Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4773-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-26-4773-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opinion article
| 
29 Sep 2022
Opinion article |
| 29 Sep 2022
| 29 Sep 2022
HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists
Center Agriculture Food Environment (C3A), University of Trento, Trento, Italy
Giuseppe Formetta
Department of Civil, Environmental and Mechanical Engineering (DICAM), Trento, Italy
Marialaura Bancheri
Institute for Mediterranean Agricultural and Forestry Systems (ISAFOM), National Research Council (CNR), Portici, Italy
Niccolò Tubini
Department of Civil, Environmental and Mechanical Engineering (DICAM), Trento, Italy
Concetta D'Amato
Center Agriculture Food Environment (C3A), University of Trento, Trento, Italy
Olaf David
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA
Christian Massari
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Related authors
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
Short summary
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
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.
Niccolò Tubini, Stephan Gruber, and Riccardo Rigon
The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, https://doi.org/10.5194/tc-15-2541-2021, 2021
Short summary
Short summary
We present a new method to compute temperature changes with melting and freezing – a fundamental challenge in cryosphere research – extremely efficiently and with guaranteed correctness of the energy balance for any time step size. This is a key feature since the integration time step can then be chosen according to the timescale of the processes to be studied, from seconds to days.
Wuletawu Abera, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 21, 3145–3165, https://doi.org/10.5194/hess-21-3145-2017, https://doi.org/10.5194/hess-21-3145-2017, 2017
Short summary
Short summary
This study documents a state-of-the-art estimation of the water budget (rainfall, evapotranspiration, discharge, and soil and groundwater storage) components for the Upper Blue Nile river. The budget uses various JGrass-NewAGE components, satellite data and all ground measurements available. The analysis shows that precipitation of the basin is 1360 ± 230 mm per year. Evapotranspiration accounts for 56 %, runoff is 33 %, and storage varies from minus 10 % to plus 17 % of the annual water budget.
Riccardo Rigon, Marialaura Bancheri, and Timothy R. Green
Hydrol. Earth Syst. Sci., 20, 4929–4947, https://doi.org/10.5194/hess-20-4929-2016, https://doi.org/10.5194/hess-20-4929-2016, 2016
Short summary
Short summary
The goal of the paper is to analyze the theory of water age inside a catchment while accounting for multiple outflows. It tries to propose the material under a new perspective where it lines up concepts, cleans the notation, discusses some classical results, and offers some examples that help to relate the modern achievements to the theory of the IUH, clarifying assets of both of them. In doing all of this, it also produces various new results, and some regarding solute transport.
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, https://doi.org/10.5194/hess-20-4641-2016, 2016
Short summary
Short summary
Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.
S. Endrizzi, S. Gruber, M. Dall'Amico, and R. Rigon
Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, https://doi.org/10.5194/gmd-7-2831-2014, 2014
Short summary
Short summary
GEOtop is a fine scale grid-based simulator that represents the heat and water budgets at and below the soil surface, reproduces the highly non-linear interactions between the water and energy balance during soil freezing and thawing and simulates snow cover. The core components of GEOtop 2.0. are described. Based on a synthetic simulation, it is shown that the interaction of processes represented in GEOtop 2.0. can result in phenomena that are relevant for applications involving frozen soils.
G. Formetta, S. K. Kampf, O. David, and R. Rigon
Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, https://doi.org/10.5194/gmd-7-725-2014, 2014
G. Formetta, R. Rigon, J. L. Chávez, and O. David
Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, https://doi.org/10.5194/gmd-6-915-2013, 2013
Søren Julsgaard Kragh, Jacopo Dari, Sara Modanesi, Christian Massari, Luca Brocca, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 28, 441–457, https://doi.org/10.5194/hess-28-441-2024, https://doi.org/10.5194/hess-28-441-2024, 2024
Short summary
Short summary
This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show that the synergy from using two equally important components in a joint approach within a baseline framework yields better irrigation estimates.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Martin Morlot, Simone Russo, Luc Feyen, and Giuseppe Formetta
Nat. Hazards Earth Syst. Sci., 23, 2593–2606, https://doi.org/10.5194/nhess-23-2593-2023, https://doi.org/10.5194/nhess-23-2593-2023, 2023
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We analyzed recent trends in heat and cold wave (HW and CW) risk in a European alpine region, defined by a time and spatially explicit framework to quantify hazard, vulnerability, exposure, and risk. We find a statistically significant increase in HW hazard and exposure. A decrease in vulnerability is observed except in the larger cities. HW risk increased in 40 % of the region, especially in highly populated areas. Stagnant CW hazard and declining vulnerability result in reduced CW risk.
Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, https://doi.org/10.5194/essd-15-1555-2023, 2023
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Irrigation is the main source of global freshwater consumption. Despite this, a detailed knowledge of irrigation dynamics (i.e., timing, extent of irrigated areas, and amounts of water used) are generally lacking worldwide. Satellites represent a useful tool to fill this knowledge gap and monitor irrigation water from space. In this study, three regional-scale and high-resolution (1 and 6 km) products of irrigation amounts estimated by inverting the satellite soil moisture signals are presented.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
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Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Sara Modanesi, Christian Massari, Michel Bechtold, Hans Lievens, Angelica Tarpanelli, Luca Brocca, Luca Zappa, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
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Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Christian Massari, Francesco Avanzi, Giulia Bruno, Simone Gabellani, Daniele Penna, and Stefania Camici
Hydrol. Earth Syst. Sci., 26, 1527–1543, https://doi.org/10.5194/hess-26-1527-2022, https://doi.org/10.5194/hess-26-1527-2022, 2022
Short summary
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Droughts are a creeping disaster, meaning that their onset, duration and recovery are challenging to monitor and forecast. Here, we provide further evidence of an additional challenge of droughts, i.e. the fact that the deficit in water supply during droughts is generally much more than expected based on the observed decline in precipitation. At a European scale we explain this with enhanced evapotranspiration, sustained by higher atmospheric demand for moisture during such dry periods.
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.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
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Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
Daniele Masseroni, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca
Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
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We evaluate 63 years of changes in annual streamflow volume across Europe, using a data set of more than 3000 stations, with a special focus on the Mediterranean basin. The results show decreasing (increasing) volumes in the southern (northern) regions. These trends are strongly consistent with the changes in temperature and precipitation.
Niccolò Tubini, Stephan Gruber, and Riccardo Rigon
The Cryosphere, 15, 2541–2568, https://doi.org/10.5194/tc-15-2541-2021, https://doi.org/10.5194/tc-15-2541-2021, 2021
Short summary
Short summary
We present a new method to compute temperature changes with melting and freezing – a fundamental challenge in cryosphere research – extremely efficiently and with guaranteed correctness of the energy balance for any time step size. This is a key feature since the integration time step can then be chosen according to the timescale of the processes to be studied, from seconds to days.
Louise Mimeau, Yves Tramblay, Luca Brocca, Christian Massari, Stefania Camici, and Pascal Finaud-Guyot
Hydrol. Earth Syst. Sci., 25, 653–669, https://doi.org/10.5194/hess-25-653-2021, https://doi.org/10.5194/hess-25-653-2021, 2021
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Soil moisture is a key variable related to droughts and flood genesis, but little is known about the evolution of soil moisture under climate change. Here, using a simulation approach, we show that changes in soil moisture are driven by changes in precipitation intermittence rather than changes in precipitation intensity or in temperature.
El Mahdi El Khalki, Yves Tramblay, Christian Massari, Luca Brocca, Vincent Simonneaux, Simon Gascoin, and Mohamed El Mehdi Saidi
Nat. Hazards Earth Syst. Sci., 20, 2591–2607, https://doi.org/10.5194/nhess-20-2591-2020, https://doi.org/10.5194/nhess-20-2591-2020, 2020
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In North Africa, the vulnerability to floods is high, and there is a need to improve the flood-forecasting systems. Remote-sensing and reanalysis data can palliate the lack of in situ measurements, in particular for soil moisture, which is a crucial parameter to consider when modeling floods. In this study we provide an evaluation of recent globally available soil moisture products for flood modeling in Morocco.
Barry Hankin, Ian Hewitt, Graham Sander, Federico Danieli, Giuseppe Formetta, Alissa Kamilova, Ann Kretzschmar, Kris Kiradjiev, Clint Wong, Sam Pegler, and Rob Lamb
Nat. Hazards Earth Syst. Sci., 20, 2567–2584, https://doi.org/10.5194/nhess-20-2567-2020, https://doi.org/10.5194/nhess-20-2567-2020, 2020
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With growing support for nature-based solutions to reduce flooding by local communities, government authorities and international organisations, it is still important to improve how we assess risk reduction. We demonstrate an efficient, simplified 1D network model that allows us to explore the
whole-systemresponse of numerous leaky barriers placed in different stream networks, whilst considering utilisation, synchronisation effects and cascade failure, and we provide advice on their siting.
Christian Massari, Luca Brocca, Thierry Pellarin, Gab Abramowitz, Paolo Filippucci, Luca Ciabatta, Viviana Maggioni, Yann Kerr, and Diego Fernandez Prieto
Hydrol. Earth Syst. Sci., 24, 2687–2710, https://doi.org/10.5194/hess-24-2687-2020, https://doi.org/10.5194/hess-24-2687-2020, 2020
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Rain gauges are unevenly spaced around the world with extremely low gauge density over places like Africa and South America. Here, water-related problems like floods, drought and famine are particularly severe and able to cause fatalities, migration and diseases. We have developed a rainfall dataset that exploits the synergies between rainfall and soil moisture to provide accurate rainfall observations which can be used to face these problems.
Luca Brocca, Paolo Filippucci, Sebastian Hahn, Luca Ciabatta, Christian Massari, Stefania Camici, Lothar Schüller, Bojan Bojkov, and Wolfgang Wagner
Earth Syst. Sci. Data, 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, https://doi.org/10.5194/essd-11-1583-2019, 2019
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SM2RAIN–ASCAT is a new 12-year (2007–2018) global-scale rainfall dataset obtained by applying the SM2RAIN algorithm to ASCAT soil moisture data. The dataset has a spatiotemporal sampling resolution of 12.5 km and 1 d. Results show that the new dataset performs particularly well in Africa and South America, i.e. in the continents in which ground observations are scarce and the need for satellite rainfall data is high. SM2RAIN–ASCAT is available at http://doi.org/10.5281/zenodo.340556.
Marialaura Bancheri, Francesco Serafin, Michele Bottazzi, Wuletawu Abera, Giuseppe Formetta, and Riccardo Rigon
Geosci. Model Dev., 11, 2189–2207, https://doi.org/10.5194/gmd-11-2189-2018, https://doi.org/10.5194/gmd-11-2189-2018, 2018
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This paper presents a new modeling package for the spatial interpolation of environmental variables. It includes 11 theoretical semivariogram models and four types of Kriging interpolations. To test the performances of the package, two applications are performed: the interpolation of 1 year of temperatures
and a rainfall event. Both interpolations gave good results. In comparison with gstat, the SIK package proved to be a good alternative, regarding both the easiness of use and the accuracy.
Luca Ciabatta, Christian Massari, Luca Brocca, Alexander Gruber, Christoph Reimer, Sebastian Hahn, Christoph Paulik, Wouter Dorigo, Richard Kidd, and Wolfgang Wagner
Earth Syst. Sci. Data, 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, https://doi.org/10.5194/essd-10-267-2018, 2018
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In this study, rainfall is estimated starting from satellite soil moisture observation on a global scale, using the ESA CCI soil moisture datasets. The new obtained rainfall product has proven to correctly identify rainfall events, showing performance sometimes higher than those obtained by using classical rainfall estimation approaches.
Christian Massari, Wade Crow, and Luca Brocca
Hydrol. Earth Syst. Sci., 21, 4347–4361, https://doi.org/10.5194/hess-21-4347-2017, https://doi.org/10.5194/hess-21-4347-2017, 2017
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The paper explores a method for the assessment of the performance of global rainfall estimates without relying on ground-based observations. Thanks to this method, different global correlation maps are obtained (for the first time without relying on a benchmark dataset) for some of the most used globally available rainfall products. This is central for hydroclimatic studies within data-scarce regions, where ground observations are scarce to evaluate the relative quality of a rainfall product
Wuletawu Abera, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 21, 3145–3165, https://doi.org/10.5194/hess-21-3145-2017, https://doi.org/10.5194/hess-21-3145-2017, 2017
Short summary
Short summary
This study documents a state-of-the-art estimation of the water budget (rainfall, evapotranspiration, discharge, and soil and groundwater storage) components for the Upper Blue Nile river. The budget uses various JGrass-NewAGE components, satellite data and all ground measurements available. The analysis shows that precipitation of the basin is 1360 ± 230 mm per year. Evapotranspiration accounts for 56 %, runoff is 33 %, and storage varies from minus 10 % to plus 17 % of the annual water budget.
Riccardo Rigon, Marialaura Bancheri, and Timothy R. Green
Hydrol. Earth Syst. Sci., 20, 4929–4947, https://doi.org/10.5194/hess-20-4929-2016, https://doi.org/10.5194/hess-20-4929-2016, 2016
Short summary
Short summary
The goal of the paper is to analyze the theory of water age inside a catchment while accounting for multiple outflows. It tries to propose the material under a new perspective where it lines up concepts, cleans the notation, discusses some classical results, and offers some examples that help to relate the modern achievements to the theory of the IUH, clarifying assets of both of them. In doing all of this, it also produces various new results, and some regarding solute transport.
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, https://doi.org/10.5194/hess-20-4641-2016, 2016
Short summary
Short summary
Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.
S. Endrizzi, S. Gruber, M. Dall'Amico, and R. Rigon
Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, https://doi.org/10.5194/gmd-7-2831-2014, 2014
Short summary
Short summary
GEOtop is a fine scale grid-based simulator that represents the heat and water budgets at and below the soil surface, reproduces the highly non-linear interactions between the water and energy balance during soil freezing and thawing and simulates snow cover. The core components of GEOtop 2.0. are described. Based on a synthetic simulation, it is shown that the interaction of processes represented in GEOtop 2.0. can result in phenomena that are relevant for applications involving frozen soils.
G. Formetta, S. K. Kampf, O. David, and R. Rigon
Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, https://doi.org/10.5194/gmd-7-725-2014, 2014
C. Massari, L. Brocca, S. Barbetta, C. Papathanasiou, M. Mimikou, and T. Moramarco
Hydrol. Earth Syst. Sci., 18, 839–853, https://doi.org/10.5194/hess-18-839-2014, https://doi.org/10.5194/hess-18-839-2014, 2014
G. Formetta, R. Rigon, J. L. Chávez, and O. David
Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, https://doi.org/10.5194/gmd-6-915-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Can the combining of wetlands with reservoir operation reduce the risk of future floods and droughts?
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
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Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-6, https://doi.org/10.5194/hess-2024-6, 2024
Revised manuscript accepted for HESS
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Climate and land-surface conditions influence the availability of fresh water resources. Their impact is quantified with data of 71 catchments in Saxony/Germany, for which distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff, that has not been seen in the observation records before.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Huy Dang and Yadu Pokhrel
EGUsphere, https://doi.org/10.5194/egusphere-2023-3158, https://doi.org/10.5194/egusphere-2023-3158, 2024
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By examining basin-wide simulations of the river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River Basin (MRB) over the long-term; however, dams have largely altered the seasonality of Mekong’s flow regime and annual flooding patterns at major downstream areas in recent years. These findings could help rethink the planning of future dams and water resource management in the MRB.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
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Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
EGUsphere, https://doi.org/10.5194/egusphere-2023-3043, https://doi.org/10.5194/egusphere-2023-3043, 2023
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An integrated cryospheric-hydrologic model, FLEX-Cryo, was developed, considered glaciers, snow cover, frozen soil, and their dynamic impacts on hydrology. We utilized the model to simulate the future changes in cryosphere and hydrology in Hulu catchment. Our projections showed that the two glaciers will melt out around 2050; snow cover will reduce; and permafrost will degrade. For hydrology, runoff will decrease after glacier melt out; and permafrost degradation will increase baseflow.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
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Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
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Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Pamela E. Tetford and Joseph R. Desloges
Hydrol. Earth Syst. Sci., 27, 3977–3998, https://doi.org/10.5194/hess-27-3977-2023, https://doi.org/10.5194/hess-27-3977-2023, 2023
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An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the fit of flow data, and (2) incorporating land use as a predictor variable improves the drainage area to discharge model.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn E. Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-258, https://doi.org/10.5194/hess-2023-258, 2023
Revised manuscript accepted for HESS
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Wouldn't it be nice to have both the accuracy of neural networks (NNs) and the interpretability of process-based models (PBMs)? Differentiable modeling gives you the best of both worlds by connecting NNs with PBMs. However, there was previously a major issue that iterative solution schemes would run into memory use trouble. This paper presents an operator called adjoint, which liberates all the iterative solvers. This is the first time adjoint is applied to large-scale hydrologic modeling.
Ana Ramos Oliveira, Tiago Brito Ramos, Lígia Pinto, and Ramiro Neves
Hydrol. Earth Syst. Sci., 27, 3875–3893, https://doi.org/10.5194/hess-27-3875-2023, https://doi.org/10.5194/hess-27-3875-2023, 2023
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This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behavior when modeling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream of the reservoir.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
EGUsphere, https://doi.org/10.5194/egusphere-2023-2169, https://doi.org/10.5194/egusphere-2023-2169, 2023
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (one or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Zhihua He, Kevin Shook, Christopher Spence, John W. Pomeroy, and Colin Whitfield
Hydrol. Earth Syst. Sci., 27, 3525–3546, https://doi.org/10.5194/hess-27-3525-2023, https://doi.org/10.5194/hess-27-3525-2023, 2023
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This study evaluated the impacts of climate change on snowmelt, soil moisture, and streamflow over the Canadian Prairies. The entire prairie region was divided into seven basin types. We found strong variations of hydrological sensitivity to precipitation and temperature changes in different land covers and basins, which suggests that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the prairies.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
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This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.
Dung Trung Vu, Thanh Duc Dang, Francesca Pianosi, and Stefano Galelli
Hydrol. Earth Syst. Sci., 27, 3485–3504, https://doi.org/10.5194/hess-27-3485-2023, https://doi.org/10.5194/hess-27-3485-2023, 2023
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The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
Francesco Fatone, Bartosz Szeląg, Przemysław Kowal, Arthur McGarity, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Nicolas Caradot
Hydrol. Earth Syst. Sci., 27, 3329–3349, https://doi.org/10.5194/hess-27-3329-2023, https://doi.org/10.5194/hess-27-3329-2023, 2023
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A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
Olivier Delaigue, Pierre Brigode, Guillaume Thirel, and Laurent Coron
Hydrol. Earth Syst. Sci., 27, 3293–3327, https://doi.org/10.5194/hess-27-3293-2023, https://doi.org/10.5194/hess-27-3293-2023, 2023
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Teaching hydrological modeling is an important, but difficult, matter. It requires appropriate tools and teaching material. In this article, we present the airGRteaching package, which is an open-source software tool relying on widely used hydrological models. This tool proposes an interface and numerous hydrological modeling exercises representing a wide range of hydrological applications. We show how this tool can be applied to simple but real-life cases.
Florian Willkofer, Raul Roger Wood, and Ralf Ludwig
EGUsphere, https://doi.org/10.5194/egusphere-2023-2019, https://doi.org/10.5194/egusphere-2023-2019, 2023
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Severe flood events pose threat to riverine areas, yet robust estimates about the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses and benefits from data from a RCM SMILE to drive a high-resolution hydrological model for 98 catchments of the Hydrological Bavaria to exploit the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
EGUsphere, https://doi.org/10.5194/egusphere-2023-1969, https://doi.org/10.5194/egusphere-2023-1969, 2023
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Due to climate change, flooding is expected to become more frequent globally in the coming decades. Locally, storm-induced channel geometry changes can drastically affect flood hazards, yet rivers are mostly treated as static elements in flood studies. This study tried to gain an understanding of the effects of major storm events on future flood hazards, promoting a framework for incorporating channel conveyance adjustments into flood hazard assessment.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
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This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
EGUsphere, https://doi.org/10.5194/egusphere-2023-1755, https://doi.org/10.5194/egusphere-2023-1755, 2023
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The authors compared different regionalization methods for river flow prediction in watersheds with few data. We collected data on precipitation, evapotranspiration, river flow, and geographical and climatic factors for 126 catchments in the Paraná state, Brazil. The regionalization method based on physiographic-climatic similarity showed to be the most robust for predicting daily and Q95 reference flow. We also found patterns in data, grouping locations based on similarities.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987, https://doi.org/10.5194/hess-27-2973-2023, https://doi.org/10.5194/hess-27-2973-2023, 2023
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Mediterranean floods are causing major damage, and recent studies have shown that, despite the increase in intense rainfall, there has been no increase in river floods. This study reveals that the seasonality of floods changed in the Mediterranean Basin during 1959–2021. There was also an increased frequency of floods linked to short episodes of intense rain, associated with a decrease in soil moisture. These changes need to be taken into consideration to adapt flood warning systems.
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
Hydrol. Earth Syst. Sci., 27, 2725–2745, https://doi.org/10.5194/hess-27-2725-2023, https://doi.org/10.5194/hess-27-2725-2023, 2023
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Reservoirs and wetlands are important regulators of watershed hydrology, which should be considered when projecting floods and droughts. We first coupled wetlands and reservoir operations into a semi-spatially-explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. We found that, overall, the risk of future floods and droughts will increase further even under the combined influence of reservoirs and wetlands.
Cited articles
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat,
s., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner,
B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: Google Brain, A
system for large-scale machine learning, in: OSDI'16: Proc. 12th USENIX Symposium on
Operating Systems Design and Implementation, 265–283, USENIX Association, 2016 a
Abbaszadeh, P., Moradkhani, H., and Daescu, D. N.: The quest for model
uncertainty quantification: A hybrid ensemble and variational data
assimilation framework, Water Resour. Res., 55, 2407–2431, 2019. a
Abbott, B. W., Bishop, K., Zarnetske, J. P., Minaudo, C., Chapin, F., Krause,
S., Hannah, D. M., Conner, L., Ellison, D., Godsey, S. E., Plont, S., Marçais, J., Huebner, A., Frei,
R. J., Hampton, T., Gu, S., Buhman, M., Sayedi, S. S., Ursache, O., Chapin, M., Henderson, K. D., and Pinay, G.: Human
domination of the global water cycle absent from depictions and perceptions, Nat. Geosci., 12, 533–540, 2019. a
Addor, N. and Melsen, L. A.: Legacy, Rather Than Adequacy, Drives the Selection
of Hydrological Models, Water Resour. Res., 55, 378–390, 2019. a
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-21-5293-2017, 2017. a
Archfield, S. A., Clark, M., Arheimer, B., Hay, L. E., McMillan, H., Kiang,
J. E., Seibert, J., Hakala, K., Bock, A., Wagener, T., Farmer, W. H.,
Andréassian, V., Attinger, S., Viglione, A., Knight, R., Markstrom, S.,
and Over, T.: Accelerating advances in continental domain hydrologic
modeling, Water Resour. Res., 51, 10078–10091, 2015. a
Argent, R. M.: An overview of model integration for environmental
applications – components, frameworks and semantics, Environ. Modell.
Softw., 19, 219–234, 2004. a
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J.,
Srinivasan, R., Santhi, C., Harmel, R. D., Van Griensven, A., Van Liew,
M. W., Kannan, N., and Jha, M.
K.: SWAT: Model use, calibration, and validation,
Trans. ASABE, 55, 1491–1508, 2012. a
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik,
S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins,
R., Chatila, R., and Herrera, F.: Explainable Artificial Intelligence (XAI): Concepts, taxonomies,
opportunities and challenges toward responsible AI, Inform. Fusion, 58,
82–115, 2020. a
Babaeian, E., Sadeghi, M., Jones, S. B., Montzka, C., Vereecken, H., and
Tuller, M.: Ground, proximal, and satellite remote sensing of soil moisture,
Rev. Geophys., 57, 530–616, 2019. a
Ballatore, A.: The myth of the Digital Earth between fragmentation and
wholeness, Wi, J. Mobile Media, 8, https://doi.org/10.48550/arXiv.1412.2078, 2014. a
Bancheri, M., Rigon, R., and Manfreda, S.: The GEOframe-NewAge Modelling
System Applied in a Data Scarce Environment, Water, 12, 86, https://doi.org/10.3390/w12010086,
2019a. a
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017. a
Berti, G.: Generic software components for Scientific Computing, Ph.D. thesis, Brandenburgischen Technischen Universitat Cottbus, https://www.researchgate.net/profile/Guntram-Berti/publication/239065936_Generic_software_components_for_Scientific_Computing/links/545fd2180cf295b56161c9b0/Generic-software-components-for-Scientific-Computing.pdf (last access: 23 September 2022), 2000. a
Bertoldi, G., Della Chiesa, S., Notarnicola, C., Pasolli, L., Niedrist, G., and
Tappeiner, U.: Estimation of soil moisture patterns in mountain grasslands by
means of SAR RADARSAT2 images and hydrological modeling, J.
Hydrol., 516, 245–257, 2014. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a, b, c
Beven, K.: Towards an alternative blueprint for a physically based digitally
simulated hydrologic response modelling system, Hydrol. Process., 16,
189–206, 2002. a
Beven, K.: Environmental modelling: an uncertain future?, CRC press, 328 pp., https://doi.org/10.1201/9781482288575, 2018. a
Beven, K.: Towards a methodology for testing models as hypotheses in the
inexact sciences, Proc. Math. Phys. Eng. Sci., 475, 20180862, https://doi.org/10.1098/rspa.2018.0862, 2019. a, b
Beven, K. J.: Rainfall-Runoff Modelling: The Primer, The Primer, John Wiley
& Sons, ISBN 9780470714591, 488 pp., 2012. a
Binley, A. M., Beven, K. J., Calver, A., and Watts, L.: Changing responses in
hydrology: assessing the uncertainty in physically based model predictions,
Water Resour. Res., 27, 1253–1261, 1991. a
Blechta, J., Hake, J., Johansson, A., and others: The FEniCS project
version 1.5, Arch. Num. Softw., 51, 3, 9-23, https://doi.org/10.11588/ans.2015.100.20553, 2015. a
Blöschl, G.: Debates-Hypothesis testing in hydrology: Introduction, Water
Resour. Res., 53, 1767–1769, 2017. a
Blöschl, G., Bierkens, M. F., Chambel, A. et al.: Twenty-three unsolved problems in hydrology (UPH) – a community
perspective, Hydrol. Sci. J., 64, 1141–1158, 2019. a
Boldrini, E., Mazzetti, P., Nativi, S., Santoro, M., Papeschi, F., Roncella,
R., Olivieri, M., Bordini, F., and Pecora, S.: WMO Hydrological Observing
System (WHOS) broker: implementation progress and outcomes, in: European
Geoscience Union General Assembly, p. 14755, Copernicus, 22nd EGU General Assembly, 4–8 May 2020, https://doi.org/10.5194/egusphere-egu2020-14755, 2020. a
Bottazzi, M., Bancheri, M., Mobilia, M., Bertoldi, G., Longobardi, A., and
Rigon, R.: Comparing Evapotranspiration Estimates from the
GEOframe-Prospero Model with Penman–Monteith and Priestley-Taylor
Approaches under Different Climate Conditions, Water, 13, 1221, https://doi.org/10.3390/w13091221, 2021. a
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S.,
Kidd, R., Dorigo, W., Wagner, W., and Levizzani, V.: Soil as a natural rain
gauge: Estimating global rainfall from satellite soil moisture data, J. Geophys. Res.-Atmos., 119, 5128–5141, 2014. a
Brunner, P. and Simmons, C. T.: HydroGeoSphere: A fully integrated,
physically based hydrological model, Ground Water, 50, 170–176, 2012. a
Busti, R.: The implementation and testing of different modeling solutions to
estimate water balance in mountain regions, Master's thesis, University of
Trento, 2021. a
CEOS: Committee on Earth Observation Satellites (Ceos),
http://database.eohandbook.com/ (last access: 23 September 2022), 2019. a
Chen, M., Voinov, A., Ames, D. P., Kettner, A. J., Goodall, J. L., Jakeman,
A. J., Barton, M. C., Harpham, Q., Cuddy, S. M., DeLuca, C., Yue, S., Wang,
J., Zhang, F., Wen, Y., and Lü, G.: Position paper: Open web-distributed
integrated geographic modelling and simulation to enable broader
participation and applications, Earth Sci. Rev., 207, 103223, https://doi.org/10.1016/j.earscirev.2020.103223, 2020. a
Chew, C. and Small, E.: Soil moisture sensing using spaceborne GNSS
reflections: Comparison of CYGNSS reflectivity to SMAP soil moisture,
Geophys. Res. Lett., 45, 4049–4057, 2018. a
Cho, E., Jacobs, J. M., Jia, X., and Kraatz, S.: Identifying subsurface
drainage using satellite big data and machine learning via Google earth
engine, Water Resour. Res., 55, 8028–8045, 2019. a
Chu X. and Steinman A.: Event and Continuous Hydrologic Modeling
with HEC-HMS, J. Irrig. Drain. Eng., 135, 119–124, 2009. a
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple
working hypotheses for hydrological modeling, Water Resour. Res., 47, https://doi.org/10.1029/2010WR009827,
2011a. a, b
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the Method of Multiple Working Hypotheses for Hydrological Modeling, Water Resour. Res., 47, https://doi.org/10.1029/2010wr009827,
2011b. a
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, 2015. a, b
Collins, N., Theurich, G., DeLuca, C., Suarez, M., Trayanov, A., Balaji, V.,
Li, P., Yang, W., Hill, C., and da Silva, A.: Design and Implementation of
Components in the Earth System Modeling Framework, Int. J. High Perform.
Comput. Appl., 19, 341–350, 2005. a
Cook, D.: Practical Machine Learning with H2O: Powerful, Scalable Techniques
for Deep Learning and AI, “O'Reilly Media, Inc.”, ISBN 9781491964576, 300 p., 2016. a
Cornelissen, T., Diekkrüger, B., and Bogena, H. R.: Significance of scale
and lower boundary condition in the 3D simulation of hydrological processes
and soil moisture variability in a forested headwater catchment, J.
Hydrol., 516, 140–153, 2014. a
Cox, R. T.: Probability, Frequency and Reasonable Expectation, Am. J. Phys.,
14, 1–13, 1946. a
Craglia, M., de Bie, K., Jackson, D., Pesaresi, M., Remetey-Fülöpp, G.,
Wang, C., Annoni, A., Bian, L., Campbell, F., Ehlers, M., van Genderen, J.,
Goodchild, M., Guo, H., Lewis, A., Simpson, R., Skidmore, A., and Woodgate,
P.: Digital Earth 2020: towards the vision for the next decade, Int. J. Digital Earth, 5, 4–21, https://doi.org/10.1080/17538947.2011.638500, 2012. a
Craig, J. R., Brown, G., Chlumsky, R., Jenkinson, R. W., Jost, G., Lee, K.,
Mai, J., Serrer, M., Sgro, N., Shafii, M., Snowdon, A. P., and Tolson, B. A.:
Flexible watershed simulation with the Raven hydrological modelling
framework, Environ. Modell. Softw., 129, 104728, https://doi.org/10.1016/j.envsoft.2020.104728, 2020. a, b
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models, Geosci. Model Dev., 14, 7047–7072, https://doi.org/10.5194/gmd-14-7047-2021, 2021. a
David, O., Lloyd, W., Rojas, K., Arabi, M., Geter, F., Ascough, J., Green, T.,
Leavesley, G., and Carlson, J.: Modeling-as-a-Service (MaaS) using the
Cloud Services Innovation Platform (CSIP), in: International Congress on
Environmental Modelling and Software, scholarsarchive.byu.edu, 13, https://digitalcommons.tacoma.uw.edu/tech_pub/13 (last access: 23 September 2022), 2014. a, b, c
Davidson, M., Chini, M., Dierking, W., Djavidnia, S., Haarpaintner, J.,
Hajduch, G., Laurin, G., Lavalle, M., López-Martínez, C., Nagler,
T., Pierdicca, N., and Su, B.: Copernicus L-band SAR Mission Requirements Document, https://esamultimedia.esa.int/docs/EarthObservation/Copernicus_L-band_SAR_mission_ROSE-L_MRD_v2.0_issued.pdf (last access: 23 September 2022),
2019. a
De Lannoy, G. J. and Reichle, R. H.: Global assimilation of multiangle and
multipolarization SMOS brightness temperature observations into the GEOS-5
catchment land surface model for soil moisture estimation, J.
Hydrometeorol., 17, 669–691, 2016. a
Dey, C., Sanders, C., Clochard, J., and Hennessy, J.: Guide to the WMO table driven code form used for the
representation and exchange of regularly spaced data in binary form: FM 92
GRIB, Tech. rep., WMO Tech. Rep., 98 pp.,
http://www.wmo.int/pages/prog/www/WMOCodes/Guides//GRIB/GRIB1-Contents.html (last access: 23 September 2022),
2007. a
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for
deriving water availability indicators: model tuning and validation, J. Hydrol., 270, 105–134, 2003. a
Duan, Q., Gupta, H. V., Sorooshian, S., Rousseau, A. N., and Turcotte, R.:
Calibration of Watershed Models, John Wiley & Sons, edited by: Duan, Q. et al., 653 pp., AGU Washington D. C., 2003. a
Eagleson, P. S.: The emergence of global-scale hydrology, Water Resour. Res.,
22, 6S–14S, 1986. a
Emerton, R. E., Stephens, E. M., Pappenberger, F., Pagano, T. C., Weerts,
A. H., Wood, A. W., Salamon, P., Brown, J. D., Hjerdt, N., Donnelly, C.,
Baugh, C. A., and Cloke, H. L.: Continental and global scale flood
forecasting systems, WIREs Water, 3, 391–418, 2016. a
Endrizzi, S., Gruber, S., Dall'Amico, M., and Rigon, R.: GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects, Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, 2014. a
Endrizzi, S., Gruber, S., Dall'Amico, M., and Rigon, R.: GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects, Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, 2014. a
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D.,
Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J.,
Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and
Tarboton, D.: An overview of current applications, challenges, and future
trends in distributed process-based models in hydrology, J. Hydrol., 537,
45–60, 2016a. a
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarbotons, D.: An overview
of current applications, challenges, and future trends in distributed
process-based models in hydrology, J. Hydrol., 537, 45–60,
2016b. a
Fenicia, F. and Kavetski, D.: Behind every robust result is a robust method:
Perspectives from a case study and publication process in hydrological
modelling, Hydrol. Process., 35, 45–60, 2021. a
Folk, M., Heber, G., Koziol, Q., Pourmal, E., and Robinson, D.: An overview of
the HDF5 technology suite and its applications, in: Proceedings of the
EDBT/ICDT 2011 Workshop on Array Databases, AD '11, 36–47, Association
for Computing Machinery, New York, NY, USA, 36–47, https://doi.org/10.1145/1966895.1966900, 2011. a
Freeze, R. A. and Harlan, R. L.: Blueprint for a physically-based,
digitally-simulated hydrologic response model, J. Hydrol., 9, 237–258, https://doi.org/10.1016/0022-1694(69)90020-1., 1969. a
Gardner, H. and Manduchi, G.: Design Patterns for e-Science, Springer Science
& Business Media, ISBN 9783540680888, 388 pp., 2007. a
Geer, A. J.: Learning earth system models from observations: machine learning
or data assimilation?, Philos. Trans. Roy. Soc. A, 379, 20200089, https://doi.org/10.1098/rsta.2020.0089, 2021. a, b, c, d
Gharari, S., Gupta, H. V., Clark, M. P., Hrachowitz, M., Fenicia, F., Matgen,
P., and Savenije, H. H. G.: Understanding the information content in the
hierarchy of model development decisions: Learning from data, Water Resour.
Res., 57, https://doi.org/10.1029/2020wr027948, 2021. a, b
Gironás, J., Roesner, L. A., Rossman, L. A., and Davis, J.: A new
applications manual for the Storm Water Management Model(SWMM),
Environ. Modell. Softw., 25, 813–814, 2010. a
Goodchild, M. F., Guo, H., Annoni, A., Bian, L., De Bie, K., Campbell, F.,
Craglia, M., Ehlers, M., Van Genderen, J., Jackson, D., Lewis, A. J., Pesaresi, M., Remetey-Fülöpp, G., Simpson, R., Skidmore,
A., Wang, C., and Woodgate, P.:
Next-generation digital earth, P. Natl. Acad.
Sci. USA, 109, 11088–11094, 2012. a
Götzinger, J. and Bárdossy, A.: Generic error model for calibration and
uncertainty estimation of hydrological models, Water Resour. Res., 44,
W00B07, https://doi.org/10.1029/2007wr006691, 2008. a
Graessler, I. and Pöhler, A.: Integration of a digital twin as human
representation in a scheduling procedure of a cyber-physical production
system, in: 2017 IEEE International Conference on Industrial Engineering and
Engineering Management (IEEM), 289–293, https://doi.org/0.1109/IEEM.2017.8289898, 2017. a
Gregersen, J. B., Gijsbers, P. J. A., and Westen, S. J. P.: OpenMI: Open
modelling interface, J. Hydroinform., 9, 175–191, 2007. a
Guo, H., Goodchild, M. F., and Annoni, A.: Manual of Digital Earth, Springer
Singapore, ISBN 9789813299146, 852 pp., 2019. a
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of automatic calibration
for hydrologic models: Comparison with multilevel expert calibration, J. Hydrol. Eng., 4, 135–143, 1999. a
Hall, C. A., Saia, S. M., Popp, A. L., Dogulu, N., Schymanski, S. J., Drost, N., van Emmerik, T., and Hut, R.: A hydrologist's guide to open science, Hydrol. Earth Syst. Sci., 26, 647–664, https://doi.org/10.5194/hess-26-647-2022, 2022. a
Hill, M. C. and Tiedeman, C. R.: Effective Groundwater Model
Calibration with Analysis of Data, Sensitivities, and Uncertainty, Hoboken, New Jersey, John
Wiley and Sons, ISBN 9780471776369, 455 pp., 2007. a
Holling, C. S.: Adaptive Environmental Assessment and Management. John
Wiley & Sons. http://pure.iiasa.ac.at/id/eprint/823/ (last access: 27 September 2022), ISBN 0471996327, 402 pp., 1978. a
Hornik, K.: Approximation capabilities of multilayer feedforward networks,
Neural Netw., 4, 251–257, 1991. a
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017. a
Hut, R., Drost, N., van de Giesen, N., van Werkhoven, B., Abdollahi, B., Aerts, J., Albers, T., Alidoost, F., Andela, B., Camphuijsen, J., Dzigan, Y., van Haren, R., Hutton, E., Kalverla, P., van Meersbergen, M., van den Oord, G., Pelupessy, I., Smeets, S., Verhoeven, S., de Vos, M., and Weel, B.: The eWaterCycle platform for open and FAIR hydrological collaboration, Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, 2022. a, b
Jasak, H., Jemcov, A., and Tukovic, Z.: OpenFOAM: A C++ library for complex
physics simulations, Coupled Method. Numer. Dynam., Dubrovnik, Croatia, http://csabai.web.elte.hu/http/simulationLab/jasakEtAlOpenFoam.pdf (last access: 27 September 2022), 2007. a
Jiang, P. and Kumar, P.: Using information flow for whole system understanding
from component dynamics, Water Resour. Res., 55, 8305–8329, 2019. a
Jiang, P., Elag, M., Kumar, P., Peckham, S. D., Marini, L., and Rui, L.: A
service-oriented architecture for coupling web service models using the Basic
Model Interface (BMI), Environ. Modell. Softw., 92, 107–118,
2017. a
Karpatne, A., Jiang, Z., Vatsavai, R. R., Shekhar, S., and Kumar, V.:
Monitoring land-cover changes: A machine-learning perspective, IEEE
Geosci. Remote Sens. Magaz., 4, 8–21, 2016. a
Kelly, D. and Sanders, R.: The challenge of testing scientific software, in:
Proceedings of the 3rd annual conference of the Association for Software
Testing (CAST 2008: Beyond the Boundaries), 30–36, Citeseer, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.464.7432&rep=rep1&type=pdf (last access: 27 September 2022), 2008. a, b
Kennedy, J. and Eberhart, R. C.: A discrete binary version of the particle
swarm algorithm, in: 1997 IEEE International Conference on Systems, Man,
and Cybernetics. Computational Cybernetics and Simulation,
4104–4108, Vol. 5, 1997. a
Knoben, W. J. M., Freer, J. E., Fowler, K. J. A., Peel, M. C., and Woods, R. A.: Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations, Geosci. Model Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, 2019. a
Knoben, W. J. M., Clark, M. P., Bales, J., Bennett, A., Gharari, S., Marsh,
C. B., Nijssen, B., Pietroniro, A., Spiteri, R. J., Tarboton, D. G., and
Wood, A. W.: Community Workflows to Advance Reproducibility in Hydrologic
Modeling: Separating model-agnostic and model-specific configuration steps in
applications of large-domain hydrologic models, https://doi.org/10.1002/essoar.10509195.1, 2021. a, b, c
Knuth, D. E. and Levy, S.: The WEB system of structured documentation, Tech.
Rep. STAN-CS-83-980, Stanford University, http://i.stanford.edu/pub/cstr/reports/cs/tr/83/980/CS-TR-83-980.pdf (last access: 27 September 2022), 210 pp., 1983. a
Konapala, G. and Mishra, A.: Quantifying climate and catchment control on
hydrological drought in the continental United States, Water Resour. Res.,
56, e2018WR024620, https://doi.org/10.1029/2018wr024620, 2020. a
Konapala, G., Kao, S.-C., Painter, S. L., and Lu, D.: Machine learning assisted
hybrid models can improve streamflow simulation in diverse catchments across
the conterminous US, Environ. Res. Lett., 15, 104022, https://doi.org/10.1088/1748-9326/aba927, 2020. a
Kramer, D.: API documentation from source code comments: a case study of
Javadoc, in: Proceedings of the 17th annual international conference on
Computer documentation, SIGDOC '99, 147–153, Association for Computing
Machinery, New York, NY, USA, 1999. a
Krapu, C., Borsuk, M., and Kumar, M.: Gradient-based inverse estimation for a
rainfall-runoff model, Water Resour. Res., 55, 6625–6639, 2019. a
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a
Kreyenberg, P. J., Bauser, H. H., and Roth, K.: Velocity field estimation on
density‐driven solute transport with a convolutional neural network, Water
Resour. Res., 55, 7275–7293, 2019. a
Kuffour, B. N. O., Engdahl, N. B., Woodward, C. S., Condon, L. E., Kollet, S., and Maxwell, R. M.: Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model, Geosci. Model Dev., 13, 1373–1397, https://doi.org/10.5194/gmd-13-1373-2020, 2020. a, b
Kumar, S. V., Peters-Lidard, C. D., Santanello, J. A., Reichle, R. H., Draper, C. S., Koster, R. D., Nearing, G., and Jasinski, M. F.: Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes, Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, 2015. a
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden,
S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., Adams, J., Mitchell,
K., Wood, E. F., and Sheffield, J.: Land information system: An interoperable
framework for high resolution land surface modeling, Environ. Modell. Softw., 21, 1402–1415, 2006. a
Lagouarde, J.-P., Bhattacharya, B., Crébassol, P., Gamet, P., Adlakha, D.,
Murthy, C., Singh, S., Mishra, M., Nigam, R., Raju, P. V., Babu, S. S., Shukla, M. V., Pandya, M. R.,
Boulet, G., Briottet, X., Dadou, I., Dedieu, G., Gouhier, M., Hagolle, O., Irvine, M., Jacob, F.,
Kumar, K. K., Laignel, B., Maisongrande, P., Mallick, K., Olioso, A., Ottlé, C., Roujean, J.-L.,
Sobrino, J., Ramakrishnan, R., Sekhar, M., and Sarkar, S. S.: Indo-French
high-resolution thermal infrared space mission for earth natural resources
assessment and monitoring-concept and definition of trishna, in:
ISPRS-GEOGLAM-ISRS Joint International Workshop on “Earth Observations for
Agricultural Monitoring”, Vol. 42, 403–407, https://doi.org/10.5194/isprs-archives-XLII-3-W6-403-2019, New Delhi, India, 2019. a
Lau, S., Drosos, I., Markel, J. M., and Guo, P. J.: The Design Space of
Computational Notebooks: An Analysis of 60 Systems in Academia and Industry,
in: 2020 IEEE Symposium on Visual Languages and Human-Centric Computing
(VL/HCC), pp. 1–11, Dunedin, New Zealand, https://doi.org/10.1109/VL/HCC50065.2020, 2020. a, b
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C.,
Bonan, G., Collier, N., Ghimire, B., Kampenhout, L., Kennedy, D., Kluzek, E.,
Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J.,
Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M.,
Bisht, G., Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M.,
Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M.,
Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts,
J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D.,
Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A.,
Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The
community land model version 5: Description of new features, benchmarking,
and impact of forcing uncertainty, J. Adv. Model. Earth Syst., 11,
4245–4287, 2019. a, b
Lee, H., Sivapalan, M., and Zehe, E.: Representative elementary watershed
(REW) approach: a new blueprint for distributed hydrological modelling at
the catchment scale, IAHS Publ., ISSN 0144-7815, 195 pp., 2005. a
Leisch, F.: Sweave: Dynamic Generation of Statistical Reports Using Literate
Data Analysis, in: Compstat, pp. 575–580, Physica-Verlag HD, 575–580, https://doi.org/10.1007/978-3-642-57489-4_89, 2002. a
Lentner, G.: Shared Memory High Throughput Computing with Apache Arrow™, in:
Proceedings of the Practice and Experience in Advanced Research Computing on
Rise of the Machines (learning), no. Article 119 in PEARC '19, pp. 1–2,
Association for Computing Machinery, New York, NY, USA, ISBN 9781450372275, 1–2 pp., 2019. a
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood,
E. F.: Inroads of remote sensing into hydrologic science during the WRR era,
Water Resour. Res., 51, 7309–7342, 2015. a
Levia, D. F., Carlyle-Moses, D. E., Michalzik, B., Nanko, K., and Tischer, A.:
Forest-water interactions, Springer, 625 p., ISBN 978-3-030-26085-9, https://doi.org/10.1007/978-3-030-26086-6, 2020. a
Lievens, H., Martens, B., Verhoest, N., Hahn, S., Reichle, R., and Miralles,
D. G.: Assimilation of global radar backscatter and radiometer brightness
temperature observations to improve soil moisture and land evaporation
estimates, Remote Sens. Environ., 189, 194–210, 2017. a
Lin, W.-F., Tsai, D.-Y., Tang, L., Hsieh, C.-T., Chou, C.-Y., Chang, P.-H., and
Hsu, L.: ONNC: A Compilation Framework Connecting ONNX to Proprietary
Deep Learning Accelerators, in: 2019 IEEE International Conference on
Artificial Intelligence Circuits and Systems (AICAS), 214-218, https://doi.org/10.1109/AICAS.2019.8771510, 2019. a
Liu, Y. and Wu, L.: Geological Disaster Recognition on Optical Remote Sensing
Images Using Deep Learning, Proc. Comput. Sci., 91, 566–575,
https://doi.org/10.1016/j.procs.2016.07.144, 2016. a
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012. a
López López, P., Sutanudjaja, E. H., Schellekens, J., Sterk, G., and Bierkens, M. F. P.: Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products, Hydrol. Earth Syst. Sci., 21, 3125–3144, https://doi.org/10.5194/hess-21-3125-2017, 2017. a
Manfreda, S., Brocca, L., Moramarco, T., Melone, F., and Sheffield, J.: A physically based approach for the estimation of root-zone soil moisture from surface measurements, Hydrol. Earth Syst. Sci., 18, 1199–1212, https://doi.org/10.5194/hess-18-1199-2014, 2014. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a, b, c
Martin, R. C.: Clean Code: A Handbook of Agile Software Craftsmanship, Prentice
Hall, ISBN 9780132350884, 431 pp., 2009. a
Mayer-Schönberger, V. and Cukier, K.: Big Data: A Revolution that Will
Transform how We Live, Work, and Think, Houghton Mifflin Harcourt, 2013. a
McCabe, M. F., Aragon, B., Houborg, R., and Mascaro, J.: CubeSats in hydrology:
Ultrahigh-resolution insights into vegetation dynamics and terrestrial
evaporation, Water Resour. Res., 53, 10017–10024,
2017a. a
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi, J., Gao, H., and Wood, E. F.: The future of Earth observation in hydrology, Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, 2017b. a, b
McCool, M., Robison, A., and Reinders, J.: Structured parallel programming:
patterns for efficient computation, Elsevier, ISBN 9780124159938, 432 pp., 2012. a
McCuen, R. H.: Modeling hydrologic change: statistical methods, CRC press,
ISBN 9781566706001, 456 pp., 2016. a
Meyer, T., Jagdhuber, T., Piles, M., Fink, A., Grant, J., Vereecken, H., and
Jonard, F.: Estimating gravimetric water content of a winter wheat field from
L-band vegetation optical depth, Remote Sens., 11, 2353, https://doi.org/10.3390/rs11202353, 2019. a
Millman, K. J. and Pérez, F.: Developing Open-Source
Scientific Practice *, in:
Implementing Reproducible Research, 149–83, Chapman and Hall/CRC,
https://www.jarrodmillman.com/publications/millman2014developing.pdf (last access: 27 September 2022), 2018. a
Modanesi, S., Massari, C., Gruber, A., Lievens, H., Tarpanelli, A., Morbidelli, R., and De Lannoy, G. J. M.: Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land, Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, 2021. a
Montanari, A. and Koutsoyiannis, D.: A blueprint for process‐based modeling
of uncertain hydrological systems, Water Resour. Res., 48, https://doi.org/10.1029/2011wr011412, 2012. a
Moore, R. V. and Hughes, A. G.: Integrated environmental modelling: achieving
the vision, Geological Society, London, Special Publications, 408, 17–34,
2017. a
Müller-Hansen, F., Schlüter, M., Mäs, M., Donges, J. F., Kolb, J. J., Thonicke, K., and Heitzig, J.: Towards representing human behavior and decision making in Earth system models – an overview of techniques and approaches, Earth Syst. Dynam., 8, 977–1007, https://doi.org/10.5194/esd-8-977-2017, 2017. a
NASA-ISRO, S.: Mission Science Users’ Handbook, Jet Propulsion Lab.,
California Inst. Technol., Pasadena, CA, USA,
https://nisar.jpl.nasa.gov/system/documents/files/26_NISAR_FINAL_9-6-19.pdf (last access: 23 September 2022),
2018. a
Nativi, S. and Bigagli, L.: Discovery, Mediation, and Access Services for Earth
Observation Data, IEEE J. Select. Top. Appl. Earth
Observ. Remote Sens., 2, 233–240, 2009. a
Nativi, S., Craglia, M., and Pearlman, J.: Earth Science Infrastructures
Interoperability: The Brokering Approach, IEEE J. Selec. Top.
Appl. Earth Observ. Remote Sens., 6, 1118–1129, 2013. a
Nativi, S., Mazzetti, P., and Craglia, M.: Digital Ecosystems for Developing
Digital Twins of the Earth: The Destination Earth Case, Remote Sens., 13,
2119, https://doi.org/10.3390/rs13112119, 2021. a, b, c
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D.,
Frame, J. M., Prieto, C., and Gupta, H. V.: What role does hydrological
science play in the age of machine learning?, Water Resour. Res., 57,
e2020WR028091, https://doi.org/10.1029/2020wr028091, 2021. a, b
Nedovic-Budic, Z., Crompvoets, J., and Georgiadou, Y.: Spatial Data
Infrastructures in Context: North and South, CRC Press, 288 pp., ISBN 9781439828038, 2011. a
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and
water assessment tool theoretical documentation version 2009, Tech. rep.,
Texas Water Resources Institute, https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/128050/TR-406_SoilandWaterAssessmentToolTheoreticalDocumentation.pdf?sequence=1 (last access: 27 September 2022), 2011. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek,
Erik Lamarque, J., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S.,
Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical
Description of version 4.5 of the Community Land Model (CLM), NCAR, 434 pp., ISSN 2153-2400, 2013. a
Ott, J., Pritchard, M., Best, N., Linstead, E., Curcic, M., and Baldi, P.: A
Fortran-Keras Deep Learning Bridge for Scientific Computing, Sci. Program.,
2020, 8888811, https://doi.org/10.1155/2020/8888811, 2020. a
Pan, B., Hsu, K., AghaKouchak, A., and Sorooshian, S.: Improving precipitation
estimation using convolutional neural network, Water Resour. Res., 55,
2301–2321, 2019. a
Paniconi, C. and Putti, M.: Physically based modeling in catchment hydrology at
50: Survey and outlook, Water Resour. Res., 51, 7090–7129, 2015. a
Peters, N. E., Freer, J., and Beven, K.: Modelling hydrologic responses in a
small forested catchment (Panola Mountain, Georgia, USA): a comparison of
the original and a new dynamic TOPMODEL, Hydrol. Process., 17, 345–362,
2003. a
Peters-Lidard, C. D., Houser, P. R., Tian, Y., Kumar, S. V., Geiger, J., Olden,
S., Lighty, L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F.,
and Sheffield, J.: High-performance Earth system modeling with NASA/GSFC's
Land Information System, Innov. Syst. Softw. Eng., 3, 157–165,
2007. a, b
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B.,
and Wagener, T.: Sensitivity analysis of environmental models: A systematic
review with practical workflow, Environ. Modell. Softw., 79,
214–232, 2016. a
Post, D. E. and Votta, L. G.: Computational Science Demands a New Paradigm,
Phys. Today, 58, 35–41, 2005. a
Post, H., Vrugt, J. A., Fox, A., Vereecken, H., and Hendricks Franssen, H.-J.:
Estimation of Community Land Model parameters for an improved assessment of
net carbon fluxes at European sites, J. Geophys. Res.-Biogeosci., 122, 661–689, 2017. a
Prieto, C., Kavetski, D., Le Vine, N., Álvarez, C., and Medina, R.:
Identification of dominant hydrological mechanisms using Bayesian inference,
multiple statistical hypothesis testing, and flexible models, Water Resour.
Res., 57, https://doi.org/10.1029/2020wr028338, 2021. a
Rädle, R., Nouwens, M., Antonsen, K., Eagan, J. R., and Klokmose, C. N.:
Codestrates: Literate Computing with Webstrates, in: Proceedings of the 30th
Annual ACM Symposium on User Interface Software and Technology, UIST '17,
pp. 715–725, Association for Computing Machinery, New York, NY, USA, https://doi.org/10.1145/3126594.3126642, 2017. a
Rahman, J. M., Seaton, S. P., Perraud, J. M., Hotham, H., Verrelli, D. I., and
Coleman, J. R.: It's TIME for a new environmental modelling
framework, in: MODSIM 2003 International Congress on Modelling and
Simulation, vol. 4, 1727–1732, Modelling and
Simulation Society of Australia and New Zealand Inc. Townsville,
http://www.research.div1.com.au/RESOURCES/research/publications/conferences/20030714ff_MODSIM2003/RahmanSeatonPerraudHothamVerrelliColeman2003_1727.n.pdf (last access: 27 September 2022), 2003. a
Ramadhan, A., Marshall, J., Souza, A., Wagner, G. L., Ponnapati, M., and
Rackauckas, C.: Capturing missing physics in climate model parameterizations
using neural differential equations, arXiv preprint arXiv:2010.12559, http://arxiv.org/abs/2010.12559, 2020. a
Refsgaard, J. C., Storm, B., and Clausen, T.: Système Hydrologique
Europeén (SHE): review and perspectives after 30 years development in
distributed physically-based hydrological modelling, Hydrol. Res., 41,
355–377, https://doi.org/10.2166/nh.2010.009, 2010. a
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv.
Water Resour., 31, 1411–1418, 2008. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Nuno, P.: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204, 2019. a
Rew, R. and Davis, G.: NetCDF: an interface for scientific data access, IEEE Comput. Graph. Appl., 10, 76–82, 1990. a
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020. a
Rodríguez-Iturbe, I. and Rinaldo, A.: Fractal River Basins: Chance and
Self-Organization, Cambridge University Press, ISBN 9780521004053, 526 pp., 2001. a
Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D.,
Simard, M., and Pavelsky, T. M.: AquaSat: A data set to enable remote
sensing of water quality for inland waters, Water Resour. Res., 55,
10012–10025, 2019. a
Rouson, D., Xia, J., and Xu, X.: Scientific Software Design: The
Object-Oriented Way, The object-oriented way, Cambridge University Press,
Cambridge, England, Cambridge, England, Cambridge University Press, ISBN 9781107415331, 406 pp., 2014. a
Savenije, H. H. G. and Hrachowitz, M.: HESS Opinions “Catchments as meta-organisms – a new blueprint for hydrological modelling”, Hydrol. Earth Syst. Sci., 21, 1107–1116, https://doi.org/10.5194/hess-21-1107-2017, 2017. a, b
Semeraro, C., Lezoche, M., Panetto, H., and Dassisti, M.: Digital twin
paradigm: A systematic literature review, Comput. Industry, 130,
103469, https://doi.org/10.1016/j.compind.2021.103469, 2021. a
Serafin, F., David, O., Carlson, J. R., Green, T. R., and Rigon, R.: Bridging
technology transfer boundaries: Integrated cloud services deliver results of
nonlinear process models as surrogate model ensembles, Environ.
Modell. Softw., 146, 105231, https://doi.org/10.1016/j.envsoft.2021.105231, 2021. a, b
Shen, C., Laloy, E., Elshorbagy, A., Albert, A., Bales, J., Chang, F.-J., Ganguly, S., Hsu, K.-L., Kifer, D., Fang, Z., Fang, K., Li, D., Li, X., and Tsai, W.-P.: HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community, Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018, 2018. a, b, c, d
Stacke, T. and Hagemann, S.: HydroPy (v1.0): a new global hydrology model written in Python, Geosci. Model Dev., 14, 7795–7816, https://doi.org/10.5194/gmd-14-7795-2021, 2021. a
Stodden, V., Borwein, J., and Bailey, D. H.: Setting the default to
reproducible, computational science research, SIAM News, 46, 4–6, 2013. a
Todini, E.: Hydrological catchment modelling: past, present and future, Hydrol. Earth Syst. Sci., 11, 468–482, https://doi.org/10.5194/hess-11-468-2007, 2007. a, b
Trambauer, P., Dutra, E., Maskey, S., Werner, M., Pappenberger, F., van Beek, L. P. H., and Uhlenbrook, S.: Comparison of different evaporation estimates over the African continent, Hydrol. Earth Syst. Sci., 18, 193–212, https://doi.org/10.5194/hess-18-193-2014, 2014. a
Tubini, N. and Rigon, R.: Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment, Geosci. Model Dev., 15, 75–104, https://doi.org/10.5194/gmd-15-75-2022, 2022.
a, b
Viglione, A., Borga, M., Balabanis, P., and Blöschl, G.: Barriers to the
exchange of hydrometeorological data in Europe: Results from a survey and
implications for data policy, J. Hydrol., 394, 63–77, 2010. a
Voinov, A. and Shugart, H. H.: “Integronsters”, integral and integrated
modeling, Environm. Modell. Softw., 39, 149–158, 2013. a
Vrugt, J., van Wijk, M. T., Hopmans, J., and Šimunek, J.: One-, two-, and
three-dimensional root water uptake functions for transient modeling, Water
Resour. Res., 37, 2457–2470, 2001. a
Vrugt, J. A. and Neuman, S. P.: Introduction to the special section in Vadose
Zone Journal: Parameter identification and uncertainty assessment in the
unsaturated zone, Vadose Zone J., 5, 915–916, 2006. a
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex
Evolution Metropolis algorithm for optimization and uncertainty assessment of
hydrologic model parameters, Water Resour. Res., 39, 279–218, https://doi.org/10.1029/2002WR001642, 2003. a
Wada, Y., Bierkens, M. F. P., de Roo, A., Dirmeyer, P. A., Famiglietti, J. S., Hanasaki, N., Konar, M., Liu, J., Müller Schmied, H., Oki, T., Pokhrel, Y., Sivapalan, M., Troy, T. J., van Dijk, A. I. J. M., van Emmerik, T., Van Huijgevoort, M. H. J., Van Lanen, H. A. J., Vörösmarty, C. J., Wanders, N., and Wheater, H.: Human–water interface in hydrological modelling: current status and future directions, Hydrol. Earth Syst. Sci., 21, 4169–4193, https://doi.org/10.5194/hess-21-4169-2017, 2017. a
Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., van den Akker, O., and
Heynert, K.: The Delft-FEWS flow forecasting system, Environ.
Modell. Softw., 40, 65–77, 2013. a
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M.
F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis,
D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade,
A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting
a grand challenge for monitoring Earth's terrestrial water: OPINION, Water
Resour. Res., 47, W05301, https://doi.org/10.1029/2010wr010090, 2011. a
Xie, Y.: knitr: A General-Purpose Package for Dynamic Report
Generation in R, R package version, https://rdrr.io/github/yihui/knitr/man/knitr-package.html (last access: 27 September 2022), 2013. a
Yeh, W. W.-G.: Review of parameter identification procedures in groundwater
hydrology: The inverse problem, Water Resour. Res., 22, 95–108, 1986. a
Yilmaz, K. K., Vrugt, J. A., Gupta, H. V., and Sorooshian, S.: Model
calibration in watershed hydrology, Advances in data-based approaches for
hydrologic modeling and forecasting, pp. 53–105, edited by: Sivakumar, B. and Berndtsson, R., WORLD SCIENTIFIC, https://doi.org/10.1142/9789814307987_0003, 2010. a
Short summary
The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We...
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