Physicochemical Parameters of Gomati River at Lucknow in Uttar Pradesh (India)

Gomati River originate from Madhoganj Tanda village in Pilibhit district, U.P. it passes through the district of Shahjahanpur, kheri, Hardoi, Sitapur, Janpur and ultimately merge in Ganga. River water is significant for every living organism as well as aquatic life. Water pollution is a major global problem. Modernization and urbanization have polluted the river water and degraded the status. All over the world we are seeing that drain is the main source of water pollution especially for rivers flowing within the city. This drain generally carries industrial effluent, domestic waste, sewage and medicinal waste resulting in poor water quality. Gomati River receives industrial as well as domestic waste from various drains of Lucknow city. As Gomati river is the only source of surface water near the communities. A total 20 parameters namely Temperature, pH, Turbidity, Conductivity, Total dissolved solids (TDS), Total suspended solids (TSS), Total solids (TS), Dissolved oxygen (DO), Biological oxygen demand (BOD) Chemical oxygen demand (COD), Alkalinity, Total hardness, Calcium as ca, Magnesium as Mg, Chloride, Fluoride, Sulphate as So4, Nickel as Ni, Lead as Pb, and Zinc as Zn where analysed and their variation is discussed to obtain the impact of effluents on water quality. From the result it was found higher than the permissible limit of WHO and BIS.

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

Accelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to -understand these events’ propagation in the near future. We have considered sixteen extreme climate indices defined by the World Meteorological Organization’s Expert Team on Climate Change Detection and Indices from a long-term dataset (1951–2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non-stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain combinations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indicating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16-year bands, and the two-year period was dominant in the global power spectrum around 1970–1990. One interesting finding is that a dominant two-year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes.

Estimation of Runoff under changed climatic scenario of a Meso Scale River by Neural Network Based Gridded Model Approach

As climate change is linked with changes in precipitation, evapotranspiration and changes in other climatological parameters, these changes will be affected runoff of a river basin. Gomati River basin is the largest river basin among all the river basin of Tripura. Due to the increase in settlement in the Gomati river basin and climate change may threaten natural flow patterns that endure its diversity. This study assesses the impact of climate change on total flow of a catchment in North East India (Gomati River catchment). For this assessment, the Group Method of Data Handling Modeling System (GMDH) model was used to simulate the rainfall-runoff relationship of the catchment, with respect to the observed data during the period of 2008–2009. The statistically downscaled outputs of HadGEM2-ES (Hadley Centre Global Environment Model version 2), general circulation models (GCMs) scenario was used to assess the impacts of climate change on the Gomati River Basin. Future projections were developed for the 2030s, 2040s and 2050s projections, respectively. The results from the present study can contribute to the development of adaptive strategies and future policies for the sustainable management of water resources in North East, Tripura.