International Journal of Contemporary Research in Multidisciplinary

Abstract:

The paper examines how the quality and diversity of plankton and of water quality were measured over time in the regulated rivers of Garhwal Himalayas region, incorporating both ecological issues and conventional analytical methods. Garhwal Himalaya is ecologically important and yet poorly represented in long-term hydro-ecological monitoring. Based on literature understanding and conceptual framework linking seasonal changes, physicochemical parameters and biotic responses, the study helps determine the role of natural forces and anthropogenic pressures, including dam construction, land-use change and urban runoff over time on the river health. The remote sensing observations and the seasonal data collection shows that indicators of water quality such as dissolved oxygen, temperature, pH, and nutrient indicators experience sharply different nutrient concentrations and water quality levels before and after monsoon seasons. Such fluctuations affect the communities of plankton especially of phytoplankton and zooplankton as vulnerable bioindicators of environmental equilibrium or disturbance. The researchers observed a close connection between degraded water quality of urbanized or controlled areas and lower plankton diversity. Moreover, geospatial analysis and AI-and-supported tools also increased the accuracy of temporal representation when identifying trends. The outcomes are aligned with using integrated monitoring solutions, which unite both biological measures and technological procedures to gain insight into the total effects of seasonal variations and of human involvement. Finally, the study underlines the need of time-integrating, ecosystem-based strategies to preserve ecological integrity of the rivers of Garhwal and to manage the rivers basin of the greater Himalaya on sustainable basis.

Keywords:

Garhwal Himalayas, water quality, plankton diversity, temporal assessment, regulated rivers, bioindicators, seasonal variation, remote sensing, AI-based monitoring, ecological health.

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