Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Monitoring agricultural drought using different indices based on remote sensing data in the Brazilian biomes of Cerrado and Atlantic Forest

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.

Temperature vegetation dryness index (TVDI) for drought monitoring in the Guangdong Province from 2000 to 2019

Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky-Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P < 0.001 and P < 0.05) compared to TVDIEVI, and the average Pearson'R correlation coefficient was slightly higher than that of TVDIEVI, indicating that TVDINDVI responded better to drought in Guangdong Province. Our conclusion reveals that drought-prone regions in Guangdong Province are concentrated in the Leizhou Peninsula in southern Guangdong and the Pearl River Delta in central Guangdong. We also analyzed the phenomenon of winter-spring drought in Guangdong Province over the past 20 years. The area coverage of different drought levels was as follows: mild drought accounted for 42% to 64.6%, moderate drought accounted for 6.96% to 27.92%, and severe drought accounted for 0.002% to 1.84%. In 2003, the winter-spring drought in the entire province was the most severe, with a drought coverage rate of up to 84.2%, while in 2009, the drought area coverage was the lowest, at 49.02%. This study offers valuable insights the applicability of TVDI, and presents a viable methodology for drought monitoring in Guangdong Province, underlining its significance to agriculture, environmental conservation, and socio-economic facets in the region.

An assessment of the impact of Indian summer monsoon droughts on atmospheric aerosols and associated radiative forcing at a semi-arid station in peninsular India

A continuing increase in droughts/floods in Asian monsoon regions and worsening air quality due to aerosols are the two biggest threats to the health and well being of over 60% of the world's population. This study focuses on in-situ observations of atmospheric aerosols and their impact on shortwave direct aerosol radiative forcing (SDARF) during the southwest monsoon season (June-September) from 2015 to 2020 over a semi-arid station in Southern India. The Standardized precipitation index (SPI) is used to identify the droughts and normal monsoon years. Based on the SPI index, 2015, 2016, and 2018 were considered the drought monsoon years, while 2017, 2019, and 2020 were chosen as the normal monsoon years. During the drought monsoon years (normal monsoon years), the monthly mean black carbon (BC) was 1.17 ± 0.25 (0.72 ± 0.18), 1.02 ± 0.31 (0.64 ± 0.17), 1.02 ± 0.38 (0.74 ± 0.28), and 1.28 ± 0.35 μg/m³ (0.88 ± 0.21 μg/m³), for June, July, August and September respectively. The lower BC concentration during the normal monsoon years is mainly due to the enhanced wet-removal rates by high rainfall over the measurement location. In July, there was a high ventilation coefficient (VC) and low concentration of BC, while in September, low VC, and a high concentration of BC was observed in both the drought and the normal monsoon years. In addition, a plane-parallel radiative transfer model was used to estimate shortwave direct aerosol radiative forcing for composite and without BC at various surfaces, including the surface (SUF), atmosphere (ATM), and top of the atmosphere (TOA). During the drought monsoon years (normal monsoon years), the estimated monthly mean ATM forcing was 17.6 ± 2.4 (13.9 ± 2.1), 17.5 ± 7.5 (12.7 ± 4.4), 17.2 ± 4.0 (13.5 ± 1.9), and 17.4 ± 2.8 Wm^-2 (14.6 ± 0.7 Wm^-2) for June, July, August, and September, respectively. During the drought monsoon years, the estimated BC forcing was substantially larger (8.8 ± 2.6 Wm^-2) than that of normal monsoon years (6.0 ± 1.5 Wm^-2). It indicates the important role of absorbing BC aerosols during the drought monsoon years in introducing additional heat to the lower atmosphere, particularly over peninsular India.

An Integrated Method for Identifying Present Status and Risk of Drought in Bangladesh

The occurrence and severity of agricultural droughts may not be dependent upon climatic variables alone. Rather increasingly, drought is affected by human interventions such as irrigation. Anthropogenic activity has introduced uncertainty in the assessment of current drought and future drought risk in many parts of the world; neither climatic nor remote sensing data alone are able to assess drought conditions effectively. In response, we present a simple approach to assess drought by combining a remote sensing-based drought index, the Temperature Vegetation Dryness Index (TVDI), climate data (i.e., rainfall and temperature), and field observations to evaluate recent drought conditions in northwestern Bangladesh (NWB). Applying this approach, we gained five insights: (i) the TVDI successfully indicated the drought conditions of NWB and agrees with field observations, (ii) the integrated use of TVDI and climate data (such as rainfall and temperature) provides the best understanding of the difference between meteorological drought and droughts resulting from surface moisture conditions, (iii) the TVDI results agree with rainfall data (r2 = 0.40 in March and r2 = 46 in April) in a part of the study area (NWB) where irrigation is not available, (iv) the TVDI can be used along with climate data to predict the potential risk of drought, and (v) while meteorological drought exists due to low rainfall and high temperature in this NWB in pre-monsoon season, because of widespread irrigation practices, meteorological drought is unable to trigger agricultural drought over most parts of the study area. The findings imply that there is a potential risk of drought in NWB, since any disruption of irrigation water supply could trigger a severe agricultural drought over the whole region. This is similar to what is currently observed over a small part of NWB.

Comparative Analysis of Drought Indicated by the SPI and SPEI at Various Timescales in Inner Mongolia, China

The global climate is noticeably warming, and drought occurs frequently. Therefore, choosing a suitable index for drought monitoring is particularly important. The standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) are commonly used indicators in drought monitoring. The SPEI takes temperature into account, but the SPI does not. In the context of global warming, what are their differences and applicability in regional drought monitoring? In this study, after calculating the SPI and SPEI at 1-, 3-, 6-, and 12-month timescales at 102 meteorological stations in Inner Mongolia from 1981 to 2018, we compared and analyzed the performances of the SPI and SPEI in drought monitoring from temporal and spatial variations, and the consistency and applicability of the SPI and SPEI were also discussed. The results showed that (1) with increasing timescale, the temporal variations in the SPI and SPEI were increasingly consistent, but there were still slight differences in the fluctuation value and continuity; (2) due to the difference in time series, the drought characteristics identified by the SPI and SPEI were quite different in space at various timescales, and with the increase in timescale, the spatial distributions of the drought trends in Inner Mongolia were basically consistent, except in Alxa; (3) at the shortest timescale, the difference between the SPI and SPEI was the largest, and the drought reflected by the SPI and SPEI may be consistent at long timescales; and (4) compared with typical drought events and vegetation indexes, the SPEI may be more suitable than the SPI for drought monitoring in Inner Mongolia. It should be noted that the adaptability of the SPI and SPEI may be different in different periods and regions, which remains to be analyzed in the future.

Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought.