Rainwater Harvesting –Significance, Types and Methods

Rainwater harvesting (RWH) is the process of collecting, storing, and utilizing rainwater for various purposes, such as domestic use, irrigation, groundwater recharge, and industrial applications. The main objective is to conserve water, reduce dependency on groundwater, and mitigate water scarcity.

Catchment Area – The surface that collects rainwater (e.g., rooftops, open fields, roads).
Conveyance System – The pipes, gutters, and channels that transport collected water.
Filtration Unit – A system that removes debris, sediments, and contaminants.
Storage Tank/Reservoir – The container used to store harvested water (underground or aboveground).
Groundwater Recharge – The process of directing harvested rainwater into the ground to replenish aquifers.
First Flush System – A mechanism that discards the initial rainwater to prevent contaminants from entering the storage system.

Types and Methods

1. Rooftop Rainwater Harvesting

Concept: Collecting rainwater from rooftops and storing it for later use.
Method:
- Rainwater is collected from roof surfaces through gutters.
- It passes through a filtration unit.
- Water is stored in tanks or directed for groundwater recharge.
Example: Many urban households install rooftop harvesting systems with PVC pipes and storage tanks for domestic use.

2. Surface Runoff Harvesting

Concept: Capturing rainwater that flows over land and directing it to storage or recharge structures.
Method:
- Constructing small check dams, percolation tanks, or ponds.
- Diverting water from roads and pavements into recharge pits.
Example: Urban stormwater harvesting projects use percolation tanks to recharge groundwater.

3. Groundwater Recharge Systems

Concept: Diverting rainwater into aquifers to replenish underground water sources.
Method:
- Constructing recharge pits, wells, or trenches.
- Using sand, gravel, and charcoal layers for filtration before water enters the ground.
Example: Farmers in drought-prone areas build recharge wells to sustain borewells.

4. Check Dams and Percolation Tanks

Concept: Small structures built across seasonal streams to slow down water flow and increase percolation.
Method:
- Constructing check dams using stones, concrete, or soil bunds.
- Water collects behind the dam and gradually infiltrates into the ground.
Example: In semi-arid regions, check dams help improve groundwater levels for agriculture.

5. Rain Gardens and Bioswales

Concept: Landscape features designed to absorb and filter rainwater.
Method:
- Creating depressions with native plants that allow water to percolate.
- Designing sloped channels (bioswales) to direct water into the soil.
Example: Cities use rain gardens in parks and roadsides to reduce urban flooding.

6. Farm Ponds and Tanks

Concept: Storing rainwater in small reservoirs for irrigation.
Method:
- Excavating farm ponds to collect rainwater.
- Lining them with clay or plastic to reduce seepage.
Example: Farmers use farm ponds to store monsoon rain for use during dry periods.

Comments

RADIOMETRIC CORRECTION

Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types 1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification: - You have prior knowledge about the c...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

Vineesh V, Geography

Search This Blog