Evaluation of baseflow modelling structure in monthly water balance models using 443 Australian catchments

Cheng, Shujie; Cheng, Lei; Liu, Pan; Zhang, Lu; Xu, Chong‐Yu; Xiong, Lihua; Xia, Jun

doi:10.1016/j.jhydrol.2020.125572

Cited by 19 publications

(7 citation statements)

References 84 publications

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“…Considering the high diversity of the Australian data set (Cheng et al., 2020; Fowler, 2021), the trained machine learning models in this study have the capability to be applied to other regions, except for situations where ω is very large and may be underestimated, as shown in Figure 2. Among the four models, the BRT model produced the minimum underestimation of extremely large ω .…”

Section: Discussionmentioning

confidence: 99%

“…Parameterizing ω with catchment properties not only improves the simulation accuracy of Fu's equation (Greve et al., 2015), but also reveals the controls of climate, physiography, and vegetation on hydrological partitioning (Abatzoglou & Ficklin, 2017). However, current understanding of controls on hydrological partitioning are still very limited, and building a physically‐based relationship between ω and the control factors is difficult due to the complex (nonlinear) interactions between climate and catchment processes (Cheng et al., 2020; Gentine et al., 2012; Ning et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

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Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

Cheng

Qin

et al. 2022

Water Resources Research

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Reliable partitioning of precipitation (P) into runoff (Q) and evapotranspiration (E a ) is crucial for hydrological research and application, especially for regions with scarce data (Yang et al., 2007;Zhang et al., 2018). Understanding the controls of catchment properties on hydrological partitioning helps achieve reliable hydrology partitioning, but remains a challenging task (Sinha et al., 2020). The Budyko framework has been widely used to establish the relationship between evaporative ratio (i.e., E a /P) and relative availability of water and energy (i.e., aridity index (AI), the ratio between long-term E P and P, AI = E p /P) (Budyko, 1974;Cheng et al., 2011). In the widely used Budyko framework proposed by Fu (1981) (i.e., Fu's equation), the controls of other secondary factors on partitioning are lumped into a landscape parameter (ω) that includes intra-annual climate variability, soil, vegetation, and topography (Cheng et al., 2021;Fu, 1981;Zhang et al., 2004). Note that Fu's equation is further explained in Section 2.1. Parameterizing ω with catchment properties not only improves the simulation accuracy of Fu's equation (Greve et al., 2015), but also reveals the controls of climate, physiography, and vegetation on hydrological partitioning (Abatzoglou & Ficklin, 2017). However, current understanding of controls on hydrological partitioning are still very limited, and building a physically-based relationship between ω and the control factors is difficult due to the complex (nonlinear) interactions between climate and catchment processes (

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

Cheng

Qin

et al. 2022

Water Resources Research

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“…TWSC can be considered as the sum of groundwater and root‐zone water storage change in the model. The model has been extensively used for runoff (Q) simulation in a large number of catchments with various hydro‐meteorological conditions (Cheng et al, 2020; Hamel et al, 2017). They usually used Q to calibrate the model, and this study also considered using ET and TWSC to calibrate the model (Section 3.2).…”

Section: Methodsmentioning

confidence: 99%

Multisource remote sensing data facilitate ecohydrological simulations without runoff calibration

Yang

Scanlon

2022

Hydrological Processes

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Observed runoff is typically the only variable applied to calibrate ecohydrological models, which can simulate runoff well but cannot guarantee reliable reproduction of other hydrological components (e.g., evapotranspiration (ET) and total terrestrial water storage change (TWSC)). This study explores the potential of estimating hydrological components (runoff, ET, TWSC, groundwater, and root‐zone water storage.) using a conceptual model driven and calibrated by multisource remote sensing data only. The performances of the model were evaluated in 13 catchments with different geological, climatic and vegetation conditions. Results show that the model was encouraging in simulating monthly runoff (median KGE, 0.71; RMSE, 19.4 mm/mon), ET (median KGE, 0.86; RMSE, 9.0 mm/mon), and TWSC (median KGE, 0.58; RMSE, 26.6 mm/mon). The good positive correlation between root‐zone water storage and gross primary productivity also indicates the effectiveness of the model in simulating root‐zone water storage. This study offers new prospects for estimating key ecohydrological components precisely, especially in catchments that are difficult to monitor (e.g., karst regions) and catchments with limited observation data (e.g., ungauged regions).

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“…Lastly, future works should focus more on improving the processbased approach while maintaining the core principles of the multiple hydrograph separation technique in which the structures were developed in order to maintain its flexibility. For example, using recession curve power functions instead of exponential functions (Tashie et al, 2020) or using nonlinear storage methods for baseflow estimations (e.g., Ceola et al, 2010;Cheng et al, 2020;Kirchner, 2009) in each tank especially in snow dominated catchments (e.g., Lane et al, 2019).…”

Section: Advantages and Limitations Of The Structural Frameworkmentioning

confidence: 99%

A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves

Leong

Yokoo

2022

Hydrological Processes

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There has been significant progress made towards understanding the process controls that govern the shapes of flow duration curves (FDC). However, the transferability of the knowledge gained to large spatial scales is still lacking, partly due to the fixed representation of the model structures representing the unobserved underground layer. In the context of improving large scale applicability/transferability of FDC studies: (1) we propose a novel approach to transform/restructure the representation of the subsurface component in hydrological models to be flexible and adaptable in any environment and (2) provide a thorough interpretation of the dominant processes that impact the shape of FDCs. The flexible structure is designed from a series of multiple connected linear reservoirs that are developed from a unique multiple hydrograph separation procedure, to estimate the number of distinct hydrological processes at play. The main results are: (1) wet catchments have more complex model structures (more reservoirs) than arid catchments; indicating that subsurface processes are potentially more present in wet catchments than in drier ones. Dry catchments have more near surface occurring processes, thus have less complex model structures. However, more complexity is potentially required within the internal reservoirs of dry catchments. (2) The strength of interference in the intermediate reservoirs in controlling the dominant processes towards the bottom determined the shape of FDCs, and this increased with aridity. The novel finding is that a model structure can be determined by the catchment's ability to generate flows as well as the strength of low flow persistence. Hence, we propose a conceptual framework for the development of flexible model structures based on the shapes of FDCs. The transformation of models to be flexible in this research, should contribute to studies that seek the transferability of models over large spatial scales.

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Evaluation of baseflow modelling structure in monthly water balance models using 443 Australian catchments

Cited by 19 publications

References 84 publications

Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

Multisource remote sensing data facilitate ecohydrological simulations without runoff calibration

A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves

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