Construction of fine resolution bioclimatic variables for Central Highlands and Southern Central Coast of Vietnam

Pham Bach Viet; Hoang Minh Duc; Tran Van Bang; Luu Hong Truong; Nguyen Dang Quang; Diep Dinh Phong

doi:10.15625/2615-9023/16062

Author affiliations

Authors

Pham Bach Viet Vietnam National Space Center, VAST, Vietnam
Hoang Minh Duc Southern Institute of Ecology, VAST, Vietnam
Tran Van Bang Vietnam National Space Center, VAST, Vietnam
Luu Hong Truong Southern Institute of Ecology, VAST, Vietnam
Nguyen Dang Quang Center for Hydro-Meteorological Technology Application, Vietnam
Diep Dinh Phong Southern Institute of Ecology, VAST, Vietnam

DOI:

https://doi.org/10.15625/2615-9023/16062

Keywords:

Bioclimatic variables, climate data, downscaling, multiple linear regression, precipitation, temperature

Abstract

In this study, 19 surface bioclimatic variables of high spatial resolution 0.00226^o (~ 250 m) are generated in a Geographic Information System by the combination of (1) the raster dataset of monthly temperature and precipitation obtained from the global WorldClim database at 0.00833^o spatial resolution for the period of 1960–2000; and (2) the climate data (temperature and precipitation) of the Central Highlands and Southern Central Coast collected from the 31 temperature and 97 precipitation recording sites for the period of 1991–2015. The statistical downscaling method is applied, using multiple linear regression analysis, in which elevation, geographic coordinates, and distance from the coast are treated as independent variables, to estimate the distribution of temperature; and the B-Spline interpolation method combined with multiple linear regression analysis is employed on precipitation over the study area. The outcomes of the two main analyses are computed to create 19 high spatial resolution bioclimatic variables. While using only local climate data on analyzing the regression models results in high fluctuation of estimated temperature, the combination of the two datasets is more informative. The spatial distribution of our interpolated precipitation is similar to the WorldClim data but has a smaller difference in the mean annual precipitation. The results, which shows higher spatial resolution and are closer to the observed data than those from the WorldClim, could be better applied for predicting species distribution in the region.

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References

Abatzoglou J. T., Dobrowski S. Z., Parks S. A. Hegewisch K. C., 2018. Data descriptor: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1985–2015. Scientific Data, 5: 170191. https://doi.org/10.1038/sdata.2017.191

Andrews D. G., 2010. An introduction to atmospheric physics (2nd ed). Cambridge University Press, pp. 237.

Bett N. N., Blair M. E., Sterling E. J., 2012. Ecological Niche Conservatism in Doucs (Genus Pygathrix). International Journal of Primatology, 33: 972–988. https://doi.org/10.1007/s10764-012-9622-3

Bohner J., Benjamin B., 2018. GIS and climatology and meteorology. In: Cova T. J., Tsou M-H. (Eds), GIS Methods and Techniques. University of Hamburg, Hamburg, Germany Elsevier Inc.

Booth T. H., 2018. Why understanding the pioneering and continuing contributions of BIOCLIM to species distribution modelling is important? Australasian Ecology, 43: 852–860.

Booth T. H., Nix H. A., Busby J. R., Hutchinson M. F., 2014. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions, 20: 1–9.

Collen B., Whitton F., Dyer E. E., Baillie J. E. M., Cumberlidge N., Darwall W. R. T., Pollock C., Richman N. I., Soulsby A. M., Böhm M., 2014. Global patterns of freshwater species diversity, threat and endemism. Global Ecological Biogeography 23: 40–51.

CGIAR Research Centers in Southeast Asia, 2016. The drought crisis in the Central Highlands of Vietnam. Assessment Report. https://core.ac.uk/download/pdf/ 132684728.pdf (last access Jan 10, 2021).

Daly C., Halbleib M., Smith J. I., Gibson W. P., Doggett M. K., Taylor G. H., Curtis J., Pasteris P.P., 2008. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology. https://doi.org/10.1002/joc.1688

Deb J. C., 2016. Assessing species vulnerability to climate change in Tropical Asia: Implications for biodiversity conservation and forest management. PhD thesis. School of Earth and Environment Sciences - University of Queensland, pp. 145.

Deb J. C., Phinn S., Butt S., McAlpine C. A., 2017. The impact of climate change on the distribution of two threatened Dipterocarp trees. Ecology and Evolution. https://doi.org/10.1002/ece3.2846

Earthdata: https://earthdata.nasa.gov/learn/ articles/nasa-shuttle-radar-topography-mission-srtm-version-3-0-global-1-arc-second-data-released-over-asia-and-australia), last access July 20, 2020.

Earthdata: NASA Shuttle Radar Topography Mission (SRTM) Version 3.0 Global 1 arc second Data Released over Asia and Australia.

Fick S. E., Hijmans R. J., 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. Wiley Online Library. https://doi.org/ 10.1002/joc.5086

Franklin J., 2010. Mapping species distributions: spatial inference and prediction, Cambridge University Press.

Hair J. F. Jr., Black W. C., Babin B.J., Anderson R. E., 2014, Multivariate Data Analysis (7th ed.). Pearson New International Edition, pp 739.

Hengl T., 2009. A practice guide to geostatistical mapping. (http://spatial-analyst.net/book/).

Hijmans R. J., Cameron S. E. Parra J. L., Jones P. G., Jarvis A., 2005. Very high-resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25: 1965–1978.

Hutchinson M. F., 1995. Interpolating mean rainfall using thin plate smoothing splines. International Journal of GIS, 9(4):

–403. https://doi.org/ 10.1080/02693799508902045

Hutchinson M. F., 1991. The application of thin plate smoothing splines to continent-wide data assimilation. In: Jasper J.D. (Ed), BMRC Research Report No. 27, Data Assimilation Systems, Bureau of Meteorology, Melbourne: 104–113.

John R. B., 1991. BIOCLIM - A Climate analysis and prediction system. In: Margules C. R., Austin M. P. (Eds), Natural Conservation: Cost effectively biological surveys and data analysis. CSIRO Australia.

Karger D. N. Conrad O., Böhner J., Kawohl T., Kreft H., Soria-Auza R. W., Zimmermann, N. E., Peter Linder H., Kessler M., 2017. Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4: 170122. https://doi.org/10.1038/sdata.2017.122

Kriticos D. J., Webber B. L., Leriche A., Ota N., Macadam I., Bathols J., Scott J. K., 2011. Methods in Ecology and Evolution. British Ecological Society, Methods in Ecology and Evolution, 3: 53–64.

Lanzante J. R., 1996. Resistant, robust and non-parametric techniques for the analysis of climate data: theory and examples, including applications to historical radiosonde station data. International Journal of Climatology, 16: 1197–1226.

Le Coz, M., François, D., Pierre, G., Guillaume, F., 2009. Assessment of Digital Elevation Model (DEM) aggregation methods for hydrological modeling: Lake Chad basin, Africa. Computers & Geosciences, 35(8): 1661–1670.

Liu C., White, M., Newell, G., Griffioen, P., 2014. Species distribution modelling for conservation planning in Victoria, Australia. Ecological Modelling 249:

–74. https://doi.org/10.1016/ j.ecolmodel. 2012.07.003

Loomis S. E., James M. R., Dirk V., Carrie M., De Cort G., Jaap, S. S. D., Daniel O., Hilde, E., Street-Perrott, F. A., Meredith A. K., 2017. The tropical lapse rate steepened during the Last Glacial Maximum. Science Advances, 3(1). https://doi.org/10.1126/sciadv.1600815

Ly S., Charles C., Degré A., 2013. Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale. A review. Biotechnol. Agron. Soc. Environ., 17(2): 392–406.

Maeda M., Yasutomi N., Yatagai A. & National Center for Atmospheric Research Staff, 2020. The Climate Data Guide: APHRODITE: Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources. (Eds). Retrieved from https://climatedataguide.ucar.edu/climate-data/aphrodite-asian-precipitation-highly-resolved-observational-data-integration-towards.

Marchi M., Sinjur I., Bozzano M., Westergren M., 2019. Evaluating WorldClim Version 1 (1961–1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate. Sustainability, 11(3043). https://doi.org/10.3390/su11113043

Ninyerola M., Pons, X., Roure J.M., 2000. A methodological approach of climatological. Journal of Climatology, 20: 1823–1841.

Nguyen D.N., Nguyen T. H., 2004. Climate and Climate Resources in Vietnam. Agricultural Publishing House, Hanoi, Vietnam, pp. 296 (in Vietnamese).

Nguyen D. Q., Renwick J., McGregor J. 2013. Variations of surface temperature and rainfall in Vietnam from 1971 to 2010. International Journal of Climatology 34(1): 249–264.

Nguyen D. Q., Renwick J., McGregor J. 2014. Variations of monsoon rainfall: A simple unified index. Geophysical Research Letters 41(2): 575–581.

Nguyen Tuan Anh, Le Duc Minh, Pham Viet Hung, Vu Thi Duyen, 2019. Modeling the Red-shanked Douc (Pygathrix nemaeus) distribution in Vietnam using Maxent. VNU Journal of Science: Earth and Environmental Sciences, 35(3): 61–71.

Peterson D. L., Schreiner E. G., Buckingham N. M., 1998. Gradients, vegetation and climate: spatial and temporal dynamics in the Olympic Mountains, U.S.A. Global Ecology and Biogeography Letters, 6(1): 7–17.

Fink A. H., Phan V. T., Pinto J., van der Linden R., Schubert D., Trinh T. L. and Ngo-Duc T., 2014. Climate change projections and selected impacts for Vietnam. In: Meon G., Pätsch M., Phuoc N. V. and Quan N. H. (eds.) EWATEC-COAST: Technologies for Environmental and Water Protection of Coastal Zones in Vietnam. EWATEC-COAST Working Group, pp. 35–56. ISBN 978-3-95404-852-6.

Porfirio L. L., Rebecca M., Harris B., Lefroy E. C., Hugh S., Gould S. F., Lee G., Bindoff N. L., Mackey B., 2014. Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change. PLoS One, 9(11): e113749. https://doi.org/ 10.1371/journal.pone.0113749

Prajapati A., Sapan N., Sheetal M., 2012. Evaluation of Different Image Interpolation Algorithms. International Journal of Computer Applications, 58(12): 6–12.

QGIS Development Team, 2019. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org.

Raghavan R. K., Goodin D. G., Hanzlicek G. A., Zolnerowich G., Dryden M. W., Anderson G. A., Ganta R. R., 2016. Maximum entropy‐based ecological niche model and bio‐climatic determinants of Lone Star Tick (Amblyomma americanum) Niche. Vector‐Borne and Zoonotic Diseases, 16(3): 205–211.

Regalado J. C., Nguyen Tien Hiep, Phan Ke Loc, Averyanov L., Harder D. K., 2005. New insights into the diversity of the Flora of Vietnam. Biologiske Skrifter, 55: 189–197.

Sharma V., Kilic A., Irmak S., 2016. Impact of scale/resolution on evapotranspiration from Landsat and MODIS images. Water Resources Research, 52(3): 1800–1819. doi:10.1002/2015WR017772.

Shen Y‐J., Yanjun S., Jason G., Alexander B., 2016. Spatial‐temporal variation of near‐surface temperature lapse rates over the Tianshan Mountains, central Asia. Advancing Earth and Space Science, 121(23): 14006–14017. https://doi.org/10.1002/2016JD025711

van Schingen M., Ha Q. Q., Pham C. T., Le T. Q., Nguyen T. Q., Bonkowski M., Ziegler T., 2016. Discovery of a new crocodile lizard population in Vietnam: Population trends, future prognoses and identification of key habitats for conservation. Revue Suisse de Zoologie, 123(2): 241–251.

Xu T., Hutchinson M., 2010. Anuclim Version 6.1 User guide. The Australian National University - Fenner School of Environment and Society.

Yang X., Xie X., Liu D. L., Ji F. and Wang L., 2015. Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region. Advances in Meteorology, 2015: Article ID 563629. doi: 10.1155/2015/563629

Yohe L. R., Flanders J., Hoang D. M., Vu L., Phung T. B., Nguyen Q. H., Sushma Reddy S., 2014. Unveiling the impact of human influence on species distributions in Vietnam: a case study using babblers (Aves: Timaliidae). Tropical Conservation Science, 7(3): 586–596.

Wagner M., Trutsching W., Bathke A. C., Ruprecht U., 2018. A first approach to calculate BIOCLIM variables and climate zones for Antarctica. Theorical Applied Climatology, 131: 1397–1451. https://doi.org/10.1007/s00704-017-2053-5

ANU: https://fennerschool.anu.edu.au/ research/products/anuclim

WorldClim1: https://worldclim.org

WorldClim2: https://worldclim.org/data/ v1.4/formats.html

WorldClim3: https://worldclim.org/data/ bioclim.html