Parameter dimensionality reduction of a conceptual model for streamflow prediction in Canadian, snowmelt dominated ungauged basins

Arsenault, R.J.; Poissant, Dominique; Brissette, François

doi:10.1016/j.advwatres.2015.08.014

Cited by 24 publications

(15 citation statements)

References 51 publications

(44 reference statements)

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“…This result is consistent with the model performance for gauged catchments (see Figure 4 and Table 6). This result tends to support the claim that there is no incentive to prefer a parsimonious hydrological model for regionalization studies rather than a model with adequate complexity (Arsenault et al, 2015;. However, hydrological models with fewer parameters are recommended when no preknowledge about the regionalization performance is available since the performance difference between the regionalization methods is relatively smaller.…”

Section: Assessment Over Hydrological Modelsmentioning

confidence: 53%

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Xue

Magnusson

Huang

et al. 2020

Journal of Hydrology

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Runoff prediction in ungauged basins (PUB) is a challenging but essential research topic from scientific and practical perspectives, and the International Association of Hydrological Sciences (IAHS) was motivated to establish a "Decade on Predictions in Ungauged Basins (PUB): 2003-2012". Regionalization methods are currently regarded as the most common solution for PUB, coupled with hydrological models. Regionalization methods are generally categorized into three classes: spatial proximity, physical similarity and regression. However, knowledge of the applicability of regionalization methods is not clear or consistent, despite the number of extensive studies that have been conducted and considerable progress that has been achieved in recent decades. Due to the nonstationary climate, the reliability of results from historical data for future applications cannot be used undoubtedly. Even worse, PUB studies in cold regions (e.g., Norway) are limited.

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Section: Assessment Over Hydrological Modelsmentioning

confidence: 53%

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Xue

Magnusson

Huang

et al. 2020

Journal of Hydrology

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“…The four models of Equation (2) may have a different structure and, consequently, a different number of regional parameters to be estimated for each of them. In particular, the various basin descriptors could have a different weight in explaining the a i optimal parameters' variability, and the identification, with consequent exclusion, of those poorly significant could be extremely useful in reducing the number of model parameters [23,24,38]; equifinality and overparameterization are, in fact, common problems for such kind of models and could reduce model robustness, introducing possible sources of model uncertainty. Initially, an attempt to fit a unique model for the entire region was carried out, trying to identify a unique set of equations characterizing the general form of Equation (2); nevertheless, as expected, better results were obtained by separately analyzing the three homogeneous sub-zones of Figure 3 and determining a different set of equations for each of them.…”

Section: Model Description and Assumptionsmentioning

confidence: 99%

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Pumo

Viola

Noto

2016

Water

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Many hydrologic applications require reliable estimates of runoff in river basins to face the widespread lack of data, both in time and in space. A regional method for the reconstruction of monthly runoff series is here developed and applied to Sicily (Italy). A simple modeling structure is adopted, consisting of a regression-based rainfall-runoff model with four model parameters, calibrated through a two-step procedure. Monthly runoff estimates are based on precipitation, temperature, and exploiting the autocorrelation with runoff at the previous month. Model parameters are assessed by specific regional equations as a function of easily measurable physical and climate basin descriptors. The first calibration step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such "optimal" sets are used at the second step, part of a regional regression analysis, to establish the regional equations for model parameters assessment as a function of basin attributes. All the gauged watersheds across the region have been analyzed, selecting 53 basins for model calibration and using the other six basins exclusively for validation. Performances, quantitatively evaluated by different statistical indexes, demonstrate relevant model ability in reproducing the observed hydrological time-series at both the monthly and coarser time resolutions. The methodology, which is easily transferable to other arid and semi-arid areas, provides a reliable tool for filling/reconstructing runoff time series at any gauged or ungauged basin of a region.

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“…It was initially designed for academic purposes but soon found applications in research as well. It has been used in multi-model ensemble studies [33] as well as in streamflow prediction in ungauged sites [34] MOHYSE is composed of a production store and a routing store and has 10 calibration parameters ( Figure 4A). The climate variables required to run MOHYSE are daily precipitation and daily mean temperature.…”

Section: Mohysementioning

confidence: 99%

Performance and Uncertainty Evaluation of Snow Models on Snowmelt Flow Simulations over a Nordic Catchment (Mistassibi, Canada)

2015

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An analysis of hydrological response to a multi-model approach based on an ensemble of seven snow models (SM; degree-day and mixed degree-day/energy balance models) coupled with three hydrological models (HM) is presented for a snowmelt-dominated basin in Canada. The present study aims to compare the performance and the reliability of different types of SM-HM combinations at simulating snowmelt flows over the 1961-2000 historical period. The multi-model approach also allows evaluating the uncertainties associated with the structure of the SM-HM ensemble to better predict river flows in Nordic environments. The 20-year calibration shows a satisfactory performance of the ensemble of 21 SM-HM combinations at simulating daily discharges and snow water equivalents (SWEs), with low streamflow volume biases. The validation of the ensemble of 21 SM-HM combinations is conducted over a 20-year period. Performances are similar to the calibration in simulating the daily discharges and SWEs, again with low model biases for streamflow. The spring-snowmelt-generated peak flow is captured only in timing by the ensemble of 21 SM-HM combinations. The results of specific hydrologic indicators show that the uncertainty related to the choice of the given HM in the SM-HM combinations cannot be neglected in a more quantitative manner in simulating snowmelt flows. The selection of the SM plays a larger role than the choice of the SM approach (degree-day versus mixed degree-day/energy balance) in simulating spring flows. Overall, the snow models provide a low degree of uncertainty to the total uncertainty in hydrological modeling for snow hydrology studies. OPEN ACCESSHydrology 2015, 2 290

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Parameter dimensionality reduction of a conceptual model for streamflow prediction in Canadian, snowmelt dominated ungauged basins

Cited by 24 publications

References 51 publications

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Performance and Uncertainty Evaluation of Snow Models on Snowmelt Flow Simulations over a Nordic Catchment (Mistassibi, Canada)

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