Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-2755-2023
© 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/essd-15-2755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An ensemble of 48 physically perturbed model estimates of the 1∕8° terrestrial water budget over the conterminous United States, 1980–2015
Hui Zheng
Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Wenli Fei
Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas 78705, USA
Jiangfeng Wei
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas 78705, USA
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Long Zhao
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas 78705, USA
School of Geographical Sciences, Southwest University, Chongqing 400715, China
Lingcheng Li
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas 78705, USA
Pacific Northwest National Laboratory, Richland, Washington 99354, USA
State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China
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Earth Syst. Sci. Data, 16, 4709–4734, https://doi.org/10.5194/essd-16-4709-2024, https://doi.org/10.5194/essd-16-4709-2024, 2024
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Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
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Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
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Earth Syst. Sci. Data, 16, 4311–4323, https://doi.org/10.5194/essd-16-4311-2024, https://doi.org/10.5194/essd-16-4311-2024, 2024
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Global water resource monitoring is crucial due to climate change and population growth. This study presents a hand-labeled dataset of 100 PlanetScope images for surface water detection, spanning diverse biomes. We use this dataset to evaluate two state-of-the-art mapping methods. Results highlight performance variations across biomes, emphasizing the need for diverse, independent validation datasets to enhance the accuracy and reliability of satellite-based surface water monitoring techniques.
Lei Huang, Yong Luo, Jing M. Chen, Qiuhong Tang, Tammo Steenhuis, Wei Cheng, and Wen Shi
Earth Syst. Sci. Data, 16, 3993–4019, https://doi.org/10.5194/essd-16-3993-2024, https://doi.org/10.5194/essd-16-3993-2024, 2024
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Timely global terrestrial evapotranspiration (ET) data are crucial for water resource management and drought forecasting. This study introduces the VISEA algorithm, which integrates satellite data and shortwave radiation to provide daily 0.05° gridded near-real-time ET estimates. By employing a vegetation index–temperature method, this algorithm can estimate ET without requiring additional data. Evaluation results demonstrate VISEA's comparable accuracy with accelerated data availability.
Sibylle Kathrin Hassler, Rafael Bohn Reckziegel, Ben du Toit, Svenja Hoffmeister, Florian Kestel, Anton Kunneke, Rebekka Maier, and Jonathan Paul Sheppard
Earth Syst. Sci. Data, 16, 3935–3948, https://doi.org/10.5194/essd-16-3935-2024, https://doi.org/10.5194/essd-16-3935-2024, 2024
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Kaihao Zheng, Peirong Lin, and Ziyun Yin
Earth Syst. Sci. Data, 16, 3873–3891, https://doi.org/10.5194/essd-16-3873-2024, https://doi.org/10.5194/essd-16-3873-2024, 2024
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We develop a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. It involves a stepwise approach to estimate the basin-level floodplain hydraulic geometry parameters that best respect the scaling law while approximating the global hydrodynamic flood maps. A ~90 m resolution global floodplain map, the Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT), is delineated with demonstrated superiority.
Yuzhong Yang, Qingbai Wu, Xiaoyan Guo, Lu Zhou, Helin Yao, Dandan Zhang, Zhongqiong Zhang, Ji Chen, and Guojun Liu
Earth Syst. Sci. Data, 16, 3755–3770, https://doi.org/10.5194/essd-16-3755-2024, https://doi.org/10.5194/essd-16-3755-2024, 2024
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We present the temporal data of stable isotopes in different waterbodies in the Beiluhe Basin in the hinterland of the Qinghai–Tibet Plateau (QTP) produced between 2017 and 2022. In this article, the first detailed stable isotope data of 359 ground ice samples are presented. This first data set provides a new basis for understanding the hydrological effects of permafrost degradation on the QTP.
Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-318, https://doi.org/10.5194/essd-2024-318, 2024
Revised manuscript accepted for ESSD
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The CAMELS-DE dataset features data from 1555 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends, and supports the development of hydrological models.
Bennet Juhls, Anne Morgenstern, Jens Hölemann, Antje Eulenburg, Birgit Heim, Frederieke Miesner, Hendrik Grotheer, Gesine Mollenhauer, Hanno Meyer, Ephraim Erkens, Felica Yara Gehde, Sofia Antonova, Sergey Chalov, Maria Tereshina, Oxana Erina, Evgeniya Fingert, Ekaterina Abramova, Tina Sanders, Liudmila Lebedeva, Nikolai Torgovkin, Georgii Maksimov, Vasily Povazhnyi, Rafael Gonçalves-Araujo, Urban Wünsch, Antonina Chetverova, Sophie Opfergelt, and Pier Paul Overduin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-290, https://doi.org/10.5194/essd-2024-290, 2024
Revised manuscript accepted for ESSD
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The Siberian Arctic is warming fast: permafrost is thawing, river chemistry is changing, and coastal ecosystems are affected. We want to understand changes to the Lena River, a major Arctic river flowing to the Arctic Ocean, by collecting 4.5 years of detailed water data, including temperature and carbon and nutrient contents. This dataset records current conditions and helps us to detect future changes. Explore it at https://doi.org/10.1594/PANGAEA.913197 and https://lena-monitoring.awi.de/.
Einara Zahn and Elie Bou-Zeid
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-272, https://doi.org/10.5194/essd-2024-272, 2024
Revised manuscript accepted for ESSD
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Quantifying water and CO2 exchanges through transpiration, evaporation, photosynthesis, and soil respiration are essential to understand how ecosystems function. We implemented five methods to estimate these fluxes over a five-year period across 47 sites. This is the first dataset representing such a large spatial and temporal coverage of soil and plant exchanges, and it has many potentials applications such as to examine the response of ecosystem to weather extremes and climate change.
Hordur Bragi Helgason and Bart Nijssen
Earth Syst. Sci. Data, 16, 2741–2771, https://doi.org/10.5194/essd-16-2741-2024, https://doi.org/10.5194/essd-16-2741-2024, 2024
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LamaH-Ice is a large-sample hydrology (LSH) dataset for Iceland. The dataset includes daily and hourly hydro-meteorological time series, including observed streamflow and basin characteristics, for 107 basins. LamaH-Ice offers most variables that are included in existing LSH datasets and additional information relevant to cold-region hydrology such as annual time series of glacier extent and mass balance. A large majority of the basins in LamaH-Ice are unaffected by human activities.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
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To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Pierre-Antoine Versini, Leydy Alejandra Castellanos-Diaz, David Ramier, and Ioulia Tchiguirinskaia
Earth Syst. Sci. Data, 16, 2351–2366, https://doi.org/10.5194/essd-16-2351-2024, https://doi.org/10.5194/essd-16-2351-2024, 2024
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Nature-based solutions (NBSs), such as green roofs, have appeared as relevant solutions to mitigate urban heat islands. The evapotranspiration (ET) process allows NBSs to cool the air. To improve our knowledge about ET assessment, this paper presents some experimental measurement campaigns carried out during three consecutive summers. Data are available for three different (large, small, and point-based) spatial scales.
Ralph Bathelemy, Pierre Brigode, Vazken Andréassian, Charles Perrin, Vincent Moron, Cédric Gaucherel, Emmanuel Tric, and Dominique Boisson
Earth Syst. Sci. Data, 16, 2073–2098, https://doi.org/10.5194/essd-16-2073-2024, https://doi.org/10.5194/essd-16-2073-2024, 2024
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The aim of this work is to provide the first hydroclimatic database for Haiti, a Caribbean country particularly vulnerable to meteorological and hydrological hazards. The resulting database, named Simbi, provides hydroclimatic time series for around 150 stations and 24 catchment areas.
Changming Li, Ziwei Liu, Wencong Yang, Zhuoyi Tu, Juntai Han, Sien Li, and Hanbo Yang
Earth Syst. Sci. Data, 16, 1811–1846, https://doi.org/10.5194/essd-16-1811-2024, https://doi.org/10.5194/essd-16-1811-2024, 2024
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Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land–atmosphere interactions.
Yuhan Guo, Hongxing Zheng, Yuting Yang, Yanfang Sang, and Congcong Wen
Earth Syst. Sci. Data, 16, 1651–1665, https://doi.org/10.5194/essd-16-1651-2024, https://doi.org/10.5194/essd-16-1651-2024, 2024
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We have provided an inaugural version of the hydrogeomorphic dataset for catchments over the Tibetan Plateau. We first provide the width-function-based instantaneous unit hydrograph (WFIUH) for each HydroBASINS catchment, which can be used to investigate the spatial heterogeneity of hydrological behavior across the Tibetan Plateau. It is expected to facilitate hydrological modeling across the Tibetan Plateau.
Ziyun Yin, Peirong Lin, Ryan Riggs, George H. Allen, Xiangyong Lei, Ziyan Zheng, and Siyu Cai
Earth Syst. Sci. Data, 16, 1559–1587, https://doi.org/10.5194/essd-16-1559-2024, https://doi.org/10.5194/essd-16-1559-2024, 2024
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Large-sample hydrology (LSH) datasets have been the backbone of hydrological model parameter estimation and data-driven machine learning models for hydrological processes. This study complements existing LSH studies by creating a dataset with improved sample coverage, uncertainty estimates, and dynamic descriptions of human activities, which are all crucial to hydrological understanding and modeling.
Pierluigi Claps, Giulia Evangelista, Daniele Ganora, Paola Mazzoglio, and Irene Monforte
Earth Syst. Sci. Data, 16, 1503–1522, https://doi.org/10.5194/essd-16-1503-2024, https://doi.org/10.5194/essd-16-1503-2024, 2024
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FOCA (Italian FlOod and Catchment Atlas) is the first systematic collection of data on Italian river catchments. It comprises geomorphological, soil, land cover, NDVI, climatological and extreme rainfall catchment attributes. FOCA also contains 631 peak and daily discharge time series covering the 1911–2016 period. Using this first nationwide data collection, a wide range of applications, in particular flood studies, can be undertaken within the Italian territory.
Wei Jing Ang, Edward Park, Yadu Pokhrel, Dung Duc Tran, and Ho Huu Loc
Earth Syst. Sci. Data, 16, 1209–1228, https://doi.org/10.5194/essd-16-1209-2024, https://doi.org/10.5194/essd-16-1209-2024, 2024
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Dams have burgeoned in the Mekong, but information on dams is scattered and inconsistent. Up-to-date evaluation of dams is unavailable, and basin-wide hydropower potential has yet to be systematically assessed. We present a comprehensive database of 1055 dams, a spatiotemporal analysis of the dams, and a total hydropower potential of 1 334 683 MW. Considering projected dam development and hydropower potential, the vulnerability and the need for better dam management may be highest in Laos.
Chuanqi He, Ci-Jian Yang, Jens M. Turowski, Richard F. Ott, Jean Braun, Hui Tang, Shadi Ghantous, Xiaoping Yuan, and Gaia Stucky de Quay
Earth Syst. Sci. Data, 16, 1151–1166, https://doi.org/10.5194/essd-16-1151-2024, https://doi.org/10.5194/essd-16-1151-2024, 2024
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The shape of drainage basins and rivers holds significant implications for landscape evolution processes and dynamics. We used a global 90 m resolution topography to obtain ~0.7 million drainage basins with sizes over 50 km2. Our dataset contains the spatial distribution of drainage systems and their morphological parameters, supporting fields such as geomorphology, climatology, biology, ecology, hydrology, and natural hazards.
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.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, and Luc Feyen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-41, https://doi.org/10.5194/essd-2024-41, 2024
Revised manuscript accepted for ESSD
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This article presents a reanalysis of Europe's rivers streamflow for the period 1950–2020, using a state-of-the-art hydrological simulation framework. The dataset, called HERA (Hydrological European ReAnalysis), uses detailed information about the landscape, climate, and human activities to estimate river flow. HERA can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources, flood and drought risks.
Daniel Kovacek and Steven Weijs
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-508, https://doi.org/10.5194/essd-2023-508, 2024
Revised manuscript accepted for ESSD
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We made a dataset for British Columbia describing the terrain, soil, land cover, and climate of over 1 million watersheds. The attributes are often used in hydrology because they are related to the water cycle. The data is meant to be used for water resources problems that can benefit from lots of basins and their attributes. The data and instructions needed to build the dataset from scratch are freely available. The permanent home for the data is https://doi.org/10.5683/SP3/JNKZVT.
Ana M. Ricardo, Rui M. L. Ferreira, Alberto Rodrigues da Silva, Jacinto Estima, Jorge Marques, Ivo Gamito, and Alexandre Serra
Earth Syst. Sci. Data, 16, 375–385, https://doi.org/10.5194/essd-16-375-2024, https://doi.org/10.5194/essd-16-375-2024, 2024
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Floods are among the most common natural disasters responsible for severe damages and human losses. Agueda.2016Flood, a synthesis of locally sensed data and numerically produced data, allows complete characterization of the flood event that occurred in February 2016 in the Portuguese Águeda River. The dataset was managed through the RiverCure Portal, a collaborative web platform connected to a validated shallow-water model.
Jiawei Hou, Albert I. J. M. Van Dijk, Luigi J. Renzullo, and Pablo R. Larraondo
Earth Syst. Sci. Data, 16, 201–218, https://doi.org/10.5194/essd-16-201-2024, https://doi.org/10.5194/essd-16-201-2024, 2024
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The GloLakes dataset provides historical and near-real-time time series of relative (i.e. storage change) and absolute (i.e. total stored volume) storage for more than 27 000 lakes worldwide using multiple sources of satellite data, including laser and radar altimetry and optical remote sensing. These data can help us understand the influence of climate variability and anthropogenic activities on water availability and system ecology over the last 4 decades.
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.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
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CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Peter Burek and Mikhail Smilovic
Earth Syst. Sci. Data, 15, 5617–5629, https://doi.org/10.5194/essd-15-5617-2023, https://doi.org/10.5194/essd-15-5617-2023, 2023
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We address an annoying problem every grid-based hydrological model must solve to compare simulated and observed river discharge. First, station locations do not fit the high-resolution river network. We update the database with stations based on a new high-resolution network. Second, station locations do not work with a coarser grid-based network. We use a new basin shape similarity concept for station locations on a coarser grid, reducing the error of assigning stations to the wrong basin.
Najwa Sharaf, Jordi Prats, Nathalie Reynaud, Thierry Tormos, Rosalie Bruel, Tiphaine Peroux, and Pierre-Alain Danis
Earth Syst. Sci. Data, 15, 5631–5650, https://doi.org/10.5194/essd-15-5631-2023, https://doi.org/10.5194/essd-15-5631-2023, 2023
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We present a regional long-term (1959–2020) dataset (LakeTSim) of daily epilimnion and hypolimnion water temperature simulations in 401 French lakes. Overall, less uncertainty is associated with the epilimnion compared to the hypolimnion. LakeTSim is valuable for providing new insights into lake water temperature for assessing the impact of climate change, which is often hindered by the lack of observations, and for decision-making by stakeholders.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
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Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Wei Wang, La Zhuo, Xiangxiang Ji, Zhiwei Yue, Zhibin Li, Meng Li, Huimin Zhang, Rong Gao, Chenjian Yan, Ping Zhang, and Pute Wu
Earth Syst. Sci. Data, 15, 4803–4827, https://doi.org/10.5194/essd-15-4803-2023, https://doi.org/10.5194/essd-15-4803-2023, 2023
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The consumptive water footprint of crop production (WFCP) measures blue and green evapotranspiration of either irrigated or rainfed crops in time and space. A gridded monthly WFCP dataset for China is established. There are four improvements from existing datasets: (i) distinguishing water supply modes and irrigation techniques, (ii) distinguishing evaporation and transpiration, (iii) consisting of both total and unit WFCP, and (iv) providing benchmarks for unit WFCP by climatic zones.
Emma L. Robinson, Matthew J. Brown, Alison L. Kay, Rosanna A. Lane, Rhian Chapman, Victoria A. Bell, and Eleanor M. Blyth
Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, https://doi.org/10.5194/essd-15-4433-2023, 2023
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This work presents two new Penman–Monteith potential evaporation datasets for the UK, calculated with the same methodology applied to historical climate data (Hydro-PE HadUK-Grid) and an ensemble of future climate projections (Hydro-PE UKCP18 RCM). Both include an optional correction for evaporation of rain that lands on the surface of vegetation. The historical data are consistent with existing PE datasets, and the future projections include effects of rising atmospheric CO2 on vegetation.
Xinyu Chen, Liguang Jiang, Yuning Luo, and Junguo Liu
Earth Syst. Sci. Data, 15, 4463–4479, https://doi.org/10.5194/essd-15-4463-2023, https://doi.org/10.5194/essd-15-4463-2023, 2023
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River flow is experiencing changes under the impacts of climate change and human activities. For example, flood events are occurring more often and are more destructive in many places worldwide. To deal with such issues, hydrologists endeavor to understand the features of extreme events as well as other hydrological changes. One key approach is analyzing flow characteristics, represented by hydrological indices. Building such a comprehensive global large-sample dataset is essential.
Tobias L. Hohenbrink, Conrad Jackisch, Wolfgang Durner, Kai Germer, Sascha C. Iden, Janis Kreiselmeier, Frederic Leuther, Johanna C. Metzger, Mahyar Naseri, and Andre Peters
Earth Syst. Sci. Data, 15, 4417–4432, https://doi.org/10.5194/essd-15-4417-2023, https://doi.org/10.5194/essd-15-4417-2023, 2023
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The article describes a collection of 572 data sets of soil water retention and unsaturated hydraulic conductivity data measured with state-of-the-art laboratory methods. Furthermore, the data collection contains basic soil properties such as soil texture and organic carbon content. We expect that the data will be useful for various important purposes, for example, the development of soil hydraulic property models and related pedotransfer functions.
Sebastien Klotz, Caroline Le Bouteiller, Nicolle Mathys, Firmin Fontaine, Xavier Ravanat, Jean-Emmanuel Olivier, Frédéric Liébault, Hugo Jantzi, Patrick Coulmeau, Didier Richard, Jean-Pierre Cambon, and Maurice Meunier
Earth Syst. Sci. Data, 15, 4371–4388, https://doi.org/10.5194/essd-15-4371-2023, https://doi.org/10.5194/essd-15-4371-2023, 2023
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Mountain badlands are places of intense erosion. They deliver large amounts of sediment to river systems, with consequences for hydropower sustainability, habitat quality and biodiversity, and flood hazard and river management. Draix-Bleone Observatory was created in 1983 to understand and quantify sediment delivery from such badland areas. Our paper describes how water and sediment fluxes have been monitored for almost 40 years in the small mountain catchments of this observatory.
Gopi Goteti
Earth Syst. Sci. Data, 15, 4389–4415, https://doi.org/10.5194/essd-15-4389-2023, https://doi.org/10.5194/essd-15-4389-2023, 2023
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Data on river gauging stations, river basin boundaries and river flow paths are critical for hydrological analyses, but existing data for India's river basins have limited availability and reliability. This work fills the gap by building a new dataset. Data for 645 stations in 15 basins of India were compiled and checked against global data sources; data were supplemented with additional information where needed. This dataset will serve as a reliable building block in hydrological analyses.
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.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
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Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Benjamin M. Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, and Sly Wongchuig
Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, https://doi.org/10.5194/essd-15-2957-2023, 2023
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The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km3. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.
Natalie Lützow, Georg Veh, and Oliver Korup
Earth Syst. Sci. Data, 15, 2983–3000, https://doi.org/10.5194/essd-15-2983-2023, https://doi.org/10.5194/essd-15-2983-2023, 2023
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Glacier lake outburst floods (GLOFs) are a prominent natural hazard, and climate change may change their magnitude, frequency, and impacts. A global, literature-based GLOF inventory is introduced, entailing 3151 reported GLOFs. The reporting density varies temporally and regionally, with most cases occurring in NW North America. Since 1900, the number of yearly documented GLOFs has increased 6-fold. However, many GLOFs have incomplete records, and we call for a systematic reporting protocol.
Hanieh Seyedhashemi, Florentina Moatar, Jean-Philippe Vidal, and Dominique Thiéry
Earth Syst. Sci. Data, 15, 2827–2839, https://doi.org/10.5194/essd-15-2827-2023, https://doi.org/10.5194/essd-15-2827-2023, 2023
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This paper presents a past and future dataset of daily time series of discharge and stream temperature for 52 278 reaches over the Loire River basin (100 000 km2) in France, using thermal and hydrological models. Past data are provided over 1963–2019. Future data are available over the 1976–2100 period under different future climate change models (warm and wet, intermediate, and hot and dry) and scenarios (optimistic, intermediate, and pessimistic).
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.
Alison L. Kay, Victoria A. Bell, Helen N. Davies, Rosanna A. Lane, and Alison C. Rudd
Earth Syst. Sci. Data, 15, 2533–2546, https://doi.org/10.5194/essd-15-2533-2023, https://doi.org/10.5194/essd-15-2533-2023, 2023
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Climate change will affect the water cycle, including river flows and soil moisture. We have used both observational data (1980–2011) and the latest UK climate projections (1980–2080) to drive a national-scale grid-based hydrological model. The data, covering Great Britain and Northern Ireland, suggest potential future decreases in summer flows, low flows, and summer/autumn soil moisture, and possible future increases in winter and high flows. Society must plan how to adapt to such impacts.
Jamie Hannaford, Jonathan D. Mackay, Matthew Ascott, Victoria A. Bell, Thomas Chitson, Steven Cole, Christian Counsell, Mason Durant, Christopher R. Jackson, Alison L. Kay, Rosanna A. Lane, Majdi Mansour, Robert Moore, Simon Parry, Alison C. Rudd, Michael Simpson, Katie Facer-Childs, Stephen Turner, John R. Wallbank, Steven Wells, and Amy Wilcox
Earth Syst. Sci. Data, 15, 2391–2415, https://doi.org/10.5194/essd-15-2391-2023, https://doi.org/10.5194/essd-15-2391-2023, 2023
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The eFLaG dataset is a nationally consistent set of projections of future climate change impacts on hydrology. eFLaG uses the latest available UK climate projections (UKCP18) run through a series of computer simulation models which enable us to produce future projections of river flows, groundwater levels and groundwater recharge. These simulations are designed for use by water resource planners and managers but could also be used for a wide range of other purposes.
Fabian A. Gomez, Sang-Ki Lee, Charles A. Stock, Andrew C. Ross, Laure Resplandy, Samantha A. Siedlecki, Filippos Tagklis, and Joseph E. Salisbury
Earth Syst. Sci. Data, 15, 2223–2234, https://doi.org/10.5194/essd-15-2223-2023, https://doi.org/10.5194/essd-15-2223-2023, 2023
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We present a river chemistry and discharge dataset for 140 rivers in the United States, which integrates information from the Water Quality Database of the US Geological Survey (USGS), the USGS’s Surface-Water Monthly Statistics for the Nation, and the U.S. Army Corps of Engineers. This dataset includes dissolved inorganic carbon and alkalinity, two key properties to characterize the carbonate system, as well as nutrient concentrations, such as nitrate, phosphate, and silica.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
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Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
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As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Cited articles
Abolafia-Rosenzweig, R., He, C., Burns, S. P., and Chen, F.: Implementation
and Evaluation of a Unified Turbulence Parameterization throughout the Canopy
and Roughness Sublayer in Noah-MP Snow Simulations, J. Adv.
Model. Earth Sy., 13, e2021MS002665, https://doi.org/10.1029/2021MS002665,
2021. a, b
Ajami, N. K., Duan, Q., and Sorooshian, S.: An Integrated Hydrologic
Bayesian Multimodel Combination Framework: Confronting Input,
Parameter, and Model Structural Uncertainty in Hydrologic Prediction, Water
Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007. a
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017. a, b
Brutsaert, W.: Evaporation into the Atmosphere: Theory, History,
and Applications, Springer, Dordrecht,
https://doi.org/10.1007/978-94-017-1497-6, 1982. a
Burnash, R. J. C., Ferral, R. L., and McGuire, R. A.: A Generalized Streamflow
Simulation System: Conceptual Modeling for Digital Computers, Technical
Report, Joint Federal-State River Forecast Center, U.S. National Weather
Service and California Department of Water Resources, Sacramento,
California, USA, https://searchworks.stanford.edu/view/753303 (last access: 6 February 2016), 1973. a
Cai, X., Yang, Z.-L., David, C. H., Niu, G.-Y., and Rodell, M.: Hydrological
Evaluation of the Noah-MP Land Surface Model for the Mississippi River
Basin, J. Geophys. Res.-Atmos., 119, 23–38,
https://doi.org/10.1002/2013JD020792, 2014a. a, b, c
Carrera, M. L., Bélair, S., and Bilodeau, B.: The Canadian Land Data
Assimilation System (CaLDAS): Description and Synthetic Evaluation
Study, J. Hydrometeorol., 16, 1293–1314,
https://doi.org/10.1175/JHM-D-14-0089.1, 2015. a
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface– Hydrology
Model with the Penn State–NCAR MM5 Modeling System. Part
I: Model Implementation and Sensitivity, Mon. Weather Rev., 129,
569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2,
2001a. a, b, c
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface– Hydrology
Model with the Penn State–NCAR MM5 Modeling System. Part
II: Preliminary Model Validation, Mon. Weather Rev., 129,
587–604, https://doi.org/10.1175/1520-0493(2001)129<0587:CAALSH>2.0.CO;2,
2001b. a
Chen, F., Mitchell, K. E., Schaake, J., Xue, Y., Pan, H. L., Koren, V., Duan,
Q., Ek, M., and Betts, A. K.: Modeling of Land Surface Evaporation by Four
Schemes and Comparison with FIFE Observations, J. Geophys.
Res.-Atmos., 101, 7251–7268, https://doi.org/10.1029/95JD02165, 1996. a, b
Chen, F., Janjić, Z., and Mitchell, K.: Impact of Atmospheric Surface-Layer
Parameterizations in the New Land-Surface Scheme of the NCEP Mesoscale
Eta Model, Bound.-Lay. Meteorol., 85, 391–421,
https://doi.org/10.1023/A:1000531001463, 1997. a, b
Chen, F., Barlage, M., Tewari, M., Rasmussen, R., Jin, J., Lettenmaier, D. P.,
Livneh, B., Lin, C., Miguez-Macho, G., Niu, G.-Y., Wen, L., and Yang,
Z.-L.: Modeling Seasonal Snowpack Evolution in the Complex Terrain and
Forested Colorado Headwaters Region: A Model Intercomparison Study,
J. Geophys. Res.-Atmos., 119, 2014JD022167,
https://doi.org/10.1002/2014JD022167, 2014. a
Clapp, R. B. and Hornberger, G. M.: Empirical Equations for Some Soil Hydraulic
Properties, Water Resour. Res., 14, 601–604,
https://doi.org/10.1029/WR014i004p00601, 1978. a, b
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the Method of Multiple
Working Hypotheses for Hydrological Modeling, Water Resour. Res., 47,
1–16, https://doi.org/10.1029/2010WR009827, 2011. a
Cloke, H. and Pappenberger, F.: Ensemble Flood Forecasting: A Review,
J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005,
2009. a
Clow, D. W., Nanus, L., Verdin, K. L., and Schmidt, J.: Evaluation of
SNODAS Snow Depth and Snow Water Equivalent Estimates for the Colorado
Rocky Mountains, USA, Hydrol. Process., 26, 2583–2591,
https://doi.org/10.1002/hyp.9385, 2012. a
Dai, A.: Increasing Drought under Global Warming in Observations and Models,
Nat. Clim. Change, 3, 52–58, https://doi.org/10.1038/nclimate1633, 2013. a
Dang, C., Zender, C. S., and Flanner, M. G.: Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryospheric surfaces, The Cryosphere, 13, 2325–2343, https://doi.org/10.5194/tc-13-2325-2019, 2019. a, b
Decker, M., Or, D., Pitman, A. J., and Ukkola, A.: New Turbulent Resistance
Parameterization for Soil Evaporation Based on a Pore-Scale Model: Impact
on Surface Fluxes in CABLE, J. Adv. Model. Earth
Sy., 9, 220–238, https://doi.org/10.1002/2016MS000832, 2017. a
Dickinson, R. E., Wang, G., Zeng, X., and Zeng, Q.: How Does the Partitioning
of Evapotranspiration and Runoff between Different Processes Affect the
Variability and Predictability of Soil Moisture and Precipitation?, Adv.
Atmos. Sci., 20, 475–478, https://doi.org/10.1007/BF02690805, 2003. a
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.:
GSWP-2: Multimodel Analysis and Implications for Our Perception of the
Land Surface, B. Am. Meteorol. Soc., 87,
1381–1398, https://doi.org/10.1175/BAMS-87-10-1381, 2006. a, b, c, d
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah Land Surface Model
Advances in the National Centers for Environmental Prediction
Operational Mesoscale Eta Model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
Emerton, R. E., Cloke, H. L., Stephens, E. M., Zsoter, E., Woolnough, S. J.,
and Pappenberger, F.: Complex Picture for Likelihood of ENSO-driven Flood
Hazard, Nat. Commun., 8, 14796, https://doi.org/10.1038/ncomms14796, 2017. a
Fang, B., Lei, H., Zhang, Y., Quan, Q., and Yang, D.: Spatio-Temporal Patterns
of Evapotranspiration Based on Upscaling Eddy Covariance Measurements in the
Dryland of the North China Plain, Agr. Forest Meteorol.,
281, 107844, https://doi.org/10.1016/j.agrformet.2019.107844, 2020. a
Fei, W., Zheng, H., Xu, Z., Wu, W.-Y., Lin, P., Tian, Y., Guo, M., She, D., Li,
L., Li, K., and Yang, Z.-L.: Ensemble Skill Gains Obtained from the
Multi-Physics versus Multi-Model Approaches for Continental-Scale
Hydrological Simulations, Water Resour. Res., 57, e2020WR028846,
https://doi.org/10.1029/2020wr028846, 2021. a, b, c, d, e, f, g, h, i, j, k
Gan, Y., Liang, X.-Z., Duan, Q., Chen, F., Li, J., and Zhang, Y.: Assessment
and Reduction of the Physical Parameterization Uncertainty for
Noah-MP Land Surface Model, Water Resour. Res., 55, 5518–5538,
https://doi.org/10.1029/2019WR024814, 2019. a
Gao, H., Tang, Q., Ferguson, C. R., Wood, E. F., and Lettenmaier, D. P.:
Estimating the Water Budget of Major US River Basins via Remote Sensing,
Int. J. Remote Sens., 31, 3955–3978,
https://doi.org/10.1080/01431161.2010.483488, 2010. a
Guo, Z., Dirmeyer, P. A., Gao, X., and Zhao, M.: Improving the Quality of
Simulated Soil Moisture with a Multi-Model Ensemble Approach, Q.
J. Roy. Meteor. Soc., 133, 731–747,
https://doi.org/10.1002/qj.48, 2007. a
He, C., Chen, F., Barlage, M., Liu, C., Newman, A., Tang, W., Ikeda, K., and
Rasmussen, R.: Can Convection-Permitting Modeling Provide Decent
Precipitation for Offline High-Resolution Snowpack Simulations over
Mountains?, J. Geophys. Res.-Atmos., 124,
12631–12654, https://doi.org/10.1029/2019JD030823, 2019. a, b, c
Hejazi, M. I., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V., Wise, M., Patel, P., Eom, J., and Calvin, K.: Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies, Hydrol. Earth Syst. Sci., 18, 2859–2883, https://doi.org/10.5194/hess-18-2859-2014, 2014. a
Jacquemin, B. and Noilhan, J.: Sensitivity Study and Validation of a Land
Surface Parameterization Using the HAPEX-MOBILHY Data Set, Bound.-Lay.
Meteorol., 52, 93–134, https://doi.org/10.1007/BF00123180, 1990. a, b
Jarvis, P. G.: The Interpretation of the Variations in Leaf Water Potential and
Stomatal Conductance Found in Canopies in the Field, Philos.
T. Roy. Soc. Lond. B, 273,
593–610, https://doi.org/10.1098/rstb.1976.0035, 1976. a
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a, b
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G.,
Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM
Ensemble of Global Land-Atmosphere Energy Fluxes, Sci. Data, 6, 74,
https://doi.org/10.1038/s41597-019-0076-8, 2019. a
Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., Durand, M., Barros, A., Kim, E. J., Forman, B. A., Gutmann, E. D., Wrzesien, M. L., Garnaud, C., Sandells, M., Marshall, H.-P., Cristea, N., Pflug, J. M., Johnston, J., Cao, Y., Mocko, D., and Wang, S.: Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling, The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, 2021. a
Koster, R. D.: “Efficiency Space”: A Framework for Evaluating Joint
Evaporation and Runoff Behavior, B. Am. Meteorol.
Soc., 96, 393–396, https://doi.org/10.1175/BAMS-D-14-00056.1, 2015. a
Koster, R. D. and Suarez, M. J.: Modeling the Land Surface Boundary in Climate
Models as a Composite of Independent Vegetation Stands, J.
Geophys. Res.-Atmos., 97, 2697–2715, https://doi.org/10.1029/91JD01696,
1992. a, b
Kumar, S., Holmes, T., Mocko, M. D., Wang, S., and Peters-Lidard, C.:
Attribution of Flux Partitioning Variations between Land Surface Models over
the Continental U.S., Remote Sensing, 10, 751,
https://doi.org/10.3390/rs10050751, 2018. a
Kumar, S. V., Wang, S., Mocko, D. M., Peters-Lidard, C. D., and Xia, Y.:
Similarity Assessment of Land Surface Model Outputs in the North American
Land Data Assimilation System, Water Resour. Res., 53, 8941–8965,
https://doi.org/10.1002/2017WR020635, 2017. a
LaFontaine, J. H., Hay, L. E., Viger, R. J., Regan, R. S., and Markstrom,
S. L.: Effects of Climate and Land Cover on Hydrology in the Southeastern
U.S.: Potential Impacts on Watershed Planning, J.
Am. Water Resour. Assoc., 51, 1235–1261,
https://doi.org/10.1111/1752-1688.12304, 2015. a
Le, P. V. V., Kumar, P., and Drewry, D. T.: Implications for the Hydrologic
Cycle under Climate Change Due to the Expansion of Bioenergy Crops in the
Midwestern United States, P. Natl. Acad. Sci. USA, 108, 15085–15090, https://doi.org/10.1073/pnas.1107177108, 2011. a
Levia, D. F., Creed, I. F., Hannah, D. M., Nanko, K., Boyer, E. W.,
Carlyle-Moses, D. E., van de Giesen, N., Grasso, D., Guswa, A. J.,
Hudson, J. E., Hudson, S. A., Iida, S., Jackson, R. B., Katul, G. G.,
Kumagai, T., Llorens, P., Ribeiro, F. L., Pataki, D. E., Peters, C. A.,
Carretero, D. S., Selker, J. S., Tetzlaff, D., Zalewski, M., and Bruen, M.:
Homogenization of the Terrestrial Water Cycle, Nat. Geosci., 13,
656–658, https://doi.org/10.1038/s41561-020-0641-y, 2020. a
Li, L., Yang, Z.-L., Matheny, A. M., Zheng, H., Swenson, S. C., Lawrence,
D. M., Barlage, M., Yan, B., McDowell, N. G., and Leung, L. R.:
Representation of Plant Hydraulics in the Noah-MP Land Surface Model:
Model Development and Multiscale Evaluation, J. Adv. Model. Earth Sy., 13, e2020MS002 214, https://doi.org/10.1029/2020ms002214,
2021. a, b
Lian, X., Piao, S., Huntingford, C., Li, Y., Zeng, Z., Wang, X., Ciais, P.,
McVicar, T. R., Peng, S., Ottlé, C., Yang, H., Yang, Y., Zhang, Y., and
Wang, T.: Partitioning Global Land Evapotranspiration Using CMIP5 Models
Constrained by Observations, Nat. Clim. Change, 8, 640–646,
https://doi.org/10.1038/s41558-018-0207-9, 2018. a
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.-Atmos., 99,
14415–14428, https://doi.org/10.1029/94JD00483, 1994. a, b
Lin, P., Hopper, L. J., Yang, Z.-L., Lenz, M., and Zeitler, J. W.: Insights
into Hydrometeorological Factors Constraining Flood Prediction Skill during
the May and October 2015 Texas Hill Country Flood Events, J.
Hydrometeorol., 19, 1339–1361, https://doi.org/10.1175/JHM-D-18-0038.1, 2018. a
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. a
Liu, X., Chen, F., Barlage, M., Zhou, G., and Niyogi, D. S.: Noah-MP-Crop:
Introducing Dynamic Crop Growth in the Noah-MP Land Surface Model,
J. Geophys. Res.-Atmos., 121, 13953–13972,
https://doi.org/10.1002/2016JD025597, 2016. a
Liu, X., Chen, F., Barlage, M., and Niyogi, D.: Implementing Dynamic Rooting
Depth for Improved Simulation of Soil Moisture and Land Surface Feedbacks in
Noah-MP-Crop, J. Adv. Model. Earth Sy., 12,
e2019MS001786, https://doi.org/10.1029/2019MS001786, 2020. a
Lv, M., Ma, Z., Li, M., and Zheng, Z.: Quantitative Analysis of Terrestrial
Water Storage Changes under the Grain for Green Program in the
Yellow River Basin, J. Geophys. Res.-Atmos., 124,
1336–1351, https://doi.org/10.1029/2018JD029113, 2019. a
Lv, M., Xu, Z., Yang, Z.-L., Lu, H., and Lv, M.: A Comprehensive Review of
Specific Yield in Land Surface and Groundwater Studies, J. Adv.
Model. Earth Sy., 13, e2020MS002270, https://doi.org/10.1029/2020MS002270,
2021. a
Ma, N., Niu, G.-Y., Xia, Y., Cai, X., Zhang, Y., Ma, Y., and Fang, Y.: A
Systematic Evaluation of Noah-MP in Simulating Land-Atmosphere Energy,
Water, and Carbon Exchanges over the Continental United States, J.
Geophys. Res.-Atmos., 122, 12245–12268,
https://doi.org/10.1002/2017JD027597, 2017. a, b, c
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi, J., Gao, H., and Wood, E. F.: The future of Earth observation in hydrology, Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, 2017. a
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C.,
Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., Higgins,
R. W., Pinker, R. T., Tarpley, J. D., Lettenmaier, D. P., Marshall, C. H.,
Entin, J. K., Pan, M., Shi, W., Koren, V., Meng, J., Ramsay, B. H., and
Bailey, A. A.: The Multi-Institution North American Land Data Assimilation
System (NLDAS): Utilizing Multiple GCIP Products and Partners
in a Continental Distributed Hydrological Modeling System, J.
Geophys. Res.-Atmos., 109, D07S90, https://doi.org/10.1029/2003JD003823,
2004. a, b, c
Niu, G.-Y. and Yang, Z.-L.: Effects of Frozen Soil on Snowmelt Runoff and Soil
Water Storage at a Continental Scale, J. Hydrometeorol., 7,
937–952, https://doi.org/10.1175/JHM538.1, 2006. a
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., and Gulden, L. E.: A Simple
TOPMODEL-based Runoff Parameterization (SIMTOP) for Use in Global
Climate Models, J. Geophys. Res.-Atmos., 110, D21106,
https://doi.org/10.1029/2005JD006111, 2005. a
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.:
Development of a Simple Groundwater Model for Use in Climate Models and
Evaluation with Gravity Recovery and Climate Experiment Data, J. Geophys Res., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007. a, b, c, d
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
Community Noah Land Surface Model with Multiparameterization Options
(Noah-MP): 1. Model Description and Evaluation with Local-Scale
Measurements, J. Geophys. Res.-Atmos., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011. a, b, c, d, e
Oleson, K. W., Dai, Y., Bonan, G. B., Bosilovich, M., Dirmeyer, P. A., Hoffman,
F. M., Houser, P. R., Levis, S., Niu, G.-Y., Thornton, P. E., Vertenstein,
M., Yang, Z.-L., and Zeng, X.: Technical Description of the Community
Land Model (CLM), Tech. rep., National Center for Atmospheric
Research, Boulder, Colorado, https://doi.org/10.5065/D6N877R0, 2004. a
Pan, M., Sahoo, A. K., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood,
E. F.: Multisource Estimation of Long-Term Terrestrial Water Budget for Major
Global River Basins, J. Climate, 25, 3191–3206,
https://doi.org/10.1175/JCLI-D-11-00300.1, 2012. a, b
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Ottlé, C., Poulter, B., Zaehle, S., and Running, S. W.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling, Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, 2020. a
Peters-Lidard, C. D., Hossain, F., Leung, L. R., McDowell, N., Rodell, M.,
Tapiador, F. J., Turk, F. J., and Wood, A.: 100 Years of Progress in
Hydrology, Meteorol. Monogr., 59, 25.1–25.51,
https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0019.1, 2018. a
Peters-Lidard, C. D., Mocko, D. M., Su, L., Lettenmaier, D. P., Gentine, P.,
and Barlage, M.: Advances in Land Surface Models and Indicators for Drought
Monitoring and Prediction, B. Am. Meteorol. Soc.,
102, E1099–E1122, https://doi.org/10.1175/BAMS-D-20-0087.1, 2021. a
Philip, J. R.: Theory of Infiltration, in: Advances in Hydroscience, Elsevier,
vol. 5, 215–296, https://doi.org/10.1016/B978-1-4831-9936-8.50010-6,
1969. a
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W.,
Dankers, R., Fekete, B. M., Franssen, W., Gerten, D., Gosling, S. N.,
Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T.,
Wada, Y., and Wisser, D.: Hydrological Droughts in the 21st Century, Hotspots
and Uncertainties from a Global Multimodel Ensemble Experiment, P.
Natl. Acad. Sci. USA, 111, 3262–3267,
https://doi.org/10.1073/pnas.1222473110, 2014. a
Quiring, S. M., Ford, T. W., Wang, J. K., Khong, A., Harris, E., Lindgren, T.,
Goldberg, D. W., and Li, Z.: The North American Soil Moisture Database:
Development and Applications, B. Am. Meteorol.
Soc., 97, 1441–1459, https://doi.org/10.1175/BAMS-D-13-00263.1, 2016. a
Rateb, A., Scanlon, B. R., Pool, D. R., Sun, A., Zhang, Z., Chen, J., Clark,
B., Faunt, C. C., Haugh, C. J., Hill, M., Hobza, C., McGuire, V. L., Reitz,
M., Schmied, H. M., Sutanudjaja, E. H., Swenson, S., Wiese, D., Xia, Y., and
Zell, W.: Comparison of Groundwater Storage Changes from GRACE Satellites
with Monitoring and Modeling of Major U.S. Aquifers, Water Resour.
Res., 56, e2020WR027556, https://doi.org/10.1029/2020wr027556, 2020. a
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85,
381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite-Based Estimates of
Groundwater Depletion in India, Nature, 460, 999–1002,
https://doi.org/10.1038/nature08238, 2009. a
Rodell, M., Beaudoing, H. K., L'Ecuyer, T. S., Olson, W. S., Famiglietti,
J. S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers,
D., Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J.,
Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A.,
Sheffield, J., and Wood, E. F.: The Observed State of the Water Cycle in the
Early Twenty-First Century, J. Climate, 28, 8289–8318,
https://doi.org/10.1175/JCLI-D-14-00555.1, 2015. a
Sakumura, C., Bettadpur, S., and Bruinsma, S.: Ensemble Prediction and
Intercomparison Analysis of GRACE Time-Variable Gravity Field Models,
Geophys. Res. Lett., 41, 1389–1397, https://doi.org/10.1002/2013GL058632,
2014. a
Save, H., Bettadpur, S., and Tapley, B. D.: High-Resolution CSR GRACE RL05
Mascons, J. Geophys. Res.-Sol. Ea., 121, 7547–7569,
https://doi.org/10.1002/2016JB013007, 2016. a
Saxe, S., Farmer, W., Driscoll, J., and Hogue, T. S.: Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates, Hydrol. Earth Syst. Sci., 25, 1529–1568, https://doi.org/10.5194/hess-25-1529-2021, 2021. a, b
Scanlon, B. R., Faunt, C. C., Longuevergne, L., Reedy, R. C., Alley, W. M.,
McGuire, V. L., and McMahon, P. B.: Groundwater Depletion and Sustainability
of Irrigation in the US High Plains and Central Valley, P.
Natl. Acad. Sci. USA, 109, 9320–9325,
https://doi.org/10.1073/pnas.1200311109, 2012. a
Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Schmied, H. M., van Beek, L.
P. H., Wiese, D. N., Wada, Y., Long, D., Reedy, R. C., Longuevergne, L.,
Döll, P., and Bierkens, M. F. P.: Global Models Underestimate Large
Decadal Declining and Rising Water Storage Trends Relative to GRACE
Satellite Data, P.
Natl. Acad. Sci. USA, 115,
E1080–E1089, https://doi.org/10.1073/pnas.1704665115, 2018. a
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B.,
Dazlich, D. A., Zhang, C., Collelo, G. D., and Bounoua, L.: A Revised Land
Surface Parameterization (SiB2) for Atmospheric GCMs. Part I:
Model Formulation, J. Climate, 9, 676–705,
https://doi.org/10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2, 1996. a
Shellito, P. J., Kumar, S. V., Santanello, J. A., Lawston-Parker, P., Bolten,
J. D., Cosh, M. H., Bosch, D. D., Holifield Collins, C. D., Livingston, S.,
Prueger, J., Seyfried, M., and Starks, P. J.: Assessing the Impact of Soil
Layer Depth Specification on the Observability of Modeled Soil Moisture and
Brightness Temperature, J. Hydrometeorol., 21, 2041–2060,
https://doi.org/10.1175/JHM-D-19-0280.1, 2020. a
Shi, C., Xie, Z., Qian, H., Liang, M., and Yang, X.: China Land Soil Moisture
EnKF Data Assimilation Based on Satellite Remote Sensing Data, Sci.
China Earth Sci., 54, 1430–1440, https://doi.org/10.1007/s11430-010-4160-3, 2011. a
Sobol', L. M.: Sensitivity Estimates for Nonlinear Mathematical Models,
Mathematical Modelling and Computational Experiment, 1, 407–414, 1993. a
Su, L., Cao, Q., Xiao, M., Mocko, D. M., Barlage, M., Li, D., Peters-Lidard,
C. D., and Lettenmaier, D. P.: Drought Variability over the Conterminous
United States for the Past Century, J. Hydrometeorol., 22,
1153–1168, https://doi.org/10.1175/JHM-D-20-0158.1, 2021. a
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. a
Telteu, C.-E., Müller Schmied, H., Thiery, W., Leng, G., Burek, P., Liu, X., Boulange, J. E. S., Andersen, L. S., Grillakis, M., Gosling, S. N., Satoh, Y., Rakovec, O., Stacke, T., Chang, J., Wanders, N., Shah, H. L., Trautmann, T., Mao, G., Hanasaki, N., Koutroulis, A., Pokhrel, Y., Samaniego, L., Wada, Y., Mishra, V., Liu, J., Döll, P., Zhao, F., Gädeke, A., Rabin, S. S., and Herz, F.: Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication, Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, 2021. a
Trenberth, K. E. and Fasullo, J. T.: North American Water and Energy
Cycles, Geophys. Res. Lett., 40, 365–369, https://doi.org/10.1002/grl.50107,
2013a. a
Trenberth, K. E. and Fasullo, J. T.: Regional Energy and Water Cycles:
Transports from Ocean to Land, J. Climate, 26, 7837–7851,
https://doi.org/10.1175/JCLI-D-13-00008.1, 2013b. a
Trenberth, K. E., Smith, L., Qian, T., Dai, A., and Fasullo, J.: Estimates of
the Global Water Budget and Its Annual Cycle Using Observational and Model
Data, J. Hydrometeorol., 8, 758–769, https://doi.org/10.1175/JHM600.1, 2007. a
Troin, M., Arsenault, R., Wood, A. W., Brissette, F., and Martel, J.-L.:
Generating Ensemble Streamflow Forecasts: A Review of Methods and
Approaches over the Past 40 Years, Water Resour. Res., 57,
e2020WR028392, https://doi.org/10.1029/2020WR028392, 2021. a, b
Voss, K. A., Famiglietti, J. S., Lo, M., de Linage, C., Rodell, M., and
Swenson, S. C.: Groundwater Depletion in the Middle East from GRACE
with Implications for Transboundary Water Management in the
Tigris-Euphrates-Western Iran Region, Water Resour. Res., 49,
904–914, https://doi.org/10.1002/wrcr.20078, 2013. a
Wang, W., Yang, K., Zhao, L., Zheng, Z., Lu, H., Mamtimin, A., Ding, B., Li,
X., Zhao, L., Li, H., Che, T., and Moore, J. C.: Characterizing Surface
Albedo of Shallow Fresh Snow and Its Importance for Snow Ablation on the
Interior of the Tibetan Plateau, J. Hydrometeorol., 21,
815–827, https://doi.org/10.1175/JHM-D-19-0193.1, 2020. a, b, c
Wang, Y.-H., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., and
Niu, G.-Y.: A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme
Improves Snowpack Prediction over the Drier Western United States,
Geophys. Res. Lett., 46, 13825–13835,
https://doi.org/10.1029/2019gl085722, 2019. a, b, c
Ward, P. J., Jongman, B., Kummu, M., Dettinger, M. D., Sperna Weiland, F. C.,
and Winsemius, H. C.: Strong Influence of El Niño Southern Oscillation
on Flood Risk around the World, P. Natl. Acad.
Sci. USA, 111, 15659–15664, https://doi.org/10.1073/pnas.1409822111, 2014. a
Wu, W.-Y., Yang, Z.-L., and Barlage, M.: The Impact of Noah-MP Physical
Parameterizations on Modeling Water Availability during Droughts in the
Texas–Gulf Region, J. Hydrometeorol., 22,
1221–1233, https://doi.org/10.1175/JHM-D-20-0189.1, 2021. a
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B. A., Sheffield, J., Luo, L., Alonge,
C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.:
Continental-Scale Water and Energy Flux Analysis and Validation for North
American Land Data Assimilation System Project Phase 2 (NLDAS-2): 2.
Validation of Model-Simulated Streamflow, J. Geophys.
Res.-Atmos., 117, D03110, https://doi.org/10.1029/2011JD016051,
2012a. a, b, c, d
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B. A., Wood, E. F.,
Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D. P.,
Koren, V., Duan, Q., Mo, K. C., Fan, Y., and Mocko, D.: Continental-Scale
Water and Energy Flux Analysis and Validation for the North American Land
Data Assimilation System Project Phase 2 (NLDAS-2): 1.
Intercomparison and Application of Model Products, J. Geophys.
Res.-Atmos., 117, D03109, https://doi.org/10.1029/2011JD016048,
2012b. a, b, c, d
Xia, Y., Ek, M. B., Wu, Y., Ford, T., and Quiring, S. M.: Comparison of
NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part
II: Impact of Soil Texture Classification and Vegetation Type Mismatches,
J. Hydrometeorol., 16, 1981–2000, https://doi.org/10.1175/JHM-D-14-0097.1,
2015a. a, b, c, d
Xia, Y., Ek, M. B., Wu, Y., Ford, T., and Quiring, S. M.: Comparison of
NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part I:
Comparison and Analysis, J. Hydrometeorol., 16, 1962–1980,
https://doi.org/10.1175/JHM-D-14-0096.1, 2015b. a, b, c, d
Xia, Y., Cosgrove, B. A., Mitchell, K. E., Peters-Lidard, C. D., Ek, M. B.,
Brewer, M., Mocko, D., Kumar, S. V., Wei, H., Meng, J., and Luo, L.:
Basin-Scale Assessment of the Land Surface Water Budget in the National
Centers for Environmental Prediction Operational and Research
NLDAS-2 Systems, J. Geophys. Res-Atmos., 121,
2750–2779, https://doi.org/10.1002/2015JD023733, 2016. a, b
Xia, Y., Hao, Z., Shi, C., Li, Y., Meng, J., Xu, T., Wu, X., and Zhang, B.:
Regional and Global Land Data Assimilation Systems: Innovations,
Challenges, and Prospects, J. Meteorol. Res., 33, 159–189,
https://doi.org/10.1007/s13351-019-8172-4, 2019. a
Xu, T., Guo, Z., Xia, Y., Ferreira, V. G., Liu, S., Wang, K., Yao, Y., Zhang,
X., and Zhao, C.: Evaluation of Twelve Evapotranspiration Products from
Machine Learning, Remote Sensing and Land Surface Models over Conterminous
United States, J. Hydrol., 578, 124105,
https://doi.org/10.1016/j.jhydrol.2019.124105, 2019. a
Xue, Y., Sellers, P. J., Kinter, J. L., and Shukla, J.: A Simplified
Biosphere Model for Global Climate Studies, J. Climate, 4,
345–364, https://doi.org/10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO;2, 1991. a, b
Yang, Z.-L. and Dickinson, R. E.: Description of the Biosphere-Atmosphere
Transfer Scheme (BATS) for the Soil Moisture Workshop and Evaluation of
Its Performance, Global Planet. Change, 13, 117–134,
https://doi.org/10.1016/0921-8181(95)00041-0, 1996. a
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The
Community Noah Land Surface Model with Multiparameterization Options
(Noah-MP): 2. Evaluation over Global River Basins, J.
Geophys. Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140,
2011. a, b, c
Yin, D. and Roderick, M. L.: Inter-annual variability of the global terrestrial water cycle, Hydrol. Earth Syst. Sci., 24, 381–396, https://doi.org/10.5194/hess-24-381-2020, 2020. a
Zaussinger, F., Dorigo, W., Gruber, A., Tarpanelli, A., Filippucci, P., and Brocca, L.: Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data, Hydrol. Earth Syst. Sci., 23, 897–923, https://doi.org/10.5194/hess-23-897-2019, 2019. a
Zhang, B., Xia, Y., Long, B., Hobbins, M., Zhao, X., Hain, C., Li, Y., and
Anderson, M. C.: Evaluation and Comparison of Multiple Evapotranspiration
Data Models over the Contiguous United States: Implications for the
next Phase of NLDAS (NLDAS-Testbed) Development, Agr.
Forest Meteorol., 280, 107810, https://doi.org/10.1016/j.agrformet.2019.107810,
2020. a, b, c, d
Zhang, G., Chen, F., and Gan, Y.: Assessing Uncertainties in the Noah-MP
Ensemble Simulations of a Cropland Site during the Tibet Joint
International Cooperation Program Field Campaign, J. Geophys.
Res.-Atmos., 121, 9576–9596, https://doi.org/10.1002/2016JD024928, 2016. a
Zhang, X., Chen, L., Ma, Z., and Gao, Y.: Assessment of Surface Exchange
Coefficients in the Noah-MP Land Surface Model for Different Land-Cover
Types in China, Int. J. Climatol., 41, 2638–2659,
https://doi.org/10.1002/joc.6981, 2021. a
Zhang, Y., Pan, M., Sheffield, J., Siemann, A. L., Fisher, C. K., Liang, M., Beck, H. E., Wanders, N., MacCracken, R. F., Houser, P. R., Zhou, T., Lettenmaier, D. P., Pinker, R. T., Bytheway, J., Kummerow, C. D., and Wood, E. F.: A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010, Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, 2018. a, b
Zhao, L. and Yang, Z.-L.: Multi-Sensor Land Data Assimilation: Toward a
Robust Global Soil Moisture and Snow Estimation, Remote Sens.
Environ., 216, 13–27, https://doi.org/10.1016/j.rse.2018.06.033, 2018.
a
Zhao, L., Yang, K., He, J., Zheng, H., and Zheng, D.: Potential of Mapping
Global Soil Texture Type from SMAP Soil Moisture Product: A Pilot Study,
IEEE T. Geosci. Remote, 60, 1–10,
https://doi.org/10.1109/TGRS.2021.3119667, 2022. a
Zheng, H., Yang, Z.-L., Lin, P., Wei, J., Wu, W.-Y., Li, L., Zhao, L., and
Wang, S.: On the Sensitivity of the Precipitation Partitioning into
Evapotranspiration and Runoff in Land Surface Parameterizations, Water
Resour. Res., 55, 95–111, https://doi.org/10.1029/2017WR022236, 2019. a, b, c, d, e, f, g
Zheng, H., Yang, Z.-L., Lin, P., Wu, W.-Y., Li, L., Xu, Z., Wei, J., Zhao, L.,
Bian, Q., and Wang, S.: Falsification-Oriented Signature-Based Evaluation for
Guiding the Development of Land Surface Models and the Enhancement of
Observations, J. Adv. Model.Earth Sy., 12,
e2020MS002132, https://doi.org/10.1029/2020MS002132, 2020. a, b, c, d, e
Zheng, H., Fei, W., Yang, Z.-L., Wei, J., Zhao, L., and Li, L.: An Ensemble of
48 Physically Perturbed Model Estimates of the 1/8∘ Terrestrial
Water Budget over the Conterminous United States, 1980–2015, Zenodo [data set],
https://doi.org/10.5281/zenodo.7109816, 2022. a
Short summary
An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP...
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