In this chapter, we will cover the remote sensing of precipitation to understand how precipitation is tracked. Precipitation is considered one of the most important components of the water cycle that drives the availability of water and its management. For example, precipitation leads to runoff and streamflow, irrigates a field of crops and provides the water for crop growth, fills up lakes, reservoirs and ponds that are a key source for water management. The understanding of precipitation remote sensing will pave the way for learning more complex water management applications that are being increasingly carried out around the world today using satellite water data. We will first cover the history of precipitation remote sensing that began with using active sensing and ground radar. Next, we will cover satellite-based sensing where the challenges and complexities are different. The pros and cons of using various electromagnetic wavelengths will be covered. Finally, we will cover the topic of multi-sensor precipitation estimation based on the synergistic use of multiple satellite sensors spanning different wavelengths of the electromagnetic spectrum.

In the previous chapter we covered how satellite remote sensing can be used to classify areas under a crop, estimate their crop water demand and actual crop water consumption. This information can be used for irrigation management using satellite data. In this chapter we will cover how satellite data can be used to estimate temperature of surface water. We will cover the basic principle behind the estimation technique, understand the limitation of the technique and then build some data literacy to derive the surface temperature of water in regulated rivers ourselves.

In this Chapter, we will explore how reservoirs can be monitored from space for water management. Today it is now possible to track the dynamic state of reservoirs at temporal and spatial scales of satellite remote sensing. This dynamic state comprises inflow, outflow, surface area, storage change and evaporative losses. Most of these variables can be modeled using satellite data or directly estimated using satellite data. This chapter will introduce readers to the Reservoir Assessment Tool (RAT) that we have developed as an open-source complete package for users to use the full power of satellite remote sensing to track reservoirs anywhere.

This chapter will explore the topic of citizen science in the context of water management using satellite remote sensing. This is a broad field and the goal here is to expose readers to a social yet important issue of using citizens to carry out science for building more robust management solutions. As mentioned earlier in Chapter 11, this chapter is in no way comprehensive. The objective here is to encourage readers to start thinking about the idea of citizen science and the positive role it can play in building more equitable satellite-based water management solutions

In this chapter and the next, we will be switching gears to transition to less-technical but more social and governance related issues where satellite remote sensing of water can play a positive role. So far, we have learned up to chapter 10 are technical aspects of satellite remote sensing of water and their applications in water management. In this chapter, we will explore the potential of satellite remote sensing for social justice in water management.

In the previous chapter we covered how satellite remote sensing can be used to detect water on the surface in terms of its spatial extent, elevation and change in storage. In this chapter, we will cover how discharge can be estimated using satellite-based observables, such as those from the newly launched Surface Water and Ocean Topography (SWOT) mission mentioned in Chapter 6.

In this chapter will focus on surface water – notably the water that is in lakes and reservoirs, rather than rivers and groundwater. This is the water that remains directly on land and represents a significant reservoir for the water cycle. The storage of such water drives many water management applications, as we shall see later, such as reservoir and flood management (chapter 8), irrigation (chapter 9). Here, we will overview the various remote sensing techniques that can be used to detect if a land is covered with water and if so, what is the extent. Later in the chapter we will learn how two successive satellite overpasses can help us estimate storage change a water body may have experience. This storage change can be a crucial component for various water management applications as it helps us understand how much water lakes or reservoirs are storing, losing (to diversion or evaporation) or releasing.

In the previous chapters, we built the basic foundation of satellite remote sensing. In this chapter we will explore a relatively recent innovation in information technology called cloud computing that has dramatically improved data accessibility and the practicality of applying large satellite remote sensing datasets for water management. Future chapters on specific targets and water management themes will have hands-on examples and assignments based on actual satellite data. Most of these chapters will assume prior knowledge of cloud computing for understanding and completing assignments. Since cloud computing is gradually proliferating in all walks of water management practice, the aim of this chapter is to introduce readers to cloud computing concepts and specific tools currently available for dealing with the very large satellite data sets on water.

In the previous chapter, we introduced ourselves to the importance of satellite remote sensing for water management and why the technique is going to take greater importance in years to come as challenges mount from climate change, competing needs and lack of ground data. In this chapter, we will overview the basics of remote sensing, define key concepts and terms. Using these concepts and terms, we will develop an understanding of the fundamental principle required for the success of remote sensing.

Earlier in Chapter 2, we had formalized our approach to remote sensing in the form of ‘target’, background and foreground. Now the first thing we need to focus on is the electromagnetic behavior of the target. This is best captured by the term ‘Black Body’. From here on, we will try to think of the target in water management relative to a black body and understand how much a black body it is under certain circumstances.

This is the first chapter of the book. The goal of this chapter is to introduce ourselves to the growing importance of using satellite remote sensing to manage our water. We will try to understand this in the context of the underlying challenges and new global forces shaping up this century that are expected to make traditional ways of managing water using in-situ data more challenging.

Critical to successful engagement in any organisation is an understanding of the important elements affecting good communication. There are many dimensions to the study of communication in the 21st century, both generally and in health service settings, in the 21st century. This chapter considers the foundational concepts, with references to help students discover more about communication in organisational, social and cultural settings. Many believe that even the definition of communication is worth questioning. As a notion it is so discursive and diverse that any definition other than the simplest becomes so complex as to cease being useful.

The complexities inherent in healthcare organisations highlight the multifaceted nature of their operations. Regardless of role, scale, procedural intricacies or governance structures, these organisations need to deal with the complexities of both internal dynamics and external landscapes. The diversity of stakeholders involved adds layers of challenge to effectively managing clinical and social processes, optimising outcomes, allocating resources equitably, developing and retaining a skilled workforce, making informed decisions and upholding ethical standards.

The advent of the digital age has brought about significant changes in how information is created, disseminated and consumed. Recent developments in the use of big data and artificial intelligence (AI) have brought all things digital into sharp focus. Big data and AI have played pivotal roles in shaping the digital landscape. The term ‘big data’ describes the vast amounts of structured and unstructured data generated every day. Advanced analytics on big data enable businesses and organisations to extract valuable insights, make informed decisions and enhance various processes. AI, on the other hand, has brought about a paradigm shift in how machines learn, reason and perform tasks traditionally associated with human intelligence. Machine-learning algorithms, a subset of AI, process vast datasets to identify patterns and make predictions. This has applications across diverse fields, including health care, finance, marketing and more. The combination of big data and AI has fuelled advancements in areas such as personalised recommendations, predictive analytics and automation in all aspects of our day-to-day lives.