Abstract:Meteorological drought during the southwest monsoon season and for the northeast monsoon season over five meteorological subdivisions of India for the period 1901-2015 has been examined using district and all India standardized precipitation index (SPI). Whenever all India southwest monsoon rainfall was less than −10% or below normal, for those years all India SPI was found as −1 or less. Composite analysis of SPI for the below normal years, viz., less than −15% and −20% of normal rainfall years indicate that … Show more
“…In other words, in the case of long-term droughts, the behavior time series has a steeper slope, with significant trends with a high degree regardless of the dataset used. This result is a pattern concerning the trend analysis of the drought behavior time series, as found in Asia 23 and Oceania 72 . Figure 6 Percentage of the area with the significant trend and spatial distribution of the Sen’s slope of the drought behavior time series over Paraíba State (1998–2017).…”
Droughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann–Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region.
“…In other words, in the case of long-term droughts, the behavior time series has a steeper slope, with significant trends with a high degree regardless of the dataset used. This result is a pattern concerning the trend analysis of the drought behavior time series, as found in Asia 23 and Oceania 72 . Figure 6 Percentage of the area with the significant trend and spatial distribution of the Sen’s slope of the drought behavior time series over Paraíba State (1998–2017).…”
Droughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann–Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region.
“…We find stronger linkages for the regions close to the ocean (southeastern peninsular 5 comprising C1 to C4) compared to the inland regions with higher elevation (northwestern India comprising C5, C7). The results mentioned above are in congruence with the findings by Guhathakurta et al (2017); Mishra et al (2012). The spatial heterogeneity in the strength of the relationship between ENSO and precipitation may be a result of the tropical convection during the ESNO events (Bansod, 2011).…”
Abstract. A better understanding of precipitation dynamics in the Indian subcontinent is required since India’s society depends heavily on reliable monsoon forecasts. We introduce a nonlinear, multiscale approach, based on wavelets and event synchronization, for unraveling teleconnection influences on precipitation. We consider those climate patterns with highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple time scales. We find substantial variation across India and across time scales. In particular, El Niño/Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the southeast at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation particularly in the northern regions. The effect of PDO stretches across the whole country, whereas AMO influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improving precipitation forecasting.
“…The SPI introduced by McKee et al (1993) has been widely used. For calculation of the SPI, the precipitation depth can be employed as an input variable, and it has been extensively used to define dry and wet conditions in many countries and regions (Guhathakurta et al, 2017;Hosseini et al, 2020). With the availability of different satellite data and the widespread use of them, it has become possible to study drought using this method (Dutta et al, 2015).…”
Drought is a natural phenomenon that causes a lot of damages annually in various sectors, including agriculture and natural resources. The aim of this study is to evaluate the influence of meteorological drought index on vegetation index. For this purpose, the standard precipitation index (SPI) as a meteorological drought index is calculated using the precipitation data of 28 meteorological stations located on the area of Lorestane province, Iran, during the years 1987–2017. Then, the vegetation condition index (VCI) is computed using normalized difference vegetation index (NDVI) images that obtained from MODIS images of Terra satellite during 2000–2017. Dry, normal, and wet years were obtained based on the SPI results for 2008, 2013, and 2016, respectively. SPI and VCI were correlated using Pearson's correlation method. The results of the relationship between VCI and SPI showed that the highest Pearson correlation coefficient related to 9-month SPI in November was equal to 0.64. Multivariate linear regression was also performed between SPI and VCI, and the results showed that SPI was significantly correlated with VCI at 5% level over a period of 9 and 12 months. Finally, a confusion matrix was used to evaluate the compliance of the SPI and VCI drought classes. Results showed that the VCI had the highest compliance in the moderate drought class with SPI.
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