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The Connecting Nature Framework is a process tool that has been co-produced and co-applied with science and practice partners in the project with the aim to give a hands-on and comprehensive guide to the Connecting Nature cities to develop their nature-based solution exemplars. It places the nature-based solution at the core of an interactive process that distinguishes three phases of development for a nature-based solution: planning, delivery, and stewardship. Throughout each phase, there are seven separate elements that need to be considered: Technical solutions, Governance, Financing and business models, Nature-based entrepreneurship, Co-production, Reflexive monitoring, and Impact assessment. While the first phase of the Framework development and application has focused on three Front-runner Cities (2017- 2019), we have in the second phase (2019-2022) transferred the Framework, including the generated knowledge and insights about how to apply it to different city contexts, to the seven Fast-follower and Multiplier Cities., Suggested citation: Hölscher, K., Allaert, K., Janssen, A., Lodder, M., McCann, S., van der Have, C., Asmaryan, S., Bakola, E., Beslagic, L., Boskidis, I., Collier, M. J., Connop, S., Dick, G., Dumitru, A., Dymek, D., Dziubala, A., Fletcher , I., García-Espina Adank, C., Georgiou, P., Gonzalez Vazquez, M. M., Gvilava, M., Kelly, S., Madajczyk, N., Malekkidou, E., Mavroudi, M., McQuaid, S., Osipiuk, A., Pasic, B., Prieto Gonzalez, A., Quartier, M., Rizzi, D., Sermpezi, R., Suljevic, N., Tomé Lourido, D., Trendafilov, I., Tsouris, V., Vandergert, P., Van De Sijpe, K., Velikova, V., Vos, P., and Xidous, D. (2022). Connecting Nature Deliverable 6: Connecting Nature Framework Reports for Fast-follower Cities. DOI: https://doi.org/10.5281/zenodo.7319263

Рассматриваются основные направления применения дистанционных технологий в Республике Армения. Изложены базовые научно-технологические ресурсы для развития перспективных направлений, которые обеспечат надежную научно-методическую основу для совершенствования и разработки дистанционных методов и создания на этой основе мониторинговой системы The main areas of the use of remote sensing technologies in Armenia are considered in the article. The basic scientific-technological resources for their perspective development are also discussed. The later will serve a background for the development of remote sensing methods and monitoring system in the future

Earth Science community depends on the exploration, analysis and reprocessing of high volumes of data as well as the modeling and simulation of complex coupled systems on multiple scales. The main aim of this article is to introduce a new hydrological modeling service based on the Soil and Water Assessment Tool (SWAT) (Arnold et al. J American Water Resour Assoc 34(1), 73–89, 1998 ; Arnold and Fohrer Hydrol Process 19(3), 563–572, 2005) model using high efficiency, resource sharing and low cost cloud computing resources (Astsatryan et al. International Journal of Scientific & Engineering Research 1(1), 1130–1133, 2014). Such a Desktop as a Service (DaaS) approach allowing users to work from anywhere, and gives centralized desktop management and great performance. Within the Spatial Data Infrastructure (SDI) and cloud platform, the DaaS service gives secure access to the model and a centralized data storage to get a SWAT model input. The article illustrates the analyses of the implementation of the SWAT model for the Sotk watershed of Lake Sevan in Armenia (Sargsyan 2007).

Armenia is a small country geographically located in the South Caucasus region. Once the most industrialized republic of the Soviet Union, the country inherited a dramatic ecological situation from the Soviet era. After the mid‐1990s, the economic policy of Armenia shifted towards a strong support for industrial development greatly ignoring ecological interests. As a result, mining‐related industries such as dressing and metallurgical plants were permitted to operate without environmental regulations, and geo‐exploration and exploitation works in deposits were conducted disregarding nature protection norms. As the key national environmental academic entity, the Center for Ecological‐Noosphere Studies of the National Academy of Sciences of the Republic of Armenia (CENS) has a strong national role in delivering authoritative environmental information and data sets. Aware of the need for enhanced and more efficient delivery of information and data sharing, CENS engaged in recent years in several international capacity building projects directed to the setting up of an environmental Spatial Data Infrastructure (SDI). The goal of this SDI was both to provide enhanced capacities for intra muro maintenance and exchange of data among research groups and\ to allow various external stakeholders to access more efficiently geospatial data sets and services. This first step was successful in showing the potential of data sharing for internal institutional organization, to gain visibility towards key stakeholders in the country and the South Caucasus region, and to start engaging in international voluntary partnerships such as the Group on Earth Observations (GEO). CENS now envisions to scale up its SDI infrastructure to a national SDI (nSDI) in order to support a wider range of geospatial services towards the Armenian environmental community. This paper will discuss several aspects and challenges of the envisioned strategy for an Armenian nSDI. First, we present how the current components of the implemented SDI benefit the scientific and environmental communities in Armenia, with an emphasis on the promising link between high performance computing and geospatial services. Second, we examine how the EGIDA methodology – a GEO capacity building suites of activities developed in the FP7 project of the same name – can be applied to support the process of scaling up the infrastructure to become a nSDI, which has been selected as a pilot study in the FP7 EOPOWER project. Finally, we discuss the potential of future full‐scale provision of geospatial services in Armenia and how these could benefit the various stakeholders involved in Armenia and in the South Caucasus region.

The main objective of this paper is to introduce a portal of geoprocessing services that can be used to compute either a single vegetation index or a combination of vegetation indices, as a workflow. High Performance Computing (HPC) resources are used for the calculations, and the Web Processing Service (WPS) standard is used to handle the requests from and the responses to the portal. In case of a workflow, a single node of the cluster is dedicated to each index, and the number of used cores depends on the complexity of the task. In addition, based on a series of experiments made to accelerate remote sensing image processing, a parallelization method within the computational node is automatically chosen depending on the complexity of the operations and the amount of data. The suggested algorithm optimizes the processing by selecting the best methodology (serial or parallel) and the number of cores to efficiently manipulate and distribute the data. The interoperable web portal, Spatial Data Infrastructure (SDI) and the heterogeneous resources of HPC cluster are located in the same local area network, and the cluster nodes have access to the data via network file system sharing. The use of standardized web services makes it possible to use remote data as inputs.

Processing of high-resolution time series satellite images typically requires a large amount of computational resources and time. We introduce here a scientific gateway for computing the Normalized Difference Vegetation Index (NDVI) time series data. Based on a distributed workflow using the Web Processing Service (WPS) standard, the gateway aims to be completely interoperable with other standardized tools. The availability of this gateway may help researchers to acquire knowledge of land cover changes more efficiently over very large spatial and temporal extents, which is especially important in the context of Armenia for which timely decisionmaking is needed.