Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5267-2022
© Author(s) 2022. 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-14-5267-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Nov 2022
Data description paper |
| 30 Nov 2022
| 30 Nov 2022
A 1 km daily soil moisture dataset over China using in situ measurement and machine learning
Qingliang Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
College of Computer Science and Technology, Changchun Normal
University, Changchun 130032, China
Gaosong Shi
College of Computer Science and Technology, Changchun Normal
University, Changchun 130032, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Vahid Nourani
Center of Excellence in Hydroinformatics and Faculty of Civil
Engineering, University of Tabriz, Tabriz 51368, Iran
Faculty of Civil and Environmental Engineering, Near East University, Near East Boulevard, Nicosia 99628, Turkey
Jianduo Li
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 10081, China
Lu Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Feini Huang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Ye Zhang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Chunyan Wang
College of Computer Science and Technology, Changchun Normal
University, Changchun 130032, China
Dagang Wang
School of Geography and Planning, Sun Yat-sen University, Guangzhou
510275, China
Jianxiu Qiu
School of Geography and Planning, Sun Yat-sen University, Guangzhou
510275, China
Xingjie Lu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
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We introduce CAMELS-INDIA (Catchment Attributes and MEteorology for Large-sample Studies – India), which provides daily hydrometeorological time series and static catchment attributes representing location, topography, climate, hydrological signatures, land-use, land cover, soil, geology, and anthropogenic influences for 472 catchments in peninsular India, to foster large-sample hydrological studies in India and promote the inclusion of Indian catchments in global hydrological research.
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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Agroforestry systems (AFSs) combine trees and crops within the same land unit, providing a sustainable land use option which protects natural resources and biodiversity. Introducing trees into agricultural systems can positively affect water resources, soil characteristics, biomass and microclimate. We studied an AFS in South Africa in a multidisciplinary approach to assess the different influences and present the resulting dataset consisting of water, soil, tree and meteorological variables.
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.
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/.
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.
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
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Albertson, J. D. and Kiely, G.: On the structure of soil moisture time
series in the context of land surface models, J. Hydrol., 243,
101–119, https://doi.org/10.1016/S0022-1694(00)00405-4, 2001.
Balenović, I., Marjanović, H., Vuletić, D., Paladinić, E.,
and Indir, K.: Quality assessment of high density digital surface model over
different land cover classes, Period. Biol., 117, 459–470,
https://doi.org/10.18054/pb.2015.117.4.3452, 2016.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015.
Baroni, G., Ortuani, B., Facchi, A., and Gandolfi, C.: The role of
vegetation and soil properties on the spatio-temporal variability of the
surface soil moisture in a maize-cropped field, J. Hydrol., 489,
148–159, https://doi.org/10.1016/j.jhydrol.2013.03.007, 2013.
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brocca, L., Morbidelli, R., Melone, F., and Moramarco, T.: Soil moisture
spatial variability in experimental areas of central Italy, J.
Hydrol., 333, 356–373, https://doi.org/10.1016/j.jhydrol.2006.09.004,
2007.
Bury, T. M., Sujith, R. I., Pavithran, I., Scheffer, M., Lenton, T. M., Anand, M., and Bauch, C. T.: Deep learning for early warning signals
of tipping points, P. Natl. Acad. Sci., 118,
e2106140118, https://doi.org/10.1073/pnas.2106140118, 2021.
Carranza, C., Nolet, C., Pezij, M., and van der Ploeg, M.: Root zone soil
moisture estimation with Random Forest, J. Hydrol., 593, 125840,
https://doi.org/10.1016/j.jhydrol.2020.125840, 2021.
Chakrabarti, S., Bongiovanni, T., Judge, J., Nagarajan, K., and Principe, J.
C.: Downscaling Satellite-Based Soil Moisture in Heterogeneous Regions Using
High-Resolution Remote Sensing Products and Information Theory: A Synthetic
Study, IEEE T. Geosci. Remote, 53, 85–101,
https://doi.org/10.1109/TGRS.2014.2318699, 2015.
Chawla, I., Karthikeyan, L., and Mishra, A. K.: A review of remote sensing
applications for water security: Quantity, quality, and extremes, J.
Hydrol., 585, 124826, https://doi.org/10.1016/j.jhydrol.2020.124826, 2020.
Chen, M., Willgoose, G. R., and Saco, P. M.: Investigating the impact of
leaf area index temporal variability on soil moisture predictions using
remote sensing vegetation data, J. Hydrol., 522, 274–284,
https://doi.org/10.1016/j.jhydrol.2014.12.027, 2015.
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, USA, 13–17 August 2016, 785–794, https://doi.org/10.1145/2939672.2939785,
2016.
Chen, Y., Feng, X., and Fu, B.: An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018, Earth Syst. Sci. Data, 13, 1–31, https://doi.org/10.5194/essd-13-1-2021, 2021.
Cong, N., Wang, T., Nan, H., Ma, Y., Wang, X., Myneni, R. B., and Piao, S.:
Changes in satellite-derived spring vegetation green-up date and its linkage
to climate in China from 1982 to 2010: a multimethod analysis, Glob. Change
Biol., 19, 881–891, https://doi.org/10.1111/gcb.12077, 2013.
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, RG2002,
https://doi.org/10.1029/2011RG000372, 2012.
Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M.
G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G., Oleson, K. W.,
Schlosser, C. A., and Yang, Z.-L.: The Common Land Model, B.
Am. Meteorol. Soc., 84, 1013–1024,
https://doi.org/10.1175/BAMS-84-8-1013, 2003.
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.
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.
Dorogush, A. V., Ershov, V., and Gulin, A.: CatBoost: gradient boosting with
categorical features support, arXiv [preprint],
https://doi.org/10.48550/arXiv.1810.11363, 24 October 2018.
Entekhabi, D., Rodriguez-Iturbe, I., and Castelli, F.: Mutual interaction of
soil moisture state and atmospheric processes, J. Hydrol., 184,
3–17, https://doi.org/10.1016/0022-1694(95)02965-6, 1996.
Entekhabi, D., Njoku, E. G., Neill, P. E. O., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsang, L., and Zyl, J. V.: The Soil Moisture Active Passive (SMAP)
Mission, P. IEEE, 98, 704–716,
https://doi.org/10.1109/JPROC.2010.2043918, 2010.
Feng, H. and Liu, Y.: Combined effects of precipitation and air temperature
on soil moisture in different land covers in a humid basin, J.
Hydrol., 531, 1129–1140, https://doi.org/10.1016/j.jhydrol.2015.11.016,
2015.
Fujii, H., Koike, T., and Imaoka, K.: Improvement of the AMSR-E Algorithm
for Soil Moisture Estimation by Introducing a Fractional Vegetation Coverage
Dataset Derived from MODIS Data, Journal of the Remote Sensing Society of
Japan, 29, 282–292, https://doi.org/10.11440/rssj.29.282, 2009.
Gaur, N. and Mohanty, B. P.: Land-surface controls on near-surface soil
moisture dynamics: Traversing remote sensing footprints, Water Resour.
Res., 52, 6365–6385, https://doi.org/10.1002/2015WR018095, 2016.
Global Climate Observing System (GCOS): The Global Observing System for Climate: Implementation Needs, World Meteorological Organization, Guayaquil, Ecuador, Rep. GCOS-200, 341 pp.,
https://doi.org/10.13140/RG.2.2.23178.26566, 2016.
Gruber, A., Su, C. H., Crow, W. T., Zwieback, S., Dorigo, W. A., and Wagner,
W.: Estimating error cross-correlations in soil moisture data sets using
extended collocation analysis, J. Geophys. Res.-Atmos.,
121, 1208–1219, https://doi.org/10.1002/2015JD024027, 2016.
Gu, X., Li, J., Chen, Y. D., Kong, D., and Liu, J.: Consistency and
Discrepancy of Global Surface Soil Moisture Changes from Multiple
Model-Based Data Sets Against Satellite Observations, J. Geophys.
Res.-Atmos., 124, 1474–1495, https://doi.org/10.1029/2018JD029304,
2019.
Guo, L. and Lin, H.: Chapter Two – Addressing Two Bottlenecks to Advance the
Understanding of Preferential Flow in Soils, in: Advances in Agronomy,
edited by: Sparks, D. L., Academic Press, 61–117,
https://doi.org/10.1016/bs.agron.2017.10.002, 2018.
Hu, Q. and Feng, S.: A Daily Soil Temperature Dataset and Soil Temperature
Climatology of the Contiguous United States, J. Appl. Meteorol.,
42, 1139–1156, https://doi.org/10.1175/1520-0450(2003)042<1139:ADSTDA>2.0.CO;2, 2003.
Joshi, C. and Mohanty, B. P.: Physical controls of near-surface soil
moisture across varying spatial scales in an agricultural landscape during
SMEX02, Water Resour. Res., 46, W12503, https://doi.org/10.1029/2010WR009152,
2010.
Karthikeyan, L. and Kumar, D. N.: A novel approach to validate satellite
soil moisture retrievals using precipitation data, J. Geophys.
Res.-Atmos., 121, 11516–11535, https://doi.org/10.1002/2016JD024829, 2016.
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.
Kemppinen, J., Niittynen, P., Virkkala, A.-M., Happonen, K., Riihimäki,
H., Aalto, J., and Luoto, M.: Dwarf Shrubs Impact Tundra Soils: Drier,
Colder, and Less Organic Carbon, Ecosystems, 24, 1378–1392,
https://doi.org/10.1007/s10021-020-00589-2, 2021.
Kerr, Y. H., Waldteufel, P., Wigneron, J., Delwart, S., Cabot, F., Boutin,
J., Escorihuela, M., Font, J., Reul, N., Gruhier, C., Juglea, S. E.,
Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg, S.:
The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water
Cycle, P. IEEE, 98, 666–687,
https://doi.org/10.1109/JPROC.2010.2043032, 2010.
Kim, S., Zhang, R., Pham, H., and Sharma, A.: A Review of Satellite-Derived
Soil Moisture and Its Usage for Flood Estimation, Remote Sensing in Earth
Systems Sciences, 2, 225–246, https://doi.org/10.1007/s41976-019-00025-7,
2019.
Kumar, S. V., Reichle, R. H., Koster, R. D., Crow, W. T., and Peters-Lidard,
C. D.: Role of Subsurface Physics in the Assimilation of Surface Soil
Moisture Observations, J. Hydrometeorol., 10, 1534–1547,
https://doi.org/10.1175/2009JHM1134.1, 2009.
Le Bissonnais, Y., Renaux, B., and Delouche, H.: Interactions between soil
properties and moisture content in crust formation, runoff and interrill
erosion from tilled loess soils, CATENA, 25, 33–46,
https://doi.org/10.1016/0341-8162(94)00040-L, 1995.
Lei, S., Shi, Z., and Zou, Z.: Coupled Adversarial Training for Remote
Sensing Image Super-Resolution, IEEE T. Geosci. Remote, 58, 3633–3643, https://doi.org/10.1109/TGRS.2019.2959020, 2020.
Li, L., Shangguan, W., Deng, Y., Mao, J., Pan, J., Wei, N., Yuan, H., Zhang,
S., Zhang, Y., and Dai, Y.: A Causal Inference Model Based on Random Forests
to Identify the Effect of Soil Moisture on Precipitation, J.
Hydrometeorol., 21, 1115–1131, https://doi.org/10.1175/JHM-D-19-0209.1,
2020.
Li, Q., Wang, Z., Shangguan, W., Li, L., Yao, Y., and Yu, F.: Improved daily
SMAP satellite soil moisture prediction over China using deep learning model
with transfer learning, J. Hydrol., 600, 126698,
https://doi.org/10.1016/j.jhydrol.2021.126698, 2021.
Lin, L. and Liu, X.: Mixture-based weight learning improves the random
forest method for hyperspectral estimation of soil total nitrogen, Comput.
Electron. Agr., 192, 106634,
https://doi.org/10.1016/j.compag.2021.106634, 2022.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L.,
and Merchant, J. W.: Development of a global land cover characteristics
database and IGBP DISCover from 1 km AVHRR data, Int. J.
Remote Sens. 21, 1303–1330, https://doi.org/10.1080/014311600210191,
2000.
Ly, H. B., Nguyen, T. A., and Pham, B. T.: Estimation of Soil Cohesion Using
Machine Learning Method: A Random Forest Approach, Advances in Civil
Engineering, 2021, 8873993, https://doi.org/10.1155/2021/8873993, 2021.
Mao, T., Shangguan, W., Li, Q., Li, L., Zhang, Y., Huang, F., Li, J., Liu, W., and Zhang, R.:
A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on
Random Forest Considering Soil Moisture Memory and Mass Conservation, Remote
Sensing, 14, 3858, https://doi.org/10.3390/rs14163858, 2022.
Meng, X., Mao, K., Meng, F., Shi, J., Zeng, J., Shen, X., Cui, Y., Jiang, L., and Guo, Z.: A fine-resolution soil moisture dataset for China in 2002–2018, Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, 2021.
Mishra, A., Vu, T., Veettil, A. V., and Entekhabi, D.: Drought monitoring
with soil moisture active passive (SMAP) measurements, J. Hydrol.,
552, 620–632, https://doi.org/10.1016/j.jhydrol.2017.07.033, 2017.
Mohamed, E., Habib, E., Abdelhameed, A. M., and Bayoumi, M.: Assessment of a
Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and
Filling the Gaps in Between Soil Moisture Observations, Frontiers in
Artificial Intelligence, 4, 636234, https://doi.org/10.3389/frai.2021.636234, 2021.
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.
Norbiato, D., Borga, M., Degli Esposti, S., Gaume, E., and Anquetin, S.:
Flash flood warning based on rainfall thresholds and soil moisture
conditions: An assessment for gauged and ungauged basins, J.
Hydrol., 362, 274–290, https://doi.org/10.1016/j.jhydrol.2008.08.023,
2008.
O, S. and Orth, R.: Global soil moisture data derived through machine
learning trained with in-situ measurements, Scientific Data, 8, 170,
https://doi.org/10.1038/s41597-021-00964-1, 2021.
Ojha, R., Morbidelli, R., Saltalippi, C., Flammini, A., and Govindaraju, R.
S.: Scaling of surface soil moisture over heterogeneous fields subjected to
a single rainfall event, J. Hydrol., 516, 21–36,
https://doi.org/10.1016/j.jhydrol.2014.01.057, 2014.
Orth, R. and Seneviratne, S. I.: Using soil moisture forecasts for
sub-seasonal summer temperature predictions in Europe, Clim. Dynam., 43,
3403–3418, https://doi.org/10.1007/s00382-014-2112-x, 2014.
Pan, J., Shangguan, W., Li, L., Yuan, H., Zhang, S., Lu, X., Wei, N., and
Dai, Y.: Using data-driven methods to explore the predictability of surface
soil moisture with FLUXNET site data, Hydrol. Process., 33, 2978–2996,
https://doi.org/10.1002/hyp.13540, 2019.
Parinussa, R. M., Lakshmi, V., Johnson, F. M., and Sharma, A.: A new
framework for monitoring flood inundation using readily available satellite
data, Geophys. Rese. Lett., 43, 2599–2605,
https://doi.org/10.1002/2016GL068192, 2016.
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.
Rosenblatt, F.: The perceptron: A probabilistic model for information
storage and organization in the brain, Psychol. Rev., 65, 386–408,
https://doi.org/10.1037/h0042519, 1958.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture–climate interactions in a changing climate: A review,
Earth-Sci. Rev., 99, 125–161,
https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Shangguan, W., Dai, Y., Liu, B., Zhu, A., Duan, Q., Wu, L., Ji, D., Ye, A.,
Yuan, H., Zhang, Q., Chen, D., Chen, M., Chu, J., Dou, Y., Guo, J., Li, H.,
Li, J., Liang, L., Liang, X., Liu, H., Liu, S., Miao, C., and Zhang, Y.: A
China data set of soil properties for land surface modeling, J.
Adv. Model. Earth Sy., 5, 212–224,
https://doi.org/10.1002/jame.20026, 2013.
Shangguan, W., Li, Q., and Shi, G.: A 1-km daily soil moisture dataset over
China based on in-situ measurement (2000–2020), National Tibetan Plateau
Data Center [data set], https://doi.org/10.11888/Terre.tpdc.272415, 2022.
Srivastava, P. K., Han, D., Ramirez, M. R., and Islam, T.: Machine Learning
Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land
Surface Temperature for Hydrological Application, Water Resour.
Manag., 27, 3127–3144, https://doi.org/10.1007/s11269-013-0337-9, 2013.
Tijdeman, E. and Menzel, L.: The development and persistence of soil moisture stress during drought across southwestern Germany, Hydrol. Earth Syst. Sci., 25, 2009–2025, https://doi.org/10.5194/hess-25-2009-2021, 2021.
Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J.,
Bogena, H., Weihermüller, L., Herbst, M., Martinez, G., and
Vanderborght, J.: On the spatio-temporal dynamics of soil moisture at the
field scale, J. Hydrol., 516, 76–96,
https://doi.org/10.1016/j.jhydrol.2013.11.061, 2014.
Wagner, W., Blöschl, G., Pampaloni, P., Calvet, J.-C., Bizzarri, B.,
Wigneron, J.-P., and Kerr, Y.: Operational readiness of microwave remote
sensing of soil moisture for hydrologic applications, Hydrol. Res.,
38, 1–20, https://doi.org/10.2166/nh.2007.029, 2007.
Wang, Y., Mao, J., Jin, M., Hoffman, F. M., Shi, X., Wullschleger, S. D., and Dai, Y.: Development of observation-based global multilayer soil moisture products for 1970 to 2016, Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, 2021.
Wei, Z., Meng, Y., Zhang, W., Peng, J., and Meng, L.: Downscaling SMAP soil
moisture estimation with gradient boosting decision tree regression over the
Tibetan Plateau, Remote Sens. Environ., 225, 30–44,
https://doi.org/10.1016/j.rse.2019.02.022, 2019.
Xu, J. W., Zhao, J. F., Zhang, W. C., and Xu, X. X.: A Novel Soil Moisture
Predicting Method Based on Artificial Neural Network and Xinanjiang Model,
Adv. Mat. Res., 121–122, 1028–1032,
https://doi.org/10.4028/www.scientific.net/AMR.121-122.1028, 2010.
Yao, Y., Qin, Q., Zhao, S., and Yuan, W.: Retrieval of soil
moisture based on MODIS shortwave infrared spectral feature, J. Infrared
Millim. Waves, 30, 9–14,
http://journal.sitp.ac.cn/hwyhmb/hwyhmben/article/abstract/100118 (last access: 25 November 2022), 2011.
Yuan, H., Dai, Y., Xiao, Z., Ji, D., and Shangguan, W.: Reprocessing the
MODIS Leaf Area Index products for land surface and climate modelling,
Remote Sens. Environ., 115, 1171–1187,
https://doi.org/10.1016/j.rse.2011.01.001, 2011.
Zeng, L., Hu, S., Xiang, D., Zhang, X., Li, D., Li, L., and Zhang, T.:
Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data
via a Machine Learning Method, Remote Sensing, 11, 284,
https://doi.org/10.3390/rs11030284, 2019.
Zhang, H., Wang, P., and Jiang, Z.: Nonpairwise-Trained Cycle Convolutional
Neural Network for Single Remote Sensing Image Super-Resolution, IEEE
T. Geosci. Remote, 59, 4250–4261,
https://doi.org/10.1109/TGRS.2020.3009224, 2021.
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, R., Kim, S., and Sharma, A.: A comprehensive validation of the SMAP
Enhanced Level-3 Soil Moisture product using ground measurements over varied
climates and landscapes, Remote Sens. Environ., 223, 82–94,
https://doi.org/10.1016/j.rse.2019.01.015, 2019.
Zhu, X., Guo, K., Ren, S., Hu, B., Hu, M., and Fang, H.: Lightweight Image
Super-Resolution With Expectation-Maximization Attention Mechanism, IEEE
T. Circ. Syst. Vid., 32, 1273–1284,
https://doi.org/10.1109/TCSVT.2021.3078436, 2022.
Zwieback, S., Chang, Q., Marsh, P., and Berg, A.: Shrub tundra ecohydrology:
rainfall interception is a major component of the water balance,
Environ. Res. Lett., 14, 055005,
https://doi.org/10.1088/1748-9326/ab1049, 2019.
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived...
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