Articles | Volume 27, issue 5
https://doi.org/10.5194/hess-27-1047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-27-1047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Mar 2023
Research article |
| 13 Mar 2023
| 13 Mar 2023
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
Symbiosis Institute of Geo-informatics, Symbiosis International (Deemed University), Pune 411016, India
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
Manu Vardhan
Computer Science and Engineering Department, National Institute of
Technology Raipur, Raipur 492010, India
Rakesh Sahu
Computer Science and Engineering Department, Chandigarh University, Mohali 140413, India
Debrupa Chatterjee
Symbiosis Institute of Geo-informatics, Symbiosis International (Deemed University), Pune 411016, India
Pankaj Chauhan
Geomorphology and Glaciology Department, Wadia Institute of Himalayan Geology, Dehradun 248001, India
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
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Yu Zhu, Shiyin Liu, Junfeng Wei, Kunpeng Wu, Tobias Bolch, Junli Xu, Wanqin Guo, Zongli Jiang, Fuming Xie, Ying Yi, Donghui Shangguan, Xiaojun Yao, and Zhen Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-255, https://doi.org/10.5194/essd-2024-255, 2024
Preprint under review for ESSD
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This study compiled a near-complete inventory of glacier mass changes across the eastern Tibetan Plateau using topographical maps. This data enhances our understanding of glacier change variability before 2000. When combined with existing research, our dataset provides a nearly five-decade record of mass balance, aiding hydrological simulations and assessments of mountain glacier contributions to sea-level rise.
Yu Zhu, Shiyin Liu, Ben W. Brock, Lide Tian, Ying Yi, Fuming Xie, Donghui Shangguan, and Yiyuan Shen
Hydrol. Earth Syst. Sci., 28, 2023–2045, https://doi.org/10.5194/hess-28-2023-2024, https://doi.org/10.5194/hess-28-2023-2024, 2024
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This modeling-based study focused on Batura Glacier from 2000 to 2020, revealing that debris alters its energy budget, affecting mass balance. We propose that the presence of debris on the glacier surface effectively reduces the amount of latent heat available for ablation, which creates a favorable condition for Batura Glacier's relatively low negative mass balance. Batura Glacier shows a trend toward a less negative mass balance due to reduced ablation.
Miaomiao Qi, Shiyin Liu, Yongpeng Gao, Fuming Xie, Georg Veh, Letian Xiao, Jinlong Jing, Yu Zhu, and Kunpeng Wu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-24, https://doi.org/10.5194/hess-2024-24, 2024
Revised manuscript accepted for HESS
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Here we propose a new mathematically robust and cost-effective model to improve glacial lake water storage estimation. We have also provided a dataset of measured water storage in glacial lakes through field depth measurements. Our model incorporates an automated calculation process and outperforms previous ones, achieving an average relative error of only 14 %. This research offers a valuable tool for researchers seeking to improve the risk assessment of glacial lake outburst floods.
Fuming Xie, Shiyin Liu, Yongpeng Gao, Yu Zhu, Tobias Bolch, Andreas Kääb, Shimei Duan, Wenfei Miao, Jianfang Kang, Yaonan Zhang, Xiran Pan, Caixia Qin, Kunpeng Wu, Miaomiao Qi, Xianhe Zhang, Ying Yi, Fengze Han, Xiaojun Yao, Qiao Liu, Xin Wang, Zongli Jiang, Donghui Shangguan, Yong Zhang, Richard Grünwald, Muhammad Adnan, Jyoti Karki, and Muhammad Saifullah
Earth Syst. Sci. Data, 15, 847–867, https://doi.org/10.5194/essd-15-847-2023, https://doi.org/10.5194/essd-15-847-2023, 2023
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In this study, first we generated inventories which allowed us to systematically detect glacier change patterns in the Karakoram range. We found that, by the 2020s, there were approximately 10 500 glaciers in the Karakoram mountains covering an area of 22 510.73 km2, of which ~ 10.2 % is covered by debris. During the past 30 years (from 1990 to 2020), the total glacier cover area in Karakoram remained relatively stable, with a slight increase in area of 23.5 km2.
Yu Zhu, Shiyin Liu, Junfeng Wei, Kunpeng Wu, Tobias Bolch, Junli Xu, Wanqin Guo, Zongli Jiang, Fuming Xie, Ying Yi, Donghui Shangguan, Xiaojun Yao, and Zhen Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-473, https://doi.org/10.5194/essd-2022-473, 2023
Preprint withdrawn
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In this study, we presented a nearly complete inventory of glacier mass change dataset across the eastern Tibetan Plateau by using topographical maps, which will enhance the knowledge on the heterogeneity of glacier change before 2000. Our dataset, in combination with the published results, provide a nearly five decades mass balance to support hydrological simulation, and to evaluate the contribution of mountain glacier loss to sea level.
Dahong Zhang, Xiaojun Yao, Hongyu Duan, Shiyin Liu, Wanqin Guo, Meiping Sun, and Dazhi Li
The Cryosphere, 15, 1955–1973, https://doi.org/10.5194/tc-15-1955-2021, https://doi.org/10.5194/tc-15-1955-2021, 2021
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Glacier centerlines are crucial input for many glaciological applications. We propose a new algorithm to derive glacier centerlines and implement the corresponding program in Python language. Application of this method to 48 571 glaciers in the second Chinese glacier inventory automatically yielded the corresponding glacier centerlines with an average computing time of 20.96 s, a success rate of 100 % and a comprehensive accuracy of 94.34 %.
Xin Wang, Xiaoyu Guo, Chengde Yang, Qionghuan Liu, Junfeng Wei, Yong Zhang, Shiyin Liu, Yanlin Zhang, Zongli Jiang, and Zhiguang Tang
Earth Syst. Sci. Data, 12, 2169–2182, https://doi.org/10.5194/essd-12-2169-2020, https://doi.org/10.5194/essd-12-2169-2020, 2020
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The theoretical and methodological bases for all processing steps including glacial lake definition and classification and lake boundary delineation are discussed based on satellite remote sensing data and GIS techniques. The relative area errors of each lake in 2018 varied 1 %–79 % with average relative area errors of ±13.2 %. In high-mountain Asia, 30 121 glacial lakes with a total area of 2080.12 ± 2.28 km2 were catalogued in 2018 with a 15.2 % average rate of increase in area in 1990–2018.
F. Xie, S. Liu, Y. Gao, Y. Zhu, K. Wu, M. Qi, S. Duan, and A. M. Tahir
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 417–424, https://doi.org/10.5194/isprs-annals-V-3-2020-417-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-417-2020, 2020
Junfeng Wei, Shiyin Liu, Te Zhang, Xin Wang, Yong Zhang, Zongli Jiang, Kunpeng Wu, and Zheng Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-259, https://doi.org/10.5194/tc-2019-259, 2020
Preprint withdrawn
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During the past three decades, Longbasaba Glacier has experienced a continuous and accelerating recession in glacier area and length but accompanied by the decelerating surface lowing and ice flow. The glacier surface lowering played a predominant role in the mass contribution of glacier shrinkage to the increase in lake water volume, while ice avalanches were the main potential trigger for failure of moraine dams and subsequent GLOF events.
Zhen Zhang, Shiyin Liu, Zongli Jiang, Donghui Shangguan, Junfeng Wei, Wanqin Guo, Junli Xu, Yong Zhang, and Danni Huang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-94, https://doi.org/10.5194/tc-2019-94, 2019
Preprint withdrawn
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We present an integrated view of the glacier area and its mass changes for Mt. Xinqingfeng and Mt. Malan of the inner Tibetan Plateau as derived from topographic maps, Landsat, ASTER, SRTM DEM, and TerraSAR-X/TanDEM-X for the period of 1970–2012 and 1970–2018, respectively. The glaciers experienced weak shrinkage and slight negative mass balance. The Monuomaha Glacier and Zu Glacier together with another 5 glaciers displayed the surging or advancing characteristics during the observation period.
Kunpeng Wu, Shiyin Liu, Zongli Jiang, Junli Xu, and Junfeng Wei
The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-90, https://doi.org/10.5194/tc-2018-90, 2018
Revised manuscript not accepted
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This study presents diminishing ice cover in the central Nyainqentanglha Range by 0.68 % ± 0.05 % a−1, and a mean mass deficit of 0.46 % ± 0.04 m w.e. a−1 since 1968. Mass losses accelerating from 0.42 % ± 0.05 m w.e. a−1 to 0.60 % ± 0.20 m w.e. a−1 during 1968–2000 and 2000–~2013, with thinning noticeably greater on the debris-covered ice than the clean ice. Surface-elevation changes can be influenced by ice cliffs, as well as debris cover, and land- or lake-terminating glaciers and supraglacial lakes.
Kunpeng Wu, Shiyin Liu, Zongli Jiang, Junli Xu, Junfeng Wei, and Wanqin Guo
The Cryosphere, 12, 103–121, https://doi.org/10.5194/tc-12-103-2018, https://doi.org/10.5194/tc-12-103-2018, 2018
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This study presents diminishing ice cover in the Kangri Karpo Mountains by 24.9 % ± 2.2 % or 0.71 % ± 0.06 % a−1 from 1980 to 2015 but with nine glaciers advancing. By utilizing geodetic methods, glaciers have experienced a mean mass deficit of 0.46 ± 0.08 m w.e. a−1 from 1980 to 2014. These glaciers showed slight accelerated shrinkage and significant accelerated mass loss during 2000–2015 compared to that during 1980–2000, which is consistent with the tendency of climate warming.
Y. Zhang, Y. Hirabayashi, K. Fujita, S. Liu, and Q. Liu
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-2413-2013, https://doi.org/10.5194/tcd-7-2413-2013, 2013
Revised manuscript not accepted
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
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To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
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The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
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Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
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This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
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Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
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Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
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We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
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Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
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We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
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Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
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This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
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A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
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We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
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Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Preprint under review for HESS
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Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-230, https://doi.org/10.5194/hess-2024-230, 2024
Revised manuscript accepted for HESS
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Water budget non-closure is a widespread phenomenon among multisource datasets, which undermines the robustness of hydrological inferences. This study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset, and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-181, https://doi.org/10.5194/hess-2024-181, 2024
Revised manuscript accepted for HESS
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Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small compared to large catchments, and that spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show an effect. The results can improve estimations of occurrence probabilities of extreme floods.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-211, https://doi.org/10.5194/hess-2024-211, 2024
Revised manuscript accepted for HESS
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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 better prepare for and respond to floods.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1464, https://doi.org/10.5194/egusphere-2024-1464, 2024
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Our study conducted a detailed analysis of runoff component and future trend in the Yarlung Tsangpo River basin owing to the existed differences in the published results, and find that the contributions of snowmelt and glacier melt runoff to streamflow were limited, both for ~5 % which were much lower than previous results. The streamflow there will continuously increase in the future, but the overestimated contribution from glacier melt would lead to an underestimation on such increasing trend.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
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Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript accepted for HESS
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
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This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-118, https://doi.org/10.5194/hess-2024-118, 2024
Revised manuscript accepted for HESS
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation-Runoff Relationships (DPRR) to explore the controls for changes in precipitation-runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation-runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
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.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2024-993, https://doi.org/10.5194/egusphere-2024-993, 2024
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This study uses deep learning to predict spatially contiguous water runoff in Switzerland from 1962–2023. It outperforms traditional models, requiring less data and computational power. Key findings include increased dry years and summer water scarcity. This method offers significant advancements in water monitoring.
Joško Trošelj and Naota Hanasaki
EGUsphere, https://doi.org/10.5194/egusphere-2024-595, https://doi.org/10.5194/egusphere-2024-595, 2024
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This study presents the first distributed hydrological simulation which confirms the claims raised by historians that the Eastward Diversion Project of the Tone River in Japan was conducted four centuries ago to increase low flows and subsequent travelling possibilities surrounding the Capitol Edo (Tokyo) using inland navigation. We reconstructed six historical river maps and indirectly validated the historical simulations with reachable ancient river ports via increased low-flow water levels.
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.
Cited articles
Abbasian, M., Moghim, S., and Abrishamchi, A.: Performance of the general circulation models in simulating temperature and precipitation over Iran, Theor. Appl. Climatol., 135, 1465–1483,
https://doi.org/10.1007/s00704-018-2456-y, 2019.
Adib, M. N. M. and Harun, S.: Metalearning Approach Coupled with CMIP6
Multi-GCM for Future Monthly Streamflow Forecasting, J. Hydrol. Eng., 27,
05022004, https://doi.org/10.1061/(ASCE)HE.1943-5584.0002176, 2022.
Adnan, R. M., Yuan, X., Kisi, O., Yuan, Y., Tayyab, M., and Lei, X.:
Application of soft computing models in streamflow forecasting. In
Proceedings of the institution of civil engineers-water, Manage., 172,
123–134, https://doi.org/10.1680/jwama.16.00075, 2019.
Adnan, R. M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., and Li,
B.: Least square support vector machine and multivariate adaptive regression
splines for streamflow prediction in mountainous basin using
hydro-meteorological data as inputs, J. Hydrol., 586, 124371,
https://doi.org/10.1016/j.jhydrol.2019.124371, 2020.
Ali, S. A., Aadhar, S., Shah, H. L., and Mishra, V.: Projected increase in
hydropower production in India under climate change, Sci. Rep., 8, 1–12, https://doi.org/10.1038/s41598-018-30489-4, 2018.
Archer, D.: Contrasting hydrological regimes in the upper Indus Basin, J.
Hydrol., 274, 198–210, https://doi.org/10.1016/S0022-1694(02)00414-6, 2003.
Beven, K. J.: Rainfall-runoff modelling: the primer, John Wiley & Sons, ISBN 978-0-470-71459-1, 2011.
Chen, H., Sun, J., Lin, W., and Xu, H.: Comparison of CMIP6 and CMIP5 models
in simulating climate extremes, Sci. Bull., 65, 1415–1418,
https://doi.org/10.1016/j.scib.2020.05.015, 2020.
Choudhury, B. A., Rajesh, P. V., Zahan, Y., and Goswami, B. N.: Evolution of the Indian summer monsoon rainfall simulations from CMIP3 to CMIP6 models, Clim. Dynam., 58, 2637–2662, https://doi.org/10.1007/s00382-021-06023-0, 2022.
Conan, C., De Marsily, G., Bouraoui, F., and Bidoglio, G.: A long-term
hydrological modelling of the Upper Guadiana River basin (Spain), Phys.
Chem. Earth. A/B/C, 28, 193–200, https://doi.org/10.1016/S1474-7065(03)00025-1 2003.
Dai, A., Qian, T., Trenberth, K. E., and Milliman, J. D.: Changes in
continental freshwater discharge from 1948 to 2004, J. Climate, 22, 2773–2792, https://doi.org/10.1175/2008JCLI2592.1, 2009.
Das, J. and Nanduri, U. V.: Assessment and evaluation of potential climate
change impact on monsoon flows using machine learning technique over
Wainganga River basin, India, Hydrol. Sci. J., 63, 1020–1046,
https://doi.org/10.1080/02626667.2018.1469757, 2018.
Easterling, D. R., Meehl G, A., Parmesan, C., Changnon S, A., Karl, T. R.,
and Mearns, L. O.: Climate extremes: observations, modeling, and impacts,
Science, 289, 2068–2074, https://doi.org/10.1126/science.289.5487.2068, 2000.
Eng, K. and Wolock D. M.: Evaluation of machine learning approaches for
predicting streamflow metrics across the conterminous United States, No. 2022-5058, US Geological Survey, https://doi.org/10.3133/sir20225058, 2022.
Fu, M., Fan, T., Ding Z, A., Salih S, Q., Al-Ansari, N., and Yaseen Z. M.:
Deep learning data-intelligence model based on adjusted forecasting window
scale: application in daily streamflow simulation, IEEE Access., 8,
32632–32651, https://doi.org/10.1109/ACCESS.2020.2974406, 2020.
Gao, Y., Gao, X., and Zhang, X.: The 2 ∘C global temperature target and the
evolution of the long-term goal of addressing climate change – from the
United Nations framework convention on climate change to the Paris
agreement, Engineering, 3, 272–278, https://doi.org/10.1016/J.ENG.2017.01.022, 2017.
Gerten, D., Rost, S., von Bloh, W., and Lucht, W.: Causes of change in 20th
century global river discharge, Geophys. Res. Lett., 35, L20405,
https://doi.org/10.1029/2008GL035258, 2008.
Ghimire, S., Yaseen, Z. M., Farooque, A. A., Deo, R. C., Zhang, J., and Tao, X.: Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks, Sci. Rep., 11, 1–26, https://doi.org/10.1038/s41598-021-96751-4, 2021.
Ghobadi, F. and Kang, D.: Improving long-term streamflow prediction in a
poorly gauged basin using geo-spatiotemporal mesoscale data and
attention-based deep learning: A comparative study, J. Hydrol., 615,
128608, https://doi.org/10.1016/j.jhydrol.2022.128608, 2022.
Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S., and
Xavier, P. K.: Increasing trend of extreme rain events over India in a
warming environment, Science., 314, 1442–1445, https://doi.org/10.1126/science.1132027, 2006.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration, J. Hydrol. Eng., 4, 135–143, 1999.
Gusain, A., Ghosh, S., and Karmakar, S.: Added value of CMIP6 over CMIP5
models in simulating Indian summer monsoon rainfall, Atmos. Res., 232,
104680, https://doi.org/10.1016/j.atmosres.2019.104680, 2020.
Haddeland, I., Heinke, J., Biemans, H., Eisner, S., Flörke, M.,
Hanasaki, N., Konzmann, M., Ludwig, F., Masaki, Y., Schewe, J., and Stacke,
T.: Global water resources affected by human interventions and climate
change, P. Natl. Acad. Sci. USA, 111, 3251–3256,
https://doi.org/10.1073/pnas.1222475110, 2014.
Hagen, J. S., Leblois, E., Lawrence, D., Solomatine, D., and Sorteberg, A.:
Identifying major drivers of daily streamflow from large-scale atmospheric
circulation with machine learning, J. Hydrol., 596, 126086,
https://doi.org/10.1016/j.jhydrol.2021.126086, 2021.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H.: The
elements of statistical learning: data mining, inference, and prediction,
Vol. 2, 1–758, Springer, New York, https://www.sas.upenn.edu/~fdiebold/NoHesitations/BookAdvanced.pdf (last access: 24 July 2022), 2009.
Hawkins, E., Osborne, T. M., Ho, C. K., and Challinor, A. J.: Calibration and
bias correction of climate projections for crop modelling: an idealised case
study over Europe, Agr. Forest Meteorol., 170, 19–31,
https://doi.org/10.1016/j.agrformet.2012.04.007, 2013.
Herath, H. M. V. V., Chadalawada, J., and Babovic, V.: Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling, Hydrol. Earth Syst. Sci., 25, 4373–4401, https://doi.org/10.5194/hess-25-4373-2021, 2021.
Hussain, D. and Khan, A. A.: Machine learning techniques for monthly river
flow forecasting of Hunza River, Pakistan, Earth. Sci. Inf., 13,
939–949, https://doi.org/10.1007/s12145-020-00450-z, 2020.
Hussain, D., Hussain, T., Khan, A. A., Naqvi, S. A. A., and Jamil, A.: A deep
learning approach for hydrological time-series prediction: A case study of
Gilgit river basin, Earth. Sci. Inf., 13, 915–927, 2020.
Jose, D. M. and Dwarakish, G. S.: Bias Correction and trend analysis of
temperature data by a high-resolution CMIP6 Model over a Tropical River
Basin, Asia-Pac. J. Atmos. Sci., 58, 97–115,
https://doi.org/10.1007/s13143-021-00240-7, 2022.
Kabir, S., Patidar, S., and Pender, G.: Investigating capabilities of
machine learning techniques in forecasting stream flow, in: Proceedings of
the Institution of Civil Engineers-Water Manage., 173, 69–86, https://doi.org/10.1680/jwama.19.00001, 2020.
Kadel, I., Yamazaki, T., Iwasaki, T., and Abdillah M, R.: Projection of
future monsoon precipitation over the central Himalayas by CMIP5 models
under warming scenarios, Clim. Res., 75, 1–21, https://doi.org/10.3354/cr01497, 2018.
Karan, K., Singh, D., Singh, P. K., Bharati, B., Singh, T. P., and Berndtsson, R.: Implications of future climate change on crop and irrigation water requirements in a semi-arid river basin using CMIP6 GCMs, J. Arid. Land., 14, 1234–1257, https://doi.org/10.1007/s40333-022-0081-1, 2022.
Kim, Y. H., Min, S. K., and Zhang, X.: Evaluation of the CMIP6 multi-model
ensemble for climate extreme indices, Weat. Clim. Extremes, 29, 100269,
https://doi.org/10.1016/j.wace.2020.100269, 2020.
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., and Inman,
D. J.: 1D convolutional neural networks and applications: A survey,
Mech. Syst. Signal Pr., 151, 107398, https://doi.org/10.1016/j.ymssp.2020.107398, 2021.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking
measurements, analyses, and models to advance the science of hydrology,
Water. Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Krysanova, V.,Wortmann, M., Bolch, T., Merz, B., Duethmann, D., Walter, J., Huang, S., Tong, J., Buda, S., and Kundzewicz, Z. W.: Analysis of current trends in climate parameters,
river discharge and glaciers in the Aksu River basin (Central Asia), Hydrol.
Sci. J., 60, 566–590, https://doi.org/10.1080/02626667.2014.925559, 2015.
Kundzewicz, Z. W., Nohara, D., Tong, J., Oki, T., Buda, S., and Takeuchi,
K.: Discharge of large Asian rivers–Observations and projections, Quat.
Int., 208, 4–10, https://doi.org/10.1016/j.quaint.2009.01.011, 2009.
Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K., and Wunderle, S.: Climate change in the High Mountain Asia in CMIP6, Earth Syst. Dynam., 12, 1061–1098, https://doi.org/10.5194/esd-12-1061-2021, 2021.
Legates, D. R. and McCabe Jr., G. J.: Evaluating the use of
“goodness-of-fit” measures in hydrologic and hydroclimatic model
validation, Water. Resour. Res., 35, 1, 233–241,
https://doi.org/10.1029/1998WR900018, 1999.
Lutz, A. F., Immerzeel, W. W., Shrestha, A. B., and Bierkens, M. F. P.:
Consistent increase in High Asia's runoff due to increasing glacier melt and
precipitation, Nat. Clim. Change., 4, 587–592,
https://doi.org/10.1038/nclimate2237, 2014.
Lutz, A. F., Ter Maat, H. W., Wijngaard, R. R., Biemans, H., Syed, A., Shrestha, A. B., Wester, P., and Immerzeel, W. W.: South Asian River basins in a 1.5 C warmer world, Reg. Enviro. Change., 19, 833–847,
https://doi.org/10.1007/s10113-018-1433-4, 2019.
Mahato, P. K., Singh, D., Bharati, B., Gagnon, A. S., Singh, B. B., and Brema, J.: Assessing the impacts of human interventions and climate change on
fluvial flooding using CMIP6 data and GIS-based hydrologic and hydraulic
models, Geocarto. Int., 37, 11483–11508, https://doi.org/10.1080/10106049.2022.2060311, 2022.
Mazrooei, A., Sankarasubramanian, A., and Wood, A. W.: Potential in improving
monthly streamflow forecasting through variational assimilation of observed
streamflow, J. Hydrol., 600, 126559, https://doi.org/10.1016/j.jhydrol.2021.126559, 2021.
Miller, J. D., Immerzeel, W. W., and Rees, G.: Climate change impacts on
glacier hydrology and river discharge in the Hindu Kush–Himalayas, Mt. Res.
Dev., 32, 461–467, https://doi.org/10.1659/MRD-JOURNAL-D-12-00027.1, 2012.
Mishra, V., Bhatia, U., and Tiwari, A. D.: Bias-corrected climate projections
for South Asia from Coupled Model Intercomparison Project-6, Sci. Data, 7,
338, https://doi.org/10.1038/s41597-020-00681-1, 2020.
Moriasi, D. N., Arnold, J. G., Van-Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Murphy, J. M., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M., and Stainforth, D. A.: Quantification of modelling uncertainties in a large ensemble of climate change simulations, Nature, 430, 768–772,
https://doi.org/10.1038/nature02771, 2004.
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)90098-3, 1970.
Nepal, S. and Shrestha, A. B.: Impact of climate change on the hydrological
regime of the Indus, Ganges and Brahmaputra River basins: a review of the
literature, Int. J. Water. Resou. Dev., 31, 201–218,
https://doi.org/10.1080/07900627.2015.1030494, 2015.
Niu, X., Wang, S., Tang, J., Lee, D. K., Gutowski, W., Dairaku, K., McGregor,
J., Katzfey, J., Gao, X., Wu, J., and Hong, S.: Projection of Indian summer
monsoon climate in 2041–2060 by multiregional and global climate models,
J. Geophys. Res.-Atmos., 120, 1776–1793, https://doi.org/10.1002/2014JD022620, 2015.
Oki, T. and Kanae, S.: Global hydrological cycles and world water
resources, Science, 313, 1068–1072, https://doi.org/10.1126/science.1128845, 2006.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Otto, F. E., Skeie, R. B., Fuglestvedt, J. S., Berntsen, T., and Allen, M.
R.: Assigning historic responsibility for extreme weather events, Nat. Clim.
Change., 7, 757–759, https://doi.org/10.1038/nclimate3419, 2017.
Pasquini, A. I. and Depetris, P. J.: Discharge trends and flow dynamics of
South American rivers draining the southern Atlantic seaboard: An overview,
J. Hydrol., 333, 385–399, https://doi.org/10.1016/j.jhydrol.2006.09.005, 2007.
Rahimzad, M., Moghaddam Nia, A., Zolfonoon, H., Soltani, J., Danandeh Mehr,
A., and Kwon, H. H.: Performance comparison of an lstm-based deep learning
model versus conventional machine learning algorithms for streamflow
forecasting, Water. Resour. Manage., 35, 4167–4187,
https://doi.org/10.1007/s11269-021-02937-w, 2021.
Rasouli, K., Hsieh, W. W., and Cannon, A. J.: Daily streamflow forecasting by
machine learning methods with weather and climate inputs, J. Hydrol, 414,
284–293, https://doi.org/10.1016/j.jhydrol.2011.10.039, 2012.
Refsgaard, J. C.: Parameterisation, calibration and validation of distributed
hydrological models, J. Hydrol., 198, 69–97, https://doi.org/10.1016/S0022-1694(96)03329-X, 1997.
Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Sameer, K. C., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, H., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The shared socioeconomic pathways and
their energy, land use, and greenhouse gas emissions implications: an
overview, Global Environ. Change, 42, 153–168,
https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Sabin, T. P., Krishnan, R., Vellore, R., Priya, P., Borgaonkar, H. P., Singh,
B. B., and Sagar, A.: Climate change over the Himalayas. In Assessment of
climate change over the Indian region, Springer, Singapore, 207–222,
https://doi.org/10.1007/978-981-15-4327-2_11, 2020.
Sanjay, J., Krishnan, R., Shrestha, A. B., Rajbhandari, R., and Ren, G. Y.:
Downscaled climate change projections for the Hindu Kush Himalayan region
using CORDEX South Asia regional climate models, Adv. Clim. Change. Res., 8,
185–198, https://doi.org/10.1016/j.accre.2017.08.003, 2017.
Schreiner-McGraw, A. P. and Ajami, H.: Impact of uncertainty in
precipitation forcing data sets on the hydrologic budget of an integrated
hydrologic model in mountainous terrain, Water. Resour. Res., 56, e2020WR027639, https://doi.org/10.1029/2020WR027639, 2020.
Shortridge, J. E., Guikema, S. D., and Zaitchik, B. F.: Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds, Hydrol. Earth Syst. Sci., 20, 2611–2628, https://doi.org/10.5194/hess-20-2611-2016, 2016.
Shu, X., Ding, W., Peng, Y., Wang, Z., Wu, J., and Li, M.: Monthly streamflow
forecasting using convolutional neural network. Water Resour. Manag., 35,
5089–5104, https://doi.org/10.1007/s11269-021-02961-w, 2021.
Shukla, S., Jain, S. K., and Kansal, M. L.: Hydrological modelling of a
snow/glacier-fed western Himalayan basin to simulate the current and future
streamflows under changing climate scenarios, Sci. Total. Environ., 795,
148871, https://doi.org/10.1016/j.scitotenv.2021.148871, 2021.
Singh, D., Gupta, R. D., and Jain, S. K.: Study of long-term trend in river
discharge of Sutlej River (NW Himalayan region), Geogr. Environ.
Sustain., 7, 87–96, https://doi.org/10.24057/2071-9388-2014-7-3-50-57, 2014.
Singh, D., Gupta, R. D., and Jain, S. K.: Assessment of impact of climate
change on water resources in a hilly river basin, Arabian J. Geosci., 8,
10625–10646, https://doi.org/10.1007/s12517-015-1985-2, 2015a.
Singh, D., Gupta, R. D., and Jain, S. K.: Statistical analysis of long term
spatial and temporal trends of temperature parameters over Sutlej River
basin, India, J. Earth. Syst. Sci., 124, 17–35,
https://doi.org/10.1007/s12517-015-1985-2, 2015b.
Singh, D., Jain, S. K., and Gupta, R. D.: Statistical downscaling and
projection of future temperature and precipitation change in middle
catchment of Sutlej River Basin, India, J. Earth. Syst. Sci., 124,
843–860, https://doi.org/10.1007/s12040-015-0575-8, 2015c.
Singh, D., Rai, S. P., and Rai, D.: Application of geospatial techniques in
hydrological modelling, in: Sustainable Green Technologies for Environmental Management, edited by: Shah, S., Venkatramanan, V., and Prasad, R., Springer, Singapore, https://doi.org/10.1007/978-981-13-2772-8_8, 2019.
Singh, D., Zhu, Y., Liu, S., Srivastava, P. K., Dharpure, J. K., Chatterjee, D., Sahu, R., and Gagnon, A. S.: Exploring the links between variations in snow cover area and climatic variables in a Himalayan catchment using earth
observations and CMIP6 climate change scenarios, J. Hydrol., 608, 127648,
https://doi.org/10.1016/j.jhydrol.2022.127648, 2022.
Singh, P. and Jain, S. K.: Snow, and glacier melt in the Satluj River at
Bhakra Dam in the western Himalayan region, Hydrol. Sci. J., 47, 93–106,
https://doi.org/10.1080/02626660209492910, 2002.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrol.
Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Sperna Weiland, F. C., van Beek, L. P. H., Kwadijk, J. C. J., and Bierkens, M. F. P.: The ability of a GCM-forced hydrological model to reproduce global discharge variability, Hydrol. Earth Syst. Sci., 14, 1595–1621, https://doi.org/10.5194/hess-14-1595-2010, 2010.
Stahl, K., Hisdal, H., Hannaford, J., Tallaksen, L. M., van Lanen, H. A. J., Sauquet, E., Demuth, S., Fendekova, M., and Jódar, J.: Streamflow trends in Europe: evidence from a dataset of near-natural catchments, Hydrol. Earth Syst. Sci., 14, 2367–2382, https://doi.org/10.5194/hess-14-2367-2010, 2010.
Stahl, K., Tallaksen, L. M., Hannaford, J., and van Lanen, H. A. J.: Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble, Hydrol. Earth Syst. Sci., 16, 2035–2047, https://doi.org/10.5194/hess-16-2035-2012, 2012.
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.
Thapa, S., Li, H., Li, B., Fu, D., Shi, X., Yabo, S., Lu, L., Qi, H., and
Zhang, W.: Impact of climate change on snowmelt runoff in a Himalayan basin,
Nepal, Environ. Monit. Assess., 193, 1–17, https://doi.org/10.1007/s10661-021-09197-6, 2021.
Trenberth, K. E.: Changes in precipitation with climate change, Clim. Res.,
47, 123–138, https://doi.org/10.3354/cr00953, 2011.
Turner A, G. and Annamalai, H.: Climate change and the South Asian summer
monsoon, Nat. Clim. Change, 2, 587–595, https://doi.org/10.1038/nclimate1495, 2012.
Van, S. P., Le, H. M., Thanh, D. V., Dang, T. D., Loc, H. H., and Anh, D. T.: Deep learning convolutional neural network in rainfall–runoff modelling, J.
Hydroinf., 22, 541–561, https://doi.org/10.2166/hydro.2020.095, 2020.
Van der Wiel, K., Wanders, N., Selten, F. M., and Bierkens, M. F. P.: Added
value of large ensemble simulations for assessing extreme river discharge in
a 2 ∘C warmer world, Geophys. Res. Lett., 46, 2093–2102,
https://doi.org/10.1029/2019GL081967, 2019.
Van-Liew, M. W., Arnold, J. G., and Garbrecht, J. D.: Hydrologic simulation on agricultural watersheds: Choosing between two models, T. ASAE, 46, 1539, https://doi.org/10.13031/2013.15643, 2003.
Wang, T., Zhao, Y., Xu, C., Ciais, P., Liu, D., Yang, H., Piao, S., and Yao, T.: Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris
climate targets, Nat. Clim. Change, 11, 219–225,
https://doi.org/10.1038/s41558-020-00974-8, 2021.
Xenarios, S., Gafurov, A., Schmidt-Vogt, D., Sehring, J., Manandhar, S., Hergarten, C., Shigaeva, J., and Foggin, M.: Climate change and adaptation of mountain societies in Central Asia: uncertainties, knowledge gaps, and data
constraints, Reg. Environ. Change, 19, 1339–1352,
https://doi.org/10.1007/s10113-018-1384-9, 2019.
Xiang, Z., Yan, J., and Demir, I.: A rainfall-runoff model with LSTM-based
sequence-to-sequence learning, Water. Resour. Res., 56, e2019WR025326,
https://doi.org/10.1029/2019WR025326, 2020.
Yang, Q., Zhang, H., Wang, G., Luo, S., Chen, D., Peng, W., and Shao, J.:
Dynamic runoff simulation in a changing environment: A data stream approach,
Environ. Modell. Soft., 112, 157–165,
https://doi.org/10.1016/j.envsoft.2018.11.007, 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.
Zhao, B., Sun, H., Yan, D., Wei, G., Tuo, Y., and Zhang, W.: Quantifying
changes and drivers of runoff in the Kaidu River Basin associated with
plausible climate scenarios, J. Hydrol.-Reg. Stud., 38, 100968,
https://doi.org/10.1016/j.ejrh.2021.100968, 2021.
Short summary
This study examines, for the first time, the potential of various machine learning models in streamflow prediction over the Sutlej River basin (rainfall-dominated zone) in western Himalaya during the period 2041–2070 (2050s) and 2071–2100 (2080s) and its relationship to climate variability. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between the 2050s and 2080s by 0.79 to 1.43 % for SSP585 and by 0.87 to 1.10 % for SSP245.
This study examines, for the first time, the potential of various machine learning models in...
