Characterizing Future Climate and Hydrology in Massachusetts using Stochastic Modeling Methods
Communities across Massachusetts may face potential consequences of climate change, ranging from more extreme rainfall to more pronounced and frequent droughts. Climate change could alter the state’s hydrology in potentially complex and unanticipated ways. Typical approaches for projecting hydrologic risk under climate change can misrepresent and underestimate the variability of climate and hydrology in the future. This limitation can severely impede risk-based decision-making for watershed and infrastructure management.
As an alternative, this project will apply emerging stochastic modeling tools that minimize future variability, including the development of a Stochastic Watershed Model. This model uses input from a Stochastic Weather Generator that estimates climate variables for future warming scenarios over the State of Massachusetts.
The goal of this project is to project and disseminate 21st century precipitation and temperature characteristics for the state of Massachusetts.
This investigation starts with a Stochastic Weather Generator developed at Cornell University. This tool will simulate precipitation and temperature for selected warming scenarios throughout Massachusetts. Output from the Stochastic Weather Generator will be used to characterize future climate in Massachusetts. Additionally, output from the Stochastic Weather Generator will be used to drive a deterministic rainfall-runoff model, which defines how much runoff is created from a specific amount of rainfall. Finally, a Stochastic Watershed Model, developed at Tuft University, will be applied to correct the biases of the deterministic models at high- and low-flow extremes, which are common with deterministic watershed models.
This project began with a pilot study of the Squannacook Basin. This has been documented in a coming SIR and data release and is being used as a starting point for the second phase of this project; applying stochastic climate data to the PRMS and stochastic watershed models to the entire commonwealth.
Pilot Study
Phase 1 of this project was a pilot study to apply the stochastic methods to one basin. The Squannacook River Watershed was selected as the focus of this study as it is representative of basin characteristics in MA and was minimally impacted by regulation.
The following have already been published by partners detailing parts of the initial phase of this project. The final data release and SIR for Phase 1 is still in preparation.
Details about the Stochastic Weather Generator developed for this project.
- Najibi, N., Mukhopadhyay, S., & Steinschneider, S. (2022). Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity. Geophysical Research Letters, 49, e2021GL097100. https://doi.org/10.1029/2021GL097100
- Najibi, N, Mukhopadhyay, S, Steinschneider, S. Identifying weather regimes for regional-scale stochastic weather generators. Int J Climatol. 2021; 41: 2456–2479. https://doi.org/10.1002/joc.6969
- Steinschneider, S., Ray, P., Rahat, S. H., & Kucharski, J. (2019). A weather-regime-based stochastic weather generator for climate vulnerability assessments of water systems in the western United States. Water Resources Research, 55, 6923–6945. https://doi.org/10.1029/2018WR024446
Details about the Stochastic Watershed Model developed for this project.
- Brodeur, Z., Wi, S., Shabestanipour, G., Lamontagne, J., & Steinschneider, S. (2024). A hybrid, non-stationary stochastic watershed model (SWM) for uncertain hydrologic simulations under climate change. Water Resources Research, 60, e2023WR035042. https://doi.org/10.1029/2023WR035042
- Shabestanipour, G., Brodeur, Z., Farmer, W. H., Steinschneider, S., Vogel, R. M., & Lamontagne, J. R. (2023). Stochastic watershed model ensembles for long-range planning: Verification and validation. Water Resources Research, 59, e2022WR032201. https://doi.org/10.1029/2022WR032201
Communities across Massachusetts may face potential consequences of climate change, ranging from more extreme rainfall to more pronounced and frequent droughts. Climate change could alter the state’s hydrology in potentially complex and unanticipated ways. Typical approaches for projecting hydrologic risk under climate change can misrepresent and underestimate the variability of climate and hydrology in the future. This limitation can severely impede risk-based decision-making for watershed and infrastructure management.
As an alternative, this project will apply emerging stochastic modeling tools that minimize future variability, including the development of a Stochastic Watershed Model. This model uses input from a Stochastic Weather Generator that estimates climate variables for future warming scenarios over the State of Massachusetts.
The goal of this project is to project and disseminate 21st century precipitation and temperature characteristics for the state of Massachusetts.
This investigation starts with a Stochastic Weather Generator developed at Cornell University. This tool will simulate precipitation and temperature for selected warming scenarios throughout Massachusetts. Output from the Stochastic Weather Generator will be used to characterize future climate in Massachusetts. Additionally, output from the Stochastic Weather Generator will be used to drive a deterministic rainfall-runoff model, which defines how much runoff is created from a specific amount of rainfall. Finally, a Stochastic Watershed Model, developed at Tuft University, will be applied to correct the biases of the deterministic models at high- and low-flow extremes, which are common with deterministic watershed models.
This project began with a pilot study of the Squannacook Basin. This has been documented in a coming SIR and data release and is being used as a starting point for the second phase of this project; applying stochastic climate data to the PRMS and stochastic watershed models to the entire commonwealth.
Pilot Study
Phase 1 of this project was a pilot study to apply the stochastic methods to one basin. The Squannacook River Watershed was selected as the focus of this study as it is representative of basin characteristics in MA and was minimally impacted by regulation.
The following have already been published by partners detailing parts of the initial phase of this project. The final data release and SIR for Phase 1 is still in preparation.
Details about the Stochastic Weather Generator developed for this project.
- Najibi, N., Mukhopadhyay, S., & Steinschneider, S. (2022). Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity. Geophysical Research Letters, 49, e2021GL097100. https://doi.org/10.1029/2021GL097100
- Najibi, N, Mukhopadhyay, S, Steinschneider, S. Identifying weather regimes for regional-scale stochastic weather generators. Int J Climatol. 2021; 41: 2456–2479. https://doi.org/10.1002/joc.6969
- Steinschneider, S., Ray, P., Rahat, S. H., & Kucharski, J. (2019). A weather-regime-based stochastic weather generator for climate vulnerability assessments of water systems in the western United States. Water Resources Research, 55, 6923–6945. https://doi.org/10.1029/2018WR024446
Details about the Stochastic Watershed Model developed for this project.
- Brodeur, Z., Wi, S., Shabestanipour, G., Lamontagne, J., & Steinschneider, S. (2024). A hybrid, non-stationary stochastic watershed model (SWM) for uncertain hydrologic simulations under climate change. Water Resources Research, 60, e2023WR035042. https://doi.org/10.1029/2023WR035042
- Shabestanipour, G., Brodeur, Z., Farmer, W. H., Steinschneider, S., Vogel, R. M., & Lamontagne, J. R. (2023). Stochastic watershed model ensembles for long-range planning: Verification and validation. Water Resources Research, 59, e2022WR032201. https://doi.org/10.1029/2022WR032201