Analyzing post-2000 groundwater level and rainfall changes in Rajasthan, India, using well observations and GRACE data.

Research on groundwater and water resources is essential for preserving viable environments. Although the arid area has been identified as a significant hotspot for groundwater depletion, the Indian desert region was not included in the initial analysis. This study intends to evaluate Rajasthan's groundwater level (GWL) and rainfall trends from 2000 to 2021 and how variations in GWLs are related to long-term rainfall. Annual GWL and rainfall data time series were collected from 921 monitoring stations for 33 districts of Rajasthan. The GWL trends and rainfall were identified using non-parametric modified Mann-Kendall test and Spearman rho techniques. Pearson's, Kendall's (tau b), and Spearman's analyses were used to determine the correlation between GWL and rainfall. The results from the modified Mann-Kendall and Spearman rho methods reveal that GWL has a significant declining trend in 38 % of districts, where 13 % have no trend, and the rest of 49 % have a rising trend. The yearly rainfall trend at 70 % and 30 % of the districts are rising and stable, respectively. A negative correlation between GWL depth and rainfall was discovered in each district, where 15 % are firm, 58 % are moderate, and 27 % are weak negative correlations. Also, the regression analysis estimates the effect of rainfall on GWL, which was observed: rainfall negatively influenced the depth of GWL at 58 % of the districts, had a positive impact at 33 %, and others had no effect. GRACE TWS anomaly shows a decreasing trend of -1.22 cm/yr, and GRACE and GWL anomalies have a positive relationship (r = 0.471). Results conclude that rainfall is the primary influencer on GWL in this semi-arid region vulnerable to drought.

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques.

Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980-2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann-Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027-2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

Phytochemical investigation and in vitro antinociceptive activity of Clerodendrum indicum leaves.

The crude ethanolic extracts of Clerodendrum indicum Linn. leaves were investigated for possible antinociceptive activity using acetic acid induced writhing model in mice. Phytochemical analysis was also carried out according to the standard procedures to identify the presence of different phytoconstituents in the ethanolic extract of the plant leaves. The study results showed 38.91 and 55.24% inhibition of writhings in the tested mice when ethanolic extract of Clerodendrum indicum Linn. leaves at doses of 250 and 500 mg kg(-1) body weight was given intraperitoneally, respectively. The study results were also compared with antinociceptive activity of the standard drug, Diclofenac sodium (68.37% inhibition) used at 25 mg kg(-1) body weight. At the above doses, the crude ethanolic extract of the plant showed significant antinociceptive activity in dose dependent fashion in acetic acid-induced writhing model in mice. The inhibition of writhings was calculated in respective to control group and it was found that p-values (<0.0001) obtained in all cases were extremely statistically significant. However, the phytochemical analysis showed the presence of alkaloid, steroid, saponin, tannin, reducing sugar and gum. The results suggest that crude ethanolic extracts of Clerodendrum indicum leaves possess significant antinociceptive properties justifying its folkloric use as analgesics and further research is necessary to isolate the principle phytochemical constituent(s) responsible for this activity.