Exploration on hydrological model calibration by considering the hydro-meteorological variability

Abstract The hydrological response is changeable for catchments with hydro-meteorological variations, which is neglected by the traditional calibration approach through using time-invariant parameters. This study aims to reproduce the variation of hydrological responses by allowing parameters to vary over clusters with hydro-meteorological similarities. The Fuzzy C-means algorithm is used to partition one-month periods into temperature-based and rainfall-based clusters. One-month periods are also classified based on seasons and random numbers for comparison. This study is carried out in three catchments in the UK, using the IHACRES rainfall-runoff model. Results show when using time-varying parameters to account for the variation of hydrological processes, it is important to identify the key factors that cause the change of hydrological responses, and the selection of the time-varying parameters should correspond to the identified key factors. In the study sites, temperature plays a more important role in controlling the change of hydrological responses than rainfall. It is found that the number of clusters has an effect on model performance, model performances for calibration period become better with the increase of cluster number; however, the increase of model complexity leads to poor predictive capabilities due to overfitting. It is important to select the appropriate number of clusters to achieve a balance between model complexity and model performance.

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Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

10.5194/hess-2019-6 ◽

2019 ◽

Author(s):

Zhengke Pan ◽

Pan Liu ◽

Shida Gao ◽

Jun Xia ◽

Jie Chen ◽

...

Keyword(s):

Spatial Coherence ◽

Model Performance ◽

Climatic Conditions ◽

Bayesian Regression ◽

Model Parameters ◽

Time Varying ◽

Hierarchical Bayesian ◽

Hydrological Models ◽

Varying Parameters ◽

Time Varying Parameters

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 the performance degradation. Many existing literatures model the time-varying parameters as functions of physically-based covariates; however, a major challenge remains 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 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 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 periods were not suitable for predicting runoff over wet periods 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.

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Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

Hydrology and Earth System Sciences ◽

10.5194/hess-23-3405-2019 ◽

2019 ◽

Vol 23 (8) ◽

pp. 3405-3421 ◽

Cited By ~ 1

Author(s):

Zhengke Pan ◽

Pan Liu ◽

Shida Gao ◽

Jun Xia ◽

Jie Chen ◽

...

Keyword(s):

Spatial Coherence ◽

Model Performance ◽

Climatic Conditions ◽

Bayesian Regression ◽

Model Parameters ◽

Time Varying ◽

Hierarchical Bayesian ◽

Hydrological Models ◽

Varying Parameters ◽

Time Varying Parameters

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.

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