Skip to main content

Indo Brahma theory and shivalik river


1. Initial River Formation: During the Miocene period, approximately 5-24 million years ago, a significant river known as the Shiwalik or Indo-Brahma traversed the entire length of the Himalayas, flowing from Assam to Punjab. This river had a massive longitudinal extent and discharged into the Gulf of Sind near lower Punjab.

2. Sedimentary Evidence: The continuity of the Shiwalik and the presence of lacustrine origin and alluvial deposits, including sands, silt, clay, boulders, and conglomerates, provide geological evidence supporting the existence of this ancient river.

3. Fragmentation into Three Drainage Systems:
   - Indus Drainage System: In the western part of the Himalayas, the Indo-Brahma river eventually fragmented, forming the Indus River and its five main tributaries. This fragmentation likely occurred due to geological events such as the Pleistocene upheaval in the western Himalayas.
   - Ganga Drainage System: In the central part of the Himalayas, the Indo-Brahma river gave rise to the Ganga River and its Himalayan tributaries. The uplift of the Potwar Plateau, also known as the Delhi Ridge, acted as a watershed dividing the Indus and Ganga drainage systems.
   - Brahmaputra Drainage System: In the eastern part of the Himalayas, the stretch of the Indo-Brahma river in Assam transformed into the Brahmaputra River and its Himalayan tributaries. The downthrusting of the Malda gap area between the Rajmahal hills and the Meghalaya plateau during the mid-Pleistocene period diverted the flow of the Ganga and Brahmaputra systems towards the Bay of Bengal.

4. Geological Processes: The Pleistocene upheaval in the western Himalayas, including the uplift of the Potwar Plateau, likely played a significant role in the fragmentation of the Indo-Brahma river. Similarly, the downward movement of the Malda gap area redirected the flow of rivers towards the Bay of Bengal, reshaping the drainage pattern in the eastern part of the Himalayas.

These geological processes, including uplifts, downthrusting, and watershed formations, have contributed to the complex and diverse drainage system of the Himalayas, shaping the landscape and influencing the distribution of rivers and tributaries in the region.




Comments

Post a Comment

Popular posts from this blog

GIS data continuous discrete ordinal interval ratio

In Geographic Information Systems (GIS) , data is categorized based on its nature (discrete or continuous) and its measurement scale (nominal, ordinal, interval, or ratio). These distinctions influence how the data is collected, analyzed, and visualized. Let's break down these categories with concepts, terminologies, and examples: 1. Discrete Data Discrete data is obtained by counting distinct items or entities. Values are finite and cannot be infinitely subdivided. Characteristics : Represent distinct objects or occurrences. Commonly represented as vector data (points, lines, polygons). Values within a range are whole numbers or categories. Examples : Number of People : Counting individuals on a train or in a hospital. Building Types : Categorizing buildings as residential, commercial, or industrial. Tree Count : Number of trees in a specific area. 2. Continuous Data Continuous data is obtained by measuring phenomena that can take any value within a range...
Post a Comment
Read more

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...
Post a Comment
Read more

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...
Post a Comment
Read more

Representation of Spatial and Temporal Relationships

Geographical Information System (GIS) is a powerful tool for analyzing and visualizing spatial data. One of the key features of GIS is its ability to represent spatial and temporal relationships between different geographic features. Spatial relationships refer to the physical location of an object or feature in relation to other objects or features, while temporal relationships refer to the sequence or timing of events. Together, these relationships are essential for understanding and analyzing complex spatial and temporal data. Representation of Spatial Relationships in GIS: Spatial relationships in GIS can be represented using a variety of techniques such as distance, proximity, and topology. For example, distance-based relationships can be used to measure the distance between two points, while proximity-based relationships can be used to determine which objects or features are closest to one another. Topology-based relationships can be used to represent the connectivity between dif...
Post a Comment
Read more

Data Generalization in GIS

Data generalization in GIS is the process of simplifying complex geographic data to make it suitable for visualization and analysis at specific map scales. It reduces unnecessary details while preserving the overall patterns and essential characteristics, ensuring that the map remains clear and interpretable at different zoom levels. Key Concepts and Terminologies Purpose of Data Generalization : To simplify spatial data for better visualization and usability at smaller scales. To prevent maps from becoming cluttered or unreadable due to excessive detail. To maintain the essence of geographic features while omitting minor details. Example : On a world map, a small island may be represented as a single point or omitted, while on a local map, it may appear with detailed boundaries. Key Data Generalization Techniques Simplification : Definition : Reduces the number of vertices or points in a line or polygon, removing minor details while retaining the general shap...
Post a Comment
Read more