Articles | Volume 25, issue 1
https://doi.org/10.5194/hess-25-193-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-193-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg
Jairo Arturo Torres-Matallana
CORRESPONDING AUTHOR
Soil Geography and Landscape Group, Wageningen University, Wageningen, the Netherlands
Research Group for Sustainable Urban and Built Environment, Department for Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
Ulrich Leopold
Research Group for Sustainable Urban and Built Environment, Department for Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
Gerard B. M. Heuvelink
Soil Geography and Landscape Group, Wageningen University, Wageningen, the Netherlands
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Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-618, https://doi.org/10.5194/essd-2024-618, 2025
Preprint under review for ESSD
Short summary
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Current understanding of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate spatially-explicit new global maps of top- and subsoil τ, with quantified uncertainties for better user applications. The new insights from resulting maps facilitate modelling efforts of carbon cycle and support effective carbon management.
Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink
Earth Syst. Sci. Data, 16, 2941–2970, https://doi.org/10.5194/essd-16-2941-2024, https://doi.org/10.5194/essd-16-2941-2024, 2024
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Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
U. Leopold, C. Braun, and P. Pinheiro
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W7-2023, 95–100, https://doi.org/10.5194/isprs-archives-XLVIII-4-W7-2023-95-2023, https://doi.org/10.5194/isprs-archives-XLVIII-4-W7-2023-95-2023, 2023
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, https://doi.org/10.5194/soil-7-217-2021, 2021
Short summary
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This paper focuses on the production of global maps of soil properties with quantified spatial uncertainty, as implemented in the SoilGrids version 2.0 product using DSM practices and adapting them for global digital soil mapping with legacy data. The quantitative evaluation showed metrics in line with previous studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations.
S. Bhattacharya, C. Braun, and U. Leopold
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 3–9, https://doi.org/10.5194/isprs-archives-XLII-4-W14-3-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W14-3-2019, 2019
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
Short summary
Short summary
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.
W. Marijn van der Meij, Arnaud J. A. M. Temme, Christian M. F. J. J. de Kleijn, Tony Reimann, Gerard B. M. Heuvelink, Zbigniew Zwoliński, Grzegorz Rachlewicz, Krzysztof Rymer, and Michael Sommer
SOIL, 2, 221–240, https://doi.org/10.5194/soil-2-221-2016, https://doi.org/10.5194/soil-2-221-2016, 2016
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This study combined fieldwork, geochronology and modelling to get a better understanding of Arctic soil development on a landscape scale. Main processes are aeolian deposition, physical and chemical weathering and silt translocation. Discrepancies between model results and field observations showed that soil and landscape development is not as straightforward as we hypothesized. Interactions between landscape processes and soil processes have resulted in a complex soil pattern in the landscape.
D. R. Cameron, M. Van Oijen, C. Werner, K. Butterbach-Bahl, R. Grote, E. Haas, G. B. M. Heuvelink, R. Kiese, J. Kros, M. Kuhnert, A. Leip, G. J. Reinds, H. I. Reuter, M. J. Schelhaas, W. De Vries, and J. Yeluripati
Biogeosciences, 10, 1751–1773, https://doi.org/10.5194/bg-10-1751-2013, https://doi.org/10.5194/bg-10-1751-2013, 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
The potential of historical hydrology in Switzerland
Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models
Improving uncertainty estimation in urban hydrological modeling by statistically describing bias
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
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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.
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.
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
Short summary
Short summary
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.
D. Del Giudice, M. Honti, A. Scheidegger, C. Albert, P. Reichert, and J. Rieckermann
Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, https://doi.org/10.5194/hess-17-4209-2013, 2013
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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Short summary
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.
This study aimed to select and characterise the main sources of input uncertainty in urban sewer...