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Publication . Article . Other literature type . 2021

EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia

Alexander Kmoch; Arno Kanal; Alar Astover; Ain Kull; Holger Virro; Aveliina Helm; Meelis Pärtel; +2 Authors
Open Access
Published: 20 Jan 2021 Journal: Earth System Science Data, volume 13, pages 83-97 (eissn: 1866-3516, Copyright policy )
Publisher: Copernicus GmbH
Country: Estonia
Abstract

To understand, model, and predict landscape evolution, ecosystem services, and hydrological processes, the availability of detailed observation-based soil data is extremely valuable. For the EstSoil-EH dataset, we synthesized more than 20 eco-hydrological variables on soil, topography, and land use for Estonia (https://doi.org/10.5281/zenodo.3473289, Kmoch et al., 2019a) as numerical and categorical values from the original Soil Map of Estonia, the Estonian 5 m lidar DEM, Estonian Topographic Database, and EU-SoilHydroGrids layers. The Soil Map of Estonia maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 and forms the basis for EstSoil-EH. It is the most detailed and information-rich dataset for soils in Estonia, with 75 % of mapped units smaller than 4.0 ha, based on Soviet-era field mapping. For each soil unit, it describes the soil type (i.e. soil reference group), soil texture, and layer information with a composite text code, which comprises not only the actual texture class, but also classifiers for rock content, peat soils, distinct compositional layers, and their depths. To use these as eco-hydrological process properties in modelling applications we translated the text codes into numbers. The derived parameters include soil layering, soil texture (clay, silt, and sand contents), coarse fragments, and rock content of the soil layers within the soil profiles. In addition, we aggregated and predicted physical variables related to water and carbon cycles (bulk density, hydraulic conductivity, organic carbon content, available water capacity). The methodology and dataset developed will be an important resource for the Baltic region, but possibly also for all other regions where detailed field-based soil mapping data are available. Countries like Lithuania and Latvia have similar historical soil records from the Soviet era that could be turned into value-added datasets such as the one we developed for Estonia.

Subjects by Vocabulary

Microsoft Academic Graph classification: Environmental science Hydrological modelling Soil texture Available water capacity Silt Soil map Soil horizon Soil type Soil water Soil science

Library of Congress Subject Headings: lcsh:Environmental sciences lcsh:GE1-350 lcsh:Geology lcsh:QE1-996.5

Subjects

General Earth and Planetary Sciences

47 references, page 1 of 5

Abbaspour, K. C., Vaghefi, S. A., Yang, H. and Srinivasan, R.: Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications, Sci. Data, 6, 263, https://doi.org/10.1038/s41597-019-0282-4, 2019.

Abdelbaki, A. M.: Evaluation of pedotransfer functions for predicting soil bulk density for U.S. soils, Ain Shams Eng. J., 9, 1611- 1619, https://doi.org/10.1016/j.asej.2016.12.002, 2018. [OpenAIRE]

Adams, W. A.: The Effect of Organic Matter on the bulk and true Densities of some Uncultivated Podzolic Soils, J. Soil Sci., 24, 10-17, https://doi.org/10.1111/j.1365-2389.1973.tb00737.x, 1973.

Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrol. Sci. B., 24, 43- 69, https://doi.org/10.1080/02626667909491834, 1979.

Breiman, L.: Random Forests, Mach. Learn., 45, 5-32, https://doi.org/10.1023/A:1010933404324, 2001.

Calhoun, T. E., Ellermäe, O., Kõlli, R., Lemetti, I., Penu, P., and Smith, C. W.: Benchmark Soils of Estonia Researched thru Baltic - American Collaboration, Problems of Estonian Soil Classification, Trans. Est. Agric. Univ., 198, 76-114, 1998.

Caruana, R. and Niculescu-Mizil, A.: An Empirical Comparison of Supervised Learning Algorithms, in: Proceedings of the 23rd International Conference on Machine Learning, 161-168, ACM, New York, NY, USA, 25-29 June 2006.

Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991-2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.

Dipak, S. and Abhijit, H.: Physical and Chemical Methods in Soil Analysis, New Age International Ltd., New Delhi, 2005.

Ditzler, C., Scheffe, K., and Monger, H. C.: Soil survey manual. USDA Handbook 18, Soil Science Division, Government Printing Office, Washington, D.C., 2017.

Funded by
EC| GLOMODAT
Project
GLOMODAT
Enhancing data fusion, parallelisation for hydrological modelling and estimating sensitivity to spatial parameterization of SWAT to model nitrogen and phosphorus runoff at local and global scale
  • Funder: European Commission (EC)
  • Project Code: 795625
  • Funding stream: H2020 | MSCA-IF-EF-RI
Validated by funder
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Rural Digital Europe
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