Reducing the uncertainty of time-varying hydrological model parameters using spatial coherence within a hierarchical Bayesian framework

Pan, Zhengke; Liu, Pan; Gao, Shanjun; Cheng, Lei; Chen, Jie; Zhang, Xiaojing

doi:10.1016/j.jhydrol.2019.123927

Cited by 11 publications

(12 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As described in the literature (Pan et al, 2019;Perrin et al, 2003;Renard et al, 2011;Westra et al, 2014), parameter θ 1 , which represents the primary storage of water in the catchment, is the most sensitive parameter in the GR4J model structure, and the stochastic variations of this parameter have the largest impact on model projection performance (Renard et al, 2011;Westra et al, 2014). In addition, the temporal variation in the catchment storage capacity was physically interpretable.…”

Section: Process Layer: Temporal Variation Of the Model Parametermentioning

confidence: 94%

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

Pan

Liu

Gao

et al. 2019

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Process Layer: Temporal Variation Of the Model Parametermentioning

confidence: 94%

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

Pan

Liu

Gao

et al. 2019

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since rainfall is one of the most important factors that influence the degree of wetness, the identification method of meteorological drought was only based on the annual rainfall data as in other studies (Li et al, 2020;Pan et al, 2019b;Saft et al, 2015;Wong et al, 2013). The method proposed by Saft et al (2015) was introduced in this study to define the meteorological-drought period (also known as dry period).…”

Section: Identification Of Meteorological Droughtmentioning

confidence: 99%

“…Furthermore, we have the same study region, i.e., catchments in southeastern Australia (but our data sources and periods are different). A more detailed process of the identification method of the dry period can be obtained in research by Saft et al (2015) and our previous study (Pan et al, 2019b).…”

Section: Identification Of Meteorological Droughtmentioning

confidence: 99%

“…It not only affects the balance of aquatic and terrestrial ecosystems but also many economic and social sectors including agriculture yield, industrial production and urban water supply (Mishra and Singh, 2010). Furthermore, recent literature indicates that improved probabilities of extreme events have been projected in many parts around the world because of increasing anthropogenic interference and global climate change, which implies more severe droughts in the future (Dai, 2011(Dai, , 2012Pan et al, 2019b). Unlike other natural hazards, e.g., floods,…”

Section: Introductionmentioning

confidence: 99%

“…For example, Deng et al (2016) calibrated the time-varying parameters of a two-parameter monthly-water-balance model with a case study in the Wudinghe catchment in China and found that one of the model parameters that denote the CWSC experienced a significant upward trend during the historical period from 1958 to 2000. Pan et al (2019b) calibrated the GR4J (modèle du Génie Rural à 4 paramètres Journalier) hydrological model with time-varying parameters in three catchments of southeastern Australia and found that the spatial coherence of adjacent catchments helps to reduce the estimation uncertainty of the CWSC and improves the model prediction performance. In addition, because of the resilience of the catchment, the shift in the CWSC might occur as a delayed step change.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The influence of a prolonged meteorological drought on catchment water storage capacity: a hydrological-model perspective

Pan

Liu

et al. 2020

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Understanding the propagation of prolonged meteorological drought helps solve the problem of intensified water scarcity around the world. Most of the existing literature studied the propagation of drought from one type to another (e.g., from meteorological to hydrological drought) with statistical approaches; there remains difficulty in revealing the causality between meteorological drought and potential changes in the catchment water storage capacity (CWSC). This study aims to identify the response of the CWSC to the meteorological drought by examining the changes of hydrological-model parameters after drought events. Firstly, the temporal variation of a model parameter that denotes that the CWSC is estimated to reflect the potential changes in the real CWSC. Next, the change points of the CWSC parameter were determined based on the Bayesian change point analysis. Finally, the possible association and linkage between the shift in the CWSC and the time lag of the catchment (i.e., time lag between the onset of the drought and the change point) with multiple catchment properties and climate characteristics were identified. A total of 83 catchments from southeastern Australia were selected as the study areas. Results indicated that (1) significant shifts in the CWSC can be observed in 62.7 % of the catchments, which can be divided into two subgroups with the opposite response, i.e., 48.2 % of catchments had lower runoff generation rates, while 14.5 % of catchments had higher runoff generation rate; (2) the increase in the CWSC during a chronic drought can be observed in smaller catchments with lower elevation, slope and forest coverage of evergreen broadleaf forest, while the decrease in the CWSC can be observed in larger catchments with higher elevation and larger coverage of evergreen broadleaf forest; (3) catchments with a lower proportion of evergreen broadleaf forest usually have a longer time lag and are more resilient. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change.

show abstract

Quantifying the flood coincidence likelihood between Huai River and its tributaries considering the nonstationarity

Zhang,

Xu,

Wang

et al. 2024

Journal of Hydrology: Regional Studies

View full text Add to dashboard Cite

Reducing the uncertainty of time-varying hydrological model parameters using spatial coherence within a hierarchical Bayesian framework

Cited by 11 publications

References 84 publications

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

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

The influence of a prolonged meteorological drought on catchment water storage capacity: a hydrological-model perspective

Quantifying the flood coincidence likelihood between Huai River and its tributaries considering the nonstationarity

Contact Info

Product

Resources

About