Modelling ungauged catchments using the catchment runoff response similarity

Tegegne, Getachew; Kim, Young-Oh

doi:10.1016/j.jhydrol.2018.07.042

Cited by 60 publications

(43 citation statements)

References 46 publications

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“…Catchment characteristics (e.g., basin surface, soil type, topography, and land use) and meteorological data (e.g., precipitation, air temperature, solar radiation, relative humidity, and wind speed) are commonly adopted as attributes to represent the physical similarity [12,[23][24][25][26][27][28][29]. The classical physical similarity technique assumes that the similarity in the input attribute is fully transferred to the output (i.e., the runoff).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Narbondo

Gorgoglione

Crisci

et al. 2020

Water

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Regionalization techniques have been comprehensively discussed as the solution for runoff predictions in ungauged basins (PUB). Several types of regionalization approach have been proposed during the years. Among these, the physical similarity one was demonstrated to be one of the most robust. However, this method cannot be applied in large regions characterized by highly variable climatic conditions, such as sub-tropical areas. Therefore, this study aims to develop a new regionalization approach based on an enhanced concept of physical similarity to improve the runoff prediction of ungauged basins at country scale, under highly variable-weather conditions. A clustering method assured that watersheds with different hydrologic and physical characteristics were considered. The novelty of the proposed approach is based on the relationships found between rainfall-runoff model parameters and watershed-physiographic factors. These relationships were successively exported and validated at the ungauged basins. From the overall results, it can be concluded that the runoff prediction in the ungauged basins was very satisfactory. Therefore, the proposed approach can be adopted as an alternative method for runoff prediction in ungauged basins characterized by highly variable-climatic conditions.

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Section: Introductionmentioning

confidence: 99%

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Narbondo

Gorgoglione

Crisci

et al. 2020

Water

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show abstract

“…In these three approaches, the HB framework has been proved as the most efficient method to incorporate the spatial coherence to reduce the estimation uncertainty because it has the advantage of shrinking the local parameter toward the common regional mean and including an estimation of its variance or covariance across the catchments (Bracken et al, 2018;. In the field of hydrological modeling, most proceeding literatures were focused on no pooling models that neglect the spatial coherence between catchments (Heuvelmans et al, 2006;Lebecherel et al, 2016;Merz and Bloschl, 2004;Oudin et al, 2008;Singh et al, 2012;Tegegne and Kim, 2018;Xu et al, 2018); little attention has been paid to the HB framework. Thus, we want to fill this gap and explore the applicability of the spatial coherence through the HB framework in hydrological modeling with the time-varying parameters.…”

Section: Imntroductmonmentioning

confidence: 99%

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

Pan¹,

Liu²,

Gao³

et al. 2019

Preprint

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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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“…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(Chiew et al, , 2014Coron et al, 2012;Merz et al, 2011;Renard et al, 2011;Seiller et al, 2012;Vaze et al, 2010). For instance, Merz et al (2011) calibrated model parameters using six consecutive 5-year periods between 1976 and 2006 for 273 catchments in Austria and found that the calibrated parameters representing snow and soil moisture processes showed a significant trend in the study area.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Merz et al (2011) calibrated model parameters using six consecutive 5-year periods between 1976 and 2006 for 273 catchments in Austria and found that the calibrated parameters representing snow and soil moisture processes showed a significant trend in the study area. Other studies have found that degradation in model performance was directly related to the difference in precipitation between the calibration and verification periods (Coron et al, 2012;Vaze et al, 2010). One proposal for managing this problem is to calibrate model parameters in periods with similar climatic conditions to the near future, but future streamflow observations are unavailable.…”

Section: Introductionmentioning

confidence: 99%

“…In these three approaches, the HB framework has been proven to be the most efficient method to incorporate the spatial coherence to reduce the estimation uncertainty because it has the advantage of shrinking the local parameter toward the common regional mean and including an estimation of its variance or covariance across the catchments (Bracken et al, 2018;Sun and Lall, 2015;Sun et al, 2015). In the field of hydrological modeling, most preceding studies were focused on no-pooling models that neglect the spatial coherence between catchments (Heuvelmans et al, 2006;Lebecherel et al, 2016;Merz and Bloschl, 2004;Oudin et al, 2008;Singh et al, 2012;Tegegne and Kim, 2018;Xu et al, 2018); little attention has been paid to the HB framework. Thus, we want to fill this gap and explore the applicability of the spatial coherence through the HB framework in hydrological modeling with the time-varying parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.

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

Modelling ungauged catchments using the catchment runoff response similarity

Cited by 60 publications

References 46 publications

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

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

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