Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California.

California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002-2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled groundwater level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical displacements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km3 from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of -5.7 ± 1.2 km3 yr-1 and -9.8 ± 1.7 km3 yr-1 occurred during the extended drought periods of January 2007-December 2009, and October 2011-September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater depletion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal.

Long-term analysis of glaciers and glacier lakes in the Central and Eastern Himalaya.

Himalayan glaciers represent both an important source of water and a major suite of geohazards for inhabitants of their downstream regions. Recent climate change has intersected with local topographic, geomorphic, and glaciological factors to drive complex patterns of glacier thinning, retreat, velocity change, and lake development. In this study, we analyze the long-term variations in surface elevation change and velocity of the glaciers in the Central and Eastern Himalaya using existing and newly generated datasets spanning 1975 to 2018. We have used modelled (e.g., debris and ice thickness) and remote sensing datasets (e.g., Corona, Hexagon, and Landsat images) to investigate the impact of debris cover and the evolution of proglacial lakes on the glacier response in the region. We found that lake-terminating glaciers (lake TGs) have significantly higher thinning, velocity, and deceleration over time than land-terminating glaciers (land TGs). Lakes have shown an overall growth of 98 % in area and 40 % in number during 1975-2017. New proglacial lakes will likely continue to develop, and existing ones will keep expanding, influencing the frontal changes and dynamics of the lake-terminating glaciers. Debris-covered glaciers have undergone similar thinning compared to clean-ice glaciers, both for lake and land TGs; however, variations exist across the ablation zones between clean and debris-covered glaciers which this study further explores using a data-driven approach. Overall, the proglacial lakes development, changes in debris coverage, and topography significantly affect the glacier responses in the regions.

Synchronization of Boron application methods and rates is environmentally friendly approach to improve quality attributes of Mangifera indica L. On sustainable basis.

Micronutrient deficiency in the soil is one of the major causes of mango fruit and yield's poor quality. Besides, the consumption of such a diet also causes a deficiency of micronutrients in humans. Boron deficiency adversely affects the flowering and pollen tube formation, thus decreasing mango yield and quality attributes. Soil and foliar application of B are considered a productive method to alleviate boron deficiency. A field experiment was conducted to explore the Boron most suitable method and application rate in mango under the current climatic scenario. There were nine treatments applied in three replications. The results showed that application of T8 = RD + Borax (75 g plant -1 as a basal application) + H3 BO3 (0.8% as a foliar spray) and T9 = RD + Borax (150 g plant -1 as a basal application) + H3 BO3 (0.8% as a foliar spray) significantly enhanced the nitrogen, potassium, proteins, ash, fats, fiber, and total soluble solids in mango as compared to the control. A significant decrease in sodium, total phenolics contents, antioxidant activity, and acidity as citric acid also validated the effective functioning of T8 = RD + Borax (75 g plant -1 as a basal application) + H3 BO3 (0.8% as a foliar spray) and T9 = RD + Borax (150 g plant -1 as a basal application) + H3 BO3 (0.8% as a foliar spray) as compared to control. In conclusion, T8 = RD + Borax (75 g plant -1 as a basal application) + H3 BO3 (0.8% as a foliar spray) and T9 = RD + Borax (150 g plant -1 as a basal application) + H3 BO3 (0.8% as a foliar spray) is a potent strategy to improve the quality attributes of mango under the changing climatic situation.