eChapters

This chapter introduces approaches for understanding, assessing, and reducing the vulnerability of forest ecosystems to climate change. It examines the impacts of changing climatic conditions on forests and considers factors such as forest fragmentation, biodiversity, tree density, disturbance, and community dependence on forest resources. The chapter discusses methods and models for vulnerability assessment, including indicator-based and spatial approaches, and explains how vulnerability can be mapped and classified. It also considers strategies for minimizing climate impacts and enhancing forest resilience through appropriate management and conservation interventions.

Abstract

Contamination groundwater and soil due to heavy metals (HMs) has aggravated into a threat not only for people inhabiting the polluted areas but also for the vegetation of the region. Relying on the use of obligate symbionts, arbuscular mycorrhizal fungi (AMF) in remediation of contaminated sites can be a productive approach. These fungi due to high cation exchange capacity have the ability to adsorb heavy metals (HMs) on fungal tissues; they also possess enzymes responsible for reduction of metals to less toxic forms. In areas with high toxicity of various HMs such as lead, AMF extends its extraradical mycelium (ERM) beyond the depletion zone of plant roots increasing the availability of nutrients such as potassium, phosphorus, magnesium, and sulphur resulting in increased plant growth and yield. Production of phenol and organic acids by the fungus have been reported to increase iron diffusion, that is a co-factor for antioxidant enzyme superoxide dismutase. AMF sequester HMs in fungal structures and bind them in their chitin cell walls preventing their translocation to aerial plant parts.

Abstract

Soil salinization is an important factor limiting crop production, mainly in the arid and semiarid regions of the world. Nevertheless, plants naturally have evolved several physiological, biochemical and molecular mechanisms to establish and thrive in salt stressed soils. In addition to breeding crops for salt tolerance, recent emphasis is on exploring the possible role of plant-microbe interactions in amelioration of salt stress in plants. It is now well established that arbuscular mycorrhizal (AM) symbiosis alleviates the effect of salt stress in plants. Further, available evidence does suggest that AM symbiosis regulates many of the plant’s salt stress alleviation mechanisms. However, the molecular basis for many of these mechanisms is yet to be ascertained. In this chapter, we summarize the current knowledge on the role of AM symbiosis in alleviation of salt stress in crop plants, which now appears to be a promising strategy to improve crop yield in salt stressed soils.

This chapter integrates case studies, scientific examples, and policy perspectives to illustrate how AI and ML can transform agricultural weather intelligence. The discussion spans from advanced predictive models in developed nations to emerging applications in technology-limited regions, while also addressing the urgent need for resilient crisis management systems in the face of natural and human-caused conditions. Advanced scientific techniques can not only optimize food production and resource management but also play a critical role in recovery efforts during times of crisis. Agriculture is intrinsically linked to the natural world—a domain defined by fluctuating weather patterns, seasonal shifts, and unpredictable environmental factors. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how we analyze and predict weather patterns, and their applications in agriculture are far-reaching (Fig. 5.1). From forecasting crop yields and fishery conditions to managing plantations and monitoring livestock health, these technologies are creating a paradigm shift in agricultural weather intelligence. Scientific organizations worldwide are leveraging AI and ML to refine predictive models, enable multi-variate analysis, and provide advanced warning systems for extreme events such as droughts, tsunamis, earthquakes, and even crises caused by human-made conflicts. Emerging nations with rapidly growing technological capabilities stand to benefit immensely from these innovations, tailoring solutions to local challenges while contributing to global food security and crisis resilience.

Introduction 
In the past, literacy in reading, writing and mathematics was considered the basis of establishment in society. With this knowledge, anyone could run all the businesses of his professional life efficiently because no technical skills other than typewriter and general computer knowledge were required. In present set up, this general knowledge is no longer enough to perform any task smoothly today. In this age of science and technology, the old concept of literacy is no longer applicable. Three other types of literacy or skills have now been added to the past concept of literacy: information literacy, technological literacy and human literacy. In this age of technology, people cannot keep themselves functional in this digitized world by practical knowledge of analog equipment alone. In present days, people live and work on the constant flow of big data, connections, and instant information flowing from every click and touch of a device. Technology is not static. Evernew technological advancement brings tides of data before people, therefore, they need data literacy to read, analyze and use them. Technological literacy gives them grounding in coding and engineering principles, so they know how their machines tick. Ultimately, human literacy teaches them the humanities, communication and design, allowing them to work in human environments. Artificial intelligence (AI) is now a fact of everyday life in our modern hightech societies. There are many definitions of AI and each of those definitions has been revised over time. Presently, most definitions state that AI solves complex cognitive problems associated with human intelligence or that AI helps as many people as possible through smartphones or healthcare, or even that AI recognizes problems and creates solutions for the benefit of technology, people, and society. However, the core concept of AI has continuously been to create machines that were capable of thinking like humans. It took about 200,000 years for human intelligence to evolve from natural to artificial and only 10 years to cut ties with the Earth and move to the clouds. During the creation of AI, mankind learned a lot about what it means to be human, how human intelligence is structured, and how humans learn and acquire expertise. With more predictive power, more efficiency and the ability to deliver better results than humans, AI is rapidly moving aside from humans into the world of expertise. However, it is impossible to imagine that someone else will replace Homo sapiens at this moment, but its probability cannot be ruled out in the days to come. The day, when mankind, in the course of time, will immerse in the world of knowledge, robotics becomes an essential part of the human body and existence, the existence of mankind is sure to be threatened. In October 2017, Saudi Arabia granted citizenship to humanoid Sofia. Perhaps this is a new breed of human being brought about by technology in the 21st century. Just as the message of the first driverless car aroused a lot of curiosity among mankind and now that curiosity has disappeared completely, it is likely that the arrival of many Sophia’s will make the environment very normal in the coming days. So here comes AI – Artificial Intelligence, the driving force behind Sophia or Driverless cars. The very simple definition of artificial intelligence is -the intelligence displayed by machines. Today, in this age of science and technology, we can say that anything imaginable by humans can be done with the help of machines. Sundar Pichai, CEO Google and Alphabet in his article “The promise of AI for India and the World” opined that every technology shift is an opportunity to advance scientific discovery, accelerate human progress and improve lives. AI will do this on an unprecedented scale and India is uniquely positioned to play a leading role.

Artificial Intelligence (AI) technology is increasingly used in our everyday lives. It is used in a variety of industries from gaming, journalism/media, to finance, as well as in the research fields from water resources to robotics. In this chapter concept and applications of AI are discussed in brief.

6.1 Historical Background

After World War II, a number of people worked independently on intelligent machines. The English mathematician Alan Turing was the first who explained the concept of intelligent machines through a lecture in 1947. He stated that intelligent machines could be developed by programming computers rather than by building machines. Since late 1950 many researchers are working on intelligent machines, mostly on programming computers

The artificial neural network (ANN), a data driven model, is inherently suited to the problems that are mathematically difficult to describe. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets, where explicit knowledge of the internal processes is not available. In this chapter general structure of ANN is described and also explained briefly with a case study.

7.1 What is Artificial Neural Network?

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous system, such as brain process the information. An ANN is an adaptive (Adaptive means that the system parameters are changed during operation i.e. training phase), most often nonlinear system that learns to perform a function (an input/output map) from data. In more practical term, neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find out patterns in the data.

Need for systematic mapping and hazard zonation is emphasized in this chapter. Though prevention of natural disaster is not possible, its destruction could be minimized through implementation of effective hazard management system. With this motive, the coastal region between Tangapatnam and Ovari, Tamilnadu State has been classified into various categories of vulnerability based on its response to the 26th December 2004 tsunami. Based on the extent of seawater inundation and coastal geomorphic features, we have assessed the tsunami impact and estimated the behavior of the beaches in case of similar havoc in future. This study had shown that the maximum seawater inundation recorded in the study area is 750m in Colachel and the minimum is 100m in Kadiapatanam, Mandakadu and Vaniakudy areas. Chinnamuttom, Kanyakumari, Manakudy, Pallam and Colachel areas are under high risk.

INTRODUCTION

On 26^th December 2004, tectonic disturbances happened near Sumatra Island with an intensity of 9.3 in the Richter scale wrecked havoc on southwest part of India. In addition to inflicting life and infrastructure loss, the resultant tsunami event had also eroded severely the coastal region and modified topography of beach and shallow coastal regions. It did raise the concern of scientists and emergency planners about the impact of larger earthquake and consequent tsunami should there be any recurrence of such event. Despite becoming aware of tsunami hazard, there has been confusion about areas at risk and areas of safety. This chapter is an attempt to assess the vulnerability of part of Tamilnadu coast based on its geomorphological conditions and the impact of recent tsunami. The hazard maps have been generated and are intended for educational purposes, to improve awareness of tsunami hazards and to encourage responsible emergency planning efforts by illustrating the range of possible tsunami events.

Abstract

Higher temperature and altered rainfall patterns accompanied by extreme weather events are being considered indications of climate change. These factors impact vegetation growth in natural forest, open scrub and in agriculture land. Vegetation index based on satellite data was used to analyse agricultural vulnerability in India to understand impact of rainfall variability. Time-series Normalized Difference Vegetation Index (NDVI) data products obtained from AVHRR (1982 to 2006) and MODIS (2000-2011) were analyzed to assess spatial and temporal variability. Coefficient of variation of maximum NDVI was assessed for the study period and results were corroborated with Standard Precipitation Index (SPI) instead of actual rainfall. Resolution of AVHRR time-series data facilitated study at regional-scale while MODIS datasets with 250m pixel resolution helped in identifying vulnerability at district-level. Study indicated that over 241 Mha areas in the country may not be vulnerable to rainfall variability induced climate change while over 81.3 Mha in arid, semi-arid and dry sub- humid regions may be vulnerable. Study indicated that over 12.1 and 1.81 Mha of Kharif cropland would be mildly and severally vulnerable while 6.86 and 0.5 Mha of Rabi cropland may be adversely affected in a similar manner. Study indicated that length of cropping season was decreasing due to delay in start of season. The paper also presents analysis of variations in SPI and resultant NDVI in typical drought and flood years in the country.

Keywords: MODIS, AVHRR, NDVI, Rainf ed Agricul ture, Vulnerability, Climate Change

Introduction 
SDG 3 emphasizes good health and well-being for people globally. In this chapter, we will focuses on the health and well-being of the Indigenous Khasi Tribe living in Meghalaya, India. The Khasi Tribe comprises 7 sub-tribes, commonly known as ‘Seven Huts’ or ‘HynniewTrep, HyniewSkum’ in the Khasi language. The 7 sub tribes are Khynriam, Pnar, Bhoi, War, Maram, Lyngngam and Dikko. Although the culture, tradition, and beliefs are shared among all subtribes, earning them the common name Khasi Tribe, there are slight differences in terms of topography. Some sub-tribes live in sloping or steep mountainous areas, engaging primarily in forestry and horticulture, while others reside in plain areas, where they cultivate agricultural crops. As an indigenous group, the Khasi community follows certain beliefs and practices related to health and well-being. The Khasi tribe is a matrilineal society where women hold great responsibility at home and in the community. Some researchers have acknowledged that the Khasi tribe is one of the tribes in India with a better status in terms of literacy rates and nutritional status. (D Agrahar- Murugkar, 2004). Approximately 83% of the Khasi tribe are agrarian. The main food items that boost their immune system and make them strong and healthy include rice, dal, potato, tomato, bamboo shoot, pumpkin, green vegetables, citrus fruits, and meat (beef, pork, fish, eggs, and dry fish), which they cook in their own indigenous style. The majority of them are non-vegetarian. A common stimulant is betel nut, which has been used since ancient times as a social mediator to connect with others and form friendships. The Khasi people are hardworking; enjoy games and sports, and traditional music. In terms of social well-being, the Khasi tribe has a strong sense of “we feeling”, supporting one another, following ethical and moral instincts, and respecting one another. They believe in the spirits of Mother Nature (ki blei, ki basa). To maintain strong bonding and enhance Khasi Tribe sentiments, various festivals are celebrated every year, such as Shad Suk Mynsiem, Shad Nongkrem, Seng Kut Snem, Pom Blang, and Beh Diengkhlam. They also pay homage to freedom fighters who have died for the Khasi land, such as Tirot Sing, Kiang Nongbah, and Phan Nonglait. Following the legacy of these freedom fighters, various leaders are heading civil groups that monitor the performance of government administration and other welfare states for common social well-being.

Introduction

Climate change and human activities have emerged as significant factors affecting water resources in diverse regions (Kumar et al. 2022; Haddeland et al. 2014). The escalation in human water consumption for domestic and industrial purposes enhanced by the impacts of climate change has resulted in an overall decline in water availability (Liu et al. 2017; Paul et al. 2021). Rising temperatures and altered precipitation patterns directly influence water availability for ecosystems and agriculture potentially leading to greater evapotranspiration rates, intensified rainfall events, and prolonged droughts (Sharannya et al. 2021; Myers et al. 2023). Furthermore, urbanization, deforestation, and agricultural practices alter landscapes, affecting water resources in these areas.

Water shortages pose threats to ecosystem health, and socio-economic development and disrupt agricultural production (Piao et al. 2010). Thus, ensuring sustainable and efficient water usage necessitates accounting for water usage and quantifying it in a changing environment. To comprehensively assess the significance of water resources in the environment, Falkenmark (1995) introduced the concept of blue water (BW) and green water (GW) water, particularly for managing water resources in water-stressed regions. Green water refers to the portion of precipitation that infiltrates into the unsaturated soil layer and is subsequently utilized by plants through transpiration and soil evaporation. In fact, “blue water” represents surface and groundwater flow in rivers, lakes, and aquifers (Abate et al 2024; Sharannya et al. 2021). While numerous studies have investigated the spatial and temporal variation of BW and GW using the Soil and Water Assessment Tool (SWAT) (Feng et al. 2021; Li et al. 2009; Lyu et al. 2019), fewer publications have focused on assessing these variations and uncertainties at the basin scale under varying climate scenarios (Sharma et al. 2023). Hydrological models such as SWAT, serve as valuable tools for simulating past and future hydrological changes in the watersheds using projected climate data. SWAT specifically can simulate both blue and green flows at the watershed scale.

Abstract

Land development in urbanized watersheds poses a potential of increasing storm runoff rates, and therefore increasing the risk of flooding in the downstream areas of a watershed. To investigate the impact of change in land use practices on the watershed hydrology, an hydrological analysis was conducted on Little Kitten Creek watershed located near Manhattan city of Riley county, Kansas, USA. Prior to development, land use types in the watershed included woodland, pasture, and fallow land. However, rapid development has occurred in the watershed in the last ten years. Post development land use practices include commercial development, residential housing, and conservation (woodland and grasslands). Data collected and analyzed included digital elevation model (DEM) of study site, stream network and soil data using ArcGIS (ESRI, 2006) and Arc-Hydro Software (Maidment, 2000). Watershed characteristics such as slopes, channel length, circularity ratios, and drainage density were also computed. The delineated watershed boundary, stream segments and soil data layers were over laid to produce soil maps. Time of concentration (Tc) was determined for the pre and post development conditions in the watershed using seven different methods. The time of concentration values computed using Kerby, Federal Aviation Agency, Kinematic Wave and NRCS (SCS) methods found more reasonable compared to the remaining three methods used.

These computed time of concentration values were observed to be shorter for the post-development condition when compared to the pre-development condition of the watershed. The reduced time of concentration in the post development condition is attributed to increase in percentage of impervious areas due to increased residential development in the watershed; therefore increased rates of runoff in the watershed are expected.

Keywords: Rezoning, Time of Concentration, DEM, Kinematic Wave, Drainage density, Circularity ratio.

Abstract

This paper is the result of strong belief that if earth observation and geospatial techniques could be applied to a village to plan its natural resources in sustainable manner, the potential of this technique for common man is harnessed maximum. So analysis of one village natural environment, predicting its impact on humans, identifying certain possible inhabitable conditions due to existing nature & suggesting ways to utilize these conditions for sustainable human's and nature's existence  is the motive behind this work.

1. Introduction

Biochar, produced from pyrolytic decomposition of lignocellulosic biomass, is a solid hard product and a unique green material for soil amendments. Its use has been identified in Amazon fertility as ancient indigenous practice. Through ‘pyrolysis’ (heating without oxygen) process (‘pyro’ which means caused by heat whereas ‘lysis’ means dis-integration), there are three types of yields we get: a solid product (biochar), a gaseous product (syngas/ synthesis gas), and a liquid (bio-oil). The solid item, biochar, is applied for soil amendments. It can be used in agricultural, industrial, and environmental as a functional material. Solid char, a common pyrolytic outcome is charcoal, typically used to designate the solid char product that is ignited for fuel, whereas “biochar” designates the unique solid char purposively used as a soil amendment agent. On the other hand, the gaseous and liquid counterparts of pyrolysis have commercial applications like green energy, feedstock raw materials, and precursors (Fig.1).

Abstract

Climate change is the biggest problem of today, resulting in thawing glaciers, changes in crop yield, and disturbances of water resources not only in India but in the entire world. Crop yield were predicted according mid century (2050s) by using IPCC Scenario A2 and studied impact of climate change. Out of 80 districts of Uttar Pradesh the three were selected for the case study areas namely Meerut, Kanpur and Hamirpur. FAO based model ET₀ Calculator 3.1 and Aquacrop 3.1 Plus were used to estimate reference evapotranspiration and crop yields respectively by using two major food crops rice and wheat. Crop yield were estimated the present climatic conditions (2005) and mid-century condition (2050s). According to the comparison of simulation results for present and future scenarios, crop yield of rice and wheat was predicted to increase in the mid- century (2050s). However, food security was predicted to become a threat in future, because the increase in population till the 2050s is projected, and yield was increases in the major food crops. In order to sustain the growing population, there would be required in the future, a large area for growing rice and wheat in comparison to the area presently, to meet the same food availability per capita. Hence, there would be a need to increase the production of the food crops in order to meet the growing demands of food in the study areas.

Keywords: Crop yield, Global warming, Future, Production

3.1 Land Use and Land Cover Analysis

Concept and Importance: Understanding the significance of land use and land cover analysis in watershed management for assessing spatial patterns of human activities and natural ecosystems.

Land use and land cover analysis is a fundamental tool in watershed management, providing valuable insights into the spatial patterns of human activities and natural ecosystems within watersheds. Here's an exploration of the concept and importance of land use and land cover analysis in watershed management:

a. Concept: Land use refers to the human activities and practices conducted on the land, including residential, commercial, industrial, agricultural, recreational, and conservation activities. Land cover, on the other hand, refers to the physical and biological features of the land surface, including vegetation, water bodies, bare soil, impervious surfaces, and artificial structures. Land use and land cover analysis involves mapping, classifying, and quantifying the spatial distribution and temporal changes of different land use and land cover types within watersheds using remote sensing, Geographic Information Systems (GIS), and field surveys.

b. Significance: Land use and land cover analysis is essential for watershed management due to its significance in understanding the interactions between human activities and natural processes, assessing environmental impacts, and guiding land management decisions. Key reasons for the importance of land use and land cover analysis in watershed management include:

Abstract

Coastal aquifers are the sources of freshwater which are being over extracted due to increase in population, agriculture and industrial activities. This over exploitation has resulted in decline in groundwater level and intrusion of sea water into the coastal aquifers making it unfit for human utilities. An attempt has been made in the coastal tracts of Pondicherry region using Electrical resistivity imaging. The survey has been attempted in six locations spread thought the study area and the interpretation has been attempted by RES2DINV software. The profile length attempted was between 150 to 300 m with electrode spacings between 5 to 10 m. The apparent resistivity ranges between 0.1 to 1183 & ohm. Sea water intrusion was prominent along the northern parts of the study area and lower resistivity isolated in kirumambakkam has been interpreted as salinity due to the presence of clay minerals. In nallavadu saline intrusion has been identified at shallow depth.

Abstract

Moisture stress is a wide spread problem seriously influencing sesame production, especially in arid regions. The present study was carried out in order to evaluate response of sesame cultivars to moisture stress and determine the best measures to increase and improve the yield of cultivars in stress condition.

ABSTRACT

A watershed is a topographically delineated area that is drained by a stream system, i.e. the total land area above some point on a stream or river that drains past that point. The watershed is a hydrologic unit that often is used as a physical – biological unit and a social – economic political unit for the planning and management of natural resources. Remote Sensing (RS) and Geographic Information System (GIS) techniques can be used effectively to generate the land use/land cover for evaluating the changes in an area. Conventional methods of runoff measurements are not easy for inaccessible terrain of Tamilnadu. Remote sensing technology can augment the conventional method to a great extent in rainfall-runoff studies. In this study, SCS Curve Number technique modified for Indian condition has been used for generation of CN for an ungauged watershed. A sub watershed of Manimuktha watershed is considered as study area. The daily rainfall data of Virudhachalam rain gauge station is collected and used to predict the daily runoff from the watershed using GIS based Soil Conservation Service -Curve Number (SCS – CN) method (USDA, 1972). Monthly and annual runoff values are obtained from the daily runoff values of the watershed. Base map, Soil map and Land use map of the study area are prepared by using Remote Sensing data and Survey of India (SOI) toposheets. PC Arc/Info GIS software is used for analysis.

Astro-meteorology is considered as one of the branches of Astronomy along with Astrophysics, Astrobiology and Astrometry. Astronomy is the science that deals with the material universe beyond the earth’s atmosphere. It studies the nature and constitution of Celestial (heavenly) bodies and the laws which govern them including the effect they produce upon one another. It seeks to unravel the probable past history of heavenly bodies and their development. There exists a confusion between the branches of Astronomy viz., Astrophysics and Astro-meteorology with the term Astrology. Astrophysics uses principles and laws of physics to explain how Celestial bodies form and function or in other words it will help to ascertain the nature of the astronomical objects, rather than their positions or motions in space. However, the Astrology and Astro-meteorology are the subjects deal in future prediction and often considered as pseudo-science which uses position and movement of stars and planets for that purpose. Astrology links these to predict the human behaviour, while that of Astro-meteorology links the same to predict the future weather.

From ancient days, these predictions are based on one’s wisdom and knowledge in astronomy and often these astrologers are astronomers who keenly watch the stars and planets movement and guide their rulers and farmers with their guesstimates. Till date it is being lore, mainly passed to followers of this group and the methodology is not documented clearly and properly. There are many incidences of falsified predictions of astrology as reported by many reviewers elsewhere and we are not discussing the same here, owing to our interest in weather prediction i.e., astro-meteorology only. However, we have to recognize the works of the ancient astrologers who helped in developing the astronomical science.

Basically, there are two approaches in astrology viz., geocentric and heliocentric as used by the practitioner of astrology worldwide. As the name implies the Geocentric astrology is based on the calculation of planetary position as seen by the observer from the earth and thus earth is centre, while Heliocentric astrology bases its interpretation upon position within solar system with reference to the sun as centre. Though in true sense, the sun is the centre, the effect of motion as manifest on the earth is the basis of most astrological interpretation. Hence, vast majority of astrologers employs the geocentric calculations of the planetary positions for their predictions.