Skip to main content

Drought prone regions India

The geography of drought-prone regions in India is influenced by various factors, including climate, topography, and hydrology. Here's a brief overview of the geography of these regions:


1. Arid and Semi-Arid Climate: Many drought-prone regions in India fall within the arid and semi-arid climatic zones. These areas receive low and erratic rainfall, making them susceptible to droughts. States like Rajasthan, Gujarat, and parts of Maharashtra and Karnataka have arid or semi-arid climates, characterized by hot temperatures and sparse vegetation.


2. Geographical Features: Certain geographical features contribute to the prevalence of drought in specific regions. For example, the Thar Desert in Rajasthan and parts of Kutch in Gujarat are arid landscapes with scanty vegetation and limited water resources. These areas experience severe water scarcity during droughts.


3. Water Bodies and Rivers: Drought-prone regions may also lack significant water bodies or river systems, exacerbating water scarcity during dry spells. For instance, some parts of Maharashtra and Karnataka have limited access to perennial rivers, relying heavily on rainfall for water supply.


4. Topography: The topography of drought-prone regions can vary from flat plains to hilly terrain. In states like Madhya Pradesh, Uttar Pradesh, and Bihar, hilly and semi-hilly regions may experience water stress due to inadequate water retention capacity and runoff during droughts.


5. Groundwater Depletion: Over-exploitation of groundwater resources is a common issue in many drought-prone regions. Unsustainable agricultural practices, such as excessive groundwater pumping for irrigation, contribute to groundwater depletion, exacerbating drought conditions.


6. Vegetation Cover: Sparse vegetation cover in arid and semi-arid regions reduces the soil's ability to retain moisture, making these areas more vulnerable to drought. Deforestation and land degradation further compound the problem, leading to soil erosion and reduced water infiltration.


7. Rainfall Patterns: Irregular rainfall patterns, with uneven distribution and seasonal variations, are characteristic of drought-prone regions. Some areas may experience long dry spells interspersed with intense rainfall events, leading to water stress for agriculture and other activities.


Drought prone regions in India


1. Rajasthan: Rajasthan is the largest state in India and is predominantly arid or semi-arid. It experiences low and erratic rainfall, with most parts receiving less than 600 mm of rainfall annually. The Thar Desert covers a significant portion of the state, leading to water scarcity and frequent drought conditions.


2. Gujarat: Gujarat has a diverse climate, with some regions experiencing arid conditions. The Saurashtra region and parts of Kutch are particularly prone to drought due to low rainfall and high evaporation rates. Additionally, unsustainable water management practices exacerbate the situation.


3. Maharashtra: Maharashtra faces droughts frequently, especially in regions like Marathwada, Vidarbha, and parts of western Maharashtra. Factors such as irregular rainfall patterns, inadequate water management infrastructure, and over-exploitation of groundwater contribute to drought vulnerability.


4. Karnataka: Northern Karnataka, including districts like Gulbarga, Bidar, and Raichur, is prone to drought due to its semi-arid climate. Rainfall variability and poor water conservation measures aggravate the situation, impacting agriculture and livelihoods.


5. Andhra Pradesh and Telangana: These states have regions like Rayalaseema and Telangana, which face water scarcity and droughts due to irregular rainfall, unsustainable agricultural practices, and over-dependence on groundwater.


6. Tamil Nadu: Tamil Nadu experiences droughts, especially in its western districts like Coimbatore, Erode, and Salem. The state's water resources are strained due to low rainfall, excessive extraction of groundwater, and poor water management practices.


7. Madhya Pradesh: Certain parts of Madhya Pradesh, such as Bundelkhand and Malwa, are vulnerable to drought due to inadequate rainfall and soil moisture retention. Deforestation, soil erosion, and inefficient irrigation systems exacerbate the situation.


8. Uttar Pradesh: Eastern Uttar Pradesh, including districts like Bundelkhand, faces recurrent droughts due to inadequate monsoon rains and poor water management. Agricultural productivity suffers, impacting the livelihoods of millions.


9. Bihar: Some regions of Bihar, such as the North Bihar Plain, are susceptible to drought due to insufficient rainfall and inadequate irrigation facilities. The state's vulnerability is compounded by factors like soil degradation and floods in certain areas.


These regions face varying degrees of water scarcity and drought, affecting agricultural productivity, water availability for drinking and sanitation, and overall socio-economic development. Efforts to improve water management, promote sustainable agricultural practices, and build resilience to climate change are essential to mitigate the impacts of drought in these regions.


Comments

Post a Comment

Popular posts from this blog

Quantitative Revolution in Geography

The Quantitative Revolution was a major change in Geography during the 1950s and 1960s. It introduced mathematical, statistical and scientific methods to understand geographical phenomena. Before this revolution, geographers mainly described places, regions, landscapes and differences between areas. After the revolution, they increasingly used numerical data, statistical analysis, mathematical models and theories to explain why geographical patterns occur and how they may change. For example: Traditional approach: Describe the distribution of population in different regions. Quantitative approach: Use statistical methods to identify population patterns, explain the factors influencing population distribution and predict future population growth. The revolution helped establish Geography as a spatial science, focusing on the location, distribution, relati...
Post a Comment
Read more

Satellite Remote Sensing: Concepts and Imaging Systems

  Satellite remote sensing relies on detectors (sensors) that measure reflected/emitted electromagnetic radiation from the Earth. How sensors collect data depends on: Spectral coverage → Multispectral (few bands), Hyperspectral (hundreds), Thermal, Microwave. Detector type → Discrete detectors, linear arrays, or area arrays. Scanning mechanism → Scanning mirrors (whiskbroom) vs. linear arrays (pushbroom). Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Whiskbroom scanners) Principle : A single detector (or a few detectors) measures radiation one pixel at a time. A scanning mirror sweeps across-track (perpendicular to the satellite path) to build up the image line by line. The forward motion of the satellite provides the along-track dimension. Known as a whiskbroom scanner . Characteristics : Good calibration stability. Narrow instantaneous field of view (IFOV). Susceptible to mechanical wear (moving ...
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

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...
Post a Comment
Read more

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...
Post a Comment
Read more