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Abstract Predicting future streamflow change is essential for water resources management and understanding the impacts of projected climate and land use changes on water availability. The Budyko framework is a useful and computationally efficient tool to model streamflow at larger spatial scales. This study predicts future streamflow changes in 889 watersheds in the contiguous United States based on projected climate and land use changes from 2040 to 2069. The temporal variability of surface water balance controls, represented by the Budykoωparameter, was modeled using multiple linear regression, random forest (RF), and gradient boosting. Results show that RF is the optimal model and can explain >85% of the variance in most watersheds. Relative cumulative moisture surplus, forest coverage, crop land and urban land are the most important variables of the time‐varyingωin most watersheds. There are statistically significant increases in mean annual precipitation, potential evapotranspiration, andωin 2040–2069, as compared to 1950–2005. This leads to a statistically significant decrease in the runoff ratio (Q/P). Streamflow is projected to decrease in the central, southwestern, and southeastern United States and increase in the northeast. These projections of water availability which are based on future climate and land use change scenarios can inform water resources management and adaptation strategies.
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This content will become publicly available on June 13, 2026
Training a hidden Markov model with PMDI and temperature to create climate informed scenarios
Understanding the nature of climatic change impacts on spatial and temporal hydroclimatic patterns is important to the development of timely and spatially explicit adaptation options. However, regime-switching behavior of hydroclimatic variables complicates the modelling process as many traditional time series methods do not capture this behavior. Accurately representing spatial correlation across hydroclimatic regimes is particularly important for water resources planning in large watersheds such as the Colorado River, and regions where interbasin transfers and shared demand nodes link multiple watersheds. Here, we developed a hidden Markov model (HMM) with covariates that generates an ensemble of plausible future regional scenarios of the Palmer modified drought index (PMDI) for any projected temperature sequence. The resulting spatially explicit scenarios represent the historical spatial and temporal patterns of the training data while incorporating non-stationarity by conditioning on temperature. These ensembles can aid water resources managers, infrastructure planners, and government policymakers tasked with building of more resilient water systems. Moreover, these ensembles can be used to generate streamflow ensembles, which, in turn, will be a valuable input to study the impact of climate change on regional hydrology.
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- Award ID(s):
- 1942370
- PAR ID:
- 10600334
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Water
- Volume:
- 7
- ISSN:
- 2624-9375
- Subject(s) / Keyword(s):
- hidden Markov model Western U.S. drought climate change paleoclimate data
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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