Developing Statistical Downscaling to Improve Water Quality Understanding and Management in the Ramganga Sub-Basin

Ray, S. , Scott, M. and Miller, C. (2020) Developing Statistical Downscaling to Improve Water Quality Understanding and Management in the Ramganga Sub-Basin. JSM 2020, 02-06 Aug 2020.

Abstract

Traditional water sampling is based on a small number of sites, and is very labour intensive and expensive, and our proposal brings together data from new in-situ sensors, delivering data at high temporal frequency, coupled with measurements from calibrated miniaturised hyperspectral imaging radiometers deployed from drones, and data from new satellite missions (Sentinel 2). Together, these provide an efficient and unprecedented means of collecting significant data across a range of environments and pollution discharge scenarios of optical water types in the Ramganga basin. We develop novel statistical downscaling and data fusion methodologies through a varying coefficient, hierarchical Bayesian modelling framework will be developed to incorporate river network structure and model quantiles of flow. These approaches support integration of disparate data sources to enable prediction of water resource condition and associated uncertainties to inform risk-based modelling under a range of socio-economic and climate change scenarios, and provide tools to inform future monitoring design.

• Item Type: Conference or Workshop Item
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Miller, Professor Claire and Scott, Professor Marian and Ray, Professor Surajit
• Authors: Ray, S., Scott, M., and Miller, C.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics

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