Web Processing Service Research Articles

Analyzing third-party data leaks on online pharmacy websites

PurposeWith digitalization, using essential digital services such as online services has become increasingly common. These services process sensitive health related data, such as customers’ prescription medicine orders, which makes ensuring stringent data privacy crucial. The current study examines third parties such as analytics services on Finnish pharmacy websites and investigates the nature and contents of data leaks on these websites.MethodsWe perform an extensive network traffic analysis to reveal data leaks among 163 Finnish online pharmacies. We also study a set of privacy policies of these online pharmacies, and provide a legal analysis regarding the interpretation of the concept of data concerning health in the context of online pharmacies.ResultsOur findings reveal serious data leaks among Finnish online pharmacies. We found 145 pharmacies had third-party services on their websites and only 18 did not. Out of all 163 online pharmacies, 57 (35.0 %) leaked a specific prescription medicine name connected with identifying personal data on the customer. We argue that the information concerning purchases on the prescription medicines should be interpreted as data concerning health to ensure efficient protection of customers’ right to data protection and privacy.ConclusionsWe hope that these concerning results will serve as a wake-up call for the developers and maintainers of online pharmacies and other web services processing sensitive data. Any third-party services incorporated into websites processing sensitive personal data should be closely inspected in terms of data leaks, or preferably not used at all.

An Open Science oriented Bayesian interpolation model for marine parameter observations

Ecological and ecosystem modellers frequently need to interpolate spatiotemporal observations of geophysical and environmental parameters over an analysed area. However, particularly in marine science, modellers with low expertise in oceanography and hydrodynamics can hardly use interpolation methods optimally. This paper introduces an Open Science oriented, open-source, scalable and efficient workflow for 2D marine environmental parameters. It combines a fast, efficient interpolation method with a Bayesian hierarchical model embedding the stationary advection–diffusion equation as a constraint. Our workflow fills the usability gap between interpolation software providers and the users’ communities. It can run entirely automatically without requiring expert parametrisation. It is also available on a cloud computing platform, with a Web Processing Service compliant interface, supporting collaboration, repeatability, reproducibility, and provenance tracking. We demonstrate that our workflow produces comparable results to a state-of-the-art model (frequently used in oceanography) in interpolating four environmental parameters at the global scale.

Cyberinformatics tool for in-season crop-specific land cover monitoring: Design, implementation, and applications of iCrop

Cyberinformatics tools have supported decision makings in agriculture through cutting-edge big data, artificial intelligence/machine learning (AI/ML), and high-performance computing technologies. An open and easy-to-use agricultural cyberinformatics tool based on the findable, accessible, interoperable, reusable (FAIR) data principle is essential for the efficient distribution of crop-specific land cover information. This paper introduces iCrop, a new cyberinformatics tool to enable in-season crop type monitoring for the Conterminous United States (CONUS). As a web-based geographic information system (GIS), iCrop not only delivers three sets of new ML-based field-level crop-specific land cover geospatial data, including pre-season crop cover maps, in-season crop cover maps, and Refined Cropland Data Layer (R-CDL), but also provides a suite of mapping and geoprocessing functionalities through the FAIR geospatial data standards, such as Web Map Service (WMS), Web Coverage Service (WCS), and Web Processing Service (WPS). Meanwhile, we outline several use cases to highlight iCrop's applications under various agricultural operation scenarios, its functionality for land use change analysis, and its interoperability with generic web-based and desktop GIS software (e.g., GeoPlatform and QGIS). Our experimental results show that the new cyberinformatics tool can provide timely and unique crop-specific land cover information through the geoprocessing functionalities to facilitate U.S. agricultural information management and decision support. Moreover, this paper can be used as a systematic guidance for the design and implementation of the cyberinformatics tool to disseminate agro-geoinformation based on the FAIR data principle.

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements.

Estimating hidden fishing activity hotspots from vessel transmitted data

Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01° spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing ~50 million records transmitted by ~1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch.

Crop-CASMA: A web geoprocessing and map service based architecture and implementation for serving soil moisture and crop vegetation condition data over U.S. Cropland

• A data service system for the selected SMAP data and its derivative data products over CONUS is developed. • All map and geoprocessing services are implemented via the OGC standard interface. • The data service system is interoperable with web and desktop clients. • Monitoring and analyzing U.S. cropland soil moisture conditions are significantly simplified. Soil moisture is an essential parameter to understand crop conditions throughout the growing season. Collecting soil moisture data by field observation is labor-intensive, especially when attempting to obtain Conterminous United States (CONUS) geographic coverage. In addition, using soil moisture for assessing current and future crop conditions is best realized by combining soil moisture estimates with concurrent observations of crop conditions. However, until recently, this capability has not been available to the public. In this paper, we present an interoperable data service application system, the Crop Condition and Soil Moisture Analytics (Crop-CASMA) system, that facilitates the retrieval, analysis, visualization, and sharing of soil moisture data for the CONUS. This system delivers a variety of satellite remote sensing based data products that are derived from Soil Moisture Active Passive (SMAP) Level-4 data and SMAP Thermal Hydraulic disaggregation of Soil Moisture (THySM) data, as well as vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. To make services interoperable and reusable, all data products are disseminated via Open Geospatial Consortium (OGC) Web Map Service (WMS) and Web Coverage Service (WCS) interface standards. Additionally, a suite of geoprocessing operations, such as geospatial statistics, time-series profile generation, PDF map production, and image composition, has been implemented in the OGC Web Processing Service (WPS) interface standard. The implementation shows the proposed web service system can significantly simplify the mapping and quantitative analysis of soil moisture and crop condition over U.S. cropland. In addition, it is interoperable with GIS software and has been successfully integrated with web-based applications.

Geospatial Web Services Discovery through Semantic Annotation of WPS

This paper presents an approach to GWS (GeospatialWeb Service) discovery through the semantic annotation of WPS (Web Processing Service) service descriptions. The rationale behind this work is that search engines that use appropriate semantic-based similarity measures in the matching process are more accurate in terms of precision and recall than those based on syntactic matching alone. The lack of semantics in the description of services using a standard such as WPS prevents the use of such a matching process and is considered a limitation of GWS discovery. The GWS discovery approach presented is based on the consideration of semantics in the service description method and in the matching process. The description of services is based on a semantic lightweight meta-model instantiated in the WPS 2.0 standard, extending the description of the service through metadata tags. The matching process is performed in three steps (functionality matching step, I/O (Input/Output) matching step and non-functional matching step). Its core is a semantic similarity measure that combines logical and non-logical matching methods. Finally, the paper presents the results of an experiment applying the proposed discovery approach on a GWS corpus, showing promising results and the added value of the three-step matching process.

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data.

This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients' compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group (P < 0.05). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment (P < 0.05), and the scores of the quality of life in the intervention group were higher than those in the control group (P < 0.05). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group (P < 0.05). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group (P < 0.001), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group (P < 0.05).

Using Ensemble and TOPSIS with AHP for Classification and Selection of Web Services

Due to the increasing number of Web Services with the same functionality, selecting a Web Service that best serves the needs of the Web Client has become a tremendously challenging task. Present approaches use non-functional parameters of the Web Services but they do not consider any preprocessing of the set of functionally Similar Web Services. The lack of preprocessing results in increased use of computational resources due to unnecessary processing of Web Services that have a very low to no chance of satisfying the consumer’s requirements. In this paper, we propose an Ensemble classification method for preprocessing and a Web Service Selection method based on the Quality of Service (QoS) parameters. Once the most eligible Web Services are enumerated through classification, they are ranked using the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) method with Analytic Hierarchy Process (AHP) used for weight calculation. A prototype of the method is developed, and experiments are conducted on a real-world Web Services dataset. Results demonstrate the feasibility of the proposed method.

Integration of Web Processing Services with Workflow-Based Scientific Applications for Solving Environmental Monitoring Problems

Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the utilization of high-performance computing. To this end, the paper addresses a new approach to automation of implementing resource-intensive computational operations of web processing services in a heterogeneous distributed computing environment. To implement such an operation, we develop a workflow-based scientific application executed under the control of a multi-agent system. Agents represent heterogeneous resources of the environment and distribute the computational load among themselves. Software development is realized in the Orlando Tools framework, which we apply to creating and operating problem-oriented applications. The advantages of the proposed approach are in integrating geographic information services and high-performance computing tools, as well as in increasing computation speedup, balancing computational load, and improving the efficiency of resource use in the heterogeneous distributed computing environment. These advantages are shown in analyzing multidimensional time series.

DESIGN OF A SERVERLESS OGC WPS BASED GEOPROCESSING SERVICE SOLUTION

Abstract. Geospatial data and related technologies have increasingly become a crucial part of big data analysis processes and even a prominent player in most of them. Serverless architectures have become today's trending and widely used technology within the cloud computing paradigm. In this paper, we review the serverless paradigm advantages over traditional cloud architecture models and infrastructures. Moreover, we examined the deployment of Open Geospatial Consortium (OGC) Web Processing Service (WPS) specification based geoprocessing Application Programming Interface (API) with serverless architecture. In this context, we propose a system design and review it in detail together with the results discussed along with use cases.

Geodesign as a support for proposing actions to fulfil the Sustainable Development Goals

Abstract. This paper discusses the state of the art in Geodesign, as a result from the evolution in the use of geospatial data for shared and co-creative planning. The evolution of Geographic Information Systems (GIS) led to significant advances in geovisualization, the use of cartographic data via the Internet and the construction of SDIs (Spatial Data Infrastructures). These advances fostered the emergence of Geodesign as one of the foundations for territorial planning. The text will also introduce a Brazilian Geodesign platform, GISColab, developed according to the standards set by the Open Geospatial Consortium (OGC). The platform introduces layer creation resources via WPS (Web Processing Service), as well as tools for measuring the performance of participatory planning workshops, presently focusing on the UN’s Sustainable Development Goals (SDGs). We introduce case studies in which SDGs were explored in different ways: in post-workshop analyses conducted by coordinators and participants, as well as its application as a supportive tool for decision-making during the workshop, via WPS. Finally, we also discuss the inclusion of SDGs to raise awareness of its key themes and support opinion building, resulting in transformative learning experiences.

Extending the cartographic capabilities of desktop GIS – Implementing less common statistical mapping methods as web services together with students

Abstract. For various reasons statistical mapping is receiving increased interest within the technological shift towards service-oriented web cartography. At the same time new teaching approaches are required at universities which combine both, the motivating of students to get a grip on active programming while learning also about less common thematic mapping methods. The paper points to those cartographic representation methods of visualizing areal distributions of quantitative area characteristics which are often overlooked either due to not being readily available in GIS software or because they are simply not known. Of the nine methods discussed in regard to their abilities and limitations, five are not readily implemented or supported by the ArcGIS Pro software. Within the new teaching course on web processing services (WPS) as part of the International Geomatics master programme at Karlsruhe University of Applied Sciences, the students learn how to conceptualize, program and publish a WPS by gaining a solution for those less common methods. The alternate band map method serves as teaching example, but students are asked to independently work on other methods, too. The WPS course acts here as an eye-opener for the students to the wider pool of cartographic visualization options, of how to get around the limitations of GIS software, and of how to automate processes including making them available to others. With that we hope to contribute to automatically rendered thematic maps becoming more versatile again.

Optimizing precision agricultural operations by standardized cloud-based functions

Aim of study: An approach to integrate knowledge into the IT-infrastructure of precision agriculture (PA) is presented. The creation of operation relevant information is analyzed and explored to be processed by standardized web services and thereby to integrate external knowledge into PA. The target is to make knowledge integrable into any software solution. Area of study: The data sampling took place at the Heidfeld Hof Research Station in Stuttgart, Germany. Material and methods: This study follows the information science’s idea to separate the process from data sampling into the final actuation through four steps: data, information, knowledge, and wisdom. The process from the data acquisition, over a professional data treatment to the actual application is analyzed by methods modelled in the Unified Modelling Language (UML) for two use-cases. It was further applied for a low altitude sensor in a PA operation; a data sampling by UAV represents the starting point. Main results: For the implemented solution, the Web Processing Service (WPS) of the Open Geospatial Consortium (OGC) is proposed. This approach reflects the idea of a function as a service (FaaS), in order to develop a demand-driven and extensible solution for irregularly used functionalities. PA benefits, as on-farm processes are season oriented and a FaaS reflects the farm’s variable demands over time by origin and extends the concept to offer external know-how for the integration into specific processes. Research highlights: The standardized implementation of knowledge into PA software products helps to generate additional benefits for PA.

AN APPROACH WEB SERVICE SELECTION BY QUALITY CRITERIA BASED ON SENSITIVITY ANALYSIS OF MCDM METHODS

Context. The problem of QoS based Web service from the list of Web services with equal or similar functionality was considered. This task is an essential part of the processes of finding, discover, matching and using Web services on the Internet due to the numerous offerings of Web services with equal or similar functionality. The reasonable selection of a suitable Web service takes into account a lot of user’s quality requirements, such as response time, throughput, reliability, cost, etc. Such a task is usually formulated as an MCDM problem, in which the parameters are the Web service quality factors and the importance degree of these factors. The object of this research is a process of selection Web services using MCDM methods, taking into accounts the user’s preferences and requirements to the Web service quality characteristics. The subject of the research is the LSP method, which, in addition to the degree of importance of the criteria used in all MCDM methods, simulates the user’s reasoning about quality, taking into account, in particular, such characteristics of the criteria as mandatory, sufficiency, desirability, simultaneity and substitutability. Objective. The objective of the work is to develop an approach for comparing the result of using the LSP method with the results of using other MCDM methods. Method. A method for calculating the weights of input criteria that are not always explicitly specified in the LSP method was proposed. For this, the conjunctive coefficients of impact are used, which are calculated as a result of the sensitivity analysis of the Web service generalized quality criterion to changes the partial quality criteria. This method underlies the proposed approach to comparing the efficiency of the LSP method with other MCDM methods, which consists of using the obtained weights as the weights of the input criteria for the MCDM methods. Results. The developed method and approach was verified experimentally. The Web service ranking produced by the LSP method was compared with the ones produced by SAW, AHP, TOPSIS and VIKOR methods. This comparison confirmed the efficiency of the proposed method and approach. Conclusions. From the obtained results of comparing the LSP method and the MCDM methods considered in this study, it follows that the proposed method and approach provide the equivalent input conditions for these methods as for the LSP method, which is a necessary condition for the correct comparison of MCDM methods. The use of the proposed approach made it possible to study the sensitivities of the considered MCDM methods. In practical applications, this approach can be used to select a suitable MCDM method. The proposed method can be useful for creating professional evaluation systems in which it is necessary to assess the importance (weights) of tens and hundreds of quality criteria.

Training students in Python programming skills and WPS wrapping for geoprocessing tasks by using examples of less commonly applied thematic mapping methods

Abstract. Python programming and the application of web services technology can be considered important skills for geomatics students when looking for employment later. A course on Web Processing Services (WPS) as part of the International Geomatics master programme at Karlsruhe University of Applied Sciences takes this into consideration while using the opportunity of making students aware of less-known thematic mapping methods. Students are introduced to automating a complex geoprocessing task and finally wrapping it into a WPS. It is achieved by following the phases of first conceptualizing the single steps before then using ArcGIS ModelBuilder and Python scripts in combination to build the processing chain. The alternate band map method serves as example in the hands-on exercise. Later students are asked to automate the generation of the geometries for the map face and/or map legend supporting other thematic mapping methods like the square density map or the absolute value raster map. The paper discusses the teaching approach and experiences made so far. Conceptualizing the automatization of a given complex geoprocessing problem proved to be the most challenging part for the students.

A Web‐based Open‐source Geoinformation Tool for Regional Water Resources Assessment

To reduce the impact of droughts and increase the resilience of regional water systems, various competing demands, such as hydropower, supply, irrigation and river ecosystems, need to be reconciled. In this perspective, for a sustainable and efficient management of the water resources, planners and practitioners would benefit from using the most recent data and easily accessible analysis tools, that can make hydrological model applications easier to apply. In this paper, a web-based open-source geoinformation tool for estimating Flow Duration Curves (FDCs) in ungauged basins is presented. The FDC estimation derives from a regional statistical model, that relates basin topographic parameters, climatic characteristic and other environmental factors to FDC features via multivariate regression. The software tool has the primary function of simplifying the GIS-intensive application of the multivariate methodology, allowing computation of the necessary basin features through web services. These are developed in compliance with the OGC specifications for geospatial Web Processing Service (WPS), while scripting and model implementation are developed server-side, which ensures the user access to always updated data, and to procedures free from software client compatibility issues. An operational application is available for the North-Western Italy.

Geospatial meteorological and climatic data services application programming interface

At present, it becomes necessary to develop software for Earth science data repositories instead of downloading data to computational servers and workstations. This is especially important taking into account the upcoming volume of big environmental datasets. WPS (Web Processing Service) is an OGC standard for distributed geospatial data processing services. The WPS services provide access to geospatial data processing packages including statistical processing software tools. The main goal of this paper is to present an approach that provides a unified access to the web processing services developed for climate and meteorological research based on the latest technological and scientific achievements. Based on the “OGC API-Processes” standard, a Web API Climate is proposed to provide a uniform access to the “Climate” platform (http://climate.scert.ru/climate/environment/v3.0a-demo/) geospatial services. The Climate Web API provides a software basis for an advanced development and use of the “Climate” platform web processing services aiming at climate change studies at regional and global levels. A correct implementation of the specification will form a solid foundation for further development of desktop client applications in the form of GIS plugins, as well as universal Web-GIS clients easily embedded into thematic virtual research environments.

Social Internet of Things: vision, challenges, and trends

IoT describes a new world of billions of objects that intelligently communicate and interact with each other. One of the important areas in this field is a new paradigm-Social Internet of Things (SIoT), a new concept of combining social networks with IoT. SIoT is an imitation of social networks between humans and objects. Objects like humans are considered intelligent and social. They create their social network to achieve their common goals, such as improving functionality, performance, and efficiency and satisfying their required services. Our article’s primary purpose is to present a comprehensive review article from the SIoT system to analyze and evaluate the recent works done in this area. Therefore, our study concentrated on the main components of the SIoT (Architecture, Relation Management, Trust Management, web services, and information), features, parameters, and challenges. To gather enough information for better analysis, we have reviewed the articles published between 2011 and December 2019. The strengths and weaknesses of each article are examined, and effective evaluation parameters, approaches, and the most used simulation tools in this field are discussed. For this purpose, we provide a scientific taxonomy for the final SIoT structure based on the academic contributions we have studied. Ultimately we observed that the evaluation parameters are different in each element of the SIoT ecosystem, for example for Relation Management, scalability 29% and navigability 22% are the most concentrated metrics, in Trust Management, accuracy 25%, and resiliency 25% is more important, in the web service process, time 23% and scalability 16% are the most mentioned and finally in information processing, throughput and time 25% are the most investigated factor. Also, Java-based tools like Eclipse has the most percentage in simulation tools in reviewed literature with 28%, and SWIM has 13% of usage for simulation.

An end-user-friendly hydrological Web Service for hydrograph prediction in ungauged basins

ABSTRACT A consolidated geomorphology-based approach for discharge prediction in ungauged basins (PUB) through deconvolution of discharge signals from gauged donor catchments, their transposition and their convolution towards target outlets, has been made available online to end users through a Web Processing Service (WPS) for the synoptic peninsular region of Brittany, France. In the spirit of hydrological services, the SIMFEN WPS allows anyone to execute the hydrological modelling package transfR online through: (1) Open Geospatial Consortium (OGC®) interoperability standards; (2) collection and use of public hydrometric data; (3) connection to the pre-existing MNTSurf WPS for geomorphometric analysis; (4) visualization using a collaboratively developed Mviewer; and (5) innovative WPS workflows. The ability to model discharge series at any ungauged outlet of the synoptic region is offered to specialists of other disciplines, non-modeller water practitioners and interested citizens to support interdisciplinarity, water monitoring and management, and related science-society-policy debates and actions.