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
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
No articles found.
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
Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data
Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data
The pan-Arctic catchment database (ARCADE)
Multi-hazard susceptibility mapping of cryospheric hazards in a high-Arctic environment: Svalbard Archipelago
High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2021
WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting
A dataset of 10-year regional-scale soil moisture and soil temperature measurements at multiple depths on the Tibetan Plateau
OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden
Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space
Weekly High Resolution Multispectral and Thermal UAS Mapping of an Alpine Catchment During Summer Snowmelt, Niwot Ridge, Colorado
Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)
HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China
The Surface Water Chemistry (SWatCh) database: a standardized global database of water chemistry to facilitate large-sample hydrological research
Hydrography90m: a new high-resolution global hydrographic dataset
GLOBMAP SWF: a global annual surface water cover frequency dataset during 2000–2020
Lake Surface Temperature Dataset in the North Slave Region Retrieved from Landsat Satellite Series – 1984 to 2021
Integrated ecohydrological hydrometric and stable water isotope data of a drought-sensitive mixed land use lowland catchment
Streamflow data availability in Europe: a detailed dataset of interpolated flow-duration curves
High-resolution streamflow and weather data (2013–2019) for seven small coastal watersheds in the northeast Pacific coastal temperate rainforest, Canada
A 500-year annual runoff reconstruction for 14 selected European catchments
A comprehensive geospatial database of nearly 100 000 reservoirs in China
Stable water isotope monitoring network of different water bodies in Shiyang River basin, a typical arid river in China
A dataset of lake-catchment characteristics for the Tibetan Plateau
QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany
A global terrestrial evapotranspiration product based on the three-temperature model with fewer input parameters and no calibration requirement
A new snow depth data set over northern China derived using GNSS interferometric reflectometry from a continuously operating network (GSnow-CHINA v1.0, 2013–2022)
Shallow groundwater level time series and a groundwater chemistry survey from a boreal headwater catchment
Microwave radiometry experiment for snow in Altay, China: time series of in situ data for electromagnetic and physical features of snowpack
An integrated dataset of daily lake surface water temperature over the Tibetan Plateau
Nunataryuk field campaigns: Understanding the origin and fate of terrestrial organic matter in the coastal waters of the Mackenzie Delta region
eFLaG: enhanced future FLows and Groundwater. A national dataset of hydrological projections based on UKCP18
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
Short summary
Short summary
Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
Jianxin Zhang, Kai Liu, and Ming Wang
Earth Syst. Sci. Data, 15, 521–540, https://doi.org/10.5194/essd-15-521-2023, https://doi.org/10.5194/essd-15-521-2023, 2023
Short summary
Short summary
This study successfully extracted global flood days based on gravity satellite and precipitation data between 60° S and 60° N from 1 April 2002 to 31 August 2016. Our flood days data performed well compared with current available observations. This provides an important data foundation for analyzing the spatiotemporal distribution of large-scale floods and exploring the impact of ocean–atmosphere oscillations on floods in different regions.
Niek Jesse Speetjens, Gustaf Hugelius, Thomas Gumbricht, Hugues Lantuit, Wouter R. Berghuijs, Philip A. Pika, Amanda Poste, and Jorien E. Vonk
Earth Syst. Sci. Data, 15, 541–554, https://doi.org/10.5194/essd-15-541-2023, https://doi.org/10.5194/essd-15-541-2023, 2023
Short summary
Short summary
The Arctic is rapidly changing. Outside the Arctic, large databases changed how researchers look at river systems and land-to-ocean processes. We present the first integrated pan-ARctic CAtchments summary DatabasE (ARCADE) (> 40 000 river catchments draining into the Arctic Ocean). It incorporates information about the drainage area with 103 geospatial, environmental, climatic, and physiographic properties and covers small watersheds , which are especially subject to change, at a high resolution
Ionut Cristi Nicu, Letizia Elia, Lena Rubensdotter, Hakan Tanyaş, and Luigi Lombardo
Earth Syst. Sci. Data, 15, 447–464, https://doi.org/10.5194/essd-15-447-2023, https://doi.org/10.5194/essd-15-447-2023, 2023
Short summary
Short summary
Thaw slumps and thermo-erosion gullies are cryospheric hazards that are widely encountered in Nordenskiöld Land, the largest and most compact ice-free area of the Svalbard Archipelago. By statistically analysing the landscape characteristics of locations where these processes occurred, we can estimate where they may occur in the future. We mapped 562 thaw slumps and 908 thermo-erosion gullies and used them to create the first multi-hazard susceptibility map in a high-Arctic environment.
Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, and Peter Bauer-Gottwein
Earth Syst. Sci. Data, 14, 5671–5694, https://doi.org/10.5194/essd-14-5671-2022, https://doi.org/10.5194/essd-14-5671-2022, 2022
Short summary
Short summary
A data gap of 338 Chinese reservoirs with their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) during 2010–2021. Validation against the in situ observations of 93 reservoirs indicates the relatively high accuracy and reliability of the datasets. The unique and novel remotely sensed dataset would benefit studies involving many aspects (e.g., hydrological models, water resources related studies, and more).
Ibrahim Demir, Zhongrun Xiang, Bekir Demiray, and Muhammed Sit
Earth Syst. Sci. Data, 14, 5605–5616, https://doi.org/10.5194/essd-14-5605-2022, https://doi.org/10.5194/essd-14-5605-2022, 2022
Short summary
Short summary
We provide a large benchmark dataset, WaterBench-Iowa, with valuable features for hydrological modeling. This dataset is designed to support cutting-edge deep learning studies for a more accurate streamflow forecast model. We also propose a modeling task for comparative model studies and provide sample models with codes and results as the benchmark for reference. This makes up for the lack of benchmarks in earth science research.
Pei Zhang, Donghai Zheng, Rogier van der Velde, Jun Wen, Yaoming Ma, Yijian Zeng, Xin Wang, Zuoliang Wang, Jiali Chen, and Zhongbo Su
Earth Syst. Sci. Data, 14, 5513–5542, https://doi.org/10.5194/essd-14-5513-2022, https://doi.org/10.5194/essd-14-5513-2022, 2022
Short summary
Short summary
Soil moisture and soil temperature (SMST) are important state variables for quantifying the heat–water exchange between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements at multiple depths are scarce on the Tibetan Plateau (TP). The presented dataset would be valuable for the evaluation and improvement of long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change.
Jafet C. M. Andersson, Jonas Olsson, Remco (C. Z.) van de Beek, and Jonas Hansryd
Earth Syst. Sci. Data, 14, 5411–5426, https://doi.org/10.5194/essd-14-5411-2022, https://doi.org/10.5194/essd-14-5411-2022, 2022
Short summary
Short summary
This article presents data from three types of sensors for rain measurement, i.e. commercial microwave links (CMLs), gauges, and weather radar. Access to CML data is typically restricted, which limits research and applications. We openly share a large CML database (364 CMLs at 10 s resolution with true coordinates), along with 11 gauges and one radar composite. This opens up new opportunities to study CMLs, to benchmark algorithms, and to investigate how multiple sensors can best be combined.
Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-403, https://doi.org/10.5194/essd-2022-403, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Irrigation is the main source of global freswhwater consumption. Despite this, a detailed knowledge of irrigation dynamics (i.e., timing, extent of irrigated areas, and amounts of water used) are generally lacking worldwide. Satellites represent a useful tool to fill this knowledge gap and monitor irrigation water from space. In this study, three regional-scale and high-resolution (1 and 6 km) products of irrigation amounts estimated by inverting the satellite soil moisture signal are presented.
Oliver Wigmore and Noah Paul Molotch
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-357, https://doi.org/10.5194/essd-2022-357, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
We flew a custom-built drone fitted with visible, near infrared, and thermal cameras every week over a summer season at Niwot Ridge in Colorado’s alpine tundra. We processed these images into seamless orthomosaics that record changes in snow cover, vegetation health, and the movement of water over the land surface. These novel datasets provide a unique centimetre resolution snapshot of ecohydrologic processes, connectivity, and spatial and temporal heterogeneity in the alpine zone.
Utkarsh Mital, Dipankar Dwivedi, James B. Brown, and Carl I. Steefel
Earth Syst. Sci. Data, 14, 4949–4966, https://doi.org/10.5194/essd-14-4949-2022, https://doi.org/10.5194/essd-14-4949-2022, 2022
Short summary
Short summary
We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning framework that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets and constitutes a valuable resource to model the water cycle in the mountains of Colorado, western United States.
Rongzhu Qin, Zeyu Zhao, Jia Xu, Jian-Sheng Ye, Feng-Min Li, and Feng Zhang
Earth Syst. Sci. Data, 14, 4793–4810, https://doi.org/10.5194/essd-14-4793-2022, https://doi.org/10.5194/essd-14-4793-2022, 2022
Short summary
Short summary
This work presents a new high-resolution daily gridded maximum temperature, minimum temperature, and precipitation dataset for China (HRLT) with a spatial resolution of 1 × 1 km for the period 1961 to 2019. This dataset is valuable for crop modelers and climate change studies. We created the HRLT dataset using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin-plate splines.
Lobke Rotteveel, Franz Heubach, and Shannon M. Sterling
Earth Syst. Sci. Data, 14, 4667–4680, https://doi.org/10.5194/essd-14-4667-2022, https://doi.org/10.5194/essd-14-4667-2022, 2022
Short summary
Short summary
Data are needed to detect environmental problems, find their solutions, and identify knowledge gaps. Existing datasets have limited availability, sample size and/or frequency, or geographic scope. Here, we begin to address these limitations by collecting, cleaning, standardizing, and compiling the Surface Water Chemistry (SWatCh) database. SWatCh contains global surface water chemistry data for seven continents, 24 variables, 33 722 sites, and > 5 million samples collected between 1960 and 2022.
Giuseppe Amatulli, Jaime Garcia Marquez, Tushar Sethi, Jens Kiesel, Afroditi Grigoropoulou, Maria M. Üblacker, Longzhu Q. Shen, and Sami Domisch
Earth Syst. Sci. Data, 14, 4525–4550, https://doi.org/10.5194/essd-14-4525-2022, https://doi.org/10.5194/essd-14-4525-2022, 2022
Short summary
Short summary
Streams and rivers drive several processes in hydrology, geomorphology, geography, and ecology. A hydrographic network that accurately delineates streams and rivers, along with their topographic and topological properties, is needed for environmental applications. Using the MERIT Hydro Digital Elevation Model at 90 m resolution, we derived a globally seamless, standardised hydrographic network: Hydrography90m. The validation demonstrates improved accuracy compared to other datasets.
Yang Liu, Ronggao Liu, and Rong Shang
Earth Syst. Sci. Data, 14, 4505–4523, https://doi.org/10.5194/essd-14-4505-2022, https://doi.org/10.5194/essd-14-4505-2022, 2022
Short summary
Short summary
Surface water has been changing significantly with high seasonal variation and abrupt change, making it hard to capture its interannual trend. Here we generated a global annual surface water cover frequency dataset during 2000–2020. The percentage of the time period when a pixel is covered by water in a year was estimated to describe the seasonal dynamics of surface water. This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland water extent.
Gifty Attiah, Homa Kheyrollah Pour, and K. Andrea Scott
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-289, https://doi.org/10.5194/essd-2022-289, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Lake surface temperature (LST) is a significant indicator of climate change and influences local weather and climate. This study developed a LST product (North Slave LST) retrieved from Landsat archives for 535 lakes across the North Slave region, Northwest Territories, Canada. The North Slave LST dataset will provide communities, scientists, and stakeholders with spatial and temporal changing trends of temperature on lakes for the past 38 years (1984–2021).
Doerthe Tetzlaff, Aaron Smith, Lukas Kleine, David Dubbert, Jonas Freymueller, Hauke Daempfling, and Chris Soulsby
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-304, https://doi.org/10.5194/essd-2022-304, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a comprehensive set of ecohydrological hydrometric and stable water isotope data of 2 years of data. The data set is unique as the different compartments of the landscape were sampled and the effects of a prolonged drought (2018–2020) was captured with a marked negative rainfall anomaly (the most severe regional drought of the 21st century). Thus, the data allow the drought effects on water storage, flux and age dynamics and persistence lowland landscapes to be investigated.
Simone Persiano, Alessio Pugliese, Alberto Aloe, Jon Olav Skøien, Attilio Castellarin, and Alberto Pistocchi
Earth Syst. Sci. Data, 14, 4435–4443, https://doi.org/10.5194/essd-14-4435-2022, https://doi.org/10.5194/essd-14-4435-2022, 2022
Short summary
Short summary
For about 24000 river basins across Europe, this study provides a continuous representation of the streamflow regime in terms of empirical flow–duration curves (FDCs), which are key signatures of the hydrological behaviour of a catchment and are widely used for supporting decisions on water resource management as well as for assessing hydrologic change. FDCs at ungauged sites are estimated by means of a geostatistical procedure starting from data observed at about 3000 sites across Europe.
Maartje C. Korver, Emily Haughton, William C. Floyd, and Ian J. W. Giesbrecht
Earth Syst. Sci. Data, 14, 4231–4250, https://doi.org/10.5194/essd-14-4231-2022, https://doi.org/10.5194/essd-14-4231-2022, 2022
Short summary
Short summary
The central coastline of the northeast Pacific coastal temperate rainforest contains many small streams that are important for the ecology of the region but are sparsely monitored. Here we present the first 5 years (2013–2019) of streamflow and weather data from seven small streams, using novel automated methods with estimations of measurement uncertainties. These observations support regional climate change monitoring and provide a scientific basis for environmental management decisions.
Sadaf Nasreen, Markéta Součková, Mijael Rodrigo Vargas Godoy, Ujjwal Singh, Yannis Markonis, Rohini Kumar, Oldrich Rakovec, and Martin Hanel
Earth Syst. Sci. Data, 14, 4035–4056, https://doi.org/10.5194/essd-14-4035-2022, https://doi.org/10.5194/essd-14-4035-2022, 2022
Short summary
Short summary
This article presents a 500-year reconstructed annual runoff dataset for several European catchments. Several data-driven and hydrological models were used to derive the runoff series using reconstructed precipitation and temperature and a set of proxy data. The simulated runoff was validated using independent observed runoff data and documentary evidence. The validation revealed a good fit between the observed and reconstructed series for 14 catchments, which are available for further analysis.
Chunqiao Song, Chenyu Fan, Jingying Zhu, Jida Wang, Yongwei Sheng, Kai Liu, Tan Chen, Pengfei Zhan, Shuangxiao Luo, Chunyu Yuan, and Linghong Ke
Earth Syst. Sci. Data, 14, 4017–4034, https://doi.org/10.5194/essd-14-4017-2022, https://doi.org/10.5194/essd-14-4017-2022, 2022
Short summary
Short summary
Over the last century, many dams/reservoirs have been built globally to meet various needs. The official statistics reported more than 98 000 dams/reservoirs in China. Despite the availability of several global-scale dam/reservoir databases, these databases have insufficient coverage in China. Therefore, we present the China Reservoir Dataset (CRD), which contains 97 435 reservoir polygons. The CRD reservoirs have a total area of 50 085.21 km2 and total storage of about 979.62 Gt.
Guofeng Zhu, Yuwei Liu, Peiji Shi, Wenxiong Jia, Junju Zhou, Yuanfeng Liu, Xinggang Ma, Hanxiong Pan, Yu Zhang, Zhiyuan Zhang, Zhigang Sun, Leilei Yong, and Kailiang Zhao
Earth Syst. Sci. Data, 14, 3773–3789, https://doi.org/10.5194/essd-14-3773-2022, https://doi.org/10.5194/essd-14-3773-2022, 2022
Short summary
Short summary
From 2015 to 2020, we studied the Shiyang River basin, which has the highest utilization rate of water resources and the most prominent contradiction of water use, as a typical demonstration basin to establish and improve the isotope hydrology observation system, including river source region, oasis region, reservoir channel system region, oasis farmland region, ecological engineering construction region, and salinization process region.
Junzhi Liu, Pengcheng Fang, Yefeng Que, Liang-Jun Zhu, Zheng Duan, Guoan Tang, Pengfei Liu, Mukan Ji, and Yongqin Liu
Earth Syst. Sci. Data, 14, 3791–3805, https://doi.org/10.5194/essd-14-3791-2022, https://doi.org/10.5194/essd-14-3791-2022, 2022
Short summary
Short summary
The management and conservation of lakes should be conducted in the context of catchments because lakes collect water and materials from their upstream catchments. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with an area from 0.2 to 4503 km2 on the Tibetan Plateau (TP), which provides exciting opportunities for lake studies in a spatially explicit context and promotes the development of landscape limnology on the TP.
Pia Ebeling, Rohini Kumar, Stefanie R. Lutz, Tam Nguyen, Fanny Sarrazin, Michael Weber, Olaf Büttner, Sabine Attinger, and Andreas Musolff
Earth Syst. Sci. Data, 14, 3715–3741, https://doi.org/10.5194/essd-14-3715-2022, https://doi.org/10.5194/essd-14-3715-2022, 2022
Short summary
Short summary
Environmental data are critical for understanding and managing ecosystems, including the mitigation of water quality degradation. To increase data availability, we present the first large-sample water quality data set (QUADICA) of riverine macronutrient concentrations combined with water quantity, meteorological, and nutrient forcing data as well as catchment attributes. QUADICA covers 1386 German catchments to facilitate large-sample data-driven and modeling water quality assessments.
Leiyu Yu, Guo Yu Qiu, Chunhua Yan, Wenli Zhao, Zhendong Zou, Jinshan Ding, Longjun Qin, and Yujiu Xiong
Earth Syst. Sci. Data, 14, 3673–3693, https://doi.org/10.5194/essd-14-3673-2022, https://doi.org/10.5194/essd-14-3673-2022, 2022
Short summary
Short summary
Accurate evapotranspiration (ET) estimation is essential to better understand Earth’s energy and water cycles. We estimate global terrestrial ET with a simple three-temperature model, without calibration and resistance parameterization requirements. Results show the ET estimates agree well with FLUXNET EC data, water balance ET, and other global ET products. The proposed daily and 0.25° ET product from 2001 to 2020 could provide large-scale information to support water-cycle-related studies.
Wei Wan, Jie Zhang, Liyun Dai, Hong Liang, Ting Yang, Baojian Liu, Zhizhou Guo, Heng Hu, and Limin Zhao
Earth Syst. Sci. Data, 14, 3549–3571, https://doi.org/10.5194/essd-14-3549-2022, https://doi.org/10.5194/essd-14-3549-2022, 2022
Short summary
Short summary
The GSnow-CHINA data set is a snow depth data set developed using the two Global Navigation Satellite System station networks in China. It includes snow depth of 24, 12, and 2/3/6 h records, if possible, for 80 sites from 2013–2022 over northern China (25–55° N, 70–140° E). The footprint of the data set is ~ 1000 m2, and it can be used as an independent data source for validation purposes. It is also useful for regional climate research and other meteorological and hydrological applications.
Jana Erdbrügger, Ilja van Meerveld, Jan Seibert, and Kevin Bishop
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-114, https://doi.org/10.5194/essd-2022-114, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Groundwater can respond quickly to precipitation and is the main source of streamflow in most catchments in humid, temperate climates. To better understand shallow groundwater dynamics, we installed a network of groundwater wells in two boreal headwater catchments in Sweden. We recorded groundwater levels in 75 wells for two years and sampled the water and analyzed its chemical composition in one summer. This paper describes these datasets.
Liyun Dai, Tao Che, Yang Zhang, Zhiguo Ren, Junlei Tan, Meerzhan Akynbekkyzy, Lin Xiao, Shengnan Zhou, Yuna Yan, Yan Liu, Hongyi Li, and Lifu Wang
Earth Syst. Sci. Data, 14, 3509–3530, https://doi.org/10.5194/essd-14-3509-2022, https://doi.org/10.5194/essd-14-3509-2022, 2022
Short summary
Short summary
An Integrated Microwave Radiometry Campaign for Snow (IMCS) was conducted to collect ground-based passive microwave and optical remote-sensing data, snow pit and underlying soil data, and meteorological parameters. The dataset is unique in continuously providing electromagnetic and physical features of snowpack and environment. The dataset is expected to serve the evaluation and development of microwave radiative transfer models and snow process models, along with land surface process models.
Linan Guo, Hongxing Zheng, Yanhong Wu, Lanxin Fan, Mengxuan Wen, Junsheng Li, Fangfang Zhang, Liping Zhu, and Bing Zhang
Earth Syst. Sci. Data, 14, 3411–3422, https://doi.org/10.5194/essd-14-3411-2022, https://doi.org/10.5194/essd-14-3411-2022, 2022
Short summary
Short summary
Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an indicator of climate change. By combining the strengths of satellites and models, we produced an integrated dataset on daily LSWT of 160 large lakes across the Tibetan Plateau (TP) for the period 1978–2017. LSWT increased significantly at a rate of 0.01–0.47° per 10 years. The dataset can contribute to research on water and heat balance changes and their ecological effects in the TP.
Martine Lizotte, Bennet Juhls, Atsushi Matsuoka, Philippe Massicotte, Gaëlle Mével, David Obie James Anikina, Sofia Antonova, Guislain Bécu, Marine Béguin, Simon Bélanger, Thomas Bossé-Demers, Lisa Bröder, Flavienne Bruyant, Gwénaëlle Chaillou, Jérôme Comte, Raoul-Marie Couture, Emmanuel Devred, Gabrièle Deslongchamps, Thibaud Dezutter, Miles Dillon, David Doxaran, Aude Flamand, Frank Fell, Joannie Ferland, Marie-Hélène Forget, Michael Fritz, Thomas J. Gordon, Caroline Guilmette, Andrea Hilborn, Rachel Hussherr, Charlotte Irish, Fabien Joux, Lauren Kipp, Audrey Laberge-Carignan, Hugues Lantuit, Edouard Leymarie, Antonio Mannino, Juliette Maury, Paul Overduin, Laurent Oziel, Colin Stedmon, Crystal Thomas, Lucas Tisserand, Jean-Éric Tremblay, Jorien Vonk, Dustin Whalen, and Marcel Babin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-163, https://doi.org/10.5194/essd-2022-163, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Permafrost thaw in the Mackenzie Delta region results in the release of organic matter into the coastal marine environment. What happens to this carbon-rich organic matter as it transits along the fresh to salty aquatic environments is still under-documented. Four expeditions were conducted from April to September 2019 in the coastal area of the Beaufort Sea to study the fate of organic matter. This paper describes a rich set of data characterizing the composition and sources of organic matter.
Jamie Hannaford, Jonathan Mackay, Matthew Ascott, Victoria Bell, Thomas Chitson, Steven Cole, Christian Counsell, Mason Durant, Christopher R. Jackson, Alison Kay, Rosanna Lane, Majdi Mansour, Robert Moore, Simon Parry, Alison Rudd, Michael Simpson, Katie Facer-Childs, Stephen Turner, John Wallbank, Steven Wells, and Amy Wilcox
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-40, https://doi.org/10.5194/essd-2022-40, 2022
Revised manuscript accepted for ESSD
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...
