Articles | Volume 12, issue 6
https://doi.org/10.5194/gmd-12-2463-2019
© Author(s) 2019. 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-12-2463-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Wouter J. M. Knoben
CORRESPONDING AUTHOR
Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK
Jim E. Freer
School of Geographical Science, University of Bristol, Bristol, BS8
1BF, UK
Keirnan J. A. Fowler
Department of Infrastructure Engineering, University of Melbourne,
Melbourne, Parkville VIC 3052, Australia
Murray C. Peel
Department of Infrastructure Engineering, University of Melbourne,
Melbourne, Parkville VIC 3052, Australia
Ross A. Woods
Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK
Related authors
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Wouter J. M. Knoben and Diana Spieler
Hydrol. Earth Syst. Sci., 26, 3299–3314, https://doi.org/10.5194/hess-26-3299-2022, https://doi.org/10.5194/hess-26-3299-2022, 2022
Short summary
Short summary
This paper introduces educational materials that can be used to teach students about model structure uncertainty in hydrological modelling. There are many different hydrological models and differences between these models impact their usefulness in different places. Such models are often used to support decision making about water resources and to perform hydrological science, and it is thus important for students to understand that model choice matters.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Shervan Gharari, Martyn P. Clark, Naoki Mizukami, Wouter J. M. Knoben, Jefferson S. Wong, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 24, 5953–5971, https://doi.org/10.5194/hess-24-5953-2020, https://doi.org/10.5194/hess-24-5953-2020, 2020
Short summary
Short summary
This work explores the trade-off between the accuracy of the representation of geospatial data, such as land cover, soil type, and elevation zones, in a land (surface) model and its performance in the context of modeling. We used a vector-based setup instead of the commonly used grid-based setup to identify this trade-off. We also assessed the often neglected parameter uncertainty and its impact on the land model simulations.
Wouter J. M. Knoben, Jim E. Freer, and Ross A. Woods
Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, https://doi.org/10.5194/hess-23-4323-2019, 2019
Short summary
Short summary
The accuracy of model simulations can be quantified with so-called efficiency metrics. The Nash–Sutcliffe efficiency (NSE) has been often used in hydrology, but recently the Kling–Gupta efficiency (KGE) is gaining in popularity. We show that lessons learned about which NSE scores are
acceptabledo not necessarily translate well into understanding of the KGE metric.
Gemma Coxon, Jim Freer, Rosanna Lane, Toby Dunne, Wouter J. M. Knoben, Nicholas J. K. Howden, Niall Quinn, Thorsten Wagener, and Ross Woods
Geosci. Model Dev., 12, 2285–2306, https://doi.org/10.5194/gmd-12-2285-2019, https://doi.org/10.5194/gmd-12-2285-2019, 2019
Short summary
Short summary
DECIPHeR (Dynamic fluxEs and ConnectIvity for Predictions of Hydrology) is a new modelling framework that can be applied from small catchment to continental scales for complex river basins. This paper describes the modelling framework and its key components and demonstrates the model’s ability to be applied across a large model domain. This work highlights the potential for catchment- to continental-scale predictions of streamflow to support robust environmental management and policy decisions.
Shervan Gharari, Paul H. Whitfield, Alain Pietroniro, Jim Freer, Hongli Liu, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-150, https://doi.org/10.5194/hess-2023-150, 2023
Preprint under review for HESS
Short summary
Short summary
This study provides insight into the practices that are incorporated into discharge estimation across the national Canadian hydrometric network operated by the Water Survey of Canada, WSC. The procedures used to estimate and correct discharge values are not always understood by end-users. Factors such as ice cover, and sedimentation limit the ability of accurate discharge estimation. Highlighting these challenges sheds light on difficulties in discharge estimation and associated uncertainty.
Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Susana Almeida, Liz Holcombe, Jim Freer, Nick Chappell, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 27, 2523–2534, https://doi.org/10.5194/hess-27-2523-2023, https://doi.org/10.5194/hess-27-2523-2023, 2023
Short summary
Short summary
This publication provides an introduction to the CREDIBLE Uncertainty Estimation (CURE) toolbox. CURE offers workflows for a variety of uncertainty estimation methods. One of its most important features is the requirement that all of the assumptions on which a workflow analysis depends be defined. This facilitates communication with potential users of an analysis. An audit trail log is produced automatically from a workflow for future reference.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
Short summary
Short summary
We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Louisa D. Oldham, Jim Freer, Gemma Coxon, Nicholas Howden, John P. Bloomfield, and Christopher Jackson
Hydrol. Earth Syst. Sci., 27, 761–781, https://doi.org/10.5194/hess-27-761-2023, https://doi.org/10.5194/hess-27-761-2023, 2023
Short summary
Short summary
Water can move between river catchments via the subsurface, termed intercatchment groundwater flow (IGF). We show how a perceptual model of IGF can be developed with relatively simple geological interpretation and data requirements. We find that IGF dynamics vary in space, correlated to the dominant underlying geology. We recommend that IGF
loss functionsmay be used in conceptual rainfall–runoff models but should be supported by perceptualisation of IGF processes and connectivities.
Keirnan Fowler, Murray Peel, Margarita Saft, Tim J. Peterson, Andrew Western, Lawrence Band, Cuan Petheram, Sandra Dharmadi, Kim Seong Tan, Lu Zhang, Patrick Lane, Anthony Kiem, Lucy Marshall, Anne Griebel, Belinda E. Medlyn, Dongryeol Ryu, Giancarlo Bonotto, Conrad Wasko, Anna Ukkola, Clare Stephens, Andrew Frost, Hansini Gardiya Weligamage, Patricia Saco, Hongxing Zheng, Francis Chiew, Edoardo Daly, Glen Walker, R. Willem Vervoort, Justin Hughes, Luca Trotter, Brad Neal, Ian Cartwright, and Rory Nathan
Hydrol. Earth Syst. Sci., 26, 6073–6120, https://doi.org/10.5194/hess-26-6073-2022, https://doi.org/10.5194/hess-26-6073-2022, 2022
Short summary
Short summary
Recently, we have seen multi-year droughts tending to cause shifts in the relationship between rainfall and streamflow. In shifted catchments that have not recovered, an average rainfall year produces less streamflow today than it did pre-drought. We take a multi-disciplinary approach to understand why these shifts occur, focusing on Australia's over-10-year Millennium Drought. We evaluate multiple hypotheses against evidence, with particular focus on the key role of groundwater processes.
Xu Zhang, Jinbao Li, Qianjin Dong, and Ross A. Woods
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-309, https://doi.org/10.5194/hess-2022-309, 2022
Manuscript not accepted for further review
Short summary
Short summary
Accurately estimating long-term evaporation is important for describing water balance. Budyko framework already incorporates precipitation and potential evaporation, while water storage capacity and climate seasonality are usually ignored. Here, we analytically generalize Budyko framework through the Ponce-Shetty model, and physically account these two factors. Our generalized equations perform better than varying Budyko-type equations, and improve the robustness and physical interpretation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Wouter J. M. Knoben and Diana Spieler
Hydrol. Earth Syst. Sci., 26, 3299–3314, https://doi.org/10.5194/hess-26-3299-2022, https://doi.org/10.5194/hess-26-3299-2022, 2022
Short summary
Short summary
This paper introduces educational materials that can be used to teach students about model structure uncertainty in hydrological modelling. There are many different hydrological models and differences between these models impact their usefulness in different places. Such models are often used to support decision making about water resources and to perform hydrological science, and it is thus important for students to understand that model choice matters.
Keirnan J. A. Fowler, Suwash Chandra Acharya, Nans Addor, Chihchung Chou, and Murray C. Peel
Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, https://doi.org/10.5194/essd-13-3847-2021, 2021
Short summary
Short summary
This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments with long-term monitoring, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://doi.pangaea.de/10.1594/PANGAEA.921850.
Thorsten Wagener, Dragan Savic, David Butler, Reza Ahmadian, Tom Arnot, Jonathan Dawes, Slobodan Djordjevic, Roger Falconer, Raziyeh Farmani, Debbie Ford, Jan Hofman, Zoran Kapelan, Shunqi Pan, and Ross Woods
Hydrol. Earth Syst. Sci., 25, 2721–2738, https://doi.org/10.5194/hess-25-2721-2021, https://doi.org/10.5194/hess-25-2721-2021, 2021
Short summary
Short summary
How can we effectively train PhD candidates both (i) across different knowledge domains in water science and engineering and (ii) in computer science? To address this issue, the Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering.
Keith J. Beven, Mike J. Kirkby, Jim E. Freer, and Rob Lamb
Hydrol. Earth Syst. Sci., 25, 527–549, https://doi.org/10.5194/hess-25-527-2021, https://doi.org/10.5194/hess-25-527-2021, 2021
Short summary
Short summary
The theory that forms the basis of TOPMODEL was first outlined by Mike Kirkby some 45 years ago. This paper recalls some of the early developments: the rejection of the first journal paper, the early days of digital terrain analysis, model calibration and validation, the various criticisms of the simplifying assumptions, and the relaxation of those assumptions in the dynamic forms of TOPMODEL, and it considers what we might do now with the benefit of hindsight.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Shervan Gharari, Martyn P. Clark, Naoki Mizukami, Wouter J. M. Knoben, Jefferson S. Wong, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 24, 5953–5971, https://doi.org/10.5194/hess-24-5953-2020, https://doi.org/10.5194/hess-24-5953-2020, 2020
Short summary
Short summary
This work explores the trade-off between the accuracy of the representation of geospatial data, such as land cover, soil type, and elevation zones, in a land (surface) model and its performance in the context of modeling. We used a vector-based setup instead of the commonly used grid-based setup to identify this trade-off. We also assessed the often neglected parameter uncertainty and its impact on the land model simulations.
Gemma Coxon, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, Rosanna Lane, Melinda Lewis, Emma L. Robinson, Thorsten Wagener, and Ross Woods
Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, https://doi.org/10.5194/essd-12-2459-2020, 2020
Short summary
Short summary
We present the first large-sample catchment hydrology dataset for Great Britain. The dataset collates river flows, catchment attributes, and catchment boundaries for 671 catchments across Great Britain. We characterise the topography, climate, streamflow, land cover, soils, hydrogeology, human influence, and discharge uncertainty of each catchment. The dataset is publicly available for the community to use in a wide range of environmental and modelling analyses.
Sebastian J. Gnann, Nicholas J. K. Howden, and Ross A. Woods
Hydrol. Earth Syst. Sci., 24, 561–580, https://doi.org/10.5194/hess-24-561-2020, https://doi.org/10.5194/hess-24-561-2020, 2020
Short summary
Short summary
In many places, seasonal variability in precipitation and evapotranspiration (climate) leads to seasonal variability in river flow (streamflow). In this work, we explore how climate seasonality is transformed into streamflow seasonality and what controls this transformation (e.g. climate aridity and geology). The results might be used in grouping catchments, predicting the seasonal streamflow regime in ungauged catchments, and building hydrological simulation models.
Wouter J. M. Knoben, Jim E. Freer, and Ross A. Woods
Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, https://doi.org/10.5194/hess-23-4323-2019, 2019
Short summary
Short summary
The accuracy of model simulations can be quantified with so-called efficiency metrics. The Nash–Sutcliffe efficiency (NSE) has been often used in hydrology, but recently the Kling–Gupta efficiency (KGE) is gaining in popularity. We show that lessons learned about which NSE scores are
acceptabledo not necessarily translate well into understanding of the KGE metric.
Rosanna A. Lane, Gemma Coxon, Jim E. Freer, Thorsten Wagener, Penny J. Johnes, John P. Bloomfield, Sheila Greene, Christopher J. A. Macleod, and Sim M. Reaney
Hydrol. Earth Syst. Sci., 23, 4011–4032, https://doi.org/10.5194/hess-23-4011-2019, https://doi.org/10.5194/hess-23-4011-2019, 2019
Short summary
Short summary
We evaluated four hydrological model structures and their parameters on over 1100 catchments across Great Britain, considering modelling uncertainties. Models performed well for most catchments but failed in parts of Scotland and south-eastern England. Failures were often linked to inconsistencies in the water balance. This research shows what conceptual lumped models can achieve, gives insights into where and why these models may fail, and provides a benchmark of national modelling capability.
Gemma Coxon, Jim Freer, Rosanna Lane, Toby Dunne, Wouter J. M. Knoben, Nicholas J. K. Howden, Niall Quinn, Thorsten Wagener, and Ross Woods
Geosci. Model Dev., 12, 2285–2306, https://doi.org/10.5194/gmd-12-2285-2019, https://doi.org/10.5194/gmd-12-2285-2019, 2019
Short summary
Short summary
DECIPHeR (Dynamic fluxEs and ConnectIvity for Predictions of Hydrology) is a new modelling framework that can be applied from small catchment to continental scales for complex river basins. This paper describes the modelling framework and its key components and demonstrates the model’s ability to be applied across a large model domain. This work highlights the potential for catchment- to continental-scale predictions of streamflow to support robust environmental management and policy decisions.
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Proc. IAHS, 380, 3–8, https://doi.org/10.5194/piahs-380-3-2018, https://doi.org/10.5194/piahs-380-3-2018, 2018
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
Keith J. Beven, Susana Almeida, Willy P. Aspinall, Paul D. Bates, Sarka Blazkova, Edoardo Borgomeo, Jim Freer, Katsuichiro Goda, Jim W. Hall, Jeremy C. Phillips, Michael Simpson, Paul J. Smith, David B. Stephenson, Thorsten Wagener, Matt Watson, and Kate L. Wilkins
Nat. Hazards Earth Syst. Sci., 18, 2741–2768, https://doi.org/10.5194/nhess-18-2741-2018, https://doi.org/10.5194/nhess-18-2741-2018, 2018
Short summary
Short summary
This paper discusses how uncertainties resulting from lack of knowledge are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. As every analysis is necessarily conditional on the assumptions made about the nature of sources of such uncertainties it is also important to follow the guidelines for good practice suggested in Part 2.
Andreas Paul Zischg, Guido Felder, Rolf Weingartner, Niall Quinn, Gemma Coxon, Jeffrey Neal, Jim Freer, and Paul Bates
Hydrol. Earth Syst. Sci., 22, 2759–2773, https://doi.org/10.5194/hess-22-2759-2018, https://doi.org/10.5194/hess-22-2759-2018, 2018
Short summary
Short summary
We developed a model experiment and distributed different rainfall patterns over a mountain river basin. For each rainfall scenario, we computed the flood losses with a model chain. The experiment shows that flood losses vary considerably within the river basin and depend on the timing of the flood peaks from the basin's sub-catchments. Basin-specific characteristics such as the location of the main settlements within the floodplains play an additional important role in determining flood losses.
Simon Brenner, Gemma Coxon, Nicholas J. K. Howden, Jim Freer, and Andreas Hartmann
Nat. Hazards Earth Syst. Sci., 18, 445–461, https://doi.org/10.5194/nhess-18-445-2018, https://doi.org/10.5194/nhess-18-445-2018, 2018
Short summary
Short summary
In this study we simulate groundwater levels with a semi-distributed karst model. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. We show that our approach is able to predict groundwater levels across all considered timescales up to the 75th percentile. We then use our approach to assess future changes in groundwater dynamics and show that projected climate changes may lead to generally lower groundwater levels.
Benoit P. Guillod, Richard G. Jones, Simon J. Dadson, Gemma Coxon, Gianbattista Bussi, James Freer, Alison L. Kay, Neil R. Massey, Sarah N. Sparrow, David C. H. Wallom, Myles R. Allen, and Jim W. Hall
Hydrol. Earth Syst. Sci., 22, 611–634, https://doi.org/10.5194/hess-22-611-2018, https://doi.org/10.5194/hess-22-611-2018, 2018
Short summary
Short summary
Assessing the potential impacts of extreme events such as drought and flood requires large datasets of such events, especially when looking at the most severe and rare events. Using a state-of-the-art climate modelling infrastructure that is simulating large numbers of weather time series on volunteers' computers, we generate such a large dataset for the United Kingdom. The dataset covers the recent past (1900–2006) as well as two future time periods (2030s and 2080s).
Mary C. Ockenden, Wlodek Tych, Keith J. Beven, Adrian L. Collins, Robert Evans, Peter D. Falloon, Kirsty J. Forber, Kevin M. Hiscock, Michael J. Hollaway, Ron Kahana, Christopher J. A. Macleod, Martha L. Villamizar, Catherine Wearing, Paul J. A. Withers, Jian G. Zhou, Clare McW. H. Benskin, Sean Burke, Richard J. Cooper, Jim E. Freer, and Philip M. Haygarth
Hydrol. Earth Syst. Sci., 21, 6425–6444, https://doi.org/10.5194/hess-21-6425-2017, https://doi.org/10.5194/hess-21-6425-2017, 2017
Short summary
Short summary
This paper describes simple models of phosphorus load which are identified for three catchments in the UK. The models use new hourly observations of phosphorus load, which capture the dynamics of phosphorus transfer in small catchments that are often missed by models with a longer time step. Unlike more complex, process-based models, very few parameters are required, leading to low parameter uncertainty. Interpretation of the dominant phosphorus transfer modes is made based solely on the data.
Katrien Van Eerdenbrugh, Stijn Van Hoey, Gemma Coxon, Jim Freer, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 21, 5315–5337, https://doi.org/10.5194/hess-21-5315-2017, https://doi.org/10.5194/hess-21-5315-2017, 2017
Short summary
Short summary
Consistency in stage–discharge data is investigated using a methodology called Bidirectional Reach (BReach). Various measurement stations in the UK, New Zealand and Belgium are selected based on their historical ratings information and their characteristics related to data consistency. When applying a BReach analysis on them, the methodology provides results that appear consistent with the available knowledge and thus facilitates a reliable assessment of (in)consistency in stage–discharge data.
Christa D. Peters-Lidard, Martyn Clark, Luis Samaniego, Niko E. C. Verhoest, Tim van Emmerik, Remko Uijlenhoet, Kevin Achieng, Trenton E. Franz, and Ross Woods
Hydrol. Earth Syst. Sci., 21, 3701–3713, https://doi.org/10.5194/hess-21-3701-2017, https://doi.org/10.5194/hess-21-3701-2017, 2017
Short summary
Short summary
In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological hypotheses. We call upon the community to develop a focused effort towards a fourth paradigm for hydrology.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
Short summary
Short summary
The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Remko Nijzink, Christopher Hutton, Ilias Pechlivanidis, René Capell, Berit Arheimer, Jim Freer, Dawei Han, Thorsten Wagener, Kevin McGuire, Hubert Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 20, 4775–4799, https://doi.org/10.5194/hess-20-4775-2016, https://doi.org/10.5194/hess-20-4775-2016, 2016
Short summary
Short summary
The core component of many hydrological systems, the moisture storage capacity available to vegetation, is typically treated as a calibration parameter in hydrological models and often considered to remain constant in time. In this paper we test the potential of a recently introduced method to robustly estimate catchment-scale root-zone storage capacities exclusively based on climate data to reproduce the temporal evolution of root-zone storage under change (deforestation).
Naoki Mizukami, Martyn P. Clark, Kevin Sampson, Bart Nijssen, Yixin Mao, Hilary McMillan, Roland J. Viger, Steve L. Markstrom, Lauren E. Hay, Ross Woods, Jeffrey R. Arnold, and Levi D. Brekke
Geosci. Model Dev., 9, 2223–2238, https://doi.org/10.5194/gmd-9-2223-2016, https://doi.org/10.5194/gmd-9-2223-2016, 2016
Short summary
Short summary
mizuRoute version 1 is a stand-alone runoff routing tool that post-processes runoff outputs from any distributed hydrologic models to produce streamflow estimates in large-scale river network. mizuRoute is flexible to river network representation and includes two different river routing schemes. This paper demonstrates mizuRoute's capability of multi-decadal streamflow estimations in the river networks over the entire contiguous Unites States, which contains over 54 000 river segments.
C. E. M. Lloyd, J. E. Freer, P. J. Johnes, and A. L. Collins
Hydrol. Earth Syst. Sci., 20, 625–632, https://doi.org/10.5194/hess-20-625-2016, https://doi.org/10.5194/hess-20-625-2016, 2016
Short summary
Short summary
This paper examines the current methodologies for quantifying storm behaviour through hysteresis analysis, and explores a new method. Each method is systematically tested and the impact on the results is examined. Recommendations are made regarding the most effective method of calculating a hysteresis index. This new method allows storm hysteresis behaviour to be directly compared between storms, parameters, and catchments, meaning it has wide application potential in water quality research.
Z. K. Tesemma, Y. Wei, M. C. Peel, and A. W. Western
Hydrol. Earth Syst. Sci., 19, 2821–2836, https://doi.org/10.5194/hess-19-2821-2015, https://doi.org/10.5194/hess-19-2821-2015, 2015
S. Ceola, B. Arheimer, E. Baratti, G. Blöschl, R. Capell, A. Castellarin, J. Freer, D. Han, M. Hrachowitz, Y. Hundecha, C. Hutton, G. Lindström, A. Montanari, R. Nijzink, J. Parajka, E. Toth, A. Viglione, and T. Wagener
Hydrol. Earth Syst. Sci., 19, 2101–2117, https://doi.org/10.5194/hess-19-2101-2015, https://doi.org/10.5194/hess-19-2101-2015, 2015
Short summary
Short summary
We present the outcomes of a collaborative hydrological experiment undertaken by five different international research groups in a virtual laboratory. Moving from the definition of accurate protocols, a rainfall-runoff model was independently applied by the research groups, which then engaged in a comparative discussion. The results revealed that sharing protocols and running the experiment within a controlled environment is fundamental for ensuring experiment repeatability and reproducibility.
M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
Short summary
Short summary
We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary stochastic replicates of monthly precipitation and temperature projections and investigate the impact of within-GCM uncertainty on projected runoff and reservoir yield. Amplification of within-GCM variability from precipitation to runoff to reservoir yield suggests climate change impact assessments ignoring within-GCM uncertainty would provide water resources managers with an unjustified sense of certainty
T. A. McMahon, M. C. Peel, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, https://doi.org/10.5194/hess-19-361-2015, 2015
Short summary
Short summary
Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
Z. K. Tesemma, Y. Wei, M. C. Peel, and A. W. Western
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-10515-2014, https://doi.org/10.5194/hessd-11-10515-2014, 2014
Revised manuscript not accepted
F. N. Outram, C. E. M. Lloyd, J. Jonczyk, C. McW. H. Benskin, F. Grant, M. T. Perks, C. Deasy, S. P. Burke, A. L. Collins, J. Freer, P. M. Haygarth, K. M. Hiscock, P. J. Johnes, and A. L. Lovett
Hydrol. Earth Syst. Sci., 18, 3429–3448, https://doi.org/10.5194/hess-18-3429-2014, https://doi.org/10.5194/hess-18-3429-2014, 2014
C. C. Sampson, T. J. Fewtrell, F. O'Loughlin, F. Pappenberger, P. B. Bates, J. E. Freer, and H. L. Cloke
Hydrol. Earth Syst. Sci., 18, 2305–2324, https://doi.org/10.5194/hess-18-2305-2014, https://doi.org/10.5194/hess-18-2305-2014, 2014
T. A. McMahon, M. C. Peel, and J. Szilagyi
Hydrol. Earth Syst. Sci., 17, 4865–4867, https://doi.org/10.5194/hess-17-4865-2013, https://doi.org/10.5194/hess-17-4865-2013, 2013
T. A. McMahon, M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar
Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, https://doi.org/10.5194/hess-17-1331-2013, 2013
Related subject area
Hydrology
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Uncertainty estimation for a new exponential filter-based long-term root-zone soil moisture dataset from C3S surface observations
NEOPRENE v1.0.1: A Python library for generating spatial rainfall based on the Neyman-Scott process
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
Enhancing the representation of water management in global hydrological models
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling
Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5
GSTools v1.3: a toolbox for geostatistical modelling in Python
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Tree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3
Effects of dimensionality on the performance of hydrodynamic models for stratified lakes and reservoirs
Computation of backwater effects in surface waters of lowland catchments including control structures – an efficient and re-usable method implemented in the hydrological open-source model Kalypso-NA (4.0)
Inishell 2.0: semantically driven automatic GUI generation for scientific models
Irrigation quality and management determine salinization in Israeli olive orchards
Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment
HydroPy (v1.0): a new global hydrology model written in Python
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
EGUsphere, https://doi.org/10.5194/egusphere-2023-47, https://doi.org/10.5194/egusphere-2023-47, 2023
Short summary
Short summary
In this study, we apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root-zone globally over 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using 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.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
EGUsphere, https://doi.org/10.5194/egusphere-2022-1104, https://doi.org/10.5194/egusphere-2022-1104, 2023
Short summary
Short summary
NEOPRENE is an open source freely available library allowing scientist and practitioners to generate synthetic time series and maps of rainfall. These outputs will us help explore plausible events, never observed in the past, that 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.
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
Short summary
Short summary
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.
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. Discuss., https://doi.org/10.5194/gmd-2023-12, https://doi.org/10.5194/gmd-2023-12, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
EGUsphere, https://doi.org/10.5194/egusphere-2022-1205, https://doi.org/10.5194/egusphere-2022-1205, 2022
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
Short summary
Short summary
A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
Short summary
Short summary
Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
Short summary
Short summary
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
Short summary
Short summary
With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
Short summary
Short summary
Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
Short summary
Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
Short summary
Short summary
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
Short summary
Short summary
This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, https://doi.org/10.5194/gmd-15-3161-2022, 2022
Short summary
Short summary
The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
Short summary
Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022, https://doi.org/10.5194/gmd-15-2917-2022, 2022
Short summary
Short summary
Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev., 15, 2619–2634, https://doi.org/10.5194/gmd-15-2619-2022, https://doi.org/10.5194/gmd-15-2619-2022, 2022
Short summary
Short summary
Our study introduces FETCH3, a ready-to-use, open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that FETCH3 can correctly simulate above- and below-ground water transport.
Mayra Ishikawa, Wendy Gonzalez, Orides Golyjeswski, Gabriela Sales, J. Andreza Rigotti, Tobias Bleninger, Michael Mannich, and Andreas Lorke
Geosci. Model Dev., 15, 2197–2220, https://doi.org/10.5194/gmd-15-2197-2022, https://doi.org/10.5194/gmd-15-2197-2022, 2022
Short summary
Short summary
Reservoir hydrodynamics is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of models with 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only be reproduced by the 3D model. Results support the selection of models and their assessment.
Sandra Hellmers and Peter Fröhle
Geosci. Model Dev., 15, 1061–1077, https://doi.org/10.5194/gmd-15-1061-2022, https://doi.org/10.5194/gmd-15-1061-2022, 2022
Short summary
Short summary
A hydrological method to compute backwater effects in surface water streams and on adjacent lowlands caused by mostly complex flow control systems is presented. It enables transfer of discharges to water levels and calculation of backwater volume routing along streams and lowland areas by balancing water level slopes. The developed, implemented and evaluated method extends the application range of hydrological models significantly for flood-routing simulation in backwater-affected catchments.
Mathias Bavay, Michael Reisecker, Thomas Egger, and Daniela Korhammer
Geosci. Model Dev., 15, 365–378, https://doi.org/10.5194/gmd-15-365-2022, https://doi.org/10.5194/gmd-15-365-2022, 2022
Short summary
Short summary
Most users struggle with the configuration of numerical models. This can be improved by relying on a GUI, but this requires a significant investment and a specific skill set and does not fit with the daily duties of model developers, leading to major maintenance burdens. Inishell generates a GUI on the fly based on an XML description of the required configuration elements, making maintenance very simple. This concept has been shown to work very well in our context.
Vladimir Mirlas, Yaakov Anker, Asher Aizenkod, and Naftali Goldshleger
Geosci. Model Dev., 15, 129–143, https://doi.org/10.5194/gmd-15-129-2022, https://doi.org/10.5194/gmd-15-129-2022, 2022
Short summary
Short summary
Salinization owing to irrigation water quality causes soil degradation and soil fertility reduction that with poor drainage conditions impede plant development and manifest in economic damage. This study provided a soil salting process evaluation procedure through a combination of soil salinity monitoring, field experiments, remote sensing, and unsaturated soil profile saline water movement modeling. The modeling results validated the soil salinization danger from using brackish irrigation.
Niccolò Tubini and Riccardo Rigon
Geosci. Model Dev., 15, 75–104, https://doi.org/10.5194/gmd-15-75-2022, https://doi.org/10.5194/gmd-15-75-2022, 2022
Short summary
Short summary
This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. WHETGEO-1D is intended to be the first building block of a new customisable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code and is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides.
Tobias Stacke and Stefan Hagemann
Geosci. Model Dev., 14, 7795–7816, https://doi.org/10.5194/gmd-14-7795-2021, https://doi.org/10.5194/gmd-14-7795-2021, 2021
Short summary
Short summary
HydroPy is a new version of an established global hydrology model. It was rewritten from scratch and adapted to a modern object-oriented infrastructure to facilitate its future development and application. With this study, we provide a thorough documentation and evaluation of our new model. At the same time, we open our code base and publish the model's source code in a public software repository. In this way, we aim to contribute to increasing transparency and reproducibility in science.
Cited articles
Addor, N. and Melsen, L. A.: Legacy, Rather Than Adequacy, Drives the
Selection of Hydrological Models, Water Resour. Res., 55, 378–390,
https://doi.org/10.1029/2018WR022958, 2019.
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data
set: catchment attributes and meteorology for large-sample studies, Hydrol.
Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-2017-169, 2017.
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N.,
Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G.,
Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment
attributes and meteorology for large sample studies – Chile dataset,
Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018,
2018.
Andréassian, V., Perrin, C., and Michel, C.: Impact of imperfect
potential evapotranspiration knowledge on the efficiency and parameters of
watershed models, J. Hydrol., 286, 19–35,
https://doi.org/10.1016/j.jhydrol.2003.09.030, 2004.
Andréassian, V., Perrin, C., Berthet, L., Le Moine, N., Lerat, J.,
Loumagne, C., Oudin, L., Mathevet, T., Ramos, M. H., and Valéry, A.:
Crash tests for a standardized evaluation of hydrological models, Hydrol.
Earth Syst. Sci., 13, 1757–1764, https://doi.org/10.5194/hess-13-1757-2009, 2009.
Arsenault, R., Poulin, A., Côté, P., and Brissette, F.: Comparison of
Stochastic Optimization Algorithms in Hydrological Model Calibration, J.
Hydrol. Eng., 19, 1374–1384, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000938,
2014.
Atkinson, S. E., Woods, R. A., and Sivapalan, M.: Climate and landscape
controls on water balance model complexity over changing timescales, Water
Resour. Res., 38, 50-1–50-17, https://doi.org/10.1029/2002WR001487, 2002.
Atkinson, S. E., Sivapalan, M., Woods, R. A., and Viney, N. R.: Dominant
physical controls on hourly flow predictions and the role of spatial
variability: Mahurangi catchment, New Zealand, Adv. Water Resour., 26,
219–235, https://doi.org/10.1016/S0309-1708(02)00183-5, 2003.
Bai, Y., Wagener, T., and Reed, P.: A top-down framework for watershed model
evaluation and selection under uncertainty, Environ. Model. Softw., 24,
901–916, https://doi.org/10.1016/j.envsoft.2008.12.012, 2009.
Bárdossy, A. and Singh, S. K.: Robust estimation of hydrological model
parameters, Hydrol. Earth Syst. Sci., 12, 1273–1283,
https://doi.org/10.5194/hess-12-1273-2008, 2008.
Bathurst, J. C., Ewen, J., Parkin, G., O'Connell, P. E., and Cooper, J. D.:
Validation of catchment models for predicting land-use and climate change
impacts. 3. Blind validation for internal and outlet responses, J. Hydrol.,
287, 74–94, https://doi.org/10.1016/j.jhydrol.2003.09.021, 2004.
Beven, K.: Towards a coherent philosophy for modelling the environment,
Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci., 458, 2465–2484,
https://doi.org/10.1098/rspa.2002.0986, 2002.
Beven, K.: Environmental modelling: an uncertain future?, Routledge,
London, ISBN 9780415457590, 2009.
Beven, K.: Rainfall-Runoff Modelling: The Primer, 2nd Edn., John Wiley and
Sons Ltd, 2012.
Beven, K. and Binley, A.: GLUE: 20 years on, Hydrol. Process., 28,
5897–5918, https://doi.org/10.1002/hyp.10082, 2014.
Beven, K. and Freer, J.: A dynamic topmodel, Hydrol. Process., 15,
1993–2011, https://doi.org/10.1002/hyp.252, 2001a.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using
the GLUE methodology, J. Hydrol., 249, 11–29, 2001b.
Beven, K., Lamb, R., Quinn, P., Romanowicz, R., and Freer, J.: TOPMODEL, in:
Computer Models of Watershed Hydrology, edited by: Singh, V. P., 627–668,
Water Resources Publications, USA, Baton Rouge, 1995.
Boyle, D. P.: Multicriteria calibration of hydrologic models, PhD thesis,
University of Arizona, 2001.
Burnash, R. J. C.: The NWS River Forecast System - catchment modeling, in:
Computer Models of Watershed Hydrology, edited by: Singh, V. P.,
311–366, 1995.
Chiew, F. H. S.: Estimating groundwater recharge using an integrated surface
and groundwater model, University of Melbourne, 1990.
Chiew, F. and McMahon, T.: Application of the daily rainfall-runoff model
MODHYDROLOG to 28 Australian catchments, J. Hydrol., 153, 383–416,
https://doi.org/10.1016/0022-1694(94)90200-3, 1994.
Chiew, F. H. S., Peel, M. C., and Western, A. W.: Application and testing of
the simple rainfall-runoff model SIMHYD, in: Mathematical Models of Small
Watershed Hydrology, edited by: Singh, V. P. and Frevert, D. K., 335–367,
Water Resources Publications LLC, USA, Chelsea, Michigan, USA, 2002.
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, W10510, 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, 2011.
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, 2015a.
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., Gochis, D. J.,
Rasmussen, R. M., Tarboton, D. G., Mahat, V., Flerchinger, G. N., and Marks,
D. G.: A unified approach for process-based hydrologic modeling: 2. Model
implementation and case studies, Water Resour. Res., 51, 2515–2542,
https://doi.org/10.1002/2015WR017200, 2015b.
Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M.,
and Hendrickx, F.: Crash testing hydrological models in contrasted climate
conditions: An experiment on 216 Australian catchments, Water Resour. Res.,
48, W05552, https://doi.org/10.1029/2011WR011721, 2012.
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.: The
suite of lumped GR hydrological models in an R package, Environ. Model.
Softw., 94, 166–171, https://doi.org/10.1016/j.envsoft.2017.05.002, 2017.
Coron, L., Delaigue, O., Thirel, G., Perrin, C., and Michel, C.: airGR: Suite
of GR Hydrological Models for Precipitation-Runoff Modelling, Version: R package version 1.2.13.16,
available at: https://cran.r-project.org/package=airGR/, last access: 8 May 2019.
Croke, B. and Jakeman, A.: A catchment moisture deficit module for the
IHACRES rainfall-runoff model, Environ. Model. Softw., 19, 1–5,
https://doi.org/10.1016/j.envsoft.2003.09.001, 2004.
Crooks, S. M. and Naden, P. S.: CLASSIC: a semi-distributed rainfall-runoff
modelling system, Hydrol. Earth Syst. Sci., 11, 516–531,
https://doi.org/10.5194/hess-11-516-2007, 2007.
de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue,
G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H.,
Thirel, G., and Willems, P.: Looking beyond general metrics for model
comparison – lessons from an international model intercomparison study,
Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017,
2017.
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge
observations: A quantitative analysis, Hydrol. Earth Syst. Sci., 13,
913–921, https://doi.org/10.5194/hess-13-913-2009, 2009.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global
Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of
a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10,
765–785, https://doi.org/10.5194/essd-10-765-2018, 2018.
Eder, G., Sivapalan, M., and Nachtnebel, H. P.: Modelling water balances in
an Alpine catchment through exploitation of emergent properties over
changing time scales, Hydrol. Process., 17, 2125–2149,
https://doi.org/10.1002/hyp.1325, 2003.
Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective
calibration approaches in hydrological modelling: a review, Hydrol. Sci. J.,
55, 58–78, https://doi.org/10.1080/02626660903526292, 2010.
Ewen, J. and Parkin, G.: Validation of catchment models for predicting
land-use and climate change impacts. 1. Method, J. Hydrol., 175, 583–594,
https://doi.org/10.1016/S0022-1694(96)80026-6, 1996.
Farmer, D., Sivapalan, M., and Jothityangkoon, C.: Climate, soil, and
vegetation controls upon the variability of water balance in temperate and
semiarid landscapes: Downward approach to water balance analysis, Water
Resour. Res., 39, 1035, https://doi.org/10.1029/2001WR000328, 2003.
Fenicia, F., McDonnell, J. J., and Savenije, H. H. G.: Learning from model
improvement: On the contribution of complementary data to process
understanding, Water Resour. Res., 44, 1–13, https://doi.org/10.1029/2007WR006386,
2008a.
Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Understanding
catchment behavior through stepwise model concept improvement, Water Resour.
Res., 44, W01402, https://doi.org/10.1029/2006WR005563, 2008b.
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.
Fowler, K. J. A., Peel, M. C., Western, A. W., Zhang, L., and Peterson, T.
J.: Simulating runoff under changing climatic conditions: Revisiting an
apparent deficiency of conceptual rainfall-runoff models, Water Resour.
Res., 52, 1820–1846, https://doi.org/10.1002/2015WR018068, 2016.
Freer, J. E., McMillan, H., McDonnell, J. J., and Beven, K. J.: Constraining
dynamic TOPMODEL responses for imprecise water table information using fuzzy
rule based performance measures, J. Hydrol., 291, 254–277,
https://doi.org/10.1016/j.jhydrol.2003.12.037, 2004.
Fukushima, Y.: A model of river flow forecasting for a small forested
mountain catchment, Hydrol. Process., 2, 167–185, 1988.
Goswami, M. and O'Connor, K. M.: A “monster” that made the SMAR conceptual
model “right for the wrong reasons,” Hydrol. Sci. J., 55, 913–927,
https://doi.org/10.1080/02626667.2010.505170, 2010.
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global
Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control,
time-series indices and homogeneity assessment, Earth Syst. Sci. Data,
10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards
a comprehensive assessment of model structural adequacy, Water Resour. Res.,
48, W08301, https://doi.org/10.1029/2011WR011044, 2012.
Hansen, N., Müller, S. D., and Koumoutsakos, P.: Reducing the Time
Complexity of the Derandomized Evolution Strategy with Covariance Matrix
Adaptation (CMA-ES), Evol. Comput., 11, 1–18,
https://doi.org/10.1162/106365603321828970, 2003.
Hutton, C., Wagener, T., Freer, J., Han, D., Duffy, C., and Arheimer, B.:
Most computational hydrology is not reproducible, so is it really science?,
Water Resour. Res., 52, 7548–7555, https://doi.org/10.1002/2016WR019285, 2016.
Jayawardena, A. W. and Zhou, M. C.: A modified spatial soil moisture storage capacity distribution curve for the Xinanjiang model, J. Hydrol., 227, 93–113, https://doi.org/10.1016/S0022-1694(99)00173-0, 2000.
Jothityangkoon, C., Sivapalan, M., and Farmer, D. .: Process controls of
water balance variability in a large semi-arid catchment: downward approach
to hydrological model development, J. Hydrol., 254, 174–198,
https://doi.org/10.1016/S0022-1694(01)00496-6, 2001.
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, 1–27, https://doi.org/10.1029/2009WR008896,
2010.
Kavetski, D. and Clark, M. P.: Numerical troubles in conceptual hydrology:
Approximations, absurdities and impact on hypothesis testing, Hydrol.
Process., 25, 661–670, https://doi.org/10.1002/hyp.7899, 2011.
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.
Kavetski, D., Kuczera, G., and Franks, S. W.: Semidistributed hydrological
modeling: A “saturation path” perspective on TOPMODEL and VIC, Water
Resour. Res., 39, 1246, https://doi.org/10.1029/2003WR002122, 2003.
Kavetski, D., Kuczera, G., and Franks, S. W.: Calibration of conceptual
hydrological models revisited: 1. Overcoming numerical artefacts, J.
Hydrol., 320, 173–186, https://doi.org/10.1016/j.jhydrol.2005.07.012, 2006.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking
measurements, analyses, and models to advance the science of hydrology,
Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Kirchner, J. W.: Aggregation in environmental systems – Part 2: Catchment mean transit times and young water fractions under hydrologic nonstationarity, Hydrol. Earth Syst. Sci., 20, 299–328, https://doi.org/10.5194/hess-20-299-2016, 2016.
Klemeš, V.: Operational testing of hydrological simulation models,
Hydrol. Sci. J., 31, 13–24, https://doi.org/10.1080/02626668609491024, 1986.
Kraft, P., Vaché, K. B., Frede, H.-G., and Breuer, L.: CMF: A
Hydrological Programming Language Extension For Integrated Catchment Models,
Environ. Model. Softw., 26, 828–830, https://doi.org/10.1016/j.envsoft.2010.12.009,
2011.
Krueger, T., Freer, J., Quinton, J. N., Macleod, C. J. A., Bilotta, G. S.,
Brazier, R. E., Butler, P., and Haygarth, P. M.: Ensemble evaluation of
hydrological model hypotheses, Water Resour. Res., 46, W07516,
https://doi.org/10.1029/2009WR007845, 2010.
Knoben, W. J. M.: wknoben/MARRMoT: MARRMoT_v1.2 (Version v1.2), Zenodo, https://doi.org/10.5281/zenodo.3235664, 30 May, 2019.
Leavesley, G. H., Lichty, R. W., Troutman, B. M., and Saindon, L. G.:
Precipitation-Runoff Modeling System: User's Manual, U.S. Geol. Surv.
Water-Resources Investig. Rep. 83-4238, 207, 1983.
Leavesley, G. H., Restrepo, P. J., Markstrom, S. L., Dixon, M., and Stannard,
L. G.: The Modular Modeling System – MMS, User's Manual, Denver, Col., 1996.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res., 99, 14415–14428, 1994.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and
Bergström, 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.
Littlewood, I. G., Down, K., Parker, J. R., and Post, D. A.: IHACRES v1.0
User Guide, 1997.
Markstrom, S. L., Regan, S., Hay, L. E., Viger, R. J., Webb, R. M. T., Payn,
R. A., and LaFontaine, J. H.: PRMS-IV, the Precipitation-Runoff Modeling
System, Version 4, in: U.S. Geological Survey Techniques and Methods, book 6,
chap. B7, p. 158., 2015.
McMahon, T. A., Peel, M. C., Lowe, L., Srikanthan, R., and McVicar, T. R.:
Estimating actual, potential, reference crop and pan evaporation using
standard meteorological data: A pragmatic synthesis, Hydrol. Earth Syst.
Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, 2013.
McMillan, H., Freer, J., Pappenberger, F., Krueger, T., and Clark, M.:
Impacts of uncertain river flow data on rainfall-runoff model calibration
and discharge predictions, Hydrol. Process., 24, 1270–1284,
https://doi.org/10.1002/hyp.7587, 2010.
McMillan, H., Krueger, T., and Freer, J.: Benchmarking observational
uncertainties for hydrology: rainfall, river discharge and water quality,
Hydrol. Process., 26, 4078–4111, https://doi.org/10.1002/hyp.9384, 2012.
Moore, R. J. and Bell, V. A.: Comparison of rainfall-runoff models for flood
forecasting. Part 1: Literature review of models, Environment Agency,
Bristol, 2001.
Nathan, R. J. and McMahon, T. A.: SFB model part l, Validation of fixed
model parameters, in: Civil Eng. Trans., 157–161., 1990.
National Weather Service: II.3-SAC-SMA: Conceptualization of the Sacramento
Soil Moisture Accounting model, in: National Weather Service River Forecast
System (NWSRFS) User Manual, pp. 1–13, 2005.
Nielsen, S. A. and Hansen, E.: Numerical simulation of he rainfall-runoff
process on a daily basis, Nord. Hydrol., 4, 171–190, https://doi.org/10.2166/nh.1973.0013, 1973.
Nijzink, R., Hutton, C., Pechlivanidis, I., Capell, R., Arheimer, B., Freer, J., Han, D., Wagener, T., McGuire, K., Savenije, H., and Hrachowitz, M.: The evolution of root-zone moisture capacities after deforestation: a step towards hydrological predictions under change?, Hydrol. Earth Syst. Sci., 20, 4775–4799, https://doi.org/10.5194/hess-20-4775-2016, 2016.
O'Connell, P. E., Nash, J. E., and Farrell, J. P.: River flow forecasting
through conceptual models part II – the Brosna catchment at Ferbane, J.
Hydrol., 10, 317–329, 1970.
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil,
F., and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model? Part 2 - Towards a simple and efficient potential
evapotranspiration model for rainfall-runoff modelling, J. Hydrol.,
303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005.
Oudin, L., Perrin, C., Mathevet, T., Andréassian, V., and Michel, C.:
Impact of biased and randomly corrupted inputs on the efficiency and the
parameters of watershed models, J. Hydrol., 320, 62–83,
https://doi.org/10.1016/j.jhydrol.2005.07.016, 2006.
Pechlivanidis, I. G., Jackson, B. M., McIntyre, N. R., and Wheater, H. S.:
Catchment scale hydrological modelling: a review of model types, calibration
approaches and uncertainty analysis methods in the context of recent
developments in technology and applications, Glob. NEST, 13, 193–214,
2011.
Peel, M. C. and Blöschl, G.: Hydrological modelling in a changing world,
Prog. Phys. Geogr., 35, 249–261, https://doi.org/10.1177/0309133311402550, 2011.
Penman, H. L.: The Dependence of Transpiration on Weather and Soil
Conditions, J. Soil Sci., 1, 74–89,
https://doi.org/10.1111/j.1365-2389.1950.tb00720.x, 1950.
Perrin, C., Michel, C., and Andréassian, V.: Does a large number of
parameters enhance model performance? Comparative assessment of common
catchment model structures on 429 catchments, J. Hydrol., 242,
275–301, https://doi.org/10.1016/S0022-1694(00)00393-0, 2001.
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.
Pianosi, F., Sarrazin, F., and Wagener, T.: A Matlab toolbox for Global Sensitivity Analysis, Environ. Model. Softw., 70, 80–85, https://doi.org/10.1016/j.envsoft.2015.04.009, 2015.
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat
Flux and Evaporation Using Large-Scale Parameters, Mon. Weather Rev.,
100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972.
Refsgaard, J. C. and Henriksen, H. J.: Modelling guidelines – Terminology
and guiding principles, Adv. Water Resour., 27, 71–82,
https://doi.org/10.1016/j.advwatres.2003.08.006, 2004.
Santos, L., Thirel, G., and Perrin, C.: Continuous state-space representation
of a bucket-type rainfall-runoff model: a case study with the GR4 model
using state-space GR4 (version 1.0), Geosci. Model Dev., 11, 1591–1605,
https://doi.org/10.5194/gmd-11-1591-2018, 2018.
Savenije, H. H. G.: “Topography driven conceptual modelling (FLEX-Topo)”,
Hydrol. Earth Syst. Sci., 14, 2681–2692, https://doi.org/10.5194/hess-14-2681-2010,
2010.
Schaefli, B., Hingray, B., Niggli, M., and Musy, A.: A conceptual
glacio-hydrological model for high mountainous catchments, Hydrol. Earth
Syst. Sci., 9, 95–109, https://doi.org/10.5194/hess-9-95-2005, 2005.
Schaefli, B., Nicotina, L., Imfeld, C., Da Ronco, P., Bertuzzo, E., and
Rinaldo, A.: SEHR-ECHO v1.0: A spatially explicit hydrologic response model
for ecohydrologic applications, Geosci. Model Dev., 7, 2733–2746,
https://doi.org/10.5194/gmd-7-2733-2014, 2014.
Schoups, G., Vrugt, J. A., Fenicia, F., and Van De Giesen, N. C.: Corruption
of accuracy and efficiency of Markov chain Monte Carlo simulation by
inaccurate numerical implementation of conceptual hydrologic models, Water
Resour. Res., 46, W10530, https://doi.org/10.1029/2009WR008648, 2010.
Seibert, J. and van Meerveld, H. J. I.: Hydrological change modeling:
Challenges and opportunities, Hydrol. Process., 30, 4966–4971,
https://doi.org/10.1002/hyp.10999, 2016.
Seibert, J. and Vis, M. J. P.: Teaching hydrological modeling with a
user-friendly catchment-runoff-model software package, Hydrol. Earth Syst.
Sci., 16, 3315–3325, https://doi.org/10.5194/hess-16-3315-2012, 2012.
Seibert, J., Vis, M. J. P., Lewis, E., and van Meerveld, H. J.: Upper and
lower benchmarks in hydrological modelling, Hydrol. Process., 32,
1120–1125, https://doi.org/10.1002/hyp.11476, 2018.
Singh, V. P. and Woolhiser, D. A.: Mathematical Modeling of Watershed
Hydrology, J. Hydrol. Eng., 7, 270–292,
https://doi.org/10.1061/(ASCE)1084-0699(2002)7:4(270), 2002.
Sivapalan, M., Ruprecht, J. K., and Viney, N. R.: Water and salt balance
modelling to predict the effects of land-use changes in forested catchments.
1. Small catchment water balance model, Hydrol. Process., 10, 393–411,
https://doi.org/10.1002/(SICI)1099-1085(199603)10:3<393::AID-HYP307>3.0.CO;2-%23, 1996.
Son, K. and Sivapalan, M.: Improving model structure and reducing parameter
uncertainty in conceptual water balance models through the use of auxiliary
data, Water Resour. Res., 43, W01415, https://doi.org/10.1029/2006WR005032, 2007.
Sugawara, M.: Automatic calibration of the tank model, Hydrol. Sci. Bull.,
24, 375–388, https://doi.org/10.1080/02626667909491876, 1979.
Sugawara, M.: Tank model, in: Computer models of watershed hydrology, edited
by: Singh, V. P., 165–214, Water Resources Publications, USA, 1995.
Tan, B. Q. and O'Connor, K. M.: Application of an empirical infiltration
equation in the SMAR conceptual model, J. Hydrol., 185, 275–295,
https://doi.org/10.1016/0022-1694(95)02993-1, 1996.
Tromp-Van Meerveld, H. J. and McDonnell, J. J.: Threshold relations in
subsurface stormflow: 2. The fill and spill hypothesis, Water Resour. Res.,
42, 1–11, https://doi.org/10.1029/2004WR003800, 2006.
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.
Vinogradov, Y. B., Semenova, O. M., and Vinogradova, T. A.: An approach to
the scaling problem in hydrological modelling: The deterministic modelling
hydrological system, Hydrol. Process., 25, 1055–1073,
https://doi.org/10.1002/hyp.7901, 2011.
Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13–26, https://doi.org/10.5194/hess-5-13-2001, 2001.
Wagener, T., Lees, M. J., and Wheater, H. S.: A toolkit for the development
and application of parsimonious hydrological models, in: Mathematical Models
of Small Watershed Hydrology – Volume 2, edited by: Singh, V. P., Frevert, D. K., and Meyer, S. P., 91–139, Water Resources Publications LLC, USA, 2002.
Wagener, T., Sivapalan, M., Troch, P. A., McGlynn, B. L., Harman, C. J.,
Gupta, H. V., Kumar, Rao, P. S. C., Basu, N. B., and Wilson, J. S.: The
future of hydrology: An evolving science for a changing world, Water Resour.
Res., 46, W05301, https://doi.org/10.1029/2009WR008906, 2010.
Ye, S., Yaeger, M., Coopersmith, E., Cheng, L., and Sivapalan, M.: Exploring
the physical controls of regional patterns of flow duration curves – Part 2:
Role of seasonality, the regime curve, and associated process controls,
Hydrol. Earth Syst. Sci., 16, 4447–4465, https://doi.org/10.5194/hess-16-4447-2012,
2012.
Ye, W., Bates, B. C., Viney, N. R., and Sivapalan, M.: Performance of
conceptual rainfall-runoff models in low-yielding ephemeral catchments,
Water Resour. Res., 33, 153–166, https://doi.org/10.1029/96WR02840, 1997.
Zhao, R.-J.: The Xinanjiang model applied in China, J. Hydrol., 135,
371–381, https://doi.org/10.1016/0022-1694(92)90096-E, 1992.
Short summary
Computer models are used to predict river flows. A good model should represent the river basin to which it is applied so that flow predictions are as realistic as possible. However, many different computer models exist, and selecting the most appropriate model for a given river basin is not always easy. This study combines computer code for 46 different hydrological models into a single coding framework so that models can be compared in an objective way and we can learn about model differences.
Computer models are used to predict river flows. A good model should represent the river basin...