A 10 km North American precipitation and land-surface  reanalysis based on the GEM atmospheric model

Gasset, Nicolas; Fortin, Vincent; Dimitrijevic, Milena; Carrera, Marco L.; Bilodeau, Bernard; Muncaster, Ryan; Gaborit, Étienne; Roy, Guy; Pentcheva, Nedka; Bulat, Maxim; Wang, Xihong; Pavlovic, Radenko; Lespinas, Franck; Khedhaouiria, Dikra; Mai, Juliane

doi:10.5194/hess-25-4917-2021

Cited by 39 publications

(13 citation statements)

References 82 publications

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“…The Regional Deterministic Reanalysis System v2 (RDRS-v2) was employed as meteorologic forcing (Gasset et al, 2021). RDRS-v2 is an hourly and 10 km by 10 km meteorologic forcing dataset covering North America (Fig.…”

Section: Meteorologic Forcings and Geophysical Datasetsmentioning

confidence: 99%

“…Generally, the smaller this value, the more subwatersheds and tributaries will be identified. The meteorologic forcings are provided by the Regional Deterministic Reanalysis System v2 (RDRS-v2; 10 km; hourly; Gasset et al, 2021). The figure shows the mean annual precipitation derived for the 18 years during which the dataset is available and used in this study (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017).…”

Section: Routing Productmentioning

confidence: 99%

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The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

Mai

Shen

Tolson

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

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Section: Meteorologic Forcings and Geophysical Datasetsmentioning

confidence: 99%

Section: Routing Productmentioning

confidence: 99%

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

Mai

Shen

Tolson

et al. 2022

Hydrol. Earth Syst. Sci.

Self Cite

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“…In addition to the global products described above, one newer surface reanalysis product that is noted to be of particular interest for transboundary and northern watersheds in North America is the Canadian 10 km North American precipitation and land-surface reanalysis (Gasset et al, 2021). This reanalysis (listed as RDRS_v2 in Table 5) is the result of initializing the Global Deterministic Reforecast System with ERA-Interim and dynamically downscaling the output using the Regional Deterministic Reforecast System (RDRS), coupled with the Canadian Land Data Assimilation System (CaLDAS) and the Canadian Precipitation Analysis (CaPA).…”

Section: Reanalysismentioning

confidence: 99%

Navigating Great Lakes Hydroclimate Data

Fry¹,

Gronewold²,

Seglenieks³

et al. 2022

Front. Water

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Despite the fact that the Great Lakes contain roughly 20% of the world's surface freshwater, there is a relatively limited body of recent work in peer reviewed literature that addresses recent trends in lake levels. This work is largely coming from a handful of authors who are most well-versed in the complexities of monitoring and modeling in a basin that spans an international border and contains vast areas of surface water connected by both natural and managed connecting channel flows. At the same time, the recent dramatic changes from record low water levels in the early 2010's to record high water levels across the Great Lakes in 2019 and 2020 have brought significant attention to the hydroclimatic conditions in the basin, underscoring the need to bring new approaches and diverse perspectives (including from outside the basin) to address hydroclimate research challenges in the Great Lakes. Significant effort has led to advancements in data and model coordination among U.S. and Canadian federal agencies throughout the decades, and at the same time research from the broader community has led to higher resolution gridded data products. In this paper, we aim to present the current state of data and models for use in hydrological simulation with the objective of providing a guide to navigating the waters of Great Lakes hydroclimate data. We focus on data for use in modeling water levels, but we expect the information to be more broadly applicable to other hydroclimate research. We approach this by including perspectives from both the Great Lakes water management community and the broader earth science community.

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“…A 3 arc seconds HydroSHEDS digital elevation model (DEM) (Lehner et al, 2008) and a 30 m North American Land Change Monitoring System (NALCMS) (Homer et al, 2017) landuse/landcover map were used to create the land surface database for the WATFLOOD model. Then hourly precipitation and temperature from a 10 km Regional Deterministic Reanalysis System (RDRS) (Gasset et al, 2021) were used as input into the WATFLOOD model at a 10 km spatial resolution. For modeling purposes, we divided the GL basin in five subdomains: Superior (SUP), Huron (HUR), Michigan (MIC), Erie (ERI) and Ontario (ONT).…”

Section: Model Inputsmentioning

confidence: 99%

The Impacts of Climate Change on Land Hydroclimatology of the Laurentian Great Lakes Basin

et al. 2022

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The freshwater resources of the Laurentian Great Lakes basin contribute significantly to the environment and economy of the region. With the impacts of climate change becoming more evident, sustainable management of the freshwater resources of the Laurentian Great Lakes basin is important. This study uses 36 simulations from 6 regional climate models to quantify trends and changes in land-area precipitation and temperature in two future periods (mid-century, 2035–2064 and end-century, 2065–2094) with reference to a baseline period (1951–2005) for two emission scenarios (RCP4.5 and RCP 8.5). Climatic forcings from these 36 simulations are used as input to a calibrated and validated hydrological model to assess changes in land snowpack and actual evapotranspiration, and runoff to lake. Ensemble results show wetter (7 to 15% increase in annual precipitation) and warmer (2.4–5.0°C increase in annual mean temperature) future conditions on GL land areas. Seasonal and monthly changes in precipitation and mean temperature are more sporadic, for instance although precipitation is projected to increase overall, in some scenarios, summer precipitation is expected to decrease. Projected increases in highest one-day precipitation and decreases in number of wet days indicate possible increases in extreme precipitation in future. Minimum temperature is expected to increase in a higher rate than maximum temperature. Ensemble results from the hydrological model show projected decrease in snowpack (29–58%). Similarly, actual evapotranspiration is projected to increase, especially during summer months (up to 0.4 mm/day). Annually, runoff is expected to increase (up to 48% in Superior, 40% in Michigan-Huron, 25% Erie and 28% in Ontario). Seasonal and monthly changes in runoff are more sporadic (e.g., projected decrease up to 17% in Erie subdomain in October). Such contrasting patterns of changes in land hydroclimatology of the GL basin will pose challenges to sustainable management of the water resources of the basin in future.

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A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model

Cited by 39 publications

References 82 publications

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

Navigating Great Lakes Hydroclimate Data

The Impacts of Climate Change on Land Hydroclimatology of the Laurentian Great Lakes Basin

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