A hybrid model for river water temperature as a function of air temperature and discharge

Toffolon, Marco; Piccolroaz, Sebastiano

doi:10.1088/1748-9326/10/11/114011

Cited by 156 publications

(160 citation statements)

References 26 publications

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“…In light of the practical and technical limitations discussed above, it appears that it is not simple to find models able to simultaneously conjugate simplicity and parsimony, limited computational demand and the ability to provide reliable long‐term estimates. Here we test different versions of air2stream (Toffolon and Piccolroaz, ), a simple lumped model that can be classified into a third alternative family of hybrid, statistical‐based semi‐empirical models. The model has been developed with the aim to retain the limited data requirements of statistical models, while preserving the intimate structure of process‐based models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

Piccolroaz

Calamita

Majone

et al. 2016

Hydrological Processes

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Abstract:River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using datademanding deterministic models, and modelling limitations, when using simpler statistical models. In this work we test a suite of models belonging to air2stream family, which are characterized by a hybrid formulation that combines a physical derivation of the key equation with a stochastic calibration of parameters. The air2stream models rely solely on air temperature and streamflow, and are of similar complexity as standard statistical models. The performances of the different versions of air2stream in predicting river water temperature are compared with those of the most common statistical models typically used in the literature. To this aim, a dataset of 38 Swiss rivers is used, which includes rivers classified into four different categories according to their hydrological characteristics: low-land natural rivers, lake outlets, snow-fed rivers and regulated rivers. The results of the analysis provide practical indications regarding the type of model that is most suitable to simulate river water temperature across different time scales (from daily to seasonal) and for different hydrological regimes. A model intercomparison exercise suggests that the family of air2stream hybrid models generally outperforms statistical models, while cross-validation conducted over a 30-year period indicates that they can be suitably adopted for long-term analyses.

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Section: Introductionmentioning

confidence: 99%

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

Piccolroaz

Calamita

Majone

et al. 2016

Hydrological Processes

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“…Such low values for δ contradict the evidence that water depth does increase along the downstream direction. This apparently counter‐intuitive behaviour results from the fact that daily averaged temperature is not sensitive to water depth in small streams (Toffolon & Piccolroaz, ). Hence, because calibration was solely conducted against stream temperature data, our models are not aimed at predicting spatial variation in hydrological variables such as water depth.…”

Section: Resultsmentioning

confidence: 99%

“…This approximation implies that the time scale for the adaptation of water temperature to the external forcing is much shorter than the time step of the model (e.g., Toffolon & Piccolroaz, ), which is 1 day in the case study here presented.…”

Section: The Modelmentioning

confidence: 99%

SESTET: A spatially explicit stream temperature model based on equilibrium temperature

Carraro

Toffolon

Rinaldo

et al. 2019

Hydrological Processes

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Stream‐water temperature is a key variable controlling chemical, biological, and ecological processes in freshwater environments. Most models focus on a single river cross‐section; however, temperature gradients along stretches and tributaries of a river network are crucial to assess ecohydrological features such as aquatic species suitability, growth and feeding rates, or disease transmission. We propose SESTET, a deterministic, spatially explicit stream temperature model for a whole river network, based on water and energy budgets at a reach scale and requiring only commonly available spatially distributed datasets, such as morphology and air temperature, as input. Heat exchange processes at the air–water interface are modelled via the widely used equilibrium temperature concept, whereas the effects of network structure are accounted for through advective heat fluxes. A case study was conducted on the prealpine Wigger river (Switzerland), where water temperatures have been measured in the period 2014–2018 at 11 spatially distributed locations. The results show the advantages of accounting for water and energy budgets at the reach scale for the entire river network, compared with simpler, lumped formulations. Because our approach fundamentally relies on spatially distributed air temperature fields, adequate spatial interpolation techniques that account for the effects of both elevation and thermal inversion in air temperature are key to a successful application of the model. SESTET allows the assessment of the magnitude of the various components of the heat budget at the reach scale and the derivation of reliable estimates of spatial gradients of mean daily stream temperatures for the whole catchment based on a limited number of conveniently located (viz., spanning the largest possible elevation range) measuring stations. Moreover, accounting for mixing processes and advective fluxes through the river network allows one to trust regionalized values of the parameters controlling the relationship between equilibrium and air temperature, a key feature to generalize the model to data‐scarce catchments.

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“…If the number of function calls is given in the paper, values used may vary severely. For example, a million function calls were used to calibrate air-to-stream temperature models with four to eight parameters by means of Particle Swarm Optimization (PSO) in Toffolon and Piccolroaz (2015), what seems a large exaggeration. As many as five million function calls are used in Bi et al (2016) for water distribution system optimization problems, however, this time such large number could be justified by the fact that even 1000-dimensional problems were tackled.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

Piotrowski

Napiorkowski

Osuch

2018

Water Resour Manage

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Various methods are used in the literature for calibration of conceptual rainfall-runoff models. However, very rarely the question on the relation between the number of model runs (or function calls) and the quality of solutions found is asked. In this study two lumped conceptual rainfall-runoff models (HBV and GR4J with added snow module) are calibrated for five catchments, located in temperate climate zones of USA and Poland, by means of three modern variants of Evolutionary Computation and Swarm Intelligence optimization algorithms with four different maximum numbers of function calls set to 1000, 3000, 10,000 and 30,000. At the calibration stage, when more than 10,000 function calls is used, only marginal improvement in model performance has been found, irrespective of the catchment or calibration algorithm. For validation data, the relation between the number of function calls and model performance is even weaker, in some cases the longer calibration, the poorer modelling performance. It is also shown that the opinion on the model performance based on different popular hydrological criteria, like the Nash-Sutcliffe coefficient or the Persistence Index, may be misleading. This is because very similar, largely positive values of Nash-Sutcliffe coefficient obtained on different catchments may be accompanied by contradictory values of the Persistence Index.

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A hybrid model for river water temperature as a function of air temperature and discharge

Cited by 156 publications

References 26 publications

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

SESTET: A spatially explicit stream temperature model based on equilibrium temperature

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

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