EASYMORE: A Python package to streamline the remapping of variables for Earth System models

Gharari, Shervan; Keshavarz, Kasra; Knoben, Wouter J.M.; Tang, Gouqiang; Clark, Martyn P.

doi:10.1016/j.softx.2023.101547

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Data for the USA were downloaded from the National Water Information System (NWIS; USGS) using the Python package dataretrieval (Hodson and Hariharan, 2023). Data for Canada were downloaded from the WSC HYDAT database (ECCC, 2018) using the EASYMORE Python package (Gharari et al, 2023). See Fig.…”

Section: Basin Shapefiles and Streamflow Datamentioning

confidence: 99%

FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America

Arnal,

Clark,

Pietroniro

et al. 2024

Preprint

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Abstract. Seasonal streamflow forecasts provide key information for decision-making in sectors 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 for predicting spring/summer volumes, while high precipitation during the target period presents challenges to forecast skill and streamflow predictability. Notably, we can predict lower and higher than normal streamflows during the spring to early summer with up to five months lead time 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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Section: Basin Shapefiles and Streamflow Datamentioning

confidence: 99%

FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America

Arnal,

Clark,

Pietroniro

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The precipitation volume is based on the ERA5 dataset (Hersbach et al, 2020) and the upstream area is based on the basin shapefile provided by WSC for active hydrometric stations. The remapping of the precipitation to the basin is done using the EARYMORE python package (Gharari & Knoben, 2021).…”

Section: Competing Interestsmentioning

confidence: 99%

Exploring the provenance of information across Canadian hydrometric stations: Implications for discharge estimation and uncertainty quantification

Gharari¹,

Whitfield²,

Pietroniro³

et al. 2023

Preprint

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Abstract. 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.

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FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America

Arnal,

Clark,

Pietroniro

et al. 2024

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. 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.

show abstract

EASYMORE: A Python package to streamline the remapping of variables for Earth System models

Cited by 3 publications

References 10 publications

FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America

FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America

Exploring the provenance of information across Canadian hydrometric stations: Implications for discharge estimation and uncertainty quantification

FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America

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