shailesh kumar singh

The Pacific Plate is one of the heaviest tectonic plates, hosting multiple Pacific Island countries. Geological research has long provided evidence of volcanic magmatic events, which have frequently caused subsurface activities. The formation of an active ring of fires across the Pacific Ocean is a geological signature of the intense volcanic events occurring over time.  The study aims to verify the close correlation between groundwater features and volcanic magmatic events, which is evident in near-surface ground signatures and geothermal systems. Hot water springs and geysers emerge as manifestations of the active molten volcanic processes, releasing intense heat flux, gases, and pressure. Pacific Island countries exhibit common geological features, particularly in terms of parent rock materials, resulting in a unique combination of aquifer characteristics essential for long-term hydrological sustainability. The sustainability of groundwater resources is intricately linked with volcanic events and their associated signatures, which emerge abruptly over time.  Furthermore, the study is to access the correlation of the pacific geological features within Fiji and various regions and its usages in terms of implementing water resources upon the identification of the active aquifers in the terrestrial and coastal plains. Fieldwork using various tomography methods from various regional countries executed by the Secretariat of the Pacific Community through the implementation of various programs has provided various supporting evidence that the magmatic activities featured by dikes, basins, and aquifers have water holding capacity which is hydrogeological connected to the main water aquifer.

The present study compared various empirical and data-driven algorithms to predict Actual Evapotranspiration (AET) using various hydro climatic variables. The AET over semi-arid climatic conditions of Hyderabad, Telangana, India, and Waipara (New Zealand) was estimated using different empirical methods-based PET using Budyko and Turc models. Modelled PET from five data-driven algorithms, such as Long short-term memory neural networks (LSTM), Artificial Neural Network (ANN), Gradient Boosting Regressor, Random Forest, and Support Vector Regression were trained to predict AET using meteorological variables. The results show simple empirical-based AET models, Budyko and Turc, can estimate AET very well. The results indicated that 99% accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with limited data. Both LSTM and ANN models based on PET have been noted as the most robust models for estimating AET with minimal climate data. It was observed that the meteorological variables of temperature and solar radiation have more significant contributions than other variables in the estimation of AET. In addition, the effects of the meteorological variables were found to be essential and effective in the estimation of AET. The research findings of the study reveal that under limited data availability, the best input combinations were identified as temperature and wind speed for estimating PET; temperature, wind speed, and precipitation for estimating AET for semi-arid climatology.

Hydrological models are used for various water resources application. To represent hydrological processes, it need parameters that achieve a discharge simulation as close to the observed series as possible. The simulation result depends on how accurately the models parameters are calibrated. The calibration of model parameters depend on various factors, such as calibration methods and selected objective functions. In this study, some of the automatic calibration methods were investigated and a comparison was made to give better prediction. Different optimization algorithms like SCE-UA, SA, and ROPE were used to illustrate and calibrate the conceptual model HBV-IWS. The study was conducted on the Upper Neckar catchment, Germany. The results show that almost all optimization algorithms gave a very similar result, but the ROPE algorithm seems to be more robust. This is due to ROPE giving a space of parameter values after calibration, instead of a single parameter set as in other optimizations.

Water access in Fiji is a critical issue as not everyone can access reticulated water sources which poses heavy dependence on groundwater sources. Along with its various uses, groundwater sources have been left vulnerable to contamination and depletion over the years due to changes in climatic patterns and human influence.  Traditional hydrometric groundwater survey is performed monthly to help determine recharge potential areas in Fiji but doesn’t cover all the water sources nor considers all the factors contributing to groundwater recharge. GIS, presents a contemporary approach to identify potential recharge areas which has been used in this study. In this study, the Multi Influencing Factor technique was used to determine the weightage of the factors aspect, slope, soil drainage, drainage density, parent materials and rainfall which are believed to have potentially contribute to groundwater recharge.  The basic GIS tools of overlaying and reclassification was used to denote the relationships between the factors and groundwater recharge and the potential groundwater recharges areas for Viti Levu and Vanua Levu.  Results have shown that higher potential areas are mostly areas with lower altitudes such as plains, wet areas (windward side), water bodies, excessively drained soils and parent materials with high exposure to weathering. Low ground water recharge potential areas are mostly mountainous, poorly drained soils, dense vegetation and poorly permeable bedrocks. These information is compared against existing boreholes and findings are to assist the government to invest more in those areas that have high recharge areas.