest activities occur. It also demonstrates the use of LiDAR data for deriving an index of the erosion regulation ecosystem service in a proxy-based approach as it had not been demonstrated previously at the watershed level. The validation method applied here fills a gap in ecosystem services mapping that could benefit studies in other watershed contexts.