Articles | Volume 11, issue 5
https://doi.org/10.5194/gmd-11-1873-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-1873-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
15 May 2018
Methods for assessment of models |
| 15 May 2018
| 15 May 2018
The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models
Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, 1350, Denmark
Mehmet Cüneyd Demirel
Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, 1350, Denmark
Department of Civil Engineering, Istanbul Technical University, 34469
Maslak, Istanbul, Turkey
Simon Stisen
Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, 1350, Denmark
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Short summary
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Utilizing peatlands for agriculture leads to large emissions of greenhouse gases worldwide. The emissions are triggered by lowering the water table, which is a necessary step in order to make peatlands arable. Many countries aim at reducing their emissions by restoring peatlands, which can be achieved by stopping agricultural activities and thereby raising the water table. We estimate a total emission of 2.6 Mt CO2-eq for organic-rich peatlands in Denmark and a potential reduction of 77 %.
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci., 26, 5859–5877, https://doi.org/10.5194/hess-26-5859-2022, https://doi.org/10.5194/hess-26-5859-2022, 2022
Short summary
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Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
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Rena Meyer, Wenmin Zhang, Søren Julsgaard Kragh, Mie Andreasen, Karsten Høgh Jensen, Rasmus Fensholt, Simon Stisen, and Majken C. Looms
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E. Bontempo, M. C. Demirel, C. Corsini, F. Martins, and D. Valeriano
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 201–206, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, 2020
Raphael Schneider, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-685, https://doi.org/10.5194/hess-2019-685, 2020
Revised manuscript not accepted
Short summary
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For groundwater models to deliver reliable results, their parameters often have to be estimated in an optimization process guided by some measure of model performance. In this context, we suggest the use of a novel performance metric, which is less prone to a fit to inadequate observations than the most frequently used metrics based on squared errors. Hence, calibration is more robust to deficiencies in model and observational data, which are common especially in larger scale models.
Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, and Eun-Sung Chung
Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, https://doi.org/10.5194/hess-23-4803-2019, 2019
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Julian Koch, Helen Berger, Hans Jørgen Henriksen, and Torben Obel Sonnenborg
Hydrol. Earth Syst. Sci., 23, 4603–4619, https://doi.org/10.5194/hess-23-4603-2019, https://doi.org/10.5194/hess-23-4603-2019, 2019
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Gorka Mendiguren, Julian Koch, and Simon Stisen
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Short summary
Short summary
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R. Guzinski, H. Nieto, S. Stisen, and R. Fensholt
Hydrol. Earth Syst. Sci., 19, 2017–2036, https://doi.org/10.5194/hess-19-2017-2015, https://doi.org/10.5194/hess-19-2017-2015, 2015
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The study compared evapotranspiration (ET) modelled by two remote sensing models and one hydrological model in a river catchment in Denmark. The results show that the spatial patterns of ET produced by the remote sensing models are more similar to each other than to the fluxes produced by the hydrological model. This indicates potential benefits to the hydrological modelling community from integrating spatial information derived through remote sensing methodology into the hydrological models.
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, https://doi.org/10.5194/hess-19-275-2015, 2015
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This paper investigates the skill of 90-day low-flow forecasts using three models. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
H. Ajami, J. P. Evans, M. F. McCabe, and S. Stisen
Hydrol. Earth Syst. Sci., 18, 5169–5179, https://doi.org/10.5194/hess-18-5169-2014, https://doi.org/10.5194/hess-18-5169-2014, 2014
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A new hybrid approach was developed to reduce the computational burden of the spin-up procedure by using a combination of model simulations and an empirical depth-to-water table function. Results illustrate that the hybrid approach reduced the spin-up period required for an integrated groundwater--surface water--land surface model (ParFlow.CLM) by up to 50%. The methodology is applicable to other coupled or integrated modeling frameworks when initialization from an equilibrium state is required.
J. Koch, X. He, K. H. Jensen, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 18, 2907–2923, https://doi.org/10.5194/hess-18-2907-2014, https://doi.org/10.5194/hess-18-2907-2014, 2014
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Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
Graphical representation of global water models
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
Geosci. Model Dev., 18, 2275–2301, https://doi.org/10.5194/gmd-18-2275-2025, https://doi.org/10.5194/gmd-18-2275-2025, 2025
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This study shows the significance of groundwater in resolving the coupled terrestrial water–energy cycle. LM4-SHARC (soil–hillslope aquifer–river continuum) describes the hillslope groundwater using its emergent properties, yielding noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implications of groundwater-mediated hydrologic interactions between hillslopes and streams need further exploration in the Earth system modeling community.
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
Geosci. Model Dev., 18, 1709–1736, https://doi.org/10.5194/gmd-18-1709-2025, https://doi.org/10.5194/gmd-18-1709-2025, 2025
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Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
Kerry L. Callaghan, Andrew D. Wickert, Richard Barnes, and Jacqueline Austermann
Geosci. Model Dev., 18, 1463–1486, https://doi.org/10.5194/gmd-18-1463-2025, https://doi.org/10.5194/gmd-18-1463-2025, 2025
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We present the Water Table Model (WTM), a new model for simulating groundwater and lake levels at continental scales over millennia. The WTM enables long-term evaluations of water-table changes. As a proof of concept, we simulate the North American water table for the present and the Last Glacial Maximum (LGM), showing that North America held more groundwater and lake water during the LGM than it does today – enough to lower sea levels by 14.98 cm. The open-source code is available on GitHub.
Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, Christian Onof, and Li-Pen Wang
Geosci. Model Dev., 18, 1357–1373, https://doi.org/10.5194/gmd-18-1357-2025, https://doi.org/10.5194/gmd-18-1357-2025, 2025
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pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett–Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
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Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
Geosci. Model Dev., 18, 547–562, https://doi.org/10.5194/gmd-18-547-2025, https://doi.org/10.5194/gmd-18-547-2025, 2025
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We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices: from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev., 17, 8817–8852, https://doi.org/10.5194/gmd-17-8817-2024, https://doi.org/10.5194/gmd-17-8817-2024, 2024
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Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP, which has been used for numerous water resource assessments since 1996. We show the effects of new model features, as well as model evaluations, against water abstraction statistics and observed streamflow and water storage anomalies. The publicly available model output for several variants is described.
Yanchen Zheng, Gemma Coxon, Mostaquimur Rahman, Ross Woods, Saskia Salwey, Youtong Rong, and Doris Wendt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-211, https://doi.org/10.5194/gmd-2024-211, 2024
Revised manuscript accepted for GMD
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Groundwater is vital for people and ecosystems, but most physical models lack surface-groundwater interactions representation, leading to inaccurate streamflow predictions in groundwater-rich areas. This study presents DECIPHeR-GW v1, which links surface and groundwater systems to improve predictions of streamflow and groundwater levels. Tested across England and Wales, DECIPHeR-GW shows high accuracy, especially in south east England, making it a valuable tool for large-scale water management.
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024, https://doi.org/10.5194/gmd-17-8115-2024, 2024
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This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
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We develop an operational forecast system, Coastlines-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model has relatively low computational requirements, and results compare well with near-real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and wave predictions can improve in accuracy.
Robert Chlumsky, James R. Craig, and Bryan A. Tolson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-184, https://doi.org/10.5194/gmd-2024-184, 2024
Revised manuscript accepted for GMD
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We aim to improve mapping of floods, and present a new method for hydraulic modelling that uses a combination of novel geospatial analysis and existing hydraulic modelling approaches. This method is wrapped into a modelling software called Blackbird. We compared Blackbird to two other existing options for flood mapping and found that the Blackbird model outperformed both. The Blackbird model has the potential to support real-time and large-scale flood mapping applications in the future.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024, https://doi.org/10.5194/gmd-17-7083-2024, 2024
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Geoscientists commonly use various potential evapotranpiration (PET) formulas for environmental studies, which can be prone to errors and sensitive to climate change. PyEt, a tested and open-source Python package, simplifies the application of 20 PET methods for both time series and gridded data, ensuring accurate and consistent PET estimations suitable for a wide range of environmental applications.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
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The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
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Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Dor Fridman, Mikhail Smilovic, Peter Burek, Sylvia Tramberend, and Taher Kahil
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-143, https://doi.org/10.5194/gmd-2024-143, 2024
Revised manuscript accepted for GMD
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Global hydrological models are applied at high spatial resolutions to quantify water availability and evaluate water scarcity mitigation options. Yet they mostly oversee important local processes. This paper presents and demonstrates the inclusion of wastewater treatment and reclamation into a global hydrological model. As a result model performance is improved, and models are capable to utilize treated wastewater as an alternative water source.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
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STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
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River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
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The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
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This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
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This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
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The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
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Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
EGUsphere, https://doi.org/10.5194/egusphere-2024-1303, https://doi.org/10.5194/egusphere-2024-1303, 2024
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Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of the reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers and data users.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
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The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
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Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Cited articles
Alexandrov, G. A., Ames, D., Bellocchi, G., Bruen, M., Crout, N.,
Erechtchoukova, M., Hildebrandt, A., Hoffman, F., Jackisch, C., Khaiter, P.,
Mannina, G., Matsunaga, T., Purucker, S. T., Rivington, M., and Samaniego,
L.: Technical assessment and evaluation of environmental models and software:
Letter to the Editor, Environ. Model. Softw., 26, 328–336,
https://doi.org/10.1016/j.envsoft.2010.08.004, 2011.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton,
S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P.,
Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B.
D., and Andreassian, V.: Characterising performance of environmental models,
Environ. Model. Softw., 40, 1–20, https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Brown, B. G., Gotway, J. H., Bullock, R., Gilleland, E., Fowler, T.,
Ahijevych, D., and Jensen, T.: The Model Evaluation Tools (MET): Community
tools for forecast evaluation, in: Preprints, 25th Conf. on International
Interactive Information and Processing Systems (IIPS) for Meteorology,
Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc. A, Vol. 9, 2009.
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple
working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301,
https://doi.org/10.1029/2010WR009827, 2011.
Cloke, H. L. and Pappenberger, F.: Evaluating forecasts of extreme events
for hydrological applications: An approach for screening unfamiliar
performance measures, Meteorol. Appl., 15, 181–197, 2008.
Corbari, C. and Mancini, M.: Calibration and Validation of a Distributed
Energy–Water Balance Model Using Satellite Data of Land Surface Temperature
and Ground Discharge Measurements, J. Hydrometeorol., 15, 376–392,
https://doi.org/10.1175/JHM-D-12-0173.1, 2014.
Cuntz, M., Mai, J., Zink, M., Thober, S., Kumar, R., Schäfer, D.,
Schrön, M., Craven, J., Rakovec, O., Spieler, D., Prykhodko, V.,
Dalmasso, G., Musuuza, J., Langenberg, B., Attinger, S., and Samaniego, L.:
Computationally inexpensive identification of noninformative model parameters
by sequential screening, Water Resour. Res., 51, 6417–6441,
https://doi.org/10.1002/2015WR016907, 2015.
Dawson, C. W., Abrahart, R. J., and See, L. M.: HydroTest: A web-based
toolbox of evaluation metrics for the standardised assessment of hydrological
forecasts, Environ. Modell. Softw., 22, 1034–1052,
https://doi.org/10.1016/j.envsoft.2006.06.008, 2007.
Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., and Stisen,
S.: Combining satellite data and appropriate objective functions for improved
spatial pattern performance of a distributed hydrologic model, Hydrol. Earth
Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, 2018a.
Demirel, M. C., Stisen, S., and Koch, J.: SPAEF: SPAtial EFficiency,
https://doi.org/10.5281/ZENODO.1158890, 2018b.
Doherty, J.: PEST: Model Independent Parameter Estimation. Fifth Edition of
User Manual, Watermark Numerical Computing, Brisbane, 2005.
Dorninger, M., Mittermaier, M. P., Gilleland, E., Ebert, E. E., Brown, B.
G., and Wilson, L. J.: MesoVICT: Mesoscale Verification Inter-Comparison over Complex Terrain. NCAR Technical Note NCAR/TN-505+STR, 23 pp., https://doi.org/10.5065/D6416V21, 2013.
Duan, Q. Y., Gupta, V. K., and Sorooshian, S.: Shuffled complex evolution
approach for effective and efficient global minimization, J. Optimiz. Theory
App., 76, 501–521, https://doi.org/10.1007/BF00939380, 1993.
Gilleland, E., Ahijevych, D., Brown, B. G., Casati, B., and Ebert, E. E.:
Intercomparison of Spatial Forecast Verification Methods, Weather Forecast.,
24, 1416–1430, 2009.
Gilleland, E., Bukovsky, M., Williams, C. L., McGinnis, S., Ammann, C. M.,
Brown, B. G., and Mearns, L. O.: Evaluating NARCCAP model performance for
frequencies of severe-storm environments, Adv. Stat. Clim. Meteorol.
Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, 2016.
Glaser, B., Klaus, J., Frei, S., Frentress, J., Pfister, L., and Hopp, L.: On
the value of surface saturated area dynamics mapped with thermal infrared
imagery for modeling the hillslope-riparian-stream continuum, Water Resour.
Res., 52, 8317–8342, https://doi.org/10.1002/2015WR018414, 2016.
Grayson, R. and Blöschl, G.: Spatial patterns in catchment hydrology:
observations and modelling, Cambridge University Press, 2001.
Grayson, R. B., Blöschl, G., Western, A. W., and McMahon, T. A.: Advances
in the use of observed spatial patterns of catchment hydrological response,
Adv. Water Resour., 25, 1313–1334, https://doi.org/10.1016/s0309-1708(02)00060-x, 2002.
Gupta, H. V., Wagener, T., and Liu, Y. Q.: Reconciling theory with
observations: elements of a diagnostic approach to model evaluation, Hydrol.
Process., 22, 3802–3813, https://doi.org/10.1002/Hyp.6989, 2008.
Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards
a comprehensive assessment of model structural adequacy, Water Resour. Res.,
48, W08301,
https://doi.org/10.1029/2011WR011044, 2012.
Hagen, A.: Fuzzy set approach to assessing similarity of categorical maps,
Int. J. Geogr. Inf. Sci., 17, 235–249, https://doi.org/10.1080/13658810210157822, 2003.
Hagen, A. and Martens, P.: Map comparison methods for comprehensive assessment
of geosimulation models, International Conference on Computational
Science and Its Applications, Springer, Berlin, Heidelberg, 2008.
Herrera-Estrada, J. E., Satoh, Y., and Sheffield, J.: Spatiotemporal dynamics
of global drought, Geophys. Res. Lett., 44, 2254–2263,
https://doi.org/10.1002/2016GL071768, 2017.
Hovadik, J. M. and Larue, D. K.: Static characterizations of reservoirs:
refining the concepts of connectivity and continuity, Petrol. Geosci., 13,
195–211, 2007.
Immerzeel, W. W. and Droogers, P.: Calibration of a distributed hydrological
model based on satellite evapotranspiration, J. Hydrol., 349, 411–424,
https://doi.org/10.1016/j.jhydrol.2007.11.017, 2008.
Jensen, K. H. and Illangasekare, T. H.: HOBE: A Hydrological Observatory,
Vadose Zone J., 10, 1–7, https://doi.org/10.2136/vzj2011.0006, 2011.
Kling, H. and Gupta, H.: On the development of regionalization relationships
for lumped watershed models: The impact of ignoring sub-basin scale
variability, J. Hydrol., 373, 337–351, https://doi.org/10.1016/j.jhydrol.2009.04.031,
2009.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425,
264–277, https://doi.org/10.1016/J.JHYDROL.2012.01.011, 2012.
Koch, J.: SEEM: Spatial Evaluation of Environmental Models,
https://doi.org/10.5281/zenodo.1154614, 2018.
Koch, J. and Stisen, S.: Citizen science: A new perspective to advance
spatial pattern evaluation in hydrology, PLoS One, 12, 1–20,
https://doi.org/10.1371/journal.pone.0178165, 2017.
Koch, J., He, X., Jensen, K. H., and Refsgaard, J. C.: Challenges in
conditioning a stochastic geological model of a heterogeneous glacial aquifer
to a comprehensive soft data set, Hydrol. Earth Syst. Sci., 18, 2907–2923,
https://doi.org/10.5194/hess-18-2907-2014, 2014.
Koch, J., Jensen, K. H., and Stisen, S.: Toward a true spatial model
evaluation in distributed hydrological modeling: Kappa statistics, Fuzzy
theory, and EOF-analysis benchmarked by the human perception and evaluated
against a modeling case study, Water Resour. Res., 51, 1225–1246,
https://doi.org/10.1002/2014WR016607, 2015.
Koch, J., Cornelissen, T., Fang, Z., Bogena, H., Diekkrüger, B., Kollet,
S., and Stisen, S.: Inter-comparison of three distributed hydrological models
with respect to seasonal variability of soil moisture patterns at a small
forested catchment, J. Hydrol., 533, 234–249,
https://doi.org/10.1016/j.jhydrol.2015.12.002, 2016a.
Koch, J., Siemann, A., Stisen, S., and Sheffield, J.: Spatial validation of
large scale land surface models against monthly land surface temperature
patterns using innovative performance metrics, J. Geophys. Res.-Atmos., 121,
5430–5452, https://doi.org/10.1002/2015JD024482, 2016b.
Koch, J., Mendiguren, G., Mariethoz, G., and Stisen, S.: Spatial sensitivity
analysis of simulated land-surface patterns in a catchment model using a set
of innovative spatial performance metrics, J. Hydrometeorol., 18, 1121–1142,
JHM-D-16-0148.1, https://doi.org/10.1175/JHM-D-16-0148.1, 2017.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Kuhnert, M., Voinov, A., and Seppelt, R.: Comparing raster map comparison
algorithms for spatial modeling and analysis, Photogramm. Eng. Remote
Sensing, 71, 975–984, 2005.
Kumar, R., Samaniego, L., and Attinger, S.: The effects of spatial
discretization and model parameterization on the prediction of extreme runoff
characteristics, J. Hydrol., 392, 54–69, https://doi.org/10.1016/j.jhydrol.2010.07.047,
2010.
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed
hydrologic model parameterization on water fluxes at multiple scales and
locations, Water Resour. Res., 49, 360–379, https://doi.org/10.1029/2012WR012195, 2013.
Kumar, S. V., Peters-Lidard, C. D., Santanello, J., Harrison, K., Liu, Y.,
and Shaw, M.: Land surface Verification Toolkit (LVT) – a generalized
framework for land surface model evaluation, Geosci. Model Dev., 5, 869–886,
https://doi.org/10.5194/gmd-5-869-2012, 2012.
McCabe, M. F., Wood, E. F., Wjcik, R., Pan, M., Sheffield, J., Gao, H., and
Su, H.: Hydrological consistency using multi-sensor remote sensing data for
water and energy cycle studies, Remote Sens. Environ., 112, 430–444,
https://doi.org/10.1016/j.rse.2007.03.027, 2008.
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R.,
Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi,
J., Gao, H., and Wood, E. F.: The future of Earth observation in hydrology,
Hydrol. Earth Syst. Sci., 21, 3879–3914,
https://doi.org/10.5194/hess-21-3879-2017, 2017.
Mendiguren, G., Koch, J., and Stisen, S.: Spatial pattern evaluation of a
calibrated national hydrological model – a remote-sensing-based diagnostic
approach, Hydrol. Earth Syst. Sci., 21, 5987–6005,
https://doi.org/10.5194/hess-21-5987-2017, 2017.
Mendoza, P. A., Clark, M. P., Barlage, M., Rajagopalan, B., Samaniego, L.,
Abramowitz, G., and Gupta, H.: Are we unnecessarily constraining the agility
of complex process-based models?, Water Resour. Res., 51, 716–728,
https://doi.org/10.1002/2014WR015820, 2015.
Mittermaier, M., Roberts, N. and Thompson, S. A.: A long-term assessment of
precipitation forecast skill using the Fractions Skill Score, Meteorol.
Appl., 20, 176–186, https://doi.org/10.1002/met.296, 2013.
Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D.,
Nijssen, B., Rakovec, O,. and Samaniego, L.: Towards seamless large-domain
parameter estimation for hydrologic models, Water Resour. Res., 53,
8020–8040, https://doi.org/10.1002/2017WR020401, 2017.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and
Veith, T. L.: Model Evaluation Guidelines for Systematic Quantification of
Accuracy in Watershed Simulations, T. ASABE, 50, 885–900,
https://doi.org/10.13031/2013.23153, 2007.
Norman, J. M., Kustas, W. P., and Humes, K. S.: Source approach for
estimating soil and vegetation energy fluxes in observations of directional
radiometric surface temperature, Agr. Forest Meteorol., 77, 263–293,
https://doi.org/10.1016/0168-1923(95)02265-Y, 1995.
Orth, R., Dutra, E., Trigo, I. F., and Balsamo, G.: Advancing land surface
model development with satellite-based Earth observations, Hydrol. Earth
Syst. Sci., 21, 2483–2495, https://doi.org/10.5194/hess-21-2483-2017, 2017.
Pokhrel, P. and Gupta, H. V.: On the ability to infer spatial catchment
variability using streamflow hydrographs, Water Resour. Res., 47, W08534,
https://doi.org/10.1029/2010wr009873, 2011.
Refsgaard, J. C. and Henriksen, H. J.: Modelling guidelines – Terminology
and guiding principles, Adv. Water Resour., 27, 71–82,
https://doi.org/10.1016/j.advwatres.2003.08.006, 2004.
Refsgaard, J. C., Auken, E., Bamberg, C. A., Christensen, B. S. B., Clausen,
T., Dalgaard, E., Effersø, F., Ernstsen, V., Gertz, F., Hansen, A. L., He,
X., Jacobsen, B. H., Jensen, K. H., Jørgensen, F., Jørgensen, L. F.,
Koch, J., Nilsson, B., Petersen, C., De Schepper, G., Schamper, C.,
Sørensen, K. I., Therrien, R., Thirup, C., and Viezzoli, A.: Nitrate
reduction in geologically heterogeneous catchments – A framework for
assessing the scale of predictive capability of hydrological models, Sci.
Total Environ., 468–469, 1278–1288, https://doi.org/10.1016/j.scitotenv.2013.07.042,
2014.
Renard, P. and Allard, D.: Connectivity metrics for subsurface flow and
transport, Adv. Water Resour., 51, 168–196,
https://doi.org/10.1016/j.advwatres.2011.12.001, 2013.
Roberts, N.: Assessing the spatial and temporal variation in the skill of
precipitation forecasts from an NWP model, Meteorol. Appl., 15, 163–169,
2008.
Roberts, N. M. and Lean, H. W.: Scale-Selective Verification of Rainfall
Accumulations from High-Resolution Forecasts of Convective Events, Mon.
Weather Rev., 136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2008.
Rongier, G., Collon, P., Renard, P., Straubhaar, J., and Sausse, J.:
Comparing connected structures in ensemble of random fields, Adv. Water
Resour., 96, 145–169, https://doi.org/10.1016/j.advwatres.2016.07.008, 2016.
Ruiz-Pérez, G., González-Sanchis, M., Del Campo, A. D., and
Francés, F.: Can a parsimonious model implemented with satellite data be
used for modelling the vegetation dynamics and water cycle in
water-controlled environments?, Ecol. Modell., 324, 45–53,
https://doi.org/10.1016/j.ecolmodel.2016.01.002, 2016.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter
regionalization of a grid-based hydrologic model at the mesoscale, Water
Resour. Res., 46, W05523,
https://doi.org/10.1029/2008wr007327, 2010a.
Samaniego, L., Bardossy, A., and Kumar, R.: Streamflow prediction in ungauged
catchments using copula-based dissimilarity measures, Water Resour. Res., 46,
W02506,
https://doi.org/10.1029/2008WR007695, 2010b.
Samaniego, L., Kumar, R., Mai, J., Zink, M., Thober, S., Cuntz, M., Rakovec,
O., Schäfer, D., Schrön, M., Brenner, J., Demirel, C. M., Kaluza, M.,
Langenberg, B., Stisen, S., and Attinger, S.: mesoscale Hydrologic Model,
https://doi.org/10.5281/ZENODO.1069203, 2017a.
Samaniego, L., Kumar, R., Thober, S., Rakovec, O., Zink, M., Wanders, N.,
Eisner, S., Müller Schmied, H., Sutanudjaja, E. H., Warrach-Sagi, K., and
Attinger, S.: Toward seamless hydrologic predictions across spatial scales,
Hydrol. Earth Syst. Sci., 21, 4323–4346,
https://doi.org/10.5194/hess-21-4323-2017, 2017b.
Schaefi, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process.,
21, 2075–2080, 2007.
Schalge, B., Rihani, J., Baroni, G., Erdal, D., Geppert, G., Haefliger, V.,
Haese, B., Saavedra, P., Neuweiler, I., Hendricks Franssen, H.-J., Ament, F.,
Attinger, S., Cirpka, O. A., Kollet, S., Kunstmann, H., Vereecken, H., and
Simmer, C.: High-Resolution Virtual Catchment Simulations of the
Subsurface-Land Surface-Atmosphere System, Hydrol. Earth Syst. Sci. Discuss.,
https://doi.org/10.5194/hess-2016-557, 2016.
Schuurmans, J. M., van Geer, F. C., and Bierkens, M. F. P.: Remotely sensed
latent heat fluxes for model error diagnosis: a case study, Hydrol. Earth
Syst. Sci., 15, 759–769, https://doi.org/10.5194/hess-15-759-2011, 2011.
Stisen, S., McCabe, M. F., Refsgaard, J. C., Lerer, S., and Butts, M. B.:
Model parameter analysis using remotely sensed pattern information in a
multi-constraint framework, J. Hydrol., 409, 337–349,
https://doi.org/10.1016/j.jhydrol.2011.08.030, 2011.
Stisen, S., Sonnenborg, T. O., Refsgaard, J. C., Koch, J., Bircher, S., and
Jensen, K. H.: Moving beyond runoff calibration – Multi-constraint
optimization of a surface-subsurface-atmosphere model, Hydrol. Process.,
in revision, 2018.
Swain, M. J. and Ballard, D. H.: Color indexing, Int. J. Comput. Vis., 7,
11–32, https://doi.org/10.1007/BF00130487, 1991.
Terink, W., Lutz, A. F., Simons, G. W. H., Immerzeel, W. W., and Droogers, P.: SPHY v2.0: Spatial Processes in HYdrology, Geosci. Model Dev., 8, 2009–2034, https://doi.org/10.5194/gmd-8-2009-2015, 2015.
van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., and
Srinivasan, R.: A global sensitivity analysis tool for the parameters of
multi-variable catchment models, J. Hydrol., 324, 10–23,
https://doi.org/10.1016/j.jhydrol.2005.09.008, 2006.
Vereecken, H., Pachepsky, Y., Simmer, C., Rihani, J., Kunoth, A., Korres,
W., Graf, A., Franssen, H. J.-H., Thiele-Eich, I., and Shao, Y.: On the role
of patterns in understanding the functioning of soil-vegetation-atmosphere
systems, J. Hydrol., 542, 63–86, https://doi.org/10.1016/j.jhydrol.2016.08.053, 2016.
Wealands, S. R., Grayson, R. B., and Walker, J. P.: Quantitative comparison
of spatial fields for hydrological model assessment – some promising
approaches, Adv. Water Resour., 28, 15–32,
https://doi.org/10.1016/j.advwatres.2004.10.001, 2005.
Western, A. W., Blöschl, G., and Grayson, R. B.: Toward capturing
hydrologically significant connectivity in spatial patterns, Water Resour.
Res., 37, 83–97, 2001.
Wolff, J. K., Harrold, M., Fowler, T., Gotway, J. H., Nance, L., and Brown,
B. G.: Beyond the Basics: Evaluating Model-Based Precipitation Forecasts
Using Traditional, Spatial, and Object-Based Methods, Weather Forecast., 29,
1451–1472, https://doi.org/10.1175/WAF-D-13-00135.1, 2014.
Short summary
Our work addresses a key challenge in earth system modelling: how to optimally exploit the information contained in satellite remote sensing observations in the calibration of such models. For this we thoroughly test a number of measures that quantify the fit between an observed and a simulated spatial pattern. We acknowledge the difficulties associated with such a comparison and suggest using measures that regard multiple aspects of spatial information, i.e. magnitude and variability.
Our work addresses a key challenge in earth system modelling: how to optimally exploit the...
