Articles | Volume 27, issue 24
https://doi.org/10.5194/hess-27-4529-2023
© Author(s) 2023. 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-27-4529-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2023
Research article |
| 20 Dec 2023
| 20 Dec 2023
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations
Yuhang Zhang
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Bita Analui
Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA
Phu Nguyen
Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA
Soroosh Sorooshian
Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA
Kuolin Hsu
Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA
Yuxuan Wang
College of Arts and Sciences, University of Virginia, Charlottesville, VA 22903, USA
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Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, Jong Cheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Tan Jackson, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-4194, https://doi.org/10.5194/egusphere-2024-4194, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Our study evaluated 23 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG and JRA-3Q, demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Song Wang, Carlos Sierra, Yiqi Luo, Jinsong Wang, Weinan Chen, Yahai Zhang, Aizhong Ye, and Shuli Niu
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-33, https://doi.org/10.5194/bg-2023-33, 2023
Manuscript not accepted for further review
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Nitrogen is important for plant growth and carbon uptake, which is uaually limited in nature and can constrain carbon storage and impact efforts to combat climate change. We developed a new method of combining data and models to determine if and how much an ecosystem is nitrogen limited. This new method can help determine if and to what extent an ecosystem is nitrogen-limited, providing insight into nutrient limitations on a global scale and guiding ecosystem management decisions.
Miguel Ricardo A. Hilario, Ewan Crosbie, Michael Shook, Jeffrey S. Reid, Maria Obiminda L. Cambaliza, James Bernard B. Simpas, Luke Ziemba, Joshua P. DiGangi, Glenn S. Diskin, Phu Nguyen, F. Joseph Turk, Edward Winstead, Claire E. Robinson, Jian Wang, Jiaoshi Zhang, Yang Wang, Subin Yoon, James Flynn, Sergio L. Alvarez, Ali Behrangi, and Armin Sorooshian
Atmos. Chem. Phys., 21, 3777–3802, https://doi.org/10.5194/acp-21-3777-2021, https://doi.org/10.5194/acp-21-3777-2021, 2021
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This study characterizes long-range transport from major Asian pollution sources into the tropical northwest Pacific and the impact of scavenging on these air masses. We combined aircraft observations, HYSPLIT trajectories, reanalysis, and satellite retrievals to reveal distinct composition and size distribution profiles associated with specific emission sources and wet scavenging. The results of this work have implications for international policymaking related to climate and health.
Ji Li, Daoxian Yuan, Jiao Liu, Yongjun Jiang, Yangbo Chen, Kuo Lin Hsu, and Soroosh Sorooshian
Hydrol. Earth Syst. Sci., 23, 1505–1532, https://doi.org/10.5194/hess-23-1505-2019, https://doi.org/10.5194/hess-23-1505-2019, 2019
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There are no long-term reasonable rainfall data to build a hydrological model in karst river basins to a large extent. In this paper, the PERSIANN-CCS QPEs are employed to estimate the precipitation data as an attempt in the Liujiang karst river basin, 58 270 km2, China. An improved method is proposed to revise the results of the PERSIANN-CCS QPEs. The post-processed PERSIANN-CCS QPE with a distributed hydrological model, the Liuxihe model, has a better performance in karst flood forecasting.
Phu Nguyen, Mohammed Ombadi, Soroosh Sorooshian, Kuolin Hsu, Amir AghaKouchak, Dan Braithwaite, Hamed Ashouri, and Andrea Rose Thorstensen
Hydrol. Earth Syst. Sci., 22, 5801–5816, https://doi.org/10.5194/hess-22-5801-2018, https://doi.org/10.5194/hess-22-5801-2018, 2018
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The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. We evaluate the products over CONUS at different spatial and temporal scales using CPC data. Daily scale is the finest temporal scale used for the evaluation over CONUS. We provide a comparison of the available products at a quasi-global scale. We highlight the strengths and limitations of the PERSIANN products.
Feng Ma, Lifeng Luo, Aizhong Ye, and Qingyun Duan
Hydrol. Earth Syst. Sci., 22, 5697–5709, https://doi.org/10.5194/hess-22-5697-2018, https://doi.org/10.5194/hess-22-5697-2018, 2018
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Predicting meteorological droughts more than 2 months in advance became difficult due to low predictability, leading to weak skill for hydrological droughts in wet seasons. Hydrological drought forecasts showed skills up to 3–6 lead months due to the memory of initial hydrologic conditions in dry seasons. Human activities have increased hydrological predictability during wet seasons in the MHRB. This fills gaps in understanding drought and predictability predictions in endorheic and arid basins.
Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, and Wen-Ping Tsai
Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018, https://doi.org/10.5194/hess-22-5639-2018, 2018
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Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.
Christine A. Shields, Jonathan J. Rutz, Lai-Yung Leung, F. Martin Ralph, Michael Wehner, Brian Kawzenuk, Juan M. Lora, Elizabeth McClenny, Tashiana Osborne, Ashley E. Payne, Paul Ullrich, Alexander Gershunov, Naomi Goldenson, Bin Guan, Yun Qian, Alexandre M. Ramos, Chandan Sarangi, Scott Sellars, Irina Gorodetskaya, Karthik Kashinath, Vitaliy Kurlin, Kelly Mahoney, Grzegorz Muszynski, Roger Pierce, Aneesh C. Subramanian, Ricardo Tome, Duane Waliser, Daniel Walton, Gary Wick, Anna Wilson, David Lavers, Prabhat, Allison Collow, Harinarayan Krishnan, Gudrun Magnusdottir, and Phu Nguyen
Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, https://doi.org/10.5194/gmd-11-2455-2018, 2018
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ARTMIP (Atmospheric River Tracking Method Intercomparison Project) is a community effort with the explicit goal of understanding the uncertainties, and the implications of those uncertainties, in atmospheric river science solely due to detection algorithm. ARTMIP strives to quantify these differences and provide guidance on appropriate algorithmic choices for the science question posed. Project goals, experimental design, and preliminary results are provided.
Xiaomang Liu, Tiantian Yang, Koulin Hsu, Changming Liu, and Soroosh Sorooshian
Hydrol. Earth Syst. Sci., 21, 169–181, https://doi.org/10.5194/hess-21-169-2017, https://doi.org/10.5194/hess-21-169-2017, 2017
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A long-term, global, high-resolution, satellite-based precipitation estimation database (PERSIANN-CDR) was recently released. We evaluate the streamflow simulation capability of PERSIANN-CDR over two major river basins on the Tibetan Plateau. Results show that PERSIANN-CDR is a good alternative for a sparse gauge network and has the potentials for future hydrological and climate studies. The streamflow uncertainties are due to the hydrological model parameters and the length of calibration data.
Xing Yuan, Feng Ma, Linying Wang, Ziyan Zheng, Zhuguo Ma, Aizhong Ye, and Shaoming Peng
Hydrol. Earth Syst. Sci., 20, 2437–2451, https://doi.org/10.5194/hess-20-2437-2016, https://doi.org/10.5194/hess-20-2437-2016, 2016
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An experimental seasonal hydrological forecasting system is established over the Yellow River basin to provide adaptive support in a changing environment. The system consists of downscaled NMME climate prediction, hydrological models calibrated against naturalized streamflow along the mainstream, and a post-processor to account for the human interventions implicitly. As the first paper of a two-part series, this paper investigates the hydrological predictability by using reverse ESP simulations.
Y. Y. Zhang, Q. X. Shao, A. Z. Ye, H. T. Xing, and J. Xia
Hydrol. Earth Syst. Sci., 20, 529–553, https://doi.org/10.5194/hess-20-529-2016, https://doi.org/10.5194/hess-20-529-2016, 2016
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We developed an integrated water system model by coupling multiple water-related processes in hydrology, biogeochemistry, water quality and ecology, and considering the interference of human activities. The parameter sensitivity and autocalibration modules were also developed to improve the simulation efficiency. The proposed model was applied in the Shaying River catchment, which is a highly regulated and heavily polluted region in China.
W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, and C. Miao
Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, https://doi.org/10.5194/hess-19-2409-2015, 2015
R. Sultana, K.-L. Hsu, J. Li, and S. Sorooshian
Hydrol. Earth Syst. Sci., 18, 3553–3570, https://doi.org/10.5194/hess-18-3553-2014, https://doi.org/10.5194/hess-18-3553-2014, 2014
Y. Mao, A. Ye, J. Xu, F. Ma, X. Deng, C. Miao, W. Gong, and Z. Di
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-7441-2014, https://doi.org/10.5194/hessd-11-7441-2014, 2014
Manuscript not accepted for further review
J. Li, Q. Y. Duan, W. Gong, A. Ye, Y. Dai, C. Miao, Z. Di, C. Tong, and Y. Sun
Hydrol. Earth Syst. Sci., 17, 3279–3293, https://doi.org/10.5194/hess-17-3279-2013, https://doi.org/10.5194/hess-17-3279-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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
Heavy-tailed flood peak distributions: What is the effect of the spatial variability of rainfall and runoff generation?
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
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.
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
Althoff, D., Rodrigues, L. N., and Bazame, H. C.: Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble, Stoch. Env. Res. Risk A., 35, 1051-1067, https://doi.org/10.1007/s00477-021-01980-8, 2021.
Bellier, J., Zin, I., and Bontron, G.: Generating coherent ensemble forecasts after hydrological postprocessing: Adaptations of ECC-based methods, Water Resour. Res., 54, 5741–5762, https://doi.org/10.1029/2018WR022601, 2018.
Beven, K.: Changing ideas in hydrology – the case of physically-based models, J. Hydrol., 105, 157–172, https://doi.org/10.1016/0022-1694(90)90161-P, 1989.
Bogner, K. and Pappenberger, F.: Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system, Water Resour. Res., 47, e2010WR009137, https://doi.org/10.1029/2010WR009137, 2011.
Bormann, K. J., Evans, J. P., and McCabe, M. F.: Constraining snowmelt in a temperature-index model using simulated snow densities, J. Hydrol., 517, 652–667, https://doi.org/10.1016/j.jhydrol.2014.05.073, 2014.
Bröcker, J.: Evaluating raw ensembles with the continuous ranked probability score, Q. J. Roy. Meteor. Soc., 138, 1611–1617, https://doi.org/10.1002/qj.1891, 2012.
Brunner, M. I., Hingray, B., Zappa, M., and Favre, A. C.: Future trends in the interdependence between flood peaks and volumes: Hydro-climatological drivers and uncertainty, Water Resour. Res., 55, 4745–4759, https://doi.org/10.1029/2019WR024701, 2019.
Chawanda, C. J., George, C., Thiery, W., Griensven, A. V., Tech, J., Arnold, J., and Srinivasan, R.: User-friendly workflows for catchment modelling: Towards reproducible SWAT+ model studies, Environ. Modell. Softw., 134, 104812, https://doi.org/10.1016/j.envsoft.2020.104812, 2020.
Chen, H., Yong, B., Shen, Y., Liu, J., Hong, Y., and Zhang, J.: Comparison analysis of six purely satellite-derived global precipitation estimates, J. Hydrol., 581, 124376, https://doi.org/10.1016/j.jhydrol.2019.124376, 2020.
Chinese Academy of Sciences Computer Network Information Center (CAS-CNIC): The National Aeronautics and Space Administration Shuttle Radar Topographic Mission (NASA SRTM): Digital elevation model data republication, GSCLOUD [data set], http://www.gscloud.cn, last access: 18 December 2023.
Chinese Academy of Sciences Resource and Environmental Science Data Center (CAS-RESDC): Landuse dataset in China (1980–2015), National Tibetan Plateau/Third Pole Environment Data Center [data set], http://data.tpdc.ac.cn/en/data/a75843b4-6591-4a69-a5e4-6f94099ddc2d (last access: 18 December 2023), 2019.
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 modelling: 1. Modelling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015.
Corzo Perez, G. A., van Huijgevoort, M. H. J., Voß, F., and van Lanen, H. A. J.: On the spatio-temporal analysis of hydrological droughts from global hydrological models, Hydrol. Earth Syst. Sci., 15, 2963–2978, https://doi.org/10.5194/hess-15-2963-2011, 2011.
Cunha, L. K., Mandapaka, P. V., Krajewski, W. F., Mantilla, R., and Bradley, A. A.: Impact of radar-rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model, Water Resour. Res., 48, W10515, https://doi.org/10.1029/2012WR012138, 2012.
Dembélé, M., Hrachowitz, M., Savenije, H. H. G., Mariéthoz, G., and Schaefli, B.: Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets, Water Resour. Res., 56, e2019WR026085, https://doi.org/10.1029/2019WR026085, 2020.
Dong, J., Crow, W. T., and Reichle, R.: Improving Rain/No-Rain Detection Skill by Merging Precipitation Estimates from Different Sources, J. Hydrometeorol., 21, 2419–2429, https://doi.org/10.1175/JHM-D-20-0097.1, 2020.
Du, C., Ye, A., Gan, Y., You, J., Duan, Q., Ma, F., and Hou, J.: Drainage network extraction from a high-resolution DEM using parallel programming in the .NET Framework, J. Hydrol., 555, 506–517, https://doi.org/10.1016/j.jhydrol.2017.10.034, 2017.
Evin, G., Lafaysse, M., Taillardat, M., and Zamo, M.: Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics, Nonlin. Processes Geophys., 28, 467–480, https://doi.org/10.5194/npg-28-467-2021, 2021.
Falck, A. S., Maggioni, V., Tomasella, J., Vila, D. A., and Diniz, F. L. R.: Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins–Araguaia basin in Brazil, J. Hydrol., 527, 943–957, https://doi.org/10.1016/j.jhydrol.2015.05.042, 2015.
Fang, K., Kifer, D., Lawson, K., Feng, D., and Shen, C.: The data synergy effects of time-series deep learning models in hydrology, Water Resour. Res., 58, e2021WR029583, https://doi.org/10.1029/2021WR029583, 2022.
Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst, L., and Wiberg, D.: Global Agroecological Zones Assessment for Agriculture (GAEZ 2008), IIASA, Laxenburg, Austria and FAO, Rome, Italy, [data set], http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 18 December 2023), 2008.
Frame, J. M., Kratzert, F., Raney, A., Rahman, M., Salas, F. R., and Nearing, G. S.: Post-Processing the National Water Model with Long Short-Term Memory Networks for Streamflow Predictions and Model Diagnostics, J. Am. Water Resour. As., 57, 885–905, https://doi.org/10.1111/1752-1688.12964, 2021.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: G-RUN ENSEMBLE: A Multi-Forcing Observation-Based Global Runoff Reanalysis, Water Resour. Res., 57, e2020WR028787, https://doi.org/10.1029/2020WR028787, 2021.
Gneiting, T. and Ranjan, R.: Comparing density forecasts using threshold-and quantile-weighted scoring rules, J. Bus. Econ. Stat., 29, 411–422, https://doi.org/10.1198/jbes.2010.08110, 2011.
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts, calibration and sharpness, J. Roy. Stat. Soc. B, 69, 243–268, https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gou, J., Miao, C., Duan, Q., Tang, Q., Di, Z., Liao, W., Wu, J., and Zhou, R.: Sensitivity Analysis-Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China, Water Resour. Res., 56, e2019WR025968, https://doi.org/10.1029/2019WR025968, 2020.
Gou, J., Miao, C., Samaniego, L., Xiao, M., Wu, J., and Guo, X.: CNRD v1.0: A High-Quality Natural Runoff Dataset for Hydrological and Climate Studies in China, B. Am. Meteorol. Soc., 102, E929–E947, https://doi.org/10.1175/BAMS-D-20-0094.1, 2021.
Herrera, P. A., Marazuela, M. A., and Hofmann, T.: Parameter estimation and uncertainty analysis in hydrological modelling, Wiley Interdisciplinary Reviews-Water, 9, e1569, https://doi.org/10.1002/wat2.1569, 2022.
Honti, M., Scheidegger, A., and Stamm, C.: The importance of hydrological uncertainty assessment methods in climate change impact studies, Hydrol. Earth Syst. Sci., 18, 3301–3317, https://doi.org/10.5194/hess-18-3301-2014, 2014.
Hou, A. Y., Kakar, R. K., Neeck, S., AA, A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation Measurement Mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2013.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD [data set], https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Tan, J.: Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation, https://gpm.nasa.gov/resources/documents/IMERG-V06-Technical-Documentation (last access: 18 December 2023), 2020.
Jajarmizadeh, M., Harun, S., and Salarpour, M.: A review on theoretical consideration and types of models in hydrology, Journal of Environmental Science and Technology, 5, 249–261, https://doi.org/10.3923/jest.2012.249.261, 2012.
Jiang, L. and Bauer-Gottwein, P.: How do GPM IMERG precipitation estimates perform as hydrological model forcing? Evaluation for 300 catchments across Mainland China, J. Hydrol., 572, 486–500, https://doi.org/10.1016/j.jhydrol.2019.03.042, 2019.
Jiang, S., Zheng, Y., Wang, C., and Babovic, V.: Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments, Water Resour. Res., 58, e2021WR030185, https://doi.org/10.1029/2021WR030185, 2022.
Jnelson18: jnelson18/pyquantrf: DOI release (v0.0.3doi), Zenodo [code], https://doi.org/10.5281/zenodo.5815105, 2022.
Jolliffe, I. T. and Stephenson, D. B.: Forecast verification: a practitioner's guide in atmospheric science, John Wiley & Sons, https://doi.org/10.1002/9781119960003, 2012.
Kasraei, B., Heung, B., Saurette, D. D., Schmidt, M. G., Bulmer, C. E., and Bethel, W.: Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning, Environ. Modell. Softw., 144, 105139, https://doi.org/10.1016/j.envsoft.2021.105139, 2021.
Khakbaz, B., Imam, B., Hsu, K., and Sorooshian, S.: From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models, J. Hydrol., 418, 61–77, https://doi.org/10.1016/j.jhydrol.2009.02.021, 2012.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G.: Uncertainty estimation with deep learning for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 26, 1673–1693, https://doi.org/10.5194/hess-26-1673-2022, 2022.
Kobold, M. and Sušelj, K.: Precipitation forecasts and their uncertainty as input into hydrological models, Hydrol. Earth Syst. Sci., 9, 322–332, https://doi.org/10.5194/hess-9-322-2005, 2005.
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.
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, 2019.
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.
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D.: NeuralHydrology – A Python library for Deep Learning research in hydrology, Journal of Open Source Software, 7, 4050, https://doi.org/10.21105/joss.04050, 2022a.
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D.: NeuralHydrology – A Python library for Deep Learning research in hydrology (v.1.2.3), Zenodo [code], https://doi.org/10.5281/zenodo.6326394, 2022b.
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., and Nevo, S.: Caravan – A global community dataset for large-sample hydrology, Sci. Data, 10, 61, https://doi.org/10.1038/s41597-023-01975-w, 2023.
Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S., Hirose, M., Takayabu, Y. N., Ushio, T., and Nakagawa, K.: Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation, IEEE T. Geosci. Remote, 45, 2259–2275, https://doi.org/10.1109/TGRS.2007.895337, 2007.
Kubota, T., Aonashi, K., Ushio, T., Shige, S., Takayabu, Y. N., Kachi, M., Arai, Y., Tashima, T., Masaki, T., and Kawamoto, N.: Global Satellite Mapping of Precipitation (GSMaP) products in the GPM era, Satellite Precipitation Measurement, 1, 355–373, https://doi.org/10.1007/978-3-030-24568-9_20, 2020.
Kubota, T., Aonashi, K., Ushio, T., Shige, S., Takayabu, Y. N., Kachi, M., Arai, Y., Tashima, T., Masaki, T., and Kawamoto, N.: Global Satellite Mapping of Precipitation (GSMaP) products, JAXA Global Rainfall Watch [data set], https://sharaku.eorc.jaxa.jp/GSMaP/index.htm (last access: 18 December 2023), 2023.
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., and Dadson, S. J.: Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models, Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, 2021.
Li, A. H. and Martin, A.: Forest-type regression with general losses and robust forest, Proceedings of the 34th International Conference on Machine Learning, 70, 2091–2100, 2017.
Li, D., Marshall, L., Liang, Z., and Sharma, A.: Hydrologic multi-model ensemble predictions using variational Bayesian deep learning, J. Hydrol., 604, 127221, https://doi.org/10.1016/j.jhydrol.2021.127221, 2022.
Li, M., Wang, Q. J., Bennett, J. C., and Robertson, D. E.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts, Hydrol. Earth Syst. Sci., 19, 1–15, https://doi.org/10.5194/hess-19-1-2015, 2015.
Li, M., Wang, Q. J., Bennett, J. C., and Robertson, D. E.: Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting, Hydrol. Earth Syst. Sci., 20, 3561–3579, https://doi.org/10.5194/hess-20-3561-2016, 2016.
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W., and Di, Z.: A review on statistical postprocessing methods for hydrometeorological ensemble forecasting, Wiley Interdisciplinary Reviews: Water, 4, e1246, https://doi.org/10.1002/wat2.1246, 2017.
Mai, J., Craig, J. R., Tolson, B. A., and Arsenault, R.: The sensitivity of simulated streamflow to individual hydrologic processes across North America, Nat. Commun., 13, 455, https://doi.org/10.1038/s41467-022-28010-7, 2022a.
Mai, J., Shen, H., Tolson, B. A., Gaborit, É., Arsenault, R., Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet, V., and Waddell, J. W.: The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL), Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, 2022b.
Meinshausen, N. and Ridgeway, G.: Quantile regression forests, J. Mach. Learn. Res., 7, 983–999, https://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf (last access: 18 December 2023), 2006.
Miao, C., Gou, J., Fu, B., Tang, Q., Duan, Q., Chen, Z., Lei, H., Chen, J., Guo, J., and Borthwick, A. G.: High-quality reconstruction of China's natural streamflow, Sci. Bull., 67, 547–556, https://doi.org/10.1016/j.scib.2021.09.022, 2022.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nasreen, S., Součková, M., Vargas Godoy, M. R., Singh, U., Markonis, Y., Kumar, R., Rakovec, O., and Hanel, M.: A 500-year annual runoff reconstruction for 14 selected European catchments, Earth Syst. Sci. Data, 14, 4035–4056, https://doi.org/10.5194/essd-14-4035-2022, 2022.
Nearing, G. S., Tian, Y., Gupta, H. V., Clark, M. P., Harrison, K. W., and Weijs, S. V.: A philosophical basis for hydrological uncertainty, Hydrolog. Sci. J., 61, 1666–1678, https://doi.org/10.1080/02626667.2016.1183009, 2016.
Nguyen, P., Ombadi, M., Gorooh, V. A., Shearer, E. J., Sadeghi, M., Sorooshian, S., Hsu, K., Bolvin, D., and Ralph, M. F.: PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset, J. Hydrometeorol., 21, 2893–2906, https://doi.org/10.1175/JHM-D-20-0177.1, 2020a.
Nguyen, P., Shearer, E. J., Ombadi, M., Gorooh, V. A., Hsu, K., Sorooshian, S., Logan, W. S., and Ralph, M.: PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation, B. Am. Meteorol. Soc., 101, E286–E302, https://doi.org/10.1175/BAMS-D-19-0118.1, 2020b.
Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., Huynh, P., Updegraff, G., Hsu, K., Kuligowski, B., Logan, W. S., and Sorooshian, S.: The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data, Scientific Data, 6, 180296, https://doi.org/10.1038/sdata.2018.296, 2019 (data available at: http://chrsdata.eng.uci.edu/, last access: 19 December 2023).
Pan, B., Anderson, G. J., Goncalves, A., Lucas, D. D., Bonfils, C. J., Lee, J., Tian, Y., and Ma, H. Y.: Learning to correct climate projection biases, J. Adv. Model. Earth Sy., 13, e2021MS002509, https://doi.org/10.1029/2021MS002509, 2021.
Parodi, M. U., Giardino, A., van Dongeren, A., Pearson, S. G., Bricker, J. D., and Reniers, A. J. H. M.: Uncertainties in coastal flood risk assessments in small island developing states, Nat. Hazards Earth Syst. Sci., 20, 2397–2414, https://doi.org/10.5194/nhess-20-2397-2020, 2020.
Razavi, S.: Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling, Environ. Modell. Softw., 144, 105159, https://doi.org/10.1016/j.envsoft.2021.105159, 2021.
Schaake, J. C., Hamill, T. M., Buizza, R., and Clark, M.: HEPEX: the hydrological ensemble prediction experiment, B. Am. Meteorol. Soc., 88, 1541–1548, https://doi.org/10.1175/BAMS-88-10-1541, 2007.
Shen, C. and Lawson, K.: Applications of deep learning in hydrology, Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, edited by: Camps-Valls, G., Tuia, D., Zhu, X., and Reichstein, M., John Wiley & Sons Ltd, 283–297, https://doi.org/10.1002/9781119646181.ch19, 2021.
Shen, Y., Ruijsch, J., Lu, M., Sutanudjaja, E. H., and Karssenberg, D.: Random forests-based error-correction of streamflow from a large-scale hydrological model: Using model state variables to estimate error terms, Comput. Geosci., 159, 105019, https://doi.org/10.1016/j.cageo.2021.105019, 2022.
Shen, Z., Yong, B., Gourley, J. J., and Qi, W.: Real-time bias adjustment for satellite-based precipitation estimates over Mainland China, J. Hydrol., 596, 126133, https://doi.org/10.1016/j.jhydrol.2021.126133, 2021.
Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y., and Demir, I.: A comprehensive review of deep learning applications in hydrology and water resources, Water Sci. Technol., 82, 2635–2670, https://doi.org/10.2166/wst.2020.369, 2020.
Sittner, W. T., Schauss, C. E., and Monro, J. C.: Continuous hydrograph synthesis with an API-type hydrologic model, Water Resour. Res., 5, 1007–1022, https://doi.org/10.1029/WR005i005p01007, 1969.
Sivapalan, M.: From engineering hydrology to Earth system science: milestones in the transformation of hydrologic science, Hydrol. Earth Syst. Sci., 22, 1665–1693, https://doi.org/10.5194/hess-22-1665-2018, 2018.
Sordo-Ward, Á., Granados, I., Martín-Carrasco, F., and Garrote, L.: Impact of Hydrological Uncertainty on Water Management Decisions, Water Resour. Manag., 30, 5535–5551, https://doi.org/10.1007/s11269-016-1505-5, 2016.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K. L.: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574, 2018.
Taillardat, M., Mestre, O., Zamo, M., and Naveau, P.: Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model Output Statistics, Mon. Weather Rev., 144, 2375–2393, https://doi.org/10.1175/MWR-D-15-0260.1, 2016.
Taillardat, M., Fougères, A., Naveau, P., and Mestre, O.: Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting, Weather Forecast., 34, 617–634, https://dio.org/10.1175/WAF-D-18-0149.1, 2019.
Tan, M. L., Gassman, P. W., Yang, X., and Haywood, J.: A review of SWAT applications, performance and future needs for simulation of hydro-climatic extremes, Adv. Water Resour., 143, 103662, https://doi.org/10.1016/j.advwatres.2020.103662, 2020.
Tian, Y., Peters-Lidard, C. D., Eylander, J. B., Joyce, R. J., Huffman, G. J., Adler, R. F., Hsu, K., Turk, F. J., Garcia, M., and Zeng, J.: Component analysis of errors in satellite-based precipitation estimates, J. Geophys. Res., 114, D24101, https://doi.org/10.1029/2009JD011949, 2009.
Troin, M., Arsenault, R., Wood, A. W., Brissette, F., and Martel, J. L.: Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years, Water Resour. Res., 57, e2020WR028392, https://doi.org/10.1029/2020WR028392, 2021.
Tsai, W., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and Shen, C.: From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modelling, Nat. Commun., 12, 1–13, https://doi.org/10.1038/s41467-021-26107-z, 2021.
Tyralis, H. and Papacharalampous, G.: Quantile-based hydrological modelling, Water, 13, 3420, https://doi.org/10.3390/w13233420, 2021.
Tyralis, H., Papacharalampous, G., Burnetas, A., and Langousis, A.: Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS, J. Hydrol., 577, 123957, https://doi.org/10.1016/j.jhydrol.2019.123957, 2019.
Wang, Q. J., Robertson, D. E., and Chiew, F. H. S.: A Bayesian joint probability modelling approach for seasonal forecasting of streamflows at multiple sites, Water Resour. Res., 45, W05407, https://doi.org/10.1029/2008WR007355, 2009.
Wu, J., Yen, H., Arnold, J. G., Yang, Y. C. E., Cai, X., White, M. J., Santhi, C., Miao, C., and Srinivasan, R.: Development of reservoir operation functions in SWAT+ for national environmental assessments, J. Hydrol., 583, 124556, https://doi.org/10.1016/j.jhydrol.2020.124556, 2020.
Xia, J.: Identification of a constrained nonlinear hydrological system described by Volterra Functional Series, Water Resour. Res., 27, 2415–2420, https://doi.org/10.1029/91WR01364, 1991.
Xia, J., Wang, G., Tan, G., Ye, A., and Huang, G. H.: Development of distributed time-variant gain model for nonlinear hydrological systems, Sci. China Ser. D, 48, 713–723, https://doi.org/10.1360/03yd0183, 2005.
Xu, L., Chen, N., Moradkhani, H., Zhang, X., and Hu, C.: Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets, Water Resour. Res., 56, e2019WR026444, https://doi.org/10.1029/2019WR026444, 2020.
Yang, Q., Wang, Q. J., and Hakala, K.: Achieving effective calibration of precipitation forecasts over a continental scale, J. Hydrol., 35, 100818, https://doi.org/10.1016/j.ejrh.2021.100818, 2021.
Ye, A., Duan, Q., Zeng, H., Li, L., and Wang, C.: A distributed time-variant gain hydrological model based on remote sensing, Journal of Resources and Ecology, 1, 222–230, https://doi.org/10.3969/j.issn.1674-764x.2010.03.005, 2010.
Ye, A., Duan, Q., Zhan, C., Liu, Z., and Mao, Y.: Improving kinematic wave routing scheme in Community Land Model, Hydrol. Res., 44, 886–903, https://doi.org/10.2166/nh.2012.145, 2013.
Ye, A., Duan, Q., Yuan, X., Wood, E. F., and Schaake, J.: Hydrologic post-processing of MOPEX streamflow simulations, J. Hydrol., 508, 147–156, https://doi.org/10.1016/j.jhydrol.2013.10.055, 2014.
Ye, A., Duan, Q., Schaake, J., Xu, J., Deng, X., Di, Z., Miao, C., and Gong, W.: Post-processing of ensemble forecasts in low-flow period, Hydrol. Process., 29, 2438–2453, https://doi.org/10.1002/hyp.10374, 2015.
Yilmaz, K. K., Gupta, H. V., and Wagener, T.: A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, https://doi.org/10.1029/2007WR006716, 2008.
Zhang, Y. and Ye, A.: Machine Learning for Precipitation Forecasts Postprocessing: Multimodel Comparison and Experimental Investigation, J. Hydrometeorol., 22, 3065–3085, https://doi.org/10.1175/JHM-D-21-0096.1, 2021.
Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., and Hsu, K.: New insights into error decomposition for precipitation products, Geophys. Res. Lett., 48, e2021GL094092, https://doi.org/10.1029/2021GL094092, 2021a.
Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., and Hsu, K.: Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modelling, Remote Sensing, 13, 3061, https://doi.org/10.3390/rs13163061, 2021b.
Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., and Hsu, K.: QRF4P-NRT Probabilistic Post-processing of Near-real-time Satellite Precipitation Estimates using Quantile Regression Forests, Water Resour. Res., 58, e2022WR032117, https://doi.org/10.1029/2022WR032117, 2022a.
Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., and Hsu, K.: Dataset and results for “Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation”, Zenodo [data set], https://doi.org/10.5281/zenodo.7187505, 2022b.
Zhao, L., Duan, Q., Schaake, J., Ye, A., and Xia, J.: A hydrologic post-processor for ensemble streamflow predictions, Adv. Geosci., 29, 51–59, https://doi.org/10.5194/adgeo-29-51-2011, 2011.
Zhou, X., Polcher, J., and Dumas, P.: Representing Human Water Management in a Land Surface Model Using a Supply/Demand Approach, Water Resour. Res., 57, e2020WR028133, https://doi.org/10.1029/2020WR028133, 2021.
Zhu, S., Luo, X., Yuan, X., and Xu, Z.: An improved long short-term memory network for streamflow forecasting in the upper Yangtze River, Stoch. Env. Res. Risk A., 34, 1313–1329, https://doi.org/10.1007/s00477-020-01766-4, 2020.
Zounemat-Kermani, M., Batelaan, O., Fadaee, M., and Hinkelmann, R: Ensemble machine learning paradigms in hydrology: A review, J. Hydrol., 598, 126266, https://doi.org/10.1016/j.jhydrol.2021.126266, 2021.
Short summary
Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Our study shows that while the quantile regression forest (QRF) and countable mixtures of...
