The goal of the proposed research is to develop two in-situ sensor systems that measure in-ground gas concentrations and strain/moisture/temperature/suction at different scales in order to provide data on the dynamics of gas flux and soil structure. One is based on distributed fiber optic sensor (DFOS) system that can provide measurements at meters to kilometers-scale, whereas the other is based on low-power sensor coupled with in-ground mesh-network wireless sensor network (WSN) system that provides data at selected local points in distributed manner. Both technologies are currently being prototyped at UC Berkeley (UCB). The developed sensor systems will be trialed first in the unique wind tunnel-soil experimental facility available at the Colorado School of Mines (CSM). We propose an experimental plan designed to manipulate soil moisture fluctuations by balancing subsurface water introduction through precipitation events and losses to evaporation and evapotranspiration as controlled by atmospheric perturbations (temperature, wind speed, and relative humidity) so as to make more informed biogeochemical predictions and soil structure changes under changing climate conditions. Under the controlled environment, we will quantify the precision errors of the developed sensor systems. The developed systems will also be implemented in the fields of Rothamsted Research (RR) to examine its feasibility in the actual field conditions. The ultimate goal is to improve the predictive understanding of how atmospheric carbon loading is affected by soil structure changes. The proposed sensor development and experimental research will lead to a substantial improvement of soil carbon models such as the RothC model developed at RR]. Each compartment in the model decomposes by a first-order process with its own characteristic rate. The IOM compartment is resistant to decomposition. The model adjusts for soil texture and its changes by altering the partitioning between CO2 evolved and (BIO+HUM) formed during decomposition, rather than by using a rate modifying factor, such as that used for temperature. Moreover, total CO2 effluxes are largely controlled by root respiration, and microbial respiration of soil organic matter including rhizospheric organic carbon and all of these processes are highly sensitive to soil structure. In this proposed research, we therefore hypothesize that soil structure change is strongly linked to soil gas generation. We will develop and implement sensor systems that measure both, which in turn will allow us to quantify the link. These new models will in the future allow the effects of soil management on carbon dynamics to be predicted and hence give an understanding of the impact of different soil management strategies (e.g. tillage) on soil sustainability. The research will complement ongoing field research at RR supported by the BBSRC in the National Capability scheme and in ISP funding streams; especially on the delivery of nutrients to plants. The processes to be studied in the project are expected to lead to improved formulations to include multi-scale, multi-physics under development at RR by: (1) more rationally representing the coupled surface-subsurface processes, (2) including vegetation hydrodynamics and carbon and nutrient allocation, and (3) incorporating soil and genome-enabled subsurface reactive transport models that have explicit and dynamic microbial representation. The project will lead to the development of spatially-distributed sensing systems in the field that can (1) sense changes in soil stricture and (2) link these changes to fluxes of N2O, CH4, CO2 and O2 into and from soils.

Overview In dense urban areas, the underground is exploited for a variety of purposes, including transport, additional residential/commercial spaces, storage, and industrial processes. With the rise in urban populations and significant improvements in construction technologies, the number of subsurface structures is expected to grow in the next decade, leading to subsurface congestion. Recently emerging data indicate a significant impact of underground construction on subsurface temperature and there is extensive evidence of underground temperature rise at the local scale. Although it is well known that urbanization coupled with climate change is amplifying the urban heat island effect above ground, the extent of the underground climate change at the city scale is unknown because of (i) limited work on modeling the historical and future underground climate change at large scale and (ii) very limited long-term underground temperature monitoring. The hypothesis of this research is that (a) the high ground temperature around tunnels and underground basements, b) the observed temperature increase within the aquifer, and (c) inefficiency in ventilation of the underground railway networks, necessitate more detailed and reliable knowledge of urban underground thermal status. The project will develop a framework for monitoring and predicting temperature and groundwater distributions at high resolutions in the presence of underground heat sources and sinks. This can be achieved via a combination of numerical modelling, continuous temperature and groundwater monitoring and statistical analyses. The ultimate goal is for every city to generate reliable maps of underground climate, with the ability to understand the influence of future urbanization scenarios. Merit The objective of this joint NSF-EPSRC research is to advance understanding of the impacts of the urban underground on subsurface temperature increase at the city-scale. A low cost and reliable underground weather station using the fiber optic sensing technologies will be developed and installed at sites in London and San Francisco. A high-performance computing based thermo-hydro coupled underground climate change code will be developed to simulate the temperature and groundwater variation with time at the whole city scale. The main scientific deliverable from the district- and city-scale numerical simulations and the experimental temperature monitoring is a series of archetype emulators, which are defined based on the geological characteristics, above ground built environment, such as surface and buildings types, and the density and type of the underground structures. These archetype emulators will allow efficient city-scale modelling and enable application of the methodology to any other city or region with similar characteristics of above and underground built environment. This new knowledge will make possible to consider precise thermal conditions around underground structures in urban areas as well as facilitate efficient utilization of geothermal resources for both heating and cooling purposes.

Many decisions in today's world are made through a complex, dynamic process of interaction and communication between people and teams with different interests and priorities - so called "distributed decision-making" (DDM). For example, many businesses work across multiple geographically dispersed offices and timezones, with teams specialising in quite diverse areas. Each team may have its own goals and reward models, which do not necessarily coincide, and may be spread across multiple organisational units (e.g. different businesses or governments). Communication may happen via several different modalities with very different timescales and properties (e.g. email, instant messenger, and face-to-face meetings). Unfortunately, although many organisations have started to document these processes and even make records available (particularly governmental organisations e.g. https://data.gov.uk/), we have no way to automatically analyse these records. If we did, we could produce tools to automatically summarise decisions, trace who made them, and why and how they were made (and why other decisions weren't made). From a societal standpoint this would help make these processes more accountable and transparent. We'd also be able to identify collaborative failures, biases and other problems, and thus help improve decision-making in future. This project will develop these urgently required methods, using a combination of natural language processing and social network analysis. We will collate, annotate and publicly release the first multimodal dataset of real-world distributed decision-making. We will devise techniques to take natural language and semi-structured data to recognise the dialogue and interaction structures in decision making, and analyse those structures to produce summaries and evaluate the efficacy of the decision making process. We will then use the outputs to inform strategic interventions that can streamline and improve decision making. Our methods will be suitably generic to span several domains. However, the project will focus on one particular global organisation as its main use case: the Internet Engineering Task Force (IETF). This is an international forum responsible for producing Internet protocol standards - formal documents which specify the languages by which software and hardware "speak" across the Internet. To produce these documents, extensive international collaboration is performed - this spans several modalities including email discussions, collaborative document editing, face-to-face meetings and teleconferencing. Importantly, all of these modalities are documented via transparency reports ranging from public email archives to minutes from meetings. This project has partnered with the IETF to help model and streamline their decision making process. We will borrow from their experience, and employ our methods to extract decision making bottlenecks. We will devise tooling which will provide advice and proposed interventions to relevant parties within the IETF. Amongst many other things, we directly benefit the IETF, and the global Internet standards community, by helping them to uncover biases and help make important decision processes accountable.

The Wallacea region, lying between the Borneo to the west and Papau New Guinea to the east, is one of the world's biodiversity hotspots, hosting incredibly high levels of biodiversity, much of which is unique to the region. This exceptional level of biodiversity and endemism reflects evolutionary diversification and radiation over millions of years in one of the world's most geologically complex and active regions. The region's exceptional biodiversity, however, is threatened by climate change, direct exploitation and habitat destruction and fragmentation from land use change. Continued habitat loss and fragmentation is expected to precipitate population declines, increase extinction rates, and could also lead to 'reverse speciation' where disturbance pushes recently diverged species together, leading to increased hybridisation, genetic homogenisation, and species' collapse. Already, approximately 1,300 Indonesian species have been listed as at risk of extinction, but the vast majority of the region's biodiversity has not been assessed and we lack basic information on the distribution and diversification of many groups, let alone understanding of what processes drove their diversification, how they will respond to future environmental change, and how to minimize species' extinctions and losses of genetic diversity while balancing future sustainable development needs. In response to the need for conservation and management strategies to minimize the loss of Wallacea's unique biodiversity under future environmental change and future development scenarios, we will develop ForeWall, a genetically explicit individual-based model of the origin and future of the region's biodiversity. ForeWall will integrate state-of-the-art eco-evolutionary modelling with new and existing ecological and evolutionary data for terrestrial and aquatic taxa including mammals, reptiles, amphibians, freshwater fish, snails, damselflies and soil microbes, to deliver fresh understanding of the processes responsible for the generation, diversification, and persistence of Wallacea's endemic biodiversity. After testing and calibrating ForeWall against empirical data, we will forecast biodiversity dynamics across a suite of taxa under multiple environmental change and economic development scenarios. We will develop a set of alternative plausible biodiversity management/mitigation options to assess the effectiveness of these for preserving ecological and evolutionary patterns and processes across the region, allowing for policy-makers to minimise biodiversity losses during sustainable development. Our project will thus not only provide novel understanding of how geological and evolutionary processes have interacted to generate this biodiversity hotspot, but also provide policy- and decision-makers with tools and evidence to help preserve it.