Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Mai, Juliane; Tolson, Bryan A.; Shen, Helen C.; Gaborit, Étienne; Fortin, Vincent; Gasset, Nicolas; Awoye, Hervé; Stadnyk, Tricia A.; Fry, Lauren M.; Bradley, Emily A.; Seglenieks, Frank; Temgoua, André Guy Tranquille; Princz, Daniel; Gharari, Shervan; Haghnegahdar, Amin; Elshamy, Mohamed; Razavi, Saman; Gauch, Martin; Lin, Jimmy; Ni, Xiaojing; Yuan, Yongping; McLeod, Meghan; Basu, N. B.; Kumar, Rohini; Rakovec, Oldřich; Samaniego, Luis; Attinger, Sabine; Shrestha, Narayan Kumar; Daggupati, Prasad; Roy, Tirthankar; Wi, Sungwook; Hunter, Timothy; Craig, James R.; Pietroniro, Alain

doi:10.1061/(asce)he.1943-5584.0002097

Cited by 20 publications

(18 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their approach is not unique. Large-sample hydrological modeling studies that also do not explicitly characterize validation performance as adequate or inadequate include Bai et al (2021); Essou et al (2016);; Fry et al (2014); Gaborit et al (2017); Guo et al (2018); Mai et al (2021); Mathevet et al (2020); Newman et al (2015Newman et al ( , 2017; Rakovec et al (2019); Smith et al (2004Smith et al ( , 2012; and Yang et al (2019). We argue that the absence of explicit model failure criteria is a suboptimal approach to model building and that model failure handling at different steps in a model building process needs to be carefully considered, especially in the context of evaluating alternative model validation/evaluation strategies.…”

Section: 1029/2021wr031523mentioning

confidence: 99%

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Shen

Tolson

Mai

2022

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling.

show abstract

Section: 1029/2021wr031523mentioning

confidence: 99%

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Shen

Tolson

Mai

2022

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Model intercomparison projects are usually massive undertakings, especially when multiple (independent) groups come together to compare a wide range of models over large regions and a large number of locations (Duan et al, 2006;Smith et al, 2012;Best et al, 2015;Kratzert et al, 2019c;Menard et al, 2020;Mai et al, 2021;Tijerina et al, 2021). The aim of such projects is diverse.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Initiated in 2014, the Great Lakes Runoff Intercomparison Projects (GRIP) are a series of studies that have focused on comparing the runoff generated by models from various academic institutions and federal agencies. The first study concentrated on Lake Michigan (Fry et al, 2014), second on Lake Ontario (Gaborit et al, 2017), and a third on Lake Erie (Mai et al, 2021a). The latest of the GRIP projects involves a wide range of lumped and distributed models that are being run over the entire Great Lakes watershed (Mai et al, 2021b), and represents an example of productive coordination between the research community and the Great Lakes water management community.…”

Section: Runoffmentioning

confidence: 99%

Navigating Great Lakes Hydroclimate Data

Fry¹,

Gronewold²,

Seglenieks³

et al. 2022

Front. Water

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Cited by 20 publications

References 68 publications

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

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

Navigating Great Lakes Hydroclimate Data

Contact Info

Product

Resources

About