The Great Lakes Runoff Intercomparison Project Phase 1: Lake Michigan (GRIP-M)

Fry, Lauren M.; Gronewold, Andrew D.; Fortin, Vincent; Buan, Steven; Clites, Anne H.; Luukkonen, Carol L.; Holtschlag, David J.; Diamond, Laura; Hunter, Timothy; Seglenieks, Frank; Durnford, Dorothy; Dimitrijevic, Milena; Subich, Christopher; Klyszejko, Erika; Kea, Kandace; Restrepo, Pedro J.

doi:10.1016/j.jhydrol.2014.07.021

Cited by 31 publications

(21 citation statements)

References 70 publications

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“…These values compare relatively well with models that were evaluated for gauges as part of the Great Lakes Runoff Intercomparison Project, for which non-assimilative models (i.e. those that did not incorporate discharge observations into simulations) resulted in median NSE values as good as 0.53 for 17 gauges in the Lake Michigan basin (Fry et al, 2014). Multiple linear regression using MRR i resulted in the smallest interquartile range for both goodness-of-fit statistics, and was the only model with no NSE values less than zero (that is, higher variance in the model's residuals than in the observed data).…”

Section: Resultssupporting

confidence: 62%

Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal scales of analysis

Kult

Fry

Gronewold

et al. 2014

Journal of Hydrology

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Keywords:Runoff Regionalization Prediction in ungauged basins Hydrologic response Regression model Great Lakes basin s u m m a r y Methods for predicting streamflow in areas with limited or nonexistent measures of hydrologic response commonly rely on regionalization techniques, where knowledge pertaining to gauged watersheds is transferred to ungauged watersheds. Hydrologic response indices have frequently been employed in contemporary regionalization research related to predictions in ungauged basins. In this study, we developed regionalization models using multiple linear regression and regression tree analysis to derive relationships between hydrologic response and watershed physical characteristics for 163 watersheds in the Great Lakes basin. These models provide an empirical means for simulating runoff in ungauged basins at a monthly time step without implementation of a rainfall-runoff model. For the dependent variable in these regression models, we used monthly runoff ratio as the indicator of hydrologic response and defined it at two temporal scales: (1) treating all monthly runoff ratios as individual observations, and (2) using the mean of these monthly runoff ratios for each watershed as a representative observation. Application of the models to 62 validation watersheds throughout the Great Lakes basin indicated that model simulations were far more sensitive to the temporal characterization of hydrologic response than to the type of regression technique employed, and that models conditioned on individual monthly runoff ratios (rather than long term mean values) performed better. This finding is important in light of the increased usage of hydrologic response indices in recent regionalization studies. Models using individual observations for the dependent variable generally simulated monthly runoff with reasonable skill in the validation watersheds (median Nash-Sutcliffe efficiency = 0.53, median R 2 = 0.66, median magnitude of the deviation of runoff volume = 13%). These results suggest the viability of empirical approaches to simulate runoff in ungauged basins. This finding is significant given the many regions of the world with sparse gauging networks and limited resources for gathering the field data required to calibrate rainfallrunoff models.

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Section: Resultssupporting

confidence: 62%

Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal scales of analysis

Kult

Fry

Gronewold

et al. 2014

Journal of Hydrology

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“…2 have more than one major tributary flowing into Lake Ontario. In this case, the mostdownstream observed flows on independent tributaries are summed and then extrapolated to the whole subbasin using the area ratio method (ARM; Fry et al, 2014). The resulting "synthetic" flows were considered as observations for GEMHydro-UH calibration over the whole subbasin, including its ungauged parts.…”

Section: Calibration Strategymentioning

confidence: 99%

“…Even if runoff observations actually consist in this case in estimations based on the ARM, computed performances are a priori reliable given that the true gauged fraction of the total area is equal to about 70 %, and that the ARM proves reliable starting from a 50 % gauged fraction (Fry et al, 2014).…”

Section: Runoff Estimation For the Whole Lake Ontario Basinmentioning

confidence: 99%

“…Before embarking on an upgrade of operational systems, GLERL and ECCC agreed to perform a number of intercomparison studies under the umbrella of the Great Lakes Runoff Intercomparison Project (GRIP), in order to better understand the status of existing systems, and to set a benchmark for model performance against which future models could be evaluated. The first study was conducted on the Lake Michigan (GRIP-M) basin by Fry et al (2014), who compared historical runoff simulations from dissimilar hydrologic models using different calibration frameworks and input data. Amongst the models compared were GLERL's Large Basin Runoff Model (LBRM; Croley and He, 2002) that is part of the AHPS, the NOAA National Weather Service model (NWS; Burnash, 1995), and ECCC's MESH distributed model (Modélisation Environnementale -Surface and Hydrology; Pietroniro et al, 2007;Haghnegahdar et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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A hydrological prediction system based on the SVS land-surface scheme: efficient calibration of GEM-Hydro for streamflow simulation over the Lake Ontario basin

Gaborit

Fortin

Xinwen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. This work explores the potential of the distributed GEM-Hydro runoff modeling platform, developed at Environment and Climate Change Canada (ECCC) over the last decade. More precisely, the aim is to develop a robust implementation methodology to perform reliable streamflow simulations with a distributed model over large and partly ungauged basins, in an efficient manner. The latest version of GEM-Hydro combines the SVS (Soil, Vegetation and Snow) land-surface scheme and the WATROUTE routing scheme. SVS has never been evaluated from a hydrological point of view, which is done here for all major rivers flowing into Lake Ontario. Two established hydrological models are confronted to GEM-Hydro, namely MESH and WATFLOOD, which share the same routing scheme (WATROUTE) but rely on different land-surface schemes. All models are calibrated using the same meteorological forcings, objective function, calibration algorithm, and basin delineation. GEM-Hydro is shown to be competitive with MESH and WATFLOOD: the NSE √ (Nash-Sutcliffe criterion computed on the square root of the flows) is for example equal to 0.83 for MESH and GEM-Hydro in validation on the Moira River basin, and to 0.68 for WATFLOOD. A computationally efficient strategy is proposed to calibrate SVS: a simple unit hydrograph is used for routing instead of WATROUTE. Global and local calibration strategies are compared in order to estimate runoff for ungauged portions of the Lake Ontario basin.Overall, streamflow predictions obtained using a global calibration strategy, in which a single parameter set is identified for the whole basin of Lake Ontario, show accuracy comparable to the predictions based on local calibration: the average NSE √ in validation and over seven subbasins is 0.73 and 0.61, respectively for local and global calibrations. Hence, global calibration provides spatially consistent parameter values, robust performance at gauged locations, and reduces the complexity and computation burden of the calibration procedure. This work contributes to the Great Lakes Runoff Inter-comparison Project for Lake Ontario (GRIP-O), which aims at improving Lake Ontario basin runoff simulations by comparing different models using the same input forcings. The main outcome of this study consists in a new generalizable methodology for implementing a distributed hydrologic model with a high computation cost in an efficient and reliable manner, over a large area with ungauged portions, using global calibration and a unit hydrograph to replace the routing component.

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“…More generally, there are many examples for different world regions of the difficulty of global models to accurately model water changes in the landscape (e.g., Bring and Destouni, 2011, 2013Jarsjö et al, 2012;Fry et al, 2014). There is therefore a need for more accurate consideration of landscape-internal drivers and effects of hydrological change, such as permafrost thaw and/or changes in land use and water use, either in special hydrological modules of GCMs or in external hydrological models connected to GCMs.…”

Section: Projection Of Future Changesmentioning

confidence: 99%

Evolution of the hydro-climate system in the Lake Baikal basin

Törnqvist

Jarsjö

Pietroń

et al. 2014

Journal of Hydrology

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Selenga River Basin Climate change CMIP5 Hydrology Permafrost s u m m a r yClimatic changes can profoundly alter hydrological conditions in river basins. Lake Baikal is the deepest and largest freshwater reservoir on Earth, and has a unique ecosystem with numerous endemic animal and plant species. We here identify long-term historical ) and projected future hydro-climatic trends in the Selenga River Basin, which is the largest sub-basin (>60% inflow) of Lake Baikal. Our analysis is based on long-term river monitoring and historical hydro-climatic observation data, as well as ensemble mean and 22 individual model results of the Coupled Model Intercomparison Project, Phase 5 (CMIP5). Study of the latter considers a historical period (from 1961) and projections for 2010-2039 and 2070-2099. Observations show almost twice as fast warming as the global average during the period 1938-2009. Decreased intra-annual variability of river discharge over this period indicates basin-scale permafrost degradation. CMIP5 ensemble projections show further future warming, implying continued permafrost thaw. Modelling of runoff change, however, is highly uncertain, with many models (64%) and their ensemble mean failing to reproduce historical behaviour, and with indicated future increase being small relative to the large differences among individual model results.

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The Great Lakes Runoff Intercomparison Project Phase 1: Lake Michigan (GRIP-M)

Cited by 31 publications

References 70 publications

Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal scales of analysis

Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal scales of analysis

A hydrological prediction system based on the SVS land-surface scheme: efficient calibration of GEM-Hydro for streamflow simulation over the Lake Ontario basin

Evolution of the hydro-climate system in the Lake Baikal basin

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