Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model?

Müller-Thomy, Hannes; Sikorska‐Senoner, Anna E.

doi:10.1080/02626667.2019.1638926

Cited by 15 publications

(10 citation statements)

References 49 publications

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“…Such a set-up of the HBV light was previously successfully applied in Swiss catchments (e.g. Sikorska and Seibert, 2018a;Brunner et al, 2018c;Brunner and Sikorska-Senoner, 2019;Müller-Thomy and Sikorska-Senoner, 2019;Westerberg et al, 2020). Model inputs are time series of precipitation and air temperature and long-term averages of seasonally varying estimates of potential evaporation, all being area-average values for the entire catchment.…”

Section: Hydrological Model and Calibration Datamentioning

confidence: 99%

“…The key is thus the construction of design hydrographs with different shapes, peak flows and volumes, with a corresponding probability of occurrence. Such approaches can be roughly classified into methods that identify the shape of these design hydrographs based on observed data (Mediero et al, 2010) or based on theoretical considerations (unit hydrographs) (Brunner et al, 2017) and regionalization (Tung et al, 1997;Brunner et al, 2018a) or methods that rely on streamflow simulations (Arnaud and Lavabre, 2002;Kuchment and Gelfan, 2011;Paquet et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Modern extensions of this approach, however, use continuous hydrological modelling for design flood estimation to generate either (i) a range of initial conditions for use in combination with design or randomly drawn storms (Paquet et al, 2013;Zeimetz et al, 2018) or (ii) long discharge time series from long observed-precipitation records or from synthetic precipitation time series obtained with a weather generator (Calver and Lamb, 1995;Cameron et al, 2000;Blazkova and Beven, 2004;Hoes and Nelen, 2005;Winter et al, 2019). The above approach (ii) is computationally intensive, especially if long time series are to be simulated using ensembles of hydrological parameter sets or if very high return periods (> 1000 years) have to be robustly estimated.…”

Section: Introductionmentioning

confidence: 99%

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Downsizing parameter ensembles for simulations of rare floods

Sikorska‐Senoner

Schaefli

Seibert

2020

Nat. Hazards Earth Syst. Sci.

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Abstract. For extreme-flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrological model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrological model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme-flood estimation so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analysing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated thanks to a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of rare floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed.

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Section: Hydrological Model and Calibration Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Downsizing parameter ensembles for simulations of rare floods

Sikorska‐Senoner

Schaefli

Seibert

2020

Nat. Hazards Earth Syst. Sci.

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“…Molnar and Burlando, 2005;Paschalis et al, 2012) represents a downscaling technique, while the microcanonical version (exact rainfall amount conservation for each time step, e.g. Olsson, 1998;Güntner et al, 2001;Licznar et al, 2011Licznar et al, , 2015Müller-Thomy et al, 2018;Müller-Thomy and Sikorska-Senoner, 2019) represents a disaggregation technique. However, for urban hydrology the majority of investigations with cascade models focus on the disaggregation of quasi-daily time series (with time step durations of 1280 min instead of 1440 min, e.g.…”

Section: Introductionmentioning

confidence: 99%

Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation

Müller-Thomy

2020

Hydrol. Earth Syst. Sci.

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Abstract. In urban hydrology rainfall time series of high resolution in time are crucial. Such time series with sufficient length can be generated through the disaggregation of daily data with a micro-canonical cascade model. A well-known problem of time series generated in this way is the inadequate representation of the autocorrelation. In this paper two cascade model modifications are analysed regarding their ability to improve the autocorrelation in disaggregated time series with 5 min resolution. Both modifications are based on a state-of-the-art reference cascade model (method A). In the first modification, a position dependency is introduced in the first disaggregation step (method B). In the second modification the position of a wet time step is redefined in addition by taking into account the disaggregated finer time steps of the previous time step instead of the previous time step itself (method C). Both modifications led to an improvement of the autocorrelation, especially the position redefinition (e.g. for lag-1 autocorrelation, relative errors of −3 % (method B) and 1 % (method C) instead of −4 % for method A). To ensure the conservation of a minimum rainfall amount in the wet time steps, the mimicry of a measurement device is simulated after the disaggregation process. Simulated annealing as a post-processing strategy was tested as an alternative as well as an addition to the modifications in methods B and C. For the resampling, a special focus was given to the conservation of the extreme rainfall values. Therefore, a universal extreme event definition was introduced to define extreme events a priori without knowing their occurrence in time or magnitude. The resampling algorithm is capable of improving the autocorrelation, independent of the previously applied cascade model variant (e.g. for lag-1 autocorrelation the relative error of −4 % for method A is reduced to 0.9 %). Also, the improvement of the autocorrelation by the resampling was higher than by the choice of the cascade model modification. The best overall representation of the autocorrelation was achieved by method C in combination with the resampling algorithm. The study was carried out for 24 rain gauges in Lower Saxony, Germany.

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“…Many of these methods are carried out on a station-by-station basis (Müller-Thomy and Sikorska-Senoner, 2019), while others also deal with the more challenging problem of temporal disaggregation of whole spatial fields (e.g., Raut et al, 2018).…”

mentioning

confidence: 99%

Technical Note: Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

Scher¹,

Peßenteiner²

2020

Preprint

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Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly under-determined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of Generative Adversarial Networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly lead to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

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Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model?

Cited by 15 publications

References 49 publications

Downsizing parameter ensembles for simulations of rare floods

Downsizing parameter ensembles for simulations of rare floods

Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation

Technical Note: Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

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