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Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000–2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion. [ABSTRACT FROM AUTHOR]

Understanding the LULC change is essential to adopt the new development activities in the particular study area. The study reveals the LULC change for four different decades (1990, 2000, 2011, and 2022) of the Sulur-Singanallur region of the Coimbatore district. In this study, the Landsat satellite data of four decades were used to assess the regional LULC changes and identified the area under Built-up land increased from 6 to 25.77% and Waterbodies decreased by 5.48% (6–0.52%). Similarly, the current scenario of agriculture has decreased as Crop lands by 12.77% (30–17.23%). At the same time, the fallow land has risen and fall from starting to the ending years. Hence, the CA-ANN method has been adopted using the Modules for Land Use Change Evaluation (MOLUSCE) plugin of QGIS software. The spatial entities such as DEM, slope, distance to river, and distance to road were utilized as drivers to predict the future LULC of 2030, 2040, and 2050. The study's findings reveal that the crop land has decreased to 10.90%, 8.89% and 8.87%, respectively. Similarly, the waterbodies will be decreased by upto 1%, and the prediction findings portrays the increasing trend was observed in Built-up area and the better kappa statistics results identified as 0.81, 0.76, and 0.76 for 2030, 2040, and 2050. The observation of post classification shows that MLC is one of the best suited method and producing realistic result in the study for the change detection analysis. Overall, the outcome of the study showcased the continuous increase Built-up land were declined the natural land environment. If it continues, the agriculture ecosystem will be affected, and minimizes the cropping pattern and further anthropogenic influences will also reduce the agricultural activities in future. This modelling study results serves the benefit to land managers, stakeholders, farmers, and researchers to take possible measures immediately to manage the land sustainably. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Carbamazepine (CBZ), an anti-seizure drug, is widely prescribed for the management of focal seizures. At a given therapeutic dose, CBZ exhibits marked interindividual variation in the plasma CBZ levels. The aim wasto study the influence of EPHX1 c.337 T>C and UGT2B7*2 genetic polymorphisms on plasma carbamazepine (CBZ) levels in persons with epilepsy (PWE) from South India. METHODS: 115 PWE belong to South India origin who are on carbamazepine monotherapy were recruited. Genotyping of the two variants weredone using RT-PCR method. PWE who had seizure freedom for one year and their last dose which was not changed for one year duration were included and their plasma levels of CBZ and its active metabolite CBZ 10,11 epoxide were analysed by reverse phase HPLC. RESULTS: In EPHX1 c. 337 (T>C) polymorphism, the PWE carrying CC had lower plasma CBZ levels when compared to CT genotype (2.45 µg/ml vs 3.15 µg/ml. In UGT2B7*2, PWE carrying homozygous mutant TT had higher levels when compared with CT (3.09 µg/ml vs 2.74 µg/ml) genotype but found no statistical significance. Mutant genotype of EPHX1 (CC) had higher metabolic ratio compared to TT genotype (1.33 vs 1.17) but not found to be statistically significant. Mutant genotype of UGT2B7*2 (TT) was found to be having lower metabolic ratio when compared with CC genotype (1.18 vs 1.35; p value =0.08). CONCLUSION: PWE carrying EPHX1 c.337 T>C (rs1051740) and UGT2B7*2 (rs7439366) genetic polymorphisms did not affect the plasma CBZ levels and metabolic ratio of PWE of South Indian origin. However, this finding should be confirmed in a larger sample size which may help in optimization and personalized CBZ therapy in South Indians. [ABSTRACT FROM AUTHOR]

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000-2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Prediction of the Indian summer monsoon rainfall is essential for the country because the food production depends on the rainfall during the summer monsoon season. India being an agricultural country, its economy also depends on the monsoon rainfall. Various institutes across the world give global prediction. An experimental prediction based on the numerical simulations produced by North American Multi-Model Ensemble (NMME) has been attempted for the initial conditions of May to understand the strength, variability, predictability, and associated changes in the Indian summer monsoon. This study is particularly aimed to find out the efficacy of NMME models over the Indian region during the southwest monsoon season. The analysis shows that all the nine models underestimate the climatology. A mismatch exists between the spatial patterns of model climatology as compared to the observed climatology. It is found that models underestimate the inter-annual variability of the precipitation as compared to the observed ones. This can be attributed to the overestimation of sea surface temperature-Indian summer monsoon rainfall (SST-ISMR) response. This might lead to poor performance of the model in terms of precipitation prediction. The spatial correlation shows varying correlation pattern as compared to the observed one. However, almost all models have positive correlation over the peninsular India. The basic idea of MME approach is to generate a single prediction from the predictions from different models. The MME approach shows positive correlation over the peninsular and central India. [ABSTRACT FROM AUTHOR]

Seasonal predictions of rainfall during the Indian monsoon from the North American Multi-Model Ensemble (NMME) have been used to compute the prediction skill. Observed and model-predicted rainfall have been put in three categories (namely below-normal, above-normal, and near-normal) based on the value of interannual standard deviation. In this study, focus is on the rainfall in below-normal rainfall category over central India. Forecast products were evaluated against the observed rainfall data for the period from 1982 to 2009. Simple model averaging and singular value decomposition (SVD) methods have been used to prepare the multi-model ensemble (MME) predictions. The root mean square error over central India is very less in the MME predictions compared to the member models. The weighted MME scheme using SVD method only marginally improves the skill over the simple MME scheme. The deterministic forecast skill is low (the correlation coefficient is non-significant) over central India even when the SVD scheme is employed. Both parametric and non-parametric methods have been applied to prepare probabilistic forecasts using multi-model ensemble for seasonal prediction. The probabilistic forecasts are reliable and usable as the hit rate is more than the false alarm rate for below-normal rainfall category. [ABSTRACT FROM AUTHOR]

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982-2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low. [ABSTRACT FROM AUTHOR]