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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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.
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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.
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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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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.
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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
Short summary
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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
Short summary
Short summary
The goal of the paper is to analyze the theory of water age inside a catchment while accounting for multiple outflows. It tries to propose the material under a new perspective where it lines up concepts, cleans the notation, discusses some classical results, and offers some examples that help to relate the modern achievements to the theory of the IUH, clarifying assets of both of them. In doing all of this, it also produces various new results, and some regarding solute transport.
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, https://doi.org/10.5194/hess-20-4641-2016, 2016
Short summary
Short summary
Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.
S. Endrizzi, S. Gruber, M. Dall'Amico, and R. Rigon
Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, https://doi.org/10.5194/gmd-7-2831-2014, 2014
Short summary
Short summary
GEOtop is a fine scale grid-based simulator that represents the heat and water budgets at and below the soil surface, reproduces the highly non-linear interactions between the water and energy balance during soil freezing and thawing and simulates snow cover. The core components of GEOtop 2.0. are described. Based on a synthetic simulation, it is shown that the interaction of processes represented in GEOtop 2.0. can result in phenomena that are relevant for applications involving frozen soils.
G. Formetta, S. K. Kampf, O. David, and R. Rigon
Geosci. Model Dev., 7, 725–736, https://doi.org/10.5194/gmd-7-725-2014, https://doi.org/10.5194/gmd-7-725-2014, 2014
C. Massari, L. Brocca, S. Barbetta, C. Papathanasiou, M. Mimikou, and T. Moramarco
Hydrol. Earth Syst. Sci., 18, 839–853, https://doi.org/10.5194/hess-18-839-2014, https://doi.org/10.5194/hess-18-839-2014, 2014
G. Formetta, R. Rigon, J. L. Chávez, and O. David
Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, https://doi.org/10.5194/gmd-6-915-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
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
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
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Projections of streamflow intermittence under climate change in European drying river networks
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Achieving water budget closure through physical hydrological processes modelling: insights from a large-sample study
Analyzing the generalization capabilities of hybrid hydrological models for extrapolation to extreme events
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric-hydrological model
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
HESS Opinions: The sword of Damocles of the impossible flood
Scale-dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
A diversity centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Metamorphic testing of machine learning and conceptual hydrologic models
The influence of human activities on streamflow reductions during the megadrought in central Chile
Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
Lack of robustness of hydrological models: A large-sample diagnosis and an attempt to identify the hydrological and climatic drivers
Exploring the Potential Processes Controls for Changes of Precipitation-Runoff Relationships in Non-stationary Environments
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Long Short-Term Memory Networks for Real-time Flood Forecast Correction: A Case Study for an Underperforming Hydrologic Model
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Catchments do not strictly follow Budyko curves over multiple decades but deviations are minor and predictable
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.
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.
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.
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.
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.
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.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
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This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
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A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
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We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
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Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
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. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Revised manuscript accepted for HESS
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Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-230, https://doi.org/10.5194/hess-2024-230, 2024
Revised manuscript accepted for HESS
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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 Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called 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.
Eduardo Acuna Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2024-2147, https://doi.org/10.5194/egusphere-2024-2147, 2024
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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.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1464, https://doi.org/10.5194/egusphere-2024-1464, 2024
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Our study conducted a detailed analysis of runoff component and future trend in the Yarlung Tsangpo River basin owing to the existed differences in the published results, and find that the contributions of snowmelt and glacier melt runoff to streamflow were limited, both for ~5 % which were much lower than previous results. The streamflow there will continuously increase in the future, but the overestimated contribution from glacier melt would lead to an underestimation on such increasing trend.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
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Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
Short summary
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
Short summary
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart Lane, and Francesco Comiti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1687, https://doi.org/10.5194/egusphere-2024-1687, 2024
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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 an overparametrization limit is reached.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript accepted for HESS
Short summary
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
Short summary
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This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-118, https://doi.org/10.5194/hess-2024-118, 2024
Revised manuscript accepted for HESS
Short summary
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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 controls for 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.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2024-1030, https://doi.org/10.5194/egusphere-2024-1030, 2024
Short summary
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Accurate early warning systems are crucial for reducing damages caused by flooding events. In this study, we demonstrate 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.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-120, https://doi.org/10.5194/hess-2024-120, 2024
Revised manuscript accepted for HESS
Short summary
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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 quantified deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
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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