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
Hossein Salehi, Daniele Andreis, Sohaib Baig, Gaia Roati, Marco Brian, Francesco Tornatore, Giuseppe Formetta, and Riccardo Rigon
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-467, https://doi.org/10.5194/essd-2026-467, 2026
Preprint under review for ESSD
Short summary
Short summary
Here we describe the interpolation procedures followed to build a 1-km gridded daily precipitation and temperature dataset for the Po River District for the 1991–2020 period, with the aim of overcoming the scarcity of high-resolution data, essential for hydrological modelling. Initial data are collected from multi-source observational stations, while topographic data derive from the European Digital Elevation Model. Interpolation performance metrics analysis shows the coherence of this dataset.
Concetta D'Amato, Paolo Benettin, Andrea Rinaldo, and Riccardo Rigon
EGUsphere, https://doi.org/10.5194/egusphere-2026-2385, https://doi.org/10.5194/egusphere-2026-2385, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Here we validate GEOSPACE, a flexible open-source tool for simulating water exchange between soil, plants, and the atmosphere. Using data from a lysimeter experiment with four vegetation covers, we show that GEOSPACE accurately reproduces soil water dynamics, evapotranspiration, and root water uptake. Its modular design allows selecting among alternative process formulations, making it a reliable tool for ecohydrological research.
Shima Azimi, Manuela Girotto, Riccardo Rigon, Gaia Roati, Silvia Barbetta, and Christian Massari
EGUsphere, https://doi.org/10.5194/egusphere-2026-793, https://doi.org/10.5194/egusphere-2026-793, 2026
Short summary
Short summary
Even ground-based precipitation observations, often considered the most reliable, can introduce substantial uncertainty into snow modeling due to sparse gauge coverage at high elevations in mountainous catchments.This challenge motivates the present study, in which we propose a data assimilation framework that integrates satellite-based snow depth into a hydrological model to correct snowfall estimates over the Italian Alps, with implications for water management in data-scarce mountain regions.
John Mohd Wani, Kelly E. Gleason, Matteo Dall’Amico, Federico Di Paolo, Stefano Tasin, Gaia Roati, Marco Brian, Francesco Tornatore, and Riccardo Rigon
EGUsphere, https://doi.org/10.5194/egusphere-2025-5520, https://doi.org/10.5194/egusphere-2025-5520, 2025
Preprint archived
Short summary
Short summary
A 30-year study of Italy's Po River Basin reveals dramatic loss in snow below 2000 meters elevation, with some areas losing over 30 % of snow-water storage. The snow season is becoming shorter, mainly because snow arrives later in winter rather than melting earlier in spring. This represents a fundamental shift from stable to volatile conditions, with major implications for water security in one of Europe's second most climate sensitive areas.
Concetta D'Amato, Niccolò Tubini, and Riccardo Rigon
Geosci. Model Dev., 18, 7321–7355, https://doi.org/10.5194/gmd-18-7321-2025, https://doi.org/10.5194/gmd-18-7321-2025, 2025
Short summary
Short summary
This paper presents GEOSPACE and its 1D implementation: an open-source tool for simulating soil–plant–atmosphere continuum (SPAC) interactions. Using object-oriented programming, GEOSPACE modularizes SPAC processes, focusing on infiltration, evapotranspiration, and root water uptake. The 1D deployment integrates plant transpiration, soil evaporation, and root growth, providing a flexible and validated framework for ecohydrological modeling and applications.
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
Short summary
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.
Hossein Salehi, Daniele Andreis, Sohaib Baig, Gaia Roati, Marco Brian, Francesco Tornatore, Giuseppe Formetta, and Riccardo Rigon
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-467, https://doi.org/10.5194/essd-2026-467, 2026
Preprint under review for ESSD
Short summary
Short summary
Here we describe the interpolation procedures followed to build a 1-km gridded daily precipitation and temperature dataset for the Po River District for the 1991–2020 period, with the aim of overcoming the scarcity of high-resolution data, essential for hydrological modelling. Initial data are collected from multi-source observational stations, while topographic data derive from the European Digital Elevation Model. Interpolation performance metrics analysis shows the coherence of this dataset.
Concetta D'Amato, Paolo Benettin, Andrea Rinaldo, and Riccardo Rigon
EGUsphere, https://doi.org/10.5194/egusphere-2026-2385, https://doi.org/10.5194/egusphere-2026-2385, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Here we validate GEOSPACE, a flexible open-source tool for simulating water exchange between soil, plants, and the atmosphere. Using data from a lysimeter experiment with four vegetation covers, we show that GEOSPACE accurately reproduces soil water dynamics, evapotranspiration, and root water uptake. Its modular design allows selecting among alternative process formulations, making it a reliable tool for ecohydrological research.
Pierre Laluet, Jacopo Dari, Louise Busschaert, Zdenko Heyvaert, Gabrielle De Lannoy, Pia Langhans, Sara Modanesi, Christian Massari, Luca Brocca, Carla Saltalippi, Renato Morbidelli, Clément Albergel, and Wouter Dorigo
Earth Syst. Sci. Data, 18, 4833–4853, https://doi.org/10.5194/essd-18-4833-2026, https://doi.org/10.5194/essd-18-4833-2026, 2026
Short summary
Short summary
We developed a long-term dataset collection of irrigation water use based on about two decades of satellite observations, three distinct approaches, and many input datasets. The collection provides monthly estimates for major agricultural regions and helps describe how irrigation varies across locations, seasons, and years. It offers a foundation for improving how irrigation is quantified, compared across methods, and integrated into large-scale hydrological and climate studies.
Francesco Avanzi, Hans Lievens, Michael Matiu, Paolo Filippucci, Oscar M. Baez Villanueva, Simone Gabellani, Fabio Delogu, Lorenzo Alfieri, Andrea Libertino, Pere Quintana-Seguì, Diego G. Miralles, Luca Brocca, Christian Massari, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2026-2851, https://doi.org/10.5194/egusphere-2026-2851, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We developed a new system to map and monitor snow and stored water across large mountain regions using satellite data and computer simulations. Tested in four major European river basins, the system reproduced snow conditions with high accuracy and realistically captured how snow changes with elevation. Because it does not rely on ground measurements, it can help provide consistent information on snow-water resources worldwide, supporting water management and climate adaptation.
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, JongCheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Jackson Tan, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 30, 3399–3423, https://doi.org/10.5194/hess-30-3399-2026, https://doi.org/10.5194/hess-30-3399-2026, 2026
Short summary
Short summary
Our study evaluated 24 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP (Multi-Source Weighted-Ensemble Precipitation) V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GDAS (Global Data Assimilation System), demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Süleyman Selim Çallı, Kübra Özdemir Çallı, Brahim Akdim, Bruno Arfib, Aleksey Benderev, Sandra Beranger, Avi Burg, Onur Can, Jean-Baptiste Charlier, Mehmet Çelik, Arda Melih Çetin, Fehdi Chemseddine, Miroslava Deliyska, Lucio Di Matteo, Marco Dionigi, Romeo Eftimi, Jutta Eybl, Davide Fronzi, Nico Goldscheider, Ergin Gökkaya, Jorge Jodar, Herve Jourde, Eva Kaminsky, Konstantina Katsanou, Alireza Kavousi, Melike Kaya, David Labat, Tanja Liesch, Peter Malik, Christian Massari, Cyril Mayaud, Naomi Mazzilli, Pavel Pracny, Natasa Ravbar, Nathan Rispal, Simon Seelig, Vianney Sivelle, Marc Steinmann, Daniela Valigi, Gerfried Winkler, Ahmet Kemal Yahşi, and Andreas Hartmann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-281, https://doi.org/10.5194/essd-2026-281, 2026
Preprint under review for ESSD
Short summary
Short summary
Karst groundwater systems are vital but highly sensitive to climate change. Their complex underground flow makes them hard to manage without good data, so we gathered 118 high-quality records of spring discharge across Mediterranean mountain ranges. With an average of 19 years of data – including one nearly century-long record – this collection provides the detailed information needed to better predict water availability and protect these vulnerable groundwater sources.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 30, 2579–2611, https://doi.org/10.5194/hess-30-2579-2026, https://doi.org/10.5194/hess-30-2579-2026, 2026
Short summary
Short summary
Two models, AquaCrop (crop model) and Noah-MP (land surface model), were compared estimating irrigation in Italy's Po Valley. Noah-MP simulated higher water use (434 mm/yr) than AquaCrop (268 mm/yr), mainly due to extra water losses like runoff. Once losses were accounted for, both aligned with basin-scale reports of around 500 to 600 mm/yr. The study highlights how complex irrigation modeling is, and the need for better observational data to validate results.
Oscar M. Baez-Villanueva, Alfredo Crespo-Otero, Sara Modanesi, Pierre Laluet, Sergio Vicente-Serrano, Jaap Schellekens, Jacopo Dari, Hylke E. Beck, Wouter Dorigo, Christian Massari, Chiara Corbari, Joppe Massant, Kwint Delbare, Olivier Bonte, Aaron Boone, Diego Fernández-Prieto, and Diego G. Miralles
EGUsphere, https://doi.org/10.5194/egusphere-2026-1856, https://doi.org/10.5194/egusphere-2026-1856, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
We developed a new method to estimate daily land evaporation at high resolution across the Iberian Peninsula, explicitly accounting for irrigation. By combining satellite and meteorological data, we show that irrigation can strongly increase evaporation in agricultural areas. The results better match ground observations and improve understanding of water use. This approach can support farming decisions and water management at the regional scale, and will be extended to global applications.
Binyam Alemu Yosef, Angelo Basile, Antonio Coppola, Fabrizio Ungaro, Claudio Zucca, and Marialaura Bancheri
SOIL, 12, 347–369, https://doi.org/10.5194/soil-12-347-2026, https://doi.org/10.5194/soil-12-347-2026, 2026
Short summary
Short summary
This study investigates the intricate relationship between soil properties and water-related processes, with a focus on their collective impact on ecosystem service provision. Key soil characteristics were analyzed for their role in regulating the overall hydrological balance in three diverse regions. The study highlights the value of process-based modelling for disentangling soil–climate interactions and cautions against the use of static indicators in hydrological and soil health assessments.
Sofia Ortenzi, Lucio Di Matteo, Daniela Valigi, Marco Donnini, Marco Dionigi, Davide Fronzi, Josie Geris, Fabio Guadagnano, Ivan Marchesini, Paolo Filippucci, Francesco Avanzi, Daniele Penna, and Christian Massari
Hydrol. Earth Syst. Sci., 30, 1755–1778, https://doi.org/10.5194/hess-30-1755-2026, https://doi.org/10.5194/hess-30-1755-2026, 2026
Short summary
Short summary
The study presents an integrated approach to analyze groundwater–surface water interactions in a Central Italy catchment, combining hydrological, hydrochemical–isotopic, thermal drone, and satellite data. Results indicate that fractured limestone aquifers sustain streamflow, with snowmelt accounting for about 18 % of recharge. The workflow is transferable and suitable for similar data-scarce Mediterranean basins.
Shima Azimi, Manuela Girotto, Riccardo Rigon, Gaia Roati, Silvia Barbetta, and Christian Massari
EGUsphere, https://doi.org/10.5194/egusphere-2026-793, https://doi.org/10.5194/egusphere-2026-793, 2026
Short summary
Short summary
Even ground-based precipitation observations, often considered the most reliable, can introduce substantial uncertainty into snow modeling due to sparse gauge coverage at high elevations in mountainous catchments.This challenge motivates the present study, in which we propose a data assimilation framework that integrates satellite-based snow depth into a hydrological model to correct snowfall estimates over the Italian Alps, with implications for water management in data-scarce mountain regions.
Francesco Avanzi, Stefano Terzi, Mariapina Castelli, Francesca Munerol, Margherita Andreaggi, Marta Galvagno, Andrea Galletti, Tessa Maurer, Christian Massari, Grace Carlson, Manuela Girotto, Giacomo Bertoldi, Edoardo Cremonese, Simone Gabellani, Umberto Morra di Cella, Marco Altamura, Lauro Rossi, and Luca Ferraris
EGUsphere, https://doi.org/10.22541/au.176055865.50800695/v2, https://doi.org/10.22541/au.176055865.50800695/v2, 2026
Short summary
Short summary
Snow droughts are periods with below-average snow accumulation and are becoming more frequent in a warming climate, yet their ecosystem and societal impacts remain poorly known. Using 13 years of data from 38 Italian catchments, we show that snow droughts reduced snow duration by ~50 %, doubled winter melt-out events, and cut summer runoff by ~50 %. Photosynthesis increased by up to 10 % due to earlier meltout. These events also caused widespread water-supply reductions, especially in foothills.
Domenico De Santis, Silvia Barbetta, Sumit Sen, Viviana Maggioni, Farhad Bahmanpouri, Ashutosh Sharma, Ankit Agarwal, Sagar Gupta, Francesco Avanzi, and Christian Massari
Nat. Hazards Earth Syst. Sci., 26, 1075–1104, https://doi.org/10.5194/nhess-26-1075-2026, https://doi.org/10.5194/nhess-26-1075-2026, 2026
Short summary
Short summary
A conceptual, semi-distributed hydrological model was tailored to simulate high flows in monsoon-dominated, glacier-influenced and flood-prone Himalayan basins. Multi-data calibration using satellite-based glacier mass loss and evapotranspiration estimates improved process realism in data-scarce environments. The proposed modelling approach captured key streamflow features despite significant input uncertainties, proving to be a useful tool for exploring the local hydrological response dynamics.
Senna Bouabdelli, Martin Morlot, Christian Massari, and Giuseppe Formetta
EGUsphere, https://doi.org/10.5194/egusphere-2026-464, https://doi.org/10.5194/egusphere-2026-464, 2026
Short summary
Short summary
Drought is becoming more common in the Alps as warmer winters reduce snow and alter river flow. We used hydrological model simulations in the Adige basin to understand when and why droughts occur. Results show that droughts are happening earlier, becoming more intense, and increasingly driven by lack of rain instead of melting snow at high elevations. This shift toward lower-elevation river behaviour calls for new strategies to manage water for hydropower, agriculture, and tourism.
John Mohd Wani, Kelly E. Gleason, Matteo Dall’Amico, Federico Di Paolo, Stefano Tasin, Gaia Roati, Marco Brian, Francesco Tornatore, and Riccardo Rigon
EGUsphere, https://doi.org/10.5194/egusphere-2025-5520, https://doi.org/10.5194/egusphere-2025-5520, 2025
Preprint archived
Short summary
Short summary
A 30-year study of Italy's Po River Basin reveals dramatic loss in snow below 2000 meters elevation, with some areas losing over 30 % of snow-water storage. The snow season is becoming shorter, mainly because snow arrives later in winter rather than melting earlier in spring. This represents a fundamental shift from stable to volatile conditions, with major implications for water security in one of Europe's second most climate sensitive areas.
Concetta D'Amato, Niccolò Tubini, and Riccardo Rigon
Geosci. Model Dev., 18, 7321–7355, https://doi.org/10.5194/gmd-18-7321-2025, https://doi.org/10.5194/gmd-18-7321-2025, 2025
Short summary
Short summary
This paper presents GEOSPACE and its 1D implementation: an open-source tool for simulating soil–plant–atmosphere continuum (SPAC) interactions. Using object-oriented programming, GEOSPACE modularizes SPAC processes, focusing on infiltration, evapotranspiration, and root water uptake. The 1D deployment integrates plant transpiration, soil evaporation, and root growth, providing a flexible and validated framework for ecohydrological modeling and applications.
Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari
Earth Syst. Sci. Data, 17, 5221–5258, https://doi.org/10.5194/essd-17-5221-2025, https://doi.org/10.5194/essd-17-5221-2025, 2025
Short summary
Short summary
Accurate rainfall data is essential, yet measuring daily precipitation worldwide is challenging. This research presents HYdroclimatic PERformance-enhanced Precipitation (HYPER-P), a dataset combining satellite, ground, and reanalysis data to estimate precipitation at a 1 km scale from 2000 to 2022. HYPER-P improves accuracy, especially in areas with few rain gauges. This dataset supports scientists and decision-makers in understanding and managing water resources more effectively.
Xiangmei Liu, Peng Shen, Jiaqi Chen, David Andrew Barry, Christian Massari, Jiansheng Chen, Mingming Feng, Xi Zhang, Fenyan Ma, Fei Yang, and Haixia Jin
EGUsphere, https://doi.org/10.5194/egusphere-2025-4263, https://doi.org/10.5194/egusphere-2025-4263, 2025
Preprint archived
Short summary
Short summary
Our research in the Songnen Basin of Northeast China has revealed why numerous lakes remain unfrozen during winter. By studying Lake Chagan, we discovered that deep groundwater sustains these lakes, likely flowing through subterranean channels from the Tibetan Plateau. When earthquakes disrupt these conduits, water rises along fault lines to replenish the lakes. This finding challenges the theory that graben lakes primarily depend on local precipitation.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Preprint archived
Short summary
Short summary
This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
Short summary
Short summary
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
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
Short summary
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...
Special issue