SWAT hydrologic model parameter uncertainty and its implications for hydroclimatic projections in snowmelt-dependent watersheds

Ficklin, Darren L.; Barnhart, Bradley

doi:10.1016/j.jhydrol.2014.09.082

Cited by 63 publications

(40 citation statements)

References 37 publications

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“…The use of only one snow model is unlikely to be a major source of overall uncertainty, as few catchments in Britain are strongly influenced by snow. Ficklin et al (2014) indicate that uncertainty in snowmelt model parameters can lead to statistically significant differences in hydrological climate change projections. However, Seiller and Anctil (2014) show that, for a catchment in Canada, the snow module is a much smaller source of uncertainty than hydrological model structure, PE formulation and natural variability.…”

Section: Resultsmentioning

confidence: 99%

An assessment of the possible impacts of climate change on snow and peak river flows across Britain

et al. 2016

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The NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. 1An assessment of the possible impacts of climate change on snow and peak river flows across Britain AbstractA temperature-based snow module has been coupled with a grid-based distributed hydrological model, to improve simulations of river flows in upland areas of Britain subject to snowfall and snowmelt. The coupled model has been driven with data from an 11-member perturbedparameter climate model ensemble, for two time-slices (1960-1990 and 2069-2099), to investigate the potential impacts of climate change. The analysis indicates large reductions in the ensemble mean of the number of lying snow days across the country. This in turn affects the seasonality of peak river flows in some parts of the country; for northerly regions, annual maxima tend to occur earlier in the water year in future. For more southerly regions the changes are less straightforward, and likely driven by changes in rainfall patterns rather than snow. The modelled percentage changes in peak flows illustrate high spatial variability in hydrological response to projected climate change, and large differences between ensemble members. When changes in projected future peak flows are compared to an estimate of current natural variability, more changes fall outside the range of natural variability in southern Britain than in the north.

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Section: Resultsmentioning

confidence: 99%

An assessment of the possible impacts of climate change on snow and peak river flows across Britain

et al. 2016

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“…Only if one single forcing dataset is employed, as for example in a flow forecasting system (Candogan Yossef et al, 2011, it may be feasible to estimate the parameters from discharge and/or other data. In addition the current study used one single hydrological model, focusing on parameter and forcing uncertainty and ignoring hydrological model uncertainty, whereas several studies demonstrated that different models may respond differently to (climate) changes in forcing data (Vansteenkiste et al, 2012;Ficklin and Barnhart, 2014). The current study should be repeated with other GHMs to test whether the statement that forcing uncertainty is larger than parameter uncertainty is in general valid.…”

Section: Possibilities For Global Parameter Estimates or Regionalizationmentioning

confidence: 99%

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Weiland

Vrugt

Beek

et al. 2015

Journal of Hydrology

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Keywords:Global hydrological modeling Forcing uncertainty Model calibration Parameter uncertainty s u m m a r yIn the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on largescale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5°and smaller.

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“…SWAT is fully capable of simulating and forecasting surface water parameters and snow melt hydrology parameters on the subcatchment and point scales (Ficklin and Barnhart 2014;Neitsch et al 2011;Arnold et al 2012). Each subcatchment contains the main channel and many hydrologic response units (HRUs), which consist of homogeneous land use/land cover (LULC), soil types and slope.…”

Section: Description Of Swat Modelmentioning

confidence: 99%

“…Each subcatchment contains the main channel and many hydrologic response units (HRUs), which consist of homogeneous land use/land cover (LULC), soil types and slope. Around 2,400 peer-reviewed publications are available related to SWAT model applications, thus the model equations and their theoretical description have been avoided in this research article (Singh et al 2015(Singh et al , 2013Ficklin and Barnhart 2014;Arnold et al 2012;Neitsch et al 2011;Jain et al 2010;Abbaspour et al 2007). The weather generator parameters, such as daily precipitation, daily minimum and maximum temperature, daily humidity, daily wind speed, and daily net solar radiation are necessarily required to set up the SWAT model.…”

Section: Description Of Swat Modelmentioning

confidence: 99%

Unsteady High Velocity Flood Flows and the Development of Rating Curves in a Himalayan Basin under Climate Change Scenarios

Singh

Goyal

2017

J. Hydrol. Eng.

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Hydrologic models are not able to compute unsteady flood flows, and the hydraulic models are sometimes found less effective in uncertainty estimation in the modeling outcomes. This study presents a coupled modeling framework to analyze the unsteady flows in the downstream of the Teesta and Lachung Rivers using hydrodynamic and hydrological models. The present approach leads to unsteady flow simulations along stream channel reach. Soil and Water Assessment Tool (SWAT) model is used for the projection of streamflow, water depth, and precipitation at various gauged and ungauged locations over the selected catchment using Coupled Model Intercomparison Phase 5 (CMIP5) CM3 model data sets with their representative concentration pathway (RCP) experiments and observed hydrometeorological data. SWAT-based projected scenarios are incorporated in the hydrodynamic model for the projection of water level, flood discharge, and flooded area at different sections in the downstream of the Teesta and Lachung Rivers. Using projected flood flows and water-level data, the rating curve equations have been developed at the outlets such as Chungthang and Lachung. The study outcomes resulting from both models show a very good agreement between the simulated streamflow and observed streamflow at the outlet locations. The uncertainties can also be associated in the modeling outcomes. Thus, a statistical-cum-stochastic downscaling method has been applied to downscale the CMIP5 CM3 model-based meteorological variables (e.g., temperature and precipitation). The modeling errors prior to project streamflow have been adjusted using a sequential parameter fitting approach (SUFI2). The hydrodynamic model-based projected unsteady flood flows are found highly uncertain in the downstream portion of both rivers, and thus several extreme flood peaks have been observed in the final outcomes. The observations show that the water velocities have increased in the projected scenarios (recorded as 8.5 m=s), as compared to historical scenarios (6.5 m=s). Using projected time series discharge and water level (stage), the rating curve equations, which have been developed as per the low to extreme emission scenarios, can be used in the future planning and management of the water resources systems.

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SWAT hydrologic model parameter uncertainty and its implications for hydroclimatic projections in snowmelt-dependent watersheds

Cited by 63 publications

References 37 publications

An assessment of the possible impacts of climate change on snow and peak river flows across Britain

An assessment of the possible impacts of climate change on snow and peak river flows across Britain

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Unsteady High Velocity Flood Flows and the Development of Rating Curves in a Himalayan Basin under Climate Change Scenarios

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