The availability of water is a key driver of agricultural productivity. It directly impacts plant growth, and in many countries and locations it is in short or over supply. The impact of water availability on global food production is seen as a key global risk and challenge. Water availability is a hugely contentious international issue, and global climate change, potentially driving increased droughts and flooding, is considered a compounding factor. This projects seeks to develop agri-tech solutions to help alleviate the issue of water in agriculture, and for producers to ultimately drive water use efficiency. Soil moisture directly impacts crop growth, it drives irrigation systems and once a soil has reached its holding capacity excess water rapidly runs into the drainage system, potentially impacting flood risk and system drainage capacity. One of the most significant challenges within the water debate is that there are few simple and reliable systems to measure soil moisture. It is difficult to accurately measure. Techniques developed include spot THD capacitance sensors, the use of neutron probes and more recently the use of remote sensing techniques. Currently, there is no system to measure soil moisture distribution accurately across a field, and the resolution of remote sensing has not been sufficient for agricultural application, or local water management to reduce flood risk. In this project we will bring together a suite of new technologies which increase the resolution of soil moisture measurement to render it applicable for agricultural application on a field, as well as, landscape scale. The project will deploy two new sensors (one static, one mobile) within China that measures soil moisture content as a function of the albedo of cosmically generated fast neutrons (Cosmos sensor, designed by Hydroinova, US). The static sensor measures soil moisture within a field up to a 200m radius from the measurement point. A mesh of static sensors will be deployed within Henan and Hebei province (which produces 40% wheat of China), China. The mobile sensor will be deployed on a bespoke autonomous vehicle or rover to measure soil moisture variation within a field. The vehicle will be developed within the project and will be the first autonomous deployment of this sensor technology. Data from the soil moisture sensors will be used to calibrate the InSARS sensor on the Sentinel-1 satellite to monitor soil moisture within China to within a 500m x 500m resolution. This is a 5-fold improvement on current resolution from SARS. Ultimately, the technology will enable near real time forecasts of soil moisture at a field scale. This information will be invaluable to agricultural producers and for flood risk forecasting, including key insights to improve water use efficiency, irrigation practices, land drainage and the implementation of precision agricultural techniques. This is an ambitious multi disciplinary project. The project coordinates the expertise of four key groups, the University of Lincoln (robotics, mapping and deployment of autonomous vehicles), the Institute of Ecology and Agrometeorology (IEAM) of Chinese Academy of Meteorological Sciences, University of Information Science &Technology, the Centre for Ecology and Hydrology (Wallingford) and the School of Geography and Earth Sciences, The University of Aberystwth. Considerable focus is placed on knowledge exchange, not just with the agricultural and hydrological communities, but also between international partners within the project. We anticipate that the UK will benefit from understanding the challenges of developing sensor networks in China, with significant differences in scale and environment. The Chinese team will spend considerable periods embedded with the UK academics to learn new skills in remote sensing, sensor deployment and autonomous vehicles.

The challenges articulated in this proposal are how to (1) accurately assess carbon emissions in urban areas; (2) help design and manage cities so that the carbon footprint is reduced; and (3) quantify the impact of urban carbon emissions on global climate change-towards the 1.5 degree climate goal. Greenhouse gas emission reduction is key to tackling global warming. In 2020, 24% of net greenhouse gas emissions in the UK were estimated to be from the transport sector, 21% from energy supply, 18% from business, 16% from the residential sector and 11% from agriculture [1]. Accurate assessment of urban carbon emissions will help policy makers in their decision-making processes and managers of public and private spaces to optimise energy use, carbon reduction and economic benefit. Models are powerful tools in understanding carbon life cycle and atmospheric processes, making predictions, uncertainty quantification and optimal control/design for decarbonisation. However, integrated assessment of the environment and human development is arguably the most difficult and important "systems" problem faced [2]. The complex carbon cycle and atmospheric physical processes act over a wide range of spatial (from meters to degrees) and temporal (from hours, days to decades) scales. Currently, there is no integrated modelling across neighbourhood, city and global scales which can be used for exploring the complex relationship between carbon emissions associated with human activities and global climate change. Here I aim to develop a hybrid AI (Artificial Intelligence)-multiscale physics-informed optimal management framework for accurate assessment and mitigation of CO2 in urban areas. Effective carbon assessment and management necessitate the implementation of multiscale carbon models that can capture adequate spatial and temporal variability of urban carbon emissions & dispersion patterns. Current models are either excessively computationally expensive, or fail to capture the detailed variability of such problems. The proposed work will advance the status of science by developing an advanced multiscale carbon model (based on our recently developed Fluidity-Urban model) where, the use of dynamically adapted meshes enables us to resolve complex urban turbulent flows and carbon dispersion processes. The effect of city infrastructures on carbon dispersion processes is considered at different scales. AI-based modelling will then be used for the optimal design of urban infrastructures and layout for mitigation of carbon emissions. Energy efficiency and carbon-based energy usage in cities are measured based on detailed datasets of existing infrastructures in the selected city-London. The modelling framework will include new carbon parameterisation schemes for urban infrastructures/layout, enabling more accurate assessment of urban carbon emissions, and their impact on climate change. Potential improvements to existing urban infrastructures, and optimal designs for new urban developments will be provided through the AI-based optimal control tool proposed here for carbon reduction and energy efficiency. Finally, an AI-based GHG parameterisation module will be developed for coupling the calculated CO2 fluxes at high resolution grids with existing Earth System modelling. The impact of carbon emissions in cities on global climate can then be evaluated accurately based on existing and improved city infrastructure and layouts. This innovative framework will allow the critical assessment of existing and new policy options on decarbonisation to be carried out, thus improving local and global climate. The tool could potentially change the way in which city infrastructure design, GI and BI for decarbonisation are used in our future cities and pave the way for accurate quantification of the impact of urban carbon emissions on global warming. [1] BEIS, N.. 2020 UK Greenhouse Gas Emissions, Final Figures. [2] Navarro et al. 2018. Earth Syst. Dynam., 9, 1045