Assessment of geostatistical methods for spatiotemporal analysis of drought patterns in East Texas, USA

Mukti Ram Subedi; Weimin Xi; Christopher B. Edgar; Sandra Rideout-Hanzak; Brent C. Hedquist

doi:10.1007/s41324-018-0216-9

Assessment of geostatistical methods for spatiotemporal analysis of drought patterns in East Texas, USA

Mukti Ram Subedi, Weimin Xi, Christopher B. Edgar, Sandra Rideout-Hanzak, Brent C. Hedquist

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Abstract

Drought is one of the most complex and least understood climate-related natural hazards. Active drought mitigation and contingency plan formulation often require a reliable drought distribution map. This study analyzed different spatial interpolation techniques to produce drought distribution map in East Texas, USA. Deterministic [inverse distance weighting (IDW) and spline], and geostatistical [ordinary kriging (Gaussian (KG) and spherical (KS))] interpolation techniques were employed as candidate methods for evaluation. Thirty-four years (1980–2013) of weather station data (N = 47) were used to calculate a 12-month Standardized Precipitation Evaporation Index (SPEI). The dataset was randomly divided into test data (70%, N = 33) and validation data (30%, N = 14). The resulting SPEI maps were cross-checked and validated through a validation dataset by calculating error matrices. The results indicate that KG tends to perform well in relatively drier conditions while IDW shows mixed results, performing well both in dry and wet conditions. The overall power of the four techniques to map 12-month drought conditions resulted in the order of IDW > KG > KS > spline.

Original language	English (US)
Pages (from-to)	11-21
Number of pages	11
Journal	Spatial Information Research
Volume	27
Issue number	1
DOIs	https://doi.org/10.1007/s41324-018-0216-9
State	Published - Feb 8 2019

Bibliographical note

Funding Information:
We would like to thank the NOAA Climate Data Online (CDO) program for providing data for this research. Sincere gratitude is also expressed to the Department of Physics and Geosciences, Texas A&M University-Kingsville, for providing access to the Geospatial Research Laboratory. Dr. W. Xi financially supported this work through his University Research Award, STEP-HG Faculty Research Award, and Research Startup Funds from Texas A&M University-Kingsville.

Funding Information:
We would like to thank the NOAA Climate Data Online (CDO) program for providing data for this research. Sincere gratitude is also expressed to the Department of Physics and Geosciences, Texas A&M University-Kingsville, for providing access to the Geospatial Research Laboratory. Dr. W. Xi financially supported this work through his University Research Award, STEP-HG Faculty Research Award, and Research Startup Funds from Texas A&M University-Kingsville.

Publisher Copyright:
© 2018, Korean Spatial Information Society.

Keywords

Drought
Interpolation
Inverse distance weighting
Kriging
SPEI
Spline
Validation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Assessment of geostatistical methods for spatiotemporal analysis of drought patterns in East Texas, USA",

abstract = "Drought is one of the most complex and least understood climate-related natural hazards. Active drought mitigation and contingency plan formulation often require a reliable drought distribution map. This study analyzed different spatial interpolation techniques to produce drought distribution map in East Texas, USA. Deterministic [inverse distance weighting (IDW) and spline], and geostatistical [ordinary kriging (Gaussian (KG) and spherical (KS))] interpolation techniques were employed as candidate methods for evaluation. Thirty-four years (1980–2013) of weather station data (N = 47) were used to calculate a 12-month Standardized Precipitation Evaporation Index (SPEI). The dataset was randomly divided into test data (70%, N = 33) and validation data (30%, N = 14). The resulting SPEI maps were cross-checked and validated through a validation dataset by calculating error matrices. The results indicate that KG tends to perform well in relatively drier conditions while IDW shows mixed results, performing well both in dry and wet conditions. The overall power of the four techniques to map 12-month drought conditions resulted in the order of IDW > KG > KS > spline.",

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AU - Hedquist, Brent C.

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Assessment of geostatistical methods for spatiotemporal analysis of drought patterns in East Texas, USA

Abstract

Bibliographical note

Keywords

UN SDGs

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