Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3053-2022
© 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-14-3053-2022
© 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
| 
06 Jul 2022
Data description paper |
| 06 Jul 2022
| 06 Jul 2022
Daily soil moisture mapping at 1 km resolution based on SMAP data for desertification areas in northern China
Pinzeng Rao
State Key Laboratory of Hydroscience and Engineering, Department of
Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Yicheng Wang
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Fang Wang
CORRESPONDING AUTHOR
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Yang Liu
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Xiaoya Wang
State Key Laboratory of Remote Sensing Science, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Zhu Wang
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Related authors
No articles found.
Yaxu Wang, Juan Lv, Jamie Hannaford, Yicheng Wang, Hongquan Sun, Lucy J. Barker, Miaomiao Ma, Zhicheng Su, and Michael Eastman
Nat. Hazards Earth Syst. Sci., 20, 889–906, https://doi.org/10.5194/nhess-20-889-2020, https://doi.org/10.5194/nhess-20-889-2020, 2020
Short summary
Short summary
Due to the specific applicability of drought impact indicators, this study identifies which drought indicators are suitable for characterising drought impacts and the contribution of vulnerability factors. The results show that the relationship varies across different drought impacts and cities; some factors have a strong positive correlation with drought vulnerability. This study can support drought planning work and provide background for the indices used in drought monitoring applications.
Related subject area
Hydrology
Multivariate characterisation of a blackberry–alder agroforestry system in South Africa: hydrological, pedological, dendrological and meteorological measurements
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
FOCA: a new quality-controlled database of floods and catchment descriptors in Italy
A Copernicus-based evapotranspiration dataset at 100-m spatial resolution over the Mediterranean region
Dams in the Mekong: a comprehensive database, spatiotemporal distribution, and hydropower potentials
A global dataset of the shape of drainage systems
An extensive spatiotemporal water quality dataset covering four decades (1980–2022) in China
Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950–2019)
Flood simulation with the RiverCure approach: the open dataset of the 2016 Águeda flood event
GloLakes: water storage dynamics for 27 000 lakes globally from 1984 to present derived from satellite altimetry and optical imaging
Satellite-based Near-Real-Time Global Daily Terrestrial Evapotranspiration Estimates
AltiMaP: altimetry mapping procedure for hydrography data
CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland
The use of GRDC gauging stations for calibrating large-scale hydrological models
A long-term dataset of simulated epilimnion and hypolimnion temperatures in 401 French lakes (1959–2020)
GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present
A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model
A gridded dataset of consumptive water footprints, evaporation, transpiration, and associated benchmarks related to crop production in China during 2000–2018
Hydro-PE: gridded datasets of historical and future Penman–Monteith potential evaporation for the United Kingdom
A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)
Soil water retention and hydraulic conductivity measured in a wide saturation range
A high-frequency, long-term data set of hydrology and sediment yield: the alpine badland catchments of Draix-Bléone Observatory
Geospatial dataset for hydrologic analyses in India (GHI): a quality-controlled dataset on river gauges, catchment boundaries and hydrometeorological time series
A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps
Lake-TopoCat: a global lake drainage topology and catchment database
Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany
A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015
A global database of historic glacier lake outburst floods
Past and future discharge and stream temperature at high spatial resolution in a large European basin (Loire basin, France)
Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs
An ensemble of 48 physically perturbed model estimates of the 1∕8° terrestrial water budget over the conterminous United States, 1980–2015
The UKSCAPE-G2G river flow and soil moisture datasets: Grid-to-Grid model estimates for the UK for historical and potential future climates
The enhanced future Flows and Groundwater dataset: development and evaluation of nationally consistent hydrological projections based on UKCP18
RC4USCoast: a river chemistry dataset for regional ocean model applications in the US East Coast, Gulf of Mexico, and US West Coast
Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning
Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts
Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands
Shallow-groundwater-level time series and a groundwater chemistry survey from a boreal headwater catchment, Krycklan, Sweden
Weekly high-resolution multi-spectral and thermal uncrewed-aerial-system mapping of an alpine catchment during summer snowmelt, Niwot Ridge, Colorado
Nunataryuk field campaigns: understanding the origin and fate of terrestrial organic matter in the coastal waters of the Mackenzie Delta region
Integrated ecohydrological hydrometric and stable water isotope data of a drought-sensitive mixed land use lowland catchment
Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space
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.
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.
Pierluigi Claps, Giulia Evangelista, Daniele Ganora, Paola Mazzoglio, and Irene Monforte
Earth Syst. Sci. Data, 16, 1503–1522, https://doi.org/10.5194/essd-16-1503-2024, https://doi.org/10.5194/essd-16-1503-2024, 2024
Short summary
Short summary
FOCA (Italian FlOod and Catchment Atlas) is the first systematic collection of data on Italian river catchments. It comprises geomorphological, soil, land cover, NDVI, climatological and extreme rainfall catchment attributes. FOCA also contains 631 peak and daily discharge time series covering the 1911–2016 period. Using this first nationwide data collection, a wide range of applications, in particular flood studies, can be undertaken within the Italian territory.
Paulina Bartkowiak, Bartolomeo Ventura, Alexander Jacob, and Mariapina Castelli
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-466, https://doi.org/10.5194/essd-2023-466, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents a 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.
Wei Jing Ang, Edward Park, Yadu Pokhrel, Dung Duc Tran, and Ho Huu Loc
Earth Syst. Sci. Data, 16, 1209–1228, https://doi.org/10.5194/essd-16-1209-2024, https://doi.org/10.5194/essd-16-1209-2024, 2024
Short summary
Short summary
Dams have burgeoned in the Mekong, but information on dams is scattered and inconsistent. Up-to-date evaluation of dams is unavailable, and basin-wide hydropower potential has yet to be systematically assessed. We present a comprehensive database of 1055 dams, a spatiotemporal analysis of the dams, and a total hydropower potential of 1 334 683 MW. Considering projected dam development and hydropower potential, the vulnerability and the need for better dam management may be highest in Laos.
Chuanqi He, Ci-Jian Yang, Jens M. Turowski, Richard F. Ott, Jean Braun, Hui Tang, Shadi Ghantous, Xiaoping Yuan, and Gaia Stucky de Quay
Earth Syst. Sci. Data, 16, 1151–1166, https://doi.org/10.5194/essd-16-1151-2024, https://doi.org/10.5194/essd-16-1151-2024, 2024
Short summary
Short summary
The shape of drainage basins and rivers holds significant implications for landscape evolution processes and dynamics. We used a global 90 m resolution topography to obtain ~0.7 million drainage basins with sizes over 50 km2. Our dataset contains the spatial distribution of drainage systems and their morphological parameters, supporting fields such as geomorphology, climatology, biology, ecology, hydrology, and natural hazards.
Jingyu Lin, Peng Wang, Jinzhu Wang, Youping Zhou, Xudong Zhou, Pan Yang, Hao Zhang, Yanpeng Cai, and Zhifeng Yang
Earth Syst. Sci. Data, 16, 1137–1149, https://doi.org/10.5194/essd-16-1137-2024, https://doi.org/10.5194/essd-16-1137-2024, 2024
Short summary
Short summary
Our paper provides a repository comprising over 330 000 observations encompassing daily, weekly, and monthly records of surface water quality spanning the period 1980–2022. It included 18 distinct indicators, meticulously gathered at 2384 monitoring sites, ranging from inland locations to coastal and oceanic areas. This dataset will be very useful for researchers and decision-makers in the fields of hydrology, ecological studies, climate change, policy development, and oceanography.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-474, https://doi.org/10.5194/essd-2023-474, 2024
Revised manuscript accepted for ESSD
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.
Ana M. Ricardo, Rui M. L. Ferreira, Alberto Rodrigues da Silva, Jacinto Estima, Jorge Marques, Ivo Gamito, and Alexandre Serra
Earth Syst. Sci. Data, 16, 375–385, https://doi.org/10.5194/essd-16-375-2024, https://doi.org/10.5194/essd-16-375-2024, 2024
Short summary
Short summary
Floods are among the most common natural disasters responsible for severe damages and human losses. Agueda.2016Flood, a synthesis of locally sensed data and numerically produced data, allows complete characterization of the flood event that occurred in February 2016 in the Portuguese Águeda River. The dataset was managed through the RiverCure Portal, a collaborative web platform connected to a validated shallow-water model.
Jiawei Hou, Albert I. J. M. Van Dijk, Luigi J. Renzullo, and Pablo R. Larraondo
Earth Syst. Sci. Data, 16, 201–218, https://doi.org/10.5194/essd-16-201-2024, https://doi.org/10.5194/essd-16-201-2024, 2024
Short summary
Short summary
The GloLakes dataset provides historical and near-real-time time series of relative (i.e. storage change) and absolute (i.e. total stored volume) storage for more than 27 000 lakes worldwide using multiple sources of satellite data, including laser and radar altimetry and optical remote sensing. These data can help us understand the influence of climate variability and anthropogenic activities on water availability and system ecology over the last 4 decades.
Lei Huang, Yong Luo, Jing M. Chen, Qiuhong Tang, Tammo Steenhuis, Wei Cheng, and Wen Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-495, https://doi.org/10.5194/essd-2023-495, 2024
Revised manuscript accepted for ESSD
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.
Menaka Revel, Xudong Zhou, Prakat Modi, Jean-François Cretaux, Stephane Calmant, and Dai Yamazaki
Earth Syst. Sci. Data, 16, 75–88, https://doi.org/10.5194/essd-16-75-2024, https://doi.org/10.5194/essd-16-75-2024, 2024
Short summary
Short summary
As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Peter Burek and Mikhail Smilovic
Earth Syst. Sci. Data, 15, 5617–5629, https://doi.org/10.5194/essd-15-5617-2023, https://doi.org/10.5194/essd-15-5617-2023, 2023
Short summary
Short summary
We address an annoying problem every grid-based hydrological model must solve to compare simulated and observed river discharge. First, station locations do not fit the high-resolution river network. We update the database with stations based on a new high-resolution network. Second, station locations do not work with a coarser grid-based network. We use a new basin shape similarity concept for station locations on a coarser grid, reducing the error of assigning stations to the wrong basin.
Najwa Sharaf, Jordi Prats, Nathalie Reynaud, Thierry Tormos, Rosalie Bruel, Tiphaine Peroux, and Pierre-Alain Danis
Earth Syst. Sci. Data, 15, 5631–5650, https://doi.org/10.5194/essd-15-5631-2023, https://doi.org/10.5194/essd-15-5631-2023, 2023
Short summary
Short summary
We present a regional long-term (1959–2020) dataset (LakeTSim) of daily epilimnion and hypolimnion water temperature simulations in 401 French lakes. Overall, less uncertainty is associated with the epilimnion compared to the hypolimnion. LakeTSim is valuable for providing new insights into lake water temperature for assessing the impact of climate change, which is often hindered by the lack of observations, and for decision-making by stakeholders.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
Short summary
Short summary
This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
Short summary
Short summary
Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Wei Wang, La Zhuo, Xiangxiang Ji, Zhiwei Yue, Zhibin Li, Meng Li, Huimin Zhang, Rong Gao, Chenjian Yan, Ping Zhang, and Pute Wu
Earth Syst. Sci. Data, 15, 4803–4827, https://doi.org/10.5194/essd-15-4803-2023, https://doi.org/10.5194/essd-15-4803-2023, 2023
Short summary
Short summary
The consumptive water footprint of crop production (WFCP) measures blue and green evapotranspiration of either irrigated or rainfed crops in time and space. A gridded monthly WFCP dataset for China is established. There are four improvements from existing datasets: (i) distinguishing water supply modes and irrigation techniques, (ii) distinguishing evaporation and transpiration, (iii) consisting of both total and unit WFCP, and (iv) providing benchmarks for unit WFCP by climatic zones.
Emma L. Robinson, Matthew J. Brown, Alison L. Kay, Rosanna A. Lane, Rhian Chapman, Victoria A. Bell, and Eleanor M. Blyth
Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, https://doi.org/10.5194/essd-15-4433-2023, 2023
Short summary
Short summary
This work presents two new Penman–Monteith potential evaporation datasets for the UK, calculated with the same methodology applied to historical climate data (Hydro-PE HadUK-Grid) and an ensemble of future climate projections (Hydro-PE UKCP18 RCM). Both include an optional correction for evaporation of rain that lands on the surface of vegetation. The historical data are consistent with existing PE datasets, and the future projections include effects of rising atmospheric CO2 on vegetation.
Xinyu Chen, Liguang Jiang, Yuning Luo, and Junguo Liu
Earth Syst. Sci. Data, 15, 4463–4479, https://doi.org/10.5194/essd-15-4463-2023, https://doi.org/10.5194/essd-15-4463-2023, 2023
Short summary
Short summary
River flow is experiencing changes under the impacts of climate change and human activities. For example, flood events are occurring more often and are more destructive in many places worldwide. To deal with such issues, hydrologists endeavor to understand the features of extreme events as well as other hydrological changes. One key approach is analyzing flow characteristics, represented by hydrological indices. Building such a comprehensive global large-sample dataset is essential.
Tobias L. Hohenbrink, Conrad Jackisch, Wolfgang Durner, Kai Germer, Sascha C. Iden, Janis Kreiselmeier, Frederic Leuther, Johanna C. Metzger, Mahyar Naseri, and Andre Peters
Earth Syst. Sci. Data, 15, 4417–4432, https://doi.org/10.5194/essd-15-4417-2023, https://doi.org/10.5194/essd-15-4417-2023, 2023
Short summary
Short summary
The article describes a collection of 572 data sets of soil water retention and unsaturated hydraulic conductivity data measured with state-of-the-art laboratory methods. Furthermore, the data collection contains basic soil properties such as soil texture and organic carbon content. We expect that the data will be useful for various important purposes, for example, the development of soil hydraulic property models and related pedotransfer functions.
Sebastien Klotz, Caroline Le Bouteiller, Nicolle Mathys, Firmin Fontaine, Xavier Ravanat, Jean-Emmanuel Olivier, Frédéric Liébault, Hugo Jantzi, Patrick Coulmeau, Didier Richard, Jean-Pierre Cambon, and Maurice Meunier
Earth Syst. Sci. Data, 15, 4371–4388, https://doi.org/10.5194/essd-15-4371-2023, https://doi.org/10.5194/essd-15-4371-2023, 2023
Short summary
Short summary
Mountain badlands are places of intense erosion. They deliver large amounts of sediment to river systems, with consequences for hydropower sustainability, habitat quality and biodiversity, and flood hazard and river management. Draix-Bleone Observatory was created in 1983 to understand and quantify sediment delivery from such badland areas. Our paper describes how water and sediment fluxes have been monitored for almost 40 years in the small mountain catchments of this observatory.
Gopi Goteti
Earth Syst. Sci. Data, 15, 4389–4415, https://doi.org/10.5194/essd-15-4389-2023, https://doi.org/10.5194/essd-15-4389-2023, 2023
Short summary
Short summary
Data on river gauging stations, river basin boundaries and river flow paths are critical for hydrological analyses, but existing data for India's river basins have limited availability and reliability. This work fills the gap by building a new dataset. Data for 645 stations in 15 basins of India were compiled and checked against global data sources; data were supplemented with additional information where needed. This dataset will serve as a reliable building block in hydrological analyses.
Rohit Mukherjee, Frederick Policelli, Ruixue Wang, Beth Tellman, Prashanti Sharma, Zhijie Zhang, and Jonathan Giezendanner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-168, https://doi.org/10.5194/essd-2023-168, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Monitoring global water resources is crucial, but trust in existing solutions remains a challenge. To address this, we present a high-resolution dataset of hand-labeled surface water from 90 diverse locations, evaluating a novel Sentinel-1 algorithm for mapping. Our evaluation provides insights into the advantages and limitations of satellite imagery and methods applied. Our study highlights the need for independent validation datasets to ensure accurate and reliable water resource monitoring.
Md Safat Sikder, Jida Wang, George H. Allen, Yongwei Sheng, Dai Yamazaki, Chunqiao Song, Meng Ding, Jean-François Crétaux, and Tamlin M. Pavelsky
Earth Syst. Sci. Data, 15, 3483–3511, https://doi.org/10.5194/essd-15-3483-2023, https://doi.org/10.5194/essd-15-3483-2023, 2023
Short summary
Short summary
We introduce Lake-TopoCat to reveal detailed lake hydrography information. It contains the location of lake outlets, the boundary of lake catchments, and a wide suite of attributes that depict detailed lake drainage relationships. It was constructed using lake boundaries from a global lake dataset, with the help of high-resolution hydrography data. This database may facilitate a variety of applications including water quality, agriculture and fisheries, and integrated lake–river modeling.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Benjamin M. Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, and Sly Wongchuig
Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, https://doi.org/10.5194/essd-15-2957-2023, 2023
Short summary
Short summary
The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km3. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.
Natalie Lützow, Georg Veh, and Oliver Korup
Earth Syst. Sci. Data, 15, 2983–3000, https://doi.org/10.5194/essd-15-2983-2023, https://doi.org/10.5194/essd-15-2983-2023, 2023
Short summary
Short summary
Glacier lake outburst floods (GLOFs) are a prominent natural hazard, and climate change may change their magnitude, frequency, and impacts. A global, literature-based GLOF inventory is introduced, entailing 3151 reported GLOFs. The reporting density varies temporally and regionally, with most cases occurring in NW North America. Since 1900, the number of yearly documented GLOFs has increased 6-fold. However, many GLOFs have incomplete records, and we call for a systematic reporting protocol.
Hanieh Seyedhashemi, Florentina Moatar, Jean-Philippe Vidal, and Dominique Thiéry
Earth Syst. Sci. Data, 15, 2827–2839, https://doi.org/10.5194/essd-15-2827-2023, https://doi.org/10.5194/essd-15-2827-2023, 2023
Short summary
Short summary
This paper presents a past and future dataset of daily time series of discharge and stream temperature for 52 278 reaches over the Loire River basin (100 000 km2) in France, using thermal and hydrological models. Past data are provided over 1963–2019. Future data are available over the 1976–2100 period under different future climate change models (warm and wet, intermediate, and hot and dry) and scenarios (optimistic, intermediate, and pessimistic).
Youjiang Shen, Karina Nielsen, Menaka Revel, Dedi Liu, and Dai Yamazaki
Earth Syst. Sci. Data, 15, 2781–2808, https://doi.org/10.5194/essd-15-2781-2023, https://doi.org/10.5194/essd-15-2781-2023, 2023
Short summary
Short summary
Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.
Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang
Earth Syst. Sci. Data, 15, 2755–2780, https://doi.org/10.5194/essd-15-2755-2023, https://doi.org/10.5194/essd-15-2755-2023, 2023
Short summary
Short summary
An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
Alison L. Kay, Victoria A. Bell, Helen N. Davies, Rosanna A. Lane, and Alison C. Rudd
Earth Syst. Sci. Data, 15, 2533–2546, https://doi.org/10.5194/essd-15-2533-2023, https://doi.org/10.5194/essd-15-2533-2023, 2023
Short summary
Short summary
Climate change will affect the water cycle, including river flows and soil moisture. We have used both observational data (1980–2011) and the latest UK climate projections (1980–2080) to drive a national-scale grid-based hydrological model. The data, covering Great Britain and Northern Ireland, suggest potential future decreases in summer flows, low flows, and summer/autumn soil moisture, and possible future increases in winter and high flows. Society must plan how to adapt to such impacts.
Jamie Hannaford, Jonathan D. Mackay, Matthew Ascott, Victoria A. Bell, Thomas Chitson, Steven Cole, Christian Counsell, Mason Durant, Christopher R. Jackson, Alison L. Kay, Rosanna A. Lane, Majdi Mansour, Robert Moore, Simon Parry, Alison C. Rudd, Michael Simpson, Katie Facer-Childs, Stephen Turner, John R. Wallbank, Steven Wells, and Amy Wilcox
Earth Syst. Sci. Data, 15, 2391–2415, https://doi.org/10.5194/essd-15-2391-2023, https://doi.org/10.5194/essd-15-2391-2023, 2023
Short summary
Short summary
The eFLaG dataset is a nationally consistent set of projections of future climate change impacts on hydrology. eFLaG uses the latest available UK climate projections (UKCP18) run through a series of computer simulation models which enable us to produce future projections of river flows, groundwater levels and groundwater recharge. These simulations are designed for use by water resource planners and managers but could also be used for a wide range of other purposes.
Fabian A. Gomez, Sang-Ki Lee, Charles A. Stock, Andrew C. Ross, Laure Resplandy, Samantha A. Siedlecki, Filippos Tagklis, and Joseph E. Salisbury
Earth Syst. Sci. Data, 15, 2223–2234, https://doi.org/10.5194/essd-15-2223-2023, https://doi.org/10.5194/essd-15-2223-2023, 2023
Short summary
Short summary
We present a river chemistry and discharge dataset for 140 rivers in the United States, which integrates information from the Water Quality Database of the US Geological Survey (USGS), the USGS’s Surface-Water Monthly Statistics for the Nation, and the U.S. Army Corps of Engineers. This dataset includes dissolved inorganic carbon and alkalinity, two key properties to characterize the carbonate system, as well as nutrient concentrations, such as nitrate, phosphate, and silica.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
Short summary
Short summary
Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Rogier van der Velde, Harm-Jan F. Benninga, Bas Retsios, Paul C. Vermunt, and M. Suhyb Salama
Earth Syst. Sci. Data, 15, 1889–1910, https://doi.org/10.5194/essd-15-1889-2023, https://doi.org/10.5194/essd-15-1889-2023, 2023
Short summary
Short summary
From 2009, a network of 20 profile soil moisture and temperature monitoring stations has been operational in the Twente region, east of the Netherlands. In addition, field campaigns have been conducted covering four growing seasons during which soil moisture was measured near 12 monitoring stations. We describe the monitoring network and field campaigns, and we provide an overview of open third-party datasets that may support the use of the Twente datasets.
Jana Erdbrügger, Ilja van Meerveld, Jan Seibert, and Kevin Bishop
Earth Syst. Sci. Data, 15, 1779–1800, https://doi.org/10.5194/essd-15-1779-2023, https://doi.org/10.5194/essd-15-1779-2023, 2023
Short summary
Short summary
Groundwater can respond quickly to precipitation and is the main source of streamflow in most catchments in humid, temperate climates. To better understand shallow groundwater dynamics, we installed a network of groundwater wells in two boreal headwater catchments in Sweden. We recorded groundwater levels in 75 wells for 2 years and sampled the water and analyzed its chemical composition in one summer. This paper describes these datasets.
Oliver Wigmore and Noah P. Molotch
Earth Syst. Sci. Data, 15, 1733–1747, https://doi.org/10.5194/essd-15-1733-2023, https://doi.org/10.5194/essd-15-1733-2023, 2023
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.
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, 15, 1617–1653, https://doi.org/10.5194/essd-15-1617-2023, https://doi.org/10.5194/essd-15-1617-2023, 2023
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 underdocumented. 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.
Doerthe Tetzlaff, Aaron Smith, Lukas Kleine, Hauke Daempfling, Jonas Freymueller, and Chris Soulsby
Earth Syst. Sci. Data, 15, 1543–1554, https://doi.org/10.5194/essd-15-1543-2023, https://doi.org/10.5194/essd-15-1543-2023, 2023
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) captured by 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 of lowland landscapes to be investigated.
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, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, https://doi.org/10.5194/essd-15-1555-2023, 2023
Short summary
Short summary
Irrigation is the main source of global freshwater 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 signals are presented.
Cited articles
Abbaszadeh, P., Moradkhani, H., and Zhan, X.: Downscaling SMAP Radiometer
Soil Moisture Over the CONUS Using an Ensemble Learning Method, Water
Resour. Res., 55, 324–344, https://doi.org/10.1029/2018WR023354, 2019.
Achieng, K. O.: Modelling of soil moisture retention curve using machine
learning techniques: Artificial and deep neural networks vs support vector
regression models, Comput. Geosci., 133, 104320,
https://doi.org/10.1016/j.cageo.2019.104320, 2019.
Ågren, A. M., Larson, J., Paul, S. S., Laudon, H., and Lidberg, W.: Use
of multiple LIDAR-derived digital terrain indices and machine learning for
high-resolution national-scale soil moisture mapping of the Swedish forest
landscape, Geoderma, 404, 115280,
https://doi.org/10.1016/j.geoderma.2021.115280, 2021.
Akoglu, H.: User's guide to correlation coefficients, Turkish J.
Emerg. Med., 18, 91–93, https://doi.org/10.1016/j.tjem.2018.08.001,
2018.
Bai, J., Cui, Q., Zhang, W., and Meng, L.: An Approach for Downscaling SMAP
Soil Moisture by Combining Sentinel-1 SAR and MODIS Data, Remote Sensing,
11, 2736, https://doi.org/10.3390/rs11232736, 2019.
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review
of applications and future directions, ISPRS J. Photogramm., 114, 24–31,
https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Cammarota, C. and Pinto, A.: Variable selection and importance in presence
of high collinearity: an application to the prediction of lean body mass
from multi-frequency bioelectrical impedance, J. Appl. Stat.,
48, 1644–1658, https://doi.org/10.1080/02664763.2020.1763930, 2021.
Chen, H., Chen, H., Liu, Z., Sun, X., and Zhou, R.: Analysis of Factors
Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model
Combining POI Data, J. Adv. Transport., 2020, 1–12,
https://doi.org/10.1155/2020/8881545, 2020.
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, KDD '16: The 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, San Francisco California
USA, 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.
Del Frate, F., Ferrazzoli, P., and Schiavon, G.: Retrieving soil moisture
and agricultural variables by microwave radiometry using neural networks,
Remote Sens. Environ., 84, 174–183,
https://doi.org/10.1016/S0034-4257(02)00105-0, 2003.
Demarchi, L., Kania, A., Ciężkowski, W., Piórkowski, H.,
Oświecimska-Piasko, Z., and Chormański, J.: Recursive Feature
Elimination and Random Forest Classification of Natura 2000 Grasslands in
Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR
Data Fusion, Remote Sensing, 12, 1842, https://doi.org/10.3390/rs12111842,
2020.
De Santis, D., Biondi, D., Crow, W. T., Camici, S., Modanesi, S., Brocca,
L., and Massari, C.: Assimilation of Satellite Soil Moisture Products for
River Flow Prediction: An Extensive Experiment in Over 700 Catchments
Throughout Europe, Water Res., 57, e2021WR029643, https://doi.org/10.1029/2021WR029643,
2021.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré,
G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J.,
Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B.,
Schröder, B., Skidmore, A. K., Zurell, D., and Lautenbach, S.:
Collinearity: a review of methods to deal with it and a simulation study
evaluating their performance, Ecography, 36, 27–46,
https://doi.org/10.1111/j.1600-0587.2012.07348.x, 2013.
Elshorbagy, A. and Parasuraman, K.: On the relevance of using artificial
neural networks for estimating soil moisture content, J. Hydrol.,
362, 1–18, https://doi.org/10.1016/j.jhydrol.2008.08.012, 2008.
Fan, J., Yue, W., Wu, L., Zhang, F., Cai, H., Wang, X., Lu, X., and Xiang,
Y.: Evaluation of SVM, ELM and four tree-based ensemble models for
predicting daily reference evapotranspiration using limited meteorological
data in different climates of China, Agr. Forest Meteorol.,
263, 225–241, https://doi.org/10.1016/j.agrformet.2018.08.019, 2018.
Fan, J., Zheng, J., Wu, L., and Zhang, F.: Estimation of daily maize
transpiration using support vector machines, extreme gradient boosting,
artificial and deep neural networks models, Agr. Water Manage.,
245, 106547, https://doi.org/10.1016/j.agwat.2020.106547, 2021.
Fang, B. and Lakshmi, V.: Soil moisture at watershed scale: Remote sensing
techniques, J. Hydrol., 516, 258–272,
https://doi.org/10.1016/j.jhydrol.2013.12.008, 2014.
Fang, B., Lakshmi, V., Bindlish, R., Jackson, T. J., Cosh, M., and Basara,
J.: Passive Microwave Soil Moisture Downscaling Using Vegetation Index and
Skin Surface Temperature, Vadose Zone J., 12, vzj2013.05.0089er,
https://doi.org/10.2136/vzj2013.05.0089er, 2013.
Feng, R., Dario, G., and Balling, N.: Imputation of missing well log data by
random forest and its uncertainty analysis, Comput. Geosci., 152, 9,
https://doi.org/10.1016/j.cageo.2021.104763, 2021.
Gu, Y., Hunt, E., Wardlow, B., Basara, J. B., Brown, J. F., and Verdin, J.
P.: Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring
using Oklahoma Mesonet soil moisture data, Geophys. Res. Lett., 35, L22401,
https://doi.org/10.1029/2008GL035772, 2008.
Hu, F., Wei, Z., Zhang, W., Dorjee, D., and Meng, L.: A spatial downscaling
method for SMAP soil moisture through visible and shortwave-infrared remote
sensing data, J. Hydrol., 590, 125360,
https://doi.org/10.1016/j.jhydrol.2020.125360, 2020.
Im, J., Park, S., Rhee, J., Baik, J., and Choi, M.: Downscaling of AMSR-E
soil moisture with MODIS products using machine learning approaches, Environ.
Earth Sci., 75, 1120, https://doi.org/10.1007/s12665-016-5917-6, 2016.
Kang, J., Jin, R., Li, X., Ma, C., Qin, J., and Zhang, Y.: High
spatio-temporal resolution mapping of soil moisture by integrating wireless
sensor network observations and MODIS apparent thermal inertia in the Babao
River Basin, China, Remote Sens. Environ., 191, 232–245,
https://doi.org/10.1016/j.rse.2017.01.027, 2017.
Khan, F., Kanwal, S., Alamri, S., and Mumtaz, B.: Hyper-Parameter
Optimization of Classifiers, Using an Artificial Immune Network and Its
Application to Software Bug Prediction, 8, 11,
https://doi.org/10.1109/ACCESS.2020.2968362, 2020.
Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F.: Fast
Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets,
arXiv [preprint], https://doi.org/10.48550/arXiv.1605.07079, 2017.
Kovalev, M. S. and Utkin, L. V.: A robust algorithm for explaining
unreliable machine learning survival models using the Kolmogorov–Smirnov
bounds, Neural Networks, 132, 1–18,
https://doi.org/10.1016/j.neunet.2020.08.007, 2020.
Lievens, H., Verhoest, N. E. C., De Keyser, E., Vernieuwe, H., Matgen, P., Álvarez-Mozos, J., and De Baets, B.: Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR, Hydrol. Earth Syst. Sci., 15, 151–162, https://doi.org/10.5194/hess-15-151-2011, 2011.
Lin, W., Gao, J., Wang, B., and Hong, Q.: An Improved Random Forest
Classifier for Imbalanced Learning, in: 2021 IEEE International Conference
on Artificial Intelligence and Computer Applications (ICAICA), 2021 IEEE
International Conference on Artificial Intelligence and Computer
Applications (ICAICA), Dalian, China, 703–707,
https://doi.org/10.1109/ICAICA52286.2021.9497933, 2021.
Liu, J., Chai, L., Lu, Z., Liu, S., Qu, Y., Geng, D., Song, Y., Guan, Y.,
Guo, Z., Wang, J., and Zhu, Z.: Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and
LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its
Surrounding Areas, Remote Sensing, 11, 792,
https://doi.org/10.3390/rs11070792, 2019.
Liu, J., Chai, L., Dong, J., Zheng, D., Wigneron, J.-P., Liu, S., Zhou, J.,
Xu, T., Yang, S., Song, Y., Qu, Y., and Lu, Z.: Uncertainty analysis of
eleven multisource soil moisture products in the third pole environment
based on the three-corned hat method, Remote Sens. Environ., 255,
112225, https://doi.org/10.1016/j.rse.2020.112225, 2021.
Liu, Y., Yao, L., Jing, W., Di, L., Yang, J., and Li, Y.: Comparison of two
satellite-based soil moisture reconstruction algorithms: A case study in the
state of Oklahoma, USA, J. Hydrol., 590, 125406,
https://doi.org/10.1016/j.jhydrol.2020.125406, 2020.
Ma, H., Zeng, J., Chen, N., Zhang, X., Cosh, M. H., and Wang, W.: Satellite
surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive
assessment using global ground-based observations, Remote Sens.
Environ., 231, 111215, https://doi.org/10.1016/j.rse.2019.111215, 2019.
Ma, M., Zhao, G., He, B., Li, Q., Dong, H., Wang, S., and Wang, Z.:
XGBoost-based method for flash flood risk assessment, J. Hydrol.,
598, 126382, https://doi.org/10.1016/j.jhydrol.2021.126382, 2021.
Mallick, K., Bhattacharya, B. K., and Patel, N. K.: Estimating volumetric
surface moisture content for cropped soils using a soil wetness index based
on surface temperature and NDVI, Agr. Forest Meteorol., 149,
1327–1342, https://doi.org/10.1016/j.agrformet.2009.03.004, 2009.
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.
O'Neill, P., Entekhabi, D., Njoku, E., and Kellogg, K.: The NASA Soil
Moisture Active Passive (SMAP) mission: Overview, in: 2010 IEEE
International Geoscience and Remote Sensing Symposium, IGARSS 2010–2010
IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI,
USA, 3236–3239, https://doi.org/10.1109/IGARSS.2010.5652291, 2010.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial
downscaling of satellite remotely sensed soil moisture: Downscale
Satellite-Based Soil Moisture, Rev. Geophys., 55, 341–366,
https://doi.org/10.1002/2016RG000543, 2017.
Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M. H.,
Crow, W. T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M. W. J., de
Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y. H.,
Lovergine, F., Mahecha, M. D., Marzahn, P., Mattia, F., Musial, J. P.,
Preuschmann, S., Reichle, R. H., Satalino, G., Silgram, M., van Bodegom, P.
M., Verhoest, N. E. C., Wagner, W., Walker, J. P., Wegmüller, U., and
Loew, A.: A roadmap for high-resolution satellite soil moisture applications
– confronting product characteristics with user requirements, 252, 15, https://doi.org/10.1016/j.rse.2020.112162, 2021.
Piles, M., Petropoulos, G. P., Sánchez, N., González-Zamora, Á.,
and Ireland, G.: Towards improved spatio-temporal resolution soil moisture
retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations,
Remote Sens. Environ., 180, 403–417,
https://doi.org/10.1016/j.rse.2016.02.048, 2016.
Piotrowski, A. P. and Napiorkowski, J. J.: A comparison of methods to avoid
overfitting in neural networks training in the case of catchment runoff
modelling, J. Hydrol., 476, 97–111,
https://doi.org/10.1016/j.jhydrol.2012.10.019, 2013.
Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z.,
Jin, R., Li, X., Guo, Z., and Zheng, J.: Rebuilding a Microwave Soil
Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness
Temperature and SMAP over the Qinghai–Tibet Plateau, China, Remote Sensing,
11, 683, https://doi.org/10.3390/rs11060683, 2019.
Rahimzadeh-Bajgiran, P., Berg, A. A., Champagne, C., and Omasa, K.:
Estimation of soil moisture using optical/thermal infrared remote sensing in
the Canadian Prairies, ISPRS J. Photogramm.,
83, 94–103, https://doi.org/10.1016/j.isprsjprs.2013.06.004, 2013.
Rao, P., Jiang, W., Hou, Y., Chen, Z., and Jia, K.: Dynamic Change Analysis
of Surface Water in the Yangtze River Basin Based on MODIS Products, Remote
Sensing, 10, 1025, https://doi.org/10.3390/rs10071025, 2018.
Rao, P., Wang, Y., Wang, F., Liu, Y., Wang, X., and Wang, Z.:
Daily soil moisture mapping at 1 km resolution based on SMAP data for
desertification areas in Northern China, figshare [data set],
https://doi.org/10.6084/m9.figshare.16430478.v6, 2022.
Sandholt, I., Rasmussen, K., and Andersen, J.: A simple interpretation of
the surface temperature/vegetation index space for assessment of surface
moisture status, Remote Sens. Environ., 79, 213–224,
https://doi.org/10.1016/S0034-4257(01)00274-7, 2002.
Shangguan, W., Dai, Y., Liu, B., Ye, A., and Yuan, H.: A soil particle-size
distribution dataset for regional land and climate modelling in China,
Geoderma, 171–172, 85–91, https://doi.org/10.1016/j.geoderma.2011.01.013,
2012.
Shi, R., Xu, X., Li, J., and Li, Y.: Prediction and analysis of train
arrival delay based on XGBoost and Bayesian optimization, Appl. Softw.
Comput., 109, 107538, https://doi.org/10.1016/j.asoc.2021.107538, 2021.
Sun, D., Xu, J., Wen, H., and Wang, D.: Assessment of landslide
susceptibility mapping based on Bayesian hyperparameter optimization: A
comparison between logistic regression and random forest, Eng. Geol., 281, 12,
https://doi.org/10.1016/j.enggeo.2020.105972, 2021.
Sun, L., Sun, R., Li, X., Liang, S., and Zhang, R.: Monitoring surface soil
moisture status based on remotely sensed surface temperature and vegetation
index information, Agr. Forest Meteorol., 166–167, 175–187,
https://doi.org/10.1016/j.agrformet.2012.07.015, 2012.
Tomaschek, F., Hendrix, P., and Baayen, R. H.: Strategies for addressing
collinearity in multivariate linguistic data, J. Phonetics, 71,
249–267, https://doi.org/10.1016/j.wocn.2018.09.004, 2018.
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S.,
Figa-Saldaña, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J.,
Kubu, G., Brugger, K., Aubrecht, C., Züger, J., Gangkofner, U.,
Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., and
Steinnocher, K.: The ASCAT Soil Moisture Product: A Review of its
Specifications, Validation Results, and Emerging Applications, Metz, 22,
5–33, https://doi.org/10.1127/0941-2948/2013/0399, 2013.
Wang, G., Zhang, X., Yinglan, A., Duan, L., Xue, B., and Liu, T.: A
spatio-temporal cross comparison framework for the accuracies of remotely
sensed soil moisture products in a climate-sensitive grassland region,
J. Hydrol., 597, 126089,
https://doi.org/10.1016/j.jhydrol.2021.126089, 2021.
Wang, S., Liu, S., Zhang, J., Che, X., Yuan, Y., Wang, Z., and Kong, D.: A
new method of diesel fuel brands identification: SMOTE oversampling combined
with XGBoost ensemble learning, Fuel, 282, 118848,
https://doi.org/10.1016/j.fuel.2020.118848, 2020.
Wang, T., Yang, D., Fang, B., Yang, W., Qin, Y., and Wang, Y.: Data-driven
mapping of the spatial distribution and potential changes of frozen ground
over the Tibetan Plateau, Sci. Total Environ., 649, 515–525,
https://doi.org/10.1016/j.scitotenv.2018.08.369, 2019.
Wang, X., Xie, H., Guan, H., and Zhou, X.: Different responses of
MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid
regions, J. Hydrol., 340, 12–24,
https://doi.org/10.1016/j.jhydrol.2007.03.022, 2007.
Yao, P., Shi, J., Zhao, T., Lu, H., and Al-Yaari, A.: Rebuilding Long Time
Series Global Soil Moisture Products Using the Neural Network Adopting the
Microwave Vegetation Index, Remote Sensing, 9, 35,
https://doi.org/10.3390/rs9010035, 2017.
Yu, H., Wu, Y., Niu, L., Chai, Y., Feng, Q., Wang, W., and Liang, T.: A
method to avoid spatial overfitting in estimation of grassland above-ground
biomass on the Tibetan Plateau, Ecol. Indic., 125, 107450,
https://doi.org/10.1016/j.ecolind.2021.107450, 2021.
Yu, Z., Liu, D., Lü, H., Fu, X., Xiang, L., and Zhu, Y.: A multi-layer
soil moisture data assimilation using support vector machines and ensemble
particle filter, J. Hydrol., 475, 53–64,
https://doi.org/10.1016/j.jhydrol.2012.08.034, 2012.
Yue, J., Tian, J., Tian, Q., Xu, K., and Xu, N.: Development of soil
moisture indices from differences in water absorption between
shortwave-infrared bands, ISPRS J. Photogramm., 154, 216–230, https://doi.org/10.1016/j.isprsjprs.2019.06.012,
2019.
Zanotti, C., Rotiroti, M., Sterlacchini, S., Cappellini, G., Fumagalli, L.,
Stefania, G. A., Nannucci, M. S., Leoni, B., and Bonomi, T.: Choosing
between linear and nonlinear models and avoiding overfitting for short and
long term groundwater level forecasting in a linear system, J.
Hydrol., 578, 124015, https://doi.org/10.1016/j.jhydrol.2019.124015, 2019.
Zawadzki, J. and Kędzior, M.: Soil moisture variability over Odra
watershed: Comparison between SMOS and GLDAS data, Int. J.f
Appl. Earth Obs., 45, 110–124,
https://doi.org/10.1016/j.jag.2015.03.005, 2016.
Zeng, J., Li, Z., Chen, Q., Bi, H., Qiu, J., and Zou, P.: Evaluation of
remotely sensed and reanalysis soil moisture products over the Tibetan
Plateau using in-situ observations, Remote Sens. Environ., 163,
91–110, https://doi.org/10.1016/j.rse.2015.03.008, 2015.
Zhang, P., Zheng, D., van der Velde, R., Wen, J., Zeng, Y., Wang, X., Wang, Z., Chen, J., and Su, Z.: Status of the Tibetan Plateau observatory (Tibet-Obs) and a 10-year (2009–2019) surface soil moisture dataset, Earth Syst. Sci. Data, 13, 3075–3102, https://doi.org/10.5194/essd-13-3075-2021, 2021.
Zhao, W. and Li, A.: A Downscaling Method for Improving the Spatial
Resolution of AMSR-E Derived Soil Moisture Product Based on MSG-SEVIRI Data,
Remote Sensing, 5, 6790–6811, https://doi.org/10.3390/rs5126790, 2013.
Zhao, W., Li, A., and Zhao, T.: Potential of Estimating Surface Soil
Moisture With the Triangle-Based Empirical Relationship Model, IEEE T.
Geosci. Remote, 55, 6494–6504,
https://doi.org/10.1109/TGRS.2017.2728815, 2017.
Zhao, W., Sánchez, N., Lu, H., and Li, A.: A spatial downscaling
approach for the SMAP passive surface soil moisture product using random
forest regression, J. Hydrol., 563, 1009–1024,
https://doi.org/10.1016/j.jhydrol.2018.06.081, 2018.
Short summary
It is urgent to obtain accurate soil moisture (SM) with high temporal and spatial resolution for areas affected by desertification in northern China. A combination of multiple machine learning methods, including multiple linear regression, support vector regression, artificial neural networks, random forest and extreme gradient boosting, has been applied to downscale the 36 km SMAP SM products and produce higher-spatial-resolution SM data based on related surface variables.
It is urgent to obtain accurate soil moisture (SM) with high temporal and spatial resolution for...
Altmetrics
Final-revised paper
Preprint