Evaluating snow models with varying process representations for hydrological applications

Magnusson, Jan; Wever, Nander; Essery, Richard; Helbig, Nora; Winstral, A. H.; Jonas, Tobias

doi:10.1002/2014wr016498

Cited by 121 publications

(136 citation statements)

References 54 publications

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“…These comparisons illustrate that forcing uncertainty cannot be discounted and that the magnitude of forcing uncertainty is a critical factor in how forcing uncertainty compares to other sources of uncertainty (e.g., structural). This resonates with the recent work of Magnusson et al (2015), who found that uncertainty in the P forcing was a greater determinant of model performance than structural considerations.…”

Section: Contextualizing Forcing and Structural Uncertaintiessupporting

confidence: 85%

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Raleigh

Lundquist

Clark

2015

Hydrol. Earth Syst. Sci.

156

128

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Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snowaffected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

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Section: Contextualizing Forcing and Structural Uncertaintiessupporting

confidence: 85%

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Raleigh

Lundquist

Clark

2015

Hydrol. Earth Syst. Sci.

156

128

View full text Add to dashboard Cite

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“…Simulating such events is of great importance, especially for operational flood forecasting purposes. While the performance of well-calibrated models may be adequate independent of model complexity (Hock, 2003;Magnusson et al, 2015), we are particularly interested in the model performance in extreme years, when the snowmelt contribution greatly increases flood risks. This is why in the second set of modeling experiments we singled out snow-rich years as a validation data set to generate both a more challenging and more relevant test scenario.…”

Section: Model Performance Across Elevation Classes: Leave-one-out Samentioning

confidence: 99%

“…Especially at low elevations and early in the year, the DDF of M2 and M3 do not differ much and therefore produces similar runoff simulations with comparable performance. According to Lang and Braun (1990) and Magnusson et al (2015), a clearer benefit of using a flexible instead of a fixed DDF would have been expected if used within a longer time window. Third, at low elevations snowmelt may occur sporadically and not necessarily within a pre-defined season.…”

Section: Model Performance For High Elevation Catchments: Leave-one-omentioning

confidence: 99%

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Griessinger

Seibert

Magnusson

et al. 2016

Hydrol. Earth Syst. Sci.

Self Cite

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Abstract. In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a timeinvariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and the correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snow water equivalent (SWE) and snowmelt rates has gained importance.

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“…Four criterions, including The Nash-Sutcliffe efficiency coefficient (NSE) [41], correlation coefficient (R), root mean square error (RMSE) and Kling-Gupta efficiency (KGE) [42][43][44] were applied to evaluate the fitness between the simulated and observed data series of streamflow as follow:…”

Section: Hydrological Model Performance Evaluationmentioning

confidence: 99%

Assessment on the Effect of Climate Change on Streamflow in the Source Region of the Yangtze River, China

Bian

Lü

Sadeghi

et al. 2017

Water

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Tuotuo River basin, known as the source region of the Yangtze River, is the key area where the impact of climate change has been observed on many of the hydrological processes of this central region of the Tibetan Plateau. In this study, we examined six Global Climate Models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios. First, the already impacted climate change was analyzed, based on the historical data available and then, the simulation results of the GCMs and RCPs were used for future scenario assessments. Results indicated that the annual mean temperature will likely be increased, ranging from −0.66 • C to 6.68 • C during the three future prediction periods (2020s, 2050s and 2080s), while the change in the annual precipitation ranged from −1.18% to 66.14%. Then, a well-known distributed hydrological soil vegetation model (DHSVM) was utilized to evaluate the effects of future climate change on the streamflow dynamics. The seasonal mean streamflows, predicted by the six GCMs and the three RCPs scenarios, were also shown to likely increase, ranging from −0.52% to 22.58%. Watershed managers and regulators can use the findings from this study to better implement their conservation practices in the face of climate change.

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Evaluating snow models with varying process representations for hydrological applications

Cited by 121 publications

References 54 publications

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Assessment on the Effect of Climate Change on Streamflow in the Source Region of the Yangtze River, China

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