A synergy of linear model and wavelet analysis towards space-time characterization of aerosol optical depth (AOD) during pre-monsoon season (2007–2016) over Indian sub-continent

The pre-monsoon aerosol concentration plays a significant role in modifying precipitation amount over the Indian sub-continent. A large variety of aerosol from different sources produces a complex radiative and climate response through the interaction with the hydrometeorological parameters. In this study, we analyzed the space-time dynamics of aerosol optical depth (AOD) in relation to the meteorological and surface parameters over Indian sub-continent during pre-monsoon season from 2007 to 2016. The level-3 daily aerosol products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) were used. The IMD gridded rainfall and temperature, ECMWF derived RH and wind velocity, and GLADS derived soil moisture data were also used at daily time scale. For the space-time model, the Mann-Kendall trend test and a pixel-based multiple linear regression were used, while, wavelet transformation was used on these daily observations for analyzing periodicities of AOD. The time-series average shows moderate to high AOD (0.4–0.8), including a consistent positive anomaly in the Indo-Gangetic basin (IGP) in north India. A significant inter-annual variation is also observed both in MODIS and MISR datasets. The trend statistics shows an increasing trend of aerosol concentration in the eastern and southern India. The linear regression shows a variable response of AOD with changing magnitude of meteorological factors. However, a substantial spatial coverage of significantly decreased AOD is observed with increasing soil moisture content (β &lt; −0.04). The wavelet analysis manifests the abundances of 32–128 days of cycle with a periodic interjection of 8–32 days of cycle suggesting occurrence of fine and coarse mode aerosols events, respectively. The coherency of time series AOD and other covariates shows varying leading and lagging dynamics in these two principal periodicities. The findings, however, evidenced a notable difference in the space-time patterns of AOD in MODIS and MISR datasets. The analyzed AOD cycles are coincided with the Madden-Julian-Oscillation (MJO) that recurs every 30–60 days interval. The findings also support the theoretical proposition of elevated heat pump theory (EHP) driven by fine mode aerosols for occurring pre-monsoon and monsoon precipitation over the Indian sub-continent. The analyzed periodicity of AOD can provide useful insights in studying the short/long term variability of precipitation over polluted environments during the pre-monsoon season.</p

Investigation of fire regime dynamics and modeling of burn area over India for the twenty-first century

The characteristics of the vegetation fire (VF) regime are strongly influenced by geographical variables such as regional physiographic settings, location, and climate. Understanding the VF regime is extremely important for managing and mitigating the impacts of fires on ecosystems, communities, and human activities in forest fire-prone regions. The present study thereby aimed to explore the potential effects of the confounding factors on VF in India to offer actionable and achievable solutions for mitigating this concurring environmental issue sustainably. A global burn area (250 m) data (Fire-CCIv5.1) and fire radiative power (FRP) were used to investigate the dynamics of VF across seven different divisions in India. The study also used the maximum and minimum temperatures, precipitation, population density, and intensity of human modification to model forest burn areas (including grassland). The Coupled Model Intercomparison Project-6 (CMIP6) was used to predict the burn area for 2030 and 2050 future climate scenarios. The present study accounted for a sizable increasing trend of VF during 2001-2019 period. The highest increasing trend was found in central India (513 and 343 km 2 year -1 in the forest and crop fire, respectively), followed by southern India (364 km 2 year -1 in forest fire), and upper Indo-Gangetic plain (128 km 2 year -1 in crop fire). The FRP has varied significantly across the divisions, with the north-eastern Himalayas exhibiting the highest FRP hotspot. The maximum and minimum temperatures have the greatest influence on forest fires, according to Random Forest (RF) modeling. The estimated pre-monsoonal burn area for 2050 and 2050 future scenarios suggested a more frequent forest fire occurrence across India, particularly in southern and central India. A comprehensive forest fire control policy is therefore essential to safeguard and conserve forest cover in the regions, affected by forest fire periodically. </p

Mapping active paddy rice area over monsoon Asia using time-series sentinel – 2 images in Google earth engine; a case study over Lower Gangetic Plain

We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (β1) = 0.77) the total reported paddy rice area, though R2 remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security