Articles | Volume 27, issue 3
https://doi.org/10.5194/hess-27-647-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-647-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river
Menaka Revel
CORRESPONDING AUTHOR
Global Hydrological Prediction Center, Institute of Industrial
Science, The University of Tokyo, 153-8505 Tokyo, Japan
Xudong Zhou
Global Hydrological Prediction Center, Institute of Industrial
Science, The University of Tokyo, 153-8505 Tokyo, Japan
Dai Yamazaki
Global Hydrological Prediction Center, Institute of Industrial
Science, The University of Tokyo, 153-8505 Tokyo, Japan
Shinjiro Kanae
Department of Civil and Environmental Engineering, Tokyo Institute of Technology, 152-8550 Tokyo, Japan
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Menaka Revel, Xudong Zhou, Prakat Modi, Jean-François Cretaux, Stephane Calmant, and Dai Yamazaki
Earth Syst. Sci. Data, 16, 75–88, https://doi.org/10.5194/essd-16-75-2024, https://doi.org/10.5194/essd-16-75-2024, 2024
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As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
Youjiang Shen, Karina Nielsen, Menaka Revel, Dedi Liu, and Dai Yamazaki
Earth Syst. Sci. Data, 15, 2781–2808, https://doi.org/10.5194/essd-15-2781-2023, https://doi.org/10.5194/essd-15-2781-2023, 2023
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Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Pilippe Bousquet, Josep G. Canadell, Nick Davidson, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, and Michele Thieme
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-204, https://doi.org/10.5194/essd-2024-204, 2024
Preprint under review for ESSD
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The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies—that is, the maximum extent—covers 18.2 million km2, equivalent to 13.4 % of total global land area.
Yuki Kimura, Yukiko Hirabayashi, and Dai Yamazaki
EGUsphere, https://doi.org/10.22541/essoar.170365204.46854879/v1, https://doi.org/10.22541/essoar.170365204.46854879/v1, 2024
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The limited number of ensemble members causes uncertainty in future climate predictions. To address this, using multiple simulations under a single future climate scenario can increase the sample size, but data availability is limited in the scenario-based future projection experiment of climate model intercomparison projects. Our proposed method integrates multiple climate scenarios at specific temperature increases, effectively reducing uncertainty in future flood hazard assessments globally.
Jingyu Lin, Peng Wang, Jinzhu Wang, Youping Zhou, Xudong Zhou, Pan Yang, Hao Zhang, Yanpeng Cai, and Zhifeng Yang
Earth Syst. Sci. Data, 16, 1137–1149, https://doi.org/10.5194/essd-16-1137-2024, https://doi.org/10.5194/essd-16-1137-2024, 2024
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Our paper provides a repository comprising over 330 000 observations encompassing daily, weekly, and monthly records of surface water quality spanning the period 1980–2022. It included 18 distinct indicators, meticulously gathered at 2384 monitoring sites, ranging from inland locations to coastal and oceanic areas. This dataset will be very useful for researchers and decision-makers in the fields of hydrology, ecological studies, climate change, policy development, and oceanography.
Menaka Revel, Xudong Zhou, Prakat Modi, Jean-François Cretaux, Stephane Calmant, and Dai Yamazaki
Earth Syst. Sci. Data, 16, 75–88, https://doi.org/10.5194/essd-16-75-2024, https://doi.org/10.5194/essd-16-75-2024, 2024
Short summary
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As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
Md Safat Sikder, Jida Wang, George H. Allen, Yongwei Sheng, Dai Yamazaki, Chunqiao Song, Meng Ding, Jean-François Crétaux, and Tamlin M. Pavelsky
Earth Syst. Sci. Data, 15, 3483–3511, https://doi.org/10.5194/essd-15-3483-2023, https://doi.org/10.5194/essd-15-3483-2023, 2023
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We introduce Lake-TopoCat to reveal detailed lake hydrography information. It contains the location of lake outlets, the boundary of lake catchments, and a wide suite of attributes that depict detailed lake drainage relationships. It was constructed using lake boundaries from a global lake dataset, with the help of high-resolution hydrography data. This database may facilitate a variety of applications including water quality, agriculture and fisheries, and integrated lake–river modeling.
Youjiang Shen, Karina Nielsen, Menaka Revel, Dedi Liu, and Dai Yamazaki
Earth Syst. Sci. Data, 15, 2781–2808, https://doi.org/10.5194/essd-15-2781-2023, https://doi.org/10.5194/essd-15-2781-2023, 2023
Short summary
Short summary
Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Yuki Kimura, Yukiko Hirabayashi, Yuki Kita, Xudong Zhou, and Dai Yamazaki
Hydrol. Earth Syst. Sci., 27, 1627–1644, https://doi.org/10.5194/hess-27-1627-2023, https://doi.org/10.5194/hess-27-1627-2023, 2023
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Since both the frequency and magnitude of flood will increase by climate change, information on spatial distributions of potential inundation depths (i.e., flood-hazard map) is required. We developed a method for constructing realistic future flood-hazard maps which addresses issues due to biases in climate models. A larger population is estimated to face risk in the future flood-hazard map, suggesting that only focusing on flood-frequency change could cause underestimation of future risk.
Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, https://doi.org/10.5194/nhess-23-823-2023, 2023
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In coastal deltas, flooding can occur from interactions between coastal, riverine, and pluvial drivers, so-called compound flooding. Global models however ignore these interactions. We present a framework for automated and reproducible compound flood modeling anywhere globally and validate it for two historical events in Mozambique with good results. The analysis reveals differences in compound flood dynamics between both events related to the magnitude of and time lag between drivers.
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences, 19, 5779–5805, https://doi.org/10.5194/bg-19-5779-2022, https://doi.org/10.5194/bg-19-5779-2022, 2022
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Wetlands are the largest natural source of methane, one of the most important climate gases. The JULES land surface model simulates these emissions. We use satellite data to evaluate how well JULES reproduces the methane seasonal cycle over different tropical wetlands. It performs well for most regions; however, it struggles for some African wetlands influenced heavily by river flooding. We explain the reasons for these deficiencies and highlight how future development will improve these areas.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
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Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Naota Hanasaki, Hikari Matsuda, Masashi Fujiwara, Yukiko Hirabayashi, Shinta Seto, Shinjiro Kanae, and Taikan Oki
Hydrol. Earth Syst. Sci., 26, 1953–1975, https://doi.org/10.5194/hess-26-1953-2022, https://doi.org/10.5194/hess-26-1953-2022, 2022
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Global hydrological models (GHMs) are usually applied with a spatial resolution of about 50 km, but this time we applied the H08 model, one of the most advanced GHMs, with a high resolution of 2 km to Kyushu island, Japan. Since the model was not accurate as it was, we incorporated local information and improved the model, which revealed detailed water stress in subregions that were not visible with the previous resolution.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Daisuke Tokuda, Hyungjun Kim, Dai Yamazaki, and Taikan Oki
Geosci. Model Dev., 14, 5669–5693, https://doi.org/10.5194/gmd-14-5669-2021, https://doi.org/10.5194/gmd-14-5669-2021, 2021
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We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
Xudong Zhou, Wenchao Ma, Wataru Echizenya, and Dai Yamazaki
Nat. Hazards Earth Syst. Sci., 21, 1071–1085, https://doi.org/10.5194/nhess-21-1071-2021, https://doi.org/10.5194/nhess-21-1071-2021, 2021
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This article assesses different uncertainties in the analysis of flood risk and found the runoff generated before the river routing is the primary uncertainty source. This calls for attention to be focused on selecting an appropriate runoff for the flood analysis. The uncertainties are reflected in the flood water depth, inundation area and the exposure of the population and economy to the floods.
Zun Yin, Catherine Ottlé, Philippe Ciais, Feng Zhou, Xuhui Wang, Polcher Jan, Patrice Dumas, Shushi Peng, Laurent Li, Xudong Zhou, Yan Bo, Yi Xi, and Shilong Piao
Hydrol. Earth Syst. Sci., 25, 1133–1150, https://doi.org/10.5194/hess-25-1133-2021, https://doi.org/10.5194/hess-25-1133-2021, 2021
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We improved the irrigation module in a land surface model ORCHIDEE and developed a dam operation model with the aim to investigate how irrigation and dams affect the streamflow fluctuations of the Yellow River. Results show that irrigation mainly reduces the annual river flow. The dam operation, however, mainly affects streamflow variation. By considering two generic operation rules, flood control and base flow guarantee, our dam model can sustainably improve the simulation accuracy.
Tomohiro Hajima, Michio Watanabe, Akitomo Yamamoto, Hiroaki Tatebe, Maki A. Noguchi, Manabu Abe, Rumi Ohgaito, Akinori Ito, Dai Yamazaki, Hideki Okajima, Akihiko Ito, Kumiko Takata, Koji Ogochi, Shingo Watanabe, and Michio Kawamiya
Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, https://doi.org/10.5194/gmd-13-2197-2020, 2020
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We developed a new Earth system model (ESM) named MIROC-ES2L. This model is based on a state-of-the-art climate model and includes carbon–nitrogen cycles for the land and multiple biogeochemical cycles for the ocean. The model's performances on reproducing historical climate and biogeochemical changes are confirmed to be reasonable, and the new model is likely to be an
optimisticmodel in projecting future climate change among ESMs in the Coupled Model Intercomparison Project Phase 6.
Xudong Zhou, Jan Polcher, Tao Yang, and Ching-Sheng Huang
Hydrol. Earth Syst. Sci., 24, 2061–2081, https://doi.org/10.5194/hess-24-2061-2020, https://doi.org/10.5194/hess-24-2061-2020, 2020
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This article proposes a new estimation approach for assessing the uncertainty with multiple datasets by fully considering all variations in temporal and spatial dimensions. Comparisons demonstrate that classical metrics may underestimate the uncertainties among datasets due to an averaging process in their algorithms. This new approach is particularly suitable for overall assessment of multiple climatic products, but can be easily applied to other spatiotemporal products in related fields.
Hiroaki Tatebe, Tomoo Ogura, Tomoko Nitta, Yoshiki Komuro, Koji Ogochi, Toshihiko Takemura, Kengo Sudo, Miho Sekiguchi, Manabu Abe, Fuyuki Saito, Minoru Chikira, Shingo Watanabe, Masato Mori, Nagio Hirota, Yoshio Kawatani, Takashi Mochizuki, Kei Yoshimura, Kumiko Takata, Ryouta O'ishi, Dai Yamazaki, Tatsuo Suzuki, Masao Kurogi, Takahito Kataoka, Masahiro Watanabe, and Masahide Kimoto
Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, https://doi.org/10.5194/gmd-12-2727-2019, 2019
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For a deeper understanding of a wide range of climate science issues, the latest version of the Japanese climate model, called MIROC6, was developed. The climate model represents observed mean climate and climate variations well, for example tropical precipitation, the midlatitude westerlies, and the East Asian monsoon, which influence human activity all over the world. The improved climate simulations could add reliability to climate predictions under global warming.
Trung Nguyen-Quang, Jan Polcher, Agnès Ducharne, Thomas Arsouze, Xudong Zhou, Ana Schneider, and Lluís Fita
Geosci. Model Dev., 11, 4965–4985, https://doi.org/10.5194/gmd-11-4965-2018, https://doi.org/10.5194/gmd-11-4965-2018, 2018
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This study presents a revised river routing scheme for the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model. The revision is carried out to benefit from the high-resolution topography provided by the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS). We demonstrate that the finer description of the catchments allows for an improvement of the simulated river discharge of ORCHIDEE in an area with complex topography.
Xudong Zhou, Jan Polcher, Tao Yang, Yukiko Hirabayashi, and Trung Nguyen-Quang
Hydrol. Earth Syst. Sci., 22, 6087–6108, https://doi.org/10.5194/hess-22-6087-2018, https://doi.org/10.5194/hess-22-6087-2018, 2018
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Model bias is commonly seen in discharge simulation by hydrological or land surface models. This study tested an approach with the Budyko hypothesis to retrospect the estimated discharge bias to different bias sources including the atmospheric variables and model structure. Results indicate that the bias is most likely caused by the forcing variables, and the forcing bias should firstly be assessed and reduced in order to perform pertinent analysis of the regional water cycle.
Naota Hanasaki, Sayaka Yoshikawa, Yadu Pokhrel, and Shinjiro Kanae
Hydrol. Earth Syst. Sci., 22, 789–817, https://doi.org/10.5194/hess-22-789-2018, https://doi.org/10.5194/hess-22-789-2018, 2018
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Six schemes were added to the H08 global hydrological model (GHM) to represent human water abstraction more accurately and ensure that all water fluxes and storage are traceable in each grid cell at a daily interval. The schemes of local reservoirs, aqueduct water transfer, and seawater desalination were incorporated into GHMs for the first time, to the best of our knowledge. H08 has become one of the most detailed GHMs for attributing water sources available to humanity.
Cherry May R. Mateo, Dai Yamazaki, Hyungjun Kim, Adisorn Champathong, Jai Vaze, and Taikan Oki
Hydrol. Earth Syst. Sci., 21, 5143–5163, https://doi.org/10.5194/hess-21-5143-2017, https://doi.org/10.5194/hess-21-5143-2017, 2017
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Providing large-scale (regional or global) simulation of floods at fine spatial resolution is difficult due to computational constraints but is necessary to provide consistent estimates of hazards, especially in data-scarce regions. We assessed the capability of an advanced global-scale river model to simulate an extreme flood at fine resolution. We found that when multiple flow connections in rivers are represented, the model can provide reliable fine-resolution predictions of flood inundation.
Nozomi Ando, Sayaka Yoshikawa, Shinichiro Fujimori, and Shinjiro Kanae
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-27, https://doi.org/10.5194/hess-2017-27, 2017
Manuscript not accepted for further review
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Electricity generation may become a key factor that accelerates water scarcity. In this study, we estimated the future global water use for electricity generation from 2005 to 2100 in 17 global sub-regions. Consequently, We indicated that the socioeconomic changes had a larger impact on water withdrawal and consumption for electricity generation, compared with the climate mitigation changes represented by the climate mitigation scenarios.
Orie Sasaki, Omi Noguchi, Yong Zhang, Yukiko Hirabayashi, and Shinjiro Kanae
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-222, https://doi.org/10.5194/tc-2016-222, 2016
Revised manuscript not accepted
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Supraglacial debris is widely spread in many high-relief mountain regions and affects glacier melting rate and resulting runoff, however, there is no global dataset of debris information. Here we present a first global map of thermal resistance of debris on glaciers at 90 m by using multi-temporal satellite images and radiation data. We believe our result provides a solid basis for evaluating debris effects in global glacier models, which could refine future predictions of glacier meltwater.
K. Fujimura, Y. Iseri, S. Kanae, and M. Murakami
Proc. IAHS, 371, 69–73, https://doi.org/10.5194/piahs-371-69-2015, https://doi.org/10.5194/piahs-371-69-2015, 2015
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The aim of this study is to clarify the properties of the parameters in the storage-discharge relations by carrying out a sensitivity analysis of efficiency using a hydrological model. The study basins are four mountainous basins in Japan with different climates and geologies. The results confirm that the two parameters in the storage-discharge relations can be expressed in an inversely proportional relationship.
S. Yoshikawa, A. Yanagawa, Y. Iwasaki, P. Sui, S. Koirala, K. Hirano, A. Khajuria, R. Mahendran, Y. Hirabayashi, C. Yoshimura, and S. Kanae
Hydrol. Earth Syst. Sci., 18, 621–630, https://doi.org/10.5194/hess-18-621-2014, https://doi.org/10.5194/hess-18-621-2014, 2014
N. K. Gunasekara, S. Kazama, D. Yamazaki, and T. Oki
Hydrol. Earth Syst. Sci., 17, 4429–4440, https://doi.org/10.5194/hess-17-4429-2013, https://doi.org/10.5194/hess-17-4429-2013, 2013
Related subject area
Subject: Rivers and Lakes | Techniques and Approaches: Stochastic approaches
Warming of the Willamette River, 1850–present: the effects of climate change and river system alterations
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Do small and large floods have the same drivers of change? A regional attribution analysis in Europe
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Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India
The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter
Stefan A. Talke, David A. Jay, and Heida L. Diefenderfer
Hydrol. Earth Syst. Sci., 27, 2807–2826, https://doi.org/10.5194/hess-27-2807-2023, https://doi.org/10.5194/hess-27-2807-2023, 2023
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Archival measurements and a statistical model show that average water temperature in a major US West Coast river has increased by 1.8 °C since 1850, at a rate of 1.1 °C per century. The largest factor driving modeled changes are warming air temperatures (nearly 75 %). The remainder is primarily caused by depth increases and other modifications to the river system. Near-freezing conditions, common historically, no longer occur, and the number of warm water days has significantly increased.
Remy Vandaele, Sarah L. Dance, and Varun Ojha
Hydrol. Earth Syst. Sci., 25, 4435–4453, https://doi.org/10.5194/hess-25-4435-2021, https://doi.org/10.5194/hess-25-4435-2021, 2021
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The acquisition of river-level data is a critical task for the understanding of flood events but is often complicated by the difficulty to install and maintain gauges able to provide such information. This study proposes applying deep learning techniques on river-camera images in order to automatically extract the corresponding water levels. This approach could allow for a new flexible way to observe flood events, especially at ungauged locations.
Miriam Bertola, Alberto Viglione, Sergiy Vorogushyn, David Lun, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 1347–1364, https://doi.org/10.5194/hess-25-1347-2021, https://doi.org/10.5194/hess-25-1347-2021, 2021
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We estimate the contribution of extreme precipitation, antecedent soil moisture and snowmelt to changes in small and large floods across Europe.
In northwestern and eastern Europe, changes in small and large floods are driven mainly by one single driver (i.e. extreme precipitation and snowmelt, respectively). In southern Europe both antecedent soil moisture and extreme precipitation significantly contribute to flood changes, and their relative importance depends on flood magnitude.
Miriam Bertola, Alberto Viglione, David Lun, Julia Hall, and Günter Blöschl
Hydrol. Earth Syst. Sci., 24, 1805–1822, https://doi.org/10.5194/hess-24-1805-2020, https://doi.org/10.5194/hess-24-1805-2020, 2020
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We investigate changes that occurred in small vs. big flood events and in small vs. large catchments across Europe over 5 decades. Annual maximum discharge series between 1960 and 2010 from 2370 gauges in Europe are analysed. Distinctive patterns of flood regime change are identified for large regions across Europe, which depend on flood magnitude and catchment size.
Theano Iliopoulou, Cristina Aguilar, Berit Arheimer, María Bermúdez, Nejc Bezak, Andrea Ficchì, Demetris Koutsoyiannis, Juraj Parajka, María José Polo, Guillaume Thirel, and Alberto Montanari
Hydrol. Earth Syst. Sci., 23, 73–91, https://doi.org/10.5194/hess-23-73-2019, https://doi.org/10.5194/hess-23-73-2019, 2019
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We investigate the seasonal memory properties of a large sample of European rivers in terms of high and low flows. We compute seasonal correlations between peak and low flows and average flows in the previous seasons and explore the links with various physiographic and hydro-climatic catchment descriptors. Our findings suggest that there is a traceable physical basis for river memory which in turn can be employed to reduce uncertainty and improve probabilistic predictions of floods and droughts.
Alessia Ferrari, Marco D'Oria, Renato Vacondio, Alessandro Dal Palù, Paolo Mignosa, and Maria Giovanna Tanda
Hydrol. Earth Syst. Sci., 22, 5299–5316, https://doi.org/10.5194/hess-22-5299-2018, https://doi.org/10.5194/hess-22-5299-2018, 2018
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The knowledge of discharge hydrographs is useful for flood modelling purposes, water resource management, and the design of hydraulic structures. This paper presents a novel methodology to estimate the unknown discharge hydrograph in an ungauged river section using only water level information recorded downstream. A Bayesian procedure is coupled with a 2-D hydraulic model parallelized for GPUs. Finally, the proposed procedure has been applied to estimate inflow hydrographs in real river reaches.
Charlotte Marie Emery, Adrien Paris, Sylvain Biancamaria, Aaron Boone, Stéphane Calmant, Pierre-André Garambois, and Joecila Santos da Silva
Hydrol. Earth Syst. Sci., 22, 2135–2162, https://doi.org/10.5194/hess-22-2135-2018, https://doi.org/10.5194/hess-22-2135-2018, 2018
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This study uses remotely sensed river discharge data to correct river storage and discharge in a large-scale hydrological model. The method is based on an ensemble Kalman filter and also introduces an additional technique that allows for better constraint of the correction (called localization). The approach is applied over the entire Amazon basin. Results show that the method is able to improve river discharge and localization to produce better results along main tributaries.
J. C. Peña, L. Schulte, A. Badoux, M. Barriendos, and A. Barrera-Escoda
Hydrol. Earth Syst. Sci., 19, 3807–3827, https://doi.org/10.5194/hess-19-3807-2015, https://doi.org/10.5194/hess-19-3807-2015, 2015
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The paper presents an index of summer flood damage in Switzerland from 1800 to 2009 and explores the influence of solar forcing, climate variability and low-frequency atmospheric circulation on flood frequencies. The flood damage index provides evidence that the 1817-1851, 1881-1927, 1977-1990 and 2005-present flood clusters are mostly in phase with palaeoclimate proxies and solar activity minima. Floods are influenced by atmospheric instability related to the principal summer mode.
T. A. Cochrane, M. E. Arias, and T. Piman
Hydrol. Earth Syst. Sci., 18, 4529–4541, https://doi.org/10.5194/hess-18-4529-2014, https://doi.org/10.5194/hess-18-4529-2014, 2014
Short summary
Short summary
Natural patterns of water levels in the Mekong are changing as a result of hydropower and irrigation development. Since 1991, significant changes in water level fluctuations and rising and falling rates have occurred along the lower Mekong. The changes were linked to temporal and spatial trends in water infrastructure development and can lead to impacts on ecosystem productivity. Climatic change is also important, but it has not been the main cause of the key water level alternations.
H. Aksoy, N. E. Unal, E. Eris, and M. I. Yuce
Hydrol. Earth Syst. Sci., 17, 2297–2303, https://doi.org/10.5194/hess-17-2297-2013, https://doi.org/10.5194/hess-17-2297-2013, 2013
I. Pal, U. Lall, A. W. Robertson, M. A. Cane, and R. Bansal
Hydrol. Earth Syst. Sci., 17, 2131–2146, https://doi.org/10.5194/hess-17-2131-2013, https://doi.org/10.5194/hess-17-2131-2013, 2013
D. A. Plaza, R. De Keyser, G. J. M. De Lannoy, L. Giustarini, P. Matgen, and V. R. N. Pauwels
Hydrol. Earth Syst. Sci., 16, 375–390, https://doi.org/10.5194/hess-16-375-2012, https://doi.org/10.5194/hess-16-375-2012, 2012
Cited articles
Anderson, J. L.: Exploring the need for localization in ensemble data
assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111, https://doi.org/10.1016/j.physd.2006.02.011, 2007.
Andreadis, K. M. and Schumann, G. J. P.: Estimating the impact of satellite
observations on the predictability of large-scale hydraulic models, Adv.
Water Resour., 73, 44–54, https://doi.org/10.1016/j.advwatres.2014.06.006, 2014.
Andreadis, K. M., Clark, E. A., Lettenmaier, D. P., and Alsdorf, D. E.:
Prospects for river discharge and depth estimation through assimilation of
swath-altimetry into a raster-based hydrodynamics model, Geophys. Res.
Lett., 34, L10403, https://doi.org/10.1029/2007GL029721, 2007.
Balsamo, G., Dutra, E., Beljaars, A. and Viterbo, P.: Evolution of
land-surface processes in the IFS, ECMWF Newsl., 127, 17–22,
https://doi.org/10.21957/x1j3i7bz, 2011.
Bannoura, W. J.: NOAA Ocean Surface Topography Mission Jason-2 Project
Overview Dwaraka Nath Srinivas The OSTM/Jason-2 Mission Phases Mission
Roles & Responsibilities Jason-2 System Overview, Proceedings of OCEANS 2005 MTS/IEEE, 1–5, 2001.
Bates, P. D., Horritt, M. S. and Fewtrell, T. J.: A simple inertial
formulation of the shallow water equations for efficient two-dimensional
flood inundation modelling, J. Hydrol., 387, 33–45,
https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Biancamaria, S., Durand, M., Andreadis, K. M., Bates, P. D., Boone, A.,
Mognard, N. M., Rodríguez, E., Alsdorf, D. E., Lettenmaier, D. P., and
Clark, E. A.: Assimilation of virtual wide swath altimetry to improve Arctic
river modeling, Remote Sens. Environ., 115, 373–381,
https://doi.org/10.1016/j.rse.2010.09.008, 2011.
Biancamaria, S., Lettenmaier, D. P. and Pavelsky, T. M.: The SWOT Mission
and Its Capabilities for Land Hydrology, Surv. Geophys., 37, 307–337,
https://doi.org/10.1007/s10712-015-9346-y, 2016.
Birkett, C. M., Mertes, L. A. K., Dunne, T., Costa, M. H., and Jasinski, M.
J.: Surface water dynamics in the Amazon Basin: Application of satellite
radar altimetry, J. Geophys. Res.-Atmos., 107, LBA 26-1–LBA 26-21,
https://doi.org/10.1029/2001JD000609, 2002.
Bjerklie, D. M., Birkett, C. M., Jones, J. W., Carabajal, C., Rover, J. A.,
Fulton, J. W., and Garambois, P. A.: Satellite remote sensing estimation of
river discharge: Application to the Yukon River Alaska, J. Hydrol.,
561, 1000–1018, https://doi.org/10.1016/j.jhydrol.2018.04.005, 2018.
Brêda, J. P. L. F., Paiva, R. C. D., Bravo, J. M., Passaia, O. A., and
Moreira, D. M.: Assimilation of Satellite Altimetry Data for Effective River
Bathymetry, Water Resour. Res., 55, 7441–7463, https://doi.org/10.1029/2018WR024010,
2019.
Builes-Jaramillo, A. and Poveda, G.: Conjoint Analysis of Surface and
Atmospheric Water Balances in the Andes-Amazon System, Water Resour. Res.,
54, 3472–3489, https://doi.org/10.1029/2017WR021338, 2018.
Burek, P., Van Der Knijff, M., J., and de Roo, A.: LISFLOOD distributed
water balance and flood simulation model revised user manual, Joint Research Centre, Ispra, Italy, https://doi.org/10.2788/24719, 2013.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011.
Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater,
A. G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with
the ensemble Kalman filter: Use of streamflow observations to update states
in a distributed hydrological model, Adv. Water Resour., 31, 1309–1324,
https://doi.org/10.1016/j.advwatres.2008.06.005, 2008.
Crétaux, J. F., Calmant, S., Romanovski, V., Shabunin, A., Lyard, F.,
Bergé-Nguyen, M., Cazenave, A., Hernandez, F., and Perosanz, F.: An
absolute calibration site for radar altimeters in the continental domain:
Lake Issykkul in Central Asia, J. Geodesdy, 83, 723–735,
https://doi.org/10.1007/s00190-008-0289-7, 2009.
De Paiva, R. C. D., Buarque, D. C., Collischonn, W., Bonnet, M. P.,
Frappart, F., Calmant, S., and Bulhões Mendes, C. A.: Large-scale
hydrologic and hydrodynamic modeling of the Amazon River basin, Water
Resour. Res., 49, 1226–1243, https://doi.org/10.1002/wrcr.20067, 2013.
Döll, P., Douville, H., Güntner, A., Müller Schmied, H., and
Wada, Y.: Modelling Freshwater Resources at the Global Scale: Challenges and
Prospects, Surv. Geophys., 37, 195–221, https://doi.org/10.1007/s10712-015-9343-1,
2016.
d'Orgeval, T., Polcher, J., and de Rosnay, P.: Sensitivity of the West African hydrological cycle in ORCHIDEE to infiltration processes, Hydrol. Earth Syst. Sci., 12, 1387–1401, https://doi.org/10.5194/hess-12-1387-2008, 2008.
Durand, M., Andreadis, K. M., Alsdorf, D. E., Lettenmaier, D. P., Moller, D.
and Wilson, M.: Estimation of bathymetric depth and slope from data
assimilation of swath altimetry into a hydrodynamic model, Geophys. Res.
Lett., 35, L20401, https://doi.org/10.1029/2008GL034150, 2008.
Dutra, E., Gianpaolo, B., Jean-Christophe, C., Munier, S., Burke, S., Fink,
G., Van Dijk, A., Martinez-de la Torre, A., van Beek, R., De Roo, A., and
Polcher, J.: Report on the improved water resources reanalysis Deliverable,
http://earth2observe.eu/files/PublicDeliverables/D5.2 - Report on the Improved Water Resources Reanalysis (WRRtier 2).pdf (last access: 12 December 2022), 2017.
earth2observe: Runoff data E2O WRR2, earth2observe [data set], https://wci.earth2observe.eu, last access: 14 December 2022.
El Gharamti, M., McCreight, J. L., Noh, S. J., Hoar, T. J., RafieeiNasab, A., and Johnson, B. K.: Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding, Hydrol. Earth Syst. Sci., 25, 5315–5336, https://doi.org/10.5194/hess-25-5315-2021, 2021.
Emery, C. M., Paris, A., Biancamaria, S., Boone, A., Calmant, S., Garambois, P.-A., and Santos da Silva, J.: Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product, Hydrol. Earth Syst. Sci., 22, 2135–2162, https://doi.org/10.5194/hess-22-2135-2018, 2018.
Emery, C. M., Biancamaria, S., Boone, A., Ricci, S., Rochoux, M. C., Pedinotti, V., and David, C. H.: Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters, Hydrol. Earth Syst. Sci., 24, 2207–2233, https://doi.org/10.5194/hess-24-2207-2020, 2020a.
Emery, C. M., Paris, A., Biancamaria, S., Boone, A., Calmant, S., Garambois,
P.-A., Da Silva, J. S., and David, C. H.: Discharge Estimation via
Assimilation of Multisatellite-Based Discharge Products: Case Study Over the
Amazon Basin, IEEE Geosci. Remote S., 19, 1–5,
https://doi.org/10.1109/lgrs.2020.3020285, 2020b.
Emery, C. M., David, C. H., Andreadis, K. M., Turmon, M. J., Reager, J. T.,
Hobbs, J. M., Pan, M., Famiglietti, J. S., Beighley, E., and Rodell, M.:
Underlying Fundamentals of Kalman Filtering for River Network Modeling, J.
Hydrometeorol., 21, 453–474, https://doi.org/10.1175/JHM-D-19-0084.1, 2020c.
Espinoza Villar, J. C., Ronchail, J., Guyot, J. L., Cochonneau, G., Naziano,
F., Lavado, W., De Oliveira, E., Pombosa, R., and Vauchel, P.:
Spatio-temporal rainfall variability in the Amazon basin countries (Brazil,
Peru, Bolivia, Colombia, and Ecuador), Int. J. Climatol., 29,
1574–1594, https://doi.org/10.1002/joc.1791, 2009.
Evensen, G.: The Ensemble Kalman Filter: Theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003.
Evensen, G. and van Leeuwen, P. J.: An Ensemble Kalman Smoother for
Nonlinear Dynamics, Mon. Weather Rev., 128, 1852–1867,
https://doi.org/10.1175/1520-0493(2000)128<1852:aeksfn>2.0.co;2,
2002.
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S.
L., Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W.,
Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague,
C., Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, J.,
Pelletier, J., Perket, J., Rouholahnejad-Freund, E., Wagener, T., Zeng, X.,
Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B.,
Safeeq, M., Shen, C., van Verseveld, W., Volk, J. and Yamazaki, D.:
Hillslope Hydrology in Global Change Research and Earth System Modeling,
Water Resour. Res., 55, 1737–1772, https://doi.org/10.1029/2018WR023903, 2019.
Fassoni-Andrade, A. C., Fleischmann, A. S., Papa, F., Paiva, R. C. D. de,
Wongchuig, S., Melack, J. M., Moreira, A. A., Paris, A., Ruhoff, A.,
Barbosa, C., Maciel, D. A., Novo, E., Durand, F., Frappart, F., Aires, F.,
Abrahão, G. M., Ferreira-Ferreira, J., Espinoza, J. C., Laipelt, L.,
Costa, M. H., Espinoza-Villar, R., Calmant, S., and Pellet, V.: Amazon
Hydrology From Space: Scientific Advances and Future Challenges, Rev.
Geophys., 59, 1–97, https://doi.org/10.1029/2020RG000728, 2021.
Feng, D., Gleason, C. J., Lin, P., Yang, X., Pan, M., and Ishitsuka, Y.:
Recent changes to Arctic river discharge, Nat. Commun., 12, 1–9,
https://doi.org/10.1038/s41467-021-27228-1, 2021.
Fleischmann, A. S., Brêda, J. P. F., Passaia, O. A., Wongchuig, S. C.,
Fan, F. M., Paiva, R. C. D., Marques, G. F., and Collischonn, W.: Regional
scale hydrodynamic modeling of the river-floodplain-reservoir continuum, J.
Hydrol., 596, 126114, https://doi.org/10.1016/j.jhydrol.2021.126114, 2021.
Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F. and
Alcamo, J.: Domestic and industrial water uses of the past 60 years as a
mirror of socio-economic development: A global simulation study, Global
Environ. Chang., 23, 144–156, https://doi.org/10.1016/j.gloenvcha.2012.10.018, 2013.
Fu, L.-L., Alsdorf, D., Morrow, R., Rodriguez, E., and Mognard, N.: SWOT: The
Surface Water and Ocean Topography Mission Wide-Swath Altimetric Measurement
of Water Elevation on Earth,
http://hdl.handle.net/2014/41996 (last access: 14 December 2022),
2012.
Gleason, C. J. and Durand, M. T.: Remote sensing of river discharge: A
review and a framing for the discipline, Remote Sens., 12, 1–28,
https://doi.org/10.3390/rs12071107, 2020.
Gleason, C. J. and Smith, L. C.: Toward global mapping of river discharge
using satellite images and at-many-stations hydraulic geometry, P. Natl.
Acad. Sci. USA, 111, 4788–4791, https://doi.org/10.1073/pnas.1317606111, 2014.
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction,
and estimation, J. Am. Stat. Assoc., 102, 359–378,
https://doi.org/10.1198/016214506000001437, 2007.
GRDC: GRDC river discharge observations, GRDC [data set], https://portal.grdc.bafg.de, last access: 14 December 2022.
Hanazaki, R., Yamazaki, D., and Yoshimura, K.: Development of a Reservoir
Flood Control Scheme for Global Flood Models, J. Adv. Model. Earth Sy., 14, e2021MS002944,
https://doi.org/10.1029/2021ms002944, 2022.
Hannah, D. M., Demuth, S., van Lanen, H. A. J., Looser, U., Prudhomme, C.,
Rees, G., Stahl, K., and Tallaksen, L. M.: Large-scale river flow archives:
Importance, current status and future needs, Hydrol. Process., 25,
1191–1200, https://doi.org/10.1002/hyp.7794, 2011.
Hersbach, H., Peubey, C., Simmons, A., Berrisford, P., Poli, P. and Dee, D.:
ERA-20CM: A twentieth-century atmospheric model ensemble, Q. J. Roy. Meteorol.
Soc., 141, 2350–2375, https://doi.org/10.1002/qj.2528, 2015.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008,
2007.
Hydroweb: Satellite altimetry data, Hydroweb [data set], https://hydroweb.theia-land.fr/, last access: 14 December 2022.
Ishitsuka, Y., Gleason, C. J., Hagemann, M. W., Beighley, E., Allen, G. H.,
Feng, D., Lin, P., Pan, M., Andreadis, K., and Pavelsky, T. M.: Combining
optical remote sensing, McFLI discharge estimation, global hydrologic
modelling, and data assimilation to improve daily discharge estimates across
an entire large watershed, Water Resour. Res., 57, 1–20,
https://doi.org/10.1029/2020wr027794, 2020.
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems,
J. Basic Eng., 82, 35, https://doi.org/10.1115/1.3662552, 1960.
Kling, H. and Gupta, H.: On the development of regionalization relationships
for lumped watershed models: The impact of ignoring sub-basin scale
variability, J. Hydrol., 373, 337–351,
https://doi.org/10.1016/j.jhydrol.2009.04.031, 2009.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res., 99, 14415, https://doi.org/10.1029/94jd00483,
1994.
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David,
C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood,
E. F.: Global Reconstruction of Naturalized River Flows at 2.94 Million
Reaches, Water Resour. Res., 55, 6499–6516, https://doi.org/10.1029/2019WR025287,
2019.
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an
integrated data assimilation framework, Water Resour. Res., 43, 1–18,
https://doi.org/10.1029/2006WR005756, 2007.
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012.
Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H.,
Snelder, T., Tockner, K., Trautmann, T., Watt, C., and Datry, T.: Global
prevalence of non-perennial rivers and streams, Nature, 594, 391–397,
https://doi.org/10.1038/s41586-021-03565-5, 2021.
Michailovsky, C. I., Milzow, C., and Bauer-Gottwein, P.: Assimilation of
radar altimetry to a routing model of the Brahmaputra River, Water Resour.
Res., 49, 4807–4816, https://doi.org/10.1002/wrcr.20345, 2013.
Miyoshi, T. and Yamane, S.: Local Ensemble Transform Kalman Filtering with
an AGCM at a T159/L48 Resolution, Mon. Weather Rev., 135, 3841–3861,
https://doi.org/10.1175/2007MWR1873.1, 2007.
Modi, P., Revel, M., and Yamazaki, D.: Multivariable Integrated Evaluation of
Hydrodynamic Modeling: A Comparison of Performance Considering Different
Baseline Topography Data, Water Resour. Res., 58, 1–20,
https://doi.org/10.1029/2021WR031819, 2022.
Nash, J. E. and Sutcliffe, J. V: River Flow Forecasting Through Conceptual
Models Part I – a Discussion of Principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Neal, J. C., Odoni, N. A., Trigg, M. A., Freer, J. E., Garcia-Pintado, J.,
Mason, D. C., Wood, M., and Bates, P. D.: Efficient incorporation of channel
cross-section geometry uncertainty into regional and global scale flood
inundation models, J. Hydrol., 529, 169–183,
https://doi.org/10.1016/j.jhydrol.2015.07.026, 2015.
Oki, T. and Kanae, S.: Global Hydrological Cycles and World Water Resources,
Science, 5790, 1068–1072, https://doi.org/10.1126/science.1128845, 2006.
Paiva, R. C. D., Collischonn, W., Bonnet, M.-P., de Gonçalves, L. G. G., Calmant, S., Getirana, A., and Santos da Silva, J.: Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon, Hydrol. Earth Syst. Sci., 17, 2929–2946, https://doi.org/10.5194/hess-17-2929-2013, 2013a.
Paiva, R. C. D., Collischonn, W., and Buarque, D. C.: Validation of a full
hydrodynamic model for large-scale hydrologic modelling in the Amazon,
Hydrol. Process., 27, 333–346, https://doi.org/10.1002/hyp.8425, 2013b.
Papa, F., Prigent, C., Aires, F., Jimenez, C., Rossow, W. B. and Matthews,
E.: Interannual variability of surface water extent at the global scale,
1993-2004, J. Geophys. Res.-Atmos., 115, D12111,
https://doi.org/10.1029/2009JD012674, 2010.
Pedinotti, V., Boone, A., Ricci, S., Biancamaria, S., and Mognard, N.: Assimilation of satellite data to optimize large-scale hydrological model parameters: a case study for the SWOT mission, Hydrol. Earth Syst. Sci., 18, 4485–4507, https://doi.org/10.5194/hess-18-4485-2014, 2014.
Pokhrel, Y., Shin, S., Lin, Z., Yamazaki, D., and Qi, J.: Potential
Disruption of Flood Dynamics in the Lower Mekong River Basin Due to Upstream
Flow Regulation, Sci. Rep.-UK, 8, 1–13, https://doi.org/10.1038/s41598-018-35823-4,
2018.
Prigent, C., Jimenez, C., and Bousquet, P.: Satellite-Derived Global Surface
Water Extent and Dynamics Over the Last 25 Years (GIEMS-2), J. Geophys. Res.-Atmos., 125, 1–18, https://doi.org/10.1029/2019JD030711, 2020.
Reis, V., Hermoso, V., Hamilton, S. K., Bunn, S. E., Fluet-Chouinard, E.,
Venables, B., and Linke, S.: Characterizing seasonal dynamics of Amazonian
wetlands for conservation and decision making, Aquat. Conserv., 29, 1073–1082, https://doi.org/10.1002/aqc.3051, 2019.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.:
Understanding predictive uncertainty in hydrologic modeling: The challenge
of identifying input and structural errors, Water Resour. Res., 46,
1–22, https://doi.org/10.1029/2009WR008328, 2010.
Resti, A., Benveniste, J., Roca, M., Milagro-Perez, M. P., and Levrini, G.:
The Envisat Radar Altimeter System (RA-2), ESA Bull., 98, 94–101,
https://doi.org/10.1117/12.452745, 2002.
Revel, M., Ikeshima, D., Yamazaki, D. and Kanae, S.: A Physically Based Empirical
Localization Method for Assimilating Synthetic SWOT Observations of a
Continental-Scale River: A Case Study in the Congo Basin, Water, 11, 829,
https://doi.org/10.3390/w11040829, 2019.
Revel, M., Ikeshima, D., Yamazaki, D., and Kanae, S.: A Framework for
Estimating Global-Scale River Discharge by Assimilating Satellite Altimetry,
Water Resour. Res., 57, 1–34, https://doi.org/10.1029/2020WR027876, 2021.
Revel, M., Zhou, X., Yamazaki, D. and Kanae, S.: HydroDA v1.0, HydroShare [data set],
https://doi.org/10.4211/hs.08e1b18aa9f240758dd13d9ac875621f, 2022a.
Revel, M., Zhou, X., Yamazaki, D., and Kanae, S.: HydroDA 1.0., Zenodo [code], https://doi.org/10.5281/zenodo.6506861, 2022b.
Saleh, F., Ducharne, A., Flipo, N., Oudin, L., and Ledoux, E.: Impact of
river bed morphology on discharge and water levels simulated by a 1D
Saint-Venant hydraulic model at regional scale, J. Hydrol., 476, 169–177,
https://doi.org/10.1016/j.jhydrol.2012.10.027, 2013.
Santos da Silva, J., Calmant, S., Seyler, F., Rotunno Filho, O. C.,
Cochonneau, G., and Mansur, W. J.: Water levels in the Amazon basin derived
from the ERS 2 and ENVISAT radar altimetry missions, Remote Sens. Environ.,
114, 2160–2181, https://doi.org/10.1016/j.rse.2010.04.020, 2010.
Shiklomanov, A. I., Lammers, R. B., and Vörösmarty, C. J.: Widespread
decline in hydrological monitoring threatens Pan-Arctic Research, EOS
T. Am. Geophys. Un., 83, 13, https://doi.org/10.1029/2002EO000007, 2002.
Shin, S., Pokhrel, Y., Yamazaki, D., Huang, X., Torbick, N., Qi, J.,
Pattanakiat, S., Ngo-Duc, T., and Duc Tuan, N.: High Resolution Modeling of
River-floodplain-reservoir Inundation Dynamics in the Mekong River Basin,
Water Resour. Res., 56, e2019WR026449, https://doi.org/10.1029/2019wr026449, 2020.
Sood, A. and Smakhtin, V.: Revue des modèles hydrologiques globaux,
Hydrolog. Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Sutanudjaja, E. H., Van Beek, L. P. H., De Jong, S. M., Van Geer, F. C., and
Bierkens, M. F. P.: Calibrating a large-extent high-resolution coupled
groundwater-land surface model using soil moisture and discharge data, Water
Resour. Res., 50, 687–705, https://doi.org/10.1002/2013WR013807, 2014.
Van Beek, L. P. H., Wada, Y. and Bierkens, M. F. P.: Global monthly water
stress: 1. Water balance and water availability, Water Resour. Res., 47, W07517, https://doi.org/10.1029/2010WR009791, 2011.
Van Der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: a
GIS-based distributed model for river basin scale water balance and flood
simulation, Int. J. Geogr. Inf. Sci., 24, 189–212,
https://doi.org/10.1080/13658810802549154, 2008.
Van Dijk, A. I. J. M., Peña-Arancibia, J. L., Wood, E. F., Sheffield, J.
and Beck, H. E.: Global analysis of seasonal streamflow predictability using
an ensemble prediction system and observations from 6192 small catchments
worldwide, Water Resour. Res., 49, 2729–2746, https://doi.org/10.1002/wrcr.20251,
2013.
Vergnes, J.-P., Decharme, B., and Habets, F.: Introduction of groundwater
capillary rises using subgrid spatial variability of topography into the
ISBA land surface model, J. Geophys. Res.-Atmos., 119, 11065–11086,
https://doi.org/10.1002/2014JD021573, 2014.
Verzano, K.: Climate change impacts on flood related hydrological processes:
Further development and application of a global scale hydrological model,
Hamburg, Germany,
https://doi.org/10.17617/2.993926, 2009.
Vörösmarty, C., Askew, A., Grabs, W., Barry, R. G., Birkett, C.,
Döll, P., Goodison, B., Hall, A., Jenne, R., Kitaev, L., Landwehr, J.,
Keeler, M., Leavesley, G., Schaake, J., Strzepek, K., Sundarvel, S. S.,
Takeuchi, K. and Webster, F.: Global water data: A newly endangered species,
EOS, 82, 1999–2001, https://doi.org/10.1029/01EO00031, 2001.
Wongchuig, S. C., de Paiva, R. C. D., Siqueira, V., and Collischonn, W.:
Hydrological reanalysis across the 20th century: A case study of the Amazon
Basin, J. Hydrol., 570, 755–773,
https://doi.org/10.1016/j.jhydrol.2019.01.025, 2019.
Wongchuig-Correa, S., Cauduro Dias de Paiva, R., Biancamaria, S., and
Collischonn, W.: Assimilation of future SWOT-based river elevations, surface
extent observations and discharge estimations into uncertain global
hydrological models, J. Hydrol., 590, 125473,
https://doi.org/10.1016/j.jhydrol.2020.125473, 2020.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based description
of floodplain inundation dynamics in a global river routing model, Water
Resour. Res., 47, 1–21, https://doi.org/10.1029/2010WR009726, 2011.
Yamazaki, D., Lee, H., Alsdorf, D. E., Dutra, E., Kim, H., Kanae, S., and
Oki, T.: Analysis of the water level dynamics simulated by a global river
model: A case study in the Amazon River, Water Resour. Res., 48, 1–15,
https://doi.org/10.1029/2012WR011869, 2012.
Yamazaki, D., De Almeida, G. A. M., and Bates, P. D.: Improving computational
efficiency in global river models by implementing the local inertial flow
equation and a vector-based river network map, Water Resour. Res., 49,
7221–7235, https://doi.org/10.1002/wrcr.20552, 2013.
Yamazaki, D., O'Loughlin, F., Trigg, M. A., Miller, Z. F., Pavelsky, T. M.,
and Bates, P. D.: Development of the Global Width Database for Large Rivers,
Water Resour. Res., 50, 3467–3480, https://doi.org/10.1002/2013WR014664, 2014a.
Yamazaki, D., Sato, T., Kanae, S., Hirabayashi, Y., and Bates, P. D.:
Regional flood dynamics in a bifurcating mega delta simulated in a global
river model, Geophys. Res. Lett., 41, 3127–3135,
https://doi.org/10.1002/2014GL059744, 2014b.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F.,
Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map
of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., and Pavelsky,
T.: MERIT Hydro: A high-resolution global hydrography map based on latest
topography datasets, Water Resour. Res., 55, 2019WR024873,
https://doi.org/10.1029/2019WR024873, 2019.
Yamazaki, D., Revel, M., Hanazaki, R., Zhou, X. and Nitta, T.: CaMa-Flood, Zenodo [code], https://doi.org/10.5281/zenodo.4609654, 2021.
Yoon, Y., Durand, M., Merry, C. J., Clark, E. A., Andreadis, K. M., and
Alsdorf, D. E.: Estimating river bathymetry from data assimilation of
synthetic SWOT measurements, J. Hydrol., 464–465, 363–375,
https://doi.org/10.1016/j.jhydrol.2012.07.028, 2012.
Zhou, X., Revel, M., Modi, P., Shiozawa, T., and Yamazaki, D.: Correction of
river bathymetry parameters using the stage-discharge rating curve, Water
Resour. Res., 58, 1–26, https://doi.org/10.1029/2021WR031226, 2022.
Zwally, H. J., Schutz, B., Abdalati, W., Abshire, J., Bentley, C., Brenner,
A., Bufton, J., Dezio, J., Hancock, D., Harding, D., Herring, T., Minster,
B., Quinn, K., Palm, S., Spinhirne, J., and Thomas, R.: ICESat's laser
measurements of polar ice, atmosphere, ocean, and land, J. Geodyn.,
34, 405–445, https://doi.org/10.1016/S0264-3707(02)00042-X, 2002.
Short summary
The capacity to discern surface water improved as satellites became more available. Because remote sensing data is discontinuous, integrating models with satellite observations will improve knowledge of water resources. However, given the current limitations (e.g., parameter errors) of water resource modeling, merging satellite data with simulations is problematic. Integrating observations and models with the unique approaches given here can lead to a better estimation of surface water dynamics.
The capacity to discern surface water improved as satellites became more available. Because...