Integrating Logistical and Technical Criteria into a Multiteam, Competitive Watershed Model Ranking Procedure

Cunderlik, Juraj M.; Fleming, Sean W.; Jenkinson, R. Wayne; Thiemann, Michael; Kouwen, N.; Quick, Michael C.

doi:10.1061/(asce)he.1943-5584.0000670

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…The Hydrologiska Byråns Vattenbalansavdelning model (HBV) is a conceptual runoff model originally developed by Bergström []. The HBV model has been widely used in northern Europe [e.g., Seibert , ; Steele‐Dunne et al ., ] and other regions of the world [e.g., Cunderlik et al ., ; Krysanova et al ., ; Razavi and Coulibaly , ]. Here we use the software implementation HBV‐light [ Seibert and Vis , ], which includes a glacier routine described in Konz and Seibert [].…”

Section: Modeling Approach and Multiple Data Set Calibrationmentioning

confidence: 99%

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The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments

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Vis

Huss

et al. 2015

Water Resources Research

128

121

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The assessment of snow, glacier, and rainfall runoff contribution to discharge in mountain streams is of major importance for an adequate water resource management. Such contributions can be estimated via hydrological models, provided that the modeling adequately accounts for snow and glacier melt, as well as rainfall runoff. We present a multiple data set calibration approach to estimate runoff composition using hydrological models with three levels of complexity. For this purpose, the code of the conceptual runoff model HBV-light was enhanced to allow calibration and validation of simulations against glacier mass balances, satellite-derived snow cover area and measured discharge. Three levels of complexity of the model were applied to glacierized catchments in Switzerland, ranging from 39 to 103 km 2 . The results indicate that all three observational data sets are reproduced adequately by the model, allowing an accurate estimation of the runoff composition in the three mountain streams. However, calibration against only runoff leads to unrealistic snow and glacier melt rates. Based on these results, we recommend using all three observational data sets in order to constrain model parameters and compute snow, glacier, and rain contributions. Finally, based on the comparison of model performance of different complexities, we postulate that the availability and use of different data sets to calibrate hydrological models might be more important than model complexity to achieve realistic estimations of runoff composition.

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Section: Modeling Approach and Multiple Data Set Calibrationmentioning

confidence: 99%

“…Indeed, when lumped calibration strategies are used, semi‐distributed models yield higher performance than fully distributed models [ Khakbaz et al ., ]. Hence, the discussion on appropriate model complexity for runoff modeling is still ongoing [ Cunderlik et al ., ].…”

Section: Introductionmentioning

confidence: 99%

The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments

Finger

Vis

Huss

et al. 2015

Water Resources Research

128

121

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“…A multitude of hydrologic modeling options are available (Singh and Woolhiser, ; Bourdin et al ., ). There is an emerging consensus that there is no single “best” model or even overall modeling approach per se, but rather that there is a need to select a model which is best for its specific intended purpose (Fleming, ; Cunderlik et al ., ). Key considerations in our case include: low time and cost requirements for development and implementation; ease, speed, and cost‐effectiveness of operational use; operational system robustness; good simulation accuracy for high‐streamflow events, including the ability to capture nonlinear hydrologic dynamics such as the well‐known nonlinearity of rainfall‐runoff relationships; generation of probabilistic forecasts to capture hydrologic simulation uncertainty; and a proven track record for the technology.…”

Section: Model Developmentmentioning

confidence: 97%

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Fleming

Bourdin²,

Campbell

et al. 2014

J American Water Resour Assoc

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Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall-and snowmelt-driven contributions to flood events. Few operational flood-forecast models are available in the region.Here, we integrated a number of proven technologies in a novel way to produce a super-ensemble forecast system for the Englishman River, a flood-prone stream on Vancouver Island. This three-day-ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network-based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high-flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013-2014 storm season suggest that the prediction system is operationally useful and robust.(KEY TERMS: flooding; meteorology; runoff; computational methods; surface water hydrology; artificial neural networks; forecasting; uncertainty assessment; probabilistic; NAEFS; numerical weather prediction; multimodel ensemble; machine learning.)

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“…And even if the model selected based on legacy is adequate, more adequate models might be available. It is, however, generally unclear how significant the gain in adequacy will be when switching to another model and whether this gain justifies the considerable effort of switching (Cunderlik et al, 2013). Furthermore, limited guidance exists on how to select a model for a given purpose or landscape (e.g., Boorman et al, 2007;McDonnell & Woods, 2004) and comparisons of multiple models given a specific task (e.g., Kauffeldt et al, 2016) are still rare.…”

Section: Are Hydrologists Missing Opportunities Because Of the Preval...mentioning

confidence: 99%

Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models

Addor

Melsen

2019

Water Resources Research

159

103

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The findings of hydrological modeling studies depend on which model was used. Although hydrological model selection is a crucial step, experience suggests that hydrologists tend to stick to the model they have experience with, and rarely switch to competing models, although these models might be more adequate given the study objectives. To gain quantitative insights into model selection, we explored the use of seven rainfall‐runoff models based on the abstract of 1,529 peer‐reviewed papers published between 1991 and 2018. The models selected were the Hydrologiska Byråns Vattenbalansavdelning model (HBV), the Variable Infiltration Capacity model (VIC), the mesoscale Hydrological model (mHM), the TOPography‐based hydrologic model (TOPMODEL), the Precipitation Runoff Modelling System (PRMS), the Génie Rural model à 4 paramètres Journaliers (GR4J), and the Sacramento soil moisture accounting model. We provide quantitative evidence of regional preferences in model use across the world and demonstrate that specific models are consistently preferred by certain institutes. Model attachment is particularly strong. In ~74% of the studies, the model selected can be predicted solely based on the affiliation of the first author. The influence of adequacy on the model selection process is less clear. Our data reveal that each model is used across a wide range of purposes, landscapes, and temporal and spatial scales (i.e., as a model of everything and everywhere). Model intercomparisons can provide guidance for model selection and improve model adequacy, but they are still rare (because each model must usually be setup individually) and the insights they provide are currently limited (because they are rarely controlled experiments). We suggest that moving from fixed‐structure models to modular modeling frameworks (master templates for model generation) can overcome these issues, enable a more collaborative and responsive model development environment, and result in improved model adequacy.

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Integrating Logistical and Technical Criteria into a Multiteam, Competitive Watershed Model Ranking Procedure

Cited by 12 publications

References 19 publications

The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments

The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models

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