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Cédric H. David, David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets, and Victor Eijkhout

Abstract

The mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into networks. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides streamflow observations at about 20 thousand gauges located on the NHDPlus river network. A river network model called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff computations provided by land surface models. RAPID is applied to the Guadalupe and San Antonio River basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditional Muskingum method when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin.

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Kurt C. Solander, John T. Reager, Brian F. Thomas, Cédric H. David, and James S. Famiglietti

Abstract

The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM.

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Yuan Yang, Ming Pan, Peirong Lin, Hylke E. Beck, Zhenzhong Zeng, Dai Yamazaki, Cédric H. David, Hui Lu, Kun Yang, Yang Hong, and Eric F. Wood

Abstract

Better understanding and quantification of river floods for very local and flashy events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-year period of 1980-2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-year return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) sub-daily modeling. This data record, referred as Global Reach-level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.

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Charlotte M. Emery, Cédric H. David, Konstantinos M. Andreadis, Michael J. Turmon, John T. Reager, Jonathan M. Hobbs, Ming Pan, James S. Famiglietti, Edward Beighley, and Matthew Rodell

Abstract

The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from −11.32 to 0.55 at observed gauges and from −12.24 to −1.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA’s Surface Water and Ocean Topography mission.

Free access
Vincent Häfliger, Eric Martin, Aaron Boone, Florence Habets, Cédric H. David, Pierre-A. Garambois, Hélène Roux, Sophie Ricci, Lucie Berthon, Anthony Thévenin, and Sylvain Biancamaria

Abstract

The Surface Water and Ocean Topography (SWOT) mission will provide free water surface elevations, slopes, and river widths for rivers wider than 50 m. Models must be prepared to use this new finescale information by explicitly simulating the link between runoff and the river channel hydraulics. This study assesses one regional hydrometeorological model’s ability to simulate river depths. The Garonne catchment in southwestern France (56 000 km2) has been chosen for the availability of operational gauges in the river network and finescale hydraulic models over two reaches of the river. Several routing schemes, ranging from the simple Muskingum method to time-variable parameter kinematic and diffusive waves schemes, are tested. The results show that the variable flow velocity schemes are advantageous for discharge computations when compared to the original Muskingum routing method. Additionally, comparisons between river depth computations and in situ observations in the downstream Garonne River led to root-mean-square errors of 50–60 cm in the improved Muskingum method and 40–50 cm in the kinematic–diffusive wave method. The results also highlight SWOT’s potential to improve the characterization of hydrological processes for subbasins larger than 10 000 km2, the importance of an accurate digital elevation model, and the need for spatially varying hydraulic parameters.

Full access
Yuan Yang, Ming Pan, Peirong Lin, Hylke E. Beck, Zhenzhong Zeng, Dai Yamazaki, Cédric H. David, Hui Lu, Kun Yang, Yang Hong, and Eric F. Wood

Abstract

Better understanding and quantification of river floods for very local and “flashy” events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) subdaily modeling. This data record, referred as Global Reach-Level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.

Full access
Catalina M. Oaida, John T. Reager, Konstantinos M. Andreadis, Cédric H. David, Steve R. Levoe, Thomas H. Painter, Kat J. Bormann, Amy R. Trangsrud, Manuela Girotto, and James S. Famiglietti

Abstract

Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA’s ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there.

Open access
Faisal Hossain, Margaret Srinivasan, Craig Peterson, Alice Andral, Ed Beighley, Eric Anderson, Rashied Amini, Charon Birkett, David Bjerklie, Cheryl Ann Blain, Selma Cherchali, Cédric H. David, Bradley Doorn, Jorge Escurra, Lee-Lueng Fu, Chris Frans, John Fulton, Subhrendu Gangopadhay, Subimal Ghosh, Colin Gleason, Marielle Gosset, Jessica Hausman, Gregg Jacobs, John Jones, Yasir Kaheil, Benoit Laignel, Patrick Le Moigne, Li Li, Fabien Lefèvre, Robert Mason, Amita Mehta, Abhijit Mukherjee, Anthony Nguy-Robertson, Sophie Ricci, Adrien Paris, Tamlin Pavelsky, Nicolas Picot, Guy Schumann, Sudhir Shrestha, Pierre-Yves Le Traon, and Eric Trehubenko
Open access
Peter Bissolli, Catherine Ganter, Tim Li, Ademe Mekonnen, Ahira Sánchez-Lugo, Eric J. Alfaro, Lincoln M. Alves, Jorge A. Amador, B. Andrade, Francisco Argeñalso, P. Asgarzadeh, Julian Baez, Reuben Barakiza, M. Yu. Bardin, Mikhail Bardin, Oliver Bochníček, Brandon Bukunt, Blanca Calderón, Jayaka D. Campbell, Elise Chandler, Ladislaus Chang’a, Vincent Y. S. Cheng, Leonardo A. Clarke, Kris Correa, Catalina Cortés, Felipe Costa, A.P.M.A. Cunha, Mesut Demircan, K. R. Dhurmea, A. Diawara, Sarah Diouf, Dashkhuu Dulamsuren, M. ElKharrim, Jhan-Carlo Espinoza, A. Fazl-Kazem, Chris Fenimore, Steven Fuhrman, Karin Gleason, Charles “Chip” P. Guard, Samson Hagos, Mizuki Hanafusa, H. R. Hasannezhad, Richard R. Heim Jr., Hugo G. Hidalgo, J. A. Ijampy, Gyo Soon Im, Annie C. Joseph, G. Jumaux, K. R. Kabidi, P-H. Kamsu-Tamo, John Kennedy, Valentina Khan, Mai Van Khiem, Philemon King’uza, Natalia N. Korshunova, A. C. Kruger, Hoang Phuc Lam, Mark A. Lander, Waldo Lavado-Casimiro, Tsz-Cheung Lee, Kinson H. Y. Leung, Gregor Macara, Jostein Mamen, José A. Marengo, Charlotte McBride, Noelia Misevicius, Aurel Moise, Jorge Molina-Carpio, Natali Mora, Awatif E. Mostafa, Habiba Mtongori, Charles Mutai, O. Ndiaye, Juan José Nieto, Latifa Nyembo, Patricia Nying’uro, Xiao Pan, Reynaldo Pascual Ramírez, David Phillips, Brad Pugh, Madhavan Rajeevan, M. L. Rakotonirina, Andrea M. Ramos, M. Robjhon, Camino Rodriguez, Guisado Rodriguez, Josyane Ronchail, Benjamin Rösner, Roberto Salinas, Hirotaka Sato, Hitoshi Sato, Amal Sayouri, Joseph Sebaziga, Serhat Sensoy, Sandra Spillane, Katja Trachte, Gerard van der Schrier, F. Sima, Adam Smith, Jacqueline M. Spence, O. P. Sreejith, A. K. Srivastava, José L. Stella, Kimberly A. Stephenson, Tannecia S. Stephenson, S. Supari, Sahar Tajbakhsh-Mosalman, Gerard Tamar, Michael A. Taylor, Asaminew Teshome, Wassila M. Thiaw, Skie Tobin, Adrian R. Trotman, Cedric J. Van Meerbeeck, A. Vazifeh, Shunya Wakamatsu, Wei Wang, Fei Xin, F. Zeng, Peiqun Zhang, and Zhiwei Zhu
Free access
Tim Li, Abdallah Abida, Laura S. Aldeco, Eric J. Alfaro, Lincoln M. Alves, Jorge A. Amador, B. Andrade, Julian Baez, M. Yu. Bardin, Endalkachew Bekele, Eric Broedel, Brandon Bukunt, Blanca Calderón, Jayaka D. Campbell, Diego A. Campos Diaz, Gilma Carvajal, Elise Chandler, Vincent. Y. S. Cheng, Chulwoon Choi, Leonardo A. Clarke, Kris Correa, Felipe Costa, A. P. Cunha, Mesut Demircan, R. Dhurmea, Eliecer A. Díaz, M. ElKharrim, Bantwale D. Enyew, Jhan C. Espinoza, Amin Fazl-Kazem, Nava Fedaeff, Z. Feng, Chris Fenimore, S. D. Francis, Karin Gleason, Charles “Chip” P. Guard, Indra Gustari, S. Hagos, Richard R. Heim Jr., Rafael Hernández, Hugo G. Hidalgo, J. A. Ijampy, Annie C. Joseph, Guillaume Jumaux, Khadija Kabidi, Johannes W. Kaiser, Pierre-Honore Kamsu-Tamo, John Kennedy, Valentina Khan, Mai Van Khiem, Khatuna Kokosadze, Natalia N. Korshunova, Andries C. Kruger, Nato Kutaladze, L. Labbé, Mónika Lakatos, Hoang Phuc Lam, Mark A. Lander, Waldo Lavado-Casimiro, T. C. Lee, Kinson H. Y. Leung, Andrew D. Magee, Jostein Mamen, José A. Marengo, Dora Marín, Charlotte McBride, Lia Megrelidze, Noelia Misevicius, Y. Mochizuki, Aurel Moise, Jorge Molina-Carpio, Natali Mora, Awatif E. Mostafa, uan José Nieto, Lamjav Oyunjargal, Reynaldo Pascual Ramírez, Maria Asuncion Pastor Saavedra, Uwe Pfeifroth, David Phillips, Madhavan Rajeevan, Andrea M. Ramos, Jayashree V. Revadekar, Miliaritiana Robjhon, Ernesto Rodriguez Camino, Esteban Rodriguez Guisado, Josyane Ronchail, Benjamin Rösner, Roberto Salinas, Amal Sayouri, Carl J. Schreck III, Serhat Sensoy, A. Shimpo, Fatou Sima, Adam Smith, Jacqueline Spence, Sandra Spillane, Arne Spitzer, A. K. Srivastava, José L. Stella, Kimberly A. Stephenson, Tannecia S. Stephenson, Michael A. Taylor, Wassila Thiaw, Skie Tobin, Dennis Todey, Katja Trachte, Adrian R. Trotman, Gerard van der Schrier, Cedric J. Van Meerbeeck, Ahad Vazifeh, José Vicencio Veloso, Wei Wang, Fei Xin, Peiqun Zhang, Zhiwei Zhu, and Jonas Zucule
Free access