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
Preprint under review 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
Preprint under review for HESS
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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
On the use of streamflow transformations for hydrological model calibration
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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
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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
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?
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 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
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
The Significance of the Leaf-Area-Index on the Evapotranspiration Estimation in SWAT-T for Characteristic Land Cover Types of Western Africa
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
A network approach for multiscale catchment classification using traits
Multi-model approach in a variable spatial framework for streamflow simulation
Advancing understanding of lake–watershed hydrology: a fully coupled numerical model illustrated by Qinghai Lake
Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins
Empirical stream thermal sensitivity cluster on the landscape according to geology and climate
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Toward interpretable LSTM-based modeling of hydrological systems
Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model
What controls the tail behaviour of flood series: rainfall or runoff generation?
Learning Landscape Features from Streamflow with Autoencoders
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Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records
On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
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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.
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.
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.
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.
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.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-131, https://doi.org/10.5194/hess-2024-131, 2024
Revised manuscript accepted for HESS
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ET is computed from vegetation (plant transpiration) and soil (soil evaporation). In Western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented with the leaf-area-index (LAI). In this study, we evaluate the importance of LAI for the ET calculation. We take a close look at the LAI-ET interaction and show the relevance to consider both, LAI and ET. Our work contributes to the understanding of the processes of the terrestrial water cycle.
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.
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.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-57, https://doi.org/10.5194/hess-2024-57, 2024
Revised manuscript accepted for HESS
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Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. In this work we investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analysis indicate that adding two vegetation is enough to improve the representation of evaporation, and the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-47, https://doi.org/10.5194/hess-2024-47, 2024
Revised manuscript accepted for HESS
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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 needed for challenging cases, associated with aridity and intermittent flow. Baseflow index, aridity, and soil/vegetation attributes strongly correlate with learned features, indicating their importance for streamflow prediction.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
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Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
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...
