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Abstract. Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering.

Abstract In water resources applications (e.g., streamflow, rainfall‐runoff, urban water demand [UWD], etc.), ensemble member selection and ensemble member weighting are two difficult yet important tasks in the development of ensemble forecasting systems. We propose and test a stochastic data‐driven ensemble forecasting framework that uses archived deterministic forecasts as input and results in probabilistic water resources forecasts. In addition to input data and (ensemble) model output uncertainty, the proposed approach integrates both ensemble member selection and weighting uncertainties, using input variable selection and data‐driven methods, respectively. Therefore, it does not require one to perform ensemble member selection and weighting separately. We applied the proposed forecasting framework to a previous real‐world case study in Montreal, Canada, to forecast daily UWD at multiple lead times. Using wavelet‐based forecasts as input data, we develop the Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, the first multiwavelet ensemble stochastic forecasting framework that produces probabilistic forecasts. For the considered case study, several variants of Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, produced using different input variable selection methods (partial correlation input selection and Edgeworth Approximations‐based conditional mutual information) and data‐driven models (multiple linear regression, extreme learning machines, and second‐order Volterra series models), are shown to outperform wavelet‐ and nonwavelet‐based benchmarks, especially during a heat wave (first time studied in the UWD forecasting literature). , Key Points A stochastic data‐driven ensemble framework is introduced for probabilistic water resources forecasting Ensemble member selection and weighting uncertainties are explicitly considered alongside input data and model output uncertainties Wavelet‐based model outputs are used as input to the framework for an urban water demand forecasting study outperforming benchmark methods

Abstract. Real time operational flood forecasting most often concentrates on issuing streamflow predictions at specific points along the rivers of a watershed. Those points often coincide with gauging stations, and the forecasts can eventually be compared with the corresponding observations for post-event analysis. We are now witnessing an increasing number of studies aimed at also including flood mapping as part of the forecasting system, by feeding the forecasted streamflow to a hydraulics model. While this additional new information (flood extent, depth, velocity, etc.) can potentially be useful for decision makers, it also has the potential to be overwhelming. This is especially true for probabilistic and ensemble forecasting systems. While ensemble streamflow forecasts for a given point in space can be visualized relatively easily, the visualization and communication of probabilistic forecasts for water depth and extent brings additional challenges. The uncertainty becomes three dimensional and it becomes difficult to convey all the important information to support decision-making, while a confusion that could arise from too much information, counter-intuitive interpretation, or simply too much complexity in the representation of the forecast. In this paper, we synthesize the results of a large-scale survey across multiple categories of users of hydrological forecasts (28 government representatives, 52 municipalities, 9 organizations, 37 citizens and farmers, for a total of 139 persons) regarding their preferences in terms of visualizing probabilistic flood forecasts over an entire river reach. Those users have different roles and realities, which influence their needs and preferences. The survey was performed through individual and group interviews during which the interviewees were asked about their needs in terms of hydrological forecasting and their preferences in terms of communication and visualization of the information. In particular, we presented the interviewees with four prototypes representing alternative visualizations of the same probabilistic forecast in order to understand their preferences in terms of colour maps, wording, and the representation of uncertainty. Our results highlight several issues related to the understanding of probabilities in the specific context of visualizing forecasted flood maps. We propose several suggestions for visualizing probabilistic flood maps in order to convey all the relevant information while limiting the confusion of decision makers, and also describe several potential adaptations for different categories of end users.

Floods can cause extensive damage proportional to their magnitude, depending on the watershed hydrology and terrain characteristics. Flood studies generally assume bathymetry as steady, while in reality it is constantly changing due to sediment transport. This study seeks to quantify the impact of different lake bathymetry conditions on flood dynamics. The Hydrotel and Telemac2D models are used to simulate floods for a lake with bathymetries from multiple year surveys. The bathymetries differ in bed elevation due to sediment accumulation and/or remobilisation. Results show that bathymetric differences produce a more noticeable effect for moderate flows than for maximum flows. During moderate flows, shallower bathymetries induce higher water levels and larger water extents. For peak flows, differences in water levels and extent are practically negligible for the different bathymetries tested. Higher water levels during moderate flows could produce longer flooding times and affect the community’s perception of flood impacts.

AbstractA snow model forced by temperature and precipitation is used to simulate the spatial distribution of snow water equivalent (SWE) over a 600,000 km2 portion of the province of Quebec, Canada. We propose to improve model simulations by assimilating SWE data from sporadic manual snow surveys with a particle filter. A temporally and spatially correlated perturbation of the meteorological forcing is used to generate the set of particles. The magnitude of the perturbations is fixed objectively. First, the particle filter and direct insertion were both applied on 88 sites for which measured SWE consist of more or less five values per year over a period of 17 years. The temporal correlation of perturbations enables to improve the accuracy and the ensemble dispersion of the particle filter, while the spatial correlation lead to a spatial coherence in the particle weights. The spatial estimates of SWE obtained with the particle filter are compared with those obtained through optimal interpolation of the sno...

<p>In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. </p><p>For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites.</p><p>The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered.</p><p>In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework.</p><p>The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys.</p><p> </p><p><strong>References:</strong></p><p>Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20.</p><p>Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)</p>

Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.