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Browsing by Author "Pietroniro, Alain"

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    FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
    (Hydrology and Earth System Sciences, 2024-09) Arnal, Louise; Clark, Martyn P.; Pietroniro, Alain; Vionnet, Vincent; Casson, David R; Whitfield, Paul; Fortin, Vincent; Wood, Andrew; Knoben, Wouter; Newton, Brandi W; Walford, Colleen
    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.
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    Modeling the lagged and nonlinear effects of weather conditions on abundance of Culex tarsalis mosquitoes in Saskatchewan, Western Canada using a bi-dimensional distributed lag nonlinear model
    (Elsevier, 2024-12-24) Gizaw, Zemichael; Vidrio-Sahagún, Cuauhtémoc Tonatiuh; Pietroniro, Alain; Schuster Wallace, Corinne
    The establishment of West Nile Virus (WNV) competent vectors continues to pose a major public health challenge in Canada, especially in the south. While studies have examined the association between weather conditions and the abundance of mosquitoes over trap weeks, there is limited research on the effects of weather conditions on the abundance of Culex tarsalis (Cx. tarsalis) mosquitoes for a lapse of time beyond the trap week in Saskatchewan, Western Canada. To address this gap, we analyzed provincially available weekly mosquito trap and co-incident meteorological station data in Saskatchewan from 2010 to 2021 using a bi-dimensional distributed lag and nonlinear model. Data indicate that 171,141 Cx. tarsalis mosquitoes were trapped across much of Saskatchewan, from 2010 to 2021. Cx. tarsalis were found to be most abundant between weeks 26 and 35 (July and August) and peaked in weeks 30 and 31. Based on the WNV-positive pools, mosquito infection rates increased from week 23 to 36. While weekly average maximum air temperatures between 20 °Cand 30 °C were associated with more Cx. tarsalis across all lags (0 – 8 weeks), higher weekly average minimum air temperatures had a strong and immediate effect that diminished over longer lags. Higher weekly average rainfall amounts (> 20 mm) were associated with fewer Cx. tarsalis mosquitoes across all lags, while average weekly rainfall between 8 and 20 mm was strongly associated with a high abundance of Cx. tarsalis mosquitoes over longer lags (5 -7 weeks). Additionally, increasing wind speed was associated with lower abundance of Cx. tarsalis across all lags. Findings identified nonlinear lag associations for weekly average maximum air temperature and rainfall, but linear associations for weekly average minimum air temperature and wind speed. Identified lags and thresholds for temperature, rainfall, and wind speed at which mosquito abundance peaked could help to inform public health authorities in timing of vector control measures to prevent WNV transmission.