Abstract
Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an effort to assess and analyse drought characteristics based on two drought indices, i.e., Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI), and model agricultural drought risk with Fast-and-frugal decision tree (FFT) model in Bangladesh from 2001 to 2016. We identified drought occurrence and its dynamics with three-time scale, i.e., SPI3J (November-January), SPI3A (February-April) and SPI6A (November-April), and three rice-growing seasons, i.e., Aus (March-July), Aman (June-November), and Boro (November-May) from TRMM (Tropical Rainfall Measuring Mission) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. The results demonstrate that TRMM had good consistency with rain gauge measurement compared to CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data to derive SPI3J, SPI3A and SPI6A. Overall results confirmed that more drought frequency observed in SPI6A than SPI3J and SPI3A time scale, representing moderate to severe drought throughout the country. Regarding agricultural drought resulting from VCI demonstrated Boro rice-growing season as more vulnerable crop growing season affected by severe to extreme drought event. Validation results of VCI exhibited a high correlation with rice yield data than in-situ soil moisture data. Results of the FFT model show that out of ten predictor variables SPI3J and SPI6A caused agricultural drought with SPI value less than -1.08 and -1.21 respectively. Additionally, the model characterized SPI3J and SPI6A as the most critical driving factors with the highest balanced accuracy triggering agricultural drought risk in Bangladesh.
Highlights
Drought is natural repetitive phenomena causing destructive disasters that considerably influence the environment, agriculture and economy of a country
Drought analysis based on Standardized Precipitation Index (SPI) & Vegetation Condition Index (VCI) index detected dry and wet periods, which is very important to identify the meteorological and agricultural drought spatially and for monitoring drought in future in the country
Regarding meteorological drought more drought frequency was observed in SPI6A time scale compared to SPI3J and SPI3A
Summary
Drought is natural repetitive phenomena causing destructive disasters that considerably influence the environment, agriculture and economy of a country. We can confess that the use of vegetation indices for drought study derived from time-series remote sensing data set or satellite images are scarce in Bangladesh. The monthly precipitation from TRMM is slightly different from that of the meteorological station due to the changes between the spatial scales of the two kinds of rainfall data This analysis confirmed the consistency of the two data sets is high with a clear linear correlation, allowing it appropriate for the calculation of the spatio-temporal distribution of drought. 3) PRINCIPAL COMPONENT ANALYSIS AND FAST- FRUGAL DECISION TREE (FFT) MODEL The association among meteorological and agricultural drought along with the contribution of each variable to the SPI and VCI time series data, a multivariate technique named principal component analysis (PCA) was used in this study. FFT analysis was done with ‘‘FFTress’’ package developed by Phillips et al [69] in R software
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