A recent trend in hydroinformatics has been the growing number of data, models, and cyber tools which are web accessible, each aiming to improve common research tasks in hydrology through web technologies. Coupling web-based models and tools holds great promise for an integrated environment that can facilitate community participation, collaboration, and scientific replication. There are many examples of server-side, hydroinformatics resource coupling, where a common standard serves as an interface. Yet, there are few, if any, examples of client-side, resource coupling, particularly cases where a common specification is employed. Towards this end, we implemented the Basic Model Interface (BMI) specification in the JavaScript programming language, the most widely used programming language on the web. Using the BMI, we coupled two comprehensive, client-side hydrological applications (HydroLang and HLM-Web) to perform rainfall-runoff simulations of historical events as a case study demonstration. Through this process, we present how a common and often tedious task – the coupling of two independent web-resources – can be made easier through the adoption of a common standard. Furthermore, applying the standard has facilitated a step towards the possibility of client-side model as a service for hydrological models.

Advances in cyber–physical technologies have enabled real-time sensing and adaptive control of stormwater infrastructure. These smart stormwater systems allow for inexpensive, minimally-invasive stormwater control interventions in lieu of new construction. However promising the area of smart stormwater control, there still remain barriers – for experts and novices alike – to access shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical and accessible, we present pystorms, an open-source Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. pystorms consists of a collection of real world-inspired smart stormwater control scenarios on which any number of control strategies can be applied and tested via an accompanying Python programming interface and coupled stormwater simulator. pystorms provides a framework for the rigorous and efficient evaluation of smart stormwater control methodologies across diverse watersheds with only a few lines of code.

Computational hydrological models and simulations are fundamental pieces of the workflow of contemporary hydroscience research, education, and professional engineering activities. In support of hydrological modelling efforts, web-enabled tools for data processing, storage, computation, and visualization have proliferated. Most of these efforts rely on server resources for computation and data tasks and client-side resources for visualization. However, continued advancements of in-browser, client-side compute performance present an opportunity to further leverage client-side resources. Towards this end, we present an operational rainfall-runoff model and simulation engine running entirely on the client side using the JavaScript programming language. To demonstrate potential uses, we also present an easy-to-use in-browser interface designed for hydroscience education. Although the use case presented here is self-contained, the core technologies can extend to leverage multi-core processing on single machines and parallelization capabilities of multiple clients or JavaScript-enabled servers. These possibilities suggest that client-side hydrological simulation can play a central role in a dynamic, interconnected ecosystem of web-ready hydrological tools.

Sensors and control technologies are being deployed extensively in both urban water networks and rural river systems, leading to unprecedented ability to sense and control our water environment. Because these sensor networks and control systems allow for higher resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, due to growth in system complexity, humans will continue to cede direct decision-making powers to decision-support technologies such as data algorithms. Systems will have ever-greater potential to effect human lives and yet humans will be insulated from direct decisions. Combined, these trends present a challenge for water resources management decision support tools to incorporate concepts ethical and normative expectations. Towards this end, we propose the Water Ethics Web Engine, (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision-support schemes. We then demonstrate the framework with a proof-of-concept use case where decision models are learned and deployed to respond to flooding scenarios. Results indicate the framework can capture group “wisdom” in learned models and use this to make decisions. We share our generalized framework and its cyber components openly with the research community.

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.