Global spatiotemporal consistency between meteorological and soil moisture drought indices

HESAMI AFSHAR M., Bulut B., DÜZENLİ E., Amjad M., YILMAZ M. T.

Agricultural and Forest Meteorology, cilt.316, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 316
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.agrformet.2022.108848
  • Dergi Adı: Agricultural and Forest Meteorology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Communication Abstracts, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Agricultural drought, SPI, SPEI, SSMI, Soil moisture, Classification, AGRICULTURAL DROUGHT, LAND-COVER, REGIONAL WATER, CLASSIFICATION, MODEL, SPEI, VEGETATION, INDICATOR, SELECTION, PRODUCTS
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier B.V.In this study, the consistency of a set of meteorological and soil moisture drought indices has been analyzed using the linear relationship between them. Commonly used Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are compared to soil moisture drought index (SSMI) between the years 1981 and 2019. The most consistent meteorological drought index with SSMI has been selected as the best representative of it for 175,840 pixels, globally. Later, different classification methods (i.e., Decision Tree, K-Nearest Neighbors, Naïve Bayesian, Support Vector Machine, and Random Forest) are trained to identify the best representative of SSMI by considering the most consistent meteorological drought index with SSMI as target, and five ancillary datasets of average precipitation, average temperature, vegetation cover (Normalized difference vegetation index; NDVI), climate class, and land cover class as input variables of them, respectively. Results show that over 48% of pixels SPI, and over 32% of pixels SPEI show significantly better consistency with SSMI (at p-value: 0.05). Overall, in regions with cooler temperatures and low and high vegetation cover densities, the SPI, and over warmer areas with mid-range of vegetation density, the SPEI provides a better correlation with SSMI. The performance of different classification methods over validation pixels showed that the K-Nearest Neighbor method can identify the best correlated meteorological drought index with SSMI better than other methods. Overall results highlight the impact of climate and land use on interactions of meteorological and soil moisture drought indices, particularly that the classification efforts showed that, on average, monitoring of drought events by combine use of SPI and SPEI (each one over the area where it is identified as the best indicator of SSMI) improves the correlation between meteorological and soil moisture drought indices by 4 and 10%, compared to the uniform use of SPI and SPEI, respectively.