Articles | Volume 15, issue 5
https://doi.org/10.5194/essd-15-2055-2023
© 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-15-2055-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 May 2023
Data description paper |
| 23 May 2023
| 23 May 2023
Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning
Yufang Zhang
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Department of Geography, The University of Hong Kong, Hong Kong
999077, China
Department of Geography, The University of Hong Kong, Hong Kong
999077, China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Qian Wang
State Key Laboratory of Remote Sensing Science, Beijing Normal
University, Beijing 100875, China
Bing Li
Key Research Institute of Yellow River Civilization and Sustainable
Development and Collaborative Innovation Center on Yellow River
Civilization of Henan Province, Henan University, Kaifeng 475001, China
Jianglei Xu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Guodong Zhang
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Xiaobang Liu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Changhao Xiong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
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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.
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.
Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang
Earth Syst. Sci. Data, 15, 2755–2780, https://doi.org/10.5194/essd-15-2755-2023, https://doi.org/10.5194/essd-15-2755-2023, 2023
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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.
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.
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
Short summary
Short summary
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.
Rogier van der Velde, Harm-Jan F. Benninga, Bas Retsios, Paul C. Vermunt, and M. Suhyb Salama
Earth Syst. Sci. Data, 15, 1889–1910, https://doi.org/10.5194/essd-15-1889-2023, https://doi.org/10.5194/essd-15-1889-2023, 2023
Short summary
Short summary
From 2009, a network of 20 profile soil moisture and temperature monitoring stations has been operational in the Twente region, east of the Netherlands. In addition, field campaigns have been conducted covering four growing seasons during which soil moisture was measured near 12 monitoring stations. We describe the monitoring network and field campaigns, and we provide an overview of open third-party datasets that may support the use of the Twente datasets.
Jana Erdbrügger, Ilja van Meerveld, Jan Seibert, and Kevin Bishop
Earth Syst. Sci. Data, 15, 1779–1800, https://doi.org/10.5194/essd-15-1779-2023, https://doi.org/10.5194/essd-15-1779-2023, 2023
Short summary
Short summary
Groundwater can respond quickly to precipitation and is the main source of streamflow in most catchments in humid, temperate climates. To better understand shallow groundwater dynamics, we installed a network of groundwater wells in two boreal headwater catchments in Sweden. We recorded groundwater levels in 75 wells for 2 years and sampled the water and analyzed its chemical composition in one summer. This paper describes these datasets.
Cited articles
Abbaszadeh, P., Moradkhani, H., and Zhan, X.: Downscaling SMAP radiometer
soil moisture over the CONUS using an ensemble learning method, Water
Resour. Res., 55, 324–344, https://doi.org/10.1029/2018WR023354, 2019.
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017.
Al-Yaari, A., Wigneron, J.-P., Dorigo, W., Colliander, A., Pellarin, T.,
Hahn, S., Mialon, A., Richaume, P., Fernandez-Moran, R., Fan, L., Kerr, Y.
H., and De Lannoy, G.: Assessment and inter-comparison of recently
developed/reprocessed microwave satellite soil moisture products using ISMN
ground-based measurements, Remote Sens. Environ., 224, 289–303,
https://doi.org/10.1016/j.rse.2019.02.008, 2019.
Anderson, W. B., Zaitchik, B. F., Hain, C. R., Anderson, M. C., Yilmaz, M. T., Mecikalski, J., and Schultz, L.: Towards an integrated soil moisture drought monitor for East Africa, Hydrol. Earth Syst. Sci., 16, 2893–2913, https://doi.org/10.5194/hess-16-2893-2012, 2012.
Babaeian, E., Sadeghi, M., Jones, S. B., Montzka, C., Vereecken, H., and
Tuller, M.: Ground, Proximal, and Satellite Remote Sensing of Soil Moisture,
Rev. Geophys., 57, 530–616, https://doi.org/10.1029/2018RG000618, 2019.
Balenzano, A., Mattia, F., Satalino, G., Lovergine, F. P., Palmisano, D.,
and Davidson, M. W. J.: Dataset of Sentinel-1 surface soil moisture time
series at 1 km resolution over Southern Italy, Data Br., 38, 107345,
https://doi.org/10.1016/J.DIB.2021.107345, 2021.
Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipal, K., Bonekamp,
H., Figa, J., and Anderson, C.: Initial soil moisture retrievals from the
METOP-A Advanced Scatterometer (ASCAT), Geophys. Res. Lett., 34, L20401,
https://doi.org/10.1029/2007GL031088, 2007.
Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S.,
Stachl, T., Modanesi, S., Massari, C., Ciabatta, L., Brocca, L., and Wagner,
W.: Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing
Assets and Overcoming Obstacles, IEEE T. Geosci. Remote, 57,
520–539, https://doi.org/10.1109/TGRS.2018.2858004, 2019.
Beaudoing, H. and Rodell, M.: GLDAS Noah Land Surface Model L4 3 hourly 0.25
x 0.25 degree V2.1, Goddard Earth Sciences Data and Information Services
Center [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020.
Belgiu, M. and Drãguþ, L.: Random forest in remote sensing: A review
of applications and future directions, ISPRS J. Photogramm., 114, 24–31,
https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Berg, A. and Sheffield, J.: Climate change and drought: the soil moisture
perspective, Current Climate Change Report, 4, 180–191,
https://doi.org/10.1007/s40641-018-0095-0, 2018.
Bindlish, R., Jackson, T., Sun, R., Cosh, M., Yueh, S., and Dinardo, S.:
Combined Passive and Active Microwave Observations of Soil Moisture During
CLASIC, IEEE Geosci. Remote S., 6, 644–648,
https://doi.org/10.1109/LGRS.2009.2028441, 2009.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A.: Soil
Moisture for Hydrological Applications: Open Questions and New
Opportunities, Water, 9, 140, https://doi.org/10.3390/w9020140, 2017.
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations, Earth Syst. Sci. Data, 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, 2019.
Chan, S. K., Bindlish, R., O'Neill, P. E., Njoku, E., Jackson, T.,
Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S.,
Entekhabi, D., Cosh, M. H., Caldwell, T., Walker, J., Wu, X., Berg, A.,
Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martínez, J.,
González, Á., Seyfried, M., Bosch, D., Starks, P., Goodrich, D.,
Prueger, J., Palecki, M., Small, E. E., Zreda, M., Calvet, J., Crow, W. T.,
and Kerr, Y.: Assessment of the SMAP passive soil moisture product, IEEE
T. Geosci. Remote, 54, 4994–5007,
https://doi.org/10.1109/TGRS.2016.2561938, 2016.
Chan, S. K., Bindlish, R., O'Neill, P., Jackson, T., Njoku, E., Dunbar, S.,
Chaubell, J., Piepmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen,
F., Cosh, M. H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault,
M., Martínez-Fernández, J., Uldall, F., Seyfried, M., Bosch, D.,
Starks, P., Holifield Collins, C., Prueger, J., van der Velde, R., Asanuma,
J., Palecki, M., Small, E. E., Zreda, M., Calvet, J., Crow, W. T., and Kerr,
Y.: Development and assessment of the SMAP enhanced passive soil moisture
product, Remote Sens. Environ., 204, 931–941,
https://doi.org/10.1016/j.rse.2017.08.025, 2018.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in:
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, 785–794,
https://doi.org/10.1145/2939672.2939785, 2016.
Colliander, A., Jackson, T. J., Bindlish, R., Chan, S., Das, N., Kim, S. B.,
Cosh, M. H., Dunbar, R. S., Dang, L., Pashaian, L., Asanuma, J., Aida, K.,
Berg, A., Rowlandson, T., Bosch, D., Caldwell, T., Caylor, K., Goodrich, D.,
al Jassar, H., Lopez-Baeza, E., Martínez-Fernández, J.,
González-Zamora, A., Livingston, S., McNairn, H., Pacheco, A.,
Moghaddam, M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T.,
Prueger, J., Pulliainen, J., Rautiainen, K., Ramos, J., Seyfried, M.,
Starks, P., Su, Z., Zeng, Y., van der Velde, R., Thibeault, M., Dorigo, W.,
Vreugdenhil, M., Walker, J. P., Wu, X., Monerris, A., O'Neill, P. E.,
Entekhabi, D., Njoku, E. G., and Yueh, S.: Validation of SMAP surface soil
moisture products with core validation sites, Remote Sens. Environ., 191,
215–231, https://doi.org/10.1016/j.rse.2017.01.021, 2017.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera,
R., De Rosnay, P., Ryu, D., and Walker, J. P.: Upscaling sparse ground-based
soil moisture observations for the validation of coarse-resolution satellite
soil moisture products, Rev. Geophys., 50, 1–20,
https://doi.org/10.1029/2011RG000372, 2012.
Crow, W. T., Chen, F., and Colliander, A.: Benchmarking downscaled
satellite-based soil moisture products using sparse, point-scale ground
observations, Remote Sens. Environ., 283, 113300,
https://doi.org/10.1016/j.rse.2022.113300, 2022.
Cui, D., Liang, S., Wang, D., and Liu, Z.: A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables, Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, 2021.
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F.: A review of the global soil property maps for Earth system models, SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, 2019.
Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A.,
Yueh, S., Jagdhuber, T., Chen, F., Crow, W., O'Neill, P. E., Walker, J. P.,
Berg, A., Bosch, D. D., Caldwell, T., Cosh, M. H., Collins, C. H.,
Lopez-Baeza, E., and Thibeault, M.: The SMAP and Copernicus Sentinel 1A/B
microwave active-passive high resolution surface soil moisture product,
Remote Sens. Environ., 233, 111380,
https://doi.org/10.1016/J.RSE.2019.111380, 2019.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D.,
Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y.,
Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C.,
Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and
Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding:
State-of-the art and future directions, Remote Sens. Environ., 203,
185–215, https://doi.org/10.1016/J.RSE.2017.07.001, 2017.
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global Automated Quality Control of In Situ Soil Moisture Data from the
International Soil Moisture Network, Vadose Zone J., 12, vzj2012.0097,
https://doi.org/10.2136/vzj2012.0097, 2013.
Entekhabi, D., Reichle, R. H., Koster, R. D., and Crow, W. T.: Performance
metrics for soil moisture retrievals and application requirements, J.
Hydrometeorol., 11, 832–840, https://doi.org/10.1175/2010JHM1223.1, 2010.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type
Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data
set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Friedman, J. H.: Greedy function approximation: a gradient boosting machine,
Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Ghulam, A., Qin, Q., Teyip, T., and Li, Z.-L.: Modified perpendicular
drought index (MPDI): a real-time drought monitoring method, ISPRS J.
Photogramm., 62, 150–164,
https://doi.org/10.1016/j.isprsjprs.2007.03.002, 2007.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R.: Random Forests
for land cover classification, Pattern Recognit. Lett., 27, 294–300,
https://doi.org/10.1016/j.patrec.2005.08.011, 2006.
Gruber, A., Dorigo, W. A., Zwieback, S., Xaver, A., and Wagner, W.:
Characterizing Coarse-Scale Representativeness of in situ Soil Moisture
Measurements from the International Soil Moisture Network, Vadose Zone J.,
12, vzj2012.0170, https://doi.org/10.2136/vzj2012.0170,
2013.
Gruber, A., Su, C.-H., Zwieback, S., Crow, W., Dorigo, W., and Wagner, W.:
Recent advances in (soil moisture) triple collocation analysis, Int. J.
Appl. Earth Obs., 45, 200–211,
https://doi.org/10.1016/j.jag.2015.09.002, 2016.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet,
J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi,
M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C.,
Muñoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rüdiger, C.,
Scanlon, T., van der Schalie, R., Wigneron, J.-P., and Wagner, W.:
Validation practices for satellite soil moisture retrievals: What are (the)
errors?, Remote Sens. Environ., 244, 111806,
https://doi.org/10.1016/j.rse.2020.111806, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Holzman, M. E., Rivas, R., and Piccolo, M. C.: Estimating soil moisture and
the relationship with crop yield using surface temperature and vegetation
index, Int. J. Appl. Earth Obs., 28, 181–192,
https://doi.org/10.1016/j.jag.2013.12.006, 2014.
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M.,
Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback
dominates land carbon uptake variability, Nature, 592, 65–69,
https://doi.org/10.1038/s41586-021-03325-5, 2021.
Karthikeyan, L. and Mishra, A. K.: Multi-layer high-resolution soil moisture
estimation using machine learning over the United States, Remote Sens.
Environ., 266, 112706, https://doi.org/10.1016/J.RSE.2021.112706, 2021.
Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B.,
Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart,
S., Al Bitar, A., Pellarin, T., Bindlish, R., Jackson, T. J., Rüdiger,
C., Waldteufel, P., Mecklenburg, S., and Wigneron, J. P.: Overview of SMOS
performance in terms of global soil moisture monitoring after six years in
operation, Remote Sens. Environ., 180, 40–63,
https://doi.org/10.1016/j.rse.2016.02.042, 2016.
Kim, S., Zhang, R., Pham, H., and Sharma, A.: A Review of Satellite-Derived
Soil Moisture and Its Usage for Flood Estimation, Remote Sens. Earth Syst.
Sci., 2, 225–246, https://doi.org/10.1007/s41976-019-00025-7, 2019.
Li, B., Liang, S., Liu, X., Ma, H., Chen, Y., Liang, T., and He, T.:
Estimation of all-sky 1 km land surface temperature over the conterminous
United States, Remote Sens. Environ., 266, 112707,
https://doi.org/10.1016/J.RSE.2021.112707, 2021.
Li, X., Wigneron, J.-P., Fan, L., Frappart, F., Yueh, S. H., Colliander, A.,
Ebtehaj, A., Gao, L., Fernandez-Moran, R., Liu, X., Wang, M., Ma, H., Moisy,
C., and Ciais, P.: A new SMAP soil moisture and vegetation optical depth
product (SMAP-IB): Algorithm, assessment and inter-comparison, Remote Sens.
Environ., 271, 112921,
https://doi.org/10.1016/j.rse.2022.112921, 2022.
Liang, S. and Wang, J. (Eds.): Chapter 18 – Soil moisture contents, in:
Advanced Remote Sensing, 2nd Edn., Academic Press, 685–711,
https://doi.org/10.1016/B978-0-12-815826-5.00018-0, 2020.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan,
W., Zhang, X., Zhao, X., and Zhou, J.: The Global Land Surface Satellite
(GLASS) Product Suite, B. Am. Meteorol. Soc., 102, E323–E337,
https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Liu, L., Gudmundsson, L., Hauser, M., Qin, D., Li, S., and Seneviratne, S.
I.: Soil moisture dominates dryness stress on ecosystem production globally,
Nat. Commun., 11, 4892, https://doi.org/10.1038/s41467-020-18631-1, 2020.
Liu, N. F., Liu, Q., Wang, L. Z., Liang, S. L., Wen, J. G., Qu, Y., and Liu, S. H.: A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data, Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, 2013.
Long, D., Bai, L., Yan, L., Zhang, C., Yang, W., Lei, H., Quan, J., Meng,
X., and Shi, C.: Generation of spatially complete and daily continuous
surface soil moisture of high spatial resolution, Remote Sens. Environ.,
233, 111364, https://doi.org/10.1016/j.rse.2019.111364, 2019.
Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., and Meng, L.:
Identifying determinants of spatio-temporal disparities in soil moisture of
the Northern Hemisphere using a geographically optimal zones-based
heterogeneity model, ISPRS J. Photogramm., 185, 111–128,
https://doi.org/10.1016/j.isprsjprs.2022.01.009, 2022.
Ma, H. and Liang, S.: Development of the GLASS 250-m leaf area index product
(version 6) from MODIS data using the bidirectional LSTM deep learning
model, Remote Sens. Environ., 273, 112985,
https://doi.org/10.1016/J.RSE.2022.112985, 2022.
Ma, H., Zeng, J., Zhang, X., Fu, P., Zheng, D., Wigneron, J.-P., Chen, N.,
and Niyogi, D.: Evaluation of six satellite- and model-based surface soil
temperature datasets using global ground-based observations, Remote Sens.
Environ., 264, 112605,
https://doi.org/10.1016/j.rse.2021.112605, 2021.
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and
Stoffelen, A.: Extended triple collocation: Estimating errors and
correlation coefficients with respect to an unknown target, Geophys. Res.
Lett., 41, 6229–6236, https://doi.org/10.1002/2014GL061322,
2014.
Molero, B., Leroux, D. J., Richaume, P., Kerr, Y. H., Merlin, O., Cosh, M.
H., and Bindlish, R.: Multi-Timescale Analysis of the Spatial
Representativeness of In Situ Soil Moisture Data within Satellite
Footprints, J. Geophys. Res.-Atmos., 123, 3–21,
https://doi.org/10.1002/2017JD027478, 2018.
Muñoz-Sabater, J.: ERA5-Land hourly data from 1981 to present,
Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2019.
Muñoz-Sabater, J.: ERA5-Land hourly data from 1950 to 1980, Copernicus
Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2021.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Naz, B. S., Kollet, S., Franssen, H.-J. H., Montzka, C., and Kurtz, W.: A 3
km spatially and temporally consistent European daily soil moisture
reanalysis from 2000 to 2015, Sci. Data, 7, 111,
https://doi.org/10.1038/s41597-020-0450-6, 2020.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V:
Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote, 41,
215–229, https://doi.org/10.1109/TGRS.2002.808243, 2003.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and
Chaubell, J.: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture,
Version 8, NASA National Snow and Ice Data Center Distributed Active Archive
Center [data set], https://doi.org/10.5067/OMHVSRGFX38O, 2021.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys. Res.-Earth, 113, F01002, https://doi.org/10.1029/2007JF000769, 2008.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial
downscaling of satellite remotely sensed soil moisture, Rev. Geophys., 55,
341–366, https://doi.org/10.1002/2016RG000543, 2017.
Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M. H.,
Crow, W. T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M. W. J., de
Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y. H.,
Lovergine, F., Mahecha, M. D., Marzahn, P., Mattia, F., Musial, J. P.,
Preuschmann, S., Reichle, R. H., Satalino, G., Silgram, M., van Bodegom, P.
M., Verhoest, N. E. C., Wagner, W., Walker, J. P., Wegmüller, U., and
Loew, A.: A roadmap for high-resolution satellite soil moisture applications
– confronting product characteristics with user requirements, Remote Sens.
Environ., 252, 112162, https://doi.org/10.1016/J.RSE.2020.112162, 2021.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., and Liu, S.:
Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo
From MODIS Data, IEEE T. Geosci. Remote, 52, 907–919,
https://doi.org/10.1109/TGRS.2013.2245670, 2014.
Rahimzadeh-Bajgiran, P., Berg, A. A., Champagne, C., and Omasa, K.:
Estimation of soil moisture using optical/thermal infrared remote sensing in
the Canadian Prairies, ISPRS J. Photogramm., 83, 94–103,
https://doi.org/10.1016/j.isprsjprs.2013.06.004, 2013.
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.
Senyurek, V., Lei, F., Boyd, D., Kurum, M., Gurbuz, A. C., and Moorhead, R.:
Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in
CONUS, Remote Sens., 12, 1168, https://doi.org/10.3390/rs12071168, 2020.
Sheffield, J., Goteti, G., Wen, F., and Wood, E. F.: A simulated soil
moisture based drought analysis for the United States, J. Geophys. Res.,
109, 1–19, https://doi.org/10.1029/2004JD005182, 2004.
Shi, J., Zhao, T., Cui, Q., and Yao, P.: Airborne and Spaceborne Passive
Microwave Measurements of Soil Moisture, in: Observation and Measurement of
Ecohydrological Processes, edited by: Li, X. and Vereecken, H., Springer
Berlin Heidelberg, Berlin, Heidelberg, 71–105,
https://doi.org/10.1007/978-3-662-48297-1_3, 2019.
Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M.,
Pipunic, R. C., Grayson, R. B., Siriwardena, L., Chiew, F. H. S., and
Richter, H.: The Murrumbidgee soil moisture monitoring network data set,
Water Resour. Res., 48, W07701,
https://doi.org/10.1029/2012WR011976, 2012.
Song, J.: Bias corrections for Random Forest in regression using residual
rotation, J. Korean Stat. Soc., 44, 321–326,
https://doi.org/10.1016/j.jkss.2015.01.003, 2015.
Song, P., Zhang, Y., Guo, J., Shi, J., Zhao, T., and Tong, B.: A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019, Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, 2022.
Starks, P. J., Fiebrich, C. A., Grimsley, D. L., Garbrecht, J. D., Steiner,
J. L., Guzman, J. A., and Moriasi, D. N.: Upper Washita River Experimental
Watersheds: Meteorologic and Soil Climate Measurement Networks, J. Environ.
Qual., 43, 1239–1249,
https://doi.org/10.2134/jeq2013.08.0312, 2014.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and
calibration using triple collocation, J. Geophys. Res.-Oceans, 103,
7755–7766, https://doi.org/10.1029/97JC03180, 1998.
Vergopolan, N., Chaney, N. W., Pan, M., Sheffield, J., Beck, H. E.,
Ferguson, C. R., Torres-Rojas, L., Sadri, S., and Wood, E. F.:
SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the
conterminous US, Sci. Data, 8, 264,
https://doi.org/10.1038/s41597-021-01050-2, 2021.
Wagner, W., Pathe, C., Doubkova, M., Sabel, D., Bartsch, A., Hasenauer, S.,
Blöschl, G., Scipal, K., Martínez-Fernández, J., and Löw,
A.: Temporal Stability of Soil Moisture and Radar Backscatter Observed by
the Advanced Synthetic Aperture Radar (ASAR), Sensors, 8, 1174–1197,
https://doi.org/10.3390/s80201174, 2008.
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S.,
Figa-Saldaña, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J.,
Kubu, G., Brugger, K., Aubrecht, C., Züger, J., Gangkofner, U.,
Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., and
Steinnocher, K.: The ASCAT Soil Moisture Product: A Review of its
Specifications, Validation Results, and Emerging Applications, Meteorol.
Z., 22, 5–33, https://doi.org/10.1127/0941-2948/2013/0399, 2013.
Wang, Y., Leng, P., Peng, J., Marzahn, P., and Ludwig, R.: Global
assessments of two blended microwave soil moisture products CCI and SMOPS
with in-situ measurements and reanalysis data, Int. J. Appl. Earth Obs., 94, 102234, https://doi.org/10.1016/J.JAG.2020.102234, 2021.
Wigneron, J.-P., Li, X., Frappart, F., Fan, L., Al-Yaari, A., De Lannoy, G.,
Liu, X., Wang, M., Le Masson, E., and Moisy, C.: SMOS-IC data record of soil
moisture and L-VOD: Historical development, applications and perspectives,
Remote Sens. Environ., 254, 112238,
https://doi.org/10.1016/j.rse.2020.112238, 2021.
Xu, X.: Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the
Great Lakes Region, Remote Sensing, 12, 3785, https://doi.org/10.3390/rs12223785,
2020.
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.
Yang, J., Zhang, P., Lu, N., Yang, Z., Shi, J., and Dong, C.: Improvements
on global meteorological observations from the current Fengyun 3 satellites
and beyond, Int. J. Digit. Earth, 5, 251–265,
https://doi.org/10.1080/17538947.2012.658666, 2012.
Yee, M. S., Walker, J. P., Rüdiger, C., Parinussa, R. M., Koike, T., and
Kerr, Y. H.: A comparison of SMOS and AMSR2 soil moisture using
representative sites of the OzNet monitoring network, Remote Sens. Environ.,
195, 297–312, https://doi.org/10.1016/j.rse.2017.04.019, 2017.
Yuan, Q., Xu, H., Li, T., Shen, H., and Zhang, L.: Estimating surface soil
moisture from satellite observations using a generalized regression neural
network trained on sparse ground-based measurements in the continental U.S,
J. Hydrol., 580, 124351, https://doi.org/10.1016/j.jhydrol.2019.124351,
2020.
Yue, J., Tian, J., Tian, Q., Xu, K., and Xu, N.: Development of soil
moisture indices from differences in water absorption between
shortwave-infrared bands, ISPRS J. Photogramm., 154, 216–230,
https://doi.org/10.1016/j.isprsjprs.2019.06.012, 2019.
Zappa, L., Forkel, M., Xaver, A., and Dorigo, W.: Deriving Field Scale Soil
Moisture from Satellite Observations and Ground Measurements in a Hilly
Agricultural Region, Remote Sensing, 11, 2596, https://doi.org/10.3390/rs11222596,
2019.
Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.
Zhang, Y., Liang, S., Ma, H., He, T., Wang, Q., and Li, B.: A global 1 km
surface soil moisture product from 2000 to 2020, Zenodo [data set],
https://doi.org/10.5281/ZENODO.7172664, 2022a.
Zhang, Y., Liang, S., Zhu, Z., Ma, H., and He, T.: Soil moisture content
retrieval from Landsat 8 data using ensemble learning, ISPRS J. Photogramm., 185, 32–47,
https://doi.org/10.1016/j.isprsjprs.2022.01.005, 2022b.
Zhao, T., Shi, J., Entekhabi, D., Jackson, T. J., Hu, L., Peng, Z., Yao, P.,
Li, S., and Kang, C. S.: Retrievals of soil moisture and vegetation optical
depth using a multi-channel collaborative algorithm, Remote Sens. Environ.,
257, 112321, https://doi.org/10.1016/j.rse.2021.112321,
2021.
Zheng, C., Jia, L., and Zhao, T.: A 21-year dataset (2000–2020) of gap-free
global daily surface soil moisture at 1 km grid resolution, Sci. Data, 10,
139, https://doi.org/10.1038/s41597-023-01991-w, 2023.
Zheng, J., Zhao, T., Lü, H., Shi, J., Cosh, M. H., Ji, D., Jiang, L.,
Cui, Q., Lu, H., Yang, K., Wigneron, J.-P., Li, X., Zhu, Y., Hu, L., Peng,
Z., Zeng, Y., Wang, X., and Kang, C. S.: Assessment of 24 soil moisture
datasets using a new in situ network in the Shandian River Basin of China,
Remote Sens. Environ., 271, 112891,
https://doi.org/10.1016/j.rse.2022.112891, 2022.
Zhou, Z.-H.: Ensemble Learning, in: Machine Learning, Springer Singapore,
Singapore, 181–210,
https://doi.org/10.1007/978-981-15-1967-3_8, 2021.
Short summary
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.
Soil moisture observations are important for a range of earth system applications. This study...
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