Drought

Drought is one of the most objectionable natural disasters The amount of precipitation at a particular location varies from year to year, but over a period of years, the average amount is fairly constant.

• Agricultural loss

• Financial loss

• Wildlife loss

• Vegetation loss

Maharashtra is one of the most drought-prone areas in India. Thirteen districts of the state are in arid regions including Pune as the driest district. Spatial variation of drought from the Satellite view involved Landsat data collected from the  USGS  web platform, during 1991–2021 in 5-year intervals. The datasets was analyzed to build yearly time series of the NDVI, and NDWI. These raster indices have been analyzed to study the spatial patterns of drought occurrence in this region.

Again, drought analysis was implemented with precipitation & temperature grid level daily data from the  India Meteorological Department . The dataset was implemented to estimate the SPI, and RAI. IDW Interpolation method was utilized to visualize maps of these meteorological indicators’ temporal patterns of drought occurrence in the region. A comparative analysis of these spatial &temporal indicators has been done to analyze the pattern of drought occurrence over the last few years.

Drought Indices have been prepared using ARCGIS Pro

Spatial Indices of drought: -

Radiance values measured in both the visible and near-infrared channels are used to calculate NDVI. It measures greenness and vigour of vegetation over a seven-day period as a way of reducing cloud contamination and can identify drought-related stress to vegetation.

Normalized Difference Vegetation Index (NDWI) has been used for monitoring of drought-affecting agriculture as a method of stress detection. NDWI values exhibited a quicker response to drought conditions than NDVI. Analysis revealed that combining information from visible, near-infrared, and short-wave infrared.

Meteorological indices of drought: -

The Standard Precipitation Index (SPI) is a relatively new drought index based only on precipitation. It's an index based on the probability of precipitation for any time scale. Some processes are rapidly affected by atmospheric behavior, such as dry land agriculture, and the relevant time scale is a month or two.

The Rainfall Anomaly Index (RAI) is an incorporation of ranking procedure to assign magnitudes to positive and negative precipitation anomalies. It is calculated to assess the intensity and frequency of dry and rainy years

A highly positive trend of drought occurrence was observed both spatially & temporally informing urban planners to concentrate on artificial water recharge structures.

Thanks a lot to Esri providing opportunity to show case the work and Also thanks Centurion University of Technology and Management,Odisha providing a fantastic environment for doing research work.