North American Lake-River Routing Product v 2.1, derived by BasinMaker GIS Toolbox

Han, Mei; Shen, Helen C.; Tolson, Bryan A.; Craig, James R.; Mai, Juliane; Lin, S. C.; Basu, Nandita B.; Awol, Frezer Seid

doi:10.5281/zenodo.4728185

Cited by 3 publications

(4 citation statements)

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“…The GRIP-GL routing product is generated by Bas-inMaker (Version 1.0; Han, 2021), which is a set of Pythonbased geographic information system (GIS) tools for supporting vector-based watershed delineation, discretization, and simplification and incorporating lakes and reservoirs into the network. This toolbox has been successfully applied for producing the Pan-Canadian (Han et al, 2020) and North American routing product (Han et al, 2021b). More detailed information, a user manual, and tutorial examples can be found on the BasinMaker website (Han et al, 2021a).…”

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.

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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: 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

View full text Add to dashboard Cite

show abstract

“…Such routing networks should have typically featured a finer spatial resolution in order to break up each basin polygon into subbasins, at least for majority of the streamflow gauging stations used for LSTM training. Furthermore, example of a hydrologic routing model includes the routing models in Han et al (2020) and Mizukami et al (2016). The distinctive aspect of our research lies in the combination of components to form the SR model.…”

Section: Overview Of Spatially Recursive (Sr) Modelmentioning

confidence: 99%

“…In this study, we tested our SR model with two routing products, the GRIP-GL common routing product (used in Mai et al 165 (2022)), and the North American Lake-River Routing Product v2.1 (NALRP; Han et al, 2020). Both products were generated by the BasinMaker Python library (Han et al, 2023), which supports delineation of vector-based routing networks from any DEM and user-defined lake polygons.…”

Section: Spatially Distributed Prediction Based On a Lake-river Routi...mentioning

confidence: 99%

“…The intermediate results from our previous step in Section 2.1.2 (i.e., the LSTM local streamflow predictions at subbasinlevel) can be seen as distributed subbasin-specific streamflow fluxes at various points within a routing network of streams and lakes (if any). The routing-only mode in the Raven framework can simulate the routing of distributed surface runoff (Han et al, 2020;Craig et al, 2020) but here we use it for the first time to route local subbasin outlet streamflow. This is 210 accomplished by representing the local streamflow input (at each subbasin) as hourly precipitation instantly flushing to the subbasin outlet.…”

Section: Routing-only Mode In the Raven Hydrological Modelling Frameworkmentioning

confidence: 99%

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Enhancing LSTM-based streamflow prediction with a spatially distributed approach

Yu,

Tolson,

Shen

et al. 2023

Preprint

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Abstract. Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially-aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model on predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 1000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.

show abstract

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

Mai

Shen

Tolson

et al. 2022

Preprint

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 trans-boundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the United States 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 million square kilometer 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 six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulated 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: (1) The comparison of models regarding streamflow reveals the superior quality of the Machine Learning based model in all experiments performance; even for the most challenging spatio-temporal validation the ML model outperforms any other physically based model. (2) 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 the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. (3) The regionally calibrated models – while losing less performance in spatial and spatio-temporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas as well as agricultural regions in the US. (4) Comparisons of additional model outputs (AET, SSM, SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to basin scale can lead to different conclusions than a comparison at the native grid scale. This is especially true for variables with large spatial variability such as SWE. (5) A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, SWE) reveals overall excellent performing locally calibrated models (i.e., HYMOD2-lumped) as well as 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 is not setup to simulate AET, SSM, and SWE. (6) 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 data and model outputs.

show abstract

North American Lake-River Routing Product v 2.1, derived by BasinMaker GIS Toolbox

Cited by 3 publications

References 0 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)

Enhancing LSTM-based streamflow prediction with a spatially distributed approach

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

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