Articles | Volume 26, issue 19
https://doi.org/10.5194/hess-26-5163-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5163-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Oct 2022
Research article |
| 14 Oct 2022
| 14 Oct 2022
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion
Bureau of Economic Geology, The University of Texas at Austin, Austin,
TX, USA
Peishi Jiang
Pacific Northwest National Laboratory, Richland, WA, USA
Zong-Liang Yang
Department of Geological Sciences, The University of Texas at Austin,
Austin, TX, USA
Yangxinyu Xie
Department of Computer Science, The University of Texas at Austin,
Austin, TX, USA
now at: The Department of Statistics and Data Science at The University
of Pennsylvania, Pennsylvania, USA
Xingyuan Chen
Pacific Northwest National Laboratory, Richland, WA, USA
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We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
A. Y. Sun, J. Chen, and J. Donges
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Terrestrial water storage (TWS) plays a key role in global water and energy cycles. This work applies complex climate networks to analyzing spatial patterns in TWS. A comparative analysis is conducted using a remotely sensed (GRACE) and a model-generated TWS data set. Our results reveal hotspots of TWS anomalies around the global land surfaces. Prospects are offered on using network connectivity as constraints to further improve current global land surface models.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
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Yong-He Liu and Zong-Liang Yang
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We present a new hydrological model based on the popular Noah-MP. It was developed by translating the FORTRAN version of Noah-MP to C# code. A river routing model was integrated. It can run in parallel on Windows systems using today's PCs. The NMP-Hydro code has been tested to ensure it produces the same results as the original WRF-Hydro. Maps and changes in variables show consistent results with the original model. We think it is a reliable replacement for Noah-MP in WRF-Hydro 3.0.
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Using Moderate Resolution Imaging Spectroradiometer data products, we quantitatively estimate the resistance and resilience of ecosystem functions to wildfires that occurred in the Columbia River Basin in 2015. Carbon state exhibits lower resistance and resilience than the ecosystem fluxes. The random forest feature importance analysis indicates that burn severity plays a minor role in the resilience of grassland, while it has a relatively major role in the resilience of forest and savanna.
James Stegen, Amy Burgin, Michelle Busch, Joshua Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian Deines, Julia Guimond, Peter Regier, Kenton Rod, Edward Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon Sweetman, Jianqiu Zheng, Daniel Allen, Elizabeth Herndon, Beth Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad Patel
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The loss and gain of surface water (variable inundation) is a common process across Earth. Global change shifts variable inundation dynamics, highlighting a need for unified understanding that transcends individual variably inundated ecosystems (VIEs). We review literature, highlight challenges, and emphasize opportunities to generate transferable knowledge by viewing VIEs through a common lens. We aim to inspire the emergence of a cross-VIE community based on a proposed continuum approach.
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This research examines oxygen use in rivers, which is central to the carbon cycle and water quality. The study focused on an environmentally diverse river basin in the western United States and found that oxygen use in river water was very slow and influenced by factors like water temperature and concentrations of nutrients and carbon in the water. Results suggest that in the study system, most of the oxygen use occurs via mechanisms directly or indirectly associated with riverbed sediments.
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
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An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
Pin Shuai, Xingyuan Chen, Utkarsh Mital, Ethan T. Coon, and Dipankar Dwivedi
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Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, https://doi.org/10.5194/hess-26-1727-2022, 2022
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It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Yilin Fang, Xingyuan Chen, Jesus Gomez Velez, Xuesong Zhang, Zhuoran Duan, Glenn E. Hammond, Amy E. Goldman, Vanessa A. Garayburu-Caruso, and Emily B. Graham
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Surface water quality along river corridors can be improved by the area of the stream bed and stream bank in which stream water mixes with shallow groundwater or hyporheic zones (HZs). These zones are ubiquitous and dominated by microorganisms that can process the dissolved nutrients exchanged at this interface of these zones. The modulation of surface water quality can be simulated by connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer representation.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Han Qiu, Dongwei Gui, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-246, https://doi.org/10.5194/hess-2019-246, 2019
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Geosci. Model Dev., 12, 3119–3133, https://doi.org/10.5194/gmd-12-3119-2019, https://doi.org/10.5194/gmd-12-3119-2019, 2019
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Observation and simulation can provide the temporal and spatial variation of vegetation characteristics, while they are not satisfactory for understanding the mechanism of the exchange between ecosystems and atmosphere. Data assimilation (DA) can combine the observation and models via mathematical statistical analysis. Results show that the ensemble adjust Kalman filter (EAKF) is the optimal algorithm. In addition, models perform better when the DA accepts a higher proportion of observations.
Gautam Bisht, Maoyi Huang, Tian Zhou, Xingyuan Chen, Heng Dai, Glenn E. Hammond, William J. Riley, Janelle L. Downs, Ying Liu, and John M. Zachara
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A fully coupled three-dimensional surface and subsurface land model, CP v1.0, was developed to simulate three-way interactions among river water, groundwater, and land surface processes. The coupled model can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.
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CO2 loss from soil to the atmosphere (
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Hui Wan, Kai Zhang, Philip J. Rasch, Balwinder Singh, Xingyuan Chen, and Jim Edwards
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X. Cai, Z.-L. Yang, J. B. Fisher, X. Zhang, M. Barlage, and F. Chen
Geosci. Model Dev., 9, 1–15, https://doi.org/10.5194/gmd-9-1-2016, https://doi.org/10.5194/gmd-9-1-2016, 2016
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A terrestrial nitrogen dynamics model is integrated into Noah-MP. The new model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). The addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. This improvement advances the capability of Noah-MP to simultaneously predict weather and water quality.
Q. Jin, J. Wei, Z.-L. Yang, B. Pu, and J. Huang
Atmos. Chem. Phys., 15, 9897–9915, https://doi.org/10.5194/acp-15-9897-2015, https://doi.org/10.5194/acp-15-9897-2015, 2015
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Satellite data show that Indian summer monsoon (ISM) rainfall is closely associated with Middle East dust aerosols. Numerical modeling shows that the increased ISM rainfall is related to the enhanced southwesterly flow and moisture transport from the Arabian Sea to the Indian subcontinent, associated with the development of an anomalous low-pressure system over the Iranian Plateau and the Arabian Sea due to dust-induced atmospheric heating.
A. Y. Sun, J. Chen, and J. Donges
Nonlin. Processes Geophys., 22, 433–446, https://doi.org/10.5194/npg-22-433-2015, https://doi.org/10.5194/npg-22-433-2015, 2015
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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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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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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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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.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-181, https://doi.org/10.5194/hess-2024-181, 2024
Revised manuscript accepted for HESS
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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 compared to large catchments, and that spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show an effect. The results can improve estimations of occurrence probabilities of extreme floods.
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
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
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
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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
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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
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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
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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
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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
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013. a
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A.,
Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., et al.:
Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nat.
Commun., 12, 1–12, 2021. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438,
303–309, 2005. a
Battaglin, W. A., Hay, L. E., and Markstrom, S. L.: Watershed Scale Response to
Climate Change-East River Basin, Colorado, US Geological Survey Fact Sheet,
3126, 6 pp., 2011. a
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the
green transition, Nat. Clim. Change, 11, 80–83, 2021. a
Beck, H. E., De Roo, A., and van Dijk, A. I.: Global maps of streamflow
characteristics based on observations from several thousand catchments,
J. Hydrometeorol., 16, 1478–1501, 2015. a
Best, J.: Anthropogenic stresses on the world's big rivers, Nat. Geosci.,
12, 7–21, 2019. a
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT mission and
its capabilities for land hydrology, in: Remote sensing and water resources,
Springer, 117–147, 2016. a
Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A., Merz, B.,
Arheimer, B., Aronica, G. T., Bilibashi, A., Bonacci, O., Borga, M., et al.:
Changing climate shifts timing of European floods, Science, 357, 588–590,
2017. a
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P.:
Geometric deep learning: going beyond Euclidean data, IEEE Signal Proc.
Mag., 34, 18–42, 2017. a
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P.: Geometric
deep learning: Grids, groups, graphs, geodesics, and gauges, arXiv [preprint], https://doi.org/10.48550/arXiv.2104.13478, 2021. a
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y.: Spectral networks and locally
connected networks on graphs, arXiv [preprint], https://doi.org/10.48550/arXiv.1312.6203, 2013. a
Bureau of Reclamation: Taylor Park Reservoir,
https://www.usbr.gov/projects/index.php?id=236, last access: 22 March 2022. a
Camps-Valls, G., Tuia, D., Zhu, X. X., and Reichstein, M.: Deep learning for
the Earth Sciences: A comprehensive approach to remote sensing, climate
science and geosciences, John Wiley & Sons, ISBN: 9781119646143, 2021. a
Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from
continents: Latitudinal and seasonal variations, J. Hydrometeorol.,
3, 660–687, 2002. a
Dauphin, Y. N., Fan, A., Auli, M., and Grangier, D.: Language modeling with
gated convolutional networks, Proceedings of the 34th International Conference on Machine Learning, PMLR, 70, 933–941, Sydney, Australia, 2017. a
Davenport, F. V., Herrera-Estrada, J. E., Burke, M., and Diffenbaugh, N. S.:
Flood size increases nonlinearly across the western United States in response
to lower snow-precipitation ratios, Water Resour. Res., 56,
e2019WR025571, https://doi.org/10.1029/2019WR025571, 2020. a
De Groot, R. S., Wilson, M. A., and Boumans, R. M.: A typology for the
classification, description and valuation of ecosystem functions, goods and
services, Ecol. Econom., 41, 393–408, 2002. a
Defferrard, M., Bresson, X., and Vandergheynst, P.: Convolutional neural
networks on graphs with fast localized spectral filtering, Adv. Neur. Info. Proc. Syst., 29, 3844–3852, 2016. 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
Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: The backbone of the climate
network, Europhys. Lett., 87, 48007, https://doi.org/10.1209/0295-5075/87/48007, 2009a. a
Dottori, F., Szewczyk, W., Ciscar, J.-C., Zhao, F., Alfieri, L., Hirabayashi,
Y., Bianchi, A., Mongelli, I., Frieler, K., Betts, R. A., and Feyen L.: Increased
human and economic losses from river flooding with anthropogenic warming,
Nat. Clim. Change, 8, 781–786, 2018. a
Feng, D., Fang, K., and Shen, C.: Enhancing streamflow forecast and extracting
insights using long-short term memory networks with data integration at
continental scales, Water Resour. Res., 56, e2019WR026793, https://doi.org/10.1029/2019WR026793, 2020. a
Feng, D., Lawson, K., and Shen, C.: Mitigating prediction error of deep
learning streamflow models in large data-sparse regions with ensemble
modeling and soft data, Geophys. Res. Lett., 48, e2021GL092999, https://doi.org/10.1029/2021GL092999,
2021. a
Gochis, D., Barlage, M., Dugger, A., FitzGerald, K., Karsten, L., McAllister,
M., McCreight, J., Mills, J., RafieeiNasab, A., Read, L., Sampson, K., Yates, D., and Yu, W.: The
WRF-Hydro modeling system technical description (Version 5.0), NCAR
Technical Note, 107 pp., https://ral.ucar.edu/sites/default/files/public/WRFHydroV5TechnicalDescription.pdf (last access: 22 March 2022), 2018. a
Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F.,
Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., et al.: Mapping the
world's free-flowing rivers, Nature, 569, 215–221, 2019. a
Hagberg, A., Swart, P., and Schult, D.: Exploring network structure, dynamics, and function using NetworkX (No. LA-UR-08-05495; LA-UR-08-5495), Los Alamos National Lab.(LANL), Los Alamos, NM (United States), https://networkx.org (last access: 22 March 2022), 2008. a
Hamilton, W. L.: Graph representation learning, Synthesis Lectures on Artifical
Intelligence and Machine Learning, 46, Morgan & Claypool Publishers, Williston, VT, ISBN: 1681739631, 2020. a
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput.,
9, 1735–1780, 1997. a
Hrachowitz, M., Savenije, H., Blöschl, G., McDonnell, J., Sivapalan, M.,
Pomeroy, J., Arheimer, B., Blume, T., Clark, M., Ehret, U., et al.: A decade
of Predictions in Ungauged Basins (PUB) – a review, Hydrol. Sci. J., 58, 1198–1255, 2013. a
Hu, Z., Dong, Y., Wang, K., Chang, K.-W., and Sun, Y.: Gpt-GNN: Generative
pre-training of graph neural networks, in: Proceedings of the 26th ACM SIGKDD
International Conference on Knowledge Discovery & Data Mining,
1857–1867, 2020. a
Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H.,
Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., Dwivedi, D., Falco, N., Faybishenko, B., Maxwell, R., Nico, P., Steefel, C., Steltzer, H., Tokunaga, T., Tran, P. A., Wainwright, H., and Varadharajan, C.: The
East River, Colorado, Watershed: A mountainous community testbed for
improving predictive understanding of multiscale hydrological–biogeochemical
dynamics, Vadose Zone J., 17, 1–25, 2018. a
Jia, J. and Benson, A. R.: A unifying generative model for graph learning
algorithms: Label propagation, graph convolutions, and combinations, arXiv [preprint], https://doi.org/10.48550/arXiv.2101.07730, 2021. a
Jia, X., Zwart, J., Sadler, J., Appling, A., Oliver, S., Markstrom, S.,
Willard, J., Xu, S., Steinbach, M., Read, J., and Kumar, V.: Physics-guided
recurrent graph model for predicting flow and temperature in river networks,
in: Proceedings of the 2021 SIAM International Conference on Data Mining
(SDM), Society for Industrial and Applied Mathematics, 612–620, 2021. a, b, c, d
Johnson, M. and Blodgett, D.: NOAA National Water Model Reanalysis Data at
RENCI, HydroShare,
https://doi.org/10.4211/hs.89b0952512dd4b378dc5be8d2093310f (last access: 22 March 2022),
2020. a, b
Kashinath, K., Mustafa, M., Albert, A., Wu, J., Jiang, C., Esmaeilzadeh, S.,
Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D., Yu, R., Walters, R., White, B., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A., Hassanzadeh, P., and Prabhat:
Physics-informed machine learning: case studies for weather and climate
modelling, Philos. T. Roy. Soc. A, 379,
20200093, https://doi.org/10.1098/rsta.2020.0093, 2021. a, b
Kitzmiller, D. H., Wu, W., Zhang, Z., Patrick, N., and Tan, X.: The analysis of
record for calibration: a high-resolution precipitation and surface weather
dataset for the united states, in: AGU Fall Meeting Abstracts, Vol. 2018, H41H–06, 2018. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and
Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting
the power of machine learning, Water Resour. Res., 55,
11344–11354, 2019a. a
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019b. a
Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S.: A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 25, 2685–2703, https://doi.org/10.5194/hess-25-2685-2021, 2021. a
Le, X.-H., Nguyen, D.-H., Jung, S., Yeon, M., and Lee, G.: Comparison of deep
learning techniques for river streamflow forecasting, IEEE Access, 9,
71805–71820, 2021. a
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Proc., 27, 2171–2186, 2013. a
Li, Q., Han, Z., and Wu, X.-M.: Deeper insights into graph convolutional
networks for semi-supervised learning, in: Proceedings of the AAAI Conference
on Artificial Intelligence, Vol. 32, ISBN 978-1-57735-800-8, 2018. a
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David,
C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood, E. F.: Global
reconstruction of naturalized river flows at 2.94 million reaches, Water
Resour. Res., 55, 6499–6516, 2019. a
Loshchilov, I. and Hutter, F.: Fixing weight decay regularization in adam,
arXiv [preprint], https://doi.org/10.48550/arXiv.1711.05101, 2018. a
Lu, D., Konapala, G., Painter, S. L., Kao, S.-C., and Gangrade, S.: Streamflow
simulation in data-scarce basins using Bayesian and physics-informed machine
learning models, J. Hydrometeorol., 22, 1421–1438, 2021. a
Malik, N., Bookhagen, B., Marwan, N., and Kurths, J.: Analysis of spatial and
temporal extreme monsoonal rainfall over South Asia using complex networks,
Clim. Dynam., 39, 971–987, 2012. a
Mizukami, N., Clark, M. P., Gharari, S., Kluzek, E., Pan, M., Lin, P., Beck,
H. E., and Yamazaki, D.: A Vector-Based River Routing Model for Earth System
Models: Parallelization and Global Applications, J. Adv. Model. Earth Syst., 13, e2020MS002434, https://doi.org/10.1029/2020MS002434, 2021. a, b, c
Moore, R. B. and Dewald, T. G.: The Road to NHDPlus–Advancements in Digital
Stream Networks and Associated Catchments, J. Am. Water Resour. Assoc., 52, 890–900, 2016. a
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015. a
Nonnenmacher, M. and Greenberg, D. S.: Deep emulators for differentiation,
forecasting, and parametrization in Earth science simulators, J. Adv. Model. Earth Syst., 13, e2021MS002554, https://doi.org/10.1029/2021MS002554, 2021. a
Palmer, M. A., Reidy Liermann, C. A., Nilsson, C., Flörke, M., Alcamo, J.,
Lake, P. S., and Bond, N.: Climate change and the world's river basins:
anticipating management options, Front. Ecol. Environ., 6,
81–89, 2008. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen,
T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: Pytorch: An imperative
style, high-performance deep learning library, Adv. Neural Info. Proc. Syst., 32, , ISBN: 9781713807933, 2019. a
Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A.,
Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., and Anandkumar A.:
FourCastNet: A Global Data-driven High-resolution Weather Model using
Adaptive Fourier Neural Operators, arXiv [preprint], https://doi.org/10.48550/arXiv.2202.11214, 2022. a, b
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a
Phillips, J. D., Schwanghart, W., and Heckmann, T.: Graph theory in the
geosciences, Earth-Sci. Rev., 143, 147–160, 2015. a
Pokhrel, Y., Hanasaki, N., Koirala, S., Cho, J., Yeh, P. J.-F., Kim, H., Kanae,
S., and Oki, T.: Incorporating anthropogenic water regulation modules into a
land surface model, J. Hydrometeorol., 13, 255–269, 2012. a
PRISM Climate Group: 30-year normals,
https://prism.oregonstate.edu/normals/, last access: 6 September 2022. a
Qin, Y., Abatzoglou, J. T., Siebert, S., Huning, L. S., AghaKouchak, A.,
Mankin, J. S., Hong, C., Tong, D., Davis, S. J., and Mueller, N. D.:
Agricultural risks from changing snowmelt, Nat. Clim. Change, 10,
459–465, 2020. a
Rasmussen, C. E. and Williams, C. K. I.: Gaussian processes for machine
learning, The MIT Press, ISBN: 9780262182539, Cambridge, MA, 2006. a
Rong, Y., Huang, W., Xu, T., and Huang, J.: Dropedge: Towards deep graph
convolutional networks on node classification,
arXiv [preprint], https://doi.org/10.48550/arXiv.1907.10903, 2019. a, b
Salas, F. R., Somos-Valenzuela, M. A., Dugger, A., Maidment, D. R., Gochis,
D. J., David, C. H., Yu, W., Ding, D., Clark, E. P., and Noman, N.: Towards
real-time continental scale streamflow simulation in continuous and discrete
space, J. Am. Water Resour. Assoc., 54, 7–27,
2018. a
Saunders, W.: Preparation of DEMs for use in environmental modeling analysis,
Hydrologic and Hydraulic Modeling Support, Redlands, CA: ESRI, 29–51,
2000. a
Schulthess, T. C., Bauer, P., Wedi, N., Fuhrer, O., Hoefler, T., and Schär,
C.: Reflecting on the goal and baseline for exascale computing: a roadmap
based on weather and climate simulations, Comput. Sci. Eng., 21, 30–41, 2018. a
Shen, C.: A transdisciplinary review of deep learning research and its
relevance for water resources scientists, Water Resour. Res., 54,
8558–8593, 2018. a
Simley, J. D. and Carswell Jr., W. J.: The national map–hydrography, US
Geological Survey Fact Sheet, 2009–3054, 4 p., 2009. a
Sun, A. Y., Wang, D., and Xu, X.: Monthly streamflow forecasting using Gaussian
process regression, J. Hydrol., 511, 72–81, 2014. a
Tejedor, A., Longjas, A., Passalacqua, P., Moreno, Y., and Foufoula-Georgiou,
E.: Multiplex networks: A framework for studying multiprocess multiscale
connectivity via coupled-network theory with an application to river deltas,
Geophys. Res. Lett., 45, 9681–9689, 2018. a
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wilhelmi, N., Wei, Y.,
Devarakonda, R., and Cook, R.: Daymet: Daily surface weather on a 1 km grid
for North America, 1980–2008, Oak Ridge National Laboratory (ORNL)
Distributed Active Archive Center for Biogeochemical Dynamics (DAAC), https://doi.org/10.3334/ORNLDAAC/1219, 2012. a
Todini, E.: A model conditional processor to assess predictive uncertainty in
flood forecasting, Int. J. River Basin Manage., 6,
123–137, 2008. a
USGS: Watershed Boundary Dataset,
https://www.usgs.gov/national-hydrography/watershed-boundary-dataset, last access: 6 September 2022b. a
Van Beek, L., Wada, Y., and Bierkens, M. F.: Global monthly water stress: 1.
Water balance and water availability, Water Resour. Res., 47, 7, https://doi.org/10.1029/2010WR009791, 2011. a
Wang, Y., Jin, J., Zhang, W., Yu, Y., Zhang, Z., and Wipf, D.: Bag of tricks
for node classification with graph neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2103.13355, 2021. a
Weerts, A. H., Winsemius, H. C., and Verkade, J. S.: Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales), Hydrol. Earth Syst. Sci., 15, 255–265, https://doi.org/10.5194/hess-15-255-2011, 2011. a
Weiler, M., McGlynn, B. L., McGuire, K. J., and McDonnell, J. J.: How does
rainfall become runoff?, A combined tracer and runoff transfer function
approach, Water Resour. Res., 39, 11, https://doi.org/10.1029/2003WR002331, 2003. a
Willard, J., Jia, X., Xu, S., Steinbach, M., and Kumar, V.: Integrating
physics-based modeling with machine learning: A survey,
ACM Computing Surveys (CSUR), https://dl.acm.org/doi/abs/10.1145/3514228, 2020. a
Winnick, M. J., Carroll, R. W., Williams, K. H., Maxwell, R. M., Dong, W., and
Maher, K.: Snowmelt controls on concentration-discharge relationships and the
balance of oxidative and acid-base weathering fluxes in an alpine catchment,
East River, Colorado, Water Resour. Res., 53, 2507–2523, 2017. a
Winsemius, H. C., Aerts, J. C., Van Beek, L. P., Bierkens, M. F., Bouwman, A.,
Jongman, B., Kwadijk, J. C., Ligtvoet, W., Lucas, P. L., Van Vuuren, D. P.,
and Ward, P. J.: Global drivers of future river flood risk, Nat. Clim. Change, 6,
381–385, 2016. a
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L.,
Alonge, C., Wei, H., Meng, J., et al.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of model
products, J. Geophys. Res.-Atmos., 117, D3, https://doi.org/10.1029/2011JD016048, 2012. a
Yang, T., Sun, F., Gentine, P., Liu, W., Wang, H., Yin, J., Du, M., and Liu,
C.: Evaluation and machine learning improvement of global hydrological
model-based flood simulations, Environ. Res. Lett., 14, 114027, https://doi.org/10.1088/1748-9326/ab4d5e,
2019. a
Ye, A., Duan, Q., Yuan, X., Wood, E. F., and Schaake, J.: Hydrologic
post-processing of MOPEX streamflow simulations, J. Hydrol., 508,
147–156, 2014. a
Yeo, I.-K. and Johnson, R. A.: A new family of power transformations to improve
normality or symmetry, Biometrika, 87, 954–959, 2000. a
Yuval, J. and O'Gorman, P. A.: Stable machine-learning parameterization of
subgrid processes for climate modeling at a range of resolutions, Nat. Commun., 11, 1–10, 2020. a
Zhang, L., Shi, Z., Han, J., Shi, A., and Ma, D.: Furcanext: End-to-end
monaural speech separation with dynamic gated dilated temporal convolutional
networks, in: International Conference on Multimedia Modeling,
Springer, 653–665, 2020. a
Zhao, F., Veldkamp, T. I., Frieler, K., Schewe, J., Ostberg, S., Willner, S.,
Schauberger, B., Gosling, S. N., Schmied, H. M., and Portmann, F. T.: The
critical role of the routing scheme in simulating peak river discharge in
global hydrological models, Environ. Res. Lett., 12, 075003, https://doi.org/10.1088/1748-9326/aa7250,
2017. a
Zhu, X. and Ghahramani, Z.: Learning from labeled and unlabeled data with label
propagation, https://doi.org/10.1.1.14.3864, 2002. a
Short summary
High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
High-resolution river modeling is of great interest to local governments and stakeholders for...
