Basinmaker 3.0: A GIS Toolbox for Distributed Watershed Delineation of Complex Lake-River Routing Networks

Han, Mei; Shen, Helen C.; Tolson, Bryan A.; Craig, James R.; Mai, Juliane; Lin, Simon G.M.; Basu, N. B.; Awol, Frezer Seid

doi:10.2139/ssrn.4135646

Cited by 3 publications

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“…This model was discretized into 521 subbasins characterized by channel properties and connectivity to adjacent subbasins, and 890 hydrologic response units (HRUs) discretized by unique combinations of land use, vegetation, terrain and soils classes using the BasinMaker GIS toolbox (Han et al, 2022). The Han et al (2022) model HRUs were divided into forest, open and lake land covers. For the current study, the model was modified to include five land classes – lake, wetland, coniferous, deciduous, and mixed woods – resulting in 2733 HRUs (but the same original 521 subbasins).…”

Section: Methodsmentioning

confidence: 99%

“…While previous research has focused on multi‐objective calibration using SWE or SWE assimilation, the combined impact of the two approaches on model performance requires further investigation. The objectives of this study are to: (1) estimate daily, basin average SWE from spatially distributed measurements of snow depth and density; (2) compare measured basin average SWE to the Copernicus SWE product; (3) calibrate and validate a baseline hydrologic model developed by Han et al (2022) using spatially distributed SWE measurements; (4) assimilate bias corrected Copernicus SWE into the models using a particle filter; and (5) evaluate and compare the performance of the model experiments to ascertain the effect of calibrating to spatially distributed SWE and assimilating bias corrected Copernicus SWE on model performance. This was accomplished using: A baseline model calibrated to lake levels and basin outlet discharge (BL) Model (i) calibrated to spatially distributed SWE (BLS) Bias corrected Copernicus SWE assimilated by perturbing snow parameters from model (i) (PFBL), (ii) (PFBLS) and a range of snow parameters between the defined upper and lower bounds (PFR). …”

Section: Introductionmentioning

confidence: 99%

“…Han et al (2022) developed a semi-distributed, temperature indexbased hydrologic model for the Petawawa basin using the Raven Hydrologic Modelling Framework(Craig et al, 2020). This model was discretized into 521 subbasins characterized by channel properties and connectivity to adjacent subbasins, and 890 hydrologic response units (HRUs) discretized by unique combinations of land use, vegetation, terrain and soils classes using the BasinMaker GIS toolbox(Han et al, 2022). TheHan et al (2022) model HRUs were divided into forest, open and lake land covers.…”

mentioning

confidence: 99%

“…For the current study, the model was modified to include five land classeslake, wetland, coniferous, deciduous, and mixed woodsresulting in 2733 HRUs (but the same original 521 subbasins). Following the methods outlined inHan et al (2022), this model was then calibrated to 15 lake level measurements, and discharge at the basin outlet (Figure1). This model uses daily precipitation, minimum and maximum air temperature forcings from four meteorological stations (Figure1) and is henceforth referred to as baseline (BL).Model processes and parameters are described below, and calibrated parameters are summarized in Appendix A. Precipitation is partitioned into rain or snow using the two-parameter linear method from the HBV model.…”

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confidence: 99%

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Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Beaton,

Han,

Tolson

et al. 2024

Hydrological Processes

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Accuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km2 mixed‐forest basin in the Great Lakes region of central Ontario, Canada. Sub‐basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi‐objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi‐objective calibration or data assimilation, as demonstrated in this study.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Beaton,

Han,

Tolson

et al. 2024

Hydrological Processes

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“…Similar issues were also acknowledged in the Global Lake area, Climate, and Population dataset (GLCP), where HydroBASINS was also used to match lakes to the surrounding sub-basins (Meyer et al, 2020). Han et al (2020Han et al ( , 2023 developed integrated lake-river routing products for Canada and North America using their "BasinMaker" GIS (geographic information system) tool, in which they redefine the flow direction to derive consistent lake-river topological connections.…”

Section: Introductionmentioning

confidence: 95%

Lake-TopoCat: a global lake drainage topology and catchment database

Sikder

Wang

Allen

et al. 2023

Earth Syst. Sci. Data

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Abstract. Lakes and reservoirs are ubiquitous across global landscapes, functioning as the largest repository of liquid surface freshwater, hotspots of carbon cycling, and sentinels of climate change. Although typically considered lentic (hydrologically stationary) environments, lakes are an integral part of global drainage networks. Through perennial and intermittent hydrological connections, lakes often interact with each other, and these connections actively affect water mass, quality, and energy balances in both lacustrine and fluvial systems. Deciphering how global lakes are hydrologically interconnected (or the so-called “lake drainage topology”) is not only important for lake change attribution but also increasingly critical for discharge, sediment, and carbon modeling. Despite the proliferation of river hydrography data, lakes remain poorly represented in routing models, partially because there has been no global-scale hydrography dataset tailored to lake drainage basins and networks. Here, we introduce the global Lake drainage Topology and Catchment database (Lake-TopoCat), which reveals detailed lake hydrography information with careful consideration of possible multifurcation. Lake-TopoCat contains the outlet(s) and catchment(s) of each lake; the interconnecting reaches among lakes; and a wide suite of attributes depicting lake drainage topology such as upstream and downstream relationship, drainage distance between lakes, and a priori drainage type and connectivity with river networks. Using the HydroLAKES v1.0 (Messager et al., 2016) global lake mask, Lake-TopoCat identifies ∼ 1.46 million outlets for ∼ 1.43 million lakes larger than 10 ha and delineates 77.5×106 km2 of lake catchments covering 57 % of the Earth's landmass except Antarctica. The global lakes are interconnected by ∼ 3 million reaches, derived from MERIT Hydro v1.0.1 (Yamazaki et al., 2019), stretching a total distance of ∼10×106 km, of which ∼ 80 % are shorter than 10 km. With such unprecedented lake hydrography details, Lake-TopoCat contributes towards a globally coupled lake–river routing model. It may also facilitate a variety of limnological applications such as attributing water quality from lake scale to basin scale, tracing inter-lake fish migration due to changing climate, monitoring fluvial–lacustrine connectivity, and improving estimates of terrestrial carbon fluxes. Lake-TopoCat is freely accessible at https://doi.org/10.5281/zenodo.7916729 (Sikder et al., 2023).

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Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

Spieler,

Schütze

2024

Water Resources Research

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Selecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro‐climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best‐performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling‐Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high‐accuracy models among the 7,488 options, with Kling‐Gupta‐Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.

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Basinmaker 3.0: A GIS Toolbox for Distributed Watershed Delineation of Complex Lake-River Routing Networks

Cited by 3 publications

References 0 publications

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Lake-TopoCat: a global lake drainage topology and catchment database

Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

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