Water Harvesting Research

Water Harvesting Research

Incorporating Storage-Dependent Losses into Reservoir Water Balance Modeling: A GLUE-Based Uncertainty Assessment of Seymareh Dam

Document Type : Research Paper

Authors
Environment Department, Research Institute of Hydraulic Engineering and Aquatic Environments, Water Research Institute, Ministry of Energy, Tehran, Iran.
Abstract
Accurate reservoir water balance modeling is critical for sound water resources management, yet conventional models often simplify unresolved closure terms (e.g., net evaporation, unmonitored seepage, and bank storage exchange) as static or negligible. This study incorporates a storage-dependent loss formulation into the annual water balance of the Seymareh Dam reservoir (Iran) over a 13-year period (2011–2023) and evaluates parameter uncertainty using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology with Monte Carlo simulations. The baseline zero-loss assumption produced significant balance closure errors, whereas the storage-dependent linear parameterization yielded a statistically robust fit ( ). Under the GLUE framework with a behavioral threshold of , approximately of parameter sets were retained, identifying optimal parameters ( ) with a peak . The 95% prediction uncertainty (95PPU) interval successfully bracketed of observed annual storage variations. Leave-one-year-out cross-validation and non-parametric bootstrap resampling confirmed the stability and non-spurious nature of the positive storage-loss dependency. A strong compensatory equifinality was identified between the slope and intercept ( ), demonstrating that parameter uncertainty governs the first-order response surface of reservoir water balance errors. The proposed framework provides a parsimonious, uncertainty-aware diagnostic tool for improving operational decision-making in large-scale impoundments.
Keywords
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