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
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This paper presents GEOSPACE and its 1D implementation: an open-source tool for simulating soil-plant-atmosphere continuum (SPAC) interactions. Using object-oriented programming, GEOSPACE modularizes SPAC processes, focusing on infiltration, evapotranspiration, and root water uptake. The 1D deployment integrates plant transpiration, soil evaporation, and root growth, providing a flexible and validated framework for ecohydrological modeling and applications.
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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.
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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.
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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 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
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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.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
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
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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
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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
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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.
Stefania Camici, Christian Massari, Luca Ciabatta, Ivan Marchesini, and Luca Brocca
Hydrol. Earth Syst. Sci., 24, 4869–4885, https://doi.org/10.5194/hess-24-4869-2020, https://doi.org/10.5194/hess-24-4869-2020, 2020
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The paper performs the most comprehensive European-scale evaluation to date of satellite rainfall products for river flow prediction. In doing so, how errors transfer from satellite-based rainfall products into flood simulation is investigated in depth and, for the first time, quantitative guidelines on the use of these products for hydrological applications are provided. This result can represent a keystone in the use of satellite rainfall products, especially in data-scarce regions.
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
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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
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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
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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
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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
Adaptation of root zone storage capacity to climate change and its effects on future streamflow in Alpine catchments: towards non-stationary model parameters
Finding process-behavioural parameterisations of a hydrological model using a multi-step process-based calibration and evaluation scheme
Merits and limits of SWAT-GL: application in contrasting glaciated catchments
Hydrological regime index for non-perennial rivers
Assessing the adequacy of traditional hydrological models for climate change impact studies: a case for long short-term memory (LSTM) neural networks
Assessing the value of high-resolution data and parameter transferability across temporal scales in hydrological modeling: a case study in northern China
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China
Evaluating the effects of topography and land use change on hydrological signatures: a comparative study of two adjacent watersheds
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: an analysis based on 63 catchments in southeast China
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Extended-range forecasting of stream water temperature with deep-learning models
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Enhanced Baseflow Separation in Rural Catchments: Event-Specific Calibration of Recursive Digital Filters with Tracer-Derived Data
Projections of streamflow intermittence under climate change in European drying river networks
Economic valuation of subsurface water contributions to watershed ecosystem services using a fully integrated groundwater–surface-water model
Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
Spatially Resolved Rainfall Streamflow Modeling in Central Europe
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Simulating the Tone River eastward diversion project in Japan carried out 4 centuries ago
Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers
Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
A Distributed Hybrid Physics-AI Framework for Learning Corrections of Internal Hydrological Fluxes and Enhancing High-Resolution Regionalized Flood Modeling
Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration
State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models
Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations
Understanding the relationship between streamflow forecast skill and value across the western US
Leveraging soil diversity to mitigate hydrological extremes with nature-based solutions in productive catchments
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Soil moisture and precipitation intensity control the transit time distribution of quick flow in a flashy headwater catchment
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
The influence of lateral flow on land surface fluxes in southeast Australia varies with model resolution
Constraining pesticide degradation in conceptual distributed catchment models with compound-specific isotope analysis (CSIA)
On the use of streamflow transformations for hydrological model calibration
Unveiling the Impact of Potential Evapotranspiration Method Selection on Trends in Hydrological Cycle Components Across Europe
Simulation-based inference for parameter estimation of complex watershed simulators
Magali Ponds, Sarah Hanus, Harry Zekollari, Marie-Claire ten Veldhuis, Gerrit Schoups, Roland Kaitna, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 3545–3568, https://doi.org/10.5194/hess-29-3545-2025, https://doi.org/10.5194/hess-29-3545-2025, 2025
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This research examines how future climate changes impact root zone storage, a key hydrological model parameter. Root zone storage – the soil water accessible to plants – adapts to climate but is often kept constant in models. We estimated climate-adapted storage in six Austrian Alps catchments. While storage increased, streamflow projections showed minimal change, which suggests that dynamic root zone representation is less critical in humid regions but warrants further study in arid areas.
Moritz M. Heuer, Hadysa Mohajerani, and Markus C. Casper
Hydrol. Earth Syst. Sci., 29, 3503–3525, https://doi.org/10.5194/hess-29-3503-2025, https://doi.org/10.5194/hess-29-3503-2025, 2025
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This study presents a process-behavioural calibration approach for water balance models. The different calibration steps aim at calibrating different hydrological processes: evapotranspiration, the runoff partitioning into surface runoff, interflow, and groundwater recharge, as well as the groundwater behaviour. This allows for selection of a model parameterisation that correctly predicts the discharge at the catchment outlet and simultaneously correctly depicts the underlying hydrological processes.
Timo Schaffhauser, Florentin Hofmeister, Gabriele Chiogna, Fabian Merk, Ye Tuo, Julian Machnitzke, Lucas Alcamo, Jingshui Huang, and Markus Disse
Hydrol. Earth Syst. Sci., 29, 3227–3256, https://doi.org/10.5194/hess-29-3227-2025, https://doi.org/10.5194/hess-29-3227-2025, 2025
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The glacier-expanded SWAT (Soil Water Assessment Tool) version, SWAT-GL, was tested in four different catchments, highlighting the capabilities of the glacier routine. It was evaluated based on the representation of glacier mass balance, snow cover and glacier hypsometry. The glacier changes over a long timescale could be adequately represented, leading to promising potential future applications in glaciated and high mountain environments and significantly outperforming standard SWAT models.
Pablo Fernando Dornes and Rocío Noelia Comas
Hydrol. Earth Syst. Sci., 29, 2901–2923, https://doi.org/10.5194/hess-29-2901-2025, https://doi.org/10.5194/hess-29-2901-2025, 2025
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The Desaguadero–Salado–Chadiluevú–Curacó (DSCC) River is a semiarid river which is heavily dammed at its tributaries which collect the snowmelt runoff. This runoff feeds mostly gravitational irrigation systems of very low efficiency. As a result, the DSCC River does not have natural runoff. The proposed hydrological regime index (HRI) is able to discriminate and quantify regime alterations under permanent and non-permanent flow conditions and with low- and high-impoundment conditions.
Jean-Luc Martel, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
Hydrol. Earth Syst. Sci., 29, 2811–2836, https://doi.org/10.5194/hess-29-2811-2025, https://doi.org/10.5194/hess-29-2811-2025, 2025
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This study compares long short-term memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrological models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 2633–2654, https://doi.org/10.5194/hess-29-2633-2025, https://doi.org/10.5194/hess-29-2633-2025, 2025
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We assessed the value of high-resolution data and parameter transferability across temporal scales based on seven catchments in northern China. We found that higher-resolution data do not always improve model performance, questioning the need for such data. Model parameters are transferable across different data resolutions but not across computational time steps. It is recommended to utilize a smaller computational time step when building hydrological models even without high-resolution data.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 29, 2361–2375, https://doi.org/10.5194/hess-29-2361-2025, https://doi.org/10.5194/hess-29-2361-2025, 2025
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Hydrologic models are needed to provide simulations of water availability, floods, and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models and thus cannot easily be used to select an appropriate model for a specific place.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025, https://doi.org/10.5194/hess-29-2201-2025, 2025
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We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Haifan Liu, Haochen Yan, and Mingfu Guan
Hydrol. Earth Syst. Sci., 29, 2109–2132, https://doi.org/10.5194/hess-29-2109-2025, https://doi.org/10.5194/hess-29-2109-2025, 2025
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Land changes and landscape features critically impact water systems. Studying two watersheds in China’s Greater Bay Area, we found slope strongly influences water processes in mountainous areas. However, this relationship is weak in the lower regions of steeper watersheds. Urbanization leads to an increase in annual surface runoff, while flatter watersheds exhibit a buffering capacity against this effect. However, this buffering capacity diminishes with increasing annual rainfall intensity.
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci., 29, 1981–2002, https://doi.org/10.5194/hess-29-1981-2025, https://doi.org/10.5194/hess-29-1981-2025, 2025
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Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite the fact that they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what they truly reflect in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and timescales for accurate drought characterization and monitoring.
Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider
Hydrol. Earth Syst. Sci., 29, 1939–1962, https://doi.org/10.5194/hess-29-1939-2025, https://doi.org/10.5194/hess-29-1939-2025, 2025
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Accurate early-warning systems are crucial for reducing the damage caused by flooding events. In this study, we explored the potential of long short-term memory networks for enhancing the forecast accuracy of hydrologic models employed in operational flood forecasting. The presented approach elevated the investigated hydrologic model’s forecast accuracy for further ahead predictions and at flood event runoff.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1919–1937, https://doi.org/10.5194/hess-29-1919-2025, https://doi.org/10.5194/hess-29-1919-2025, 2025
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Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conduct simulation experiments using data with various temporal resolutions across multiple catchments and find that higher-resolution data do not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
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The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025, https://doi.org/10.5194/hess-29-1725-2025, 2025
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In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until we observe overparameterization.
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025, https://doi.org/10.5194/hess-29-1685-2025, 2025
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We generate operational forecasts of daily maximum stream water temperature for 32 consecutive days at 54 stations in Switzerland with our best-performing data-driven model. The average forecast error is 0.38 °C for 1 d ahead and increases to 0.90 °C for 32 d ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025, https://doi.org/10.5194/hess-29-1749-2025, 2025
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Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use inputs of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.
Fernanda Helfer, Felipe Bernardi, Claudia Alessandra Peixoto de Barros, Daniel Gustavo Allasia, Jean Paolo Gomes Minella, Rutinéia Tassi, and Néverton Scariot
EGUsphere, https://doi.org/10.5194/egusphere-2025-244, https://doi.org/10.5194/egusphere-2025-244, 2025
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We explored how water flows in small rural streams to improve tools for better managing water resources. Using a new method, we adjusted existing models to consider the size of rainfall events, showing that water movement patterns change depending on the rain’s intensity. This approach makes predictions more accurate and helps scientists and managers understand water availability and protect ecosystems.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025, https://doi.org/10.5194/hess-29-1615-2025, 2025
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Our study projects how climate change will affect the drying of river segments and stream networks in Europe, using advanced modelling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent and intense and will start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists for evaluating the ecological health of river ecosystem.
Tariq Aziz, Steven K. Frey, David R. Lapen, Susan Preston, Hazen A. J. Russell, Omar Khader, Andre R. Erler, and Edward A. Sudicky
Hydrol. Earth Syst. Sci., 29, 1549–1568, https://doi.org/10.5194/hess-29-1549-2025, https://doi.org/10.5194/hess-29-1549-2025, 2025
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This study determines the value of subsurface water for ecosystem services' supply in an agricultural watershed in Ontario, Canada. Using a fully integrated water model and an economic valuation approach, the research highlights subsurface water's critical role in maintaining watershed ecosystem services. The study informs on the sustainable use of subsurface water and introduces a new method for managing watershed ecosystem services.
Wouter J. M. Knoben, Kasra Keshavarz, Laura Torres-Rojas, Cyril Thébault, Nathaniel W. Chaney, Alain Pietroniro, and Martyn P. Clark
EGUsphere, https://doi.org/10.5194/egusphere-2025-893, https://doi.org/10.5194/egusphere-2025-893, 2025
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Many existing data sets for hydrologic analysis tend treat catchments as single, spatially homogeneous units, focus on daily data and typically do not support more complex models. This paper introduces a data set that goes beyond this setup by: (1) providing data at higher spatial and temporal resolution, (2) specifically considering the data requirements of all common hydrologic model types, (3) using statistical summaries of the data aimed at quantifying spatial and temporal heterogeneity.
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, https://doi.org/10.5194/hess-29-1277-2025, 2025
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Data-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, https://doi.org/10.5194/hess-29-1061-2025, 2025
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This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025, https://doi.org/10.5194/hess-29-1033-2025, 2025
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Owing to differences in the existing published results, we conducted a detailed analysis of the runoff components and future trends in the Yarlung Tsangpo River basin and found that the contributions of snowmelt and glacier melt runoff to streamflow (both ~5 %) are limited and much lower than previous results. The streamflow in this area will continuously increase in the future, but the overestimated contribution of glacier melt could lead to an underestimation of this increasing trend.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Marc Aurel Vischer, Noelia Otero, and Jackie Ma
EGUsphere, https://doi.org/10.5194/egusphere-2024-3649, https://doi.org/10.5194/egusphere-2024-3649, 2025
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We use a neural network to predict the amount of water flowing into rivers. Our focus is on large river catchment areas in central Europe with pronounced human activity. Our model scales efficiently to large amounts of data and is thus able to processes the input without prior aggregation, capturing fine spatial detail and improving prediction in large catchments. Our model’s internal states can be adapted to allow capturing human activity more explicitly in the future.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025, https://doi.org/10.5194/hess-29-785-2025, 2025
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Joško Trošelj and Naota Hanasaki
Hydrol. Earth Syst. Sci., 29, 753–766, https://doi.org/10.5194/hess-29-753-2025, https://doi.org/10.5194/hess-29-753-2025, 2025
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This study presents the first distributed hydrological simulation which confirms claims raised by historians that the eastward diversion project of the Tone River in Japan was conducted 4 centuries ago to increase low flows and subsequent travelling possibilities surrounding the capital, Edo (Tokyo), using inland navigation. We showed that great steps forward can be made for improving quality of life with small human engineering waterworks and small interventions in the regime of natural flows.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
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This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci., 29, 627–653, https://doi.org/10.5194/hess-29-627-2025, https://doi.org/10.5194/hess-29-627-2025, 2025
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Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
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Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Ngo Nghi Truyen Huynh, Pierre-André Garambois, Benjamin Renard, François Colleoni, Jérôme Monnier, and Hélène Roux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3665, https://doi.org/10.5194/egusphere-2024-3665, 2025
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Understanding and modeling flash flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing development of learnable and interpretable process-based models.
Alonso Pizarro, Demetris Koutsoyiannis, and Alberto Montanari
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-389, https://doi.org/10.5194/hess-2024-389, 2025
Revised manuscript accepted for HESS
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We introduce RUMI, a new metric to improve rainfall-runoff simulations. RUMI better captures the link between observed and simulated stream flows by considering uncertainty at a core computation step. Tested on 99 catchments and with the GR4J model, it outperforms traditional metrics by providing more reliable and consistent results. RUMI paves the way for more accurate hydrological predictions.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
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Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
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To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Parthkumar A. Modi, Jared C. Carbone, Keith S. Jennings, Hannah Kamen, Joseph R. Kasprzyk, Bill Szafranski, Cameron W. Wobus, and Ben Livneh
EGUsphere, https://doi.org/10.5194/egusphere-2024-4046, https://doi.org/10.5194/egusphere-2024-4046, 2025
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This study shows that in unmanaged snow-dominated basins, high forecast accuracy doesn’t always lead to high economic value, especially during extreme conditions like droughts. It highlights how irregular errors in modern forecasting systems weaken the connection between accuracy and value. These findings call for forecast evaluations to focus not only on accuracy but also on economic impacts, providing valuable guidance for better water resource management under uncertainty.
Benjamin Guillaume, Adrien Michez, and Aurore Degré
EGUsphere, https://doi.org/10.5194/egusphere-2024-3978, https://doi.org/10.5194/egusphere-2024-3978, 2025
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Nature-based solutions (NbS) can mitigate floods and agricultural droughts by enhancing soil health and restoring hydrological cycles. This study highlights that leveraging soil diversity is key to optimizing NbS performance.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
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The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
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Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
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This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
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Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
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Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
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We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Karsten Schulz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-359, https://doi.org/10.5194/hess-2024-359, 2024
Revised manuscript accepted for HESS
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Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or are also controlled by precipitation intensity. We used soil moisture-dependent and precipitation intensity-conditional transfer functions. We showed that significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential flow paths) in dry soil conditions for both low and high-intensity precipitation.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
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Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Anjana Devanand, Jason Evans, Andy Pitman, Sujan Pal, David Gochis, and Kevin Sampson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3148, https://doi.org/10.5194/egusphere-2024-3148, 2024
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Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.
Sylvain Payraudeau, Pablo Alvarez-Zaldivar, Paul van Dijk, and Gwenaël Imfeld
EGUsphere, https://doi.org/10.5194/egusphere-2024-2840, https://doi.org/10.5194/egusphere-2024-2840, 2024
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Our study focuses on the rising concern of pesticides damaging aquatic ecosystems, which puts drinking water, the environment, and human health at risk. We provided more accurate estimates of how pesticides break down and spread in small water systems, helping to improve pesticide management practices. By using unique chemical markers in our analysis, we enhanced the accuracy of our predictions, offering important insights for better protection of water sources and natural ecosystems.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
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We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Vishal Thakur, Yannis Markonis, Rohini Kumar, Johanna Ruth Thomson, Mijael Rodrigo Vargas Godoy, Martin Hanel, and Oldrich Rakovec
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-341, https://doi.org/10.5194/hess-2024-341, 2024
Revised manuscript accepted for HESS
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Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
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Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
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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