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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Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-47, https://doi.org/10.5194/hess-2024-47, 2024
Preprint under review for HESS
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature needed for challenging cases, associated with aridity and intermittent flow. Baseflow index, aridity, and soil/vegetation attributes strongly correlate with learned features, indicating their importance for streamflow prediction.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
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CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Richard Laugesen, Mark Thyer, David McInerney, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 27, 873–893, https://doi.org/10.5194/hess-27-873-2023, https://doi.org/10.5194/hess-27-873-2023, 2023
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Forecasts may be valuable for user decisions, but current practice to quantify it has critical limitations. This study introduces RUV (relative utility value, a new metric that can be tailored to specific decisions and decision-makers. It illustrates how critical this decision context is when evaluating forecast value. This study paves the way for agencies to tailor the evaluation of their services to customer decisions and researchers to study model improvements through the lens of user impact.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
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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Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 26, 5085–5102, https://doi.org/10.5194/hess-26-5085-2022, https://doi.org/10.5194/hess-26-5085-2022, 2022
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Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.
Marco Dal Molin, Mario Schirmer, Massimiliano Zappa, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 24, 1319–1345, https://doi.org/10.5194/hess-24-1319-2020, https://doi.org/10.5194/hess-24-1319-2020, 2020
Lorenz Ammann, Fabrizio Fenicia, and Peter Reichert
Hydrol. Earth Syst. Sci., 23, 2147–2172, https://doi.org/10.5194/hess-23-2147-2019, https://doi.org/10.5194/hess-23-2147-2019, 2019
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The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
Andreas Moser, Devon Wemyss, Ruth Scheidegger, Fabrizio Fenicia, Mark Honti, and Christian Stamm
Hydrol. Earth Syst. Sci., 22, 4229–4249, https://doi.org/10.5194/hess-22-4229-2018, https://doi.org/10.5194/hess-22-4229-2018, 2018
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Many chemicals such as pesticides, pharmaceuticals or household chemicals impair water quality in many areas worldwide. Measuring pollution everywhere is too costly. Models can be used instead to predict where high pollution levels are expected. We tested a model that can be used across large river basins. We find that for the selected chemicals predictions are generally within a factor of 2 to 4 from observed concentrations. Often, knowledge about the chemical use limits the predictions.
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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In this study, the rainfall–runoff models of eight international research groups were compared for a set of subcatchments of the Meuse basin to investigate the influence of certain model components on the modelled discharge. Although the models showed similar performances based on general metrics, clear differences could be observed for specific events. The differences during drier conditions could indeed be linked to differences in model structures.
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EGUsphere, https://doi.org/10.5194/egusphere-2023-2562, https://doi.org/10.5194/egusphere-2023-2562, 2024
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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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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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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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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.
Manuel Felipe Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-98, https://doi.org/10.5194/gmd-2023-98, 2023
Revised manuscript accepted for GMD
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STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source, and user-friendly modeling 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.
Robin Schwemmle, Hannes Leistert, Steinbrich Andreas, and Markus Weiler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-118, https://doi.org/10.5194/gmd-2023-118, 2023
Revised manuscript accepted for GMD
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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 an effective water resources management. This paper presents the toolbox concept and provides a simple example on providing estimations to water resources management.
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.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
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...
