Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-505-2022
© 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-26-505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2022
Research article |
| 31 Jan 2022
| 31 Jan 2022
Regionalization of hydrological model parameters using gradient boosting machine
Zhihong Song
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Jun Xia
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Key Laboratory of Water Cycle and Related Land Surface Processes,
Chinese Academy of Sciences, Beijing, 10010, China
Gangsheng Wang
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Institute for Water-Carbon Cycles and Carbon Neutrality, Wuhan
University, Wuhan, 430072, China
Dunxian She
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Chen Hu
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Si Hong
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
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Yongyong Zhang, Yongqiang Zhang, Xiaoyan Zhai, Jun Xia, Qiuhong Tang, Wei Wang, Jian Wu, Xiaoyu Niu, and Bing Han
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-126, https://doi.org/10.5194/hess-2024-126, 2024
Revised manuscript accepted for HESS
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It is challenging to investigate flood variabilities and their formation mechanisms from massive event samples. This study explores spatiotemporal variabilities of 1446 flood events using hierarchical and partitional clustering methods. Control mechanisms of meteorological and physio-geographical factors are explored for individual flood event classes using constrained rank analysis. It provides insights into comprehensive changes of flood events, and aids in flood prediction and control.
Song Liu, Dunxian She, Liping Zhang, and Jun Xia
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-414, https://doi.org/10.5194/hess-2022-414, 2023
Publication in HESS not foreseen
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Quantifying the uncertainty in streamflow predictions is a major challenge, with research and operational significance. This study advances the field of catchment-scale hydrological modelling by developing an improved uncertainty analysis technique that provides more reliable and accurate probabilistic streamflow predictions. This finding provides hydrologists with robust modelling tools for handling hydrological modelling uncertainties in engineering practices.
Yunfan Zhang, Lei Cheng, Lu Zhang, Shujing Qin, Liu Liu, Pan Liu, Yanghe Liu, and Jun Xia
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-5, https://doi.org/10.5194/hess-2021-5, 2021
Manuscript not accepted for further review
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We use statistical methods and data assimilation method with physical model to verify that prolonged drought can induce non-stationarity in the control catchment rainfall-runoff relationship, which causes three inconsistent results at the Red Hill paired-catchment site. The findings are fundamental to correctly use long-term historical data and effectively assess ecohydrological impacts of vegetation change given that extreme climate events are projected to occur more frequently in the future.
Yuhan Yang, Jie Yin, Mingwu Ye, Dunxian She, and Jia Yu
Nat. Hazards Earth Syst. Sci., 20, 181–195, https://doi.org/10.5194/nhess-20-181-2020, https://doi.org/10.5194/nhess-20-181-2020, 2020
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Emergency medical service (EMS) response is important for pre-hospital lifesaving, but disasters increase the difficulty of rescue, which increases the pressure on EMS facilities. In order to avoid the failure of EMS facilities during disasters, we propose a multi-coverage optimal location model for EMS facilities based on results of disaster risk assessment. Results showed that the optimized EMS locations reduced the delay in response and significantly increased the number of rescued people.
Bin Xiong, Lihua Xiong, Jun Xia, Chong-Yu Xu, Cong Jiang, and Tao Du
Hydrol. Earth Syst. Sci., 23, 4453–4470, https://doi.org/10.5194/hess-23-4453-2019, https://doi.org/10.5194/hess-23-4453-2019, 2019
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We develop a new indicator of reservoir effects, called the rainfall–reservoir composite index (RRCI). RRCI, coupled with the effects of static reservoir capacity and scheduling-related multivariate rainfall, has a better performance than the previous indicator in terms of explaining the variation in the downstream floods affected by reservoir operation. A covariate-based flood frequency analysis using RRCI can provide more reliable downstream flood risk estimation.
Zhengke Pan, Pan Liu, Shida Gao, Jun Xia, Jie Chen, and Lei Cheng
Hydrol. Earth Syst. Sci., 23, 3405–3421, https://doi.org/10.5194/hess-23-3405-2019, https://doi.org/10.5194/hess-23-3405-2019, 2019
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Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.
Hong Wang, Fubao Sun, Jun Xia, and Wenbin Liu
Hydrol. Earth Syst. Sci., 21, 1929–1945, https://doi.org/10.5194/hess-21-1929-2017, https://doi.org/10.5194/hess-21-1929-2017, 2017
Xingguo Mo, Xuejuan Chen, Shi Hu, Suxia Liu, and Jun Xia
Hydrol. Earth Syst. Sci., 21, 295–310, https://doi.org/10.5194/hess-21-295-2017, https://doi.org/10.5194/hess-21-295-2017, 2017
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Attributing changes in ET and GPP is crucial to impact and adaptation assessment of climate change over the NCP. Simulations with the VIP ecohydrological model illustrated relative contributions of climatic change, CO2 fertilization, and management to ET and GPP. Global radiation was the cause of GPP decline in summer, while air warming intensified the water cycle and advanced plant productivity in spring. Agronomical improvement was the main driver of crop productivity enhancement.
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Extended-range forecasting of stream water temperature with deep-learning models
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Projections of streamflow intermittence under climate change in European drying river networks
Economic valuation of subsurface water contributions to watershed ecosystem services using a fully integrated groundwater–surface-water model
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Simulating the Tone River eastward diversion project in Japan carried out 4 centuries ago
Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers
Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
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
CONCN: A high-resolution, integrated surface water-groundwater ParFlow modeling platform of continental China
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
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)
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
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
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?
Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: An analysis based on 63 catchments in southeast China
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
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
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The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025, https://doi.org/10.5194/hess-29-1725-2025, 2025
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In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until we observe overparameterization.
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025, https://doi.org/10.5194/hess-29-1685-2025, 2025
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We generate operational forecasts of daily maximum stream water temperature for 32 consecutive days at 54 stations in Switzerland with our best-performing data-driven model. The average forecast error is 0.38 °C for 1 d ahead and increases to 0.90 °C for 32 d ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025, https://doi.org/10.5194/hess-29-1749-2025, 2025
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Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use inputs of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025, https://doi.org/10.5194/hess-29-1615-2025, 2025
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Our study projects how climate change will affect the drying of river segments and stream networks in Europe, using advanced modelling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent and intense and will start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists for evaluating the ecological health of river ecosystem.
Tariq Aziz, Steven K. Frey, David R. Lapen, Susan Preston, Hazen A. J. Russell, Omar Khader, Andre R. Erler, and Edward A. Sudicky
Hydrol. Earth Syst. Sci., 29, 1549–1568, https://doi.org/10.5194/hess-29-1549-2025, https://doi.org/10.5194/hess-29-1549-2025, 2025
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This study determines the value of subsurface water for ecosystem services' supply in an agricultural watershed in Ontario, Canada. Using a fully integrated water model and an economic valuation approach, the research highlights subsurface water's critical role in maintaining watershed ecosystem services. The study informs on the sustainable use of subsurface water and introduces a new method for managing watershed ecosystem services.
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, https://doi.org/10.5194/hess-29-1277-2025, 2025
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Data-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, https://doi.org/10.5194/hess-29-1061-2025, 2025
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This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025, https://doi.org/10.5194/hess-29-1033-2025, 2025
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Owing to differences in the existing published results, we conducted a detailed analysis of the runoff components and future trends in the Yarlung Tsangpo River basin and found that the contributions of snowmelt and glacier melt runoff to streamflow (both ~5 %) are limited and much lower than previous results. The streamflow in this area will continuously increase in the future, but the overestimated contribution of glacier melt could lead to an underestimation of this increasing trend.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025, https://doi.org/10.5194/hess-29-785-2025, 2025
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Joško Trošelj and Naota Hanasaki
Hydrol. Earth Syst. Sci., 29, 753–766, https://doi.org/10.5194/hess-29-753-2025, https://doi.org/10.5194/hess-29-753-2025, 2025
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This study presents the first distributed hydrological simulation which confirms claims raised by historians that the eastward diversion project of the Tone River in Japan was conducted 4 centuries ago to increase low flows and subsequent travelling possibilities surrounding the capital, Edo (Tokyo), using inland navigation. We showed that great steps forward can be made for improving quality of life with small human engineering waterworks and small interventions in the regime of natural flows.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
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This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci., 29, 627–653, https://doi.org/10.5194/hess-29-627-2025, https://doi.org/10.5194/hess-29-627-2025, 2025
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Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
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Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
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.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-292, https://doi.org/10.5194/hess-2024-292, 2024
Revised manuscript accepted for HESS
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We developed the first high-resolution, integrated surface water-groundwater hydrologic model of the entire continental China using ParFlow. The model shows good performance of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
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.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279, https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript accepted for HESS
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Hydrologic models are needed to provide simulations of water availability, floods and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models, and thus cannot easily be used to select an appropriate model for a specific place.
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.
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.
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-221, https://doi.org/10.5194/hess-2024-221, 2024
Revised manuscript accepted for HESS
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Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what it truly reflects in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and time scales for accurate drought characterization and monitoring.
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.
Jean-Luc Martel, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
EGUsphere, https://doi.org/10.5194/egusphere-2024-2133, https://doi.org/10.5194/egusphere-2024-2133, 2024
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This study compares Long Short-Term Memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrolgical models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1438, https://doi.org/10.5194/egusphere-2024-1438, 2024
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Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conducted simulation experiments using data with various temporal resolutions across multiple catchments, and found that higher resolution data does not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.
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.
Cited articles
Adnan, R. M., Liang, Z., Trajkovic, S., Zounemat-Kermani, M., Li, B., and
Kisi, O.: Daily streamflow prediction using optimally pruned extreme learning machine, J. Hydrol., 577, 123981, https://doi.org/10.1016/j.jhydrol.2019.123981, 2019.
Akbarimehr, M. and Naghdi, R.: Assessing the relationship of slope and runoff volume on skid trails (Case study: Nav 3 district), J. Forest Sci., 58, 357–362, https://doi.org/10.17221/26/2012-JFS, 2012.
Bai, P., Liu, X., Zhang, Y., and Liu, C.: Incorporating vegetation dynamics
noticeably improved performance of hydrological model under vegetation greening, Sci. Total Environ., 643, 610–622, https://doi.org/10.1016/j.scitotenv.2018.06.233, 2018a.
Bai, P., Liu, X., and Liu, C.: Improving hydrological simulations by
incorporating GRACE data for model calibration, J. Hydrol., 557, 291–304,
https://doi.org/10.1016/j.jhydrol.2017.12.025, 2018b.
Bao, Z., Zhang, J., Liu, J., Fu, G., Wang, G., He, R., Yan, X., Jin, J., and
Liu, H.: Comparison of regionalization approaches based on regression and
similarity for predictions in ungauged catchments under multiple hydro-climatic conditions, J. Hydrol., 466–467, 37–46,
https://doi.org/10.1016/j.jhydrol.2012.07.048, 2012.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Miralles, D. G., Mcvicar, T. R., Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of
hydrologic model parameters, Water Resour. Res., 52, 3599–3622,
https://doi.org/10.1002/2015WR018247, 2016.
Beck, H. E., Pan, M., Lin, P., Seibert, J., Dijk, A. I. J. M., and Wood, E. F.: Global Fully Distributed Parameter Regionalization Based on Observed
Streamflow From 4,229 Headwater Catchments, J. Geophys. Res.-Atmos., 125, e2019JD031485, https://doi.org/10.1029/2019JD031485, 2020.
Beven, K. J.: Rainfall-runoff modelling: the primer, John Wiley & Sons,
https://doi.org/10.1002/9781119951001, 2001.
Blöschl, G.: Rainfall-Runoff Modeling of Ungauged Catchments, in: Encyclopedia of Hydrological Sciences, edited, 1–19, https://doi.org/10.1002/0470848944.hsa140, 2005.
Bouwman, A., Savchuk, A., Abbaspourghomi, A., and Visser, B.: Automated Step
Detection in Inertial Measurement Unit Data From Turkeys, Front. Genet., 11,
207, https://doi.org/10.3389/fgene.2020.00207, 2020.
Cai, M., Yang, S., Zeng, H., Zhao, C., and Wang, S.: A Distributed Hydrological Model Driven by Multi-Source Spatial Data and Its Application
in the Ili River Basin of Central Asia, Water Resour. Manage., 28,
2851–2866, https://doi.org/10.1007/s11269-014-0641-z, 2014.
Castiglioni, S., Lombardi, L., Toth, E., Castellarin, A., and Montanari, A.:
Calibration of rainfall-runoff models in ungauged basins: A regional maximum
likelihood approach, Adv. Water Resour., 33, 1235–1242, https://doi.org/10.1016/j.advwatres.2010.04.009, 2010.
Chaplot, V. A. M. and Le Bissonnais, Y.: Runoff Features for Interrill Erosion at Different Rainfall Intensities, Slope Lengths, and Gradients in an Agricultural Loessial Hillslope, Soil Sci. Soc. Am. J., 67, 844–851,
https://doi.org/10.2136/sssaj2003.8440, 2003.
Chiew, F. H. S. and Siriwardena, L.: Estimation of SIMHYD Parameter Values
for Application in Ungauged Catchments, in: MODSIM 2005 International Congress on Modelling and Simulation, Modelling and Simulation Simulation Society of Australia and New Zealand, Melbourne, Australia, 2883–2889, 2005.
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.
Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., and Stisen,
S.: Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model, Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, 2018.
Donohue, R. J., Roderick, M. L., and Mcvicar, T. R.: On the importance of
including vegetation dynamics in Budyko's hydrological model, Hydrol. Earth
Syst. Sci., 11, 983–995, https://doi.org/10.5194/hess-11-983-2007, 2007.
Donohue, R. J., Roderick, M. L., and Mcvicar, T. R.: Can dynamic vegetation
information improve the accuracy of Budyko's hydrological model?, J. Hydrol.,
390, 23–34, https://doi.org/10.1016/j.jhydrol.2010.06.025, 2010.
Duan, Q., Sorooshian, S., and Gupta, V. K.: Effective and efficient global
optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015–1031, https://doi.org/10.1029/91WR02985, 1992.
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global
optimization method for calibrating watershed models, J. Hydrol., 158, 265–284, https://doi.org/10.1016/0022-1694(94)90057-4, 1994.
Dunne, T. and Leopold, L. B.: Water in environmental planning, Macmillan,
ISBN 978-0716700791, 1978.
Fan, C., Song, C., Liu, K., Ke, L., Xue, B., Chen, T., Fu, C., and Cheng, J.: Century-Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach, Water Resour. Res., 57, e2020WR028831, https://doi.org/10.1029/2020WR028831, 2021.
Fang, H., Liang, S., Townshend, J. R., and Dickinson, R. E.: Spatially and
temporally continuous LAI data sets based on an integrated filtering method:
Examples from North America, Remote Sens. Environ., 112, 75–93,
https://doi.org/10.1016/j.rse.2006.07.026, 2008.
Finger, D., Vis, M., Huss, M., and Seibert, J.: The value of multiple data set calibration versus model complexity for improving the performance of
hydrological models in mountain catchments, Water Resour. Res., 51, 1939–1958, https://doi.org/10.1002/2014WR015712, 2015.
Freeze, R. A.: Role of subsurface flow in generating surface runoff: 2. Upstream source areas, Water Resour. Res., 8, 1272–1283,
https://doi.org/10.1029/WR008i005p01272, 1972.
Freeze, R. A.: Streamflow generation, Rev. Geophys., 12, 627–647,
https://doi.org/10.1029/RG012i004p00627, 1974.
Friedman, J., Hastie, T., and Tibshirani, R.: Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), Ann. Stat., 28, 337–407, https://doi.org/10.1214/aos/1016218223, 2000.
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine,
Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Garg, V., Nikam, B. R., Thakur, P. K., and Aggarwal, S. P.: Assessment of the
effect of slope on runoff potential of a watershed using NRCS-CN method, Int. J. Hydrol. Sci. Technol., 3, 141–159, https://doi.org/10.1504/IJHST.2013.057626, 2013.
Gerten, D.: A vital link: water and vegetation in the Anthropocene, Hydrol.
Earth Syst. Sci., 17, 3841–3852, https://doi.org/10.5194/hess-17-3841-2013, 2013
Guo, Y., Zhang, Y., Zhang, L., and Wang, Z.: Regionalization of hydrological
modeling for predicting streamflow in ungauged catchments: A comprehensive
review, Wires Water, 8, e1487, https://doi.org/10.1002/wat2.1487, 2021.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration, J. Hydrol. Eng., 4, 135–143, https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135), 1999.
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.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first
high-resolution meteorological forcing dataset for land process studies over
China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
He, Y., Bárdossy, A., and Zehe, E.: A review of regionalisation for
continuous streamflow simulation, Hydrol. Earth Syst. Sci., 15, 3539–3553, https://doi.org/10.5194/hess-15-3539-2011, 2011.
Heuvelmans, G., Muys, B., and Feyen, J.: Regionalisation of the parameters
of a hydrological model: Comparison of linear regression models with artificial neural nets, J. Hydrol., 319, 245–265,
https://doi.org/10.1016/j.jhydrol.2005.07.030, 2006.
Hobeichi, S., Abramowitz, G., Evans, J., and Beck, H. E.: Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product, Hydrol. Earth Syst. Sci., 23, 851–870, https://doi.org/10.5194/hess-23-851-2019, 2019.
Hollander, M., Wolfe, D. A., and Chicken, E.: Nonparametric statistical
methods, John Wiley & Sons, New York, https://doi.org/10.1002/9781119196037, 2013.
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., Mcdonnell, J. J.,
Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret,
U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013.
Huang, C.: Empirical Analysis of Slope and Runoff For Sediment Delivery from
Interrill Areas, Soil Sci. Soc. Am. J., 59, 982–990,
https://doi.org/10.2136/sssaj1995.03615995005900040004x, 1995.
Huang, P., Li, Z., Chen, J., Li, Q., and Yao, C.: Event-based hydrological
modeling for detecting dominant hydrological process and suitable model
strategy for semi-arid catchments, J. Hydrol., 542, 292–303,
https://doi.org/10.1016/j.jhydrol.2016.09.001, 2016.
Huang, Q., Qin, G., Zhang, Y., Tang, Q., Liu, C., Xia, J., Chiew, F. H. S.,
and Post, D.: Using Remote Sensing Data-Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments, Water Resour. Res., 56, e2020WR028205, https://doi.org/10.1029/2020WR028205, 2020.
Hundecha, Y. and Bárdossy, A.: Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model, J. Hydrol., 292, 281–295, https://doi.org/10.1016/j.jhydrol.2004.01.002, 2004.
Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., and Yang, H.: Machine learning and artificial intelligence to aid climate
change research and preparedness, Environ. Res. Lett., 14, 124007,
https://doi.org/10.1088/1748-9326/ab4e55, 2019.
Immerzeel, W. W. and Droogers, P.: Calibration of a distributed hydrological
model based on satellite evapotranspiration, J. Hydrol., 349, 411–424,
https://doi.org/10.1016/j.jhydrol.2007.11.017, 2008.
Ivanov, V. Y., Bras, R. L., and Vivoni, E. R.: Vegetation-hydrology dynamics in complex terrain of semiarid areas: 1. A mechanistic approach to modeling
dynamic feedbacks, Water Resour. Res., 44, W03429, https://doi.org/10.1029/2006WR005588, 2008.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425,
264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Knoben, W. J. M., Woods, R. A., and Freer, J. E.: A Quantitative Hydrological
Climate Classification Evaluated With Independent Streamflow Data, Water
Resour. Res., 54, 5088–5109, https://doi.org/10.1029/2018WR022913, 2018.
Koskinen, M., Tahvanainen, T., Sarkkola, S., Menberu, M. W., Laurén, A.,
Sallantaus, T., Marttila, H., Ronkanen, A., Parviainen, M., Tolvanen, A.,
Koivusalo, H., and Nieminen, M.: Restoration of nutrient-rich forestry-drained peatlands poses a risk for high exports of dissolved organic carbon, nitrogen, and phosphorus, Sci. Total Environ., 586, 858–869,
https://doi.org/10.1016/j.scitotenv.2017.02.065, 2017.
Kuczera, G. and Mroczkowski, M.: Assessment of hydrologic parameter uncertainty and the worth of multiresponse data, Water Resour. Res., 34,
1481–1489, https://doi.org/10.1029/98WR00496, 1998.
Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A.,
Cooper, T., Mayer, Z., Kenkel, B., and Benesty, M.: R Package `caret':
classification and Regression Training, GitHub, https://github.com/topepo/caret/ (last access: 28 January 2022), 2020.
Lawrence, R.: Classification of remotely sensed imagery using stochastic
gradient boosting as a refinement of classification tree analysis, Remote
Sens. Environ., 90, 331–336, https://doi.org/10.1016/j.rse.2004.01.007, 2004.
Lei, H., Yang, D., and Huang, M.: Impacts of climate change and vegetation
dynamics on runoff in the mountainous region of the Haihe River basin in the
past five decades, J. Hydrol., 511, 786–799, https://doi.org/10.1016/j.jhydrol.2014.02.029, 2014.
Leuning, R., Zhang, Y., Rajaud, A., Cleugh, H., and Tu, K.: A simple surface
conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation, Water Resour. Res., 44, W10419, https://doi.org/10.1029/2007WR006562, 2008.
Li, H., Zhang, Y., Chiew, F. H. S., and Xu, S.: Predicting runoff in ungauged
catchments by using Xinanjiang model with MODIS leaf area index, J. Hydrol.,
370, 155–162, https://doi.org/10.1016/j.jhydrol.2009.03.003, 2009.
Liao, S., Liu, Z., Liu, B., Cheng, C., Jin, X., and Zhao, Z.: Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees, Hydrol. Earth Syst. Sci., 24, 2343–2363, https://doi.org/10.5194/hess-24-2343-2020, 2020.
Lima, A. R., Cannon, A. J., and Hsieh, W. W.: Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation, Environ. Model. Softw., 73, 175–188,
https://doi.org/10.1016/j.envsoft.2015.08.002, 2015.
Liu, F., Zhang, G., Song, X., Li, D., Zhao, Y., Yang, J., Wu, H., and Yang,
F.: High-resolution and three-dimensional mapping of soil texture of China,
Geoderma, 361, 114061, https://doi.org/10.1016/j.geoderma.2019.114061, 2020.
Livneh, B. and Lettenmaier, D. P.: Regional parameter estimation for the
unified land model, Water Resour. Res., 49, 100–114, https://doi.org/10.1029/2012WR012220, 2013.
Lohmann, D., Nolte-Holube, R., and Raschke, E.: A large-scale horizontal
routing model to be coupled to land surface parametrization schemes, Tellus A, 48, 708–721, https://doi.org/10.3402/tellusa.v48i5.12200, 1996.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Mckenzie, N. J., Jacquier, D. W., Ashton, L. J., and Cresswell, H. P.: Estimation of soil properties using the Atlas of Australian Soils, CSIRO
Land and Water Technical Report 11/00, available at: https://www.asris.csiro.au/themes/Atlas.html#Atlas_Digital (last access: 28 January 2022), 2000.
Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D., Nijssen, B., Rakovec, O., and Samaniego, L.: Towards seamless large-domain parameter estimation for hydrologic models, Water Resour. Res., 53, 8020–8040, https://doi.org/10.1002/2017WR020401, 2017.
Mohan, S. and Bhaskaran, P. K.: Evaluation of CMIP5 climate model projections for surface wind speed over the Indian Ocean region, Clim. Dynam., 53, 5415–5435, https://doi.org/10.1007/s00382-019-04874-2, 2019.
Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren,
L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl,
G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer,
B., Boegh, E., Schymanski, S. J., Di Baldassarre, G., Yu, B., Hubert, P.,
Huang, Y., Schumann, A., Post, D. A., Srinivasan, V., Harman, C., Thompson,
S., Rogger, M., Viglione, A., Mcmillan, H., Characklis, G., Pang, Z., and
Belyaev, V.: “Panta Rhei – Everything Flows”: Change in hydrology and
society – The IAHS Scientific Decade 2013–2022, Hydrolog. Sci. J., 58, 1256–1275, https://doi.org/10.1080/02626667.2013.809088, 2013.
Montgomery, D. R. and Dietrich, W. E.: Runoff generation in a steep, soil-mantled landscape, Water Resour. Res., 38, 1–7, https://doi.org/10.1029/2001WR000822, 2002.
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.
Natekin, A. and Knoll, A.: Gradient boosting machines, a tutorial, Front.
Neurorobot., 7, 21, https://doi.org/10.3389/fnbot.2013.00021, 2013.
Newman, A. J., Mizukami, N., Clark, M. P., Wood, A. W., Nijssen, B., and Nearing, G.: Benchmarking of a Physically Based Hydrologic Model, J.
Hydrometeorol., 18, 2215–2225, https://doi.org/10.1175/JHM-D-16-0284.1, 2017.
Nijzink, R. C., Almeida, S., Pechlivanidis, I. G., Capell, R., Gustafssons, D., Arheimer, B., Parajka, J., Freer, J., Han, D., Wagener, T., Nooijen, R. R. P., Savenije, H. H. G., and Hrachowitz, M.: Constraining Conceptual Hydrological Models With Multiple Information Sources, Water Resour. Res., 54, 8332–8362, https://doi.org/10.1029/2017WR021895, 2018.
Ning, L., Xia, J., Zhan, C., and Zhang, Y.: Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis, J. Arid Land, 8, 506–520,
https://doi.org/10.1007/s40333-016-0126-4, 2016.
Oubeidillah, A. A., Kao, S. C., Ashfaq, M., Naz, B. S., and Tootle, G.: A
large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US, Hydrol. Earth Syst. Sci., 18, 67–84, https://doi.org/10.5194/hess-18-67-2014, 2014.
Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Le Moine, N.:
Spatial proximity, physical similarity, regression and ungaged catchments: A
comparison of regionalization approaches based on 913 French catchments, Water Resour. Res., 44, W03413, https://doi.org/10.1029/2007WR006240, 2008.
Oudin, L., Kay, A., Andréassian, V., and Perrin, C.: Are seemingly physically similar catchments truly hydrologically similar?, Water Resour.
Res., 46, W11558, https://doi.org/10.1029/2009WR008887, 2010.
Pagliero, L., Bouraoui, F., Diels, J., Willems, P., and Mcintyre, N.: Investigating regionalization techniques for large-scale hydrological modelling, J. Hydrol., 570, 220–235, https://doi.org/10.1016/j.jhydrol.2018.12.071, 2019.
Parajka, J., Merz, R., and Blöschl, G.: A comparison of regionalisation
methods for catchment model parameters, Hydrol. Earth Syst. Sci., 9, 157–171, https://doi.org/10.5194/hess-9-157-2005, 2005.
Parajka, J., Andreassian, V., Archfield, S., Rdossy, A., Chiew, F., Duan, Q., Gelfan, A., Hlavcova, K., Merz, R., Mcintyre, N., Oudin, L., Perrin, C., Rogger, M., Salinas, J. L., Savenije, H., Skoien, J. O., Wagener, T., Zehe, E., and Zhang, Y.: Predictions of runoff hydrographs in ungauged basins, in: Runoff prediction in ungauged basins: Synthesis across processes, places and scales, edited, Cambridge University Press, 227–269, https://doi.org/10.1017/CBO9781139235761.013, 2013a.
Parajka, J., Viglione, A., Rogger, M., Salinas, J. L., Sivapalan, M., and
Blöschl, G.: Comparative assessment of predictions in ungauged basins –
Part 1: Runoff-hydrograph studies, Hydrol. Earth Syst. Sci., 17, 1783–1795, https://doi.org/10.5194/hess-17-1783-2013, 2013b.
Post, D. A. and Jakeman, A. J.: Relationships Between Catchment Attributes And Hydrological Response Characteristics In Small Australian Mountain Ash Catchmants, Hydrol. Process., 10, 877–892,
https://doi.org/10.1002/(SICI)1099-1085(199606)10:6<877::AID-HYP377>3.0.CO;2-T, 1996.
Prieto, C., Le Vine, N., Kavetski, D., García, E., and Medina, R.: Flow
Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests, Water Resour. Res., 55, 4364–4392, https://doi.org/10.1029/2018WR023254, 2019.
Rajaee, T., Ebrahimi, H., and Nourani, V.: A review of the artificial intelligence methods in groundwater level modeling, J. Hydrol., 572, 336–351, https://doi.org/10.1016/j.jhydrol.2018.12.037, 2019.
Razavi, T. and Coulibaly, P.: Streamflow Prediction in Ungauged Basins: Review of Regionalization Methods, J. Hydrol. Eng., 18, 958–975,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000690, 2013.
Ruffin, C., King, R. L., and Younan, N. H.: A Combined Derivative Spectroscopy and Savitzky–Golay Filtering Method for the Analysis of Hyperspectral Data, Gisci. Remote Sens., 45, 1–15, https://doi.org/10.2747/1548-1603.45.1.1, 2008.
Samuel, J., Coulibaly, P., and Metcalfe, R. A.: Estimation of Continuous
Streamflow in Ontario Ungauged Basins: Comparison of Regionalization Methods, J. Hydrol. Eng., 16, 447–459, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000338, 2011.
Schütz, N., Leichtle, A. B., and Riesen, K.: A comparative study of pattern recognition algorithms for predicting the inpatient mortality risk
using routine laboratory measurements, Artif. Intel. Rev., 52, 2559–2573, https://doi.org/10.1007/s10462-018-9625-3, 2019.
Sefton, C. E. M. and Howarth, S. M.: Relationships between dynamic response
characteristics and physical descriptors of catchments in England and Wales,
J. Hydrol., 211, 1–16, https://doi.org/10.1016/S0022-1694(98)00163-2, 1998.
Seibert, J. and Vis, M. J. P.: Teaching hydrological modeling with a user-friendly catchment-runoff-model software package, Hydrol. Earth Syst.
Sci., 16, 3315–3325, https://doi.org/10.5194/hess-16-3315-2012, 2012.
Shen, C.: A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists, Water Resour. Res., 54, 8558–8593, https://doi.org/10.1029/2018WR022643, 2018.
Shu, C. and Ouarda, T. B. M. J.: Improved methods for daily streamflow
estimates at ungauged sites, Water Resour. Res., 48, W02523, https://doi.org/10.1029/2011WR011501, 2012.
Song, Z. H.: Songzh101/GBM_HydroModel_Regionalization: The regionalization of hydrological model, Zenodo [code], https://doi.org/10.5281/zenodo.5914086, 2022.
Sun, A. Y., Wang, D., and Xu, X.: Monthly streamflow forecasting using Gaussian Process Regression, J. Hydrol., 511, 72–81, https://doi.org/10.1016/j.jhydrol.2014.01.023, 2014.
Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C.,
Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K.,
Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O.,
Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.
Tarboton, D. G.: Rainfall-runoff processes, 1, Utah State University, available at: https://hydrology.usu.edu/rrp/ (last access: 28 January 2022), 2003.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Tesfa, T. K., Tarboton, D. G., Chandler, D. G., and Mcnamara, J. P.: Modeling
soil depth from topographic and land cover attributes, Water Resour. Res., 45, W10438, https://doi.org/10.1029/2008WR007474, 2009.
Thompson, S. E., Harman, C. J., Konings, A. G., Sivapalan, M., Neal, A., and
Troch, P. A.: Comparative hydrology across AmeriFlux sites: The variable
roles of climate, vegetation, and groundwater, Water Resour. Res., 47, W00J07, https://doi.org/10.1029/2010WR009797, 2011.
Vandewiele, G. L. and Elias, A.: Monthly water balance of ungauged catchments obtained by geographical regionalization, J. Hydrol., 170, 277–291, https://doi.org/10.1016/0022-1694(95)02681-E, 1995.
van Dijk, A. I. J. M. and Bruijnzeel, L. A.: Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description, J. Hydrol., 247, 230–238,
https://doi.org/10.1016/s0022-1694(01)00392-4, 2001.
Wang, G., Xia, J., and Chen, J.: Quantification of effects of climate variations and human activities on runoff by a monthly water balance model:
A case study of the Chaobai River basin in northern China, Water Resour. Res., 45, W00A11, https://doi.org/10.1029/2007WR006768, 2009.
Wang, Y., Nan, Z., Chen, H., and Wu, X.: Correction of daily precipitation
data of ITPCAS dataset over the Qinghai-Tibetan Plateau with KNN model, in:
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 10–15 July 2016, Beijing, China, 593–596, 2016.
Waseem, M., Ajmal, M., and Kim, T.: Improving the flow duration curve
predictability at ungauged sites using a constrained hydrologic regression technique, KSCE J. Civ. Eng., 20, 3012–3021, https://doi.org/10.1007/s12205-016-0038-z, 2016.
Wei, Z., Meng, Y., Zhang, W., Peng, J., and Meng, L.: Downscaling SMAP soil
moisture estimation with gradient boosting decision tree regression over the
Tibetan Plateau, Remote Sens. Environ., 225, 30–44, https://doi.org/10.1016/j.rse.2019.02.022, 2019.
Wheater, H., Sorooshian, S., and Sharma, K. D.: Hydrological modelling in arid and semi-arid areas, Cambridge University Press, https://doi.org/10.1017/CBO9780511535734, 2007.
Xenochristou, M., Hutton, C., Hofman, J., and Kapelan, Z.: Water Demand Forecasting Accuracy and Influencing Factors at Different Spatial Scales Using a Gradient Boosting Machine, Water Resour. Res., 56, e2019WR026304, https://doi.org/10.1029/2019WR026304, 2020.
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.
Xia, R., Wang, G., Zhang, Y., Yang, P., Yang, Z., Ding, S., Jia, X., Yang, C., Liu, C., Ma, S., Lin, J., Wang, X., Hou, X., Zhang, K., Gao, X., Duan, P., and Qian, C.: River algal blooms are well predicted by antecedent
environmental conditions, Water Res., 185, 116221,
https://doi.org/10.1016/j.watres.2020.116221, 2020.
Xie, K., Liu, P., Zhang, J., Wang, G., Zhang, X., and Zhou, L.: Identification of spatially distributed parameters of hydrological models
using the dimension-adaptive key grid calibration strategy, J. Hydrol.,
598, 125772, https://doi.org/10.1016/j.jhydrol.2020.125772, 2020.
Xu, C. Y.: Estimation of Parameters of a Conceptual Water Balance Model for
Ungauged Catchments, Water Resour. Manage., 13, 353–368, https://doi.org/10.1023/A:1008191517801, 1999.
Yan, J., Jia, S., Lv, A., and Zhu, W.: Water Resources Assessment of China's
Transboundary River Basins Using a Machine Learning Approach, Water Resour.
Res., 55, 632–655, https://doi.org/10.1029/2018WR023044, 2019.
Yang, K. and He, J.: China meteorological forcing dataset (1979–2018),
National Tibetan Plateau Data Center, https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file, 2019.
Yang, K., He, J., Tang, W., Qin, J., and Cheng, C. C. K.: On downward shortwave and longwave radiations over high altitude regions: Observation
and modeling in the Tibetan Plateau, Agr. Forest Meteorol., 150, 38–46,
https://doi.org/10.1016/j.agrformet.2009.08.004, 2010.
Yang, X., Yong, B., Ren, L., Zhang, Y., and Long, D.: Multi-scale validation
of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements, Int. J. Remote Sens., 38, 5688–5709,
https://doi.org/10.1080/01431161.2017.1346400, 2017.
Yang, X., Magnusson, J., Rizzi, J., and Xu, C.: Runoff prediction in ungauged catchments in Norway: comparison of regionalization approaches, Hydrol. Res., 49, 487–505, https://doi.org/10.2166/nh.2017.071, 2018.
Yang, X., Magnusson, J., Huang, S., Beldring, S., and Xu, C.: Dependence of
regionalization methods on the complexity of hydrological models in multiple
climatic regions, J. Hydrol., 582, 124357, https://doi.org/10.1016/j.jhydrol.2019.124357, 2020.
Yang, Y., Pan, M., Beck, H. E., Fisher, C. K., Beighley, R. E., Kao, S. C.,
Hong, Y., and Wood, E. F.: In Quest of Calibration Density and Consistency in
Hydrologic Modeling: Distributed Parameter Calibration against Streamflow
Characteristics, Water Resour. Res., 55, 7784–7803, https://doi.org/10.1029/2018WR024178, 2019.
Yaseen, Z. M., El-Shafie, A., Jaafar, O., Afan, H. A., and Sayl, K. N.:
Artificial intelligence based models for stream-flow forecasting: 2000–2015, J. Hydrol., 530, 829–844, https://doi.org/10.1016/j.jhydrol.2015.10.038, 2015.
Young, A. R.: Stream flow simulation within UK ungauged catchments using a
daily rainfall-runoff model, J. Hydrol., 320, 155–172,
https://doi.org/10.1016/j.jhydrol.2005.07.017, 2006.
Zecharias, Y. B. and Brutsaert, W.: Recession characteristics of groundwater
outflow and base flow from mountainous watersheds, Water Resour. Res., 24, 1651–1658, https://doi.org/10.1029/WR024i010p01651, 1988.
Zeng, S., Xia, J., Chen, X., Zou, L., Du, H., and She, D.: Integrated land-surface hydrological and biogeochemical processes in simulating water,
energy and carbon fluxes over two different ecosystems, J. Hydrol., 582,
124390, https://doi.org/10.1016/j.jhydrol.2019.124390, 2020.
Zhan, C., Song, X., Xia, J., and Tong, C.: An efficient integrated approach
for global sensitivity analysis of hydrological model parameters, Environ.
Model. Softw., 41, 39–52, https://doi.org/10.1016/j.envsoft.2012.10.009, 2013.
Zhang, X., Tang, Q., Pan, M., and Tang, Y.: A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China, J. Hydrometeorol., 15,
2067–2084, https://doi.org/10.1175/JHM-D-13-0170.1, 2014.
Zhang, Y., Chiew, F. H. S., Zhang, L., Leuning, R., and Cleugh, H. A.:
Estimating catchment evaporation and runoff using MODIS leaf area index and
the Penman–Monteith equation, Water Resour. Res., 44, W10420, https://doi.org/10.1029/2007WR006563, 2008.
Zhang, Y., Chiew, F. H. S., Zhang, L., and Li, H.: Use of Remotely Sensed
Actual Evapotranspiration to Improve Rainfall–Runoff Modeling in Southeast
Australia, J. Hydrometeorol., 10, 969–980, https://doi.org/10.1175/2009JHM1061.1, 2009.
Zhang, Y., Chiew, F. H. S., Peña-Arancibia, J., Sun, F., Li, H., and
Leuning, R.: Global variation of transpiration and soil evaporation and the
role of their major climate drivers, J. Geophys. Res.-Atmos., 122, 6868–6881, https://doi.org/10.1002/2017JD027025, 2017.
Zhang, Y., Chiew, F. H. S., Li, M., and Post, D.: Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches, Water Resour. Res., 54, 7859–7878, https://doi.org/10.1029/2018WR023325, 2018.
Zhang, Y., Chiew, F. H. S., Liu, C., Tang, Q., Xia, J., Tian, J., Kong, D.,
and Li, C.: Can Remotely Sensed Actual Evapotranspiration Facilitate Hydrological Prediction in Ungauged Regions Without Runoff Calibration?, Water Resour. Res., 56, e2019WR026236, https://doi.org/10.1029/2019WR026236, 2020.
Zhang, Y. Q. and Chiew, F.: Evaluation of regionalisation methods for
predicting runoff in ungauged catchments in southeast Australia, in: 18th World IMACS/MODSIM Congress, 13–17 July 2009, Cairns, Australia, 3442–3448, 2009.
Zhang, Z., Zhang, Q., and Singh, V. P.: Univariate streamflow forecasting using commonly used data-driven models: literature review and case study,
Hydrolog. Sci. J., 63, 1091–1111, https://doi.org/10.1080/02626667.2018.1469756, 2018.
Zhou, Y., Zhang, Y., Vaze, J., Lane, P., and Xu, S.: Improving runoff estimates using remote sensing vegetation data for bushfire impacted catchments, Agr. Forest Meteorol., 182–183, 332–341,
https://doi.org/10.1016/j.agrformet.2013.04.018, 2013.
Zhuo, L. and Han, D.: Could operational hydrological models be made compatible with satellite soil moisture observations?, Hydrol. Process., 30, 1637–1648, https://doi.org/10.1002/hyp.10804, 2016.
Zou, L., Zhan, C., Xia, J., Wang, T., and Gippel, C. J.: Implementation of
evapotranspiration data assimilation with catchment scale distributed
hydrological model via an ensemble Kalman Filter, J. Hydrol., 549, 685–702,
https://doi.org/10.1016/j.jhydrol.2017.04.036, 2017.
Short summary
We performed a machine learning approach to regionalize the parameters of a China-wide hydrological model by linking six model parameters with 10 physical attributes (terrain and soil properties). The results show the superiority of machine-learning-based regionalization approach compared with the traditional linear regression method in ungauged regions. We also obtained the relative importance of attributes against model parameters.
We performed a machine learning approach to regionalize the parameters of a China-wide...
