Drought directly impacts the agricultural ecosystem, thus causing significant threat to regional and global food security. Investigating the occurrence and propagation patterns of drought events is crucial for its better understanding and mitigation. The study investigates different agro-climatic regions of the Ganga River basin from 2001 to 2020 to quantify meteorological drought using the Standardized Precipitation Index (SPI). Additionally, the assessment of agricultural drought was conducted using the Standardized Soil Moisture Index (SSMI) and Normalized Difference Vegetation Index (NDVI). For understanding the propagation dynamics of drought, Pearson Correlation Coefficient (PCC)-based approach was employed to compute the propagation time between meteorological and agricultural drought types. Stronger correlations were observed between SPI and SSMI compared to SPI and NDVI anomaly, highlighting the direct connection between precipitation and soil moisture. The results of the present study show that the time for propagation ranges within 1–11 months across the Ganga basin as inferred from the maximum PCC values between the SPI and SSMI time series. The propagation rate from meteorological drought to agricultural drought varied from 29.03 to 73.33% among different agro-climatic regions. The insights gained from this analysis on propagation time and rate can inform policymakers in formulating appropriate measure.

  • Characterized the meteorological and agricultural drought using multiple indices.

  • Analyzed the agricultural drought propagation time in the Ganga basin.

  • Quantified the drought propagation rate for different agro-climatic zones.

  • Assessed the relative contribution of climatic factors and human activities to agricultural drought.

The effects of drought on the environment, agriculture, economy, and society are extensive as it is a complex extreme event having variable duration and severity levels. The scientific processes behind the origin, propagation, and termination of drought are perplexing aspects and poorly understood in the world of research as drought can occur in all types of climatic zones at different forms with different intensities and duration (Mishra & Singh 2010). According to certain studies, droughts in India cause an average annual economic loss of a significant amount of GDP and it also depends on the drought category (e.g., mild, moderate, and intense droughts). Extreme climatic events in India are showing an increasing trend in recent years in which anthropogenic activities also have great influence. The land use change, the over-exploitation of water resources, etc. affects the hydrological balance of the planet which also contributes to extreme climatic events. The propagation of drought is a complex process impacted by several interconnected elements, including temperature, precipitation, evapotranspiration, humidity, climatic characteristics, land use practices, topography, etc. making it challenging to predict and understand its full extent (Wilhite & Glantz 1985). There are four basic categories for drought, firstly, meteorological drought, which occurs whenever precipitation is insufficient when compared to long-term normal; second, agricultural drought, which is a result of reduced soil moisture to support plant growth; thirdly, hydrological drought, which occurs as a result of reduced streamflow, and finally, socio-economic drought, where the human activities are affected due to the inadequate water availability (Das et al. 2022).

Considering food security in a region, it is important to monitor and predict natural disaster events like droughts as these can severely affect agricultural productivity (Mishra & Singh 2010). If the meteorological drought propagates into the agricultural drought, it affects the crop yield which can lead to insufficient food production (Hameed et al. 2020). As drought propagates from the atmosphere to agricultural areas, competition for limited water resources intensifies, and, therefore, in developing nations, the agricultural sector is one of those most impacted by drought. Drought is a major threat to India as well, causing increased susceptibility of crops to stress and various biotic and abiotic factors, and this condition is making it one of the most vulnerable countries in the world to drought. According to some studies, drought affects 68% of India's agricultural area, with 33% classified as ‘chronically drought-prone’ (Kaur et al. 2022). One of the main driving elements for triggering soil moisture stress is precipitation deficiency (Li et al. 2022) and hence studying the transition from meteorological to agricultural drought is crucial. Similar research work is of significant importance in any country as the propagation of drought from atmospheric conditions to agricultural drought can have several significant multifaceted effects such as increased evapotranspiration and competition for scarce water resources, serious soil moisture deficit, crop stress, and vulnerability, impacts on agricultural income and food security, impacts on rural livelihood and associated economic losses and other environmental impacts such as habitat degradation and biodiversity losses (Wei et al. 2022).

Drought propagation is a very complex and non-linear process, hence the study of the progression of conditions of negative rainfall anomalies to soil moisture water stress is intricately intertwined. Nevertheless, researchers have used a variety of techniques such as propagation threshold, correlation coefficients, aggregate drought indexes, standardized indicators, multivariate indicators, and copula-based approaches. Some researchers have combined the application of hydrological models and land surface process models with the intensity and duration of drought propagation (e.g., JULES land surface model). Some studies have proved that the application of hydrological and land surface models can help researchers understand how meteorological drought conditions propagate through the hydrological system, affecting soil moisture, river flows, and groundwater availability. To assess drought propagation nature in a simple and realistic manner, several standardized drought indices are developed and employed (Li et al. 2022) and these indices helped to derive new knowledge on drought characteristics like frequency, duration, and intensity. Meteorological drought can be assessed using various indices like the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), etc. (Mckee et al. 1993). The Normalized Difference Vegetation Index (NDVI) (Cai et al. 2023), Vegetation Condition Index (Al Kafy et al. 2023), Vegetation Health Index (VHI) (Weng et al. 2023), and Standardized Soil Moisture Index (SSMI) (Wei et al. 2022) are the most commonly used indices for agricultural drought assessment. The lag time to agricultural drought from meteorological drought can be assessed effectively using correlation analysis. Traditionally, Pearson's correlation analysis has been used in many studies to assess drought propagation time (DPT) (Zhou & Shi 2021). Previous studies on meteorological drought to agricultural drought have shown very interesting but sometimes contrasting results. For example, the duration of drought propagation from meteorological to agricultural drought, in humid regions, typically does not exceed 3 months, whereas over the arid as well as semi-arid regions of China, it varies between 1 and 8 months. A study by Xu et al. (2023) identified that agricultural droughts are getting triggered on an average time of 48 days from the onset of meteorological drought when Improved Soil Moisture Anomaly Percentage Index (ISMAPI) and SPI were employed in China's Yangtze River Basin. The observed differences in DPT between regions may be attributed to limitations associated with the linear nature of correlation analysis, potential biases arising from the temporal resolution of the developed indices, or the absence of sub-monthly drought indices which can provide a more comprehensive representation of spatial and temporal distributions in the time for the propagation of water stress conditions. Regarding the propagation dynamics of agricultural drought in the Indian setting, there is a significant research gap. Globally, only a few studies have been conducted clearly pointing to the drought propagation values in terms of days or month, and these studies are primarily focusing on catchment or river basin scales. A recent study by Das et al. (2023) made an important contribution by suggesting that the DPT varies within the range of 5–7 months for the onset of drought and 9–15 months for the occurrence of drought peak in India, utilizing a non-stationary based approach. However, additional study is required to completely comprehend the complexities of drought propagation in India and close the current research gap. Several studies exist on agricultural and hydrological characteristics of the Ganga River basin (see Supplementary materials Table S1). The major gap in the existing literature on Ganga basin exists in terms of extensive analysis on transition from meteorological drought to agricultural drought.

Apart from drought propagation analysis, another important aspect from the agricultural drought point of view is the drivers in terms of hydro-climatic variables and anthropogenic activities. Climatic elements such as precipitation and vapor pressure deficit are identified as the major driver for the agricultural drought by many previous researchers (Dai et al. 2022), whereas, human alterations such as altered cropping patterns and supplementary irrigation can affect the development of agricultural drought (Ding et al. 2020; Shah et al. 2021). Wu et al. (2023) have attempted to assess the relative contribution of the climatic factors (temperature, precipitation, wind, and solar radiation) and human actions using the simple multiple linear regression (MLR) model. A similar approach has been used in the present study with the limited climate variables, details of which are discussed in the method section.

Motivated by the above mentioned aspects, this study is addressing the existing research gap regarding drought propagation and influencing factors in the Indian scenario, particularly in the Ganga River basin. Since the high vulnerability to drought in India due to its agrarian nature and reliance on agriculture, understanding the propagation from meteorological to agricultural drought along with its driving factors is crucial. This study aims to quantify meteorological drought using the SPI, agricultural drought using the SSMI, and vegetation health using the NDVI within the Ganga River basin. Additionally, to assess the time for drought propagation from meteorological to agricultural drought Pearson's correlation coefficient-based lag time analysis is employed. As different weather conditions and cropping patterns significantly affect the drought propagation mechanism, the present study also investigates the relative contribution elements (climatic and human factors) over the regions with different agro-climatic settings using an MLR-based approach.

The specific objectives of this study are as follows:

  • To characterize the meteorological and agricultural drought patterns over the Ganga River basin from 2001 to 2020.

  • To assess the DPT and rates in different agro-climatic regions with diverse climatic conditions within the Ganga River basin.

  • To quantify the relative contribution of climatic factors and human activities to the agricultural drought for the 20 years of time span (2001–2020).

By achieving these objectives, the study aims to enhance the understanding of agricultural drought propagation dynamics and the influence of climatic and human factors from the Indian context, specifically within the Ganga River basin, and provide valuable insights for drought management and mitigation measures in the region.

Study area

The Ganga River basin is one of the major basins of India with 65.57% of the area contributing to agriculture. Ganga River basin lies between 22°30′ and 31°30′ north latitude and 73°30′ and 89°00′ east longitude in northern India. It is one of the most active hydrological systems in the world (Moench 2010). Almost 26% of India's total area is comprised of the Ganga River basin, which is 0.86 million km2 (Anand et al. 2018). Gangotri Glacier in the western Himalayas is the source of the Ganga River. As it traverses different topographies, it joins the Bay of Bengal after 275.8 km (Sharma et al. 2021). Rainfall in the basin varies throughout the year and is present for a few months only. Most of this occurs during the monsoon season in the months ranging from June to October. Ganga is the largest contributor to India's agricultural sector because of the region's fertile soil (Ghirardelli et al. 2021). Food insecurity in the area may result from unfavorable changes in the meteorological variables and diminishing trends in the potential on-farm yield of rice (Mishra et al. 2013).

The changes in meteorological variables like precipitation can have a significant impact on agriculture. The precipitation deficit can be reflected in agriculture with a time lag. The vulnerability of the region to changing meteorological conditions may depend on the agro-climatic characteristics of that region. To analyze the impact of various agro-climatic characteristics on drought propagation, the study was done on the 10 agro-climatic regions (Ganga Basin Report 2014) as depicted in Figure 1. The 10 agro-climatic regions are: (1) Central plateau and hills region (CPHR), (2) Eastern Himalayan region, (3) Eastern plateau and hills region, (4) Lower Gangetic plain region, (5) Middle Gangetic plain (MGP) region, (6) Trans-Gangetic plain region, (7) Upper Gangetic plain region, (8) Western dry region, (9) Western Himalayan region, and (10) Western plateau and hills region have different agricultural and climatic conditions as mentioned in Table 1.
Table 1

Agro-climatic zones of the Ganga River basin and the agricultural activities involved (Ganga Basin Report, 2014)

Sl. NoAgro-climatic zonePercentage areaAgricultural activitiesLand and water resources characteristics
Central Plateau and Hills Region 31 Coarse grains Large volume of land and water resources with low productivity. 
Upper Gangetic Plains Region 17 Wheat, Poultry, Cattle Rich in soil and water resources with medium productivity 
Middle Gangetic Plains Region 17 Rice, Poultry, Fishery, Dairy Receives high amount of rainfall 
Eastern Plateau and Hills Region 13 Rice, Coarse cereals, Poultry, Cattle Large amount of water resources and land with very low productivity 
Lower Gangetic Plains Region Rice, Pisciculture, Cattle Rich in water and soil resources 
Western Plateau and Hills Region Coarse grain, Poultry, Goat rearing Large runoff and soil erosion 
Western Himalayan Region Wheat, Sheep, Yaks, Quail, Turkeys, Horses Cold region 
Trans-Gangetic Plains Region Rice, Wheat, Poultry, Cattle Rich water and soil resources 
Eastern Himalayan Region 0.2 High levels of forest cover High rainfall and high forest cover 
10 Western Dry Region 0.05 Coarse cereals, Poultry farming, Cattle Arid condition 
Sl. NoAgro-climatic zonePercentage areaAgricultural activitiesLand and water resources characteristics
Central Plateau and Hills Region 31 Coarse grains Large volume of land and water resources with low productivity. 
Upper Gangetic Plains Region 17 Wheat, Poultry, Cattle Rich in soil and water resources with medium productivity 
Middle Gangetic Plains Region 17 Rice, Poultry, Fishery, Dairy Receives high amount of rainfall 
Eastern Plateau and Hills Region 13 Rice, Coarse cereals, Poultry, Cattle Large amount of water resources and land with very low productivity 
Lower Gangetic Plains Region Rice, Pisciculture, Cattle Rich in water and soil resources 
Western Plateau and Hills Region Coarse grain, Poultry, Goat rearing Large runoff and soil erosion 
Western Himalayan Region Wheat, Sheep, Yaks, Quail, Turkeys, Horses Cold region 
Trans-Gangetic Plains Region Rice, Wheat, Poultry, Cattle Rich water and soil resources 
Eastern Himalayan Region 0.2 High levels of forest cover High rainfall and high forest cover 
10 Western Dry Region 0.05 Coarse cereals, Poultry farming, Cattle Arid condition 
Figure 1

Study area map of the Ganga basin showing 10 different agro-climatic regions.

Figure 1

Study area map of the Ganga basin showing 10 different agro-climatic regions.

Close modal

The largest portion (31%) of the Ganga basin is contributed by the CPHR. CPHR is distributed in the states of Rajasthan, Madhya Pradesh, and Uttar Pradesh. Both Uttar Pradesh and Uttarakhand are included in the Upper Gangetic, while Uttar Pradesh and Bihar are included in the MGP. The Eastern Plateau and Hills Region includes the states of Madhya Pradesh, Jharkhand, West Bengal, and Chhattisgarh. The states of West Bengal fully encompass the Lower Gangetic Plain Region and Eastern Himalayan Region. Madhya Pradesh and Rajasthan are the states that make up the Western Plateau and Hills Region. The Western Himalayan Region includes Himachal Pradesh and Uttarakhand. Only 2% of the basin's overall area is made up of the Trans-Gangetic Plain region, which is located in parts of Delhi and Haryana. The state of Rajasthan contains the Western Dry Region, the smallest of all the agro-climatic zones.

Datasets

The daily gridded precipitation data from the India Meteorological Department (https://www.imdpune.gov.in/cmpg/Griddata/Rainfall_25_NetCDF.html) are used for the assessment of meteorological drought. The precipitation data having a spatial resolution of 0.25° × 0.25° was used from 2001 to 2020. Monthly precipitation time series was prepared from daily data to calculate the SPI at 1-month, 2-month, 3-month, ….., 12-month timescales. Monthly area-averaged (for agro-climatic regions) soil moisture data time series from ERA5 was extracted using the Google Earth Engine (GEE) platform for 20 years from 2001 to 2020. In the present study, soil moisture data from ERA5-Land (https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_RAW) were used which is originally at 0.1° × 0.1° spatial and 1 hourly temporal resolution. MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250 m (https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13Q1) was used in this study for assessing the agricultural drought. The PET data at 8-Day Global 500 m resolution are downloaded from GEE platform with MOD16A2 version for the required duration (https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2). MODIS data were available only from the year 2001 and hence the time period of the study was selected from 2001. Two decades during 2001 to 2020 were chosen for the study for this reason.

Methodology

As indices are one of the most effective ways to quantify drought, this study is based on index-based drought quantification. The SPI calculated from IMD daily gridded precipitation data was used in assessing the meteorological drought. The SSMI calculated from ERA5-Land monthly aggregated data and NDVI calculated using MODIS vegetation products were used to assess the agricultural drought based on soil moisture and vegetation response, respectively. Pearson's Correlation Coefficients (PCCs) have been successfully used in various studies for the assessment of DPT (Ding et al. 2021; Cao et al. 2022). In this study, we use the Maximum Pearson Correlation Coefficient (MPCC)-based approach to assess the time for propagation from meteorological to agricultural drought. The agricultural drought was assessed by using the soil moisture variations (SSMI) as well as the vegetation response (NDVI). The SPI at multiple timescales was correlated with 1-month SSMI as well as 1-month NDVI anomaly during the period from 2001 to 2020 for the 10 agro-climatic zones of the Ganga River basin. Also, the drought propagation rate (DPR) for different agro-climatic zones was assessed. Additionally, to study the relative influence of climatic factors and human activities, a MLR model is employed based on the study done by Wu et al. (2023). The methodological framework adopted for the present study is given in Figure 2.
Figure 2

The methodological framework to study the drought propagation features and to assess the relative contribution of CC and human activities to the agricultural drought for the Ganga basin.

Figure 2

The methodological framework to study the drought propagation features and to assess the relative contribution of CC and human activities to the agricultural drought for the Ganga basin.

Close modal

Drought characterization using standardized drought indices

This study uses the SPI for meteorological drought assessment due to its ability to calculate drought characteristics across multiple timescales and requires only one input, precipitation. The ratio of deviation of precipitation in a particular period from the mean to the standard deviation is known as standardized precipitation, where the mean, as well as standard deviation, are calculated using historical data (Mckee et al. 1993). The index corresponds to the formula given in Equation (1).
formula
(1)
where is the cumulative rainfall of the month; is the annual rainfall observed for the time series; is the standard deviation of the annual rainfall for the observed time series. This index was calculated by fitting the observed values of precipitation totals to a gamma distribution over various time intervals (e.g., 1, 2, 3, …, 12 months) (He et al. 2009). The gamma distribution, known for its flexibility, adeptly captures diverse probability density function shapes, making it ideal for precipitation data representation. Many studies have shown that, given the influence of random atmospheric factors on precipitation events, the gamma distribution, reflecting cumulative effects of independent processes, is a fitting choice (Scheuerer & Hamill 2015). The probability density function (PDF) for the gamma distribution is (Asadi Zarch et al. 2015) given in Equation (2).
formula
(2)
where α > 0 is a shape factor, β > 0 is a scale factor, and xk > 0 is the amount of precipitation over k consecutive months.

The SPI's multiple timescales can accurately quantify water scarcity in soil moisture and streamflow, making it an ideal tool for modeling drought events (Bonaccorso et al. 2003). Many previous studies have demonstrated that spatial variation of drought severities can be captured based on different severity classes of the SPI (Mckee et al. 1993; Spinoni et al. 2019; Pachore & Remesan 2022) such as extreme, severe, moderate, and near-normal dry conditions.

The NDVI, which measures the productivity and health of vegetation, is widely used to evaluate drought conditions (Al Kafy et al. 2023). NDVI is calculated as a ratio of the observed reflectance in the red and near-infrared regions of the electromagnetic spectrum. Data from the red and near-infrared sections, which are channel 1 and channel 2 of the MODIS radiometer's channels, are used to calculate NDVI (Wassie et al. 2022). Higher values indicate more vegetation cover and productivity. NDVI values vary from −1 to 1. Lower values can result from declines brought on by drought. Vegetation development and health can be evaluated by tracking the NDVI over time. NDVI can be calculated using the following equation.
formula
(3)
where NIR represents the near-infrared and R represents the red band of the electromagnetic spectrum.
This study uses NDVI anomaly to assess the vegetation conditions since NDVI anomaly is one of the most accurate methods of drought monitoring when compared to NDVI (Li et al. 2014). Several studies have used NDVI anomaly to assess vegetation conditions in different areas (Krishna et al. 2009; Li et al. 2022). Positive NDVI anomaly represents normal conditions whereas severe drought conditions are indicated by negative values (Nanzad et al. 2019). NDVI anomaly can be computed using Equation (4).
formula
(4)

where n is equal to the number of years equal to 20 years in the present study.

Seasonal NDVI anomaly is derived for each region as per Equation (5).
formula
(5)
where is the NDVI anomaly for the growing season during ith year.

The Soil Moisture Index (SSMI) measures soil water stress by analyzing its interaction with hydro-meteorological variables like precipitation and evapotranspiration (Afshar et al. 2022). Soil moisture being a significant factor for crop growth, SSMI is widely used for the assessment of agricultural drought (Um et al. 2022). SSMI, with its similar calculation principle to the SPI, offers all the advantages of the SPI, including the ability to analyze agricultural drought at multiple timescales (Dai et al. 2022).

Assessment of agricultural drought connection with the meteorological drought

The linear relationship between two random variables was measured using the PCC, which is popular among the relationship metrics (Zhou et al. 2016). The propagation link between the various types of drought can be examined using this methodology (Ding et al. 2021). Many studies have employed correlation analysis to assess the connection between different drought types (Ding et al. 2020). In this study, this correlation-based approach was employed to study the strength of the connection between multiscale SPI, SSMI-1, and monthly NDVI anomaly, which were used to assess meteorological and agricultural drought conditions, respectively. The SPI at different timescales was correlated with the NDVI using Pearson's Correlation Coefficient. The SPI at 1-, 2-, 3-, 4-,….., till 12-month timescales were correlated with the monthly NDVI anomaly and SSMI-1 time series. For two variables X and Y, the correlation coefficient is described in Equation (6).
formula
(6)
where r is the PCC, is covariance, indicates the standard deviation, and and are proxies for the mean of and , respectively.

Investigation of drought propagation mechanism

To assess the time of propagation from the meteorological to agricultural drought, the SPI timescale having the highest correlation with the monthly NDVI anomaly was used. The SPIn was calculated for multiple timescales (n = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) at 1-month intervals for the period from 2001 to 2020 (20 years). Similarly, the NDVI anomaly was also calculated at 1-month timescale with 1-month intervals from 2001 to 2020. To study the effects of agro-climatic conditions on drought propagation, the study was done independently over the different agro-climatic regions of the Ganga basin. The timescale of the SPI showing the highest correlation with the NDVI anomaly was considered to be the lag time from the meteorological drought to the agricultural drought in terms of vegetation dynamics.

The propagation relation between various types of droughts was examined using the MPCC method. Many studies (Xu et al. 2019; Ding et al. 2021) have successfully demonstrated that the PCC is effective in showing the response of one type of drought to the other. Since soil moisture also gives significant information regarding the incidents of agricultural drought, the SPIn calculated for multiple timescales was correlated with the 1-month SSMI. Propagation time taken by soil-based agricultural drought for responding to meteorological drought was computed using the SPI timescale having the highest correlation with the SSMI-1 time series. Also, the effect of soil moisture variation on the vegetation dynamics was studied by correlating the SSMI-n calculated for multiple timescales (n = 1, 2, 3, 4, 5, 6, 9, 12) at 1-month intervals with 1-month NDVI anomaly. We chose this analysis as the MPCC is a widely demonstrated and accepted analysis for addressing drought response time and propagation, as evidenced by studies (Zhou & Shi 2021; Weng et al. 2023) spanning transitions from meteorological to agricultural or hydrological droughts. Shi et al. (2022) conducted a comprehensive global evaluation of drought propagation using spatial distributions of MPCC. Additionally, studies such as Das et al. (2022) and Zhou et al. (2021) emphasize its utility in quantifying the relationship between SPI and other drought indices across different timescales, including the decadal scale and lengths. These studies highlight the robustness and reliability of MPCC, offering insights into the potential impact of meteorological drought on hydrological drought propagation and elucidating the impacts of human activities.

Drought propagation rate

The DPR is a measure of how quickly meteorological droughts spread to agricultural droughts. A high propagation rate (propagation percentage) indicates that the agricultural drought was more susceptible to the meteorological drought. The association between the two forms of drought was less when the propagation rate was smaller. The mathematical equation for the propagation rate is given in Equation (7) (Sattar et al. 2019):
formula
(7)
where the rate of drought propagation in percentage is given by ‘Rr’, the number of agricultural drought events triggered by meteorological drought events is given by ‘n’, whereas the number of meteorological drought events throughout the time series is denoted by ‘m’. Here, Rr typically varies from 0 to 1 (Rr levels can occasionally surpass 1 when there are significant human disturbances). High propagation rate values denote the strong sensitivity (or strong response) of agricultural drought to the meteorological drought.

Influence of climatic factors and human activities on agricultural drought

Soil moisture-based agricultural drought conditions are greatly influenced by the precipitation and other climatic factors such as temperature, wind speed, vapor pressure deficit (Dai et al. 2022). To study this effect of climatic factors, the MLR based residual trend method is used in the present study (Equation (8)). Here, MLR is used to get the predicted SSMI value which is the function of SPI and Penman-Monteith equation based potential evapotranspiration (PET) (Wu et al. 2023). SPI is used to account for the precipitation anomaly and PET is used as a proxy for the other climatic factors which are considered in the Penman-Monteith equation.
formula
(8)
Here, SSMIP is the predicted time series of monthly SSMI based on the SPIn and monthly PET, where n is the timescale selected based on the MPCC values between SSMI-1 and multiscale SPI. Residual of the MLR model can be attributed to the additional influence apart from precipitation and PET considered here, which can be due the anthropogenic activities.
formula
(9)
where SSMIR is the residual SSMI, SSMI is the observed value, and SSMIP is the predicted value of the standardized soil moisture index.
After computing the predicted SSMI based on the climatic factors, the relative contribution (RC) of climatic and human factors to the agricultural drought is calculated using Equation 10.
formula
(10)

Here, RC (i) is the relative contribution of the term , where i belongs to the predicted (climatic factors) and residual (human activities and other factors) SSMI factors, whereas, denominator of the Equation (10) gives the overall trend of the SSMI contributed by each term.

Meteorological and agricultural drought assessment using SPI, SSMI, and NDVI

In past works, many authors have attempted to quantify the meteorological and agricultural drought in propagation studies using SPI and SSMI (Zhang et al. 2021; Dai et al. 2022). However, the present analysis is being done in a similar direction with more focus on accurate quantification of agricultural drought using soil moisture (SSMI) as well as vegetation (NDVI)-based drought indices. The SPI for the 10 agro-climatic regions spanning the Ganga River basin for the years 2001 to 2020 was calculated using the precipitation information available from the Indian Meteorological Department. The SPI was calculated at multiple timescales in the initial stage which is necessary for the computation of DPT in the further steps. Spatio-temporal variation of SPI calculated at multiple timescales for all 10 agro-climatic regions is as given in Figure 3. Meteorological drought severity has been observed to vary spatially over Ganga, with wet conditions being present in regions like EHR, EPHR, LGP, and WHR, and dry conditions being evident in places like CPHR, MGP, TGP, and WPHR.
Figure 3

The heat map of the SPI in 10 agro-climatic regions of the Ganga River basin which are as follows: (a) Central Plateau and Hills Region (CPHR); (b) Eastern Himalayan Region (EHR); (c) Eastern Plateau and Hills Region (EPHR); (d) Lower Gangetic Plain Region (LGP); (e) MGP Region; (f) Trans-Gangetic Plain Region (TGP); (g) Upper Gangetic Plain Region (UGP); (h) Western Dry Region (WDR); (i) Western Himalayan Region (WHR); and (j) Western Plateau and Hills Region (WPHR).

Figure 3

The heat map of the SPI in 10 agro-climatic regions of the Ganga River basin which are as follows: (a) Central Plateau and Hills Region (CPHR); (b) Eastern Himalayan Region (EHR); (c) Eastern Plateau and Hills Region (EPHR); (d) Lower Gangetic Plain Region (LGP); (e) MGP Region; (f) Trans-Gangetic Plain Region (TGP); (g) Upper Gangetic Plain Region (UGP); (h) Western Dry Region (WDR); (i) Western Himalayan Region (WHR); and (j) Western Plateau and Hills Region (WPHR).

Close modal

Based on heatmaps shown in Figure 3, it is evident that, in the CPHR, the years 2002, 2007, and 2018 were the major drought years, with 2007 being the year having prominent moderate drought conditions. The Eastern Himalayan region showed more intense drought conditions than the CPHR, under the category of ‘Severely Dry’ with the year 2014 being more prominent. For the Eastern Plateau and Hills region, the year 2010 falls under severe drought conditions, whereas, in the Lower Gangetic Plain region, severe drought was observed in 2012. In the MGP, moderately dry condition was experienced in 2010 whereas the Trans-Gangetic Plain experienced near-normal condition in 2009 and 2016. In the Upper Gangetic Plain and Western Plateau and Hills region, moderately dry conditions were experienced in 2002 and 2016, respectively. The Western Dry Region and Western Himalayan Region experienced severely dry conditions in 2002 and 2009, respectively.

Different regions experienced meteorological drought in different degrees but how the vegetation responded to this depends on various factors including the agro-climatic and hydrological conditions of the region (Ding et al. 2021). The agricultural drought can be determined effectively by the soil moisture-based SSMI or vegetation-based indices like NDVI and VHI. As the soil moisture is strongly related to crop conditions and will give reliable information regarding the vegetation characteristics, SSMI is being used in the present analysis. As the health of vegetation can be determined by the reflectance of the vegetation and hence the NDVI which represents the reflectance of vegetation in red and NIR bands has also been used as an effective tool for the assessment of the vegetation condition of agro-climatic regions for the present analysis.

According to the results of this study, the NDVI anomaly distribution range was higher for the Eastern Himalayan region and Western Dry region as shown in Figure 4(a), which indicates vegetation with widely varying conditions. Also, the Lower Gangetic Plain showed a higher density of NDVI anomaly values within the range of −1 to 1 which indicated similar health conditions of the vegetation. According to Figure 4(b), the SSMI range varied uniformly in the 10 agro-climatic regions with most of the SSMI concentrated in a similar range.
Figure 4

Distribution of (a) NDVI anomaly and (b) SSMI-1 over the Ganga River basin.

Figure 4

Distribution of (a) NDVI anomaly and (b) SSMI-1 over the Ganga River basin.

Close modal
The soil moisture conditions in all agro-climatic zones varied differently during the period from 2001 to 2020 as shown in Figure 5(a). In CPHR, the agricultural drought was observed in the year 2002 with SSMI-1 of −2.99. The Eastern Himalayan Region experienced agricultural drought with SSMI-1 of −3.22 in the year 2005. With −3.6 and −2.65 values of SSMI, the Eastern Plateau and Hills Region and Lower Gangetic Plain region experienced drought conditions in the years 2010 and 2012, respectively. MGP and Trans-Gangetic Plain experienced similar droughts with SSMI-1 being −2.92 and −2.15 in the years 2002 and 2008, respectively. With −1.39 in 2010 as the highest value of SSMI-1, the Upper Gangetic Plain experienced the least severe agricultural drought when compared to all other regions. The Western Dry Region and Western Plateau and Hills Region with SSMI-1 of −2.09 and −3.14, respectively, experienced agricultural drought in the years 2007 and 2009. The Western Himalayan Region, with −2.37 as the highest value of SSMI-1, experienced agricultural drought in the year 2002. The Eastern Plateau and Hills Region has shallow and medium-depth soil and is also eroded and infertile, experiencing the highest agricultural drought with SSMI-1 of −3.6.
Figure 5

Heat map of SSMI-1 and NDVI over different agro-climatic zones of the Ganga basin from 2001 to 2020: (a) SSMI and (b) NDVI.

Figure 5

Heat map of SSMI-1 and NDVI over different agro-climatic zones of the Ganga basin from 2001 to 2020: (a) SSMI and (b) NDVI.

Close modal

The variable severity of drought conditions in each region is influenced by agro-climatic factors as well as the soil characteristics of each region. The Upper Gangetic region with rich soil and water resources experienced the least drought conditions among all. The use of groundwater for irrigation practices can also be a reason for the fair soil moisture conditions in this region (Ganga Basin Report 2014).

The agricultural drought assessment using NDVI anomaly is depicted in Figure 5(b). The CPHR experienced a vegetation-based agricultural drought in the year 2003 with an NDVI anomaly of −1.76, which corresponds with the soil moisture-based agricultural drought of the same region in the year 2002. Agricultural drought in the Eastern Himalayan Region was noticeable in 2007 with an NDVI anomaly of −3.85. The Eastern Plateau and Hills region and Lower Gangetic Plain region, with NDVI anomaly of −1.74 and −1.03, faced agricultural drought in 2016 and 2019. The MGP region and Trans-Gangetic Plain region with NDVI anomaly of −1.68 went through an agricultural drought in the year 2001. The Upper Gangetic Plain in the year 2003 had drought conditions with an NDVI anomaly of −1.93. The Western Dry region with an NDVI anomaly of −1.46 experienced the drought condition in 2010. In the Western Himalayan Region and Western Plateau and Hills region, drought condition was evident with NDVI anomalies of −1.51 and −1.76 in the years 2003 and 2014, respectively.

Studies done in the past have highlighted that the occurrence of agricultural drought depends on various factors like meteorological conditions of the area, soil and crop characteristics, etc. Results of the present study have led to similar observations, as the agricultural drought of a higher degree, when compared to all other regions, was experienced in the Eastern Himalayan region which is suffering from soil erosions and degradation with NDVI anomaly of −3.85 in 2007. However, the Lower Gangetic Plain region, which is rich in soil and water resources, experienced the least agricultural drought conditions with NDVI anomaly of −1.03 in the year 2019.

Propagation from meteorological to agricultural drought

Correlation analysis between meteorological and agricultural drought

To assess the agricultural drought response (propagation time) to meteorological drought, this study uses the PCC-based lag time analysis. Many studies on drought propagation (Weng et al. 2023; Zhou & Shi 2021) from one type to another (e.g., meteorological to agricultural, meteorological to hydrological) have successfully demonstrated the reliability of MPCC on assessing the DPT. Through MPCC, the linear correlation between the meteorological drought and agricultural drought was assessed using SPI-NDVI and SPI-SSMI correlation. The main focus of the study was assessing the DPT and DPR and the effect of agro-climatic conditions on these two variables in the Ganga River basin of India. Twenty-year data was found to be good to get the trend and draw conclusions for the assessment of DPT and DPR.

Based on the MPCC for multiscale SPI with SSMI-1 and monthly NDVI anomaly, the SPI timescale having the maximum correlation was used to determine the DPT. According to the SPI-SSMI correlation as depicted in Figure 6(a), the MPCC values of SPI with SSMI for different regions varied from 0.23 to 0.72. CPHR had an MPCC of 0.72 for SPI-10, whereas, EPHR, LGP, MGP, UGP, and WHR showed the strongest correlation for the SPI-3 with 0.68, 0.46, 0.61, 0.24, and 0.62 as correlation coefficients, respectively. SPI-11, SPI-9, SPI-4, and SPI-1 of WDR, WPHR, TGP, and EHR, respectively, showed the highest correlation values of 0.58, 0.5, 0.51, and 0.23, respectively. The strongest connection was observed in CPHR with an MPCC of 0.72 between SPI-10 and SSMI-1.
Figure 6

Correlation matrix of multiscale SPI with monthly SSMI and NDVI anomaly for 10 agro-climatic regions of the Ganga basin: (a) SPIn-SSMI-1 and (b) SPIn-NDVI.

Figure 6

Correlation matrix of multiscale SPI with monthly SSMI and NDVI anomaly for 10 agro-climatic regions of the Ganga basin: (a) SPIn-SSMI-1 and (b) SPIn-NDVI.

Close modal

To assess the propagation characteristics from meteorological to agricultural drought, the MPCC between the SPI-SSMI-1 and SPI-NDVI anomaly for each agro-climatic region was assessed. The SPI-n (n = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) at multiple timescales was correlated with 1-month SSMI and NDVI anomaly as depicted in Figure 6. It was observed that SPI-3 of EPHR, LGP, MGP, UGP, and WHR showed the highest correlation with SSMI-1. SPI-1 of EHR, SPI-4 of TGP, SPI-9 of WPHR, SPI-10 of CPHR, and SPI-11 of WDR showed the highest correlation with SSMI-1. Similarly, based on the MPCC of SPI-NDVI, CPHR, EPHR, and WHR, SPI-8 showed the highest correlation with the 1-month SSMI for the corresponding regions. The LGP, WDR, and WPHR showed a higher correlation at SPI-11 while MGP and UGP showed a higher correlation at SPI-1. SPI-5 and SPI-3 showed the highest strength of association in EHR and TGP.

The MPCC between SPI and the NDVI anomaly varied from 0.02 to 0.186 for different agro-climatic regions. In CPHR, EPHR, and WHR, SPI-8 showed the highest correlation with the monthly NDVI anomaly, with MPCCs of 0.186, 0.06, and 0.07, respectively. Similarly, SPI-11 showed the highest correlation with NDVI anomaly for LGP, WDR, and WPHR with MPCCs of 0.023, 0.22, and 0.20, respectively. SPI-5 in EHR showed the highest correlation of 0.06 with NDVI anomaly and in TGP, SPI-3 showed the highest correlation with MPCC of 0.14. In MGP and UGP, SPI-1 showed the highest correlation with MPCC values of 0.10 and 0.13, respectively. Li et al. (2023) have found the non-linear and complex connection of agricultural drought with meteorological drought for different climatic regions in their study. The findings of the present analysis also support the same with varied strength of PCC for regions with different agro-climatic conditions, highlighting the control of climate on drought propagation.

DPT based on the MPCC method

Agricultural drought (or soil moisture stress) typically responds quickly, usually within a few months, to meteorological drought. Based on the region's climate and type of ecosystem, this study found that the effects of drought varied greatly. The drought resistance of various ecosystem types may vary due to varying climatic and vegetational characteristics (Cao et al. 2022). Agricultural drought is generally driven by meteorological drought and follows it with a certain amount of delay. Many researchers in the past have acknowledged that a particular period marks the transition for meteorological drought to get reflected into agricultural and hydrological drought which is termed as propagation or response time. Correlation analysis is one of the most popular approaches for determining the propagation time between different drought types. The present study uses the Pearson correlation coefficients-based method to examine the correlations between the SPI at multiple timescales and SSMI-1, along with NDVI anomaly to determine when a meteorological drought transition can take place into an agricultural drought.

The DPT assessed based on the MPCC of SPI-SSMI varied between 1 and 11 months for all 10 agro-climatic regions of the Ganges as shown in Figure 7(a). The Western Dry Region has arid conditions with hot weather, incessant rainfall, high evaporation, sparse vegetation, and a fragile ecosystem. The precipitation may not instantly reflect in the soil moisture since the soil is relatively dry. It can be due to various reasons like the characteristics of the vegetation, soil properties, etc. In the Western Plateau and Hills Region, the soil moisture response to the variations in precipitation takes 8 months and in the Trans-Gangetic Plain, it takes 4 months. Six months was the lag time in the CPHR. For regions that are rich in water resources like the Western Himalayan Region, Upper Gangetic Plain, MGP, Lower Gangetic Plain, Eastern Plateau, and Hills Region, the lag time from meteorological to agricultural drought was observed to be 3 months. In the Eastern Himalayan Region with a high amount of rainfall, the lag time was 1 month based on data from 2001 to 2020. From the results of the present analysis, it is evident that arid climatic regions show higher response time (11 months) as compared to humid regions (1 month) toward meteorological drought. Similar observations have been reported by for an agricultural drought propagation study done on different climatic regions of China showing the influence of long-term climatic conditions on DPT.
Figure 7

DPT based on MPCC between multiscale SPI and SSMI-1 along with NDVI anomaly: (a) SPI-SSMI based DPT and (b) SPI-NDVI based DPT.

Figure 7

DPT based on MPCC between multiscale SPI and SSMI-1 along with NDVI anomaly: (a) SPI-SSMI based DPT and (b) SPI-NDVI based DPT.

Close modal

Similar to SPI-SSMI, the lag time analysis was also done based on SPI-NDVI to find the DPT. Based on the results, the lag time between the two droughts under consideration, i.e., meteorological (SPI) and agricultural (NDVI) drought, was observed to be 11 months in the LGP, WDR, and WPHR. In the CPHR, EPHR, and WHR, the time for drought propagation was observed to be 8 months. The EHR and TGP regions showed a DPT of 5 months and 3 months, respectively. The MGP and UGP showed a DPT of 1 month in both regions. The highest DPT was observed in LGP, WDR, and WPHR whereas the least was observed in MGP and UGP. SPI-NDVI based analysis also showed a similar result to that of SPI-SSMI computations for agricultural DPT quantification with similar evidence for control of climatic conditions on DPT as shown in Figure 7(b). MPCC analysis indicated statistically insignificant SPI-NDVI correlations. Consequently, for DPT and DPR assessment, SPI-SSMI correlations were employed, assigning 100% weightage to SSMI for agricultural drought assessment and 0% weightage to the NDVI.

Drought propagation rate

Many researchers (Xu et al. 2023; Sattar et al. 2019) have used the propagation rate as a crucial indicator of how sensitive the agricultural drought is to meteorological dryness. Similarly, in the current study, the percentage of propagation between meteorological and soil moisture-based agricultural droughts was expressed as DPR. Due to the effect of soil characteristics and crop conditions or other factors, not all meteorological droughts will result in agricultural drought, and not all agricultural droughts will correspond to meteorological drought. The DPR was quantified based on meteorological drought events characterized by SPI (timescale selected from the MPCC-based drought propagation analysis) and agricultural drought events by SSMI-1.

The DPR varied from 29.03 to 73.33% across different agro-climatic regions as shown in Figure 8. In the Lower Gangetic Plain, no meteorological drought events were propagating to agricultural drought because the region is rich in soil and water resources. The CPHR with subsistence agriculture experienced the lowest propagation rate of 29.03% from meteorological drought to agricultural drought. The Eastern Himalayan region and Western Plateau and Hills region experienced the highest DPR of 73.33%. The Eastern Plateau and Hills region and the MGP region experienced a DPR of 34.37 and 39.29% respectively. The trans-Gangetic Plain region, which has a delicate water balance and rice/wheat-based cropping system, experienced a DPR of 50%. The Upper Gangetic Plain, Western Dry Region, and Western Himalayan Region experienced drought propagation rates of 67.74, 30.77, and 51.85%, respectively.
Figure 8

DPR between two drought types (meteorological and agricultural) for 10 agro-climatic regions of the Ganga basin.

Figure 8

DPR between two drought types (meteorological and agricultural) for 10 agro-climatic regions of the Ganga basin.

Close modal

The drought propagation rates were different for all the agro-climatic regions. The highest DPR was observed in the Eastern Himalayan Region and Western Plateau and Hills region as shown in Figure 8. Mostly, the meteorological drought is not the only cause of agricultural drought but various factors like the initial condition of the catchment, irrigation, climate type, crop characteristics, and soil characteristics also play an important role in the transition toward agricultural drought (Li et al. 2022; Xu et al. 2023). Apart from the meteorological factors, human activities and climate change (CC) play crucial roles in the variations of DPT.

Relative contribution of climatic factors and human activities

Relative contributions of the climatic factors (SPI and PET) and human activities (residual term of the MLR model) is quantified and is as given in Figure 9. From the results, it can be inferred that the control of the climatic and human factors on the soil moisture-based agricultural drought (SSMI) is showing the greater spatial variability over the 10 agro-climatic regions of the Ganga River basin. Human activities and allied factors other than climatic conditions are having the positive contribution for all the agro-climatic regions. On the other hand, relative contribution of the climatic factors is having mixed responses, with CPHR, TGP, UDR, WDR, WHR, and WPHR regions having positive contributions, whereas, EHR, EPHR, LGP, and MGP regions are having negative contributions to the agricultural drought.
Figure 9

Relative contribution of the climatic factors: RC (CC) and human activities: RC (HA) to the soil moisture-based agricultural drought (SSMI) over 10 agro-climatic zones of the Ganga River basin.

Figure 9

Relative contribution of the climatic factors: RC (CC) and human activities: RC (HA) to the soil moisture-based agricultural drought (SSMI) over 10 agro-climatic zones of the Ganga River basin.

Close modal
Figure 10 demonstrates the absolute values of the relative contribution of the climatic factors (CC) and human actions (HA) to learn about the dominance of each element on the agricultural drought occurrence over the Ganga River basin. From the absolute values (Figure 10), it is observed that only EHR (62.50%), UGP (72.73%), and WDR (84.21%) regions are having greater control of climactic factors on the agricultural drought, whereas, for the remaining regions, human activities and other factors are more impactful, except for WPHR with both elements having almost equal contribution (CC: 45.83%, HA:54.17%).
Figure 10

Absolute values of the relative contribution (%) of the climatic factors (CC) and human activities (HA).

Figure 10

Absolute values of the relative contribution (%) of the climatic factors (CC) and human activities (HA).

Close modal

Overall, it is observed that human activities are more dominant as compared to climatic factors on agricultural drought occurrence over most parts of the basin. This can be attributed to the perennial nature of the Ganga River which makes it rich in surface and ground water resources even during the non-monsoon season with better irrigation facilities, resulting in the weaker climate control on agricultural drought occurrence (Swarnkar et al. 2021). These inferences are useful from the CC point of view, demonstrating that the changing climatic conditions may have fewer impacts than human activities on agricultural drought over most of the Ganga basin provided it is not affecting the rainfall and overall annual water balance over the basin. As discussed by Wu et al. (2023), people in general identify agricultural drought primarily on the basis of precipitation amount and its variations, human influences pose significance influences on areas with lower or variable rainfall just as reported in the TGP region of Ganges.

The diverse agro-climatic zones within the Ganga River basin exhibit distinct agricultural practices and human interventions in terms of irrigation, etc., leading to variations in agricultural drought propagation rates and the relative contributions of CC and human activities (HA). This heterogeneity is evident in the findings of Singh et al. (2023), who noted diverse behavior in extreme precipitation events across different agro-climatic regions, attributed to variations in basin topography, ranging from 8 to 7798 m in elevation. Additionally, Bhatla et al. (2020) highlighted significant diversity in cropping patterns across the 10 agro-climatic regions, with the lower Gangetic plain predominantly cultivating rice, while other regions favor wheat as the dominant crop. This variation in cropping patterns and irrigation conditions can markedly influence evapotranspiration rates, leading to variable rates of soil moisture decline and subsequently impacting DPT and rate. Sun et al. (2023) supported these findings, emphasizing the variability in DPTs associated with different vegetation types characterized by varying crop root zone depths in different regions.

While this study focuses on the two-decadal analyses of drought propagation, the findings underscore potential future repercussions across various sectors such as energy, water supply and sanitation, agriculture, industry, fisheries, forestry, and other ecosystems in the study region due to anticipated increases in drought frequency and severity, presenting challenges that are intricate to comprehend (Teutschbein et al. 2023). This highlights the need for future studies that focus on drought scenarios, employing robust climate projections and scenarios utilizing appropriate General Circulation Models (GCMs) and Regional Climate Models (RCMs). Such investigations are crucial for quantifying the impending impacts of future droughts and formulating effective mitigation strategies through climate adaptation (Mukherjee et al. 2018). Several studies have emphasized key strategies essential for future drought mitigation across diverse contexts, including: (i) strategic interventions in land use and land, (ii) the augmentation of soil water retention, (iii) the adoption of infield water conservation techniques, and (iv) the promotion of public awareness regarding droughts, their consequences, and the imperative for potential countermeasures. Implementation of such measures can collectively contribute to a comprehensive framework for effective future agricultural drought mitigation in the Ganga River basin.

A word of caution

The Ganga basin stands out as one of the most dynamic river basins in terms of water availability, intricately connected to drought patterns across regions with diverse agro-climatic conditions. Though the present study effectively reported relative contribution of climatic as well as human elements on agricultural droughts on various agro-climatic zones, it is crucial to acknowledge potential limitations inherent in the chosen approaches, models and datasets. However, the results of drought propagation have revealed distinct strengths of association between the SPI and both the NDVI and SSMI. The sensitivity of soil and crop health to meteorological drought variations may extend its influence to other variables not encompassed in this study, possibly by adding uncertainties to the reported results. Even though the initial calculations were on a pixel-based approach, it is important to highlight that area-averaged time series are employed to yield an average condition for each agro-climatic region. Furthermore, the whole analysis was performed on annual time series, and this could potentially mitigate the influence of rainfall seasonality especially in the monsoon season. Above mentioned caveats might have led to the averaging out of certain seasonal information in the Ganga basin as a dominant factor in India's regions characterized by seasonal climate and especially monsoon.

Various studies have shown additional influencing factors such as local climatic conditions, land use practices, irrigation facilities, and rainfall seasonality. Ding et al. (2020) demonstrated that cropland, forest, grassland, and desert exhibit diverse responses to meteorological drought due to variations in vegetation characteristics. Our study does not consider these specific factors, potentially introducing uncertainties into the results. Our study has employed relatively simple but very effective residual trend method initially proposed by Jin et al. (2020) and subsequently adapted for drought assessments by Wu et al. (2023) with the help of a simple MLR model which may have ignored the non-linear interactions of the variables considered. The relationship of different climatic factors to drought responses may be non-linear and this may be making interpretations of MLR model results difficult (Seddon et al. 2016). Furthermore, the MLR model relied solely on SPI and PET due to limited data availability for ground variables (such as supplementary irrigation and local agricultural practices). This limitation might have led to an exaggerated relative contribution of human elements in certain regions, such as EPHR, TGP, and WHR.

Despite the challenges outlined in the above paragraph, the present study contributes fresh insights into the variable nature of meteorological conditions affecting the propagation time and rate of agricultural drought. This extends to sub-regional spatial scales and highlights the interconnections with various features of agro-climatic zones within the Ganga River basin. The quantified understanding of the relative impact of climatic factors and human activities on agricultural drought can prove valuable for policymakers in formulating future strategies, particularly with a focus on agro-climatic zones in the basin.

Ten agro-climatic regions of the Ganga responded differently to the meteorological drought and exhibited different lag times between the drought types (meteorological and agricultural) along with varying propagation rates. This variability may be due to the soil characteristics, crop characteristics, and climatic conditions of the region along with the human influences like irrigation water use and land use alterations. The present study has analyzed the drought propagation mechanism along with the relative contribution of the climatic as well as human factors and drawn the following specific conclusions:

  • The regions rich in water resources having fertile soil, like the Upper Gangetic Region, Lower Gangetic Plain Region, Trans-Gangetic Plain Region, and Eastern Himalayan Region, experienced a lesser degree of agricultural drought with DPT varying from 1 to 4 months, whereas the regions with shallow soil conditions experienced a higher degree of agricultural drought.

  • For the CPHR, with an MPCC value of 0.72, the DPT was found to be 6 months.

  • The Eastern Himalayan Region and Upper Gangetic Plain Region showed comparatively lesser sensitivity of SSMI to the SPI with MPCC values of 0.23 and 0.24 corresponding to the DPT of 1 month and 3 months, respectively.

  • The drier regions like Western Dry Region, Western Plateau and Hills region, etc. responded very slowly to changes in the meteorological drought conditions with a DPT of 11 and 9 months, respectively.

  • Different agro-climatic regions showed different drought propagation rates varying from 29.03% to 73.33% under the influence of different agricultural and climatic conditions.

  • The changing climatic conditions may not affect the agricultural drought as most of the regions of the Ganga basin show the dominance of human activities in agricultural drought development.

In the future work of this research team on Ganga basin analysis, enhanced outcomes could be achieved by integrating an advanced regression model that considers additional variables, providing a more accurate representation of local catchment conditions (Dai et al. 2022). Thus the assessment of DPT and rate could be extended to future climate scenarios by employing suitable GCMs and RCMs to reveal and anticipate potential future instances of agricultural drought in the region.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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Author notes

Equal contributions.

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