Impact assessment of drought characteristics using NDVI, NDWI and NDDI parameters under treated micro watershed area using remote sensing and GIS application for Pre and Post watershed development Period

Introduction

In 2024, India's agriculture faced significant challenges due to extreme weather conditions, including droughts and excessive rainfall, which impacted both the area under cultivation and overall production. The total Kharif food grain production for 2024-25 was projected at 1,647.05 lakh metric tonnes (LMT), an increase of 89.37 LMT compared to the previous year. In India, contribution of rainfed production to nutritious cereals and pulses is about 84-87%, cotton-60% and 77% in oilseeds and support 60% of livestock and 40% of human population. As of February 2024, approximately 26% of India's land area was affected by drought conditions, more than double the area affected the previous year. Notably, 9% of the land experienced extreme drought. By March 2024, the drought situation had escalated, with 125 districts across 23 states and union territories facing drought conditions. This intensification raised concerns about agriculture, water resources, and rural livelihoods.

The challenges of land degradation, water shortage, and droughts pose a threat not only to the global climate and environment but also to achieving Sustainable Development Goal (SDG)-2 (Zero Hunger) and SDG-13 (Climate Action). Spatial and temporal distribution of rainfall and the increase in extreme events are impacting the rainfed production systems in the country. While stability and enhancing cropping intensity is observed in irrigated systems, lower cropping intensity and higher risk are prevalent in rainfed systems. Marathwada region is traditionally a drought prone region. The region receives mean annual rainfall of 880 mm and major area comes under assured rainfall zone. In Marathwada region, out of total cultivated area of 57.94 lakh ha, 49.60 lakh ha area is rain fed. The South-West monsoon account for nearly 75% of the precipitation and exerts a strong influence on the kharif food grain production and the economy in terms of agricultural output, farmers income and price stability. The probability of erratic monsoon rains is about 40%. Watershed management is a widely accepted approach which is effective and efficient in environment regeneration. Though several approaches have been tried and adopted in the past for integrated development of people and area, watershed approach offers certain distinct advantages -technologically, socioeconomically, and philosophically. It is a shift from exploitation-oriented development to regeneration oriented development. Conservation of the basic resources like land and water is the first step in the process. A watershed is a living system governed by natural laws. The upper, middle, and lower reaches of a watershed are associated with different kinds of life support systems -agriculture, bio-mass, flora, livestock, fauna and people. They behave differently to flow or transmission of rainwater. They demand specific types of management practices and provide economic returns in specific ways. Watershed approach enables us to take care of this neglected lot of people, land, forests, and other elements of the ecology. The ultimate objective of watershed management is to improve the standard of living for the common man in the basin by increasing his earning capacity through increased productivity of land and water. Good farming practices, rational land use, and efficient management of soil water, crops and livestock results in sustained high yields, which provide the best basis for ensuring adequate returns to the farmer and the country as a whole. Employment generation from sustained use of regenerated land and water resources is far higher than that generated through the process of resource regeneration itself. Economic viability, management of technology and equity could be considered as three main indicators of success. Aurangabad is the district headquarters situated in upper Godavari basin and in the extreme northwest of Marathwada region. It lies between 19°15' and 20 °40' N and 74°37' and 76°52'E. Major part of the district falls in Godavari basin with a small area in north eastern parts in Tapi Basin. Depending on the drainage and geomorphology, the district has been divided into 52 watersheds. Ground water has special significance for agricultural development in the district as 71% of irrigation is ground water based. The climate of the district is characterized by a hot summer and a general dryness throughout the year. December is the coldest month with the mean maximum temperature of 28.9 °C, while the mean minimum temperature is 10.3 °C. May is the hottest month with the mean maximum temperature of 39.8 °C and the mean minimum temperature of 24.6 °C. The Normal rainfall of the district for the period 1998 to 2022 is 581.7 mm [4] .

A large area under the Chhatrapati Sambhajinagar district comes under scarcity rainfall zone of rainfed conditions, hence change in rainfall pattern causes a significant impact on the agricultural production of the study area. Chhatrapati Sambhajinagar district is one of the most severe and frequent drought-prone areas of marathwada region of Maharastra. The interval of drought events in the study area was every 4-5 years, which has declined to 3 year return period in the last three decades. Watershed management is a widely accepted approach which is effective and efficient in environment regeneration by technologically, socio-economically, and philosophically sound. As per GSDA in Marathwada region irrigation wells depth increases > 60 feet's Groundwater level depletion > 1 m and 247 villages, groundwater draft has exceeded recharge in September 2022. As per IMD region have Average annual rainfall 750 mm. 60 blocks have rainfall deficient, and 15 blocks have deficient < 20%. Average rainfall is 44 per cent below normal under drought Years. Two important cash crops such cotton and sugarcane are severely affected due to capital input and cultivation cost and crop failed successively. Marathwada has 70 sugar mills. Total water required for sugarcane cultivation is 4,300 million cubic metres. Impact assessment is the systematic identification of the effects -positive or negative, intended or not -on individual households, institutions, and the environment caused by a given watershed development activity or programs. The main object of the study is to assess the impact of watershed development activities on combat the drought situation of study area through the assessment of NDVI, NDWI and NDDI indexes related to the drought.

Materials and Methodology Details about the Study Area

The proposed study will be undertaken in Dahegaon watershed in Gangapur block of Aurangabad district. It consists of 14 villages covering 7097 ha and 2347 household. It lies between 19.764 N and 19.658 N and 75.101E and 75.177 E. The study area is falling in Survey of India topographical map 47 M/2 and 47 M/1.out of 14 villages the 4 villages are in upper reach, 6 villages are in middle reach and remaining 5 villages are in lower reach. The project area consists of watersheds with streams joining Shivana River near 'Nathsagar dam' at Paithan in Godavari basin. The upper reach portion villages are facing lower productivity due to degradation and water scarcity. The watershed area comprises relatively low-lying areas. The productivity of agriculture especially in rain fed areas depends on the temporal and spatial distribution of rain. As per the Watershed Atlas of India the watershed area falls under 4E4C3 contains total 12 micro catchments. Climate of The study area always face scarcity conditions, as the scarcity of water is observed in the dry period because there is scanty rainfall, uncertainty and unevenness of rainfall. Mean average annual rainfall of the study area was 770mm. (WALMI Aurangabad). The ground water occurs under water table and semi confined to confined conditions in Deccan Trap Basalt. The major part of the Study area is covered by black cotton soil or 'Regur' formed by the weathering of Deccan Trap Basalt, The soils in the region are deep black, medium shallow (NBSS &LUP). The predominant soil in the region is Vertisol (Black cotton soil). It is rich in plant nutrients such as lime, magnesia, iron and alkalises. Under rainfed agriculture, major kharif crops of the region are sorghum, cotton, pigeon pea, green gram, black gram, pearl millet and soybean, whereas major rabi crops are rabi sorghum, safflower, chickpea and sunflower etc.

Methods

The data were processed using image analysis and GIS software. The satellite images were collected free from USGS and Google earth engine and image analysis done by using GIS. The calculation of the top of atmosphere (TOA), layer stacking, extraction of areas of interest, computation of the band indices for producing the spectral differences between the ground features, and cloud masking for the Landsat imageries were included during the pre-processing [12] . The Data Re-sampling method was carried out to ensure comparability between the Landsat data from 2014-2023 (with a spatial resolution of 30m). Re-sampling is method for changing the spatial scale of data to a common resolution, in this study, 10m, so that they can be properly compared.

Normalized difference vegetation index (NDVI) Details

Normalized difference vegetation index (NDVI) Drought Severity can be evaluated using remote sensing as the crop plants' reflectance features vary due to physiological and morphological changes resulting from water stress in plants and soil. The normalized difference vegetation index (NDVI) is the normalized reflectance difference between the two spectral bands, i.e., near-infrared (NIR) and visible red bands is used mainly for monitoring vegetation of earth's surface. The normalized difference vegetation index (NDVI) is a modest graphical indicator that can be cast off to evaluate vegetation cover over the earth's surface using the remote sensing technique. It is based on the known radiation properties of plants, using visible (red) and near-infrared (NIR) radiation. The concept of NDVI was given by Deering and Tucker [1] . NDVI calculated using equation (1)

……. (1)

NDVI-Based Drought Classification

Normalized difference water index (NDWI):

NDWI is derived from two channels, i.e. NIR and Shortwave Infrared (SWIR) channels; absorption of SWIR radiation respond to deviations in both the water content, whereas reflectance of NIR radiation is related to spongy mesophyll in vegetation canopies. In Landsat 8, there are two SWIR bands with wavelengths of 1640 nm and 2130 nm. The 1640 nm band is affected by environmental factors like water vapour and aerosol, while the 2130 nm band is least affected by the atmosphere, mainly by ozone and Rayleigh scattering. Hence, NDWI derived with SWIR 2130 nm is more beneficial in extracting the vegetation water status and in drought detection [1] . NDWI calculated using equation (3)

……. (2)

Drought categories based on NDWI classes as below [2] NDWI-Based Drought Classification Normalized Difference Drought Index (NDDI) details: Normalized Difference Drought Index (NDDI) is used for drought monitoring. This index is the combination of the above two NDVI and NDWI indices. NDDI is the ratio of the difference between NDVI and NDWI to the sum of NDVI and NDWI bands. NDDI is the more sensitive indicator for drought than the NDVI and NDWI indicators [1] . Where the maximum value of NDDI indicates drought conditions at the same time NDVI and NDWI become low. In contrast, the minimum NDDI value indicates no drought or non-water scarcity conditions. When NDDI becomes low, NDVI and NDWI values increase, indicating high vegetation with sufficient water. The formulas for calculating the NDDI indices are presented in Equations 3, as below: [8] ……… (3)

NDDI-Based Drought Classification

On the basis of classification of calculated NDDI values the drought can be categorized into seven drought severity levels based on predefined threshold values. The classification is as follows: Each category represents a progressive decrease or increases in vegetation and moisture availability, indicating increasing or decreasing drought severity. The drought classification is applied to monitor regional drought patterns and support decision-making for water resource management, agriculture, and climate adaptation strategies. The results help policymakers implement drought mitigation measures efficiently.

Results and Discussion

NDVI Estimation

As per the NDVI drought Classification for the study area the ten year Data of NDVI was collected using Landsat-8 imagery.

The average mean of first five year (2014-2018) was consider as pre development period NDVI and the average mean of last five year (2019-2023) was consider as Post development period NDVI (as shown in table 3).

NDWI Estimation

As per the NDWI drought Classification for the study area the ten year Data of NDWI was collected using Landsat-8 imagery.

The average mean of first five year (2014-2018) was consider as pre development period NDWI and the average mean of last five year (2019-2023) was consider as Post development period NDWI (as shown in table 4). Exceptional drought reduces by 5%. Mild, Moderate and severe drought increases by about 2.5%, 7% and 2.5% respectively. Hence 34% area shows positive changes against drought as per NDDI classifications.

Acknowledgement

AcknowledgmentsWe would like to acknowledge JBGVS Aurangabad and BAIF Pune for Sponsoring and implementing Bajaj Water Conservation Project. Such a great CSR project implemented it on the field for the deprived community is challenging task. This scientific research study was only possible due to both these organisation. Also the special thanks to Er. Ranjit Patil of JBGVS and Er. Shashikant Mehetre for provide ideas of possibility about such research studies.

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