Although a natural process, human actions and extreme climatic events can accentuate slope instability, leading to disastrous slope failures and loss of life, like the one that occurred in the Brazilian city of Petrópolis on February 17, 2022. Over 200 people died in the mudflows, caused by intense rainfall (258 mm in three hours) and the deforestation of upslope areas. Understanding how and why materials move downslope helps geomorphologists to predict where and when future mass movement events may occur.

Except for perhaps volcanic eruptions and earthquakes, the most impressive (and deadly) geomorphic “events” involve the downslope movement of rock, debris, and sediment – referred to as mass movements because the material moves en masse. In their simplest sense, mass movements represent the downslope transport of rock and soil materials. Examples range from massive, fast-moving landslides and debris flows, to the inexorably slow process of soil creep.

As we think and act, the brain is constantly producing Big Data in the firing of its neurons and in the connections that are strengthened and weakened. This chapter discusses how we can study the brain and the Big Data that it creates. First, we discuss how clever behavioral tasks, looking at development and other species, and natural variation across people are our first tools for understanding the brain. Next, we delve into describing several popular brain imaging methods – direct recording, electroencephalography, magnetoencephalography, magnetic resonance imaging, and a few others. We discuss how to interpret the Big Data shown by brain maps, and some Big Data methods like multiple comparisons correction to consider when viewing this data. Finally, we end the chapter discussing the ethical question of whether such neuroimaging allows mindreading.

This chapter describes the important role of artificial intelligence (AI) in Big Data psychology research. First, we discuss the main goals of AI, and then delve into an example of machine learning and what is happening under the hood. The chapter then describes the Perceptron, a classic simple neural network, and how this has grown into deep learning AI which has become increasingly popular in recent years. Deep learning can be used both for prediction and generation, and has a multitude of applications for psychology and neuroscience. This chapter concludes with the ethical quandaries around fake data generated by AI and biases that exist in how we train systems, as well as some exciting clinical applications of AI relevant to psychology and neuroscience.

Water is central to life. Geomorphologists know that running water also plays a key role in sculpting the land surface. This chapter covers physical hydrology – the science concerned with the occurrence, distribution, and movement of water – and the movement and storage of water-borne sediment within the various Earth systems. In this chapter, we focus on streams and how they transport sediment, from source to sink. The material presented here forms an important background for Chapter 16, which focuses on landforms developed by running water.

Climate and landforms are intimately tied together. Indeed, much of geomorphology is concerned with how landforms, climate, and other surficial processes (like erosion) interact. Landforms are often studied to understand past climates, and vice versa. Thus, a complete understanding of landform genesis requires knowledge of past climates, generally termed paleoclimate.

Climate can be viewed as the prevailing weather/atmospheric conditions for a site, but over long timescales. If a geomorphologist was interested in how sand dunes in a modern desert migrate, they might look at climate over the last few decades. However, a geomorphologist interested in the origin and evolution of the entire desert would need to examine climate over tens of thousands, or even millions, of years. Thus, climate is a somewhat slippery concept, especially when one considers that climate is always changing.

Water, in all its forms, is the most important agent responsible for shaping the landscape. Some water is at the surface in rivers and lakes (surface water), but much of it eventually penetrates underground. Groundwater, present in the pore spaces of soil, regolith, and bedrock, plays a fundamental role in our lives, and (a focus of this chapter) in the dissolution of bedrock, which is perhaps the most important geomorphic effect of groundwater. Because all rocks are at least partially soluble, parts (or all) of them will dissolve and go into solution when exposed to water and its associated acids – the essence of dissolution (Fig. 12.1).

Glaciers are perennial bodies of ice and snow whose movement is driven by gravity. They vary greatly in size and morphology; most glaciers cover small areas of a mountain slope, while the largest glaciers cover entire continents! Glaciers interact with the lithosphere as they erode their beds, depressing the land below them as they grow, and allowing the lithosphere to rebound as they shrink. Along the way, glaciers are effective agents of rock weathering, erosion, transport, and deposition, and important sources of water.

Glaciers add to the natural beauty of mountain and continental landscapes, both in currently glaciated landscapes and in relict landscapes formed during past ice ages. Nonetheless, their ice and water can also pose deadly hazards.

Glacial systems include the glacier and its adjacent lakes, streams, and landscapes – a system that is also closely linked to the atmosphere.

Ice sheets have dramatically shaped the landscape across the northern regions of North America and Europe. Ice sheets are so vast that they are sometimes referred to as continental glaciers. Their deposits have directly influenced human history by rerouting river systems and by providing nutrient-rich parent materials for soils. Abundant lakes and rivers, many of which were newly formed by the ice, became early transportation arteries and supplied aquatic resources to early cultures. Indirectly, glacial sediments were transported by wind to form thick and extensive blankets of loess – home to many of the world’s best soils. Ice sheets reduced the overall relief of the landscape, as valleys were widened and filled, providing for ease of transportation, growth of agriculture, and the rise of civilizations.

Mountains are among the most prominent and inspiring landforms on Earth. Earth’s internal (tectonic, or endogenic) and external (surface, or exogenic) processes have conspired to produce a wealth of mountainous landscapes that span almost every region of our planet. No strict definition of a mountain exists, other than they rise abruptly and prominently above the surrounding land, usually in the form of peaks and ridges. Thus, mountains have considerable local relief. Some mountains may rise only a few hundred meters above sea level (asl), such as the highest mountain in the United Kingdom, Ben Nevis (1,099 m asl [above sea level]). Nonetheless, it is one of the most formidable mountains in the Scottish Highlands (Fig. 6.1A). Other mountains are far more prominent. Mount Everest, the highest point on Earth at 8,849 m asl (Fig. 6.1B), is undoubtedly the most famous of all mountains.

Now equipped with broader participant samples and more diverse stimuli, we can create Big Data experiments. This chapter reviews research methods involved in running Big Data surveys and experiments. The chapter discusses overt and covert measurements that we can collect via online experiments. The chapter then discusses practical logistics to keep in mind when running a Big Data experiment, including experimental design decisions, and a behind-the-scenes look at how data is saved online via server-side coding. Next, once you have the data from an experiment, how do you clean the data and how do you visualize it? The chapter ends with discussion on the ethical implications of collecting covert measures and the useful applications of web-coding skills to create public-facing websites.

Strong interactions are, well, strong. You have hadrons interacting and breaking up into a huge number of other hadrons. It is hard to understand what is going on. Physicists stumbled along during the 1960s trying out many ideas (the key words here are current algebra, analytic S-matrix, Regge trajectories and string theory) without much real understanding.