Photographic record of Lesser Flamingo Phoeniconaias minor (Aves: Phoenicopteridae) in Ramganga river, Bareilly, India

Photographic record of Lesser Flamingo Phoeniconaias minor in Ramganga river, Bareilly, Uttar Pradesh, India. This is the first photographic record and northern most distribution of Lesser Flamingo from India.

Bayesian spatiotemporal statistical modelling of water quality within rivers

<p>Rivers are vital parts of the hydrosphere, providing ecosystem services and water for drinking and agriculture. However, rapid industrialisation and urbanisation globally leads to increasing pollution in many rivers. On their own, many in-river monitoring efforts in lower middle income countries do not yet provide enough information to adequately understand the general state or trends in freshwater ecosystems, presenting difficulties in efforts to mitigate water quality degradation. However, new sources of data such as satellites, drones and sondes provide better spatial and temporal coverage of the river network. This talk presents a statistical downscaling approach for the fusion of data from these different sources into a single product with improved accuracy and coverage compared to that of an individual source, through a Bayesian hierarchical modelling approach. The model development is motivated by the Ramganga river in northern India, a source of irrigation for crops and drinking water that supports millions of people, but suffers from heavy metal and nutrient pollution from population pressures, intensive agriculture and industries along its length, leading to water quality degradation and biodiversity loss. The work takes place as part of the Ramganga Water Data Fusion Project, funded by the UK Global Challenges Research Fund with the aim of informing work such as risk-based modelling and developing future monitoring design to improve mitigation efforts.</p>

Wavelet and Statistical Analysis of Dissolved Oxygen and Biological Oxygen Demand of Ramganga River Water

Ramganga river is the main tributary of holy river Ganga and navigates through various cities of Uttarakhand and Uttar Pradesh of India. Its water quality is very important because a lot of population is directly connected to this river. Wavelet transforms is a new analytical tool to analyze non-stationary signals/data because it captures the localized time frequency information of a signal. In wavelet transforms, the Approximation gives the low frequency terms and average behaviour of any data, while Detail gives the high frequency terms and differential behaviour of any data. The trend represents the slowest part of the signal and corresponds to the greatest scale value. As the scale increases, the resolution decreases, producing a better estimate of the unknown trend of the signal. The dissolved oxygen and biological oxygen demand data of station Kannauj, Uttar Pradesh from October 2015 to June 2020 are studied and processed by Haar wavelet transforms. The statistical parameters like skewness, kurtosis and correlation coefficient are determined and discussed. The strong agreement between wavelet analytical and statistical results is obtained.

Retrieval of Turbidity on a Spatio-Temporal Scale Using Landsat 8 SR: A Case Study of the Ramganga River in the Ganges Basin, India

Nowadays, space-borne imaging spectro-radiometers are exploited for many environmental applications, including water quality monitoring. Turbidity is a standout amongst the essential parameters of water quality that affect productivity. The current study aims to utilize Landsat 8 surface reflectance (L8SR) to retrieve turbidity in the Ramganga River, a tributary of the Ganges River. Samples of river water were collected from 16 different locations on 13 March and 27 November 2014. L8SR images from 6 March and 17 November 2014 were downloaded from the United States Geological Survey (USGS) website. The algorithm to retrieve turbidity is based on the correlation between L8SR reflectance (single and ratio bands) and insitu data. The b2/b4 and b2/b3 bands ratio are proven to be the best predictors of turbidity, with R2 = 0.560 (p < 0.05) and R2 = 0.726 (p < 0.05) for March and November, respectively. Selected models are validated by comparing the concentrations of predicted and measured turbidity. The results showed that L8SR is a promising tool for monitoring surface water from space, even in relatively narrow river channels, such as the Ramganga River.