Published October 15, 2024 | Version 0
Dataset Open

CLSoilSWAT

Authors/Creators

  • 1. Doctorado en Ciencias de Recursos Naturales, Unoversidad de la Frontera
  • 2. ROR icon Center for Climate and Resilience Research
  • 3. Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibañez
  • 4. Department of Civil Engineering, Faculty of Engineering and Sciences, Universidad de la Frontera

Contributors

  • 1. Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibanez
  • 2. ROR icon Center for Climate and Resilience Research
  • 3. Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibañez
  • 4. Department of Civil Engineering, Faculty of Engineering and Sciences, Universidad de la Frontera

Description

The CLSoilSWAT is a 100 m spatial resolution soil classification dataset for Continental Chile, covering latitudes -35.68, -55.76 . It provides soil map classification based on CLSoilMaps product (Dinamarca et al., 2023; Galleguillos et al., 2022), created by cluster k-means algorithm developed for SWAT hydrological model application. 

The soil textures, hydraulic conductivity, available water capacity, bulk dencity, soil layers and soil depth are directly extrated an summarised by class from CLSoilMaps, while the soil albedo and soil erodibility factors are calculated based on  Abbaspour et al. (2019).

The optimal cluster number was calculate using CluesGaps Algorithm (Tibshirani et al., 2001) implemented in R (Meachler 2023) and modify to work on raster dataset.

Notes

Center for Climate and Resilience Research, Award: CR2, CONICYT/ FONDAP/15110009 Improving forest water yield and productivity quantification at the catchment scale by mapping root depth and ecophysiological thresholds with remote sensing and water transfer modeling, Award: ANID Fondecyt 1210932 The catchment's memory: understanding how hydrological extremes are modulated by antecedent soil moisture conditions in a warmer climate, Award: ANID Fondecyt 1212071 Management of global change impacts on hydrological extremes by coupling remote sensing data and an interdisciplinary modeling approach (Chi)2, Award: PCI ANID Chile-NSFC China NSFC190018 Drought and water security platform for catchment planning: historical evolution and future trajectories under global change, Award: Fondo de Investigación Estratégica en Sequía FSEQ210001

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Additional details

Related works

Cites
Dataset: 10.5281/zenodo.7464210 (DOI)

Dates

Available
2024-10

References

  • Abbaspour, S. Ashraf Vaghefi, K. Yang, and R. Srinivasan. Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications. Nature Scientific Data, 6:263, 2019
  • Dinamarca, D.I., Galleguillos, M., Seguel, O. et al. CLSoilMaps: A national soil gridded database of physical and hydraulic soil properties for Chile. Sci Data 10, 630 (2023). https://doi.org/10.1038/s41597-023-02536-x
  • Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the Number of Clusters in a Data Set via the Gap Statistic. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 63(2), 411–423. http://www.jstor.org/stable/2680607