Developing statistical downscaling to improve water quality understanding and management in the Ramganga sub-basin

通过“改善所有人获得清洁水和卫生设施的机会”来增强社会包容，需要对与水有关的生态系统及其可以为社会带来的好处有一个全面的了解。然而，“目前通过可持续发展目标进程收集的全球数据并未反映淡水生态系统的一般状况或已知趋势”。新颖的数学科学研究对于融合地球观测和实地数据源以填补知识空白、提供对水质的更好理解和解决发展中国家面临的水管理挑战至关重要。该提案将提供世界领先的统计研究，支持为恒河流域的Ramganga子流域开发水质监测和建模框架。传统的水采样是基于少数地点，非常劳动密集型和昂贵的，我们的建议汇集了来自新型原位传感器的数据，以高时间频率提供数据，再加上密集耦合的高端原位Ramganga的水下生物地理光学特性特征，以及由无人机部署的校准的小型化高光谱成像辐射计的测量结果。以及来自新卫星任务（哨兵2号）的数据。总之，这些提供了一种高效和前所未有的方法，可以收集Ramganga盆地各种环境和光水类型污染排放情景的重要数据。将这些数据与传统的WQ测量相结合，将提供非常理想的框架，以迄今无法实现的空间和时间分辨率推断WQ数据。恒河流域的水质和水资源占印度陆地总面积的26%，对印度这个全球人口最多、密度最大的国家之一（占印度人口的43%）的福祉至关重要。然而，由于快速工业化和城市化等活动，它们正在受到损害，而且由于缺乏历史和当代排放和质量数据，减缓努力受到阻碍。该项目将专门为Ramganga子流域开发和实施新的统计方法，将新的和现有的水质数据与遥感卫星数据（来自多个传感器的历史数据和最近哨兵任务的新检索数据）相结合。为了解决Ramganga子流域的水质挑战并充分利用新的数据流，将通过不同系数开发新的统计降尺度和数据融合方法，分层贝叶斯建模框架将纳入河网结构和流量的模型分位数。这些方法支持整合不同的数据源，从而能够预测水资源状况和相关的不确定性，为一系列社会经济和气候变化情景下基于风险的建模提供信息，并为未来的监测设计提供信息工具。全流域WQ估计的产出将提供给决策者和未来的研究人员，以指导政策和设计未来的采样地点和时间频率。

Empowering social inclusion through 'improving access to clean water and sanitation' for all requires arobust understanding of water-related ecosystems and the benefits that they can provide to society.However, 'the global data currently collected through the SDG process do not reflect the general state or trends known about freshwater ecosystems'. Novel mathematical sciences research is essential to enable fusion of Earth observation and on-the-ground data sources to fill the knowledge gaps, provide improved understanding of water quality and address the water management challenges faced by developing countries. This proposal will deliver world leading statistical research supporting the development of a water quality monitoring and modelling framework for the Ramganga sub-basin of the Ganges river basin.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 intensive coupling high-end in-situ above and below water characterisation of the biogeo-optical properties of the Ramganga 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. Coupling these data with conventional measures of WQ will provide the much desired framework for extrapolating WQ data at hitherto unachievable spatial and temporal resolutions.Covering 26% of India's total landmass, water quality and water resources in the Ganges basin are vitalfor the wellbeing of one of the largest and densest global populations (43% of India's population).However, they are being compromised due to activities such as rapid industrialization andurbanization, and mitigation efforts are hampered by lack of historical and contemporary discharge and quality data. This project will develop and implement new statistical methodology specifically for the Ramganga sub-basin to integrate the new and existing water quality data with remote sensing satellite data (both historical data from multiple sensors and new retrievals from recent Sentinel missions). To address the water quality challenges in the Ramganga sub-basin and to fully utilise the new data streams, 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. The output of catchment-wide WQ estimates will be made available to policy makers and future researchers to guide policy and design future sampling sites and temporal frequency.