Skip to main content

India's National Water Policy


The National Water Policy (NWP) is India's central framework for managing and utilizing its water resources. It lays down principles, objectives, and strategies for optimal water development, management, and regulation across the country.

Year of Initiation and Revisions:

  • The first NWP was adopted in 1987, focusing on increasing irrigation, drinking water access, and sanitation facilities.
  • It was revised in 2002 to address emerging challenges like inter-state water disputes and environmental considerations.
  • The current policy, NWP 2012, emphasizes integrated water management, conservation, and sustainability.

Ministry of Jal Shakti:

  • In 2019, the Ministry of Water Resources, River Development and Ganga Rejuvenation was merged with the Ministry of Drinking Water and Sanitation to form the Ministry of Jal Shakti.
  • This unified ministry oversees the implementation of the NWP and other water-related programs and initiatives.

Key Highlights of the NWP 2012:

  • Equity and social justice: Prioritizes equitable water allocation for various uses, including drinking, irrigation, industry, and ecology.
  • Decentralized management: Promotes participatory decision-making through local water user groups and basin-level planning.
  • Water conservation and efficiency: Encourages rainwater harvesting, efficient irrigation practices, and wastewater reuse.
  • Environmental protection: Recognizes the ecological needs of rivers and aquatic ecosystems.
  • Pricing and tariffs: Advocates for volumetric water pricing to promote efficient usage and cost recovery.

Related Information:

  • Jal Jeevan Mission: Aims to provide piped water supply to all rural households by 2024.
  • Catch the Rain campaign: Promotes rainwater harvesting structures across the country.
  • Atal Mission for Rejuvenation and Urban Transformation (AMRUT): Focuses on water supply and sanitation infrastructure in urban areas.
  • National Water Mission: Launched under the National Action Plan on Climate Change, it aims to improve water availability and efficiency through various interventions.

Challenges and Concerns:Implementing the NWP effectively requires coordinated efforts from various stakeholders at central, state, and local levels.

  • Balancing different water demands and ensuring equitable access remains a challenge.
  • Addressing issues like climate change, pollution, and groundwater depletion requires urgent attention.




Comments

Post a Comment

Popular posts from this blog

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...
Post a Comment
Read more

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...
Post a Comment
Read more

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...
Post a Comment
Read more

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...
Post a Comment
Read more

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...
Post a Comment
Read more