Abstract
Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. Many existing studies model the time-varying parameters as functions of physically based covariates; however, a major challenge remains in finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes and one scenario with a stationary scheme for model parameters were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine the validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with the stationary setting of model parameters, (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance, and (3) model parameters calibrated over dry years were not suitable for predicting runoff over wet years because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.
Highlights
Long-term streamflow projection is an important part of effective water resources planning because it can predict future scarcity in water supply and help prevent floods
The median NSEsqrt performance in scenario 4 was 0.80 % higher than scenario 5; the variation range in scenario 4 was 53 % wider than the latter. These results demonstrate that the time-varying scheme for model parameters improved the median NSEsqrt performance and amplified the projection uncertainty compared with the results from the stationary scheme for model parameters
A two-level hierarchical Bayesian (HB) framework was used to incorporate the spatial coherence of adjacent catchments to improve the hydrological projection performance of sensitive timevarying parameters for a lumped conceptual rainfall–runoff model (GR4J) under contrasting climatic conditions
Summary
Long-term streamflow projection is an important part of effective water resources planning because it can predict future scarcity in water supply and help prevent floods. Many previous studies have explored the transferability of stationary parameters to periods with different climatic conditions. They have concluded that hydrological model pa-. Z. Pan et al.: Improving hydrological projection performance under contrasting climatic conditions rameters are sensitive to the climatic conditions of the calibration period (Chiew et al, 2009, 2014; Coron et al, 2012; Merz et al, 2011; Renard et al, 2011; Seiller et al, 2012; Vaze et al, 2010). It is still necessary to reduce the magnitude of performance loss and improve the robustness of the projection performance using calibrated parameters based on the historical records, even though the climatic conditions in the future may be dissimilar to those used for model calibration
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