Community Cloud Computing Infrastructure to Support Equitable Water Research and Education

Castronova, Anthony M.; Nassar, Ayman; Knoben, Wouter; Fienen, Michael N.; Arnal, Louise; Clark, M. Diane

doi:10.1111/gwat.13337

Cited by 3 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the recent proliferation of artificial intelligence/machine learning (AI/ML) shows promise for hydrologic simulation (Kratzert et al., 2019), but causal AI/ML methods (e.g., Althoff et al., 2021; Tsai et al., 2021) that link changes in drivers, like pumping, to outputs, like streamflow, have not yet been explored for streamflow depletion applications. The development of an international network of community streamflow depletion benchmarking data sets and computational resources (i.e., Castronova et al., 2023) would allow rigorous quantification of model selection impacts on streamflow depletion estimates and the rapid testing and development of new approaches, thus improving both model capabilities and partner trust in model outputs.…”

Section: Research Priorities To Meet Current and Emerging Management ...mentioning

confidence: 99%

Streamflow Depletion Caused by Groundwater Pumping: Fundamental Research Priorities for Management‐Relevant Science

Zipper,

Brookfield,

Ajami

et al. 2024

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Reductions in streamflow caused by groundwater pumping, known as “streamflow depletion,” link the hydrologic process of stream‐aquifer interactions to human modifications of the water cycle. Isolating the impacts of groundwater pumping on streamflow is challenging because other climate and human activities concurrently impact streamflow, making it difficult to separate individual drivers of hydrologic change. In addition, there can be lags between when pumping occurs and when streamflow is affected. However, accurate quantification of streamflow depletion is critical to integrated groundwater and surface water management decision making. Here, we highlight research priorities to help advance fundamental hydrologic science and better serve the decision‐making process. Key priorities include (a) linking streamflow depletion to decision‐relevant outcomes such as ecosystem function and water users to align with partner needs; (b) enhancing partner trust and applicability of streamflow depletion methods through benchmarking and coupled model development; and (c) improving links between streamflow depletion quantification and decision‐making processes. Catalyzing research efforts around the common goal of enhancing our streamflow depletion decision‐support capabilities will require disciplinary advances within the water science community and a commitment to transdisciplinary collaboration with diverse water‐connected disciplines, professions, governments, organizations, and communities.

show abstract

Section: Research Priorities To Meet Current and Emerging Management ...mentioning

confidence: 99%

Streamflow Depletion Caused by Groundwater Pumping: Fundamental Research Priorities for Management‐Relevant Science

Zipper,

Brookfield,

Ajami

et al. 2024

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Building workflows that are both intuitive (i.e., that can represent our understanding of local hydro-meteorological processes; (Veiga et al, 2014)) and reproducible is essential to providing platforms for progressive and purposeful testing of new scientific advances, and to pave the way for applying research outcomes in practice. Furthermore, it fosters more equitable water research and education (Castronova et al, 2023).…”

Section: Workflow Developersmentioning

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

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.

show abstract

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

Community Cloud Computing Infrastructure to Support Equitable Water Research and Education

Cited by 3 publications

References 3 publications

Streamflow Depletion Caused by Groundwater Pumping: Fundamental Research Priorities for Management‐Relevant Science

Streamflow Depletion Caused by Groundwater Pumping: Fundamental Research Priorities for Management‐Relevant Science

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

Contact Info

Product

Resources

About