An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools

Viviroli, Daniel; Zappa, Massimiliano; Gurtz, J.; Weingartner, Rolf

doi:10.1016/j.envsoft.2009.04.001

Cited by 250 publications

(267 citation statements)

References 86 publications

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“…There have been many attempts to improve the parameterization of lumped and semi-distributed models by further discretizing the sub-basins into a given number of regions that exhibit nearly similar hydrologic behavior, i.e., the so-called HRU concept initially proposed by Leavesley et al (1983) and further developed by others (e.g., Flügel, 1995;Beldring et al, 2003;Blöschl et al, 2008;Viviroli et al, 2009;Zehe et al, 2014). Unfortunately, results obtained in these parameterization attempts have not been very successful in realistically representing the spatial variability of model parameters, states, and fluxes because of the lack of regionalized parameters and the unabridged reliance on parameter calibration to improve model performance (Kumar et al, 2010).…”

Section: The State Of the Artmentioning

confidence: 99%

Toward seamless hydrologic predictions across spatial scales

Samaniego

Kumar

Thober

et al. 2017

Hydrol. Earth Syst. Sci.

101

134

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Abstract. Land surface and hydrologic models (LSMs/HMs) are used at diverse spatial resolutions ranging from catchment-scale (1-10 km) to global-scale (over 50 km) applications. Applying the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the chosen resolution, i.e., fulfills a fluxmatching condition across scales. An analysis of state-of-theart LSMs and HMs reveals that most do not have consistent hydrologic parameter fields. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB, and WaterGAP models demonstrate the pitfalls of deficient parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge's 1982 statement on the unsolved problem of parameterization in these models remains true. Based on a review of existing parameter regionalization techniques, we postulate that the multiscale parameter regionalization (MPR) technique offers a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. Herein, we develop a general model protocol to describe how MPR can be applied to a particular model and present an example application using the PCR-GLOBWB model. Finally, we discuss potential advantages and limitations of MPR in obtaining the seamless prediction of hydrological fluxes and states across spatial scales.

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Section: The State Of the Artmentioning

confidence: 99%

Toward seamless hydrologic predictions across spatial scales

Samaniego

Kumar

Thober

et al. 2017

Hydrol. Earth Syst. Sci.

101

134

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“…Daily values of actual evapotranspiration (ET) were calculated using a radiation-based approach included in the PRE-VAH model (Gurtz et al, 1999;Viviroli et al, 2009). The PREVAH model uses observed global radiation at a daily temporal resolution as input to the Penman-Monteith equation to compute potential evapotranspiration (PET).…”

Section: Datamentioning

confidence: 99%

Importance of maximum snow accumulation for summer low flows in humid catchments

Jeníček

Seibert

Zappa

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Winter snow accumulation obviously has an effect on the following catchment runoff. The question is, however, how long this effect lasts and how important it is compared to rainfall inputs. Here we investigate the relative importance of snow accumulation on one critical aspect of runoff, namely the summer low flow. This is especially relevant as the expected increase of air temperature might result in decreased snow storage. A decrease of snow will affect soil and groundwater storages during spring and might cause low streamflow values in the subsequent warm season. To understand these potential climate change impacts, a better evaluation of the effects of inter-annual variations in snow accumulation on summer low flow under current conditions is central. The objective in this study was (1) to quantify how long snowmelt affects runoff after melt-out and (2) to estimate the sensitivity of catchments with different elevation ranges to changes in snowpack. To find suitable predictors of summer low flow we used long time series from 14 Alpine and pre-Alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. In general, the results indicated that maximum winter snow water equivalent (SWE) influenced summer low flow, but could expectedly only partly explain the observed inter-annual variations. On average, a decrease of maximum SWE by 10 % caused a decrease of minimum discharge in July by 6-9 % in catchments higher than 2000 m a.s.l. This effect was smaller in middle-and lower-elevation catchments with a decrease of minimum discharge by 2-5 % per 10 % decrease of maximum SWE. For higher-and middle-elevation catchments and years with below-average SWE maximum, the minimum discharge in July decreased to 70-90 % of its normal level. Additionally, a reduction in SWE resulted in earlier low-flow occurrence in some cases. One other important factor was the precipitation between maximum SWE and summer low flow. When only dry preceding conditions in this period were considered, the importance of maximum SWE as a predictor of low flows increased. We assessed the sensitivity of individual catchments to the change of maximum SWE using the non-parametric Theil-Sen approach as well as an elasticity index. Both sensitivity indicators increased with increasing mean catchment elevation, indicating a higher sensitivity of summer low flow to snow accumulation in Alpine catchments compared to lower-elevation pre-Alpine catchments.

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“…The model is structured so that a specific combination of storages can be defined for each output class of a given hydrological classification, with one storage system for the plantavailable soil moisture (SSM), one for the runoff generation (SUZ) controlled by four parameters, and a third for Gurtz et al, 2003;Viviroli et al, 2009b). The separation of rainfall between the storage of plantavailable soil moisture and the runoff generation module is controlled by a non-linearity parameter (BETA) fixed here at a value of 3 (Viviroli et al, 2009a). In SUZ, the storage times for overland flow (K0H) and subsurface flow (K1H) regulate the generation of the runoff.…”

Section: The Runoff Generation Module Rgm-promentioning

confidence: 99%

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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An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools

Cited by 250 publications

References 86 publications

Toward seamless hydrologic predictions across spatial scales

Toward seamless hydrologic predictions across spatial scales

Importance of maximum snow accumulation for summer low flows in humid catchments

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

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