







































Ecology, Economy and Society–the INSEE Journal 6(1): 109-122, January 2023 

RESEARCH PAPER 

Attributing Vegetation Recovery During the Indian 
Summer Monsoon to Climate Drivers in Central India 

Vikram Chandel and Tejasvi Chauhan 

Abstract: Increasing droughts and heat waves as a result of global warming pose a 
major threat to forests and croplands in India. Monitoring the dynamics of 
vegetation during a drought and its recovery is essential for the Indian socio-
economy and biodiversity. We investigate vegetation recovery from a stressed state 
in the pre-monsoon (May) period to the end of the monsoon period (September). 
We then attribute net change during the monsoon period to climate drivers such as 
temperature, precipitation, and soil moisture. To delineate non-linear interactions, 
we use an information-theoretic metric to understand the relative association of 
climate variables with vegetation productivity on a daily scale. We found that pre-
monsoon vegetation stress is influenced by soil moisture (r = 0.8, p < 0.01), which 
is driven by variations in temperature and precipitation. During the monsoons, 
precipitation contributes to vegetation recovery from pre-monsoon stress through 
soil moisture recharge while inhibiting vegetation productivity by limiting the 
amount of radiation available for photosynthesis. Linear regression shows the 
significant negative dependence of vegetation recovery on precipitation (β = –0.7, p 
< 0.01) and positive dependence on soil moisture (β = 0.4, p < 0.1) indicating 
radiation limitation on photosynthesis. We also found that post-monsoon 
vegetation recovery is independent of pre-monsoon vegetation stress (p > 0.1). 
Mutual information showed the stronger, non-linear dependence of vegetation 
recovery on soil moisture than on precipitation, which is a contrasting result that 
highlights the importance of including non-linear measures in analyses of natural 
systems. Our results show that vegetation recovery in central India is driven by soil 
moisture during the Indian summer monsoon and is independent of pre-monsoon 
vegetation stress. 

 
 Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, 
India. vikram.chandel63@gmail.com   

 Department of Civil Engineering, Indian Institute of Technology Bombay, India. 
chauhan.tejasvi9@gmail.com   

Copyright © Chandel and Chauhan 2023. Released under Creative Commons Attribution © 

NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of  Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581–6152 (print); 2581–6101 (web).  

DOI: https://doi.org/10.37773/ees.v6i1.927  

 

mailto:vikram.chandel63@gmail.com
mailto:chauhan.tejasvi9@gmail.com
https://doi.org/10.37773/ees.v6i1.927


 Ecology, Economy and Society–the INSEE Journal [110] 

1. INTRODUCTION 

The frequency, intensity, and spatial extent of droughts has been increasing 
in India due to global warming (Mishra, Shah, and Thrasher 2014; Gupta 
and Jain 2018). Droughts are often accompanied by heat waves, leading to 
increased mortality due to heat stress (Rohini, Rajeevan, and Srivastava 
2016; Panda, AghaKouchak, and Ambast 2017; Mazdiyasni et al. 2017). 
Apart from affecting human life, droughts adversely affect vegetation by 
changing carbon fluxes, causing soil water storages, and disrupting 
ecosystem services. Consequently, food production (Zhang et al. 2017) in 
the country is threatened. Prolonged droughts can lead to irreversible shifts 
in vegetation phenology, thus threatening the biodiversity of a region (Clark 
et al. 2016; Wendling et al. 2019). 
Vegetation responds to drought conditions at varying time scales depending 
on its physiology, and this response that vegetation has to any stressor can 
be quantified using different metrics (Vicente-Serrano et al. 2013). Resilience 
is defined as the ability of a system to return to its original state after a 
disturbance (Scheffer et al. 2009). Resilience can be measured as the time 
taken by a system to return to its original state after a disturbance. 
Resistance is defined as the ability to persist and resist change during a 
disturbance (Nimmo et al. 2015). It can be quantified as the deviation of 
processes and variables from the mean state during a disturbance; the more 
the deviation, the less the resistance. Recovery is the process by which an 
ecosystem retreats toward the pre-disturbance state (Fraccascia, 
Giannoccaro, and Albino 2018). These metrics help us study the response 
of different vegetation ecosystems ranging from croplands to tropical 
rainforests. 

Forests serve as carbon sinks in the global carbon cycle (Soepadmo 1993; 
Whitehead 2011; Martin et al. 2001). Understanding the response of forests 
to drought is important for understanding carbon cycles and any potential 
adversities to biodiversity. Drought and heat stress cause large-scale tree 
mortality, which affects the forest phenology and the carbon cycle and 
turns forests into carbon sources (Ciais et al. 2005; Anderegg, Kane, and 
Anderegg 2013; Ma et al. 2016; 2015, Li et al. 2019; Jiao et al. 2020; Schuldt et 
al. 2020; Senf et al. 2020).  

The drivers of recovery in a forested ecosystem are climate variables, forest 
type, and forest stock volumes (Anderson-Teixeira et al. 2013, Luo et al. 
2022). An ecosystem requires an optimum amount of soil moisture, soil 
nutrients, air temperature, and solar radiation for a speedy recovery. Soil 
moisture is recharged by precipitation and depleted by evapotranspiration 
driven by the air temperature and vapour pressure deficit in the 



[111] Chandel and Chauhan  

atmosphere. The optimum temperature for photosynthesis in an ecosystem 
depends on the biodiversity of the ecosystem (Bonan 2015). Sunlight is an 
essential ingredient in the process of photosynthesis, and cloudy days result 
in a decline in photosynthetic rates due to a lack of direct solar radiation. 
Here, we focus on the interplay of climate variables such as soil moisture, 
temperature, and precipitation in driving vegetation recovery. 

We use the leaf area index (LAI), to measure the state of vegetation. LAI is 
defined as the one-sided green leaf area per unit ground area in broadleaf 
forests and as half the total needle surface area per unit ground area in 
coniferous forests. The recovery rate of vegetation system can be measured 
by the rate of photosynthesis. Gross primary productivity (GPP), a proxy 
for the photosynthetic rate, is the amount of chemical energy produced by 
primary producers during photosynthesis. We use both LAI and GPP to 
study vegetation recovery and its climate drivers in a forested ecosystem. 

Figure 1: Study Region Lies in Central India, Bounded by 20°N, 77°E, 23°N, and 
82°E 

 

Source: Authors 



 Ecology, Economy and Society–the INSEE Journal [112] 

We selected a biodiversity-rich forested region in central India (Figure 1a), 
bounded by 20°N, 77°E, 23°N and 82°E. The region encompasses the 
Melghat Tiger Reserve, Satpura National Park, Pench National Park, and 
some agricultural areas. We investigate the ecosystem’s recovery from a 
stressed state in the pre-monsoon (May) period to the end of the monsoon 
period (September). We analyse deviations in the vegetation amount during 
the pre-monsoon and post-monsoon periods and attribute the net change 
during the monsoon period to climate drivers such as temperature, 
precipitation, and soil moisture. We also examined the dependence of post-
monsoon vegetation stress to pre-monsoon vegetation stress. To delineate 
non-linear interactions, we use an information-theoretic metric to 
understand the relative association of climate variables with vegetation 
productivity on a daily scale.  

2. DATA AND METHODS 

We use the leaf area index (LAI) obtained from the moderate resolution 
imaging spectroradiometer (MODIS). This product has a spatial resolution 
of 500 m and a temporal resolution of eight days. We used rainfall (P) (Pai 
et al. 2014) and temperature (T) gridded data from the Indian 
Meteorological Department (IMD). We used the Global Land Evaporation 
Amsterdam Model (GLEAM V3) dataset to determine soil moisture (SM) 
(Martens et al. 2017). We also used daily gross primary productivity (GPP) 
data from FluxSat v2.0, which is a combined dataset of the FLUXNET 
eddy covariance tower site data and satellite data from MODIS (Joiner and 
Yoshida 2020).  

2.1 VEGETATION STRESS 

We use percent anomaly in the month of May (Δmay) as an indicator of 
stress in vegetation. We look at the percent anomaly after the monsoons 
and calculate the Δsep for September. Both  Δmay and Δsep (Figure 2) can take 
positive and negative values. We use May and September, as during the 
monsoon period, satellite products like LAI are unreliable due to frequent 
cloud cover.  

 

 

 

 



[113] Chandel and Chauhan  

Figure 2: (a) Schematic Diagram Representing Vegetation Anomalies Proposed for 

Measuring Vegetation Recovery; (b) Climatology of the Leaf Area Index (LAI) over 

the Study Region 

 

Source: Authors’ analysis 

2.2  INFORMATION THEORY 

Uncertainty (or variability) of a time series  which 

can be classified into ‘m’ different states can be quantified using Shannon’s 
entropy as 

              (1) 



 Ecology, Economy and Society–the INSEE Journal [114] 

where  is the probability (or relative frequency) of the  state and 

H is bound as . H can be normalized using  to 

form Hnorm. In dynamical systems, in any interaction between two variables 

 uncertainty regarding one variable reduces when we have 

information about another. This reduction in uncertainty is called Mutual 
Information (MI) and can be computed as  

           (2) 

Where  is the joint entropy computed using the joint probabilities 

of X and Y in equation 1 or 2. MI is bound as 

. It can measure non-linear 

associations between variables and hence has been argued to be a measure 
of true statistical independence (Knuth et al. 2013). To compare the 
associations between various components inside a system, MI can be 

normalized as  which indicates the 

percentage of variance of one variable that be explained using another. 

In this work, to compare the linear and non-linear dependencies of 
vegetation on precipitation, soil moisture, and temperature, we compute 

pairwise normalized mutual information (  of daily datasets of soil 

moisture (SM), temperature (T), and precipitation (P) with gross primary 
productivity (GPP) to understand the association of hydrometeorological 
variables with vegetation productivity for the various above-discussed 
transitions of vegetation stress from May to September for our study 
region. We partition each time series into 11 bins and compute pairwise 

 using Equation 3 for each year separately and then compute the 

average  for the years falling in the above-discussed transition 

types. 

3. RESULTS 

Figure 3 shows the climatology of precipitation (P), temperature (T), soil 
moisture (SM), and gross primary productivity (GPP) in the study area in 
central India (figure 1). Central India receives rainfall during the Indian 
summer monsoons (ISMR) in June–September (JJAS). The pre-monsoon 
period sees high temperatures along with minimal rainfall, while the onset 
of ISMR provides respite from the heat of summer. The region experiences 
temperatures as high as 40°C during April and May, which reduce on the 
arrival of the monsoons and vary from 30–35°C during and after the 
monsoons. GPP (Figure 3), a measure of photosynthesis, remains low in 
the pre-monsoon period due to the unavailability of soil moisture and rises 



[115] Chandel and Chauhan  

with an increase in soil moisture after the onset of the monsoons. Since 
GPP is influenced by the air temperature and the amount of radiation and 
moisture available, if the condition of air temperature and radiation is 
fulfilled, it is mainly driven by water availability. We observe similar 
behavior in our region as the seasonal variation of GPP is similar to 
variation in SM. 

Figure 3: The Climatology of Precipitation, Temperature, and Soil Moisture and 

Gross Primary Productivity 

 

Source: Authors 

To understand the recovery of our system, we see the magnitude of stress 
in vegetation in different years (Figure 4). The negative values of percent 
anomaly (Δ) indicate stress in vegetation. Δmay varies from -20% to 25%. We 
performed Pearson correlation of Δmay with precipitation, temperature and 



 Ecology, Economy and Society–the INSEE Journal [116] 

soil moisture during the pre-monsoon period of March, April and May. We 
found that temperature has a correlation of –0.7 (p<0.01) which signifies 
that hotter pre-monsoon period puts stress on vegetation. We found 
positive correlation of delta with soil-moisture (0.80; p<0.01) and 
precipitation (0.62; p<0.01), which implies that these variables reduce the 
stress in month of May. 

Figure 4: Scatter plot of percentage anomaly in May LAI (Δmay) and corresponding 
change in September (Δsep ) in same year 

 
Source: Authors 

Δsep  varies from –30% to 25% across the years (figure 4). To understand 
the dominant hydro-meteorological drivers of vegetation productivity 
during monsoon, we performed a linear regression of Δsep with cumulative 
precipitation anomalies, mean temperature anomaly, and mean soil moisture 
anomaly during JJAS. We found that Δsep has a significant (p-value < 0.05) 
association with soil moisture and precipitation with coefficients –10.1 and 
6.2 respectively. Negative coefficient between P and GPP is counter-
intuitive and indicates a reduction in vegetation productivity due to 
radiation limitation imposed on photosynthesis by cloud cover. Hence, high 



[117] Chandel and Chauhan  

(and continuous) precipitation can lead to a loss of vegetation productivity. 
SM on the other hand has a positive coefficient with GPP as soil moisture 
recharged by precipitation takes time to deplete and can aid photosynthesis 
when solar radiation is available.  

The negative anomaly in September is present for both positive and 
negative values of Δmay (Figure 4). This demands further investigation to 
find out whether Δsep depends on Δmay or not. To investigate, we added Δmay 
as an additional predictor to the above regression. We did not find any 
statistically significant coefficient for Δmay and there was no change in the 
coefficients of climate other variables. This shows that variations in Δsep  are 
insensitive to variations in Δmay . Since linear regression fails to capture non-
linear associations, we employ information-theory based metrics to capture 
the non-linearities. 

Figure 5(a) shows average normalised Shannon’s entropy of P, T, SM, and 
GPP across all years. Shannon’s entropy quantifies the variability of a time 
series. P and SM are the variables with the lowest and highest entropies, 
respectively, with the entropy of P at around 40% and that of SM at 90% of 
the maximum possible value of log(m). Entropies of T is slightly lesser than 
SM with values between 80%–90% of Hmax. Entropy of GPP is between 
60%–70% of Hmax. While Shannon’s entropy measures the variability, 
mutual information (MI) measures the amount of variability that can be 
explained between any two variables in the system. MI measures non-linear 
associations as well, and, hence, is a better measure of association between 
any two variables. To understand the dynamic association of 
hydrometeorological variables with GPP, we computed mutual information 
between these variables. Figure 5(b) shows the average normalised mutual 
information (MInorm) of P, T, and SM, with GPP, where MInorm is ratio of 
MI to maximum MI to enable comparison across multiple variable pairs 
(See methods). MInorm indicates the percentage variance of a variable that 
can be explained by another variable.  

SM has highest MI with GPP, varying from 20% to 25%, showing the 
strong association between variability in soil moisture and variability of 
vegetation productivity in the region. While linear regression showed the 
almost equal (and opposite) strength of association between GPP and P and 
SM, MI shows that GPP has a stronger association with SM—the 
association is almost twice as strong as than with P. Different relative 
strengths of association across linear regression and MI indicate strong non-
linear associations that linear regression fails to capture. The extra strength 
of the association between SM and GPP, hence, comes from non-linear 
interactions. The overall positive association between SM and GPP offsets 
the negative coefficient of P seen in the linear regression and hence leads to 



 Ecology, Economy and Society–the INSEE Journal [118] 

positive vegetation productivity. This shows that for our study region, 
recovery of vegetation from stressed pre-monsoon conditions has a strong 
dependence on soil moisture. 

Figure 5: (a) Average Normalised Shannon’s Entropy of P, T, SM, and GPP across 
All Years; (b) Average Normalised Mutual Information (MInorm) of P, T, and SM, 
with GPP Where MInorm Is the Ratio of MI to the Maximum MI to Make It Useful 
for Comparison across Multiple Variable Pairs 

Source: Authors 

(a) 

(b) 



[119] Chandel and Chauhan  

While our study provides novel insights on the vegetation dynamics of 
forest regions, it ignores agricultural areas that are heavily affected by 
human interventions such as irrigation. Dynamics of vegetation may differ 
in the presence of anthropogenic stresses, which needs further examination. 
The independence of September vegetation from May vegetation may not 
hold for extreme events such as forest fires, heatwaves, and prolonged 
droughts. During such events, vegetation may cross a tipping point and 
experience irreversible changes. Precipitation shows a negative correlation 
with vegetation productivity, which is counter-intuitive. Since precipitation 
in our study also acts as a proxy for solar radiation, including data such as 
photosynthetically active radiation (PAR) will provide more accurate 
inferences.  

4. CONCLUSIONS 

Our study tries to measure the recovery of vegetation from pre-monsoon 
stress between the months of May and September using deviations from 
climatology. Since good quality observed datasets are not available for the 
monsoon period, our approach is a novel method to characterise vegetation 
recovery and attribute it to climate variables using datasets for the months 
of May and September, which are mostly available. We tried to attribute 
vegetation stress to climate variables and found that pre-monsoon 
monsoon vegetation stress depends on soil moisture, which is driven by 
variations in temperature and precipitation. Since pre-monsoon 
precipitation is sparse, heat waves during the pre-monsoon period can 
deplete soil moisture rapidly. During the monsoons, precipitation 
contributes to vegetation recovery from pre-monsoon stress through soil 
moisture recharge, while it also inhibits vegetation productivity by limiting 
the available radiation for photosynthesis. While linear methods show the 
strong negative dependence of vegetation on precipitation, indicating 
radiation-limited photosynthesis, metrics that include non-linear 
dependencies such as mutual information show stronger dependence on 
soil moisture. These contrasting results highlight the importance of 
including non-linear measures in analysis of natural systems. 

ACKNOWLEDGEMENTS 

This study was supported by Prime Minister’s Research Fellowship 
(PMRF), Ministry of Education, Government of India — RSPMRF0262. 

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https://doi.org/10.1088/1748-9326/ab3dde
https://doi.org/10.1093/treephys/tpr063
https://doi.org/10.1038/srep44552

