Articles | Volume 17, issue 10
https://doi.org/10.5194/hess-17-4209-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-17-4209-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Improving uncertainty estimation in urban hydrological modeling by statistically describing bias
D. Del Giudice
Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
ETHZ: Swiss Federal Institute of Technology Zürich, 8093 Zürich, Switzerland
M. Honti
Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
A. Scheidegger
Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
C. Albert
Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
P. Reichert
Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
ETHZ: Swiss Federal Institute of Technology Zürich, 8093 Zürich, Switzerland
J. Rieckermann
Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
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Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Kire Micev, Jan Steiner, Asude Aydin, Jörg Rieckermann, and Tobi Delbruck
Atmos. Meas. Tech., 17, 335–357, https://doi.org/10.5194/amt-17-335-2024, https://doi.org/10.5194/amt-17-335-2024, 2024
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This paper reports a novel rain droplet measurement method that uses a neuromorphic event camera to measure droplet sizes and speeds as they fall through a shallow plane of focus. Experimental results report accuracy similar to a commercial laser sheet disdrometer. Because these measurements are driven by event camera activity, this approach could enable the economical deployment of ubiquitous networks of solar-powered disdrometers.
Simone Ulzega and Carlo Albert
Hydrol. Earth Syst. Sci., 27, 2935–2950, https://doi.org/10.5194/hess-27-2935-2023, https://doi.org/10.5194/hess-27-2935-2023, 2023
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Embedding input uncertainties in hydrological modelling naturally leads to stochastic models, which render parameter calibration an often computationally intractable problem. We use a case study from urban hydrology based on a stochastic rain model, and we employ a highly efficient Hamiltonian Monte Carlo inference algorithm with a timescale separation to demonstrate that fully fledged Bayesian inference with stochastic models is no longer off-limits for hydrological applications.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 26, 5085–5102, https://doi.org/10.5194/hess-26-5085-2022, https://doi.org/10.5194/hess-26-5085-2022, 2022
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Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.
Anna Špačková, Vojtěch Bareš, Martin Fencl, Marc Schleiss, Joël Jaffrain, Alexis Berne, and Jörg Rieckermann
Earth Syst. Sci. Data, 13, 4219–4240, https://doi.org/10.5194/essd-13-4219-2021, https://doi.org/10.5194/essd-13-4219-2021, 2021
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An original dataset of microwave signal attenuation and rainfall variables was collected during 1-year-long field campaign. The monitored 38 GHz dual-polarized commercial microwave link with a short sampling resolution (4 s) was accompanied by five disdrometers and three rain gauges along its path. Antenna radomes were temporarily shielded for approximately half of the campaign period to investigate antenna wetting impacts.
Lorenz Ammann, Fabrizio Fenicia, and Peter Reichert
Hydrol. Earth Syst. Sci., 23, 2147–2172, https://doi.org/10.5194/hess-23-2147-2019, https://doi.org/10.5194/hess-23-2147-2019, 2019
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The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
Andreas Moser, Devon Wemyss, Ruth Scheidegger, Fabrizio Fenicia, Mark Honti, and Christian Stamm
Hydrol. Earth Syst. Sci., 22, 4229–4249, https://doi.org/10.5194/hess-22-4229-2018, https://doi.org/10.5194/hess-22-4229-2018, 2018
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Many chemicals such as pesticides, pharmaceuticals or household chemicals impair water quality in many areas worldwide. Measuring pollution everywhere is too costly. Models can be used instead to predict where high pollution levels are expected. We tested a model that can be used across large river basins. We find that for the selected chemicals predictions are generally within a factor of 2 to 4 from observed concentrations. Often, knowledge about the chemical use limits the predictions.
Mark Honti, Nele Schuwirth, Jörg Rieckermann, and Christian Stamm
Hydrol. Earth Syst. Sci., 21, 1593–1609, https://doi.org/10.5194/hess-21-1593-2017, https://doi.org/10.5194/hess-21-1593-2017, 2017
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We present a new catchment model that covers most major pollutants and is suitable for uncertainty analysis. The effects of climate change, population dynamics, socio-economic development, and management strategies on water quality are demonstrated in a small catchment in the Swiss Plateau. Models and data are still the largest sources of uncertainty for some water quality parameters. Uncertainty assessment helps to select robust management and focus research and monitoring efforts.
Martin Fencl, Michal Dohnal, Jörg Rieckermann, and Vojtěch Bareš
Hydrol. Earth Syst. Sci., 21, 617–634, https://doi.org/10.5194/hess-21-617-2017, https://doi.org/10.5194/hess-21-617-2017, 2017
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Commercial microwave links (CMLs) can provide rainfall observations with high space–time resolution. Unfortunately, CML rainfall estimates are often biased because we lack detailed information on the processes that attenuate the transmitted microwaves. We suggest removing the bias by continuously adjusting CMLs to cumulative data from rain gauges (RGs), which can be remote from the CMLs. Our approach practically eliminates the bias, which we demonstrate on unique data from several CMLs and RGs.
João P. Leitão, Matthew Moy de Vitry, Andreas Scheidegger, and Jörg Rieckermann
Hydrol. Earth Syst. Sci., 20, 1637–1653, https://doi.org/10.5194/hess-20-1637-2016, https://doi.org/10.5194/hess-20-1637-2016, 2016
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Precise and detailed DEMs are essential to accurately predict overland flow in urban areas. In this this study we evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs. In addition, we compared the best quality UAV DEM to a conventional lidar-based DEM; the two DEMs are of comparable quality.
P. Tokarczyk, J. P. Leitao, J. Rieckermann, K. Schindler, and F. Blumensaat
Hydrol. Earth Syst. Sci., 19, 4215–4228, https://doi.org/10.5194/hess-19-4215-2015, https://doi.org/10.5194/hess-19-4215-2015, 2015
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We investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and using this information as input for urban drainage models. We show that imperviousness maps generated using UAV imagery processed with modern classification methods achieve accuracy comparable with standard, off-the-shelf aerial imagery. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications.
T. Doppler, M. Honti, U. Zihlmann, P. Weisskopf, and C. Stamm
Hydrol. Earth Syst. Sci., 18, 3481–3498, https://doi.org/10.5194/hess-18-3481-2014, https://doi.org/10.5194/hess-18-3481-2014, 2014
M. Honti, A. Scheidegger, and C. Stamm
Hydrol. Earth Syst. Sci., 18, 3301–3317, https://doi.org/10.5194/hess-18-3301-2014, https://doi.org/10.5194/hess-18-3301-2014, 2014
A. E. Sikorska, A. Scheidegger, K. Banasik, and J. Rieckermann
Hydrol. Earth Syst. Sci., 17, 4415–4427, https://doi.org/10.5194/hess-17-4415-2013, https://doi.org/10.5194/hess-17-4415-2013, 2013
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Uncertainty analysis
Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model
All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling
Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg
The potential of historical hydrology in Switzerland
Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models
Informal uncertainty analysis (GLUE) of continuous flow simulation in a hybrid sewer system with infiltration inflow – consistency of containment ratios in calibration and validation?
Simone Ulzega and Carlo Albert
Hydrol. Earth Syst. Sci., 27, 2935–2950, https://doi.org/10.5194/hess-27-2935-2023, https://doi.org/10.5194/hess-27-2935-2023, 2023
Short summary
Short summary
Embedding input uncertainties in hydrological modelling naturally leads to stochastic models, which render parameter calibration an often computationally intractable problem. We use a case study from urban hydrology based on a stochastic rain model, and we employ a highly efficient Hamiltonian Monte Carlo inference algorithm with a timescale separation to demonstrate that fully fledged Bayesian inference with stochastic models is no longer off-limits for hydrological applications.
Agnethe Nedergaard Pedersen, Annette Brink-Kjær, and Peter Steen Mikkelsen
Hydrol. Earth Syst. Sci., 26, 5879–5898, https://doi.org/10.5194/hess-26-5879-2022, https://doi.org/10.5194/hess-26-5879-2022, 2022
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A framework for assessing the reliability of urban drainage models is developed in this paper. The method applies observation data from water level sensors and model results for up to 10 years of data for 23 sites in two case areas in Odense, Denmark. With the use of signatures as a method to extract information from the time series, it is possible to differentiate the performance for different model objectives.
Jairo Arturo Torres-Matallana, Ulrich Leopold, and Gerard B. M. Heuvelink
Hydrol. Earth Syst. Sci., 25, 193–216, https://doi.org/10.5194/hess-25-193-2021, https://doi.org/10.5194/hess-25-193-2021, 2021
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This study aimed to select and characterise the main sources of input uncertainty in urban sewer systems, while accounting for temporal correlations of uncertain model inputs, by propagating input uncertainty through the model. We discuss the water quality impact of the model outputs to the environment, specifically in combined sewer systems, in relation to the uncertainty analysis, which constitutes valuable information for the environmental authorities and decision-makers.
Oliver Wetter
Hydrol. Earth Syst. Sci., 21, 5781–5803, https://doi.org/10.5194/hess-21-5781-2017, https://doi.org/10.5194/hess-21-5781-2017, 2017
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This paper aims to describe the strengths and weaknesses of the available historical hydrological evidence, to shed light on the existing basic methodologies leading to long-term frequency, seasonality and magnitude reconstructions of pre-instrumental hydrological events, to discuss the comparability of reconstructed pre-instrumental flood events compared to current events and to provide an outlook for future analysis with a focus on the situation in Switzerland.
Manoranjan Muthusamy, Alma Schellart, Simon Tait, and Gerard B. M. Heuvelink
Hydrol. Earth Syst. Sci., 21, 1077–1091, https://doi.org/10.5194/hess-21-1077-2017, https://doi.org/10.5194/hess-21-1077-2017, 2017
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In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a small urban catchment are used in this study. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. Results from this study can be used for uncertainty analyses of hydrologic modelling.
A. Breinholt, M. Grum, H. Madsen, F. Örn Thordarson, and P. S. Mikkelsen
Hydrol. Earth Syst. Sci., 17, 4159–4176, https://doi.org/10.5194/hess-17-4159-2013, https://doi.org/10.5194/hess-17-4159-2013, 2013
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