For water management, models used to be derived by scientists without the involvement of policy makers – they were mostly derived purely from analysis and observation of the natural world, thereby contributing an objective opinion to decisions without accounting for the values, knowledge or priorities of the human system that affects and is affected by the system being modelled. As a result, models were frequently being rejected, especially when the scientific findings demonstrated a need for unpopular decisions related to human behaviour. The shift towards more open and integrated planning processes is one way to avoid potential misunderstandings (and even litigation), and has required the adaptation of the scientific modelling process to incorporate community knowledge, perspective and values. Such open and integrated planning processes include participatory modelling and Integrated Water Resource Management (IWRM) [Voinov, 2008] (see Section 4.1).

Monitoring the stream discharge is considered to be of vital importance for the management of water resources, and developed in time from on-site measurements to complex, ICT based systems. While the measurement protocols for steady flows are well established and allow continuous monitoring, during unsteady flows the measurement protocols for monitoring systems are still under development and evaluation (see Section 4.2).

Monitoring environments require ubiquitous sensing nodes that can be conveniently deployed and operate for extended periods of time (several years) while requiring minimal or no maintenance effort [Puccinelli, 2005]. While a broad spectrum of environmental sensor modalities has been utilized and evaluated for data management and control, implementations of mobile application that use Cloud Computing paradigm in a standard framework that fulfill these requirements are rare. Monitoring and data processing for developing sound solutions for a sustainable management of water ecosystems became more flexible, exiting and accurate with the widespread of information and communication technology solutions in this domain. Application of ICT in varied areas led to a rapid development of advanced information systems for e-services, creating what is known as an information society (see Section 4.3).

Environmental data is often observed and shared by multiple devices and organizations (geo-graphically distributed). Moreover, some applications may require data processing across sites. Recently, Hadoop emerged as the de facto state-of-the-art for data analytics. Hadoop is optimized to co-locate data and computation and therefore mitigate the network bottleneck when moving data [Hadoop, 2014]. However, as data may not be equally distributed across sites and since intermediate data are required to be aggregated to produce final results, Hadoop may suffer severe performance degradation in such distributed settings. Thus, in our research activities we intend to address Hadoop limitations and therefore to explore new data distribution techniques and scheduling policies that can co-operatively deal with distributed big data processing for single and multiple concurrent applications (see Section 4.4).

The access to data and processing services from any device is important for different categories of users. Mobile cloud computing is a new paradigm that evolves from cloud computing and adapts the access to services to advanced ICT technologies (see Section 4.5).

A new approach is base on IoT systems. The IoT trends and relation to water resources management is presented in Section 4.6, considering several hardware and software technologies.

Failure and network partitions are common in large-scale distributed systems. The solutions elaborated for large scale distributed systems consider that failures are a norm, not an exception. The common approach is based on data replication, having as result the avoidance of single points of failure (see Section 4.7).

In contrast with the traditional one-sectorial approach to water management, the integrated approaches recognize the fundamental linkages between water uses (e.g., agriculture, water supply, navigation, hydropower, environment, recreation) and their impact on the watershed resources viewed as a system [Pangare, 2006]. The most widely used frameworks for integrative and adaptive management are IWRM and AM. In essence, IWRM (see Section 4.8, Integrated Water Resources Management (IWRM)) is a participatory planning and implementation process, based on sound scientific, which brings together stakeholders to determine how to meet society’s long-term needs for water resources while maintaining essential ecological services and economic benefits. In addition of these fundamental features, the Global Water Partnership (GWP) focuses on the importance of the IWRM in addressing the issues of poverty reduction and sustainable development in the context of the less-developed countries. AM (see Section 4.8, Adaptive Management (AM)), as a concept, has been designed primarily to support managers in dealing with uncertainties inherent in complex ecological system required to meet multiple objectives. AM combines multidisciplinary scientific research, policy development, and local practice in a cyclic learning process aimed at leading to more effective decision making and enhanced environmental, social, and economic benefits [Williams, 2007].

The chapter end with several approaches of water resources management based on Information-centric systems (ICS) for watershed investigation and management (see Section 4.9), supervisory control and data acquisition systems (see Section 4.10) and decision-support systems for water community-driven efforts considering an use case on IoWaDSS Technological Framework (see Section 4.11).