Friday, November 28, 2025

Inside Chile’s Foggy Desert: How Camanchaca Defies Nature

Camanchaca fog in Atacama desert and fog harvesting nets 

Inside Chile’s Foggy Desert: How Camanchaca Defies Nature

Imagine standing in a desert where rain hasn’t fallen for centuries—yet the sand beneath your feet hides traces of life clinging on against impossible odds. Welcome to northern Chile’s Atacama Desert, the driest non-polar desert on Earth, and home to one of nature’s most astonishing survival tricks: Camanchaca, the life-giving fog.

The Desert That Shouldn’t Support Life

When most people imagine a desert, they picture scorching heat, blinding sunlight, and endless lifeless dunes. But the Atacama is no ordinary desert.

Location and geographical setting in Atacama Desert
 

Shielded on all sides by extreme geography, the region is trapped in a perpetual double rain shadow. To the east, the Andes Mountains block humid air from the Amazon rainforests. While to the west, the cold Peru Current chills the Pacific. It causes moisture to condense over the ocean long before reaching land.

Due to being in a double rain shadoe region, some parts of the Atacama receive less than 0.1 mm of rainfall per year. A few weather stations have never recorded a single drop of rain. And yet every morning, something happens that prevent this desert from becoming a true wasteland.

Meet Camanchaca: The Fog That Sustains an Entire Desert

Camanchaca is not rain. It is a cold dense coastal fog composed of microscopic droplets—each just 1 to 40 microns wide—so light they float endlessly in the air.

Born over the chilled Pacific Ocean, this fog drifts inland under the right conditions. Camanchaca, the life-giving fog of northern Chile, forms only when a rare combination of ocean, wind, and terrain come together. It begins over the cold waters of the Peru (Humboldt) Current, which cools the moist air moving above it until tiny droplets condense into fog. A persistent temperature inversion traps this cool, moist layer near the surface, keeping the fog low and dense.

Gentle onshore winds then push the fog inland, where Chile’s steep coastal cliffs and hills guide and lift it into the Atacama Desert. Early-morning temperature contrasts—cool nights followed by rapid warming—help draw the fog farther inland. When all these conditions align, Camanchaca forms a thick, ghostly layer that drifts through canyons and over barren soil.

This thick fog hugs the ground, slides through canyons, clings to cactus spines, and lingers over the dry earth long enough to deliver precious moisture.

Life That Drinks from the Air

To survive in a place almost entirely devoid of rain, plants and animals have evolved astonishing adaptations. Cacti and shrubs collect fog droplets on thorns and leaves. Fine leaf hairs pull moisture into the plant like tiny moisture channels. Rock faces and cliffs trap mist, creating micro-drips that soak into the soil. Even guanacos, wild relatives of the llama, lick condensation from the spines of cacti. Life in the Atacama doesn’t wait for the rain.

The Ancient Art of Harvesting Fog

Humans have been doing the same for more than a thousand years. Indigenous communities in northern Chile once hung animal skins to catch fog droplets, collecting precious water in clay pots. Similar methods appeared in the dry regions of Peru and even faraway islands like the Canary Islands of Spain.

Fog Catchers: A Modern Solution to an Ancient Challenge

Camanchaca fog and fog harvesting nets

 
Today, communities have transformed this ancient wisdom into an elegant technology: fog catchers. These systems use large vertical nets—each opening about 1 millimeter wide—mounted on frames along fog-prone hillsides. As Camanchaca drifts through, Tiny droplets collide with the mesh. They merge and grow heavy. And finally, gravity pulls the water down into pipes and into storage tanks. So without electricity, pumps and no environmental impact, life survives in Atacama.

A single square meter of mesh can capture 7 to 10 liters of water on a good day. Large installations (8–32 meters wide) can collect hundreds of liters daily, sometimes even 1,000 liters under ideal conditions. And this water supports communities with drinking water, farming and even reforestation.

Global Fog harvesting Sites

From here, this idea of Fog harvesting has now spread to Peru, Mexico, and other dry regions worldwide—proof that the simplest ideas can be the most powerful.

A Lifeline with Limits

But fog harvesting is not foolproof. Its success depends entirely on nature. When fog is thin or wind patterns shift, yields can drop dramatically. Climate change may further reduce fog frequency, alter droplet size, or push the fog layer higher than the nets can reach. For many communities, fog catchers are a blessing—but not a complete replacement for other water sources. Still, when the fog is generous, these systems quite literally pull pure water out of thin air.

The Atacama reminds us that even in Earth’s harshest places, resilience finds a way to flourish. It challenges our ideas about deserts—proving they are far more dynamic, mysterious, and alive than we imagine. So, the Atacama and the Camanchaca fog, are not just a natural phenomenon. It is a lesson in adaptation, ingenuity, and the quiet power of Earth’s most subtle forces.

 Watch this video on youtube to know more - 


 

 

 

Monday, September 15, 2025

What Is a Geographic Information System (GIS)? Popular Definitions Explained

 

When people talk about Geographic Information Systems (GIS), they often mean slightly different things. Over the years, researchers and institutions have defined GIS in various ways, each focusing on a different aspect—whether it’s data, tools, workflows, or decision-making. Let’s look at some of the most popular and widely accepted definitions, and what they really mean in simple terms.


The General Definition

At its core, a GIS is described as a system of hardware and databases that can assemble, store, update, analyze, and display information tied to locations on Earth. In practice, this means GIS is a computer-based system that works with maps, images, and location data. Think of it as a smart tool that can collect and organize spatial data, analyze patterns, and present them as maps or charts.

👉 A simple example is Google Maps, which not only shows roads but also adds real-time traffic updates and the best routes to take.


Parker (1988) – Spatial and Non-Spatial Data

Parker defined GIS as “an information technology which stores, analyses and displays both spatial and non-spatial data.”

  • Spatial data: the “where” (like roads, rivers, or boundaries).
  • Non-spatial data: the “what” (like population, rainfall, or income).

👉 Imagine a city map: it doesn’t just show streets and buildings, but also information like traffic accidents or property ownership. That’s GIS combining the where with the what.


Burrough (1986) – A Toolbox of Functions

Burrough saw GIS as “a powerful set of tools for collecting, storing, retrieving, transforming, and displaying spatial data.” This definition emphasizes what GIS can do. It’s less about the type of data and more about the functions: from capturing field data to creating layered digital maps.


Department of Environment (1987) – A Systematic Workflow

The DoE defined GIS as “a system for capturing, storing, checking, manipulating, analyzing and displaying data which are spatially referenced to the earth.” This one presents GIS as a step-by-step process: collect the data, check it, analyze it, and then display it. It highlights GIS as a workflow system, rather than just a collection of tools.


Smith et al. (1987) – A Database System

Smith and colleagues described GIS as “a database system in which most of the data are spatially indexed… to answer queries about spatial entities.” In simple words, they viewed GIS as a location-based database. With spatial indexing, GIS can answer questions like:

  • “Which schools are within 5 km of this hospital?”
  • “Which farms are inside a flood-prone zone?”

👉 This makes GIS very powerful for planning and research.


Cowen (1988) – A Decision Support System

Cowen took things further, calling GIS “a decision support system involving the integration of spatially referenced data in a problem-solving environment.”
This highlights GIS as a decision-making tool. It’s not just about storing maps or analyzing data—it’s about solving real problems, like choosing the best site for a new road, planning urban growth, or managing disaster relief.


Ozemoy, Smith & Sicherman (1981) – Automation and Professional Use

Earlier on, Ozemoy and colleagues described GIS as “an automated set of functions that provide professionals with advanced capabilities.” This reflects the early shift from traditional paper maps to computerized mapping and analysis. It emphasizes automation—reducing manual effort and giving professionals tools for complex tasks.


Wrapping It Up

So, what do all these definitions tell us?

  • Some focus on data types (like Parker’s spatial + non-spatial).
  • Others focus on tools and workflows (Burrough, DoE).
  • Some highlight databases and queries (Smith et al.).
  • Others emphasize decision-making (Cowen).
  • And a few point to automation and advanced functions (Ozemoy et al.).

Despite the different wording, they all agree on one thing: GIS is a powerful way to connect location with information, making it possible to analyze the world around us and make smarter decisions.

 

Watch this video here about GIS -  

 


 References - 

  1. Ozemoy, V.M., Smith, D.R., and A. Sicherman. (1981). Evaluating computerized geographic information systems using decision analysis. Interfaces, 11:92-8. 
  2. Burrough P.A (1986). Principles of Geographical Information System for land resources assessment. Oxford University Press, Oxford, 194 pp.
  3. Department of Environment. (DoE 1987). Handling Geographic Information. HMSO, London.
  4. Smith, T.R., Menon, S., Starr, J.L., and J.E. Estes. (1987). Requirements and principles for the implementation and cinstruction of large-scale geographic information systems. Internation Journal of Geographical Information Systems, 1: 13-31. 
  5. Cowen, D.J. (1988). GIS versus CAD versus DBMS: What are the differences? Photogrammetric Engineering and Remote Sensing, 54: 1551-4. 
  6.  Parker, H.D. (1988) The unique qualities of a geographic information system: a commentary. Photogrammetric Engineering and Remote Sensing, 54: 1547-9.
  7. Aronoff, S. (1989) Geographic Information System – A Management Perspective. WDL Publications, Ottawa, Canada.
  8. Burrough P.A and R.A. McDonnell. (1998). Principles of Geographic Information System. Oxford university Press, Oxford, 10 pp.