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
Related authors
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
Short summary
Short summary
In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Shulei Zhang, Hongbin Liang, Fang Li, Xingjie Lu, and Yongjiu Dai
Hydrol. Earth Syst. Sci., 29, 3119–3143, https://doi.org/10.5194/hess-29-3119-2025, https://doi.org/10.5194/hess-29-3119-2025, 2025
Short summary
Short summary
This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025, https://doi.org/10.5194/hess-29-2201-2025, 2025
Short summary
Short summary
We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Short summary
Short summary
Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Shuyang Guo, Yongjiu Dai, Hua Yuan, and Hongbin Liang
EGUsphere, https://doi.org/10.5194/egusphere-2025-230, https://doi.org/10.5194/egusphere-2025-230, 2025
Short summary
Short summary
The Snow, Ice, and Aerosol Radiation Model Version 4 has only been used to evaluate bare ice albedo in land surface models, with necessary ice property data lacking quality control. We integrated this model into our land surface model and improved bare ice properties using quality-controlled satellite data. Our findings show regional warming and reduced snow cover in Greenland’s bare ice region, driven by changes in bare ice properties through the bare ice-snow-albedo feedback.
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
Short summary
Short summary
In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
Short summary
Short summary
Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Yueli Chen, Xingwu Duan, Minghu Ding, Wei Qi, Ting Wei, Jianduo Li, and Yun Xie
Earth Syst. Sci. Data, 14, 2681–2695, https://doi.org/10.5194/essd-14-2681-2022, https://doi.org/10.5194/essd-14-2681-2022, 2022
Short summary
Short summary
We reconstructed the first annual rainfall erosivity dataset for the Tibetan Plateau in China. The dataset covers 71 years in a 0.25° grid. The reanalysis precipitation data are employed in combination with the densely spaced in situ precipitation observations to generate the dataset. The dataset can supply fundamental data for quantifying the water erosion, and extend our knowledge of the rainfall-related hazard prediction on the Tibetan Plateau.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
Short summary
Short summary
We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 1569–1586, https://doi.org/10.5194/hess-25-1569-2021, https://doi.org/10.5194/hess-25-1569-2021, 2021
Short summary
Short summary
The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.
Zhengang Wang, Jianxiu Qiu, and Kristof Van Oost
Geosci. Model Dev., 13, 4977–4992, https://doi.org/10.5194/gmd-13-4977-2020, https://doi.org/10.5194/gmd-13-4977-2020, 2020
Short summary
Short summary
This study developed a spatially distributed carbon cycling model applicable in an eroding landscape. It includes all three carbon isotopes so that it is able to represent the carbon isotopic compositions. The model is able to represent the observations that eroding area is enriched in 13C and depleted of 14C compared to depositional area. Our simulations show that the spatial variability of carbon isotopic properties in an eroding landscape is mainly caused by the soil redistribution.
Jianxiu Qiu, Wade T. Crow, Jianzhi Dong, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 24, 581–594, https://doi.org/10.5194/hess-24-581-2020, https://doi.org/10.5194/hess-24-581-2020, 2020
Short summary
Short summary
Accurately estimating coupling of evapotranspiration (ET) and soil water content (θ) at different depths is key to investigating land–atmosphere interaction. Here we examine whether the model can accurately represent surface θ (θs) versus ET coupling and vertically integrated θ (θv) versus ET coupling. We find that all models agree with observations that θs contains slightly more information with fPET than θv. In addition, an ET scheme is crucial for accurately estimating coupling of θ and ET.
Yongjiu Dai, Wei Shangguan, Nan Wei, Qinchuan Xin, Hua Yuan, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, Dagang Wang, and Fapeng Yan
SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, https://doi.org/10.5194/soil-5-137-2019, 2019
Short summary
Short summary
Soil data are widely used in various Earth science fields. We reviewed soil property maps for Earth system models, which can also offer insights to soil data developers and users. Old soil datasets are often based on limited observations and have various uncertainties. Updated and comprehensive soil data are made available to the public and can benefit related research. Good-quality soil data are identified and suggestions on how to improve and use them are provided.
Qinchuan Xin, Yongjiu Dai, and Xiaoping Liu
Biogeosciences, 16, 467–484, https://doi.org/10.5194/bg-16-467-2019, https://doi.org/10.5194/bg-16-467-2019, 2019
Short summary
Short summary
Terrestrial biosphere models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. A time-stepping scheme is proposed to simulate leaf area index, phenology, and gross primary production via climate variables. The method performs well on simulating deciduous broadleaf forests across the eastern United States; it provides a simplified and improved version of the growing production day model for use in land surface modeling.
Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
Biogeosciences, 15, 6909–6925, https://doi.org/10.5194/bg-15-6909-2018, https://doi.org/10.5194/bg-15-6909-2018, 2018
Short summary
Short summary
Land-surface models have been widely used to predict the responses of terrestrial ecosystems to climate change. A better understanding of model mechanisms that govern terrestrial ecosystem responses to rising atmosphere [CO2] is needed. Our study for the first time shows that the expansion of leaf area under rising [CO2] is the most important response for the stimulation of land carbon accumulation by a land-surface model: CABLE. Processes related to leaf area should be better calibrated.
Xingjie Lu, Ying-Ping Wang, Yiqi Luo, and Lifen Jiang
Biogeosciences, 15, 6559–6572, https://doi.org/10.5194/bg-15-6559-2018, https://doi.org/10.5194/bg-15-6559-2018, 2018
Short summary
Short summary
How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
Fapeng Yan, Wei Shangguan, Jing Zhang, and Bifeng Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-103, https://doi.org/10.5194/essd-2018-103, 2018
Revised manuscript not accepted
Short summary
Short summary
A depth-to-bedrock map of China was produced at 100-meter resolution based on 6,382 observations and 133 covariates.
This map is the most detailed and accurate one at the national scale.
The uncertainty map was provided as a map quality reference.
The ensemble machine learning model explains 57% of variation in spatial distribution.
The four most important covariates for the map production are topographic wetness index, landform, topographic openness index, and slope.
Wei Wei, Hongsheng Zhang, Bingui Wu, Yongxiang Huang, Xuhui Cai, Yu Song, and Jianduo Li
Atmos. Chem. Phys., 18, 12953–12967, https://doi.org/10.5194/acp-18-12953-2018, https://doi.org/10.5194/acp-18-12953-2018, 2018
Short summary
Short summary
Heavy particulate pollution events have frequently occurred in the North China Plain. Using the intermittency factor, we found that the turbulence during the transport stage is intermittent and not locally generated. Turbulence results from the wind shear of low-level jets and then transports downward, causing intermittent turbulence at lower levels. The intermittent turbulence contributes positively to the vertical dispersion of particulate matter and improves the air quality near the surface.
Dagang Wang, Guiling Wang, Dana T. Parr, Weilin Liao, Youlong Xia, and Congsheng Fu
Hydrol. Earth Syst. Sci., 21, 3557–3577, https://doi.org/10.5194/hess-21-3557-2017, https://doi.org/10.5194/hess-21-3557-2017, 2017
Short summary
Short summary
Land surface models bear substantial biases. To reduce model biases, we apply a simple but efficient bias correction method to a land surface model. We first derive a relationship between observations and model simulations, and apply this relationship in the application period. While the bias correction method improves model-based estimates without improving the model physical parameterization, results do provide guidance for physically based model development effort.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
Short summary
Short summary
Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Shengyun Chen, Wenjie Liu, Qian Zhao, Lin Zhao, Qingbai Wu, Xingjie Lu, Shichang Kang, Xiang Qin, Shilong Chen, Jiawen Ren, and Dahe Qin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-80, https://doi.org/10.5194/tc-2016-80, 2016
Revised manuscript not accepted
Short summary
Short summary
Experimental warming was manipulated using open top chambers in alpine grassland ecosystem in the permafrost regions of the Qinghai-Tibet Plateau. The results revealed variations of earlier thawing, later freezing and longer freezing-thawing periods in shallow soil. Further, the estimated permafrost table declined under the warming scenarios. The work will be helpful to evaluate the stability of Qinghai-Tibet Railway/Highway and estimate the release of carbon under the future climate warming.
Related subject area
Domain: ESSD – Land | Subject: Hydrology
OLIGOTREND, a global database of multi-decadal chlorophyll a and water quality time series for rivers, lakes, and estuaries
A 3 h, 1 km surface soil moisture dataset for the contiguous United States from 2015 to 2023
Comprehensive inventory of large hydropower systems in the Italian Alpine Region
An integrated high-resolution bathymetric model for the Danube Delta system
LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles
Benchmark dataset for hydraulic simulations of flash floods in the French Mediterranean region
Transformation rate maps of dissolved organic carbon in the contiguous US
A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res)
Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data
Mapping the world's inland surface waters: an upgrade to the Global Lakes and Wetlands Database (GLWD v2)
One year of high-frequency monitoring of groundwater physico-chemical parameters in the Weierbach experimental catchment, Luxembourg
Discrete global grid system-based flow routing datasets in the Amazon and Yukon basins
GRILSS: opening the gateway to global reservoir sedimentation data curation
A worldwide event-based debris flow barrier dam dataset from 1800 to 2023
CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations
An in situ daily dataset for benchmarking temporal variability of groundwater recharge
CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking
Development of HYPER-P: HYdroclimatic PERformance-enhanced Precipitation at 1 km/daily over the Europe-Mediterranean region from 2007 to 2022
Features of Italian large dams and their upstream catchments
Gridded rainfall erosivity (2014–2022) in mainland China using 1 min precipitation data from densely distributed weather stations
High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland
CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India
HERA: a high-resolution pan-European hydrological reanalysis (1951–2020)
BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada
A 1 km soil moisture data over eastern CONUS generated through assimilating SMAP data into the Noah-MP land surface model
Northern Hemisphere in situ snow water equivalent dataset (NorSWE, 1979–2021)
A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model
ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates
Lena River biogeochemistry captured by a 4.5-year high-frequency sampling program
CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany
Observational partitioning of water and CO2 fluxes at National Ecological Observatory Network (NEON) sites: a 5-year dataset of soil and plant components for spatial and temporal analysis
A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022
GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre
CIrrMap250: annual maps of China's irrigated cropland from 2000 to 2020 developed through multisource data integration
HANZE v2.1: an improved database of flood impacts in Europe from 1870 to 2020
A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins
Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950–2019)
A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps
Satellite-based near-real-time global daily terrestrial evapotranspiration estimates
Multivariate characterisation of a blackberry–alder agroforestry system in South Africa: hydrological, pedological, dendrological and meteorological measurements
CAMELS-AUS v2: updated hydrometeorological timeseries and landscape attributes for an enlarged set of catchments in Australia
SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains
First comprehensive stable isotope dataset of diverse water units in a permafrost-dominated catchment on the Qinghai–Tibet Plateau
LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland
High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020
Evapotranspiration evaluation using three different protocols on a large green roof in the greater Paris area
Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti
CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data
A hydrogeomorphic dataset for characterizing catchment hydrological behavior across the Tibetan Plateau
A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies
Camille Minaudo, Andras Abonyi, Carles Alcaraz, Jacob Diamond, Nicholas J. K. Howden, Michael Rode, Estela Romero, Vincent Thieu, Fred Worrall, Qian Zhang, and Xavier Benito
Earth Syst. Sci. Data, 17, 3411–3430, https://doi.org/10.5194/essd-17-3411-2025, https://doi.org/10.5194/essd-17-3411-2025, 2025
Short summary
Short summary
Many waterbodies undergo nutrient decline, called oligotrophication, globally, but a comprehensive dataset to understand ecosystem responses is lacking. The OLIGOTREND database comprises multi-decadal chlorophyll a and nutrient time series from rivers, lakes, and estuaries with 4.3 million observations from 1894 unique measurement locations. The database provides empirical evidence for oligotrophication responses with a spatial and temporal coverage that exceeds previous efforts.
Haoxuan Yang, Jia Yang, Tyson E. Ochsner, Erik S. Krueger, Mengyuan Xu, and Chris B. Zou
Earth Syst. Sci. Data, 17, 3391–3409, https://doi.org/10.5194/essd-17-3391-2025, https://doi.org/10.5194/essd-17-3391-2025, 2025
Short summary
Short summary
We developed a 3 h, 1 km surface soil moisture dataset for the contiguous United States from 2015 to 2023 using the spatio-temporal fusion method. This dataset effectively combines the distinct advantages of two long-term surface soil moisture datasets, which is also the first hourly-level 1 km soil moisture dataset at the continental US scale. The new dataset could provide new insight into the fast changes in soil moisture along with drought and wet spell occurrences.
Andrea Galletti, Soroush Zarghami Dastjerdi, and Bruno Majone
Earth Syst. Sci. Data, 17, 3353–3373, https://doi.org/10.5194/essd-17-3353-2025, https://doi.org/10.5194/essd-17-3353-2025, 2025
Short summary
Short summary
IAR-HP (Italian Alpine Region HydroPower) is a detailed inventory of large hydropower systems in Italy's Alpine Region, aimed at improving their inclusion in hydrological modeling by providing relevant information with a consistent level of detail. It includes structural, geographic, and operational data for over 300 hydropower plants and their related reservoirs and water intakes. Validated through modeling, IAR-HP accurately reproduces observed hydropower, capturing 96.2 % of actual production.
Lauranne Alaerts, Jonathan Lambrechts, Ny Riana Randresihaja, Luc Vandenbulcke, Olivier Gourgue, Emmanuel Hanert, and Marilaure Grégoire
Earth Syst. Sci. Data, 17, 3125–3140, https://doi.org/10.5194/essd-17-3125-2025, https://doi.org/10.5194/essd-17-3125-2025, 2025
Short summary
Short summary
We created the first comprehensive, high-resolution, and easily accessible bathymetry dataset for the three main branches of the Danube Delta. By combining four data sources, we obtained a detailed representation of the riverbed, with resolutions ranging from 2 to 100 m. This dataset will support future studies on water and nutrient exchanges between the Danube and the Black Sea and provide insights into the delta's buffer role within the understudied Danube–Black Sea continuum.
Bennett J. McAfee, Aanish Pradhan, Abhilash Neog, Sepideh Fatemi, Robert T. Hensley, Mary E. Lofton, Anuj Karpatne, Cayelan C. Carey, and Paul C. Hanson
Earth Syst. Sci. Data, 17, 3141–3165, https://doi.org/10.5194/essd-17-3141-2025, https://doi.org/10.5194/essd-17-3141-2025, 2025
Short summary
Short summary
LakeBeD-US is a dataset of lake water quality data collected by multiple long-term monitoring programs around the United States. This dataset is designed to foster collaboration between lake scientists and computer scientists to improve predictions of water quality. By offering a way for computer models to be tested against real-world lake data, LakeBeD-US offers opportunities for both sciences to grow and to give new insights into the causes of water quality changes.
Juliette Godet, Pierre Nicolle, Nabil Hocini, Eric Gaume, Philippe Davy, Frederic Pons, Pierre Javelle, Pierre-André Garambois, Dimitri Lague, and Olivier Payrastre
Earth Syst. Sci. Data, 17, 2963–2983, https://doi.org/10.5194/essd-17-2963-2025, https://doi.org/10.5194/essd-17-2963-2025, 2025
Short summary
Short summary
This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data, 17, 2713–2733, https://doi.org/10.5194/essd-17-2713-2025, https://doi.org/10.5194/essd-17-2713-2025, 2025
Short summary
Short summary
We have developed new maps that reveal how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2500 gauges and a wealth of climate and environmental information. The maps are a critical step in understanding and predicting how carbon moves through our environment, hence making them a useful tool for tackling climate challenges.
Shanti Shwarup Mahto, Simone Fatichi, and Stefano Galelli
Earth Syst. Sci. Data, 17, 2693–2712, https://doi.org/10.5194/essd-17-2693-2025, https://doi.org/10.5194/essd-17-2693-2025, 2025
Short summary
Short summary
The MSEA-Res database offers an open-access dataset tracking absolute water storage for 186 large reservoirs across Mainland Southeast Asia from 1985 to 2023. It provides valuable insights into how reservoir storage grew by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
Nehar Mandal, Prabal Das, and Kironmala Chanda
Earth Syst. Sci. Data, 17, 2575–2604, https://doi.org/10.5194/essd-17-2575-2025, https://doi.org/10.5194/essd-17-2575-2025, 2025
Short summary
Short summary
Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected using a novel Bayesian network (BN) approach for simulating terrestrial water storage anomalies (TWSAs). TWSAs are reconstructed (BNML_TWSA) with grid-specific leader models (among four machine learning models) from January 1960 to December 2022 to generate a continuous global gridded dataset. The uncertainty in the reconstructed BNML_TWSA product is also assessed in terms of standard error.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
Short summary
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Karl Nicolaus van Zweel, Laurent Gourdol, Jean François Iffly, Loïc Léonard, François Barnich, Laurent Pfister, Erwin Zehe, and Christophe Hissler
Earth Syst. Sci. Data, 17, 2217–2229, https://doi.org/10.5194/essd-17-2217-2025, https://doi.org/10.5194/essd-17-2217-2025, 2025
Short summary
Short summary
Our study monitored groundwater in a Luxembourg forest over a year to understand water and chemical changes. We found seasonal variations in water chemistry, influenced by rainfall and soil interactions. These data help predict environmental responses and manage water resources better. By measuring key parameters like pH and dissolved oxygen, our research provides valuable insights into groundwater behaviour and serves as a resource for future environmental studies.
Chang Liao, Darren Engwirda, Matthew G. Cooper, Mingke Li, and Yilin Fang
Earth Syst. Sci. Data, 17, 2035–2062, https://doi.org/10.5194/essd-17-2035-2025, https://doi.org/10.5194/essd-17-2035-2025, 2025
Short summary
Short summary
Discrete global grid systems, or DGGS, are digital frameworks that help us organize information about our planet. Although scientists have used DGGS in areas like weather and nature, using them in the water cycle has been challenging because some core datasets are missing. We created a way to generate these datasets. We then developed the datasets in the Amazon and Yukon basins, which play important roles in our planet's climate. These datasets may help us improve our water cycle models.
Sanchit Minocha and Faisal Hossain
Earth Syst. Sci. Data, 17, 1743–1759, https://doi.org/10.5194/essd-17-1743-2025, https://doi.org/10.5194/essd-17-1743-2025, 2025
Short summary
Short summary
Trustworthy and independently verifiable information on declining storage capacity or sedimentation rates worldwide is sparse and suffers from inconsistent metadata and curation to allow global-scale archiving and analyses. The Global Reservoir Inventory of Lost Storage by Sedimentation (GRILSS) dataset addresses this challenge by providing organized, well-curated, and open-source data on sedimentation rates and capacity loss for 1013 reservoirs in 75 major river basins across 54 countries.
Haiguang Cheng, Kaiheng Hu, Shuang Liu, Xiaopeng Zhang, Hao Li, Qiyuan Zhang, Lan Ning, Manish Raj Gouli, Pu Li, Anna Yang, Peng Zhao, Junyu Liu, and Li Wei
Earth Syst. Sci. Data, 17, 1573–1593, https://doi.org/10.5194/essd-17-1573-2025, https://doi.org/10.5194/essd-17-1573-2025, 2025
Short summary
Short summary
After reviewing 2519 literature and media reports, we compiled the first comprehensive global dataset of 555 debris flow barrier dams (DFBDs) from 1800 to 2023. Our dataset meticulously documents 38 attributes of DFBDs, and we have utilized Google Earth for validation. Additionally, we discussed the applicability of landslide dam stability and peak-discharge models to DFBDs. This dataset offers a rich foundation of data for future studies on DFBDs.
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, Anker Lajer Højberg, Hans Thodsen, Mark F. T. Hansen, and Raphael J. M. Schneider
Earth Syst. Sci. Data, 17, 1551–1572, https://doi.org/10.5194/essd-17-1551-2025, https://doi.org/10.5194/essd-17-1551-2025, 2025
Short summary
Short summary
We developed a CAMELS-style dataset in Denmark, which contains hydrometeorological time series and landscape attributes for 3330 catchments (304 gauged). Many catchments in CAMELS-DK are small and at low elevations. The dataset provides information on groundwater characteristics and dynamics, as well as quantities related to the human impact on the hydrological system in Denmark. The dataset is especially relevant for developing data-driven and hybrid physically informed modeling frameworks.
Pragnaditya Malakar, Aatish Anshuman, Mukesh Kumar, Georgios Boumis, T. Prabhakar Clement, Arik Tashie, Hitesh Thakur, Nagaraj Bhat, and Lokendra Rathore
Earth Syst. Sci. Data, 17, 1515–1528, https://doi.org/10.5194/essd-17-1515-2025, https://doi.org/10.5194/essd-17-1515-2025, 2025
Short summary
Short summary
Groundwater dynamics depend on groundwater recharge, but daily benchmark data of recharge are scarce. Here we present a daily groundwater recharge per unit specified yield (RpSy) data at 485 US groundwater monitoring wells. RpSy can be used to validate the temporal consistency of recharge products from land surface and hydrologic models and facilitate assessment of recharge-driver functional relationships in them.
Olivier Delaigue, Guilherme Mendoza Guimarães, Pierre Brigode, Benoît Génot, Charles Perrin, Jean-Michel Soubeyroux, Bruno Janet, Nans Addor, and Vazken Andréassian
Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, https://doi.org/10.5194/essd-17-1461-2025, 2025
Short summary
Short summary
This dataset covers 654 rivers all flowing in France. The provided time series and catchment attributes will be of interest to those modelers wishing to analyze hydrological behavior and perform model assessments.
Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-156, https://doi.org/10.5194/essd-2025-156, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Accurate rainfall data is essential, yet measuring daily precipitation worldwide is challenging. This research presents HYdroclimatic PERformance-enhanced Precipitation (HYPER-P), a dataset combining satellite, ground, and reanalysis data to estimate precipitation at a 1 km scale from 2000 to 2023. HYPER-P improves accuracy, especially in areas with few rain gauges. This dataset supports scientists and decision-makers in understanding and managing water resources more effectively.
Giulia Evangelista, Paola Mazzoglio, Daniele Ganora, Francesca Pianigiani, and Pierluigi Claps
Earth Syst. Sci. Data, 17, 1407–1426, https://doi.org/10.5194/essd-17-1407-2025, https://doi.org/10.5194/essd-17-1407-2025, 2025
Short summary
Short summary
This paper presents the first comprehensive dataset of 528 large dams in Italy. It contains structural characteristics of the dams, such as coordinates, reservoir surface areas and volumes, together with a range of geomorphological, climatological, extreme rainfall, land cover and soil-related attributes of their upstream catchments.
Yueli Chen, Yun Xie, Xingwu Duan, and Minghu Ding
Earth Syst. Sci. Data, 17, 1265–1274, https://doi.org/10.5194/essd-17-1265-2025, https://doi.org/10.5194/essd-17-1265-2025, 2025
Short summary
Short summary
Rainfall erosivity maps are crucial for identifying key areas of water erosion. Due to the limited historical precipitation data, there are certain biases in rainfall erosivity estimates in China. This study develops a new rainfall erosivity map for mainland China using 1 min precipitation data from 60 129 weather stations, revealing that areas exceeding 4000 MJ mm ha−1 h−1yr−1 of annual rainfall erosivity are mainly concentrated in southern China and on the southern Tibetan Plateau.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
Short summary
Short summary
In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Nikunj K. Mangukiya, Kanneganti Bhargav Kumar, Pankaj Dey, Shailza Sharma, Vijaykumar Bejagam, Pradeep P. Mujumdar, and Ashutosh Sharma
Earth Syst. Sci. Data, 17, 461–491, https://doi.org/10.5194/essd-17-461-2025, https://doi.org/10.5194/essd-17-461-2025, 2025
Short summary
Short summary
We introduce CAMELS-IND (Catchment Attributes and MEteorology for Large-sample Studies – India), which provides daily hydrometeorological time series and static catchment attributes representing the 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.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, Cinzia Mazzetti, and Luc Feyen
Earth Syst. Sci. Data, 17, 293–316, https://doi.org/10.5194/essd-17-293-2025, https://doi.org/10.5194/essd-17-293-2025, 2025
Short summary
Short summary
This article presents a reanalysis of Europe's river streamflow for the period 1951–2020. Streamflow is estimated through a state-of-the-art hydrological simulation framework benefitting from detailed information about the landscape, climate, and human activities. The resulting Hydrological European ReAnalysis (HERA) can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources and flood and drought risks.
Daniel Kovacek and Steven Weijs
Earth Syst. Sci. Data, 17, 259–275, https://doi.org/10.5194/essd-17-259-2025, https://doi.org/10.5194/essd-17-259-2025, 2025
Short summary
Short summary
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 are meant to be used for water resources problems that can benefit from lots of watersheds 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.
Sheng-Lun Tai, Zhao Yang, Brian Gaudet, Koichi Sakaguchi, Larry Berg, Colleen Kaul, Yun Qian, Ye Liu, and Jerome Fast
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-599, https://doi.org/10.5194/essd-2024-599, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Our study created a high-resolution soil moisture dataset for the eastern U.S. by integrating satellite data with a land surface model and advanced algorithms, achieving 1-km scale analyses. Validated against multiple networks and datasets, it demonstrated superior accuracy. This dataset is vital for understanding soil moisture dynamics, especially during droughts, and highlights the need for improved modeling of clay soils to refine future predictions.
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-602, https://doi.org/10.5194/essd-2024-602, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Finland and Russia over the period 1979–2021. It includes >11 million observations from >10 thousand different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-553, https://doi.org/10.5194/essd-2024-553, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning techniques. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable tool for applications like SM trend analysis, drought monitoring, and assessing vegetation responses.
Wolfgang Preimesberger, Pietro Stradiotti, and Wouter Dorigo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-610, https://doi.org/10.5194/essd-2024-610, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We introduce the official ESA CCI Soil Moisture GAPFILLED climate data record. A univariate interpolation algorithm is applied to predict missing data points without relying on ancillary variables. The dataset includes gap-free uncertainty estimates for all predictions and was validated with independent in situ reference measurements. The data are recommended for applications, which require global long-term gap-free satellite soil moisture data.
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, 17, 1–28, https://doi.org/10.5194/essd-17-1-2025, https://doi.org/10.5194/essd-17-1-2025, 2025
Short summary
Short summary
The Siberian Arctic is warming fast: permafrost is thawing, river chemistry is changing, and coastal ecosystems are affected. We aimed to understand changes in 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/.
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, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, https://doi.org/10.5194/essd-16-5625-2024, 2024
Short summary
Short summary
The CAMELS-DE dataset features data from 1582 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.
Einara Zahn and Elie Bou-Zeid
Earth Syst. Sci. Data, 16, 5603–5624, https://doi.org/10.5194/essd-16-5603-2024, https://doi.org/10.5194/essd-16-5603-2024, 2024
Short summary
Short summary
Quantifying water and CO2 exchanges through transpiration, evaporation, net photosynthesis, and soil respiration is essential for understanding how ecosystems function. We implemented five methods to estimate these fluxes over a 5-year period across 47 sites. This is the first dataset representing such large spatial and temporal coverage of soil and plant exchanges, and it has many potential applications, such as examining the response of ecosystems to weather extremes and climate change.
Wangyipu Li, Zhaoyuan Yao, Yifan Qu, Hanbo Yang, Yang Song, Lisheng Song, Lifeng Wu, and Yaokui Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-460, https://doi.org/10.5194/essd-2024-460, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Due to shortcomings such as extensive data gaps and limited observation durations in current ground-based latent heat flux (LE) datasets, we developed a novel gap-filling and prolongation framework for ground-based LE observations, establishing a benchmark dataset for global evapotranspiration (ET) estimation from 2000 to 2022 across 64 sites at various time scales. This comprehensive dataset can strongly support ET modelling, water-carbon cycle monitoring, and long-term climate change analysis.
Claudia Färber, Henning Plessow, Simon Mischel, Frederik Kratzert, Nans Addor, Guy Shalev, and Ulrich Looser
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-427, https://doi.org/10.5194/essd-2024-427, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Large-sample datasets are essential in hydrological science to support modelling studies and advance process understanding. Caravan is a community initiative to create a large-sample hydrology dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world. This dataset is a subset of hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy.
Ling Zhang, Yanhua Xie, Xiufang Zhu, Qimin Ma, and Luca Brocca
Earth Syst. Sci. Data, 16, 5207–5226, https://doi.org/10.5194/essd-16-5207-2024, https://doi.org/10.5194/essd-16-5207-2024, 2024
Short summary
Short summary
This study presented new annual maps of irrigated cropland in China from 2000 to 2020 (CIrrMap250). These maps were developed by integrating remote sensing data, irrigation statistics and surveys, and an irrigation suitability map. CIrrMap250 achieved high accuracy and outperformed currently available products. The new irrigation maps revealed a clear expansion of China’s irrigation area, with the majority (61%) occurring in the water-unsustainable regions facing severe to extreme water stress.
Dominik Paprotny, Paweł Terefenko, and Jakub Śledziowski
Earth Syst. Sci. Data, 16, 5145–5170, https://doi.org/10.5194/essd-16-5145-2024, https://doi.org/10.5194/essd-16-5145-2024, 2024
Short summary
Short summary
Knowledge about past natural disasters can help adaptation to their future occurrences. Here, we present a dataset of 2521 riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The dataset contains available information on the inundated area, fatalities, persons affected, or economic loss and was obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers.
Paulina Bartkowiak, Bartolomeo Ventura, Alexander Jacob, and Mariapina Castelli
Earth Syst. Sci. Data, 16, 4709–4734, https://doi.org/10.5194/essd-16-4709-2024, https://doi.org/10.5194/essd-16-4709-2024, 2024
Short summary
Short summary
This paper presents the Two-Source Energy Balance evapotranspiration (ET) product driven by Copernicus Sentinel-2 and Sentinel-3 imagery together with ERA5 climate reanalysis data. Daily ET maps are available at 100 m spatial resolution for the period 2017–2021 across four Mediterranean basins: Ebro (Spain), Hérault (France), Medjerda (Tunisia), and Po (Italy). The product is highly beneficial for supporting vegetation monitoring and sustainable water management at the river basin scale.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
Short summary
Short summary
Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
Rohit Mukherjee, Frederick Policelli, Ruixue Wang, Elise Arellano-Thompson, Beth Tellman, Prashanti Sharma, Zhijie Zhang, and Jonathan Giezendanner
Earth Syst. Sci. Data, 16, 4311–4323, https://doi.org/10.5194/essd-16-4311-2024, https://doi.org/10.5194/essd-16-4311-2024, 2024
Short summary
Short summary
Global water resource monitoring is crucial due to climate change and population growth. This study presents a hand-labeled dataset of 100 PlanetScope images for surface water detection, spanning diverse biomes. We use this dataset to evaluate two state-of-the-art mapping methods. Results highlight performance variations across biomes, emphasizing the need for diverse, independent validation datasets to enhance the accuracy and reliability of satellite-based surface water monitoring techniques.
Lei Huang, Yong Luo, Jing M. Chen, Qiuhong Tang, Tammo Steenhuis, Wei Cheng, and Wen Shi
Earth Syst. Sci. Data, 16, 3993–4019, https://doi.org/10.5194/essd-16-3993-2024, https://doi.org/10.5194/essd-16-3993-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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.
Keirnan J. A. Fowler, Ziqi Zhang, and Xue Hou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-263, https://doi.org/10.5194/essd-2024-263, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS v2 comprises data for an increased number (561) of catchments, each with with long-term monitoring, combining hydrometeorological time series with attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://zenodo.org/doi/10.5281/zenodo.12575680.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...
Altmetrics
Final-revised paper
Preprint