Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy

Xie, Kang; Liu, Pan; Zhang, Jianyun; Wang, Guoqing; Zhang, Xiaojing; Zhou, Liting

doi:10.1016/j.jhydrol.2020.125772

Cited by 19 publications

(10 citation statements)

References 57 publications

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“…Previous studies have established the effectiveness of the multivariate calibration framework for hydrological models (Bai et al, 2018b;Dembélé et al, 2020;Demirel et al, 2018;Finger et al, 2015;Nijzink et al, 2018;Xie et al, 2020). The current study performed a multiple variable calibration strategy to calibrate model parameters in each grid against the reference datasets (VIC runoff and GLEAM ET) during the 15year calibration period, followed by an independent model validation against the reference gridded runoff and ET during the 16-year validation period.…”

Section: Performance Of Calibration and Regionalization For Dtvgm-pmlmentioning

confidence: 99%

Regionalization of hydrological model parameters using gradient boosting machine

Song

Xia

Wang

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25∘; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.

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Section: Performance Of Calibration and Regionalization For Dtvgm-pmlmentioning

confidence: 99%

Regionalization of hydrological model parameters using gradient boosting machine

Song

Xia

Wang

et al. 2022

Hydrol. Earth Syst. Sci.

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“…Therefore, the parameters are calibrated on a spatial grid of the same area in this study. Research has shown that calibration on the global grids can significantly reduce parameter discontinuities compared to calibration on individual catchments (Xie et al, 2020a). The conceptual parameters in three monthly water balance models (Table 2) are calibrated against the agreement between simulated and observed hydrographs until the optimal value is obtained.…”

Section: Parameter Calibration Strategymentioning

confidence: 99%

Global soil moisture storage capacity at 0.5° resolution for geoscientific modelling

Xie

Liu

Xia

et al. 2022

Preprint

Self Cite

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Abstract. Soil moisture storage capacity (SMSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the SMSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the SMSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global SMSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. SMSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global SMSC parameter dataset as a benchmark for geoscientific modelling by users.

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“…To our knowledge, Xie et al. (2021) is the only study that used a GRP to calibrate a hydrological model. Xie et al.…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, Xie et al (2021) is the only study that used a GRP to calibrate a hydrological model. Xie et al (2021) show that the combined use of a GRP and an ET product improved the prediction of ET. However, this approach made the streamflow prediction worse.…”

mentioning

confidence: 99%

Can Hydrological Models Benefit From Using Global Soil Moisture, Evapotranspiration, and Runoff Products as Calibration Targets?

Mei

Mai

Hong

et al. 2023

Water Resources Research

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Hydrological models are usually calibrated to in‐situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single‐objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill‐posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single‐ and multi‐objective model calibration schemes based on different combinations of gaged streamflow, global‐scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process‐based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best‐performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow‐dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.

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Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy

Cited by 19 publications

References 57 publications

Regionalization of hydrological model parameters using gradient boosting machine

Regionalization of hydrological model parameters using gradient boosting machine

Global soil moisture storage capacity at 0.5° resolution for geoscientific modelling

Can Hydrological Models Benefit From Using Global Soil Moisture, Evapotranspiration, and Runoff Products as Calibration Targets?

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