Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion  by neural network models

Snauffer, A. M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, M.

doi:10.5194/tc-12-891-2018

Cited by 39 publications

(34 citation statements)

References 39 publications

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“…This result is consistent with the general notion that, for many applications, a single hidden layer is adequate for modelling any nonlinear continuous function (e.g. Hsieh,2009;Snauffer, et al, 2018). Similarly, use of a linear activation function has yielded NNET models with better accuracy compared to the commonly used sigmoidal function.…”

Section: Discussionsupporting

confidence: 87%

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Teweldebrhan

Schuler

Burkhart

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to acquire an adequate sample size, which may take from days to months – especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual-based response surfaces. Here, we apply emulators of an MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time-relaxed limits of acceptability concept (pLoA). Three machine-learning models (MLMs) were built using model parameter sets and response surfaces with a limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time-relaxed limits of acceptability approach, based on the predicted pLoA values, and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations with an R2 value of 0.7 to 0.92. Similarly, the models identified using the coupled machine-learning (ML) emulators and limits of acceptability approach have performed very well in reproducing the median streamflow prediction during the calibration and validation periods, with an average Nash–Sutcliffe efficiency value of 0.89 and 0.83, respectively.

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Section: Discussionsupporting

confidence: 87%

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Teweldebrhan

Schuler

Burkhart

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…This result is consistent with the general notion, that for many applications a single hidden layer is adequate to model any nonlinear continuous function (e.g. Hsieh,2009;Snauffer, et al, 2018). Similarly, use of a linear activation function has yielded NNET models with better accuracy as compared to the commonly used sigmoidal function.…”

supporting

confidence: 88%

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Teweldebrhan¹,

Burkhart²,

Schuler³

et al. 2019

Preprint

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Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to get an adequate sample size which may take from days to months especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual based response surfaces. Here, we apply emulators of MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time relaxed limits of acceptability concept (pLoA). Three machine learning models (MLMs) were built using model parameter sets and response surfaces with limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time relaxed limits of acceptability approach based on the predicted pLoA values; and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations; and the models identified using the coupled ML emulators and the limits of acceptability approach have performed very well in reproducing the median streamflow prediction both during the calibration and validation periods.

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“…A similar survey count (n = 194) exists above 44.5 • N but with sparser spatial coverage due to the region's larger size and lower population density. The measurement tools used for retrieving in situ observations vary between locations over the time span of our study (Authorities, 1985;Sturm et al, 2010). Common strategies for collecting SWE measurements by hand include the use of snow corers, which are portable, handheld tubes that are inserted into the snowpack down to the soil layer and weighed to retrieve a SWE estimate at that point within the snowpack (López-Moreno et al, 2013).…”

Section: In Situ Datamentioning

confidence: 99%

Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

King

Erler²,

Frey

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

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Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

Cited by 39 publications

References 39 publications

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

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