Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4525-2022
© 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-14-4525-2022
© 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
| 
17 Oct 2022
Data description paper |
| 17 Oct 2022
| 17 Oct 2022
Hydrography90m: a new high-resolution global hydrographic dataset
Giuseppe Amatulli
CORRESPONDING AUTHOR
Yale University, School of the Environment, 195 Prospect Street, New Haven, CT, 06511, USA
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department of Community and Ecosystem Ecology, Müggelseedamm 310, 12587 Berlin, Germany
Spatial Ecology, 35A, Hazlemere Road, Penn, Buckinghamshire, HP10 8AD, UK
Jaime Garcia Marquez
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department of Community and Ecosystem Ecology, Müggelseedamm 310, 12587 Berlin, Germany
Tushar Sethi
Spatial Ecology, 35A, Hazlemere Road, Penn, Buckinghamshire, HP10 8AD, UK
Margosa Environmental Solutions Ltd, 35A, Hazlemere Road, Penn, Buckinghamshire, HP10 8AD, UK
Jens Kiesel
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department of Community and Ecosystem Ecology, Müggelseedamm 310, 12587 Berlin, Germany
Christian-Albrechts-University Kiel, Institute for Natural Resource Conservation, Department of Hydrology and Water Resources Management, Olshausenstr. 75, 24118 Kiel, Germany
Afroditi Grigoropoulou
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department of Community and Ecosystem Ecology, Müggelseedamm 310, 12587 Berlin, Germany
Christian-Albrechts-University Kiel, Institute for Natural Resource Conservation, Department of Hydrology and Water Resources Management, Olshausenstr. 75, 24118 Kiel, Germany
Maria M. Üblacker
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department of Community and Ecosystem Ecology, Müggelseedamm 310, 12587 Berlin, Germany
Freie Universität Berlin, Department of Biology, Chemistry, Pharmacy, Institute of Biology, Königin-Luise-Str. 1–3, Berlin, 14195 Germany
Longzhu Q. Shen
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department of Community and Ecosystem Ecology, Müggelseedamm 310, 12587 Berlin, Germany
Spatial Ecology, 35A, Hazlemere Road, Penn, Buckinghamshire, HP10 8AD, UK
Carnegie Mellon University, Center for Green Science, Pittsburgh, PA 15213, USA
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department of Community and Ecosystem Ecology, Müggelseedamm 310, 12587 Berlin, Germany
Related authors
Holger Virro, Giuseppe Amatulli, Alexander Kmoch, Longzhu Shen, and Evelyn Uuemaa
Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, https://doi.org/10.5194/essd-13-5483-2021, 2021
Short summary
Short summary
Water quality modeling is essential for understanding and mitigating water quality deterioration in river networks due to agricultural and industrial pollution. Improving the availability and usability of open data is vital to support global water quality modeling efforts. The GRQA extends the spatial and temporal coverage of previously available water quality data and provides a reproducible workflow for combining multi-source water quality datasets.
P. A. Strobl, C. Bielski, P. L. Guth, C. H. Grohmann, J.-P. Muller, C. López-Vázquez, D. B. Gesch, G. Amatulli, S. Riazanoff, and C. Carabajal
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 395–400, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021, 2021
Hendrik Rathjens, Jens Kiesel, Jeffrey Arnold, Gerald Reinken, and Robin Sur
EGUsphere, https://doi.org/10.5194/egusphere-2025-877, https://doi.org/10.5194/egusphere-2025-877, 2025
Short summary
Short summary
We improved the widely used SWAT model to better predict how pesticides move through the environment. We added a new process that considers how plants take-up chemicals from the soil. Testing this updated model in two catchments showed very good prediction capabilities and a reduction of chemicals in river water by up to 17 % due to the plant uptake. The enhanced model offers a valuable tool for assessing the environmental impacts of agricultural management.
Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova
Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, https://doi.org/10.5194/essd-16-5625-2024, 2024
Short summary
Short summary
The CAMELS-DE dataset features data from 1582 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends and supports the development of hydrological models.
Hendrik Rathjens, Jens Kiesel, Michael Winchell, Jeffrey Arnold, and Robin Sur
Hydrol. Earth Syst. Sci., 27, 159–167, https://doi.org/10.5194/hess-27-159-2023, https://doi.org/10.5194/hess-27-159-2023, 2023
Short summary
Short summary
The SWAT model can simulate the transport of water-soluble chemicals through the landscape but neglects the transport through groundwater or agricultural tile drains. These transport pathways are, however, important to assess the amount of chemicals in streams. We added this capability to the model, which significantly improved the simulation. The representation of all transport pathways in the model enables watershed managers to develop robust strategies for reducing chemicals in streams.
Holger Virro, Giuseppe Amatulli, Alexander Kmoch, Longzhu Shen, and Evelyn Uuemaa
Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, https://doi.org/10.5194/essd-13-5483-2021, 2021
Short summary
Short summary
Water quality modeling is essential for understanding and mitigating water quality deterioration in river networks due to agricultural and industrial pollution. Improving the availability and usability of open data is vital to support global water quality modeling efforts. The GRQA extends the spatial and temporal coverage of previously available water quality data and provides a reproducible workflow for combining multi-source water quality datasets.
Nariman Mahmoodi, Jens Kiesel, Paul D. Wagner, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 25, 5065–5081, https://doi.org/10.5194/hess-25-5065-2021, https://doi.org/10.5194/hess-25-5065-2021, 2021
Short summary
Short summary
In this study, we assessed the sustainability of water resources in a wadi region with the help of a hydrologic model. Our assessment showed that the increases in groundwater demand and consumption exacerbate the negative impact of climate change on groundwater sustainability and hydrologic regime alteration. These alterations have severe consequences for a downstream wetland and its ecosystem. The approach may be applicable in other wadi regions with different climate and water use systems.
P. A. Strobl, C. Bielski, P. L. Guth, C. H. Grohmann, J.-P. Muller, C. López-Vázquez, D. B. Gesch, G. Amatulli, S. Riazanoff, and C. Carabajal
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 395–400, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021, 2021
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
Short summary
Short summary
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Björn Guse, Matthias Pfannerstill, Abror Gafurov, Jens Kiesel, Christian Lehr, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 21, 5663–5679, https://doi.org/10.5194/hess-21-5663-2017, https://doi.org/10.5194/hess-21-5663-2017, 2017
Short summary
Short summary
Performance measures are used to evaluate the representation of hydrological processes in parameters of hydrological models. In this study, we investigated how strongly model parameters and performance measures are connected. It was found that relationships are different for varying flow conditions, indicating that precise parameter identification requires multiple performance measures. The suggested approach contributes to a better handling of parameters in hydrological modelling.
Related subject area
Domain: ESSD – Land | Subject: Hydrology
A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res)
Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data
Mapping the world's inland surface waters: an upgrade to the Global Lakes and Wetlands Database (GLWD v2)
One year of high-frequency monitoring of groundwater physico-chemical parameters in the Weierbach experimental catchment, Luxembourg
Discrete global grid system-based flow routing datasets in the Amazon and Yukon basins
GRILSS: opening the gateway to global reservoir sedimentation data curation
A worldwide event-based debris flow barrier dam dataset from 1800 to 2023
CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations
An in situ daily dataset for benchmarking temporal variability of groundwater recharge
CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking
Features of Italian large dams and their upstream catchments
Gridded rainfall erosivity (2014–2022) in mainland China using 1 min precipitation data from densely distributed weather stations
OLIGOTREND, towards a global database of multi-decadal chlorophyll-a and water quality timeseries for rivers, lakes and estuaries
High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland
A 3-hour, 1-km surface soil moisture dataset for the contiguous United States from 2015 to 2023
CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India
LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles
HERA: a high-resolution pan-European hydrological reanalysis (1951–2020)
BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada
Comprehensive inventory of large hydropower systems in the Italian Alpine Region
Northern Hemisphere in situ snow water equivalent dataset (NorSWE, 1979–2021)
ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates
Lena River biogeochemistry captured by a 4.5-year high-frequency sampling program
CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany
Observational partitioning of water and CO2 fluxes at National Ecological Observatory Network (NEON) sites: a 5-year dataset of soil and plant components for spatial and temporal analysis
An integrated high-resolution bathymetric model for the Danube Delta system
A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022
Benchmark dataset for hydraulic simulations of flash floods in the French Mediterranean region
GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre
CIrrMap250: annual maps of China's irrigated cropland from 2000 to 2020 developed through multisource data integration
HANZE v2.1: an improved database of flood impacts in Europe from 1870 to 2020
A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins
Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950–2019)
A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps
Satellite-based near-real-time global daily terrestrial evapotranspiration estimates
Multivariate characterisation of a blackberry–alder agroforestry system in South Africa: hydrological, pedological, dendrological and meteorological measurements
CAMELS-AUS v2: updated hydrometeorological timeseries and landscape attributes for an enlarged set of catchments in Australia
SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains
First comprehensive stable isotope dataset of diverse water units in a permafrost-dominated catchment on the Qinghai–Tibet Plateau
LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland
High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020
Evapotranspiration evaluation using three different protocols on a large green roof in the greater Paris area
Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti
CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data
A hydrogeomorphic dataset for characterizing catchment hydrological behavior across the Tibetan Plateau
Deriving a Transformation Rate Map of Dissolved Organic Carbon over the Contiguous U.S.
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
Dams in the Mekong: a comprehensive database, spatiotemporal distribution, and hydropower potentials
A global dataset of the shape of drainage systems
Shanti Shwarup Mahto, Simone Fatichi, and Stefano Galelli
Earth Syst. Sci. Data, 17, 2693–2712, https://doi.org/10.5194/essd-17-2693-2025, https://doi.org/10.5194/essd-17-2693-2025, 2025
Short summary
Short summary
The MSEA-Res database offers an open-access dataset tracking absolute water storage for 186 large reservoirs across Mainland Southeast Asia from 1985 to 2023. It provides valuable insights into how reservoir storage grew by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
Nehar Mandal, Prabal Das, and Kironmala Chanda
Earth Syst. Sci. Data, 17, 2575–2604, https://doi.org/10.5194/essd-17-2575-2025, https://doi.org/10.5194/essd-17-2575-2025, 2025
Short summary
Short summary
Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected using a novel Bayesian network (BN) approach for simulating terrestrial water storage anomalies (TWSAs). TWSAs are reconstructed (BNML_TWSA) with grid-specific leader models (among four machine learning models) from January 1960 to December 2022 to generate a continuous global gridded dataset. The uncertainty in the reconstructed BNML_TWSA product is also assessed in terms of standard error.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
Short summary
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Karl Nicolaus van Zweel, Laurent Gourdol, Jean François Iffly, Loïc Léonard, François Barnich, Laurent Pfister, Erwin Zehe, and Christophe Hissler
Earth Syst. Sci. Data, 17, 2217–2229, https://doi.org/10.5194/essd-17-2217-2025, https://doi.org/10.5194/essd-17-2217-2025, 2025
Short summary
Short summary
Our study monitored groundwater in a Luxembourg forest over a year to understand water and chemical changes. We found seasonal variations in water chemistry, influenced by rainfall and soil interactions. These data help predict environmental responses and manage water resources better. By measuring key parameters like pH and dissolved oxygen, our research provides valuable insights into groundwater behaviour and serves as a resource for future environmental studies.
Chang Liao, Darren Engwirda, Matthew G. Cooper, Mingke Li, and Yilin Fang
Earth Syst. Sci. Data, 17, 2035–2062, https://doi.org/10.5194/essd-17-2035-2025, https://doi.org/10.5194/essd-17-2035-2025, 2025
Short summary
Short summary
Discrete global grid systems, or DGGS, are digital frameworks that help us organize information about our planet. Although scientists have used DGGS in areas like weather and nature, using them in the water cycle has been challenging because some core datasets are missing. We created a way to generate these datasets. We then developed the datasets in the Amazon and Yukon basins, which play important roles in our planet's climate. These datasets may help us improve our water cycle models.
Sanchit Minocha and Faisal Hossain
Earth Syst. Sci. Data, 17, 1743–1759, https://doi.org/10.5194/essd-17-1743-2025, https://doi.org/10.5194/essd-17-1743-2025, 2025
Short summary
Short summary
Trustworthy and independently verifiable information on declining storage capacity or sedimentation rates worldwide is sparse and suffers from inconsistent metadata and curation to allow global-scale archiving and analyses. The Global Reservoir Inventory of Lost Storage by Sedimentation (GRILSS) dataset addresses this challenge by providing organized, well-curated, and open-source data on sedimentation rates and capacity loss for 1013 reservoirs in 75 major river basins across 54 countries.
Haiguang Cheng, Kaiheng Hu, Shuang Liu, Xiaopeng Zhang, Hao Li, Qiyuan Zhang, Lan Ning, Manish Raj Gouli, Pu Li, Anna Yang, Peng Zhao, Junyu Liu, and Li Wei
Earth Syst. Sci. Data, 17, 1573–1593, https://doi.org/10.5194/essd-17-1573-2025, https://doi.org/10.5194/essd-17-1573-2025, 2025
Short summary
Short summary
After reviewing 2519 literature and media reports, we compiled the first comprehensive global dataset of 555 debris flow barrier dams (DFBDs) from 1800 to 2023. Our dataset meticulously documents 38 attributes of DFBDs, and we have utilized Google Earth for validation. Additionally, we discussed the applicability of landslide dam stability and peak-discharge models to DFBDs. This dataset offers a rich foundation of data for future studies on DFBDs.
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, Anker Lajer Højberg, Hans Thodsen, Mark F. T. Hansen, and Raphael J. M. Schneider
Earth Syst. Sci. Data, 17, 1551–1572, https://doi.org/10.5194/essd-17-1551-2025, https://doi.org/10.5194/essd-17-1551-2025, 2025
Short summary
Short summary
We developed a CAMELS-style dataset in Denmark, which contains hydrometeorological time series and landscape attributes for 3330 catchments (304 gauged). Many catchments in CAMELS-DK are small and at low elevations. The dataset provides information on groundwater characteristics and dynamics, as well as quantities related to the human impact on the hydrological system in Denmark. The dataset is especially relevant for developing data-driven and hybrid physically informed modeling frameworks.
Pragnaditya Malakar, Aatish Anshuman, Mukesh Kumar, Georgios Boumis, T. Prabhakar Clement, Arik Tashie, Hitesh Thakur, Nagaraj Bhat, and Lokendra Rathore
Earth Syst. Sci. Data, 17, 1515–1528, https://doi.org/10.5194/essd-17-1515-2025, https://doi.org/10.5194/essd-17-1515-2025, 2025
Short summary
Short summary
Groundwater dynamics depend on groundwater recharge, but daily benchmark data of recharge are scarce. Here we present a daily groundwater recharge per unit specified yield (RpSy) data at 485 US groundwater monitoring wells. RpSy can be used to validate the temporal consistency of recharge products from land surface and hydrologic models and facilitate assessment of recharge-driver functional relationships in them.
Olivier Delaigue, Guilherme Mendoza Guimarães, Pierre Brigode, Benoît Génot, Charles Perrin, Jean-Michel Soubeyroux, Bruno Janet, Nans Addor, and Vazken Andréassian
Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, https://doi.org/10.5194/essd-17-1461-2025, 2025
Short summary
Short summary
This dataset covers 654 rivers all flowing in France. The provided time series and catchment attributes will be of interest to those modelers wishing to analyze hydrological behavior and perform model assessments.
Giulia Evangelista, Paola Mazzoglio, Daniele Ganora, Francesca Pianigiani, and Pierluigi Claps
Earth Syst. Sci. Data, 17, 1407–1426, https://doi.org/10.5194/essd-17-1407-2025, https://doi.org/10.5194/essd-17-1407-2025, 2025
Short summary
Short summary
This paper presents the first comprehensive dataset of 528 large dams in Italy. It contains structural characteristics of the dams, such as coordinates, reservoir surface areas and volumes, together with a range of geomorphological, climatological, extreme rainfall, land cover and soil-related attributes of their upstream catchments.
Yueli Chen, Yun Xie, Xingwu Duan, and Minghu Ding
Earth Syst. Sci. Data, 17, 1265–1274, https://doi.org/10.5194/essd-17-1265-2025, https://doi.org/10.5194/essd-17-1265-2025, 2025
Short summary
Short summary
Rainfall erosivity maps are crucial for identifying key areas of water erosion. Due to the limited historical precipitation data, there are certain biases in rainfall erosivity estimates in China. This study develops a new rainfall erosivity map for mainland China using 1 min precipitation data from 60 129 weather stations, revealing that areas exceeding 4000 MJ mm ha−1 h−1yr−1 of annual rainfall erosivity are mainly concentrated in southern China and on the southern Tibetan Plateau.
Camille Minaudo, Andras Abonyi, Carles Alcaraz, Jacob Diamond, Nicholas J. K. Howden, Michael Rode, Estela Romero, Vincent Thieu, Fred Worrall, Qian Zhang, and Xavier Benito
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-58, https://doi.org/10.5194/essd-2025-58, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Many waterbodies undergo nutrient decline globally, called oligotrophication, but a comprehensive dataset to understand ecosystem responses is lacking. The OLIGOTREND database comprises multi-decadal chlorophyll-a and nutrient timeseries from rivers, lakes, and estuaries with 4.3 million observations from 1,894 unique measurement locations. The database provides empirical evidence for oligotrophication responses with a spatial and temporal coverage exceeding previous efforts.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
Short summary
Short summary
In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Haoxuan Yang, Jia Yang, Tyson E. Ochsner, Erik S. Krueger, Mengyuan Xu, and Chris B. Zou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-55, https://doi.org/10.5194/essd-2025-55, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We developed a 3-hour, 1-km surface soil moisture dataset for the contiguous United States from 2015 to 2023 using the spatio-temporal fusion method. This dataset effectively combines the distinct advantages of two long-term SSM datasets, which is also the first hour-level 1-km soil moisture dataset at the continental US scale. The new dataset could provide new insight into the fast changes in soil moisture along with drought and wet spell occurrences.
Nikunj K. Mangukiya, Kanneganti Bhargav Kumar, Pankaj Dey, Shailza Sharma, Vijaykumar Bejagam, Pradeep P. Mujumdar, and Ashutosh Sharma
Earth Syst. Sci. Data, 17, 461–491, https://doi.org/10.5194/essd-17-461-2025, https://doi.org/10.5194/essd-17-461-2025, 2025
Short summary
Short summary
We introduce CAMELS-IND (Catchment Attributes and MEteorology for Large-sample Studies – India), which provides daily hydrometeorological time series and static catchment attributes representing the location, topography, climate, hydrological signatures, land use, land cover, soil, geology, and anthropogenic influences for 472 catchments in Peninsular India to foster large-sample hydrological studies in India and promote the inclusion of Indian catchments in global hydrological research.
Bennett J. McAfee, Aanish Pradhan, Abhilash Neog, Sepideh Fatemi, Robert T. Hensley, Mary E. Lofton, Anuj Karpatne, Cayelan C. Carey, and Paul C. Hanson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-27, https://doi.org/10.5194/essd-2025-27, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
LakeBeD-US is a dataset of lake water quality data collected by multiple long-term monitoring programs around the United States. This dataset is designed to foster collaboration between lake scientists and computer scientists to improve predictions of water quality. By offering a way for computer models to be tested against real-world lake data, LakeBeD-US offers opportunities for both sciences to grow and to give new insights into the causes of water quality changes.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, Cinzia Mazzetti, and Luc Feyen
Earth Syst. Sci. Data, 17, 293–316, https://doi.org/10.5194/essd-17-293-2025, https://doi.org/10.5194/essd-17-293-2025, 2025
Short summary
Short summary
This article presents a reanalysis of Europe's river streamflow for the period 1951–2020. Streamflow is estimated through a state-of-the-art hydrological simulation framework benefitting from detailed information about the landscape, climate, and human activities. The resulting Hydrological European ReAnalysis (HERA) can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources and flood and drought risks.
Daniel Kovacek and Steven Weijs
Earth Syst. Sci. Data, 17, 259–275, https://doi.org/10.5194/essd-17-259-2025, https://doi.org/10.5194/essd-17-259-2025, 2025
Short summary
Short summary
We made a dataset for British Columbia describing the terrain, soil, land cover, and climate of over 1 million watersheds. The attributes are often used in hydrology because they are related to the water cycle. The data are meant to be used for water resources problems that can benefit from lots of watersheds and their attributes. The data and instructions needed to build the dataset from scratch are freely available. The permanent home for the data is https://doi.org/10.5683/SP3/JNKZVT.
Andrea Galletti, Soroush Zarghami Dastjerdi, and Bruno Majone
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-521, https://doi.org/10.5194/essd-2024-521, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We propose IAR-HP, a detailed inventory of large hydropower systems in Italy's Alpine Region, aimed at improving hydrological modeling for climate impact studies by providing the most relevant information with a consistent level of detail. It includes structural, geographical, and operational data for over 300 hydropower plants and their related reservoirs and water intakes. Validated through modeling, IAR-HP accurately reproduces observed hydropower, capturing 96.2 % of actual production.
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-602, https://doi.org/10.5194/essd-2024-602, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Finland and Russia over the period 1979–2021. It includes >11 million observations from >10 thousand different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
Wolfgang Preimesberger, Pietro Stradiotti, and Wouter Dorigo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-610, https://doi.org/10.5194/essd-2024-610, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We introduce the official ESA CCI Soil Moisture GAPFILLED climate data record. A univariate interpolation algorithm is applied to predict missing data points without relying on ancillary variables. The dataset includes gap-free uncertainty estimates for all predictions and was validated with independent in situ reference measurements. The data are recommended for applications, which require global long-term gap-free satellite soil moisture data.
Bennet Juhls, Anne Morgenstern, Jens Hölemann, Antje Eulenburg, Birgit Heim, Frederieke Miesner, Hendrik Grotheer, Gesine Mollenhauer, Hanno Meyer, Ephraim Erkens, Felica Yara Gehde, Sofia Antonova, Sergey Chalov, Maria Tereshina, Oxana Erina, Evgeniya Fingert, Ekaterina Abramova, Tina Sanders, Liudmila Lebedeva, Nikolai Torgovkin, Georgii Maksimov, Vasily Povazhnyi, Rafael Gonçalves-Araujo, Urban Wünsch, Antonina Chetverova, Sophie Opfergelt, and Pier Paul Overduin
Earth Syst. Sci. Data, 17, 1–28, https://doi.org/10.5194/essd-17-1-2025, https://doi.org/10.5194/essd-17-1-2025, 2025
Short summary
Short summary
The Siberian Arctic is warming fast: permafrost is thawing, river chemistry is changing, and coastal ecosystems are affected. We aimed to understand changes in the Lena River, a major Arctic river flowing to the Arctic Ocean, by collecting 4.5 years of detailed water data, including temperature and carbon and nutrient contents. This dataset records current conditions and helps us to detect future changes. Explore it at https://doi.org/10.1594/PANGAEA.913197 and https://lena-monitoring.awi.de/.
Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova
Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, https://doi.org/10.5194/essd-16-5625-2024, 2024
Short summary
Short summary
The CAMELS-DE dataset features data from 1582 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends and supports the development of hydrological models.
Einara Zahn and Elie Bou-Zeid
Earth Syst. Sci. Data, 16, 5603–5624, https://doi.org/10.5194/essd-16-5603-2024, https://doi.org/10.5194/essd-16-5603-2024, 2024
Short summary
Short summary
Quantifying water and CO2 exchanges through transpiration, evaporation, net photosynthesis, and soil respiration is essential for understanding how ecosystems function. We implemented five methods to estimate these fluxes over a 5-year period across 47 sites. This is the first dataset representing such large spatial and temporal coverage of soil and plant exchanges, and it has many potential applications, such as examining the response of ecosystems to weather extremes and climate change.
Lauranne Alaerts, Jonathan Lambrechts, Ny Riana Randresihaja, Luc Vandenbulcke, Olivier Gourgue, Emmanuel Hanert, and Marilaure Grégoire
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-529, https://doi.org/10.5194/essd-2024-529, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We created the first comprehensive, high-resolution, and easily-accessible bathymetry dataset for the three main branches of the Danube Delta. By combining four data sources, we obtained a detailed representation of the riverbed, with resolutions ranging from 2 to 100 m. This dataset will support future studies on water and nutrient exchanges between the Danube and the Black Sea, and provide insights into the Delta’s buffer role within the understudied Danube-Black Sea continuum.
Wangyipu Li, Zhaoyuan Yao, Yifan Qu, Hanbo Yang, Yang Song, Lisheng Song, Lifeng Wu, and Yaokui Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-460, https://doi.org/10.5194/essd-2024-460, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Due to shortcomings such as extensive data gaps and limited observation durations in current ground-based latent heat flux (LE) datasets, we developed a novel gap-filling and prolongation framework for ground-based LE observations, establishing a benchmark dataset for global evapotranspiration (ET) estimation from 2000 to 2022 across 64 sites at various time scales. This comprehensive dataset can strongly support ET modelling, water-carbon cycle monitoring, and long-term climate change analysis.
Juliette Godet, Pierre Nicolle, Nabil Hocini, Eric Gaume, Philippe Davy, Frederic Pons, Pierre Javelle, Pierre-André Garambois, Dimitri Lague, and Olivier Payrastre
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-472, https://doi.org/10.5194/essd-2024-472, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.
Claudia Färber, Henning Plessow, Simon Mischel, Frederik Kratzert, Nans Addor, Guy Shalev, and Ulrich Looser
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-427, https://doi.org/10.5194/essd-2024-427, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Large-sample datasets are essential in hydrological science to support modelling studies and advance process understanding. Caravan is a community initiative to create a large-sample hydrology dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world. This dataset is a subset of hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy.
Ling Zhang, Yanhua Xie, Xiufang Zhu, Qimin Ma, and Luca Brocca
Earth Syst. Sci. Data, 16, 5207–5226, https://doi.org/10.5194/essd-16-5207-2024, https://doi.org/10.5194/essd-16-5207-2024, 2024
Short summary
Short summary
This study presented new annual maps of irrigated cropland in China from 2000 to 2020 (CIrrMap250). These maps were developed by integrating remote sensing data, irrigation statistics and surveys, and an irrigation suitability map. CIrrMap250 achieved high accuracy and outperformed currently available products. The new irrigation maps revealed a clear expansion of China’s irrigation area, with the majority (61%) occurring in the water-unsustainable regions facing severe to extreme water stress.
Dominik Paprotny, Paweł Terefenko, and Jakub Śledziowski
Earth Syst. Sci. Data, 16, 5145–5170, https://doi.org/10.5194/essd-16-5145-2024, https://doi.org/10.5194/essd-16-5145-2024, 2024
Short summary
Short summary
Knowledge about past natural disasters can help adaptation to their future occurrences. Here, we present a dataset of 2521 riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The dataset contains available information on the inundated area, fatalities, persons affected, or economic loss and was obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers.
Paulina Bartkowiak, Bartolomeo Ventura, Alexander Jacob, and Mariapina Castelli
Earth Syst. Sci. Data, 16, 4709–4734, https://doi.org/10.5194/essd-16-4709-2024, https://doi.org/10.5194/essd-16-4709-2024, 2024
Short summary
Short summary
This paper presents the Two-Source Energy Balance evapotranspiration (ET) product driven by Copernicus Sentinel-2 and Sentinel-3 imagery together with ERA5 climate reanalysis data. Daily ET maps are available at 100 m spatial resolution for the period 2017–2021 across four Mediterranean basins: Ebro (Spain), Hérault (France), Medjerda (Tunisia), and Po (Italy). The product is highly beneficial for supporting vegetation monitoring and sustainable water management at the river basin scale.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
Short summary
Short summary
Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
Rohit Mukherjee, Frederick Policelli, Ruixue Wang, Elise Arellano-Thompson, Beth Tellman, Prashanti Sharma, Zhijie Zhang, and Jonathan Giezendanner
Earth Syst. Sci. Data, 16, 4311–4323, https://doi.org/10.5194/essd-16-4311-2024, https://doi.org/10.5194/essd-16-4311-2024, 2024
Short summary
Short summary
Global water resource monitoring is crucial due to climate change and population growth. This study presents a hand-labeled dataset of 100 PlanetScope images for surface water detection, spanning diverse biomes. We use this dataset to evaluate two state-of-the-art mapping methods. Results highlight performance variations across biomes, emphasizing the need for diverse, independent validation datasets to enhance the accuracy and reliability of satellite-based surface water monitoring techniques.
Lei Huang, Yong Luo, Jing M. Chen, Qiuhong Tang, Tammo Steenhuis, Wei Cheng, and Wen Shi
Earth Syst. Sci. Data, 16, 3993–4019, https://doi.org/10.5194/essd-16-3993-2024, https://doi.org/10.5194/essd-16-3993-2024, 2024
Short summary
Short summary
Timely global terrestrial evapotranspiration (ET) data are crucial for water resource management and drought forecasting. This study introduces the VISEA algorithm, which integrates satellite data and shortwave radiation to provide daily 0.05° gridded near-real-time ET estimates. By employing a vegetation index–temperature method, this algorithm can estimate ET without requiring additional data. Evaluation results demonstrate VISEA's comparable accuracy with accelerated data availability.
Sibylle Kathrin Hassler, Rafael Bohn Reckziegel, Ben du Toit, Svenja Hoffmeister, Florian Kestel, Anton Kunneke, Rebekka Maier, and Jonathan Paul Sheppard
Earth Syst. Sci. Data, 16, 3935–3948, https://doi.org/10.5194/essd-16-3935-2024, https://doi.org/10.5194/essd-16-3935-2024, 2024
Short summary
Short summary
Agroforestry systems (AFSs) combine trees and crops within the same land unit, providing a sustainable land use option which protects natural resources and biodiversity. Introducing trees into agricultural systems can positively affect water resources, soil characteristics, biomass and microclimate. We studied an AFS in South Africa in a multidisciplinary approach to assess the different influences and present the resulting dataset consisting of water, soil, tree and meteorological variables.
Keirnan J. A. Fowler, Ziqi Zhang, and Xue Hou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-263, https://doi.org/10.5194/essd-2024-263, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS v2 comprises data for an increased number (561) of catchments, each with with long-term monitoring, combining hydrometeorological time series with attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://zenodo.org/doi/10.5281/zenodo.12575680.
Kaihao Zheng, Peirong Lin, and Ziyun Yin
Earth Syst. Sci. Data, 16, 3873–3891, https://doi.org/10.5194/essd-16-3873-2024, https://doi.org/10.5194/essd-16-3873-2024, 2024
Short summary
Short summary
We develop a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. It involves a stepwise approach to estimate the basin-level floodplain hydraulic geometry parameters that best respect the scaling law while approximating the global hydrodynamic flood maps. A ~90 m resolution global floodplain map, the Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT), is delineated with demonstrated superiority.
Yuzhong Yang, Qingbai Wu, Xiaoyan Guo, Lu Zhou, Helin Yao, Dandan Zhang, Zhongqiong Zhang, Ji Chen, and Guojun Liu
Earth Syst. Sci. Data, 16, 3755–3770, https://doi.org/10.5194/essd-16-3755-2024, https://doi.org/10.5194/essd-16-3755-2024, 2024
Short summary
Short summary
We present the temporal data of stable isotopes in different waterbodies in the Beiluhe Basin in the hinterland of the Qinghai–Tibet Plateau (QTP) produced between 2017 and 2022. In this article, the first detailed stable isotope data of 359 ground ice samples are presented. This first data set provides a new basis for understanding the hydrological effects of permafrost degradation on the QTP.
Hordur Bragi Helgason and Bart Nijssen
Earth Syst. Sci. Data, 16, 2741–2771, https://doi.org/10.5194/essd-16-2741-2024, https://doi.org/10.5194/essd-16-2741-2024, 2024
Short summary
Short summary
LamaH-Ice is a large-sample hydrology (LSH) dataset for Iceland. The dataset includes daily and hourly hydro-meteorological time series, including observed streamflow and basin characteristics, for 107 basins. LamaH-Ice offers most variables that are included in existing LSH datasets and additional information relevant to cold-region hydrology such as annual time series of glacier extent and mass balance. A large majority of the basins in LamaH-Ice are unaffected by human activities.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
Short summary
Short summary
To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Pierre-Antoine Versini, Leydy Alejandra Castellanos-Diaz, David Ramier, and Ioulia Tchiguirinskaia
Earth Syst. Sci. Data, 16, 2351–2366, https://doi.org/10.5194/essd-16-2351-2024, https://doi.org/10.5194/essd-16-2351-2024, 2024
Short summary
Short summary
Nature-based solutions (NBSs), such as green roofs, have appeared as relevant solutions to mitigate urban heat islands. The evapotranspiration (ET) process allows NBSs to cool the air. To improve our knowledge about ET assessment, this paper presents some experimental measurement campaigns carried out during three consecutive summers. Data are available for three different (large, small, and point-based) spatial scales.
Ralph Bathelemy, Pierre Brigode, Vazken Andréassian, Charles Perrin, Vincent Moron, Cédric Gaucherel, Emmanuel Tric, and Dominique Boisson
Earth Syst. Sci. Data, 16, 2073–2098, https://doi.org/10.5194/essd-16-2073-2024, https://doi.org/10.5194/essd-16-2073-2024, 2024
Short summary
Short summary
The aim of this work is to provide the first hydroclimatic database for Haiti, a Caribbean country particularly vulnerable to meteorological and hydrological hazards. The resulting database, named Simbi, provides hydroclimatic time series for around 150 stations and 24 catchment areas.
Changming Li, Ziwei Liu, Wencong Yang, Zhuoyi Tu, Juntai Han, Sien Li, and Hanbo Yang
Earth Syst. Sci. Data, 16, 1811–1846, https://doi.org/10.5194/essd-16-1811-2024, https://doi.org/10.5194/essd-16-1811-2024, 2024
Short summary
Short summary
Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land–atmosphere interactions.
Yuhan Guo, Hongxing Zheng, Yuting Yang, Yanfang Sang, and Congcong Wen
Earth Syst. Sci. Data, 16, 1651–1665, https://doi.org/10.5194/essd-16-1651-2024, https://doi.org/10.5194/essd-16-1651-2024, 2024
Short summary
Short summary
We have provided an inaugural version of the hydrogeomorphic dataset for catchments over the Tibetan Plateau. We first provide the width-function-based instantaneous unit hydrograph (WFIUH) for each HydroBASINS catchment, which can be used to investigate the spatial heterogeneity of hydrological behavior across the Tibetan Plateau. It is expected to facilitate hydrological modeling across the Tibetan Plateau.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-43, https://doi.org/10.5194/essd-2024-43, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We have developed a new map that reveals how organic carbon from soil leaches into headwater streams over the contiguous United States. We use advanced artificial intelligence techniques and a massive amount of data, including observations at over 2,500 gauges and a wealth of climate and environmental information. The map is a critical step in understanding and predicting how carbon moves through our environment, hence a useful tool for tackling climate challenges.
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.
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.
Cited articles
Abell, R., Allan, J. D., and Lehner, B.: Unlocking the potential of protected
areas for freshwaters, Biol. Conserv., 134, 48–63, 2007. a
Ågren, A. M., Lidberg, W., and Ring, E.: Mapping Temporal Dynamics in a Forest
Stream Network-Implications for Riparian Forest Management, Forests, 6,
2982–3001, https://doi.org/10.3390/f6092982, 2015. a
Allen, G. H., David, C. H., Andreadis, K. M., Hossain, F., and Famiglietti,
J. S.: Global estimates of river flow wave travel times and implications for
low-latency satellite data, Geophys. Res. Lett., 45, 7551–7560,
2018. a
Altermatt, F.: Diversity in riverine metacommunities: a network perspective,
Aquat. Ecol., 47, 365–377, 2013. a
Altermatt, F., Seymour, M., and Martinez, N.: River network properties shape
α-diversity and community similarity patterns of aquatic insect
communities across major drainage basins, J. Biogeogr., 40,
2249–2260, 2013. a
Amatulli, G.: A new and extendable global watershed and stream network
delineation using GRASS-GIS, Geomorphometry, 205, 205–208, 2020. a
Amatulli, G.: Using GRASS for stream-network extraction and basins delineation as a direct link, https://hydrography.org/hydrography90m/hydrography90m_workflow/,
last access: 5 October 2022a.
Amatulli, G.: Hydrography90m layers download script,
https://hydrography.org/hydrography90m/hydrography90m_layers/, last access: 05 October 2022b.
Amatulli, G., Casalegno, S., D’Annunzio, R., Haapanen, R., Kempeneers, P.,
Lindquist, E., Pekkarinen, A., M., W. A., and R., Z.-M.: Teaching
spatiotemporal analysis and efficient data processing in open source
environment, in: Proceedings of the 3rd Open Source Geospatial Research &
Education Symposium, Helsinki,
Finland, 10–13 June 2014, 13–26, 2014. a
Amatulli, G., Domisch, S., Kiesel, J., Sethi, T., Yamazaki, D., and Raymond,
P.: High-resolution stream network delineation using digital elevation
models: assessing the spatial accuracy, Tech. rep., PeerJ Preprints, https://doi.org/10.7287/peerj.preprints.27109v1,
2018a. a
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A.,
Malczyk, J., and Jetz, W.: A suite of global, cross-scale topographic
variables for environmental and biodiversity modeling, Sci. Data, 5,
180040, https://doi.org/10.1038/sdata.2018.40, 2018b. a
Amatulli, G., Garcia Marquez, J., Sethi, T., Kiesel, J., Grigoropoulou, A., Üblacker, M., Shen, L., and Domisch, S.:
Hydrography90m: A new high-resolution global hydrographic dataset, IGB Leibniz-Institute of
Freshwater Ecology and Inland Fisheries [data set], https://doi.org/10.18728/igb-fred-762.1, 2022a. a, b, c, d, e
matulli, G., Garcia Marquez, J., Sethi, T., Kiesel, J.,
Grigoropoulou, A., Üblacker, M. M., Shen, L. Q., and Domisch, S.:
Hydrography90m,
https://doi.org/10.5446/56343, 2022b.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area
model of basin hydrology/Un modèle à base physique de zone d'appel
variable de l'hydrologie du bassin versant, Hydrolog. Sci. J.,
24, 43–69, 1979. a
Bishop, A. P., Amatulli, G., Hyseni, C., Pless, E., Bateta, R., Okeyo, W. A.,
Mireji, P. O., Okoth, S., Malele, I., Murilla, G., Aksoy, S., Caccone, A., Saarman, N. S.: A machine learning
approach to integrating genetic and ecological data in tsetse flies (Glossina
pallidipes) for spatially explicit vector control planning,
Evol.Appl., 14, 1762–1777, https://doi.org/10.1111/eva.13237, 2021. a
Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A., Merz, B.,
Arheimer, B., Aronica, G. T., Bilibashi, A., Bonacci, O., Borga, M., Čanjevac, I., Castellarin, A., Chirico, G. B., Claps, P., Fiala, K., Frolova, N., Gorbachova, L.,
Gül, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T. R., Kohnová, S., Koskela J. J., Ledvinka, O.,
Macdonald, N., Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M.,
Ovcharuk, V. Radevski, I., Rogger, M., Salinas, J. L., Sauquet, E., Šraj, M., Szolgay, J., Viglione, A., Volpi, E.,
Wilson, D., Zaimi, K., and Živković, N.:
Changing climate shifts timing of European floods, Science, 357, 588–590,
2017. a
Brenden, T., Wang, L., Seelbach, P., Clark, R., Wiley, M., and Sparks-Jackson,
B.: A spatially constrained clustering program for river valley segment
delineation from GIS digital river networks, Environ. Modell.
Softw., 23, 638–649, 2008. a
Bunn, S. E., Thoms, M. C., Hamilton, S. K., and Capon, S. J.: Flow variability
in dryland rivers: boom, bust and the bits in between,
River Res.
Appl., 22, 179–186, https://doi.org/10.1002/rra.904, 2006. a
Buraas, E. M., Renshaw, C. E., Magilligan, F. J., and Dade, W. B.: Impact of
reach geometry on stream channel sensitivity to extreme floods, Earth Surf.
Proc. Land., 39, 1778–1789, 2014. a
Buto, S. G. and Anderson, R. D.: NHDPlus High Resolution (NHDPlus HR)–A
hydrography framework for the Nation, Tech. rep., US Geological Survey, https://doi.org/10.3133/fs20203033, 2020. a, b
Connor, R.: The United Nations world water development report 2015: water for a
sustainable world, vol. 1, UNESCO publishing, ISBN 978-92-3-100080-5 (set), 978-92-3-100071-3, 978-92-3-100099-7 (ePub), 2015. a
Datry, T., Larned, S. T., and Tockner, K.: Intermittent Rivers: A Challenge
for Freshwater Ecology, BioScience, 64, 229–235, 2014. a
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018. a
Domisch, S., Jaehnig, S. C., Simaika, J. P., Kuemmerlen, M., and Stoll, S.:
Application of species distribution models in stream ecosystems: the
challenges of spatial and temporal scale, environmental predictors and
species occurrence data, Fund. Appl. Limnol., 186, 45–61, https://doi.org/10.1127/fal/2015/0627,
2015b. a
Domisch, S., Friedrichs, M., Hein, T., Borgwardt, F., Wetzig, A., Jähnig,
S. C., and Langhans, S. D.: Spatially explicit species distribution models: A
missed opportunity in conservation planning?, Divers. Distrib.,
25, 758–769, 2019. a
Erskine, R. H., Green, T. R., Ramirez, J. A., and MacDonald, L. H.: Comparison
of grid-based algorithms for computing upslope contributing area, Water
Resour. Res., 42, W09416, https://doi.org/10.1029/2005WR004648, 2006. a, b
Farquharson, F., Meigh, J., and Sutcliffe, J.: Regional flood frequency
analysis in arid and semi-arid areas, J. Hydrol., 138, 487–501,
https://doi.org/10.1016/0022-1694(92)90132-F, 1992. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar
topography mission, Rev. Geophys., 45, https://doi.org/10.1029/2005RG000183, 2007. a
Gallant, J. and Wilson, J.: Terrain analysis: principles and applications, John
Wiley & Sons, ISBN 978-0-471-32188-0, 2000. a
GDAL Development Team: GDAL – Geospatial Data Abstraction Library, Version
3.1.0, Open Source Geospatial Foundation,
http://www.gdal.org (last access: 5 October 2022), 2020. a
Grant, E. H. C., Lowe, W. H., and Fagan, W. F.: Living in the branches:
population dynamics and ecological processes in dendritic networks, Ecol.
Lett., 102, 165–75, 2007. a
GRASS Development Team: Geographic Resources Analysis Support System (GRASS
GIS) Software, Version 7.8.0, Open Source Geospatial Foundation,
http://grass.osgeo.org (last access: 5 October 2022), 2019. a
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018. a
Hack, J. T.: Studies of longitudinal stream profiles in Virginia and Maryland,
vol. 294, US Government Printing Office, https://doi.org/10.3133/pp294B, 1957. a
Hankin, B., Metcalfe, P., Beven, K., and Chappell, N. A.: Integration of
hillslope hydrology and 2D hydraulic modelling for natural flood management,
Hydrol. Res., 50, 1535–1548, 2019. a
Hannon, J.: Hydrography90m layers map interface, https://hydrography.org/hydrography90m/hydrography90m_layers/, last access: 5 October 2022.
Harding, M. and Carabajal, C.: ICESat waveform measurements of
within‐footprint topographic relief and vegetation vertical structure,
Geophys. Res. Lett., 32, 0094-8276, https://doi.org/10.1029/2005GL023471, 2005. a
Haubrock, P. J., Ahmed, D. A., Cuthbert, R. N., Stubbington, R., Domisch, S.,
Marquez, J. R., Beidas, A., Amatulli, G., Kiesel, J., Shen, L. Q., Soto, I., Angeler, D. G., Bonada, N., Cañedo-Argüelles, M., Csabai, Z.,
Datry, T., de Eyto, E., Dohet, A., Drohan, E., England, J., Feio, M. J., Forio, M. A. E., Goethals, P., Graf, W.,
Heino, J., Hudgins, E. J., Jähnig, S. C., Johnson, R. K., Larrañaga, A., Leitner, P., L'Hoste, L., Lizee, M. H., Maire,
A., Rasmussen, J. J., Schäfer, R. B., Schmidt-Kloiber, A., Vannevel, R., Várbíró, G., Wiberg-Larsen, P., Haase, and
P.:
Invasion impacts and dynamics of a European-wide introduced species, Glob.
Change Biol., https://doi.org/10.1111/gcb.16207, 2022. a
Heine, R. A., Lant, C. L., and Sengupta, R. R.: Development and comparison of
approaches for automated mapping of stream channel networks,
Ann. Assoc. Am. Geogr., 94, 477–490, 2004. a
Hirt, C.: Artefact detection in global digital elevation models (DEMs): The
Maximum Slope Approach and its application for complete screening of the
SRTM v4. 1 and MERIT DEMs, Remote Sens. Environ., 207, 27–41,
2018. a
Hong, H., Tsangaratos, P., Ilia, I., Chen, W., and Xu, C.: Comparing the
performance of a logistic regression and a random forest model in landslide
susceptibility assessments. The Case of Wuyaun Area, China,
World Landslide Forum, 1043–1050, https://doi.org/10.1007/978-3-319-53498-5_118, 2017. a
Horton, R. E.: Erosional development of streams and their drainage basins;
hydrophysical approach to quantitative morphology, Geol. Soc.
Am. Bull., 56, 275–370, 1945. a
Hosen, J. D., Allen, G. H., Amatulli, G., Breitmeyer, S., Cohen, M. J., Crump, B. C., Lu, Y., Payet, J. P., Poulin,
B. A., Stubbins, A., Yoon, B., and Raymond, P. A.: River
network travel time is correlated with dissolved organic matter composition
in rivers of the contiguous United States, Hydrol. Process., 35,
e14124, https://doi.org/10.1002/hyp.14124, 2021. a
Jackson, M. C., Weyl, O., Altermatt, F., Durance, I., Friberg, N., Dumbrell, A., Piggott, J., Tiegs, S., Tockner, K.,
Krug, C., Leadley, P. W., and Woodward, G.: Recommendations
for the next generation of global freshwater biological monitoring tools,
Adv. Ecol. Res., 55, 615–636, https://doi.org/10.1016/bs.aecr.2016.08.008, 2016. a
Kempeneers, P.: PKTOOLS – Processing Kernel for geospatial data, Version
2.6.7.6, Open Source Geospatial Foundation,
http://pktools.nongnu.org/html/index.html (last access: 5 October 2022), 2018. a
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and
Nearing, G. S.: Toward Improved Predictions in Ungauged Basins: Exploiting
the Power of Machine Learning, Water Resour. Res., 55,
11344–11354, 2019. a
Lay, U. S., Pradhan, B., Yusoff, Z. B. M., Abdallah, A. F. B., Aryal, J., and
Park, H.-J.: Data mining and statistical approaches in debris-flow
susceptibility modelling using airborne LiDAR data, Sensors, 19, 3451, https://doi.org/10.3390/s19163451, 2019. a
Leopold, L. B., Wolman, M. G., Miller, J. P., and Wohl, E.: Fluvial processes
in geomorphology, Courier Dover Publications, ISBN 0486685888, 1964. a
Liang, C. and MaCkay, D. S.: A general model of watershed extraction and
representation using globally optimal flow paths and up-slope contributing
areas, Int. J. Geogr. Inf. Sci., 14,
337–358, 2000. a
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M.,
Allen, G. H., and Gleason, C. J.: Global
reconstruction of naturalized river flows at 2.94 million reaches, Water
Resour. Res., 55, 6499–6516, 2019. a
Linke, S., Lehner, B., Dallaire, C. O., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell,
S., Moidu, H., Tan, F., and Thieme, M.: Global
hydro-environmental sub-basin and river reach characteristics at high spatial
resolution, Sci. Data, 6, 1–15, 2019. a
Liu, S., She, D., Gao, C., Amatulli, G., Wang, L., Lu, X., Raymond, P. A., and
Xia, X.: Groundwater as a limited carbon dioxide source in a large river (the
Yangtze River), Sci. Total Environ., 760, 143336, https://doi.org/10.1016/j.scitotenv.2020.143336, 2021. a
Maasri, A., Jähnig, S., Adamescu, M., Adrian, R., Baigun, C., Baird, D.,
Batista-Morales, A., Bonada, N., Brown, L., Cai, Q., et al.: A Global Agenda
for Advancing Freshwater Biodiversity Research, Ecol. Lett., 25, 255–263,
https://doi.org/10.1111/ele.13931, 2021a. a
Maasri, A., Thorp, J. H., Kotlinski, N., Kiesel, J., Erdenee, B., and Jähnig,
S. C.: Variation in macroinvertebrate community structure of functional
process zones along the river continuum: New elements for the interpretation
of the river ecosystem synthesis, River Res. Appl., 37,
665–674, 2021b. a
Marani, A., Rigon, R., and Rinaldo, A.: A note on fractal channel networks,
Water Resour. Res., 27, 3041–3049, 1991. a
Marzadri, A., Amatulli, G., Tonina, D., Bellin, A., Shen, L. Q., Allen, G. H.,
and Raymond, P. A.: Global riverine nitrous oxide emissions: The role of
small streams and large rivers, Sci. Total Environ., 776,
145148, https://doi.org/10.1016/j.scitotenv.2021.145148, 2021. a
McInerney, D. and Kempeneers, P.: Open Source Geospatial Tools – Applications
in Earth Observation, Springer Verlag, ISBN 13 9783319018232, 2015. a
Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder,
T., Tockner, K., Trautmann, T., Watt, C., and Datry, T.: Global prevalence of
non-perennial rivers and streams, Nature, 594, 391–397, 2021. a
Metz, M., Mitasova, H., and Harmon, R. S.: Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search, Hydrol. Earth Syst. Sci., 15, 667–678, https://doi.org/10.5194/hess-15-667-2011, 2011. a
Moore, I. D. and Burch, G. J.: Physical basis of the length-slope factor in the
universal soil loss equation, Soil Sci. Soc. Am. J., 50,
1294–1298, 1986. a
Moore, R. B., McKay, L. D., Rea, A. H., Bondelid, T. R., Price, C. V., Dewald,
T. G., and Johnston, C. M.: User's guide for the National Hydrography
Dataset plus (NHDPlus) High Resolution, Open-File Report-US Geological
Survey, 66 p., https://doi.org/10.3133/ofr20191096, 2019. a, b
Moudrỳ, V., Lecours, V., Gdulová, K., Gábor, L., Moudrá, L.,
Kropáček, J., and Wild, J.: On the use of global DEMs in ecological
modelling and the accuracy of new bare-earth DEMs, Ecol. Modell., 383,
3–9, 2018. a
Neteler, M. and Mitasova, H.: Open source GIS: a GRASS GIS approach, vol. 689,
Springer Science & Business Media, ISBN 978-0-387-68574-8, 2013. a
Neteler, M., Bowman, H., Landa, M., and Metz, M.: GRASS GIS: A multi-purpose
open source GIS, Environ. Modell. Softw., 31, 124–130, 2012. a
O'Loughlin, F., Paiva, R., Durand, M., Alsdorf, D., and Bates, P.: A
multi-sensor approach towards a global vegetation corrected SRTM DEM product,
Remote Sens. Environ., 182, 49–59, 2016. a
Orlandini, S., Moretti, G., Franchini, M., Aldighieri, B., and Testa, B.:
Path-based methods for the determination of nondispersive drainage directions
in grid-based digital elevation models, Water Resour. Res., 39, 1144, https://doi.org/10.1029/2002WR001639, 2003. a
Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Le Moine, N.: Spatial
proximity, physical similarity, regression and ungaged catchments: A
comparison of regionalization approaches based on 913 French catchments,
Water Resour. Res., 44, W03413, https://doi.org/10.1029/2007WR006240, 2008. a
Ozdemir, A.: Using a binary logistic regression method and GIS for evaluating
and mapping the groundwater spring potential in the Sultan Mountains
(Aksehir, Turkey), J. Hydrol., 405, 123–136, 2011. a
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584.,
2016. a
Pless, E., Saarman, N. P., Powell, J. R., Caccone, A., and Amatulli, G.: A
machine-learning approach to map landscape connectivity in Aedes aegypti with
genetic and environmental data, P. Natl. Acad.
Sci. USA, 118, e2003201118, https://doi.org/10.1073/pnas.2003201118, 2021. a
Pourghasemi, H., Pradhan, B., Gokceoglu, C., and Moezzi, K. D.: Landslide
susceptibility mapping using a spatial multi criteria evaluation model at
Haraz Watershed, Iran, in: Terrigenous mass movements, https://doi.org/10.1007/978-3-642-25495-6_2, 23–49, 2012. a, b
Quinn, P., Beven, K., and Lamb, R.: The in (a/tan/β) index: How to
calculate it and how to use it within the topmodel framework, Hydrol.
Process., 9, 161–182, 1995. a
Raymond, P. A., Zappa, C. J., Butman, D., Bott, T. L., Potter, J., Mulholland,
P., Laursen, A. E., McDowell, W. H., and Newbold, D.: Scaling the gas
transfer velocity and hydraulic geometry in streams and small rivers,
Limnol. Oceanogr., 2, 41–53, 2012. a
Raymond, P. A., Saiers, J. E., and Sobczak, W. V.: Hydrological and
biogeochemical controls on watershed dissolved organic matter transport:
Pulse-shunt concept, Ecology, 97, 5–16, 2016. a
Read, D. S., Gweon, H. S., Bowes, M. J., Newbold, L. K., Field, D., Bailey,
M. J., and Griffiths, R. I.: Catchment-scale biogeography of riverine
bacterioplankton, ISME J., 9, 516–526, 2015. a
Reichl, J. P. C., Western, A. W., McIntyre, N. R., and Chiew, F. H. S.:
Optimization of a similarity measure for estimating ungauged streamflow,
Water Resour. Res., 45, https://doi.org/10.1029/2008WR007248, 2009. a, b
Rodríguez, E., Morris, C. S., and Belz, J. E.:
A Global Assessment of the SRTM Performance,
Photogramm. Eng. Rem. S., 72, 249–260, https://doi.org/10.14358/PERS.72.3.249, 2006. a, b
Román-Sánchez, A., Vanwalleghem, T., Peña, A., Laguna, A., and
Giráldez, J.: Controls on soil carbon storage from topography and
vegetation in a rocky, semi-arid landscapes, Geoderma, 311, 159–166, 2018. a
Saarman, N., Burak, M., Opiro, R., Hyseni, C., Echodu, R., Dion, K., Opiyo,
E. A., Dunn, A. W., Amatulli, G., Aksoy, S., Caccone, A.: A spatial genetics
approach to inform vector control of tsetse flies (Glossina fuscipes
fuscipes) in Northern Uganda, Ecol. Evol., 8, 5336–5354, 2018. a
Saarman, N., Pless, E., Amatulli, G., and Caccone, A.: Integrating genetic and
environmental data to model and forecast movement and habitat use in the
major insect vector of sleeping sickness in Uganda (Glossina fuscipes
fuscipes), in: Entomology 2019, ESA, https://esa.confex.com/esa/2019/meetingapp.cgi/Paper/144209 (last access: 5 October 2022), 2019. a
Scheidegger, A. E.: The algebra of stream-order numbers, United States
Geological Survey Professional Paper, 525, 187–189, 1965. a
Seibert, J. and McGlynn, B. L.: A new triangular multiple flow direction
algorithm for computing upslope areas from gridded digital elevation models,
Water Resour. Res., 43, W04501, https://doi.org/10.1029/2006WR005128, 2007. a, b, c
Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., and Shirzadi, A.:
Novel forecasting approaches using combination of machine learning and
statistical models for flood susceptibility mapping, J. Environ.
Manage., 217, 1–11, 2018. a
Shanafield, M., Bourke, S. A., Zimmer, M. A., and Costigan, K. H.: An overview
of the hydrology of non-perennial rivers and streams, Wiley Interdisciplinary
Reviews: Water, 8, e1504, https://doi.org/10.1002/wat2.1504, 2021. a
Shen, L. Q., Amatulli, G., Sethi, T., Raymond, P., and Domisch, S.: Estimating
nitrogen and phosphorus concentrations in streams and rivers, within a
machine learning framework, Sci. Data, 7, 1–11, 2020. a
Shreve, R. L.: Infinite topologically random channel networks, J.
Geol., 75, 178–186, 1967. a
Shumilova, O., Zak, D., Datry, T., von Schiller, D., Corti, R., Foulquier, A.,
Obrador, B., Tockner, K., Allan, D. C., Altermatt, F., Arce, M. I., Arnon,
S., Banas, D., Banegas-Medina, A., Beller, E., Blanchette, M. L.,
Blanco-Libreros, J. F., Blessing, J., Boëchat, I. G., Boersma, K., Bogan,
M. T., Bonada, N., Bond, N. R., Brintrup, K., Bruder, A., Burrows, R.,
Cancellario, T., Carlson, S. M., Cauvy-Fraunié, S., Cid, N., Danger, M.,
de Freitas Terra, B., Girolamo, A. M. D., del Campo, R., Dyer, F., Elosegi,
A., Faye, E., Febria, C., Figueroa, R., Four, B., Gessner, M. O., Gnohossou,
P., Cerezo, R. G., Gomez-Gener, L., Graça, M. A., Guareschi, S., Gücker,
B., Hwan, J. L., Kubheka, S., Langhans, S. D., Leigh, C., Little, C. J.,
Lorenz, S., Marshall, J., McIntosh, A., Mendoza-Lera, C., Meyer, E. I.,
Miliša, M., Mlambo, M. C., Moleón, M., Negus, P., Niyogi, D.,
Papatheodoulou, A., Pardo, I., Paril, P., Pešić, V., Rodriguez-Lozano, P.,
Rolls, R. J., Sanchez-Montoya, M. M., Savić, A., Steward, A., Stubbington,
R., Taleb, A., Vorste, R. V., Waltham, N., Zoppini, A., and Zarfl, C.:
Simulating rewetting events in intermittent rivers and ephemeral streams: A
global analysis of leached nutrients and organic matter, Glob. Change
Biol., 25, 1591–1611, 2019. a
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne lidar, J. Geophys. Res.- Biogeo., 116, 2011. a
Strobl, P. A., Bielski, C., Guth, P. L., Grohmann, C. H., Muller, J.-P.,
López-Vázquez, C., Gesch, D. B., Amatulli, G., Riazanoff, S., and
Carabajal, C.: The Digital Elevation Model Intercomparison eXperiment DEMIX,
a community-based approach at global DEM benchmarking, The International
Archives of the Photogrammetry, Remote Sensing and Spatial Information
Sciences, 43, 395–400, 2021. a
Sugarbaker, L., Constance, E. W., Heidemann, H. K., Jason, A. L., Lucas, V.,
Saghy, D., and Stoker, J. M.: The 3D Elevation Program initiative: a call for
action, US Geological Survey, https://doi.org/10.3133/cir1399, 2014. a
Tadono, T., Takaku, J., Tsutsui, K., Oda, F., and Nagai, H.: Status of “ALOS
World 3D (AW3D)” global DSM generation, in: 2015 IEEE International
Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26–31 July 2015, 3822–3825, IEEE, https://doi.org/10.1109/IGARSS.2015.7326657, 2015. a
Takaku, J., Iwasaki, A., and Tadono, T.: Adaptive filter for improving quality
of ALOS PRISM DSM, in: 2016 IEEE International Geoscience and Remote Sensing
Symposium (IGARSS), 5370–5373, IEEE, Beijing, China, 10–15 July 2016, https://doi.org/10.1109/IGARSS.2016.7730399, 2016. a
Thalacker, R. J.: Mapping techniques for soil erosion: Modeling stream power
index in eastern North Dakota, The University of North Dakota, 2014. a
USGS: Global 30 Arc-Second Elevation (GTOPO30), United States Geological
Survey, https://doi.org/10.5066/F7DF6PQS, 1996. a
USGS: Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global, United
States Geological Survey, https://doi.org/10.5066/F7PR7TFT, 2015. a
USGS EROS Archive: USGS EROS Archive – Digital Elevation – HYDRO1K, HYDRO1k Elevation Derivative Database,
https://doi.org/10.5066/F77P8WN0, 2018. a, b, c
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R., and Gushing,
C. E.: The River Continuum Concept, Can. J. Fish. Aquat.
Sci., 37, 130–137, 1980. a
Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, 2021. a
Vogt, J. V., Colombo, R., and Bertolo, F.: Deriving drainage networks and
catchment boundaries: a new methodology combining digital elevation data and
environmental characteristics, Geomorphology, 53, 281–298, 2003. a
Walling, D. E.: The sediment delivery problem, J. Hydrol., 65,
209–237, 1983. a
Wollheim, W. M., Bernal, S., Burns, D. A., Czuba, J., Driscoll, C., Hansen, A.,
Hensley, R., Hosen, J., Inamdar, S., Kaushal, S., Koenig, L. E., Lu, Y. H., Marzadri, A., Raymond, P. A., Scott, D., Stewart, R. J., Vidon, P. G., and
Wohl, E.: River network
saturation concept: factors influencing the balance of biogeochemical supply
and demand of river networks, Biogeochemistry, 141, 503–521, https://doi.org/10.1007/s10533-018-0488-0, 2018. a
Yang, C. T.: The movement of sediment in rivers, Geophysical Surveys, 3,
39–68, 1977. a
Yang, W., Hou, K., Yu, F., Liu, Z., and Sun, T.: A novel algorithm with heuristic information for extracting drainage networks from raster DEMs, Hydrol. Earth Syst. Sci. Discuss., 7, 441–459, https://doi.org/10.5194/hessd-7-441-2010, 2010. a
Zhang, L., Wang, G., Dai, B., and Li, T.: Classification and codification
methods of stream network in a river basin, a review,
Environmental Informatics Archives, 5, 364–372, 2007a. a
Zhang, Y., Liu, Y., and Chen, Z.: Multi-flow direction algorithms for
extracting drainage network based on digital elevation model, in:
Geoinformatics 2007: Geospatial Information Science, 6753, 67532B, https://doi.org/10.1117/12.761930,
2007b. a
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.
Streams and rivers drive several processes in hydrology, geomorphology, geography, and ecology....
Altmetrics
Final-revised paper
Preprint