Modelling land-use and climate change impacts on hydrology: the Upper Ganges river basin

This thesis explores the effects that large-scale land-use/cover change (LUCC) and climate change pose to the terrestrial water cycle, by developing a case study in the Upper Ganges (UG) river basin, in India. In an area experiencing rapid rates of LUCC and changes in irrigation practices, historic land-use maps are developed, based on satellite images, to investigate historical trends of LUCC. Future projection scenarios of LUCC for years up to 2035 are derived from Markov chain analysis. To explore the impacts of those changes in hydrology, the generated maps are used to force the Land Surface Model (LSM) JULES. JULES is found to be reasonably skilful in terms of its ability to reproduce observed streamflow. However, the results indicate that there is much room left for improved estimates of evapotranspiration (ET) fluxes, which JULES is found to over-predict. By dynamically coupling JULES with the crop model InfoCrop, the simulated ET fluxes are improved, compared to the original JULES model. The difference in mean annual ET between the two models (coupled and original) is approximately 150 mm/yr and indicates the potential error in ET flux estimations of an LSM without dynamic vegetation. The impact of LUCC and climate change on the hydrological response of the UG basin is quantified, by calculating variations in hydrological components (streamflow, ET and soil moisture) during the period 2000–2035. Severe increases in the high extremes of flows (+40% in the multi-model mean) are being projected for the nearby future (2030–2035). The changes in all examined hydrological components are greater in the combined land-use and climate change scenario, whilst climate change is the main driver of those changes. These results provide the necessary evidence-base to support regional land-use planning, advanced irrigation practices and develop future-proof water resource management strategies under a water-limited environment.Open Acces

D-MOSS: An integrated dengue early warning system in Vietnam driven by Earth Observations

Dengue is the fastest-growing mosquito-borne viral infection in the world today. It is present in over 150 countries, meaning that around 40 percent of the world’s population now live in countries where dengue is a daily risk. It has been estimated that annually dengue affects 390 million people and has a global cost of almost US$9 billion per year. Since 2000, there has been an increase of over 100% in the number of cases of dengue fever in Vietnam, with approximately 185,000 cases occurring in 2017 alone. In Vietnam, there is currently no system for forecasting future dengue outbreaks. D-MOSS is the first fully integrated dengue fever forecasting system incorporating Earth Observation data and seasonal climate forecasts to issue warnings on a routine basis. D-MOSS integrates multiple stressors such as water availability, land-cover, precipitation and temperature with data on past dengue fever incidents. This information is used to develop statistical models of disease incidence, that can then be used to forecast dengue outbreaks based on seasonal weather and hydrological forecasts as well as other factors. An overview of the D-MOSS web page and the forecasts it produces are shown in the accompanying figure. D-MOSS takes the form of a web-based platform. The system’s architecture is based on open and non-proprietary software, where possible, and on flexible deployment into platforms including cloud-based virtual storage and application processing. D-MOSS is currently being piloted in Vietnam. When the system becomes fully operational it should assist the Vietnamese Ministry of Health, to meet its goal of actively to forecast, detect early and prevent the occurrence of epidemics, especially major ones. The project is funded by the UK Space Agency’s International Partnership Programme and we have been recently awarded with an extension to our grant to implement D-MOSS to another six countries in South East Asia

D-MOSS: Dengue forecasting MOdel Satellite-based System

D-MOSS, Dengue forecasting MOdel Satellite-based System, is a dengue fever early warning system for Vietnam being developed by a project funded by the UK Space Agency’s International Partnerships Programme. The D-MOSS project is developing a suite of innovative tools that will allow public health authorities to identify areas of high risk for disease epidemics before an outbreak occurs, in order to target resources to reduce spreading of epidemics and improve disease control. Since 2000, there has been an increase of over 100% in the number of cases of dengue fever in Vietnam, with 185,000 cases occurring in 2017 alone, and there is currently no system for forecasting future dengue outbreaks. The D-MOSS early warning platform includes a water availability component. Water availability directly impacts dengue epidemics due to the provision of mosquito breeding sites. These dynamics are often non-linear; too much rainfall can fill outdoor containers, while too little can lead to people storing water in open containers within their homes. Both increase the population of Aedes aegypti mosquitoes and in turn the risk of dengue outbreaks. However, water availability or water resource management is rarely accounted for in dengue prediction models. The system generates monthly water stress assessments and uses them as inputs to a component of the dengue early warning system which also improves the skill of the system’s predictions. In addition, these forecasts of water stress will help to improve Vietnam’s water management. Vietnam’s Sustainable Development Strategy for 2011-2020 identifies one of the major challenges facing Vietnam as the issue of transboundary water management, because 63% of the surface water comes from upstream countries. The D-MOSS project is developing a forecasting system in which Earth Observation datasets are combined with weather forecasts and a hydrological model to predict the likelihood of future dengue epidemics up to eight months in advance. The system is calibrated against historical data. The water availability forecasts are fed into statistical forecasting models of disease incidence. This dengue early warning system model integrates the water stress forecast with a range of other covariates important for dengue transmission. The D-MOSS project is within the first year of its three-year term and is currently focused on platform and model development, while gathering the key input data and engaging with the Vietnamese government to ensure that all components are fit for purpose. The portrayal system is designed to communicate the dengue and water availability forecasts to the Vietnamese Ministries of Health and Natural Resources and Environment, respectively. A user interface will also incorporate supporting information on recommended actions, provided by the decision makers and based on the forecasts and associated uncertainty

Development of an Industry 4.0 Demonstrator Using Sequence Planner and ROS2

In many modern automation solutions, manual off-line programming is being replaced by online algorithms that dynamically perform tasks based on the state of the environment. Complexities of such systems are pushed even further with collaboration among robots and humans, where intelligent machines and learning algorithms are replacing more traditional automation solutions. This chapter describes the development of an industrial demonstrator using a control infrastructure called Sequence Planner (SP), and presents some lessons learned during development. SP is based on ROS2 and it is designed to aid in handling the increased complexity of these new systems using formal models and online planning algorithms to coordinate the actions of robots and other devices. During development, SP can auto generate ROS nodes and message types as well as support continuous validation and testing. SP is also designed with the aim to handle traditional challenges of automation software development such as safety, reliability and efficiency. In this chapter, it is argued that ROS2 together with SP could be an enabler of intelligent automation for the next industrial revolution

Human robot collaboration in the MTA SZTAKI learning factory facility at Gyor

In recent years, interest has grown in environments where humans and robots collaborate, complementing the strengths and advantages of humans and machines. Design, construction and adjustment of such environments, as well as the training of operating personnel, requires thorough understanding of the nature of human robot collaboration which previous automation expertise does not necessarily provide. The learning factory currently being constructed by MTA SZTAKI in Gyor aims to provide hands-on experience in the design and operation of facilities supporting human robot collaboration, mainly in assembly tasks. The work-in progress paper presents design principles, functionalities and structure of the facility, and outlines deployment plans in education, training, research and development in the academic and industrial sectors. (C) 2018 The Authors. Published by Elsevier B.V

Using seasonal forecasts to inform the management of water resources during drought

Water is considered to be one of the main mechanisms through which people will experience climate change, with the number of people estimated to become exposed to water scarcity projected to increase sharply in the future. Water resource managers in the UK have access to a range of meteorological and hydrological indicators of drought. However these data are limited in their utility to directly forecast how systems should be managed to reduce impacts on water users. At present there is no generically applicable method to provide such an outlook. We are working with practitioners and regulators in the UK water industry to demonstrate the use of seasonal forecasts to support decision-making during drought. The work is funded by Copernicus through the European Centre for Medium-Range Weather Forecasts (ECMWF) with the aim of showing how Copernicus Climate Change Service (C3S) data can be used in sectoral contexts. C3S data are combined with both operational practices and the latest UK water resources planning developments to provide metrics of value tailored to the needs of water resource managers. National and industry stakeholders have been fully engaged from the outset, co-creating a tool to evaluate, visualise, and communicate the potential impact of emerging droughts in a meaningful way. The tool reads water supply system information presented by water companies including drought response surfaces, operational decisions, and demand. The water supply system is simulated using seasonal forecasts, and an assessment of drought likelihood and vulnerability is provided along with an estimate of the associated uncertainty. Impacts are presented in terms of consequences for stakeholders and contextualised in terms of system vulnerabilities and the historic record. This tool supports operational decision-making, in particular when deliberating the timing of supply and demand-side interventions as a drought develops, and communicating such risks to stakeholders

On the manipulation of articulated objects in human-robot cooperation scenarios

Articulated and flexible objects constitute a challenge for robot manipulation tasks, but are present in different real-world settings, including home and industrial environments. Approaches to the manipulation of such objects employ ad hoc strategies to sequence and perform actions on them depending on their physical or geometrical features, and on a priori target object configurations, whereas principled strategies to sequence basic manipulation actions for these objects have not been fully explored in the literature. In this paper, we propose a novel action planning and execution framework for the manipulation of articulated objects, which (i) employs action planning to sequence a set of actions leading to a target articulated object configuration, and (ii) allows humans to collaboratively carry out the plan with the robot, also interrupting its execution if needed. The framework adopts a formally defined representation of articulated objects. A link exists between the way articulated objects are perceived by the robot, how they are formally represented in the action planning and execution framework, and the complexity of the planning process. Results related to planning performance, and examples with a Baxter dualarm manipulator operating on articulated objects with humans are shown

Cooperative transport tasks with robots using adaptive non-conventional sliding mode control

[EN] This work presents a hybrid position/force control of robots aimed at handling applications using multi-task and sliding mode ideas. The proposed robot control is based on a novel adaptive non-conventional sliding mode control used to robustly satisfy a set of inequality constraints defined to accomplish the cooperative transport task. In particular, these constraints are used to guarantee the reference parameters imposed by the task (e.g., keeping the load at a desired orientation) and to guide the robot using the human operator's forces detected by a force sensor located at the robot tool. Another feature of the proposal is the multi-layered nature of the strategy, where a set of four tasks are defined with different priorities. The effectiveness of the proposed adaptive non-conventional sliding mode control is illustrated by simulation results. Furthermore, the applicability and feasibility of the proposed robot control for transport tasks are substantiated by experimental results using a redundant 7R manipulator.This work was supported in part by the Spanish Government under Project DPI2017-87656-C2-1-R, and the Generalitat Valenciana under Grants VALi + d APOSTD/2016/044 and BEST/2017/029.Gracia Calandin, LI.; Solanes Galbis, JE.; Muñoz-Benavent, P.; Esparza Peidro, A.; Valls Miro, J.; Tornero Montserrat, J. (2018). Cooperative transport tasks with robots using adaptive non-conventional sliding mode control. Control Engineering Practice. 78:35-55. https://doi.org/10.1016/j.conengprac.2018.06.005S35557

Selection and integration of earth observation-based data for an operational disease forecasting system

The current increase in the volume and quality of Earth Observation (EO) data being collected by satellites offers the potential to contribute to applications across a wide range of scientific domains. It is well established that there are correlations between characteristics that can be derived from EO satellite data, such as land surface temperature or land cover, and the incidence of some diseases. Thanks to the reliable frequent acquisition and rapid distribution of EO data it is now possible for this field to progress from using EO in retrospective analyses of historical disease case counts to using it in operational forecasting systems. However, bringing together EO-based and non-EO-based datasets, as is required for disease forecasting and many other fields, requires carefully designed data selection, formatting and integration processes. Similarly, it requires careful communication between collaborators to ensure that the priorities of that design process match the requirements of the application. Here we will present work from the D-MOSS (Dengue forecasting MOdel Satellite-based System) project. D-MOSS is a dengue fever early warning system for South and South East Asia that will allow public health authorities to identify areas at high risk of disease epidemics before an outbreak occurs in order to target resources to reduce spreading of epidemics and improve disease control. The D-MOSS system uses EO, meteorological and seasonal weather forecast data, combined with disease statistics and static layers such as land cover, as the inputs into a dengue fever model and a water availability model. Water availability directly impacts dengue epidemics due to the provision of mosquito breeding sites. The datasets are regularly updated with the latest data and run through the models to produce a new monthly forecast. For this we have designed a system to reliably feed standardised data to the models. The project has involved a close collaboration between remote sensing scientists, geospatial scientists, hydrologists and disease modelling experts. We will discuss our approach to the selection of data sources, data source quality assessment, and design of a processing and ingestion system to produce analysis-ready data for input to the disease and water availability models