Enhancing Season-Ahead Streamflow Forecasts with GCMs, Climate Indices, and Their Interactions
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 10
Abstract
Streamflow forecasts play an important role in water resources operation and management, and skillful seasonal forecasts can significantly facilitate the decision-making process. Streamflow variability is often associated with various large-scale, slowly evolving climate phenomena (e.g., El Nino Southern Oscillation), promoting the value of climate indices for streamflow forecast development, and has been investigated extensively. Separately, global climate models (GCMs), which provide climate forecasts out to 12 months globally, have been demonstrated to enhance seasonal streamflow predictability. However, neither the combination nor the interaction of these two sources of predictability has been given much attention. In this work, we propose a framework that can simultaneously account for antecedent climate indices and GCM forecasts in statistical streamflow forecasts and address how their interactions affect predictability. More specifically, we build a streamflow forecast framework combining statistical forecast models conditioned on climate indices with dynamical North American Multi-Model Ensemble (NMME) precipitation forecasts to generate streamflow forecast ensembles, and merge ensemble members with a Bayesian model averaging with bootstrap aggregating (BMA-bagging) approach. The framework is applied to streamflow on the Blue Nile River upstream of the Grand Ethiopian Renaissance Dam (GERD). Most NMME models tend to improve one-month-ahead GERD inflow forecasts; however, longer lead times prove challenging, and require specific NMME model selection. In contrast, although the value of climate indices varies with forecast lead time, their potential contribution grows at multimonth leads. The GERD inflow forecasts including NMME models and climate indices can simultaneously take advantage of both sources of predictability and prove superior across lead times as compared to including either source individually.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research has been supported by the National Science Foundation (Grant No. 1639214). The authors declare that they have no conflicts of interest in this work.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 10 • October 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 24, 2022
Accepted: Jun 20, 2023
Published online: Aug 10, 2023
Published in print: Oct 1, 2023
Discussion open until: Jan 10, 2024
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Climates
- Engineering fundamentals
- Environmental engineering
- Flow (fluid dynamics)
- Fluid dynamics
- Fluid mechanics
- Forecasting
- Hydrologic engineering
- Inflow
- Mathematics
- River engineering
- Rivers and streams
- Seasonal variations
- Statistical analysis (by type)
- Statistics
- Streamflow
- Water and water resources
- Water management
- Water policy
- Water resources
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