research paper on climate change in maharashtra

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Research Article

Vulnerability of agriculture to climate change increases the risk of child malnutrition: Evidence from a large-scale observational study in India

Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – original draft

* E-mail: [email protected] , [email protected]

Affiliation Population Council, Zone 5A, India Habitat Centre, New Delhi, India

Roles Data curation, Formal analysis, Writing – review & editing

Affiliation International Food Policy Research Institute, New Delhi & Ex-Population Council, New Delhi, India

Contributed equally to this work with: Chitiprolu Anantha Rama Rao, Bellapukonda Murali Krishna Raju, Niranjan Saggurti

Roles Conceptualization, Investigation, Validation, Visualization, Writing – review & editing

¶ ‡ CARR and NS are joint senior authors on this work.

Affiliation ICAR-Central Research Institute for Dryland Agriculture, Santoshnagar, Saidabad, Hyderabad, India

Roles Data curation, Formal analysis

Roles Methodology, Supervision, Writing – review & editing

• Bidhubhusan Mahapatra, 
• Monika Walia, 
• Chitiprolu Anantha Rama Rao, 
• Bellapukonda Murali Krishna Raju, 
• Niranjan Saggurti

• Published: June 28, 2021
• https://doi.org/10.1371/journal.pone.0253637
• Peer Review
• Reader Comments

Introduction

The impact of climate change on agriculture and food security has been examined quite thoroughly by researchers globally as well as in India. While existing studies provide evidence on how climate variability affects the food security and nutrition, research examining the extent of effect vulnerability of agriculture to climate change can have on nutrition in India are scarce. This study examined a) the association between the degree of vulnerability in agriculture to climate change and child nutrition at the micro-level b) spatial effect of climate vulnerability on child nutrition, and c) the geographical hotspots of both vulnerability in agriculture to climate change and child malnutrition.

The study used an index on vulnerability of agriculture to climate change and linked it to child malnutrition indicators (stunting, wasting, underweight and anaemia) from the National Family Health Survey 4 (2015–16). Mixed-effect and spatial autoregressive models were fitted to assess the direction and strength of the relationship between vulnerability and child malnutrition at macro and micro level. Spatial analyses examined the within-district and across-district spill-over effects of climate change vulnerability on child malnutrition.

Both mixed-effect and spatial autoregressive models found that the degree of vulnerability was positively associated with malnutrition among children. Children residing in districts with a very high degree of vulnerability were more like to have malnutrition than those residing in districts with very low vulnerability. The analyses found that the odds of a child suffering from stunting increased by 32%, wasting by 42%, underweight by 45%, and anaemia by 63% if the child belonged to a district categorised as very highly vulnerable when compared to those categorised as very low. The spatial analysis also suggested a high level of clustering in the spatial distribution of vulnerability and malnutrition. Hotspots of child malnutrition and degree of vulnerability were mostly found to be clustered around western-central part of India.

Study highlights the consequences that vulnerability of agriculture to climate change can have on child nutrition. Strategies should be developed to mitigate the effect of climate change on areas where there is a clustering of vulnerability and child malnutrition.

Citation: Mahapatra B, Walia M, Rao CAR, Raju BMK, Saggurti N (2021) Vulnerability of agriculture to climate change increases the risk of child malnutrition: Evidence from a large-scale observational study in India. PLoS ONE 16(6): e0253637. https://doi.org/10.1371/journal.pone.0253637

Editor: Srinivas Goli, University of Western Australia, AUSTRALIA

Received: January 3, 2021; Accepted: June 9, 2021; Published: June 28, 2021

Copyright: © 2021 Mahapatra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The NFHS-4 data is available at the DHS website and can be downloaded from https://dhsprogram.com/data/available-datasets.cfm . The climate data can be accessed by giving request at: http://dsp.imdpune.gov.in/ . The authors had no special access privileges to the data others would not have.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Climate change is probably the most complex and challenging environmental problem faced by the world today and is increasingly being recognized as a potent threat to agriculture in general, and specifically to food security [ 1 , 2 ]. Climate scientists have predicted that climate change is going to have a significant impact on agriculture which will ultimately affect the quality and quantity of food production [ 1 , 3 ]. It is estimated that agricultural output in developing countries will decline by 10–20% by 2080 [ 4 ]. This will have adverse consequences in achieving universal food security and meeting the nutritional requirement of communities [ 2 , 5 , 6 ]. Estimates suggest that with the changing climate, in 2050, there will be 62% more severe stunting cases than what could be without any change in the current climatic scenario [ 7 ]. Currently, about one billion people are deprived of enough food [ 8 ], over 150 million children are stunted, and another 50 million are wasted [ 9 ]. Though recent evidence suggests that there have been some improvements in nutritional indicators, climate change can undermine ongoing efforts to reduce hunger and enhance food security [ 7 , 10 ]. The situation in India is much like the global scenario where with changing climate and ever-growing population, the demand for food is bound to increase further. An increase in 1–2°C in temperature is going to have a negative impact on the yield of major cereal crops in low altitude countries like India [ 11 ] which in turn will impact the nutritional status of the population [ 12 ].

The literature review for this study focused on reviewing documents on issues of climate change, agriculture, food security, and nutrition. The literature search suggests that there have been several studies globally and in the Indian context that have examined the impact of climate change on agriculture and food security. The available body of evidence estimating impact of climate change on agriculture, food security and nutrition have documented the impact of rainfall and temperature variability (including level and pattern) as well as of extreme weather events on undernutrition among children [ 3 , 13 – 17 ]. A study conducted in Mali, Africa found that by 2025, due to climate and livelihood changes an additional million children will be exposed to increased risk of malnutrition [ 13 ]. Similarly, a longitudinal study conducted in Ethiopia between 1996–2004 estimated that while one standard deviation (SD) increase in rainfall may lead to 0.24 SD increase in moderate stunting, one SD increase in temperature may lead to 0.22 SD decrease in moderate stunting [ 13 , 14 ]. In Indian context, studies examining the impact of climate change on malnutrition found that children in flood affected households were twice more likely to be stunted and underweight compared to their counterparts living in non-flooded areas. Research specific to India suggests that with the current level of crop yields remaining constant till 2050, there will be a severe shortage of micronutrient supply to the households [ 18 ]. Prior research has also examined the impact of rainfall and temperature variability (including level and pattern) on undernutrition among children. While existing studies provide evidence on how climate variability affects the food security and nutrition, there has been dearth of research examining the extent of effect vulnerability of agriculture to climate change can have on nutrition in India. The current study contributes to existing body of evidence on climate change and nutrition by assessing whether vulnerability of agriculture to climate change is linked to the nutritional status of communities. The study aims to answer three research questions: (i) Is there an association between the degree of vulnerability in agriculture to climate change and child nutrition at the micro-level? (ii) Is there any spatial effect of climate vulnerability on child nutrition? and (iii) Which are the geographical hotspots of both vulnerability in agriculture to climate change and child malnutrition?

The study used two data sources: (i) climate vulnerability index developed under National Initiative on Climate Resilient Agriculture (NICRA) project of the Indian Council of Agricultural Research (ICAR) [ 12 , 19 ] and (ii) children’s nutritional status derived from National Family Health Survey 4 (NFHS-4).

Climate vulnerability index.

X i = value of indicator in original units for i th district

X min = minimum value of the indicator in original units across the districts

X max = maximum value of the indicator in original units across the districts

This was followed by computing the weighted mean of assigned indicators to construct indices for sensitivity, exposure, and adaptive capacity. Lastly, the vulnerability index was computed by taking weighted average of the three indices—with weights of 25, 40 and 35 to exposure, sensitivity and adaptive capacity respectively [ 12 , 19 ]. All census districts were categorized into five equal quintiles where the districts with top 20% vulnerability score were considered very highly vulnerable and those in the bottom 20% were considered as very low vulnerable. More information on the various definitions, formulas, and weights used to compute component-wise and vulnerability index can be found in detail in the study report [ 19 ].

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https://doi.org/10.1371/journal.pone.0253637.t001

National Family Health Survey-4 (NFHS-4).

The Indian equivalent of the Demographic and Health Survey (DHS)—NFHS is conducted at regular intervals to generate information on various fertility, mortality, child health, and nutrition indicators at the district, state, and national levels. The fourth round of NFHS was conducted in 2015–16 and 699,686 women aged 15–49 years old were interviewed from 601,509 households across all states and union territories (UTs) of India. Data on stunting, wasting and underweight for 243,213 children and anaemia for 216,049 children born to ever-married women in the last five years preceding the survey was available. The women were recruited through a stratified two-stage sampling process. In the first stage, primary sampling units (PSUs) were selected systematically using a probability proportional to size approach, and a fixed number of households and eligible women were selected within the PSUs. In rural areas, a village was considered as the PSU, whereas in urban areas it was a census enumeration block. More information on the sampling procedure along with the distribution of socio-demographic, household-level and individual-level characteristics at the state as well as district level can be found in the NFHS-4 Reports [ 21 ].

Matching vulnerability index data with NFHS-4.

While the vulnerability index was computed for 572 districts as per Census 2001, NFHS-4 provided information on the nutritional status of children under five years of age for all 640 districts as listed in Census 2011. Therefore, to conduct the analysis, a district-level mapping exercise was carried out. A list of 572 districts, for which vulnerability index data was computed, was first matched with NFHS districts based on the district/town names. Districts that were common across both data were assigned the corresponding overall vulnerability, sensitivity, exposure, and adaptive capacity indices. For newly formed districts that were available in NFHS-4 data but not in the vulnerability data, indices corresponding to their origin district were assigned. For example, Anjaw district of Arunachal Pradesh was assigned the indices corresponding to its origin district Lohit as available in the vulnerability data. In four instances where new districts were carved out from more than one Census 2001 district, all four indices for newly formed districts were computed by calculating the median of origin district indices. All 16 metropolitan cities/ UTs for which vulnerability index was not available were excluded from the analysis. Following the assumption that these 16 districts were not considered as they are mostly urban and may not have relevant indicators required for constructing the index, 10 more districts were dropped from the remaining UTs. This resulted in observations from 614 districts of all states sans UTs. After these matching, the district level vulnerability map was recreated for the 614 districts ( S1 Fig ) and compared with the map based on 572 districts created originally by Rao et al. [ 19 ] and found no difference in district categorization.

Ethics statement

The authors did not collect any primary data for this study. Further, the climate change vulnerability index did not include any data collected from human participants. The nodal agency for collecting NFHS-4 data was International Institute for Population Sciences (IIPS), Mumbai. The protocol for NFHS-4 data collection was approved by institutional review boards of IIPS and ORC Macro.

Nutritional status outcomes.

Among all living children under the age of 5 years, nutritional status outcomes considered for this study were stunting, severe stunting, wasting, severe wasting, underweight, severe underweight, anaemia, multiple malnutrition, and all forms of malnutrition. DHS definitions per the World Health Organization’s (WHO) child growth standard were used to compute measures on children’s nutritional status. Any child whose height-for-age z score was below minus 2 (‑2.0) SD of the mean value was defined as stunted, whereas a child with height-for-age z score below ‑3.0 SD of the mean was defined as severely stunted. A child was defined as wasted if his/her weight-for-height z score was below ‑2.0 SD of the mean value. Severely wasted children had a weight-for-height z score below ‑3.0 SD of the mean. Any child whose weight-for-age z score was below ‑2.0 SD of the mean value was defined as underweight, whereas a child with a weight-for-age z score below ‑3.0 SD of the mean was defined as severely underweight. Children aged 6–59 months who stayed in the household the night before the interview with haemoglobin count lower than 11.0 grams per decilitre (g/dl) were defined as anaemic. All living children under the age of 5 years were defined to have multiple malnutrition if out of the four considered nutritional outcomes—stunting, wasting, underweight, and anaemia—they had at least two. If a child was stunted, wasted, underweight as well as anaemic s/he was defined to have all forms of malnutrition. The socio-economic and demographic characteristics that were used as covariates in multivariable analyses are religion, caste, wealth index, place of residence of the household, number of household members, age of the child, sex of the child, mother’s education, and birth order. These variables were recoded from the original questions to make them suitable for the present analysis.

Statistical analyses

Bivariate and multivariable analyses were conducted to examine the association of degree of vulnerability with the nutritional status of children. Spatial analysis was also conducted to understand the macro-level association and spill-over effect a district’s climate vulnerability can have on child malnutrition. The analysis was started by conducting bivariate analysis between the degree of vulnerability and nutritional status of children. To answer the first research question, mixed-effect multilevel models were fitted to examine the strength of association between vulnerability and child nutrition. In the mixed-effect model, births were nested within primary sampling units (as defined in NFHS-4 data), which were nested within a district and controlled for socio-demographic, household, and maternal characteristics.

Spatial analysis was conducted at the district-level where child malnutrition indicators were transformed into proportions. First, spatial autocorrelation was computed using Moran’s I and Geary’s C to understand the extent of spatial clustering in child malnutrition and climate vulnerability. Both these indices provide an idea on the extent to which a spatial regression is suitable. The Moran’s I value ranges from -1 to +1 where a positive value indicates positive spatial autocorrelation, and a negative value indicates the negative autocorrelation. Higher the absolute Moran’s I value, stronger is the spatial autocorrelation and vice-versa [ 22 ]. The Geary’s C ranges from 0 to 2; where 1 is no spatial autocorrelation, values near 0 are positively spatially correlated and those closer to 2 are highly negatively autocorrelated. Additionally, hotspots and coldspots were identified using bivariate Local Indicators of Spatial Association (LISA) (Research question # 3). The bivariate LISA generates a choropleth map highlighting the districts with a significant local Moran statistic and classifies them into high-high and low-low spatial clusters, and high-low and low-high spatial outliers. The high-high pairing suggests clustering of values, whereas high-low and low-high locations indicate spatial outliers.

Subsequently, mixed spatial autoregressive error models were fitted for each of the nutrition outcome indicators independently that considered both spatial lag and spatial error. In these spatial regression models, the degree of vulnerability was considered as the key predictor and shares of poor population (head count ratio [ 23 ]), proportion of population who belong to rural areas, general caste and Hindu religion were included as covariates. Given that coefficients from a spatial autoregression should not be directly interpreted [ 24 , 25 ], calculations were within the district (direct) and spill-over (indirect) based on the model coefficients to answer the second research question. Stata module spregress followed by estat impact was used to derive these estimates. In addition to the spatial and multivariable analyses, districts burdened with vulnerability and malnutrition were also identified by filtering out districts categorized as having high/very high vulnerability and listing out those districts with child malnutrition levels higher than country average (Research question # 3). Except for the Bivariate LISA, the rest of the analyses were performed using STATA 16.1 (StataCorp., TX, USA). The maps from Bivariate LISA were generated using GeoDa.

In the study sample, about one-fifth (21%) of children were found to be wasted, two-fifths were stunted (39%) and underweight (36%), and three-fifths had anaemia (59%) ( Fig 1 ). Nearly half of the children (48%) had multiple malnutrition and one in twenty (5%) had all the form of malnutrition.

https://doi.org/10.1371/journal.pone.0253637.g001

Q1. Is there an association between the degree of vulnerability in agriculture to climate change and child nutrition at micro-level?

The degree of vulnerability was positively associated with malnutrition among children ( Table 2 ). For example, children residing in districts with very high degree of vulnerability were more like to have stunting (41% vs 31%, Adjusted Odds Ratio [AOR]: 1.32, 95% CI: 1.21–1.44), wasting (24% vs 19%, AOR: 1.42, 95% CI: 1.27–1.60), underweight (39% vs 30%, AOR: 1.45, 95% CI: 1.30–1.61) and anaemia (63% vs 52%, AOR: 1.75, 95% CI: 1.47–2.08) than those living in districts considered to have very low degree of vulnerability. The magnitude of difference between very high and very low degree of vulnerability was observed to be higher for children severely stunted, severely wasted and severely underweight.

https://doi.org/10.1371/journal.pone.0253637.t002

Q2. Is there any spatial effect of climate vulnerability on child nutrition?

The spatial autocorrelation assessed using Moran’s I and Geary’s C suggests that there is clear evidence of geographic clustering in both nutrition indicators and degree of vulnerability ( Table 3 ). The evidence of clustering was found to be strongest for children being underweight, followed by stunting and anaemia. The spatial autoregressive model suggests that malnutrition among children is likely to be more in districts that are very highly vulnerable to climate compared to those that have a very low degree of vulnerability ( Table 4 ). For example, stunting is likely to be 3% more in very highly vulnerable districts than those with very low vulnerability. Similarly, compared to districts categorised as very low in terms of vulnerability, children from the very high category are 4% more likely to have wasting and underweight, and 6% more likely to have anaemia. Similar within district effects were noted for those districts with high vulnerability. The study also examined if the district’s vulnerability has a spill-over across districts. Districts categorized as very high vulnerability were also found to be more likely to have a spill-over effect across the neighbouring districts. For example, districts with very high vulnerability are likely to have a spillover effect of stunting by 0.24 percentage point compared to very low vulnerability district.

https://doi.org/10.1371/journal.pone.0253637.t003

https://doi.org/10.1371/journal.pone.0253637.t004

Q3. Which are the geographical hotspots of the degree of vulnerability in agriculture to climate change and child malnutrition?

The Bivariate LISA maps ( Fig 2 ) show the hotspots and coldspots in the spatial relationship between the degree of vulnerability and child malnutrition indicators. The number of high-high clusters varied across child nutrition indicators: 92 for underweight, 79 for stunting, 75 for wasting and 65 for anaemia. Similarly, the number of low-low clusters were highest for underweight (113) and least for wasting (82). The LISA maps suggest that hotspots of child malnutrition and degree of vulnerability are mostly clustered around western-central part of India though there were some hotspots for stunting in the eastern part of the country as well. Further drill-down of the district-level data found a total of 69 districts that had high levels of stunting, wasting, underweight and anaemia together with high/very high level of vulnerability ( S1 Table ). These districts belonged to the states of Bihar, Chhattisgarh, Gujarat, Haryana, Jharkhand, Karnataka, Madhya Pradesh, Maharashtra, Rajasthan, and Uttar Pradesh ( Table 5 ).

https://doi.org/10.1371/journal.pone.0253637.g002

https://doi.org/10.1371/journal.pone.0253637.t005

Climate scientists have predicted that climate change is going to have a significant impact on agriculture which will ultimately affect the quality and quantity of food production [ 1 , 3 ]. This study examined how the vulnerability of a district to climate can affect child nutrition. The study found that districts highly vulnerable to climate change can have more child malnutrition than districts which are relatively less vulnerable. The mixed-effect analysis found that the odds of a child suffering from stunting increased by 32%, wasting by 42%, underweight by 45% and anaemia by 63% if the child belonged to a district categorised as very highly vulnerable when compared to those categorised as very low. The magnitude of effects was stronger when examined for severe- stunting, wasting and underweight. The macro-level spatial analysis demonstrated that rates of child malnutrition were higher by 3–5% for very highly vulnerable districts than very low vulnerable ones. The study also investigated if the effect of high/very high vulnerability on child nutrition transferred to neighbouring districts and found significant evidence of spill-over for stunting but not for wasting, underweight and anaemia. Lastly, the study used bivariate spatial maps and macro-level data to identify the clusters where child malnutrition and vulnerability were high. Further, the study identified 69 districts that were battling the double burden of high/very high climate vulnerability as well as child malnutrition.

India being the second largest populous country with a heavy dependency on agriculture, high vulnerability of certain regions to climate change can be cause of concern to agriculturalists and policymakers [ 26 ]. Though the country has seen significant economic development in the last couple of decades, similar progress has not been made in addressing child malnutrition [ 27 ]. Child malnutrition is prevalent across states whether they are at the forefront of economic development (e.g. Gujarat) or lagging (e.g. Bihar, and Uttar Pradesh) [ 28 ]. While the study provides indisputable evidence on effect agriculture’s vulnerability to climate change, this effect may be further explained by inadequate health infrastructure and poverty. A closer look at the 69 districts facing the double burden of climate vulnerability and child malnutrition suggests that most of these districts and states are characterized by poor health infrastructure in rural areas, low literacy, rudimentary sanitation, and poverty. A study by Khan and Mohanty has highlighted how poverty has a significant impact on child malnutrition in India [ 28 ]. Consistent with earlier studies, the hotspots of child malnutrition and degree of vulnerability are concentrated in the areas where hotspots of poverty and child malnutrition have been identified. This suggests a close relationship between the degree of vulnerability and poverty level which should be explored further in future research. The clustering of vulnerability levels and child malnutrition indicates the extent to which climate change can affect the food production system and ultimately the nutrition of children in the short run and adults in the long run. However, the early evidence from this study provided an opportunity to governments and programmers to develop sustainable solutions towards mitigating the effects that climate change will have on agriculture and human health.

Of the notable findings in this study is the estimation of within-district and spill-over effect of climate vulnerability on child malnutrition. Among all the malnutrition indicators, the effect of vulnerability was most on anaemia (5% [within-district + spill-over]), followed by underweight, stunting and wasting (4%). Notably, the malnutrition indicators had higher spatial autocorrelation suggesting geographical clustering. Within-district effects of climate vulnerability, particularly for high and very highly vulnerable districts were substantially significant. This indicated that there will be a significant effect of climate vulnerability on child malnutrition among districts categorized as very high/high, irrespective of the neighbouring districts’ vulnerability level. The spill-over effect of vulnerability was significant for all malnutrition indicators except for anaemia. This again highlighted that the effect of vulnerability is not limited by the geographical boundaries rather the effect can extend to neighbouring districts as well. Interestingly, the spill-over was not present when severe malnutrition was examined.

The findings of the study should be interpreted in the light of following limitations. First, union territories and completely urban districts were excluded from the analysis as the vulnerability index values were not available for those areas. Second, the original index was based on 572 districts which re-mapped into 614 districts, as a result some of the district’s vulnerability ranking may have been wrongly assigned. However, it is assumed that such misplacing would be very minimal and not likely to change the results presented in the study. To ensure that mapping of degree of vulnerability is robust, the vulnerability maps provided by Rao et al. [ 19 ] for 572 districts were matched with the one generated for 614 districts. Third, the study did not examine the dietary intake pattern (both quantity and quality) of children and their families which is likely to have an influence on their nutritional status. Future research should collect dietary intake data and examine if vulnerability to climate change has an influence on dietary intake and whether the pattern of consumption play a role in determining the relationship between vulnerability and nutritional status. Lastly, obtaining data on all the variables/indicators for a uniform reference period at the district-level is extremely difficult. While vulnerability index computation used the most recent data available for each unit of analysis, for missing data statistical methods such as using nearest neighbourhood value, average value of respective state, simulation and extrapolation methods were used to derive the indicators at the district level for computing vulnerability index. While not a limitation to this study, it is also to be noted that the vulnerability index created were assigned unequal weights to the three dimensions of adaptive capacity, exposure, and sensitivity. Though unequal weight assignment is well justified by the authors [ 12 , 19 ], it would have been worth exploring how the vulnerability index would look if equal weights were assumed and how that, in turn, would affect the evidence generated by the study.

The study has important implications for both research and policy to address climate vulnerability and child malnutrition. Existing and future programs in India, specifically those focussing on nutrition and agriculture, should consider the vulnerability of agriculture to climate change in developing their strategies. For areas where agriculture is vulnerable to climate change, there should be increasing efforts to grow staple crops that can sustain in given climatic conditions as well as meet the nutritional requirements of the population. Given that the current research identifies such geographic cluster, it would be important to develop cluster-specific agricultural plans based on the nutritional requirements of the area. While this study identified clusters of geographies where vulnerability and malnutrition exist, it would be important to further drill down and identify the sub-clusters (sub-district or panchayat ) within those areas where the problem lies. This will help more specific targeted programming for agriculture and providing nutrition supplements to children. While this study identified the effect of vulnerability to climate change on child malnutrition, future research should explore whether the climate vulnerability has an impact on adults’ nutritional status and other co-morbidities emerging from malnutrition. In conclusion, this is the first study to examine the relationship between the degree of vulnerability in agriculture to climate change and child malnutrition. The study found strong evidence at both micro and macro levels on how the vulnerability of agriculture to climate change can result in child malnutrition. The clustering of vulnerability and child malnutrition at few select states and districts that are historically known for multiple deprivations further highlights the need to have a holistic approach to bring change in the lives of people living in those geographical areas. Finally, this effect of climate vulnerability is not limited to that district, but it spills to the adjoining areas as well.

Supporting information

S1 fig. degree of vulnerability of agriculture to climate change at district level..

https://doi.org/10.1371/journal.pone.0253637.s001

S1 Table. Districts categorized as “high” or “very high” in degree of vulnerability and having child malnutrition levels higher than India average.

https://doi.org/10.1371/journal.pone.0253637.s002

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PERSPECTIVE article

Unprecedented climate change in india and a three-pronged method for reliable weather and climate prediction.

• 1 National Institute for Space Research, São José dos Campos, Brazil
• 2 Department of Meteorology and Oceanography, Andhra University, Visakhapatnam, India
• 3 Centre for Earth, Ocean and Atmospheric Sciences, University of Hyderabad, Hyderabad, India

India, one of the most disaster-prone countries in the world, has suffered severe economic losses as well as life losses as per the World Focus report. 1 More than 80% of its land and more than 50 million of its people are affected by weather disasters. Disaster mitigation necessitates reliable future predictions, which need focused climate change research. From the climate change perspective, the summer monsoon, the main lifeline of India, is predicted to change very adversely. The duration of the rainy season is going to shrink, and pre-monsoon drying can also occur. These future changes can impact the increase of vector-borne diseases, such as malaria, dengue, and others. In another recent study, 29 world experts from various institutions found that the largest exposure to disasters, such as tropical cyclones (TCs), river floods, droughts, and heat waves, is over India. For improved and skillful prediction, we suggest a three-stage cumulative method, namely, K is for observational analysis, U is for knowledge and understanding, and M is for modeling and prediction. In this brief note, we report our perspective of imminent weather disasters to India, namely, monsoons and TCs, and how the weather and climate forecasting can be improved, leading to better climate change adaptation.

Introduction

The Indian economy still significantly depends on agriculture, which, in turn, depends on the summer monsoon rains occurring from June to September. In the present scenario of climate change, it is essential to know how the Indian summer monsoon rainfall is going to change in the future. In a recent detailed study with regional climate model projections, Ashfaq et al. (2020) suggest that an important adverse signal of future climate change over the Indian monsoon region in the RCP8.5 scenario ( Krishnan et al., 2020 ; Jyoteeshkumar Reddy et al., 2021 ) can occur. The sinking of the Indian monsoon rainy season onset is projected to delay by five to eight pentads and a shrinking of the monsoon rainy season. India can experience pre-monsoon drying as well.

In a recent innovative study, 29 world experts ( Lange et al., 2020 ) from different institutions and different countries, reached some important conclusions. These inferences deserve urgent attention and action plans by policymakers. They considered six categories of extreme climate impact events, namely, river floods, cyclones, crop failures, wildfires, heat waves, and droughts. These authors ( Lange et al., 2020 ) quantified the pure effect of climate change on the exposure of the global population to the events mentioned. One important conclusion, which is of grave concern to India, is that the largest increase in exposure is projected here. Thus, to avoid huge damages due to these disasters, such as deaths and loss of property, urgent and more reliable predictions are needed. We, however, must clarify that there has been tremendous improvement in numerical prediction of tropical cyclones (TCs) in the last few decades in India [e.g., Pattanaik and Mohapatra, 2021 ; Saranya Ganesh et al., 2021 ; Sarkar et al., 2021 , and all other papers in January 2021 of Mausam, a special issue on the state of the art on TC prediction in the North Indian Ocean (NIO)], but what we claim is that applying theory can enhance the skills from the current day model outputs substantially more as discussed in the following section. To provide an analogy, in a recent study, Rao et al. (2021) attempted to connect observations, theory, and a prediction plan for heat waves. This prediction method can be applied to a numerical weather prediction model to predict deadly heat waves; thus, Rao et al. (2021) used a K, U, and M approach for the prediction of deadly heat waves over India.

From the context of the three-pronged K, U, and M method (hereafter, KUM), there are sufficient observational studies, or K, and also some attempts have been made using highly sophisticated, state-of-the-art (atmosphere and ocean) coupled models for predictions, M. What is most lacking, however, are theoretical studies (U) aiming to find out the causes for disastrous TCs or the highly complex regional monsoons.

According to a recent 2021 overview of current research results by the Geophysical Fluid Dynamics Laboratory of Global Warming and Hurricanes 2 , the severity and frequency of TCs are increasing globally. A recent study ( Balaguru et al., 2015 ) also suggests an increase of TCs globally even over the NIO. Essentially, the increase in the strong TCs has far-reaching implications for society because these include the most harmful aspects, namely, storm surges and heavy rains with intense wind speeds. Indeed, TC rainfall rates will possibly increase in the future due to various anthropogenic effects and accompanying increases in atmospheric moisture. Rapid intensification of TCs poses forecast challenges and increased risks for coastal communities ( Emanuel, 2017 ). Recent modeling studies ( Emanuel, 2020 ) show an increase of 10–15% for precipitation rates averaged within about 100 km of the cyclone for a 2°C global warming scenario. As per IPCC AR5, higher levels of coastal flooding due to TCs are expected to occur, all else assumed to be constant due to rising sea levels. In this situation, together with the rise in sea level, the impact due to the strong TCs deteriorates the conditions of the increasing coastal population across India and the neighborhood. As the NIO is one of the typical regions with a population of 1.353 billion (2018), about 18% of the global population by 2020, it is highly susceptible to strong TCs causing adverse living conditions, and the implication is that stronger TCs will be worse.

According to reports from a respected BBC newspaper 3 , 4 , and a potential report 5 from the Indian Meteorological Department, Amphan is a very severe cyclone that transited the west coast of India in 2020 and also caused a lot of damage. The super cyclonic storm Amphan is the costliest case in the recorded history of TCs with damage of US$15.78 billion and also total fatalities of 269. Similarly, in the year 2019, a loss of US$11 billion occurred due to TCs. In the year 2020, there was a record-breaking occurrence of eight TCs over the NIO: five cyclones and three major cyclones compared to the climatology of 4.9, 1.5, and 0.7. We note a drastic increase in category 3 and beyond hurricanes occurring in the NIO and also a significant increase in the Northern and Southern hemispheres ( Figure 1 ). Also, there is a substantial increase in accumulated cyclone energy (ACE) in the last two decades in the NIO and Northern and Southern hemispheres ( Figure 2 ). In 2019, record-breaking ACE of 85 × 10 4 knots 2 , occurred in the NIO, nearly twice the previous record ( Singh et al., 2021 ; Wang et al., 2021 , BAMS). The decrease in the projected number of TCs found in some studies ( Sugi et al., 2017 ) is overcompensated by the huge increase in intensity similar to that found over the NIO in 2019 and 2020. Furthermore, as if to worsen the situation in a colloquial sense, Wang and Murakami (2020) show that the general atmospheric and ocean parameters, which show a high global correlation with the number of TCs, nevertheless show only a very low correlation with TCs of the NIO. Thus, urgent research should be carried out to understand the causes of the occurrence of TCs over the NIO. Even globally, in the last 39 years (1980–2018), weather disasters caused about 23,000 fatalities and US$100 billion in damages worldwide. Each year, weather events displace huge populations, drive people into poverty, and dampen economic growth globally ( Kousky, 2014 ; Munich, 2020 ; Hoegh-Guldberg et al., in press ). The underlying causes show a marked signal of anthropogenic roots and global warming (e.g., Sobel et al., 2016 ; Im et al., 2017 ).

Figure 1 . The number of category 3+ hurricanes that occurred in the Northern and Southern hemispheres and the NIO (black dotted line indicates a linear trend, and orange line indicates significance at the 95% confidence level) ( http://tropical.atmos.colostate.edu/Realtime/index.php?archandloc=northindian ).

Figure 2 . ACE (in 10 4 Knots 2 ) in the Northern and Southern hemispheres and the NIO (black dotted line indicates a linear trend, and orange line indicates significance at 95% confidence level) ( http://tropical.atmos.colostate.edu/Realtime/index.php?archandloc=northindian ).

Henceforth, we focus on the TCs as well as summer monsoons, which are the two most relevant weather and climate phenomena for the Indian region.

A Three-Stage Method to Study and Plan Reliable Prediction

Because India is rigorously prone to natural disasters as well as impacts due to anticipated changes in the summer monsoon in the future, there is indeed a serious question as to how to study the causal mechanisms of these disasters and plan to mitigate them. In this context, the late Gill (1985) , an accomplished geosciences expert, suggested almost 35 years ago the KUM method, namely, knowledge, understanding, and modeling, a three-pronged approach. The first step (K) is to improve observational knowledge of calamity-causing weather events and next a theoretical understanding to find out the cause of a specific effect, probably utilizing linear analytical mathematical solutions (U). Finally, the third one (M), using the presently available highly complex coupled (atmosphere and ocean) models giving numerical solutions to non-linear equations, pioneered by Phillips (1956) , predicting future occurrences. The order of KUM seems to be important. Although relatively substantial observational results are available in the Indian context for meteorological and oceanographic events, very few theoretical studies have been made delineating the causal mechanisms. Thus, this aspect should be given priority. In a recent comment, Emanuel (2020) also stressed the need for theoretical studies. Finally, only after acquiring the observational, knowledge, and cause-and-effect relationships in theoretical studies, only then , should one embark on numerical or climate modeling to successfully predict the future.

In this context, it is illuminating to recall the comments of Phillips (1970) , one of the founding fathers of theoretical meteorology and numerical weather prediction: “in making a numerical forecast, one takes a set of numbers.regardless of.synoptic structures.by another set of numbers, representing the forecast. The computation of a set of numbers depicting the formation of a front, is of course, not a theory of fronts (unless one is content to point to the equation of motion as theory!!!!!)” Thus, one should be very careful using numerical models to develop a theory of TCs, and in the Indian context, monsoon depressions (MDs) are crucial for monsoon rainfall. Today, many students and scientists worldwide spend most of their valuable time dealing with huge data sets and running numerical models to simulate rather than to develop a theory. Tellingly, Emanuel (2020) , mentions that presently there is “computing too much and thinking too little.” Indeed, there is an urgent need for curiosity-driven theoretical research even in the Indian context. One interesting example to stress the importance of theory is, today, that the best numerical weather prediction is in mid and high latitudes in winter. This is because the basic theory behind the mechanism of winter weather changes, the baroclinic instability, was discovered more than 70 years ago by Charney (1947) , and models and observations evolved accordingly. Thus, it is important to realize, without the correct understanding of the causal mechanisms through theory, one will never be able to predict correctly and completely the required weather or climate or its changes with just the brute force of computers available today!!!

TCs Over the NIO

Regarding the theory of the generation mechanisms of TCs, there are two well-known hypotheses, namely, (a) the conditional instability of the second kind (CISK) and (b) wind-induced surface heat exchange (WISHE) (please refer to Tomassini, 2020 for a comprehensive discussion of these two processes). A detailed discussion of these two is beyond the scope of the present short article. However, the authors quickly discuss these two mechanisms in the context of TCs over NIO.

In the case of TCs, the pre-synoptic disturbances get their energy by the complex interaction of two different horizontal scales, namely, cumulus convection of about 1 km and synoptic systems of about 500 km. How this interaction happens is a topic of debate, though, and most of the research in the published literature is about TCs in tropical ocean basins other than the NIO region.

Briefly, we discuss the basic characteristics of CISK and quasi-equilibrium (or WISHE). In the process of CISK, the buoyant convection can occur only when low-level stability is weakened (see Figure 2 ; Ooyama, 1969 ), and in the other, moist convection is governed by the vertically integrated measure of instability. As noted by Tomassini (2020) , meteorological conditions vary greatly from one region to the other in the tropics and also in the same region from one season to another (see Ashok et al., 2000 ; Rao et al., 2000 ; Raymond et al., 2015 ). Raymond mentions two tropical places, Sahel and the Western Pacific, where conditions are very different. Now, how do the conditions vary, during (i) pre-monsoon, (ii) MDs, and (iii) post-monsoon TCs? Similar to Bony et al. (2017) , we suggest that more detailed observations of both satellite measurements and data developed in field programs should be used to understand the convection and circulation coupling of TCs over NIO. For example, the INCOMPASS IOP field program, which collects data from strategically installed ground-based instruments in India, is one such program ( Fletcher et al., 2018 ).

Another, synoptic disturbance of importance is a MD. Despite several observational and theoretical studies by many authors (for example, Sikka, 1977 ; Mishra and Salvekar, 1979 ; Aravequia et al., 1995 ; Boos et al., 2017 ) trying to understand the basic mechanism of origin, some fundamental questions remain unanswered. Similar to TCs, the lack of understanding of how convection and MD circulation couple hinders the prediction. For both TCs and MDs, we suggest analyzing time vertical sections of potential temperature, equivalent potential temperature, and saturated equivalent potential temperature such that one can get an idea of the relative importance of CISK or the quasi-equilibrium hypothesis discussed briefly above.

Another method for elucidating the study is to examine the system's energetics, i.e., TCs or MDs. Lorenz (1960) mentions, “one enlightening method of studying the behaviour of the atmosphere, or a portion of it, consists of examining the behaviour of the energy involved.” Earlier Mishra and Rao (2001) used limited area energetics to infer the mechanism of generation of Northeast Brazil's upper tropospheric vortices. Also, Rao and Rajamani (1972) examined the energetics of MDs. These methods of energy analysis, for example, can be used to isolate or single out the basic mechanism of generation of TCs or MDs, using more recent well-covered data, such as the INCOMPASS IOP program ( Turner et al., 2019 ). Later, targeted numerical model studies should be used to not only verify the process/processes identified in energetic and diagnostic studies, but to design dynamics-based indices related to TC formation that are relatively easier to predict. For example, a CISK parameter may be easier to predict with a longer lead as compared with the TC rainfall. These methods are again akin to the KUM approach. Such carefully verified and designed indices, when operationalized, will substantially help in extending the lead prediction time. Probabilistic dynamical-statistical downscaling tools can also be developed to relate local rainfall with these indices. This will also potentially enhance the lead time of the TC-related deluge. Similarly, a better understanding of model ability in capturing the conversions between different forms of energy.

Again, several aspects of monsoons, particularly, the Indian Monsoon are still not completely clear and hinder the mechanisms of prediction. In a recent exhaustive study, Geen et al. (2020) , discussed several aspects, primarily from a theoretical standpoint even though this study was developed based on the concept of a global monsoon, Figure 2 of Geen et al. (2020) shows only a very low correlation in interannual variations of rainfall, the main meteorological element that must be predicted. However, the different regions of monsoons with different geographical boundaries raise serious objections about the global monsoon concept.

Several studies exist in the literature regarding the observed aspects of the Indian summer monsoon (the K part of the three-pronged method), and modern numerical models are employed to improve prediction skills ( Sahai et al., 2016 ; Rao et al., 2019 ; Mohanty et al., 2020 ). From an almost zero skill, we have reached a stage at which the skills for predicting the area-averaged Indian summer monsoon are found to be statistically significant. This is great progress. Having said that, there is a great scope for further improvement. Although the broad regionally averaged skills are statistically significant, they are modest. Further, improving the skills such that they are locally useful is the obvious goal but still a long way ahead. Although the prediction skill improved through better methods of, for example, data assimilation and parametrization schemes, to improve the predictions further, we need to diagnose the improved representation (e.g., Halder et al., 2016 ; Saha et al., 2019 ; Hazra et al., 2020 ), better replication of physical processes and scale interactions.

Notwithstanding all these technical improvements, the large-scale physical causal mechanisms are not clear yet. This can only be done with the studies aiming to understand the cause-and-effect relation or the U in the three-pronged method. As mentioned earlier, with more observational studies aiming to identify the correct interaction mechanism over NIO between convection and large-scale monsoon circulation (either CISK or WHISE), then this mechanism can be included in the numerical models. Also, controlled experiments using simple models, such as the one by Rao et al. (2000) , can be used to identify relative roles of mountains and thermal contrast in generating the Indian summer monsoon. In the state-of-the-art coupled models, because of extremely complex non-linear interactions among various physical mechanisms, it is almost impossible to isolate the cause of a specific effect.

Again, the diagnostic study based on energetics, such as the generation of available potential energy (PE) by latent heat and the baroclinic conversions, for example, may reveal relative roles of some physical processes, such as convection in the Indian monsoon. In a recent companion study (Rao et al., under review), comparing the South American and Indian monsoons, we found that, in the Indian monsoon, the baroclinic conversions P ¯ (mean available PE) to P ′ (eddy PE) to kinetic energy (KE) is non-existent, and the KE of monsoon is mainly furnished by the generation of perturbation PE by latent heating (rainfall) and subsequent conversion to KE. In contrast, over the South American monsoon, both the baroclinic conversions and generation terms are equally important. This is probably because the Himalayas extend from East to West across the cardinal northern border of the country, which does not allow mid-latitude baroclinic waves to penetrate at lower levels while the Andes mountains in South America extend along North to South, permit these waves to penetrate even as low latitude as Manaus, where even austral summer cold waves (FRAIAGENS) are noted. Furthermore, studies are necessary to verify how energetics vary between wet and dry monsoons in these two regions.

In a review article by Geen et al. (2020) , the authors discuss attempts to understand fundamental dynamics (U in our three-pronged method). Geen et al. (2020) mention a very similar KUM approach for monsoons (their section 3). Such efforts are urgently needed from the context of the Indian monsoon. They even discuss the south Asian monsoon (their section 3.1.2). Although they tried to reconcile between global and regional monsoon features, the differences are more striking as we mentioned earlier, regarding the Indian and South American monsoons. In the case of the East Asian monsoon, at least one author ( Molnar et al., 2010 ) mentions, “‘monsoon' is somewhat of a misnomer.”

Although there are some uncertainties in the methods used by Lange et al. (2020) , the importance of their conclusion is unambiguous. They mention that “anthropogenic” climate change has already substantially increased the exposure to extreme global climatic impacts, and anthropogenic warming is projected to exacerbate the pattern of climate change that we are already noticing nowadays. Thus, it is urgent to restrain the increase in global average temperature well below 2°C, which would significantly reduce the risks and impacts of climate change 6 ( Benitez, 2009 ; Dash et al., 2013 ). All this, therefore, underscores the urgency for climate action expressed in the Paris agreement of 2015. Even in a climate change context, using the KUM approach will help in a better diagnosis of the changes in regional implications for large-scale instabilities to diabatic processes. These can help in design model-based indices that can inform the stakeholders working on climate change mitigation and adaptation.

Recommendations

We are in an era in which observational data availability in the tropics has improved significantly and is going to be further improved. In this context, it is recommended that the forecasters and researchers of Indian weather and climate use this excellent opportunity to build theoretical knowledge unique to the regional weather and climate. The knowledge gained should be translated to identify tangible, large-scale dynamical process indices. Such indices will be very useful to extend the lead prediction skills of important weather and climate phenomenon, such as TCs, MDs, etc. Similarly, (i) evaluating the model capacity in predicting and calibration of association between hindcast perturbation PE, latent heating, and subsequent conversion to KE, and (ii) comparing the observations will potentially provide us with indices that can be directly used to predict subseasonal monsoonal rainfall with longer leads. The above recommendations are just examples. In summary, identifying the key dynamics behind important weather and climate processes at discernible time scales and designing useful dynamical indices that can be used to extend the lead forecast envelope will be the way forward.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://tropical.atmos.colostate.edu .

Author Contributions

VB conceived the idea. VB wrote the manuscript with inputs from KA and using the results from DG analysis. KA comprehensively revised the article. All authors contributed to the article and approved the submitted version.

The publication charge of this article is fully funded by the Frontiers in Climate Journal.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors thank Prof. Matthew Collins, Specialty Chief Editor, Frontiers in Climate Journal, and reviewers for their helpful feedback and recommendations in improving the manuscript quality. The authors are grateful to the Frontiers in Climate Journal Committee for waiving the article's publishing fees. We thank the reviewers for their critical comments, which helped to improve the quality of the article.

1. ^ World focus-special issue July 2014, editorial (peer-reviewed, refereed research journal).

2. ^ https://www.gfdl.noaa.gov/global-warming-and-hurricanes/

3. ^ https://www.bbc.com/news/world-asia-india-52749935

4. ^ https://en.wikipedia.org/wiki/2020_North_Indian_Ocean_cyclone_season

5. ^ https://mausam.imd.gov.in/Forecast/marquee_data/indian111.pdf

6. ^ https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement

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Keywords: KUM method, extreme weather, human suffering, tropical cyclone, monsoon, Indian summer monsoon (ISM)

Citation: Brahmananda Rao V, Ashok K and Govardhan D (2021) Unprecedented Climate Change in India and a Three-Pronged Method for Reliable Weather and Climate Prediction. Front. Clim. 3:716507. doi: 10.3389/fclim.2021.716507

Received: 28 May 2021; Accepted: 04 October 2021; Published: 15 November 2021.

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Copyright © 2021 Brahmananda Rao, Ashok and Govardhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Vadlamudi Brahmananda Rao, raovadlamud@gmail.com orcid.org/0000-0001-5905-9806

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Climate-responsive vernacular Wada housing of Pune, India

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Mihir N. Vakharia , Mahendra Joshi; Climate-responsive vernacular Wada housing of Pune, India. AIP Conf. Proc. 8 September 2023; 2800 (1): 020190. https://doi.org/10.1063/5.0162691

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Buildings consume more energy, and this is predicted to continue to rise as people’s living standards improve and the world’s population grows. Energy saving may be done by incorporating proper Climate sensitive components into a structure. Vernacular building design links with microclimate and human thermal comfort conditions and enhances the energy efficiency of the building. People have implemented climate sensitive approaches in vernacular structures all across the world since prehistoric times. Vernacular architecture has many different forms, each with its own set of reasons. This is based on the premise that people from various origins and cultures react differently to a variety of physical situations as well as the interaction of socio-cultural elements. The climatic responsiveness of Wada Architecture in the Pune region of India is discussed in this study, which includes building shape and orientation, envelope design, shading, natural ventilation, interior space layouts, and inhabitants’ activities.

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Climate Change Is Stretching Mumbai to Its Limit

Facing sea-level rise, flooding, and landslides, the city’s residents are finding resilience—because they have little other choice.

R ain had been falling for several hours on the night of July 18, 2021, when the Tiwaris called their relatives and told them to evacuate their house.

The Tiwaris live in suburban Mumbai, in the hillside shantytown of Surya Nagar, and their relatives were perched in one of a row of single-room tenements atop the steep terrain. Some of those homes had collapsed in a landslide two years earlier and had been recently rebuilt. Now, as the rain thundered on, the Tiwaris began to worry.

Suddenly, the slope disintegrated into a torrent of mud and rock, the sound of the slide drowned out by the heavy rain. Before the Tiwaris’ relatives could make it to safety, their ceiling collapsed. Mud and debris washed down to the Tiwaris’ door. They struggled to get out of their own house, and then out of their narrow lane. By the end of the night, 10 people in Surya Nagar, including three children, had been crushed to death. The Tiwaris lost three family members, and 21 more lives were lost in another landslide nearby.

By the end of last year’s monsoon season , an estimated 50 Mumbai residents had died in landslides or wall and house collapses triggered by heavy rain—one of the city’s worst tolls in recent memory.

In the geography of climate risk, some places are more vulnerable than others, and coastal megacities like Mumbai face the combined threat of rising sea levels and extreme weather events. As their populations expand—by 2050, most of the world’s people will live in urban areas—the paving over of permeable soil for houses and roads further increases the risk of flooding.

Like the rest of India, Mumbai is no stranger to what headline writers like to call “monsoon fury.” The city receives an average of about 94 inches of rain annually, more than double New York’s rainfall, and most of it arrives during the four-month rainy season. For years, the city and its residents have met the monsoon with precautionary measures including the clearing of municipal drains and the plastering of leaky roofs.

Those measures have never been quite enough: The season has long been marked by disruptions in train services, upticks in water-borne diseases, and occasional landslides and building collapses. Mumbaikars have tolerated these hazards in exchange for the economic opportunity offered by India’s commercial capital. People here are known for getting back to work quickly after a disaster, whether the disaster is a terrorist strike or a deluge.

But climate change could stretch Mumbai’s fortitude to its limits. Severe flooding used to occur once every few years. Now, intense-rainfall events occur almost every year. As the number of cyclones in the Arabian Sea increases, sea levels rise, and the city continues to sprawl over floodplains and hills—from 1991 to 2018, the city lost 58 percent of its already limited open space—Mumbai is routinely ranked high on lists of the world’s cities most vulnerable to climate impacts.

City authorities, now finalizing a climate action plan, must confront long-standing inadequacies in housing, drainage, and sanitation, and resolve historical tensions between development interests and environmental protection. Working-class communities in hillside areas such as Surya Nagar may have to think about eventual relocation, however difficult. At every level, Mumbai is facing new dangers and new decisions.

L ike many coastal settlements , Mumbai stands on land hewn from water. In the 18th and 19th centuries, British colonists leveled the hills on the small islands of what is now called the Mithi River estuary, using the resulting debris to join the archipelago into a narrow peninsula on the northwest coast of India.

One British official, describing how the original seven islands had been shaken loose from the mainland by tectonic shifts, suggested that the reclamation was fated. “Providence … decreed,” he wrote, “they should be once more united by the genius and energy of man.”

Despite the location’s challenges—malarial swamps, a lack of fresh water, and the need to build bunds and embankments to protect areas at or below sea level—the city became one of the most important ports in the region, a magnet for trade, industry, and labor. “Is it not an astounding feat,” marveled an Indian writer in 1863, “to recover the land from the sea and make it habitable and free of disease and earn lakhs [hundreds of thousands] of rupees in the process?”

During the 20th century, Mumbai expanded to accommodate its growing economy and population. More creeks, streams, and mangroves began to vanish under roads, buildings, and sewage. But every year, the rivers reminded the city of their existence. From June to September, the southwest monsoon sweeps up the west coast of India and into the hinterlands, filling lakes and reconnecting rivers with the sea. For Mumbai, one of the world’s most crowded cities, the season’s cool, clean air and leafy shade is a relief—but it’s also a warning, especially in neighborhoods where the tides once flowed.

The city’s defensive rituals, already well established, have multiplied in recent years. Before the monsoon, people buy umbrellas and plastic footwear. Apartment-building owners and residents repair or waterproof their walls and roofs. During the monsoon, shops in low-lying neighborhoods remove merchandise from the bottom shelves. Commuters brace for traffic or train disruptions and parents look out for school cancellations.

All kinds of improvised measures are on display in Kranti Nagar, a settlement of old and new migrants sandwiched between the airport and a series of metal scrap yards on the banks of the Mithi River. On a weekday in September, the sky is gray and the ground dark from a morning drizzle. Tarpaulins cover Kranti Nagar’s roofs, and clothes are hanging out to dry as best they can. Inside the maze of single- and double-story brick-and-tin tenements, it’s impossible to tell that a river runs nearby. But the residents know.

In a tiny room close to the river, Ranju Devi, a mother of two, reaches up to the light switch just above her head, a little more than five feet from the ground. That’s how high the water can rise, she tells me, when high tides and heavy rain combine and the river swells beyond its banks. Devi and her husband store the family’s clothes and documents on a high shelf so that important possessions don’t get ruined. When the water rises, they take their children and some food to the municipal school, which is located on higher ground nearby. Sometimes, they have to move quickly—one night, she says, she woke up to water at her feet.

The Sonar family, a few twisting lanes away, can afford a double-story tenement. They watch the news for weather alerts and know their escape routine: first switch off their lane’s power mains, then move their belongings and themselves upstairs. On the second floor, they’re protected from electrocution and drowning, but they’re still exposed to the chemicals and sewage in the floodwaters, which cause outbreaks of gastroenteritis, and the malaria and dengue that spread as the water stagnates. But Tulsa Sonar, the family matriarch, doesn’t see a way out of the neighborhood: Kranti Nagar is in the heart of the city, surrounded by schools, hospitals, small factories, and offices. A family of 11, the Sonars would either have to pay three to five times as much to live in more secure housing in the same area or endure long commutes to the city’s schools and jobs. Besides, their local elected representative has promised them a safer home nearby.

The Sonars first moved here from Nepal in search of work in the mid-’70s, when Tulsa was a teenager, and she says there was less flooding then. The problems started when the regional planning agency reclaimed hundreds of acres of mangrove-covered floodplains downstream and covered the newly elevated land with glass-and-steel office complexes. Then airport authorities extended the airport’s runways, narrowing and bending the Mithi River. More settlements and small factories rose along the riverbanks, their sewage and effluent further choking the river’s flow. More recently, the city raised the main road near Kranti Nagar, creating a steep slope to the riverbank settlements.

Some wealthy neighborhoods face flooding too. In a prosperous seaside housing development in the western suburb of Khar, ground-floor residents such as Shalini Balsavar move their clothes and valuables to higher shelves during the monsoon. Balsavar has swapped her wooden furniture for sofas and tables with aluminum legs. Flooding in the area started in the ’80s and ’90s, her daughter Reetha tells me, when settlements and residential buildings replaced mangrove stands along the shore, reducing the capacity of the land to drain water. In the 2000s, the problem was aggravated when the city raised the main road, increasing water flow into the Balsavars’ property. Some ground-floor residents in the area have left, while others hope to add extra floors to their building.

The Balsavars own all three floors of their building, so during a bad flood, Shalini can easily move to safety. “We have an alternative,” says Reetha, who lives on the second floor. “Others are not so lucky.”

That “luck” is becoming more important. Instead of steady rain through the monsoon season, Mumbai now experiences more days of heavy rainfall, defined as more than two and a half inches in 24 hours, interspersed with long dry breaks. Throughout western India, extreme-precipitation events increased threefold from 1950 to 2015 due to an increase in atmospheric moisture from a warming Arabian Sea. Research suggests that the short bursts of extreme rain that trigger flash floods and landslides will continue to increase as temperatures rise. The city itself may amplify these trends: Local scientists have found that clusters of concrete structures generate warmer temperatures and atmospheric instability that could be intensifying monsoon rainfall.

And there are new threats: From 2001 to 2019, rising ocean temperatures led to a 52 percent increase in the region’s cyclone frequency and a 150 percent increase in the number of very severe storms, while cyclone duration increased by 80 percent. Mumbai has not suffered a serious hit from a cyclone since 1948, but a few storms have recently come close.

For reasons scientists don’t fully understand, the monsoon season is also ending later, meaning that city residents must stay vigilant into the fall. “We’ve never been flooded in October,” says Kranti Nagar’s Tulsa Sonar, “but this year, who can tell?”

O n July 26, 2005 , three feet of rain fell on Mumbai, taking more than a thousand lives in flash floods and landslides and causing millions of dollars in damages. In many areas, residents were rescued from rooftops and couldn’t return home for days. Though flooding had been increasing for a decade, the deluge awakened Mumbaikars to the geography of their city—its hemmed-in streams and rivers and its vulnerability to the tides—and the dangers of the monsoon.

Since 2005, civic authorities have spent more and more money on flood-mitigation measures, largely due to prodding from citizens’ groups and judicial orders. In recent years, they’ve begun installing floodgates and pumping stations along parts of the seashore—only six of the city’s 174 stormwater outfalls lie above the high-tide line, so when heavy rain combines with high tide, gates are needed to stop tidal inflow and pumps must physically push rainwater out. Authorities have also set up smaller water pumps along parts of the Mithi River and are experimenting with large underground tanks designed to catch and store water below one of the city’s lowest-lying areas. A long-delayed plan to expand the capacity of the city’s century-old stormwater drains has been revived and updated. And the desilting and unclogging of open drains, streams, and rivers increases every year: By the end of last year’s monsoon, workers planned to remove nearly 220,000 tons of gunk from the Mithi. The city has built retaining walls along some rivers and has improved weather monitoring and disaster-response systems. Now when high tide coincides with heavy rain, evacuation alerts are issued to riverside settlements such as Kranti Nagar.

No one knows the limitations of these measures better than Mahesh Narvekar, the head of the municipality’s disaster-management unit. Set up in 2000, the unit became active after the 2005 floods and has been expanding since. The department now runs a state-of-the-art control room in the municipal headquarters, where staff monitor feeds from 60 automated weather stations; 147 hospitals; 5,000 CCTVs; and social media.

During the monsoon season, staff must coordinate responses to instances such as landslides, housing collapses, tree falls, and power outages. Inside the department’s headquarters, an official shows me old CCTV footage of a tree falling on a moving car; passersby leap into action to rescue the motorist. In another video, a car drifts into a flooded street while bystanders watch to see if they need to intervene. “See how our Mumbaikars respond,” the officer remarks proudly, adding that more people should receive emergency training.

Over the past two decades, the unit has dealt with not only floods and landslides but multiple terrorist strikes and a global pandemic. “Disaster response is okay; we can do it. We have the experience,” Narvekar says. But he’s worried about the future. Though the department is working to communicate more quickly with local residents, expand backup-power supplies, and improve its hazard mapping, that may not be enough to protect residents from possible superstorms, or the roughly six inches of sea-level rise expected by 2050.

Even expanded drains and river dredging can do only so much. “How much can you expand [stormwater] pipes and widen streams in a city that’s so densely developed?” he asks. What’s needed, he believes, is a paradigm shift—a climate-adaptation plan equal to Mumbai’s future. “You can’t stop excess inundation,” he says. “The whole city must become a drain.”

O n August 27 , state environment and tourism minister Aaditya Thackeray, the environment and tourism minister of the state of Maharashtra, launched the Mumbai Climate Action Plan website in coordination with municipal officials, touting it as the first such initiative of its kind in urban India and South Asia. At the launch, the municipal commissioner, Iqbal Singh Chahal, noted that most of July’s rainfall had fallen in just four days and that cyclones in the region were increasing in frequency. He warned, rather hyperbolically, that much of the office and government district, located in the historic southern tip of the city, could be “underwater” by 2050. Climate change “has come to our doorstep,” he said.

The India office of the Washington, D.C.–based World Resources Institute was entrusted with helping design the climate plan, and its first step was to hold a series of public consultations with local groups and experts. Lubaina Rangwala, the program head of the urban-development-and-resilience team at WRI India, told me in September that her objective was to come up with a “high-level road map” rather than a detailed plan. Engineering solutions such as sea walls, pumps, and underground tanks are important, she says. But like Narvekar, the head of the disaster-management unit, she doubts they will suffice in the long run. “Infrastructure is designed for certain thresholds,” she says. “A tank has a capacity; it can hold a certain amount of water until the tide ebbs and the water can drain.” But if rainfall or tides are extreme, that capacity may fall short, she notes. “The uncertainty of extreme occurrences is what makes us believe that [engineering solutions] won’t be enough.”

In recent years, local architects and researchers have pointed out that walls and other barriers can harden the battle lines between land and water, and have argued that the rivers and sea need to be integrated into the urban landscape—by, for instance, maintaining natural riverbanks that can help absorb overflow. “We do need to see the city as an estuary,” Rangwala says. “We need to think about the percolative nature of the land, and about protecting the natural infrastructures of mudflats and wetlands.”

Development interests have long stood in the way of such measures. After the 2005 disaster, for instance, a high-level state-government committee laid out a series of measures for mitigating floods. Although the city implemented engineering solutions such as pumps, drains, and retaining walls, the recommendations that carried even short-term costs for development interests—such as flood-risk zoning around waterways that would affect the real-estate market—were ignored.

Rangwala acknowledges that in a city driven by commerce, systematic reforms are difficult. But she thinks the political moment is ripe for progress, and not only because of the new global attention on climate change. “We used to say environment was anti-development; now we talk of the two in tandem,” she says. “That change has happened with this [state] government coming in.”

Thackeray, the state environment minister, is a scion of one of Mumbai’s most prominent political families. His grandfather founded the Shiv Sena, a nativist party known for decades for its attacks on migrants from the rest of the country. In 2019, the party took power in the state government after a prolonged tussle with the Bharatiya Janata Party, its erstwhile ally and the party of Prime Minister Narendra Modi. Though the Shiv Sena has dominated the city’s government for two decades, its greater power in the state—along with the rise of new, younger leaders—appears to be reshaping its agenda. Thackeray’s father, Uddhav, the head of the state government, enjoys wildlife photography, and his sons are also interested in ecology: Thackeray’s younger brother, Tejas, a wildlife researcher, has discovered several new species—including a swamp eel, a snake, and a lizard—in the state’s lush and underdocumented Western Ghats mountain range.

The younger Thackerays’ interest in the environment is partly generational, says D. Parthasarathy, a sociology professor at the Indian Institute of Technology Bombay, an elite research institution. Young people in India and around the world are more concerned about climate change and environmental issues, and 31-year-old Aaditya was one of the few state-level Indian politicians to attend the COP26 talks at Glasgow. But the agenda is also politically strategic: Before they took office, both Aaditya and his father backed a popular residents’ movement to save a piece of suburban forest from an infrastructure project, much to the annoyance of their then-allies. They promised to clean up a massive and polluting landfill in another part of the city that had become a political issue for the local community. “The [state] government came to power on such issues,” Parthasarathy says. “They are under pressure to fulfill their promises too.”

That doesn’t mean they can or will break with business as usual in Mumbai. The Shiv Sena government is not halting one of the city’s most controversial and expensive projects, a 29-kilometer coastal freeway along Mumbai’s western waterfront. The road has been opposed by environmentalists and architects who believe it could lead to coastal erosion and flooding while serving relatively few commuters, and by local fishing communities that say their fishing grounds are being destroyed by extensive reclamation. It’s also not clear how the government will handle recently relaxed national-government regulations that permit more development along the coastline. Political parties need funds, points out Parthasarathy, which makes it risky for them to alienate the city’s wealthy developers. He adds that infrastructure projects have their own political logic: “There’s an imagination to [infrastructure], a sense that it represents modernity.”

M ore than a month after the landslide that killed their relatives, the Tiwaris were still staying with friends in a nearby community. But their neighbors, the Vishwakarmas, had returned home to Surya Nagar, ignoring the warning that the city had pasted on their door. They are a three-generation family of seven. “How long can we stay with friends?” shrugged 29-year-old Sudhir Vishwakarma, the youngest son.

Sudhir and his friends were among the first responders to the landslide; they helped evacuate people and, later, dig out bodies. Ambulances and earth movers couldn’t access the site without destroying homes. The boys didn’t sleep for days, and hardly ate. Since then, every time it rains at night, families in the neighborhood keep their doors open and call out to one another.

Many climate-adaptation projects talk of making cities and communities more “resilient,” more able to cope with or adapt to extreme events like floods and heat waves. But without accompanying efforts to address social vulnerabilities, initiatives to increase resilience can place the burden of adaptation on individuals and on local communities, especially in developing countries, Parthasarathy says.

Mumbaikars’ ability to help one another in times of crisis or bounce back after a disaster is often celebrated by political leaders and the media. But “the spirit of resilience exists out of compulsion,” Parthasarathy says, “because the state is not doing its job. The people must cope somehow.” Resilience also has hidden costs, he adds. Even people who respond stoically to chronic hazards—“water comes and water goes,” as one resident of Kranti Nagar told me—lose time and money in dealing with them, and may sacrifice their health. Interruptions in schooling become more frequent, and saving money becomes more difficult. “People who are busy surviving aren’t able to invest in the future,” Parthasarathy says.

Parthasarathy prefers to use the word transformation . Instead of adapting to and coping with a particular hazard, he says, “we need to reimagine the city and the idea of development.” His research group is working with officials and local groups on a mangrove-restoration project to achieve both environmental and social objectives. Mangroves can mitigate flooding while also providing a livelihood to local fishers, he notes.

For the people of Surya Nagar and Kranti Nagar, transformation might mean moving to safe and affordable housing elsewhere in the city. Because of the lack of affordable options, people who are evicted from hazardous neighborhoods often go and live in even more vulnerable ones, notes Roshni Nuggehalli, the executive director of YUVA, a nonprofit that works with the urban poor. Over the past few decades, the government has tried to incentivize developers to rehabilitate informal dwellings—but many of the resulting housing projects have been poorly constructed or unsanitary. “What we need to address is not the climate event,” Nugehalli says, “but the systemic things that aggravate the climate event.”

A fter the landslide in Surya Nagar , city authorities quickly announced compensation to the families of those who died—several hundred thousand rupees per life, enough to support a poor family for a year or two but of little comfort to the bereaved. One man lost his wife and two children; he survived only because he happened to be working that night. “If there are no people, what’s the point of money?” asks Jaya, Sudhir’s sister-in-law.

When Sudhir’s late father moved here from northern India more than 30 years ago, the neighborhood was set among forested hills and mangrove stands and surrounded by new factories with jobs for migrant workers. He built a good life for his wife and children, and Sudhir, now an engineer, is one of the first professionals in the family. But he still can’t afford to live in the upscale residential complexes that now crowd the neighborhood.

Sudhir and his neighbors don’t know much about climate change, but they do sense that their home is becoming more dangerous. Many point out that though the 2017 landslide took two lives, last summer’s took 10. (Local officials and politicians likely knew of the danger: In early 2021, an internal report warned of the risk to precariously perched settlements in the area.) Yet most are not ready to leave the neighborhood. Nearby homes down the slope or on level ground are more expensive, and the pandemic’s toll on income and work has put them even further out of reach. Cheaper digs are distant. What they want is for the municipality to protect Surya Nagar with a strong retaining wall. And they seem likely to get it: After the landslide, Aaditya Thackeray directed authorities to speed up retaining-wall construction in unstable areas.

Yet a wall did not help Ambedkar Nagar, a much poorer settlement on the other side of the hill. Its shanties stand above a network of apartment complexes, and just below a water reservoir surrounded by forest. In the 2010s, authorities built a 15-foot-high boundary wall to protect the reservoir from urban expansion. But on one extremely rainy night in 2019, water built up behind the barrier and then broke through. The torrent, carrying chunks of concrete, swept away the bamboo and tarpaulin shanties below, killing about 30 people and injuring another 130. Later, an audit commissioned by the city found that the wall had been poorly designed and constructed. Eighty-six families who had lost homes and relatives were rehoused in low-income developments on the other side of the city; 75 more are still waiting to be relocated.

A few residents have refused to move, including Bomba Devi, a mother of three. When she came to Ambedkar Nagar in the mid-’90s, it was not unusual for leopards from the nearby forest to prowl the hillside. Though she lost a young granddaughter in the flash flood of 2019, she rejected the alternative housing because it was located next to chemical plants and a refinery, and residents there had already fallen ill. (Residents’ health problems were so serious that they sued, resulting in a court decision that blocked the city from relocating people to the area.) Besides, her son works in the local packing firm and her grandchildren go to the nearby public school. To protect her home, she and her children have created small channels and drains in the mud floor that direct the water downstream. Even so, their home is flooded with a foot of water every year.

In May, a cyclone brought record rain and winds strong enough to dislodge some boulders above the neighborhood. The residents took refuge in a clearing of sorts—a slightly elevated, flat patch of land kept dry with a thatched roof and strategically dug channels. It was safer than being inside their homes, says Moli Sheikh, Bomba Devi’s neighbor, adding, “We draw strength from being together.”

The morning I visited, more than a dozen residents were sitting in the clearing, participating in an ongoing sit-in. They are demanding that the wall be rebuilt and alternative housing be provided nearby. Many of them had paid into a public-housing scheme intended to provide them with new homes, but so far they have received nothing. “Every time there’s an incident, they come and do a survey” of the damage done and the families that need to be relocated, Sheikh says, pointing to the row of numbers that officials have chalked on his door. “What are they waiting for?”

This Atlantic Planet story was supported by the HHMI Department of Science Education.

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Mangroves help fight the effects of climate change. so why is mumbai destroying them.

Sushmita Pathak

Mangroves by the water in Mumbai. Bhaskar Paul/The India Today Group/Getty Images hide caption

Mangroves by the water in Mumbai.

Why Mumbai Needs More Mangroves

Bare trees with slender branches line a half-built highway overpass in eastern Mumbai. These are mangroves, trees or shrubs found in tropical swampy marshland with roots that grow above the ground. But construction has blocked their lifeblood — salt water. Their aerial roots poke through dry, caked mud instead of brackish water.

Environmentalist B.N. Kumar points to a small channel under the highway where seawater once entered the mangrove patch. It's now littered with rocks and construction debris.

"All the mangroves, about 5,000 of them, have dried up. They can only be used as firewood now," Kumar says. "It's very sad to see these mangroves dying like this."

The roots of mangroves, poking through swamp mud, serve as breeding areas for fish. Sushmita Pathak/NPR hide caption

The roots of mangroves, poking through swamp mud, serve as breeding areas for fish.

Thousands of acres of velvety green mangroves line the border between the Arabian sea and the city of Mumbai in western India. They act as natural buffers against coastal erosion and flooding, and they store up to four times as much carbon as other forests. With sea level rise inevitable, Mumbai's mangroves are more important now than ever. A new report by climate change researchers predicts much of Mumbai, which is India's financial capital, will be underwater by 2050 if global carbon emissions aren't reduced. The city, originally a cluster of seven islands, is especially vulnerable. Many parts of it have been built by reclaiming land from the sea.

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Mangroves, climate change and hurricanes.

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"At a time when we require more and more mangroves, we are destroying, unfortunately, more and more mangroves," Kumar says.

Studies show that Mumbai lost nearly 40 percent of its mangroves between 1991 and 2001 — about 9,000 acres. And rapid urbanization continues to threaten them.

A short car ride from the dried mangroves along the highway, a municipal garbage truck dumps trash on the edge of a mangrove patch. Sludge and plastic waste cover the roots of the mangroves, slowly choking them. In another area nearby, hundreds of acres of mangroves are being cut for the construction of the Navi Mumbai international airport.

Mangroves (green) were plentiful in this Google Earth image from Nov. 12, 2003 showing the site of the future Navi Mumbai International Airport (left). By Nov. 12, 2019 (right), the development work for the airport had wiped out hundreds of acres of mangroves. Google Maps hide caption

Mangroves (green) were plentiful in this Google Earth image from Nov. 12, 2003 showing the site of the future Navi Mumbai International Airport (left). By Nov. 12, 2019 (right), the development work for the airport had wiped out hundreds of acres of mangroves.

One of India's most glamorous infrastructure projects, the country's first bullet train — which will run between Mumbai and Ahmedabad, in the western Indian state of Gujarat — is estimated to destroy at least 32,000 mangroves .

Journalist-turned-activist Kumar, who runs a blog called The Nature Connect , has raised concerns about mangrove destruction with authorities, including the Japanese government agency that is helping build the bullet train. Kumar and other activists organized an exhibition in Mumbai earlier this year displaying large posters about the environmental impacts of such projects.

Kumar says environmentalists are often branded as anti-development, especially when they oppose projects like the bullet train, which, for many Indians, is a source of pride.

"We are not against any development," Kumar says. "Our question is, does it need to happen at the cost of the environment?"

People need to understand that they cannot survive without nature, says Debi Goenka, executive trustee of the Mumbai-based nonprofit Conservation Action Trust . "Just chasing the mirage of GDP growth is not development," he adds.

Authorities say they will plant five mangroves for each one that is cut for the bullet train. But activists say promises about replanting are a sham.

"There is actually no land [within the city] to replant mangroves, no suitable habitat available," says Goenka. On the rare occasion that mangrove restoration does happen, most of the saplings don't survive, Goenka adds.

With these mangroves gone, Mumbai will be left without a vital line of defense when natural disasters strike. And that has happened before.

In 2005, when the city experienced unprecedented monsoon rainfall leading to catastrophic flooding, one of the worst affected areas was a commercial hub in central Mumbai, full of shopping malls and skyscrapers. It's been constructed by reclaiming low-lying areas on the banks of the Mithi River, previously home to a sprawling mangrove forest that acted as a natural stormwater drain.

While floods were ravaging most of Mumbai, Nandakumar Pawar recalls being surprised that his fishing village in a northeastern suburb of Mumbai escaped the worst. A more than 2,000-acre stretch of mangroves nearby acted as a sponge to hold water and didn't allow it to flood his village, he says.

"That was a truly eye-opening incident for me and my community," Pawar says.

In a creek lined with mangroves in Mumbai, boats wait to take tourists to spot flamingos and other migratory birds. Sushmita Pathak/NPR hide caption

In a creek lined with mangroves in Mumbai, boats wait to take tourists to spot flamingos and other migratory birds.

As a fisherman, Pawar already knew mangroves are breeding grounds for fish, which lay their eggs on the roots. He realized mangroves were crucial not just for the fishing community but for everyone.

He started a nongovernmental group called Shree Ekvira Aai Pratishthan (named for a local deity) that has enlisted fishermen to help with mangrove and wetland conservation. When they go out to fish, they act as mangrove vigilantes. If they spot illegal activities, like debris or garbage dumping or illegal encroachment in mangrove areas, they alert the organization, Pawar says.

Authorities appear to be slowly realizing the urgency of mangrove conservation.

In 2012, the government in the state of Maharashtra, where Mumbai is located, set up a mangrove conservation unit , the first in India. The unit helps regenerate mangroves through a variety of projects, explains former range forest officer Seema Adgaonkar, as she walks through a nursery she helped set up in a northeastern suburb of Mumbai. The gnarled roots of the mangroves rise out of damp, mossy earth and pools of tidal water.

Adgaonkar rattles off the names of mangrove species that used to found in the wild in Mumbai but can only be seen in a protected nursery now. It's part of the city's first mangrove and coastal biodiversity tourism center, which offers regular tours, including flamingo safaris, to heighten awareness.

Seema Adgaonkar, 57, helped set up this mangrove nursery when she worked as a forest ranger with the state mangrove conservation unit. Sushmita Pathak/NPR hide caption

Seema Adgaonkar, 57, helped set up this mangrove nursery when she worked as a forest ranger with the state mangrove conservation unit.

The unit also deploys security officers armed with buckshot guns along the periphery of mangrove forests. The Bombay High Court ruled in 2018 that the destruction of mangroves "offends the fundamental rights of citizens," and several people have been arrested, mostly for encroachment.

But a handful of officers of the mangrove cell are not enough to save all of Mumbai's mangroves, Adgaonkar says. It's important to educate the public — especially children — about the urgency of saving these indispensable trees, she adds.

"If mangroves are saved, Mumbai will be saved," Adgaonkar says. "Otherwise, as sea levels rise, this bustling metropolis will collapse like a house of cards."

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About the Author

An assistant editor (infrastructure) at The Times of India, Mumbai, Chittaranjan been covering institutions involved in providing urban infrastructure, power and telecom services for seven years. Read More

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Carbon Monitor, a near-real-time daily dataset of global CO 2 emission from fossil fuel and cement production

Accountability and data-driven urban climate governance

Background & summary.

The Covenant of Mayors (CoM) initiative was launched by the European Commission (EC) in 2008 with an initial target for participating cities to reduce Greenhouse Gas (GHG) emissions in their territories by at least 20% by 2020. Later on, in 2014, based on the experience of the CoM and recognizing the vulnerability of urban areas to suffer the inevitable impacts of climate change, the EC launched a similar voluntary initiative (called Mayors Adapt) with a focus on climate adaptation in cities. Later, in 2015, the Mayors Adapt merged with the CoM, also setting a new target aligned with the EU overarching target of 40% GHG emission reduction by 2030. Nowadays, after joining forces with the Compact of Mayors in 2017, the Global Covenant of Mayors (GCoM) is currently the world’s largest coalition of cities and local governments voluntarily committed to fighting climate change. In Europe, cities commit to becoming climate neutral by 2050 (with an interim target which is recommended to be set at−55% GHG emissions by 2030), including actions on climate change adaptation, and more recently (launched in 2022), on energy poverty and energy access.

Cities and local authorities joining the GCoM commit to take the lead and enhance the transparency of local climate and energy policies. This is supported by setting realistic and ambitious quantified emission reduction targets; measuring the level of their GHG emissions in a reference base year according to a standardized methodological approach 1 ; assessing climate risks and vulnerabilities, as well as energy access and energy poverty in their territories; defining a strategy and concrete actions to mitigate and adapt to climate change, and to increase energy access for the most vulnerable population groups; approving and making their action plan publicly available; aiming at regularly reporting on the implementation of their action plan; and sharing their vision, outcomes, expertise, and knowledge with other local and regional authorities within the EU and globally through direct collaboration and peer-to-peer exchange.

GCoM signatories come from all parts of the world, setting their commitment and reporting their Climate Action Plans (CAP) through two reporting platforms, MyCovenant ( http://mycovenant.eumayors.eu/ ) and CDP-ICLEI Track ( https://www.cdp.net/en/cities ). Cities choose on which platform to report their CAP. In this paper we analyze only data coming from the first platform (covered by the Data Policy agreement 2 ), which contains more than 90% of the GCoM signatories. By adhering to this initiative under the Data Policy agreement, signatories agree on considering all Covenant Data as open data (published and made available for re-use for both commercial and non-commercial purposes), timely, comprehensive, accessible, usable, comparable and inter-operable. Additionally, signatories bear responsibility for the lawfulness of sharing Covenant Data and they ensure that Covenant Data is of good quality, accurate and regularly updated. Nonetheless, the agreement also specifies that the EC-Joint Research Centre (JRC) may, in order to further improve overall quality, i) remove errors or irregularities, and ii) add new parts or functionalities 2 .

The CAP covers the geographical area under the jurisdiction of the local authority, including actions by both public and private sectors, aiming at translating the local vision for mitigation and adaptation to climate change, and also for alleviating energy poverty and advancing in energy access. GCoM signatories develop their CAP following the available methodological guidance 1 , presenting the measures to be implemented to achieve their climate mitigation, adaptation and energy access ambitions.

With regards to the mitigation pillar, the CAP contains a Baseline GHG Emissions Inventory (BEI), following a methodology that should be consistent with the IPCC framework 3 ; a GHG emission reduction target, which should be in line with the Nationally Determined Contribution to the United Nations Framework Convention on Climate Change, mainly for target years 2020, 2030 and/or 2050; and a clear outline of the actions that the local authority intends to take in order to reduce its GHG emissions. The reduction target is measured against the BEI, and the progress made by the signatory is monitored through a Monitoring Emissions Inventory (MEI), every 4 years following the submission of the BEI. It is important to highlight that the BEI is not meant to be an exhaustive inventory of all emission sources occurring under the jurisdiction of the signatory, but it rather focuses on the energy consumption and on the sectors upon which the local authority has a potential influence.

Concerning the adaptation pillar, the CAP includes the assessment of climate risks and vulnerabilities within the territory, adaptation goals and a set of actions to increase the resilience of the local authority sectors and vulnerable groups. As for the energy poverty pillar (officially launched only recently, in 2022, being not yet mandatory to report on), it includes policies for increasing the level of energy access and/or reduce energy poverty within the boundaries of the jurisdiction, considering measurable indicators for the key attributes of secure energy, sustainable energy and affordable energy.

Under this approach, where cities voluntarily self-report on their action plans and advances in their implementation, assuring a fair level of data quality becomes a key challenge. In this sense, cities might be reporting biased estimations or evident errors with respect to the GCoM reporting framework 1 , or might be missing information or suffering of lack of coherence 4 .

Therefore, a quality harnessing procedure was developed in order to publish a structured collection of datasets holding relevant information on the CAP and monitoring reports (previous in-house JRC-reports 5 , 6 outline the statistical procedures described here in the Technical validation for harnessing the quality of the GCoM data sets 7 , 8 , 9 , being the first time this collection and its curating methods are described in peer-reviewed scientific literature). Such a procedure also aimed at facilitating its potential reuse, allowing to assess the policy impact for mitigation, adaptation and energy access ambitions inside the GCoM initiative 6 , 10 , 11 , but also, across different energy and climate initiatives and compatible databases (such as CDP-ICLEI Track 12 ).

It should be noted that a previous GCoM data set has been published 13 , considering only European and Southern Mediterranean signatories, which made use of the data gathered at MyCovenant at the time (2019). Such a data set followed a different curation process than the one presented here, and was used as benchmark for developing the comparison with the EDGAR database in the Technical validation.

Focusing on the third release for the GCoM datasets 9 (with closing date September 2022), Fig.  1 shows a European close-up on the location of the different municipalities and cities with active action plans. The red circles correspond with signatories having presented a BEI, and the yellow ones with signatories having also presented a MEI. In total, there are 6,850 signatories with a BEI, and 2,279 having also reported at least one MEI. The complete summary of GCoM signatories by geographical area is presented in Table  1 .

European close-up of active municipalities and cities committing under the GCoM, having reported a BEI and MEI (on top of the BEI), including Azores and Canary Islands.

Recollection of data

The GCoM collection of action plans and monitoring reports is the result of the ongoing efforts of signatory cities in planning, implementing and self-reporting their advances towards climate change mitigation, adaptation and energy access. All Covenant Data, including the local climate and energy strategy, the amount of final energy consumption and energy production and associated emissions by energy carrier and by sector, the main climate vulnerabilities, hazards, the impacts and risks assessment, the climate change mitigation and adaptation actions, together with stakeholders and estimated impacts, falls under the Data Policy agreement 2 . Therefore, after a quality scrutiny, Covenant Data may be published and regularly updated as an open dataset, on the public websites of Regional Covenants, as well as on the European Union Open Data Portal ( https://data.europa.eu/euodp/en/home or the website of the European Commission ( https://ec.europa.eu/jrc/ ).

The present third release of the GCoM dataset collection, with closing date September 2022, follows the previous first 7 (with closing date May 2021), and second 8 (closing date March 2022) GCoM releases, following the FAIR guiding principles for scientific data management and stewardship 14 , being easily accessible, inter-operable and reusable. All previous releases have been followed by their respective scientific and technical assessment reports 5 , 6 , 15 , 16 .

Data extraction

The GCoM datasets include data that is submitted via the password protected MyCovenant web application ( https://mycovenant.eumayors.eu/ ). The web application points to a externally managed database, which is copied into the JRC servers on a daily basis (accessed through the European Commission Authentication System login credentials). Then, a series of SQL-scripts can be executed, feeding different data tables to the overall harnessing process articulating the GCoM datasets.

In this way, the extraction of the GCoM data is carried out using SQL queries tailored to retrieve the necessary data from the JRC servers, connecting data points across the 260 tables that constitute the whole database structure. The complexity of the data extraction reflects the one of the database structure, which has undergone, and continues to undergo changes throughout the life of the project to accommodate renewed ambitions and methodological developments.

Firstly, the complete list of signatories is analysed, identifying the ones to be included in the datasets. Namely, they are published signatories, which are compliant with the GCoM reporting requirements, and on-hold signatories, which are temporarily suspended, due to non-compliance with mandatory requirements. This process leaves out signatories that never concluded the registration process or are not yet formally confirmed as GCoM signatories. Additionally, some administrative information, such as the population, the date of adhesion, the commitments subscribed to, or the type of adhesion (whether individual or in a group with other signatories), is retrieved in this phase.

Secondly, the action plans and monitoring reports with a “submitted” or “resubmitted” status are retrieved for those published or on-hold signatories (identified in the previous phase). A preliminary validation process looks at the completion of some key mandatory fields, thereby excluding entire action plans and monitoring reports that do not fulfil the required standards. For example, excluding a CAP with an emission reduction target outside the 20%−100% range (the minimum percentage reduction of GCoM signatories should be of at least 20% by 2020), or with incomplete data, i.e., lacking a baseline emission inventory or an explicit reduction target. Lastly, a manual check is performed to analyse case by case and attribute the action plan or monitoring report to their corresponding commitment.

Data Records

The present third release of the GCoM dataset collection has been published in the JRC data repository, as well as in the European open data portal 9 (CC-BY public access, under the European Commission Reuse and Copyright Notice https://data.jrc.ec.europa.eu/licence/com_reuse ). It consists of three Excel files (spreadsheets), one ( df1 ) presenting the whole set of GCoM signatories (either submitting through MyCovenant or CDP-ICLEI Track), and a second ( df2 ) and a third one ( df3 ), holding all the information related to the action plans and monitoring reports, respectively, that have been submitted through MyCovenant.

The data is organized for publication into three main datasets: df 1 - Signatories , df 2 - Action Plans and df 3 - Monitoring Reports . The first file, df 1 - Signatories , contains the identification and description of the GCoM signatories. It includes the key IDs: organisation_id , which allows relating this df1 file with the other GCoM df2 and df3 files, and the gcom_id , which allows identifying a city in the GCoM eco-system. Table  2 contains the general description of the information contained in this file.

The second file, df 2 - Action Plans , comprises the information related to submitted and resubmitted plans, spanning from mitigation, adaptation and energy access commitments, to energy and emissions inventories, actions overviews and local policies. It allows identifying each city by their corresponding organisation_id , relating it with the df1 file, and also has an action_plan_id , which allows relating all the tables in this dataset (df2). Tables  3 , 4 , 5 contains the general description of the information contained in this file.

Lastly, the third file df 3 - Monitoring Reports , holds the main information from the monitoring reports. It allows identifying each city by their corresponding organisation_id , relating it with the df1 and df2 files, each action plan by the corresponding action_plan_id , relating it directly to the df2 file, and it also has a monitoring_report_id , which allows relating all the tables in this dataset (df3). Table  6 contains the general description of the information contained in this file.

Technical Validation

A quality-harnessing computational procedure is developed, aiming at enhancing the internal coherence and contents reliability of the datasets that are published. The work-flow for such a procedure is presented in Fig.  2 , structuring the information into three datasets.

Flowchart for the GCoM data extraction and quality-harnessing process, as developed by the JRC, prior to the publication of the datasets. DF1, DF2 and DF3 appear in capital letters to distinguish the initial (pre-processed) versions from the final open data sets: df1, df2 and df3.

Additionally, a comparison is developed with the Emissions Database for Global Atmospheric Research (EDGAR v7), assessing the usability of the GCoM datasets for relevant research on local policies and their effects on reducing the impact of climate change.

Energy consumption validation

Following the data extraction and the preliminary identification of valid action plans and monitoring reports (as described above), the validation process is extended by evaluating the activity on energy consumption. Hence, a general methodology is developed for screening outlier energy activity observations with respect to national per capita references 17 , 18 . It should be noted here that, for the purpose of this process, labeling an observation as an outlier does not entail its direct elimination, but rather, that it deserves a close-up examination to judge if it can be justified, if it is an evident mistake, or if it has to be discarded.

Simplifying the analysis, the initial step for identifying outlier energy activity involves segregating electricity from other carriers. For the national consumption references, the industry sector is omitted for both categories, taking only the commercial and public services, road, and residential sectors. Therefore, the energy consumption declared by signatories is aggregated and transformed into per capita terms, and it is labelled as outlier if it is greater than a clustering-based maximum threshold (as explained below), or less than a minimum threshold set up to 0.01 (MWh/year per capita).

To determine the maximum threshold for each category (electricity and other carriers), signatories are grouped together based only on their per capita energy consumption national references. For doing so, the k-medoids technique is used 19 (aiming at a more robust grouping under the presence of outlier observations), developing a heuristic search in the same fashion as in the k -means, but using a real observation as cluster centroid instead of an average. Partitions of 2 to 5 clusters are explored, and an optimal partition is identified according to the overall satisfaction of various statistical indices measuring the density and separation between clusters 20 . As a result, the best partition consists of three clusters with maximum thresholds of 2.6, 3.3 and 7.3 (MWh/year per capita), for electricity, and with maximum thresholds of 14, 18.7 and 25 (MWh/year per capita), for other carriers.

Following the completion of the outlier screening process, a more thorough analysis is conducted on the subset of inventories initially identified. Some outliers might be rare but plausible, and if an evident error is detected, it can be corrected. For example, if the city reports in kWh instead of MWh, or in activity per capita instead of absolute activity. Only if the reported values appear to be incomplete or to make no proper sense, then the inventory is removed. After such an analysis, the emission inventories from 7 signatories were removed.

In this way, before this analysis, there were 6,883 signatories with a BEI, corresponding with 10,478 inventories (counting all the available BEIs and MEIs). Then, 26 signatories were removed due to their nationalities from Russia and Belarus (banned due to the Russian invasion to Ukraine), leaving a total of 10,447 inventories. In consequence, leaving out the 7 signatories mentioned above, 6,850 signatories are left, holding a total of 10,391 inventories.

Additionally, internal coherence is checked by focusing on action plans with a monitoring history, which is available for arbitrary years counting from their corresponding base year. Here, the reported energy consumption should follow a reasonable trend, and if a particular MEI falls out of such a trend, then the inventory is eliminated as long as no evident correction can be identified. Lastly, advanced outlier detection techniques are applied, namely the Isolation Forest 21 and the Local Outlier Factor 22 algorithms, over the remaining set of inventories. This is done in order to refine the search for outlier values, paying special attention to the inventories with higher outlier values. Here again, if atypical values are identified and no reasonable explanation or correction is found for them, then the inventory is taken out from the GCoM datasets. After this internal coherence process, we have the same number of signatories (6,850), but less inventories (excluding outlier MEIs), for a total of 10,372 inventories.

Emission factors

After removing evident mistakes from the energy consumption activity, the emissions have to be estimated by multiplying the energy activity (MWh/year) and the corresponding emission factors (i.e., for each energy carrier being reported, there is an associated emission factor). This entails a second validation phase, now on the emission factors used. Emission factors could be made available by cities when reporting their own estimation of the emissions, but this is not always the case, as sometimes signatories fail to report the explicit emission factor that they used. For the case where the emission factors are reported, they have to be assessed and validated. As for the case of missing emission factors, an imputation process had to be applied.

Therefore, emission factors are verified using carrier-specific references sourced from the JRC repository 23 , 24 . Only in case the emission factors provided by the cities are absent, significantly divergent from the carrier-specific reference (as it will be explained below), or negative, then an appropriate reference is utilized instead. The assessment on the difference between the reported values and the references considers separately emission factors for electricity with national or local origin, and for all other energy carriers.

For national electricity factors, outliers are identified if they exceed the threshold of being 50% off from their national yearly references, or if their value is greater than the national historical maximum. In the case of local electricity emission factors, outliers consist in values 2 times bigger than the national historical maximum. The higher tolerance for local than for national electricity emission factors lies in the fact that there is more uncertainty on the exact mix of energy sources that is being used locally. And for all other energy carriers, outlier emission factors are identified if they surpass the threshold of being 20% higher.

In total, 12.6% of all signatories presented some emission factors that had to be revised. In consequence, 10.9% of all inventories had some emission factor, associated to a particular energy carrier, which had to be replaced by its respective reference. Concerning missing emission factors, not being reported by signatories, the percentage was higher. In total, 36.8% of all signatories were missing some information regarding their emission factors, entailing that 30% of all inventories had some emission factor, associated to a particular energy carrier, which had to be filled in by their respective reference.

Energy production

A separate procedure was implemented, following internal consistency rules (as explained below), for validating the reported energy supply values for local electricity and heat/cold energy production, as well as for certified green electricity sales and purchases.

Initially, an implicit emission factor was calculated for local heat/cold energy and locally distributed electricity production by comparing the reported supply and emissions, categorizing them by renewable and fossil sources. Therefore, emissions were validated only if that implicit emission factor was less than 2 and greater or equal than 0.1 (tCO2-eq/MWh) for fossil sources (0 for renewable). Here again, as with local electricity emission factors and the high level of uncertainty on the specific mix of energy sources being implemented locally, those thresholds were established as broad upper and lower bounds according to the existing body of literature on emission factor references 23 , 24 .

Secondly, considering renewable energy only for locally distributed electricity production, the reported energy supply was compared with energy consumption, and only if the energy produced was far greater than the declared consumption (exceeding it in more than 150 times), it was taken out from the validated dataset.

Lastly, in the case of certified green electricity acquisitions, the reported purchases were cross-referenced with electricity consumption. Only those purchases were validated and retained for publication if the energy procured did not exceed 1.05 times the electricity consumption. Hence, a small 5% tolerance was allowed on the deviation of the declared purchases with respect to what the city actually used.

Risks and Vulnerability Assessment and adaptation goals

The Climate Risk and Vulnerability Assessment (RVA) and the adaptation goals are also subject of a revision and validation, verifying that the data reported into the platform complies with completeness, consistency, and coherence standards.

Concerning the RVA, it is noted that the adaptation pillar of the GCoM was initially introduced as a separate initiative 25 , and later merged with the mitigation pillar in 2015, and therefore, signatories that were initially committed to mitigation were allowed to report information on adaptation on an optional basis. Thus, data on adaptation was not consistently collected across all signatories, and a separate deadline was established for signatories committed to adaptation, giving two additional years to report information on it. As a result, data on the RVA was collected through the platform using different templates over time, resulting in potentially incomplete and inconsistent records.

The RVA dataset includes information on climate hazards (type, current impact and probability, expected magnitude and frequency), vulnerable sectors (and their vulnerability level), adaptive capacity factors, and population groups. To ensure that the data is minimally coherent, incoherent RVA reports that are submitted by signatories are filtered out, as well as old (reported before 2020) or redundant information, being submitted elsewhere on the platform. Lastly, climate hazards are cleaned from invalid text entries. Figure  3 presents the close up on active signatories holding an RVA and planning on adaptation actions.

Close-up of the active GCoM signatories with an RVA and adaptation actions (on top of the RVA).

On the other hand, regarding adaptation goals, they started to be collected since in the platform since 2020, as a text entry related to its base and target years. In 2022, the adaptation goal collection was enhanced with additional data fields, which included the collection of a baseline value, a target value, and the main climate hazard being addressed.

The process to clean the adaptation goals involves filtering out incoherent adaptation goals reported by signatories without an adaptation commitment or without a CAP covering the adaptation pillar, and removing any invalid text entries related to mitigation, emissions reduction, and the progress towards the target or the monitoring of the adaptation goal. The summary of this process, regarding the cleaning of the adaptation goals, is presented in Table  7 . Overall, 65% of all the submitted goals were kept in the datasets. Because adaptation plans may comprise a range of goals, from several to none, there is no direct relationship between the number of plans submitted and the number of goals published.

Local policies and their estimated impact

The GCoM datasets contain all the individual policies reported in MyCovenant by the local governments, which can be described in English but also in local languages, making it a multi-lingual dataset. The quality-harnessing procedure focused on the aggregate estimates for the impact of those policies at sector level, but also on the individual policies.

Firstly, at sector level, the validation process involved an initial screening to identify any clear inconsistencies between the commitments and mitigation measures. In this way, the estimated emissions to be reduced by the target year, as reported by signatories by sector, were aggregated into a total figure, and compared with the total baseline emissions reduction, as declared in their commitment. Then, in case the estimates by sector entailed a percentage reduction of 2 times greater than the absolute target percentage reduction declared in the respective commitment (taking into account that different actions could be redundant on their expected achievements), or exceeded the total baseline emissions, then the estimated emissions reduction was discarded. Similarly, the estimated impact on energy savings was verified, such that values that were greater than 1.2 times the energy consumption were excluded.

Further analysis was developed by sector as well as on an individual basis for all actions, computing an implicit factor between the estimates of CO 2 reduction and the sum of energy savings and renewable energy production. Here, following the same logic as with local heat/cold energy and locally distributed electricity production, if such a factor was greater than 2 or less than 0.01 (tCO2-eq/MWh), then the action-sector was excluded from the validated dataset, or for individual actions, the corresponding estimates were discarded.

Comparison with EDGAR database

The EDGAR database v7 is used to compare with the CO 2 emissions data available in the GCoM (a recent comparison of the emissions inventories reported by ICLEI C40 cities and EDGAR gridded datasets can be found in 26 ). Each one, EDGAR and GCoM, follows a different methodology, being there some disagreement in terms of spatial/geographical coverage, emissions sources, emissions allocation or the type of emissions considered.

In general terms, the GCoM datasets contain emissions data that are accounted from locally available information, which is then aggregated at municipal or city level, regarding the local administrative boundaries. Meanwhile, EDGAR 27 uses national activity data and reference emission factors for accounting GHG emissions at global scale, which are then spatially distributed by sector, with respect to the human population, on a 0.1 × 0.1 degree resolution.

Therefore, controlling for the divergences between both approaches, the comparison between the GCoM and EDGAR data is performed on direct CO 2 emissions, taking only the buildings (IPCC codes 1A4 and 1A5) and road transport (IPCC code 1a3b) sectors. Focus is placed on a sample of European cities (enabling the geographical matching between the GCoM cities identified by a LAU code, and the EDGAR grid-based emissions), taking the GCoM annual inventories for every year after 1999, for all of the available cities having a LAU code and population above 50,000 inhabitants, and matching them with the corresponding geographical polygon of the EDGAR grid.

To develop this analysis, emissions are extracted from the EDGAR database for each one of the cities (polygons) included in this study, combining different LAUs if needed to have the most complete match based on the LAU(s) and the EDGAR gridded data. Greater uncertainty should be associated to small areas, as such a match is more difficult to implement given the resolution of the EDGAR grid. Hence, the sample of cities to compare is composed of medium to big cities, although there are some small polygons in the sample, where 54 cities have surface of less than 100 km 2 .

In total, there are 322 different cities that could be matched with sufficient confidence between GCoM and EDGAR. For emissions in the buildings and road transport sectors, there are 587 and 582 observations, respectively, which include different yearly inventories for the same city. The difference between sectors is due to the fact that not all cities consistently report for all sectors throughout their emissions accounting.

Tables  8 – 9 present the results for this comparison, for both datasets for the buildings and road transport sectors, respectively. Firstly, this study replicates a previous study presented back on 2019, based on a previous sample of the Covenant data 13 , which can be taken as a benchmark on the reliability of the GCoM data with respect to EDGAR. In that paper 13 , the analysis presented a correlation (CORR) coefficient, the normalized root mean squared error (NRMSE) and the bias estimation between the Covenant and the EDGAR data, taken for the same sectors and by size of the city in terms of population. Here, we extend that analysis by including the Mean Absolute Percentage Error (MAPE), which can be understood as the estimated bias in absolute value, and consider the data by country.

Focusing on Tables  8 , 9 it is observed that the 2019 benchmark focused mainly in small cities, on the contrary to this study, which considers a higher number of medium and big sized cities. The overall correlation of 0.83 in the buildings sector is lower than the benchmark of 0.92, meanwhile the overall correlation is very similar to the one of the benchmark in the road transport sector, obtaining 0.68. Regarding medium sized cities, there are 284 medium cities in the buildings sector obtaining a correlation of 0.84, surpassing the benchmark of 0.64 for 96 cities. On the other hand, on the road transport sector, there is a higher correlation of 0.63 for 283 medium cities, against the 0.37 of 96 cities included in the 2019 benchmark. Additionally to the correlation statistic, which may not be the most suitable for the comparison between EDGAR and GCoM emissions, the NRMSE and the bias also throw better values for the GCoM datsets against the 2019 benchmark, having an overall lower NRMSE (0.09 against 3.3) and bias (10% against 35%).

Examining the MAPE, the average absolute deviation is 71% and 157%, respectively for the buildings and the road transport sectors. Aiming at understanding the source of such discrepancies, Table  10 presents a comparison by country. In the buildings sector, the greatest discrepancies come from 2 signatories in Ireland (277%), 4 signatories in Cyprus (222%), and 4 signatories in France (210%). Meanwhile, in the road transport sector, the greatest discrepancies come from 1 signatory in Lithuania (814%), 4 signatories in Cyprus (509%), 1 signatory in Estonia (476%) and 3 signatories in Latvia (467%). Such big outliers allow to partially explain the high absolute deviations by sector, identifying a low number of geographically located cities with atypical inventories.

Usage Notes

The datasets are available in structured tables in the format of Excel spreadsheets, having their own metadata description with the name, type and short description of each field. This makes the GCoM collection of datasets easily accessible, inter-operable and re-usable (taking into account their specific attributes in order to using them properly).

An important discussion should still be addressed regarding the identification of outlier observations in the GCoM data sets. As mentioned throughout the description of the quality-harnessing procedure in the Technical validation, the thresholds that were defined for identifying outlier observations on energy consumption and production, emission factors, and local policy impacts, were the result of a detailed procedure aiming at removing or revising only evident mistakes (ensuring high quality standards while aiming at keeping as much data points as possible as they were originally submitted). Therefore, outlier observations were identified not for direct removal, but for a close up analysis. Following such an analysis, only evident mistakes were revised if their nature could be inferred. Otherwise, if the nature of a mistake could not be resolved, then it was removed. In this way, the procedure focused on ensuring the usability of the available data points, with the main purpose of harnessing the data sets from extremely low quality values. On the contrary, if a stronger approach would have been taken, then the results would have been conditioned by stronger assumptions, suggesting the removal of more data points and affecting the availability of these data for applied research. In consequence, depending on the research purpose for using these data, a more in-depth analysis could be necessary for detecting (statistical or common sense) outlier values.

Upcoming releases intend to include translated fields for individual actions and adaptation goals, offering a multi-lingual corpus of actions and goals with its respective English translation, as well as energy access and energy poverty fields. It should be mentioned that for this third release, only 0.6% of all individual actions are labelled as belonging to the (recently launched) energy access and energy poverty pillar, the majority of them, designed in combination with mitigation and/or adaptation plans. For future releases, it is expected that signatories will adhere to this pillar and present their energy access and energy poverty evaluations and actions together with their CAP.

Examining the local policies for adaptation, it is important to acknowledge that a full revision of the coherence and completeness of the adaptation actions was not performed, given that signatories only report on selected climate hazards, vulnerabilities, and actions. Hence, there might be instances where an action is missing its associated climate hazard or vulnerability, which does not invalidate the action but simply lacks an explicitly complete RVA relation. Additionally, various circumstances faced by local governments, such as lacking jurisdictional power to plan actions for certain vulnerable sectors or lacking resources to address major hazards, can make it challenging to assess the full coherence and completeness of these actions, often resulting in prioritization of solutions with limited gains. Despite these challenges, an analysis of the RVA reports submitted by signatories reveals promising progress in addressing high-risk hazards. Currently, 57% of these hazards have been effectively addressed through at least one adaptation action. Moreover, when examining the action plans of signatories that report at least one high-risk hazard, an encouraging 72% of them include matching actions to address the identified hazards.

Similarly, among all the high-vulnerable sectors reported in the RVA by signatories, 60% of them have already been addressed through at least one adaptation action. Furthermore, 85% of the signatories’ action plans that report at least one high-vulnerable sector include corresponding actions to mitigate the vulnerabilities associated with these sectors.

These findings highlight the progress made by signatories in addressing high-risk hazards and vulnerable sectors through adaptation actions. However, to ensure a more comprehensive understanding of the effectiveness of these actions, future research should focus on further evaluating the coherence and completeness, e.g., as in 28 , considering the inherent limitations imposed by the dataset.

The potential applications of the GCoM datasets span different research areas, ranging from multilevel governance of climate change to financing climate action, from energy and emissions to climate risks and vulnerabilities, including socio-economic aspects. The analysis of information about governance aspects, such as the presence of a regional authority (GCoM coordinator) or a city network (GCoM supporter) next to the local authority, or the decision of a signatory to join as individual or as group, can shed lights into the benefits that may stem from these approaches. The availability of emission inventory data calculated according to a common methodology for several years, for various cities, can help gain a better understanding of emission trends in cities. E.g., as it has been used in previous JRC-reports 5 , 6 , the datasets allow answering the question on how much GHG emissions have all cities in the GCoM reduced by year. Overall, these datasets contain information for assessing and estimating the impact of local policies for climate change mitigation, adaptation and energy poverty, addressing how much is actually under the control of the local governments inside a city, and what are other important factors besides those local policies, also affecting the GHG emissions inside a city.

Code availability

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Acknowledgements

M.G.B. is a consultant for JRC-European Commission. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

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Camilo Franco, Giulia Melica, Aldo Treville, Valentina Palermo, Paolo Bertoldi, Enrico Pisoni, Fabio Monforti-Ferrario & Monica Crippa

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M.G.B., G.M., C.F. and A.T. analysed the raw GCoM data, assessing the internal consistency of the action plans and monitoring reports, C.F., V.P., E.P. and F.M. conducted the GCoM comparison with the EDGAR grid-based CO 2 emissions, and P.B., G.M. and M.C. supervised the study. All authors reviewed the manuscript.

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Franco, C., Melica, G., Treville, A. et al. GCoM datasets: a collection of climate and energy action plans with mitigation, adaptation and energy access commitments. Sci Data 11 , 969 (2024). https://doi.org/10.1038/s41597-024-03613-5

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Research on the impact of climate change on food security in Africa

39 Pages Posted: 4 Sep 2024

Jinglei liu

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Fangyu Ding

Chinese Academy of Sciences (CAS) - Institute of Geographic Sciences & Natural Resources Research

Mengmeng Hao

China Academy of Space Technology

Hanwei Liang

Nanjing University of Information Science and Technology

Against the backdrop of global warming and increasingly frequent extreme climate events, assessing the impact of climate change on food security has become an important issue of global concern. We examine the influence of climate change on food security in Sub-Saharan Africa, with a specific emphasis on four key crops: maize, rice, wheat, and soybeans. A random forest model is used to estimate the temporal and spatial trends of agricultural yields on the basis of climate data, land use, and irrigation ratios. We also studied the differential impacts of climate change on various crop types, taking into account their physiological characteristics and responses to changing environmental conditions. This prediction is performed under three shared socioeconomic pathway (SSP) scenarios—SSP2-4.5, SSP3-7.0, and SSP5-8.5—using three global climate models (GCMs): BCC-CSM2-MR, CanESM5, and IPSL-CM6A-LR.The findings suggest the following: (1) Maize, a C4 crop, is projected to experience a severe decrease in future harvests, especially under the SSP5-8.5 scenario. The worst declines are forecasted in eastern South Africa and Zambia. (2) Both rice and wheat are C3 crops that experience a "CO2 fertilization effect," resulting in an increase in yields over time. The SSP5-8.5 scenario primarily focuses on the increase in rice production in West Africa, highlighting this phenomenon. Conversely, significant increases in wheat yield are observed in South Africa and Nigeria. (3) Soybean, a C3 nitrogen-fixing crop, is projected to retain consistent yields overall but with a modest decline in comparison with past norms. The general distribution pattern of soybean yields remains mostly consistent across the SSP scenarios, with the increase in high-yield regions occurring primarily in South Africa.

Keywords: Climate change, Food security, random forest, spatial and temporal evolution, Sub-Saharan Africa

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Jinglei Liu

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Chinese Academy of Sciences (CAS) - Institute of Geographic Sciences & Natural Resources Research ( email )

Mengmeng hao (contact author), china academy of space technology ( email ), nanjing university of information science and technology ( email ).

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Causal analysis of unprecedented landslides during July 2021 in the Western Ghats of Maharashtra, India

• Recent Landslides
• Published: 18 October 2023
• Volume 21 , pages 99–109, ( 2024 )

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• Nirmala Jain 1 ,
• Priyom Roy 1 ,
• Tapas R. Martha 1 ,
• Nataraja P. Sekhar 1 &
• K. Vinod Kumar 1  

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The occurrence of landslides is not uncommon in the Western Ghats of Maharashtra, India. However, during the last week of July 2021, an unprecedented number of landslides due to heavy rainfall were reported in this region. To determine the cause of the large-scale landsliding, we mapped an event-based landslide inventory using high-resolution satellite imagery and identified 5012 landslides. We analysed rainfall data for the 2005–2021 period to identify the anomaly in rainfall quantity and its distribution which triggered such large number of landslides, particularly in 2021, even though heavy annual monsoonal rainfall is observed every year in this region. It is observed that the quantity of rainfall in 2021 is much lesser than that seen in most of the previous years. Analysis of antecedent rainfall for time-stamped landslides, such as Jui (2005), Malin (2014) and Taliye (2021), shows that two-day antecedent rainfall is the primary trigger of landslides in the region. An in-depth comparison of rainfall variability for all the years which recorded more rainfall than 2021 shows that the amount of two-day consecutive rainfall is significantly higher in 2021, although the cumulative seasonal rainfall is less than in preceding years. Further, this anomalously high rainfall was concentrated around the 600 m – 900 m elevation range. The probable consequence of such spatiotemporally localised heavy rainfall over higher elevations was rapid soil saturation triggering a large number of landslides in 2021. In times when the effect of climate change is visible in the form of weather extremes, the present study may aid in preparedness for response to such disasters.

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Acknowledgements

We thank Dr. Prakash Chauhan, Director, NRSC and Dr. K. Sreenivas, Deputy Director, RSA, NRSC, for their support and guidance in this work. We thank to NRSC Data Centre for providing Indian Remote Sensing satellite data and for their quick response. We would like to thank the International Charter for Space and Major Disasters for providing high-resolution satellite data acquired before and after the event. We would also like to thank Dr. Shantanu Bhatawdekar, Scientific Secretary, ISRO, Dr. J V Thomas, Director, EDPO, Dr. John Mathew, Associate Director, EDPO, Dr. K. H. V. Durga Rao, Group Head, DMSG and Dr. I. C. Das, Group Head, GSG for their active support to this study. We would also like to thank Bhukosh, GSI for providing a geological map, GPM and India Meterorological department (IMD), Mumbai team for providing rainfall data, of the study area.

This study was not supporeted by other Orgnisations. No funding was used for this work.

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Nirmala Jain, Priyom Roy, Tapas R. Martha, Nataraja P. Sekhar & K. Vinod Kumar

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Jain, N., Roy, P., Martha, T.R. et al. Causal analysis of unprecedented landslides during July 2021 in the Western Ghats of Maharashtra, India. Landslides 21 , 99–109 (2024). https://doi.org/10.1007/s10346-023-02165-w

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Received : 20 July 2023

Accepted : 09 October 2023

Published : 18 October 2023

Issue Date : January 2024

DOI : https://doi.org/10.1007/s10346-023-02165-w

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