Search

Seasonal streamflow forecasts provide key information for decision-making in fields such as water supply management, hydropower generation, and irrigation scheduling. The predictability of streamflow on seasonal timescales relies heavily on initial hydrological conditions, such as the presence of snow and the availability of soil moisture. In high-latitude and high-altitude headwater basins in North America, snowmelt serves as the primary source of runoff generation. This study presents and evaluates a data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America (Canada and the USA). The workflow employs snow water equivalent (SWE) measurements as predictors and streamflow observations as predictands. Gap-filling of SWE datasets is accomplished using quantile mapping from neighboring SWE and precipitation stations, and principal component analysis is used to identify independent predictor components. These components are then utilized in a regression model to generate ensemble hindcasts of streamflow volumes for 75 nival basins with limited regulation from 1979 to 2021, encompassing diverse geographies and climates. Using a hindcast evaluation approach that is user-oriented provides key insights for snow-monitoring experts, forecasters, decision-makers, and workflow developers. The analysis presented here unveils a wide spectrum of predictability and offers a glimpse into potential future changes in predictability. Late-season snowpack emerges as a key factor in predicting spring and summer volumes, while high precipitation during the target period presents challenges to forecast skill and streamflow predictability. Notably, we can predict lower-than-normal and higher-than-normal streamflows during spring to early summer with lead times of up to 5 months in some basins. Our workflow is available on GitHub as a collection of Jupyter Notebooks, facilitating broader applications in cold regions and contributing to the ongoing advancement of methodologies. [ABSTRACT FROM AUTHOR]

Accurate discharge values play a critical role in water resource planning and management. However, it is common for users, modelers, and decision-makers to consider these values as true and deterministic, despite the subjective and uncertain nature of the estimation process. To address the issue, this study was conducted to identify the discharge estimation methods and associated uncertainties of hydrometric measurements in Canada. The study involved an exploration of multiple operating procedures for rating curve construction and discharge estimation across 1800 active Water Survey of Canada (WSC) hydrometric stations using an independent workflow. The first step involved understanding the discharge estimation process used by the WSC and the standard operating procedures (SOP) for inferring discharge from stage measurements. During the implementation of the workflow, it was observed that manual intervention and interpretation by hydrographers were required for time-series sequences labeled as "override" and/or "temporary shift". The workflow demonstrated that 67 % of existing records could be adequately recreated following the rating curve and temporary shift concept, while 33 % followed the other discharge estimation methods (override). Novel methods for discharge uncertainty estimation should be sought given the practices of override and temporary shift by the WSC. This study attempts to reconcile the significant issue of estimating uncertainty in published discharge values, particularly in the context of open science and Earth System modeling. By collaborating with the WSC, this research aims to improve the understanding of the processes used for discharge estimation and promote wider access to metadata and measurements for more accurate uncertainty quantification. [ABSTRACT FROM AUTHOR]

The hydrology and hydrography of the Canadian Prairies are complex and difficult to represent in hydrological models. Recent studies suggest that runoff velocities in the Canadian Prairies may be much smaller than are generally assumed. Times to peak, basin-scale flow velocities and roughnesses were derived from hourly streamflow hydrographs from 23 basins in the central Alberta Prairies. The estimated velocities were much smaller than would be estimated from most commonly used empirical equations suggesting that many existing methods are not suitable for estimating time to peak or lag times in these basins. Basin area was found to be a poor predictor of basin-scale rainfall-runoff flow velocity. Estimated velocities generally increased with basin scale, indicating that slow basin response at small scales could be related to predominance of overland and/or shallow subsurface flow over the very level topography. Basin-scale Manning's roughness parameters, commonly used in hydrological models, were found to be orders of magnitude greater than values commonly used for streams in other parts of the world. The very large values of roughness call into question whether the Manning equation should be used for modelling runoff on the Prairies. These results have important implications for modelling rainfall-runoff in this region since using widely published values of roughness will result in poor model performance. It is likely that the Darcy-Weisbach equation, which is applicable to all flow regimes, may perform better in hydrological models of this region. Further modelling and field research will be required to determine the physical causes of these very small basin-scale velocities. [ABSTRACT FROM AUTHOR]