Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-7047-2021
© 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/gmd-14-7047-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
19 Nov 2021
Model description paper |
| 19 Nov 2021
| 19 Nov 2021
SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models
Marco Dal Molin
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Dübendorf, Switzerland
Centre of Hydrogeology and Geothermics (CHYN), University of
Neuchâtel, Neuchâtel, Switzerland
Dmitri Kavetski
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Dübendorf, Switzerland
School of Civil, Environmental and Mining Engineering, University
of Adelaide, Adelaide, SA, Australia
Civil, Surveying and Environmental Engineering, University of
Newcastle, Callaghan, NSW, Australia
Fabrizio Fenicia
CORRESPONDING AUTHOR
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Dübendorf, Switzerland
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Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022, https://doi.org/10.5194/hess-26-5669-2022, 2022
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Tanja de Boer-Euser, Laurène Bouaziz, Jan De Niel, Claudia Brauer, Benjamin Dewals, Gilles Drogue, Fabrizio Fenicia, Benjamin Grelier, Jiri Nossent, Fernando Pereira, Hubert Savenije, Guillaume Thirel, and Patrick Willems
Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, https://doi.org/10.5194/hess-21-423-2017, 2017
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S. Gharari, M. Hrachowitz, F. Fenicia, H. Gao, and H. H. G. Savenije
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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.
Concetta D'Amato, Niccolò Tubini, and Riccardo Rigon
EGUsphere, https://doi.org/10.5194/egusphere-2024-4128, https://doi.org/10.5194/egusphere-2024-4128, 2025
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This paper presents GEOSPACE and its 1D implementation: an open-source tool for simulating soil-plant-atmosphere continuum (SPAC) interactions. Using object-oriented programming, GEOSPACE modularizes SPAC processes, focusing on infiltration, evapotranspiration, and root water uptake. The 1D deployment integrates plant transpiration, soil evaporation, and root growth, providing a flexible and validated framework for ecohydrological modeling and applications.
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.
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.
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.
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.
Cited articles
Ammann, L., Doppler, T., Stamm, C., Reichert, P., and Fenicia, F.:
Characterizing fast herbicide transport in a small agricultural catchment
with conceptual models, J. Hydrol., 586, 124812, https://doi.org/10.1016/j.jhydrol.2020.124812, 2020.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large
area hydrologic modeling and assessment, Part I: model development,
J. Am. Water Res. Assoc., 34, 73–89,
https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M.
J., Srinivasan, R., Santhi, C., Harmel, R. D., van Griensven, A., Van Liew,
M. W., Kannan, N., and Jha, M. K.: SWAT: Model Use, Calibration, and
Validation, Transactions of the ASABE, 55, 1491–1508, https://doi.org/10.13031/2013.42256, 2012.
Bancheri, M., Serafin, F., and Rigon, R.: The Representation of Hydrological
Dynamical Systems Using Extended Petri Nets (EPN), Water Resour. Res.,
55, 8895–8921, https://doi.org/10.1029/2019WR025099, 2019.
Bertuzzo, E., Thomet, M., Botter, G., and Rinaldo, A.: Catchment-scale
herbicides transport: Theory and application, Adv. Water Resour.,
52, 232–242, https://doi.org/10.1016/j.advwatres.2012.11.007,
2013.
Beven, K.: Changing ideas in hydrology – The case of physically-based
models, J. Hydrol., 105, 157–172, https://doi.org/10.1016/0022-1694(89)90101-7, 1989.
Beven, K. J.: Uniqueness of place and process representations in
hydrological modelling, Hydrol. Earth Syst. Sci., 4, 203–213,
https://doi.org/10.5194/hess-4-203-2000, 2000.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing
area model of basin hydrology/Un modèle à base physique de zone
d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci.
Bull., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Boyle, D. P.: Multicriteria calibration of hydrologic models, The University
of Arizona, 2001.
Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., and Smith,
M.: Toward improved streamflow forecasts: value of semidistributed modeling,
Water Resour. Res., 37, 2749–2759, https://doi.org/10.1029/2000wr000207, 2001.
Butcher, J. C. and Goodwin, N.: Numerical methods for ordinary differential
equations, Wiley Online Library, 2008.
Clark, M. P. and Kavetski, D.: Ancient numerical daemons of conceptual
hydrological modeling: 1. Fidelity and efficiency of time stepping schemes,
Water Resour. Res., 46, 10, https://doi.org/10.1029/2009WR008894, 2010.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta,
H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural
Errors (FUSE): A modular framework to diagnose differences between
hydrological models, Water Resour. Res., 44, W00b02
https://doi.org/10.1029/2007wr006735, 2008.
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, 2011a.
Clark, M. P., McMillan, H. K., Collins, D. B. G., Kavetski, D., and Woods,
R. A.: Hydrological field data from a modeller's perspective: Part 2:
process-based evaluation of model hypotheses, Hydrol. Process., 25,
523–543, https://doi.org/10.1002/hyp.7902, 2011b.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E.,
Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D.,
Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for
process-based hydrologic modeling: 1. Modeling concept, Water Resour.
Res., 51, 2498–2514, https://doi.org/10.1002/2015wr017198, 2015.
Craig, J. R., Brown, G., Chlumsky, R., Jenkinson, R. W., Jost, G., Lee, K.,
Mai, J., Serrer, M., Sgro, N., Shafii, M., Snowdon, A. P., and Tolson, B.
A.: Flexible watershed simulation with the Raven hydrological modelling
framework, Environ. Modell. Softw., 129, 104728, https://doi.org/10.1016/j.envsoft.2020.104728, 2020.
Dal Molin, M., Schirmer, M., Zappa, M., and Fenicia, F.: Understanding
dominant controls on streamflow spatial variability to set up a
semi-distributed hydrological model: the case study of the Thur catchment,
Hydrol. Earth Syst. Sci., 24, 1319–1345, https://doi.org/10.5194/hess-24-1319-2020, 2020.
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy: The flexible language of hydrological modelling, SuperflexPy [code], available at: https://pypi.org/project/superflexpy and https://github.com/dalmo1991/superflexPy, last access: 18 October 2021a.
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy 1.3.0, Zenodo [code], https://doi.org/10.5281/zenodo.5235158, 2021b.
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy, SuperflexPy, available at: https://superflexpy.readthedocs.io, last access: 18 October 2021c.
David, P. C., Oliveira, D. Y., Grison, F., Kobiyama, M., and Chaffe, P. L.
B.: Systematic increase in model complexity helps to identify dominant
streamflow mechanisms in two small forested basins, Hydrol. Sci.
J., 64, 455–472, https://doi.org/10.1080/02626667.2019.1585858, 2019.
Dowell, M. and Jarratt, P.: The “Pegasus” method for computing the root
of an equation, BIT, 12, 503–508, https://doi.org/10.1007/BF01932959,
1972.
Eckhardt, K. and Ulbrich, U.: Potential impacts of climate change on
groundwater recharge and streamflow in a central European low mountain
range, J. Hydrol., 284, 244–252, https://doi.org/10.1016/j.jhydrol.2003.08.005, 2003.
Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Is the
groundwater reservoir linear? Learning from data in hydrological modelling,
Hydrol. Earth Syst. Sci., 10, 139–150, https://doi.org/10.5194/hess-10-139-2006, 2006.
Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Understanding
catchment behavior through stepwise model concept improvement, Water
Resour. Res., 44, 1, https://doi.org/10.1029/2006WR005563,
2008.
Fenicia, F., Wrede, S., Kavetski, D., Pfister, L., Hoffmann, L., Savenije,
H. H. G., and McDonnell, J. J.: Assessing the impact of mixing assumptions
on the estimation of streamwater mean residence time, Hydrol.
Process., 24, 1730–1741, https://doi.org/10.1002/hyp.7595, 2010.
Fenicia, F., Kavetski, D., and Savenije, H. H. G.: Elements of a flexible
approach for conceptual hydrological modeling: 1. Motivation and theoretical
development, Water Resour. Res., 47, W11510, https://doi.org/10.1029/2010wr010174,
2011.
Fenicia, F., Kavetski, D., Savenije, H. H. G., Clark, M. P., Schoups, G.,
Pfister, L., and Freer, J.: Catchment properties, function, and conceptual
model representation: is there a correspondence?, Hydrol. Process.,
28, 2451–2467, https://doi.org/10.1002/hyp.9726, 2014.
Fenicia, F., Kavetski, D., Savenije, H. H. G., and Pfister, L.: From
spatially variable streamflow to distributed hydrological models: Analysis
of key modeling decisions, Water Resour. Res., 52, 954–989,
https://doi.org/10.1002/2015wr017398, 2016.
Feyen, L., Kalas, M., and Vrugt, J. A.: Semi-distributed parameter
optimization and uncertainty assessment for large-scale streamflow
simulation using global optimization/Optimisation de paramètres
semi-distribués et évaluation de l'incertitude pour la simulation de
débits à grande échelle par l'utilisation d'une optimisation
globale, Hydrol. Sci. J., 53, 293–308, 2008.
Formetta, G., Antonello, A., Franceschi, S., David, O., and Rigon, R.:
Hydrological modelling with components: A GIS-based open-source framework,
Environ. Modell. Softw., 55, 190–200, https://doi.org/10.1016/j.envsoft.2014.01.019, 2014.
Futter, M. N., Erlandsson, M. A., Butterfield, D., Whitehead, P. G., Oni, S.
K., and Wade, A. J.: PERSiST: a flexible rainfall-runoff modelling toolkit
for use with the INCA family of models, Hydrol. Earth Syst. Sci., 18,
855–873, https://doi.org/10.5194/hess-18-855-2014, 2014.
Gao, H., Hrachowitz, M., Fenicia, F., Gharari, S., and Savenije, H. H. G.:
Testing the realism of a topography-driven model (FLEX-Topo) in the nested
catchments of the Upper Heihe, China, Hydrol. Earth Syst. Sci., 18,
1895–1915, https://doi.org/10.5194/hess-18-1895-2014, 2014.
Henn, B., Clark, M. P., Kavetski, D., Newman, A. J., Hughes, M., McGurk, B.,
and Lundquist, J. D.: Spatiotemporal patterns of precipitation inferred from
streamflow observations across the Sierra Nevada mountain range, J.
Hydrol., 556, 993–1012, https://doi.org/10.1016/j.jhydrol.2016.08.009, 2018.
Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L.: SPOTting Model
Parameters Using a Ready-Made Python Package, PLoS One, 10,
e0145180, https://doi.org/10.1371/journal.pone.0145180, 2015.
Hrachowitz, M., Fovet, O., Ruiz, L., Euser, T., Gharari, S., Nijzink, R.,
Freer, J., Savenije, H. H. G., and Gascuel-Odoux, C.: Process consistency in
models: The importance of system signatures, expert knowledge, and process
complexity, Water Resour. Res., 50, 7445–7469, https://doi.org/10.1002/2014wr015484,
2014.
Ibbitt, R. P. and O'Donnell, T.: Designing conceptual catchment models for
automatic fitting methods, IAHS Publication, 101, 462–475, 1971.
Jakeman, A. J. and Hornberger, G. M.: How Much Complexity Is Warranted in a
Rainfall-Runoff Model, Water Resour. Res., 29, 2637–2649, https://doi.org/10.1029/93wr00877, 1993.
Jansen, K. F., Teuling, A. J., Craig, J. R., Dal Molin, M., Knoben, W. J.
M., Parajka, J., Vis, M., and Melsen, L. A.: Mimicry of a conceptual
hydrological model (HBV): What's in a name?, Water Resour. Res., 57,
e2020WR029143, https://doi.org/10.1029/2020WR029143, 2021.
Kavetski, D. and Clark, M. P.: Ancient numerical daemons of conceptual
hydrological modeling: 2. Impact of time stepping schemes on model analysis
and prediction, Water Resour. Res., 46, 10, https://doi.org/10.1029/2009wr008896, 2010.
Kavetski, D. and Fenicia, F.: Elements of a flexible approach for
conceptual hydrological modeling: 2. Application and experimental insights,
Water Resour. Res., 47, W11511, https://doi.org/10.1029/2011wr010748, 2011.
Kavetski, D. and Kuczera, G.: Model smoothing strategies to remove
microscale discontinuities and spurious secondary optima in objective
functions in hydrological calibration, Water Resour. Res., 43,
W03411, https://doi.org/10.1029/2006wr005195, 2007.
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment
characterization, rainfall-runoff modeling, and doing hydrology backward,
Water Resour. Res., 45, W02429, https://doi.org/10.1029/2008wr006912, 2009.
Kneis, D.: A lightweight framework for rapid development of object-based
hydrological model engines, Environ. Modell. Softw., 68, 110–121, https://doi.org/10.1016/j.envsoft.2015.02.009, 2015.
Knoben, W. J. M., Freer, J. E., Fowler, K. J. A., Peel, M. C., and Woods, R.
A.: Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) v1.2: an
open-source, extendable framework providing implementations of 46 conceptual
hydrologic models as continuous state-space formulations, Geosci. Model. Dev.,
12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, 2019.
Kraft, P., Vaché, K. B., Frede, H.-G., and Breuer, L.: CMF: A
Hydrological Programming Language Extension For Integrated Catchment Models,
Environ. Modell. Softw., 26, 828–830, https://doi.org/10.1016/j.envsoft.2010.12.009, 2011.
Kuczera, G., Kavetski, D., Franks, S., and Thyer, M.: Towards a Bayesian
total error analysis of conceptual rainfall-runoff models: Characterising
model error using storm-dependent parameters, J. Hydrol., 331,
161–177, https://doi.org/10.1016/j.jhydrol.2006.05.010, 2006.
Lam, S. K., Pitrou, A., and Seibert, S.: Numba: a LLVM-based Python JIT
compiler, Proceedings of the Second Workshop on the LLVM Compiler
Infrastructure in HPC, Association for Computing Machinery, Austin, Texas,
7 pp., 2015.
Leavesley, G. H.: Precipitation-runoff modeling system: User's manual, 4238,
US Department of the Interior, U.S. Geological Survey, Water Resources Division, 1984.
Lerat, J., Andreassian, V., Perrin, C., Vaze, J., Perraud, J.-M., Ribstein,
P., and Loumagne, C.: Do internal flow measurements improve the calibration
of rainfall-runoff models?, Water Resour. Res., 48, https://doi.org/10.1029/2010WR010179, 2012.
Lindstrom, G., Johansson, B., Persson, M., Gardelin, M., and Bergstrom, S.:
Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
Marsh, C. B., Pomeroy, J. W., and Wheater, H. S.: The Canadian Hydrological
Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity
hydrological model – design and overview, Geosci. Model Dev., 13, 225–247,
https://doi.org/10.5194/gmd-13-225-2020, 2020.
Matgen, P., Fenicia, F., Heitz, S., Plaza, D., de Keyser, R., Pauwels, V. R.
N., Wagner, W., and Savenije, H.: Can ASCAT-derived soil wetness indices
reduce predictive uncertainty in well-gauged areas? A comparison with in
situ observed soil moisture in an assimilation application, Adv.
Water Resour., 44, 49–65, https://doi.org/10.1016/j.advwatres.2012.03.022, 2012.
Maxwell, R. M.: A terrain-following grid transform and preconditioner for
parallel, large-scale, integrated hydrologic modeling, Adv. Water
Resour., 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013.
McInerney, D., Thyer, M., Kavetski, D., Githui, F., Thayalakumaran, T., Liu,
M., and Kuczera, G.: The Importance of Spatiotemporal Variability in
Irrigation Inputs for Hydrological Modeling of Irrigated Catchments, Water
Resour. Res., 54, 6792–6821, https://doi.org/10.1029/2017wr022049, 2018.
Meyer, B.: Object-oriented software construction, Prentice Hall, New York,
1988.
Moore, R. J. and Clarke, R. T.: A distribution function approach to
rainfall runoff modeling, Water Resour. Res., 17, 1367–1382,
https://doi.org/10.1029/WR017i005p01367, 1981.
Moradkhani, H. and Sorooshian, S.: General review of rainfall-runoff
modeling: model calibration, data assimilation, and uncertainty analysis,
in: Hydrological modelling and the water cycle, Springer, 1–24, 2009.
Moser, A., Wemyss, D., Scheidegger, R., Fenicia, F., Honti, M., and Stamm,
C.: Modelling biocide and herbicide concentrations in catchments of the
Rhine basin, Hydrol. Earth Syst. Sci., 22, 4229–4249,
https://doi.org/10.5194/hess-22-4229-2018, 2018.
Nash, J.: The form of the instantaneous unit hydrograph, Int.
Assoc. Sci. Hydrol., 3, 114–121, 1957.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M.,
Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of
topography-controlled sub-grid process heterogeneity and semi-quantitative
prior constraints in distributed hydrological models, Hydrol. Earth Syst.
Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.,
Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L.: PyTorch: An imperative
style, high-performance deep learning library, Adv. Neur. In., 8024–8035, 2019.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn:
Machine learning in Python, J. Mach. Learn. Res., 12,
2825–2830, 2011.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a
parsimonious model for streamflow simulation, J. Hydrol., 279,
275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Press, W. H., Teukolsky, S. A., Flannery, B. P., and Vetterling, W. T.:
Numerical recipes in Fortran 77, Vol. 1, Volume 1 of Fortran numerical
recipes: the art of scientific computing, Cambridge University Press, 1992.
Refsgaard, J.: Terminology, Modelling Protocol And Classification of
Hydrological Model Codes, in: Distributed Hydrological Modelling, 22, p. 17, 1996.
Refsgaard, J. C. and Storm, B.: MIKE SHE, in: Computer Models of Watershed
Hydrology, edited by: Singh, V. P., Water Resources Publications, Colorado,
809–846, 1995.
Reichert, P. and Mieleitner, J.: Analyzing input and structural uncertainty
of nonlinear dynamic models with stochastic, time-dependent parameters,
Water Resour. Res., 45, 10, https://doi.org/10.1029/2009wr007814, 2009.
Renard, B., Kavetski, D., Leblois, E., Thyer, M., Kuczera, G., and Franks,
S. W.: Toward a reliable decomposition of predictive uncertainty in
hydrological modeling: Characterizing rainfall errors using conditional
simulation, Water Resou. Res., 47, 11, https://doi.org/10.1029/2011WR010643, 2011.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter
regionalization of a grid-based hydrologic model at the mesoscale, Water
Resour. Res., 46, 5, https://doi.org/10.1029/2008wr007327, 2010.
Seibert, J. and McDonnell, J. J.: On the dialog between experimentalist and
modeler in catchment hydrology: Use of soft data for multicriteria model
calibration, Water Resour. Res., 38, 23-21–23-14,
https://doi.org/10.1029/2001wr000978, 2002.
Seibert, J., Rodhe, A., and Bishop, K.: Simulating interactions between
saturated and unsaturated storage in a conceptual runoff model, Hydrol.
Process., 17, 379–390, 2003.
Sivapalan, M., Beven, K., and Wood, E. F.: On hydrologic similarity: 2. A
scaled model of storm runoff production, Water Resour. Res., 23,
2266–2278, https://doi.org/10.1029/WR023i012p02266, 1987.
Sivapalan, M., Blöschl, G., Zhang, L., and Vertessy, R.: Downward
approach to hydrological prediction, Hydrol. Process., 17, 2101–2111,
https://doi.org/10.1002/hyp.1425, 2003.
van Esse, W. R., Perrin, C., Booij, M. J., Augustijn, D. C. M., Fenicia, F., Kavetski, D., and Lobligeois, F.: The influence of conceptual model structure on model performance: a comparative study for 237 French catchments, Hydrol. Earth Syst. Sci., 17, 4227–4239, https://doi.org/10.5194/hess-17-4227-2013, 2013.
Vitolo, C., Wells, P., Dobias, M., and Buytaert, W.: fuse: An R package for
ensemble Hydrological Modelling, Journal of Open Source Software, 1, 52,
https://doi.org/10.21105/joss.00052, 2016.
Wagener, T., Sivapalan, M., Troch, P., and Woods, R.: Catchment
Classification and Hydrologic Similarity, Geography Compass, 1, 901–931,
https://doi.org/10.1111/j.1749-8198.2007.00039.x, 2007.
Walt, S. V. D., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A
Structure for Efficient Numerical Computation, Comput. Sci.
Eng., 13, 22–30, https://doi.org/10.1109/mcse.2011.37, 2011.
Westra, S., Thyer, M., Leonard, M., Kavetski, D., and Lambert, M.: A
strategy for diagnosing and interpreting hydrological model nonstationarity,
Water Resour. Res., 50, 5090–5113, https://doi.org/10.1002/2013wr014719, 2014.
Wrede, S., Fenicia, F., Martínez-Carreras, N., Juilleret, J., Hissler,
C., Krein, A., Savenije, H. H. G., Uhlenbrook, S., Kavetski, D., and
Pfister, L.: Towards more systematic perceptual model development: a case
study using 3 Luxembourgish catchments, Hydrol. Process., 29,
2731–2750, https://doi.org/10.1002/hyp.10393, 2015.
Young, P.: Data-based mechanistic modelling of environmental, ecological,
economic and engineering systems, Environ. Modell. Softw., 13, 105–122,
https://doi.org/10.1016/S1364-8152(98)00011-5, 1998.
Young, P. C.: Stochastic, dynamic modelling and signal processing: time
variable and state dependent parameter estimation, Nonlinear and
nonstationary signal processing, in: Nonstationary and Nonlinear Time Series Analysis, 74–114, 2000.
Young, P. C., Tych, W., and Taylor, C. J.: The Captain Toolbox for Matlab,
IFAC Proceedings Volumes, 42, 758–763, https://doi.org/10.3182/20090706-3-FR-2004.00126, 2009.
Short summary
This paper introduces SuperflexPy, an open-source Python framework for building flexible conceptual hydrological models. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work.
This paper introduces SuperflexPy, an open-source Python framework for building flexible...
