Evaluation and bias correction of SNODAS snow water equivalent (SWE) for streamflow simulation in eastern Canadian basins

Zahmatkesh, Zahra; Tapsoba, Dominique; Leach, James M.; Coulibaly, Paulin

doi:10.1080/02626667.2019.1660780

Cited by 11 publications

(10 citation statements)

References 56 publications

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“…Wrzesien et al (2017) performed a comparison between SNODAS and in situ SWE over alpine regions in North America and found SNODAS performed best in areas with a high density of in situ measurements; however, SNODAS still exhibited a general overestimation of SWE. Additional recent studies by Leach et al (2018), Lv et al (2019) and Zahmatkesh et al (2019) also suggest similar positive biases in SNODAS SWE estimates throughout other North American regions. This work builds on the comparison methods outlined in previous bias-correction studies by Li et al (2010), Themeßl et al (2011) and Teutschbein and Seibert (2012) to examine an ensemble of bias-correction techniques, quantify the skill of each model, and apply the model over a larger spatio-temporal domain to produce a gridded bias-corrected SWE product.…”

Section: Introductionsupporting

confidence: 54%

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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Section: Introductionsupporting

confidence: 54%

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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“…The existing error after applying CDFM may be caused by the random component of the radar QPE errors that are not removed by the CDFM method. Even though the CDFM method is not often used in radar-gauge merging, it has been successfully applied to bias correct different other gridded hydrological inputs to hydrological models in previous literature such as soil moisture [103][104][105] and snow depths [106,107]. For example, Leach et al [106] have reported a significant reduction of average RMSE after applying CDFM bias correction for Snow Data Assimilation System (SNODAS) snow depths (67.30 mm to 38.45 mm) as well as SNODAS snow water equivalent data (SWE) (19.99 mm to 5.19 mm).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Radar-Gauge Merging Techniques to Be Used in Operational Flood Forecasting in Urban Watersheds

Wijayarathne

Coulibaly

Boodoo

et al. 2020

Water

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Demand for radar Quantitative Precipitation Estimates (QPEs) as precipitation forcing to hydrological models in operational flood forecasting has increased in the recent past. It is practically impossible to get error-free QPEs due to the intrinsic limitations of weather radar as a precipitation measurement tool. Adjusting radar QPEs with gauge observations by combining their advantages while minimizing their weaknesses increases the accuracy and reliability of radar QPEs. This study deploys several techniques to merge two dual-polarized King City radar (WKR) C-band and two KBUF Next-Generation Radar (NEXRAD) S-band operational radar QPEs with rain gauge data for the Humber River (semi-urban) and Don River (urban) watersheds in Ontario, Canada. The relative performances are assessed against an independent gauge network by comparing hourly rainfall events. The Cumulative Distribution Function Matching (CDFM) method performed best, followed by Kriging with Radar-based Error correction (KRE). Although both WKR and NEXRAD radar QPEs improved significantly, NEXRAD Level III Digital Precipitation Array (DPA) provided the best results. All methods performed better for low- to medium-intensity precipitation but deteriorated with the increasing rainfall intensities. All methods outperformed radar only QPEs for all events, but the agreement is best in the summer.

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“…There are some relevant papers which are not addressed: e.g. Zahmatkesh et al (2019) 3) I have to say that the part related to evaluation of the impacts of different bias corrected SWE estimates on snowmelt is not clear to me. Using monthly estimates without accounting for evapotranspiration and other processes is somewhat less robust.…”

Section: Interactive Commentmentioning

confidence: 99%

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2020

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Evaluation and bias correction of SNODAS snow water equivalent (SWE) for streamflow simulation in eastern Canadian basins

Cited by 11 publications

References 56 publications

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

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

Evaluation of Radar-Gauge Merging Techniques to Be Used in Operational Flood Forecasting in Urban Watersheds

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