Process‐based hydrological modelling: The potential of a bottom‐up approach for runoff predictions in ungauged catchments

Antonetti, Manuel; Scherrer, Simon C.; Kienzler, Peter; Margreth, Michael; Zappa, Massimiliano

doi:10.1002/hyp.11232

Cited by 18 publications

(62 citation statements)

References 72 publications

(153 reference statements)

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“…Scherrer, 1997). The third mapping approach we used is based on the experimentalist approach introduced by Schmocker-Fackel et al (2007) and Margreth (2010), which has already been used 10 in, for instance, Antonetti et al (2016) and Antonetti et al (2017). Basically, the approach consists of the following steps.…”

Section: Study Area and Process Maps 20mentioning

confidence: 99%

“…To perform the hydrological simulations for this study the newly developed conceptual PROcess-based Runoff Generation Module (RGM-PRO) was therefore used (Antonetti et al, 2017).…”

Section: The Runoff Generation Module Rgm-promentioning

confidence: 99%

“…At the beginning of the 15 simulations, therefore, the soil moisture value simulated with the PREVAH hydrological system was assigned to each output class of the corresponding cell. Alternatively, as the spatial variability of the soil moisture is higher than the model resolution (500 m), the hydrological downscaling technique described in Blöschl et al (2009) and used in Antonetti et al (2017) was implemented. The technique relies on three assumptions: (i) the soil moisture pattern at the smaller scale is time invariant, which allows the process maps to be used as fingerprint; (ii) the spatial variance of the soil moisture at the smaller scale is 20 linked with the one at the larger scale by a scaling theory taken from literature (Blöschl et al, 2009); and (iii) the soil moisture is mass conserving.…”

Section: Model Initialisationmentioning

confidence: 99%

“…For the bottom-up setup, RGM-PRO was configured as in Antonetti et al (2017). The main catchment was first subdivided into several sub-catchments up to 2 km 2 in area.…”

Section: Bottom-up Setup: a Priori Definition Of Parameter Rangesmentioning

confidence: 99%

“…Runoff Generation Module (RGM-PRO; Antonetti et al, 2017), which was forced with input data of varying quality. Finally, an analysis of variance (ANOVA) was performed to quantify the uncertainty arising from forcing data, process maps, model parameterisation and parameter allocation strategy.…”

Section: Introductionmentioning

confidence: 99%

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How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

Antonetti

Zappa

2017

Preprint

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Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, 10 applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter.Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test to which extent expert knowledge can 15 improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the Inverse Distance Weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with 20 different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down setup relying on parameter and process constraints, and an experimentalists' setup based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed. 25The simulation results showed that: (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up setup performed better than the top-down one when simulating short-duration events, but similarly to the top-down setup when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up

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Section: Study Area and Process Maps 20mentioning

confidence: 99%

“…To perform the hydrological simulations for this study the newly developed conceptual PROcess-based Runoff Generation Module (RGM-PRO) was therefore used (Antonetti et al, 2017).…”

Section: The Runoff Generation Module Rgm-promentioning

confidence: 99%

Section: Model Initialisationmentioning

confidence: 99%

“…For the bottom-up setup, RGM-PRO was configured as in Antonetti et al (2017). The main catchment was first subdivided into several sub-catchments up to 2 km 2 in area.…”

Section: Bottom-up Setup: a Priori Definition Of Parameter Rangesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

Antonetti

Zappa

2017

Preprint

Self Cite

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Assessing effects of model complexity and structure on predictions of hydrological responses using serial and parallel model design

Chien

Mackay

2019

Hydrological Processes

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By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub-model with a water quality sub-model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25-year high-resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial-based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub-model with one set of scale assumptions onto the front end of another submodel with a different set of scale assumptions. K E Y W O R D S model complexity and structure, time series model, serial and parallel modeling approaches, Watershed scale hydrological responses

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A parameter allocation approach for flow simulation using the WetSpa‐Python model

Bahremand

Ahmadyousefi

Sheikh

et al. 2020

Hydrological Processes

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Hydrologic models are simplified representations of natural hydrologic systems. Since these models rely on assumptions and simplifications to capture some aspects of hydrological processes, calibration of parameters is unavoidable. However, utilizing the philosophy of a recent modelling framework proposed by Bahremand (2016), we show how calibration of most model parameters can be avoided by allocating or presetting these parameters utilizing knowledge gained from sensitivity analyses, field observations and a priori specifications as a part of a parameter allocation procedure. This paper details the simulation of daily river flow of the Shemshak‐Roudak watershed performed using the Python version of the WetSpa model. The WetSpa‐Python model is a distributed model of hydrological processes applied at the watershed scale. The model was applied to the Shemshak‐Roudak watershed of Iran with parameter allocation. Model calibration involved only two parameters. Straightforward methods were proposed for allocating model parameters, including three baseflow‐related parameters and the determination of maximum active groundwater storage using a mass curve technique. Also, the Budyko curve was used to constrain a correction factor for potential evapotranspiration. The WetSpa‐Python model was extended to include the influence of snowmelt. A failure to include snow in the hydrological processes of the WetSpa‐Python model creates a significant discrepancy between the observed and simulated hydrographs during the spring. The results of daily simulations for 12 years (2002–2014) are in good agreement with observations of discharge (Kling‐Gupta Efficiency = 0.84). These results demonstrate that it is feasible to simulate hydrographs with limited calibration given a knowledge of hydrological processes and an understanding of relationships between catchment characteristics and model parameters.

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Process‐based hydrological modelling: The potential of a bottom‐up approach for runoff predictions in ungauged catchments

Cited by 18 publications

References 72 publications

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

Assessing effects of model complexity and structure on predictions of hydrological responses using serial and parallel model design

A parameter allocation approach for flow simulation using the WetSpa‐Python model

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