eChapters

How can we develop a sustainable water security system in an era of climate change and global warming ?  In my view, every country should develop a sustainable water security system with the following four major components.

Supply augmentation

The major sources of irrigation water are from rainfall, rivers, tanks, reservoirs and other surface water resources, groundwater, industrial and domestic effluents, and seawater.  Seawater comprises almost 97 per cent of the global water resource and is an important social asset.  With the melting of Arctic and Antarctic ice, sea level will go up with disastrous consequences. The tsunami of December 2004 gave a glimpse of what could happen in the future.  To augment supplies, we must harvest rainwater and store it carefully, both above and below ground.  Also, we should ensure that all waste water which emerges from industry and domestic use is purified and recycled.  Rainwater harvesting should become a way of life, so that it becomes everybody’s business.

Introduction 
Having explored the fundamental principles and various types of elementary crop growth models in the previous chapter, we now arrive at a pivotal juncture in their journey from theoretical constructs to practical, dependable tools. It‘s one thing to conceptualize and build a mathematical representation of crop growth; it‘s quite another to ensure that this model accurately reflects realworld plant behavior and can be reliably used for predicting outcomes and informing critical agricultural decisions. This chapter, Calibration, Validation, Verification, and Sensitivity of Elementary Crop Models,” will guide you through the indispensable processes that establish a model‘s credibility and utility. We will delve into the systematic steps involved in calibration, where we meticulously fine-tune a model‘s parameters to accurately reflect a specific crop‘s response within a particular environment, effectively adapting the general model to local conditions. Following this, we will explore validation, a rigorous process of independently testing the model‘s predictive power against datasets not used during calibration, ensuring its robustness and generalizability. Beyond accuracy, we will also cover verification, which confirms the model‘s logical consistency and computational correctness, ensuring that its internal workings are sound. Finally, we will examine sensitivity analysis, a crucial technique for understanding how changes in various input factors—especially weather parameters—or internal model coefficients influence the model‘s outputs, thereby revealing the model‘s critical drivers and potential vulnerabilities. These four processes are far more than mere technical formalities; they form the bedrock upon which the utility, accuracy, and trustworthiness of any crop model, no matter how elementary, are firmly built. By engaging with these essential steps, we can transform theoretical frameworks into powerful instruments capable of supporting sustainable agricultural planning, risk management, and adaptive strategies in a dynamic climate.

Abstract

Traditionally remote sensing technology has been widely used in the mapping and change detection of vegetation features. Of late research has shown that remote sensing technology can be successfully employed for retrieval of biophysical parameters of vegetation, either by statistical techniques or by employing radiative transfer models (RTMs). This paper gives an introduction to leaf and canopy RTMs, especially about PROSAIL, which is the most commonly used model. It reviews different techniques for inverting RTM models, their merits and performance comparison. The main techniques briefly described are iterative optimization, look-up table, artificial neural network, genetic algorithm and support vector machine. At the end, it gives examples of applications which use canopy biophysical parameters retrieved by inverting RTM.

Keywords: Radiative transfer, PROSAIL, Inversion, Vegetation

1.0 Introduction

Accurate quantitative estimation of vegetation biophysical variables is useful for a large variety of agricultural, ecological, and meteorological applications (Houborg et al., 2007). The spatial and temporal distribution of these variables are important inputs into models quantifying the exchange of energy and matter between the land surface and the atmosphere, developing better yield prediction models and detecting abiotic stresses at regional scales. Among the many vegetation characteristics leaf area index, leaf chlorophyll content and leaf water content are of prime importance (Bacour et al., 2006; Houborg et al., 2007).

Abstract

The drainage pattern and the architecture are the reflections of not only the tectono fabric of a region, but also the active tectonic processes. From amongst many drainage patterns and anomalies, the eyed drainages, deflected drainages and the compressed meanders interpreted from satellite images for Pudukkottai region of Southern Tamil Nadu infer to reflect the active N-S block faults and NE-SW sinistral and NW-SE dextral faults related to post collision tectonic phenomena.

Introduction

The Geoscientists have long back recognized that the drainages are the reflections of the surface and subsurface lithology, structure and tectano fabric and also the active tectonic movements especially of the Quaternary period (Thornbury, 1985). Hence, the Geoscientists have all along been showing greater interest in mapping the drainage patterns. However, the mapping of the drainage patterns and detection of tectonic features there from have gained greater momentum only after the advent of modern Remote Sensing technology. Since the airborne and satellite borne spectral images have the credentials of showing the drainage pattern, the drainage anomalies and the tectonic features related to them all of which were studied in conjunction to bring out information on the geological history of the Quaternary period by many workers (Vaidyanadhan, 1971; Murthy and Sastri, 1981; Agarwal and Mitra, 1991; Ramasamy, 1991; Saravanavel and Ramasamy, 2014).

Abstract

The strenuous field trekking, inaccessibility, perception of geology only within the visibility range of the eyes etc. have remained matters of major constraints in the past in field based geological mapping. The remote sensing technology not only eradicated these but also provided lot many newer vistas in the field of geology. In addition, the application of such unique technology has brought out a hierarchy of newer concepts in the field of geology. The paper narrates the newer knowledge gathered on the geology of South India in the post remote sensing Era.

Abstract

As the available surface water is too inadequate to fulfill the man’s requirements he was forced to locate newer groundwater reservoirs on one side and to control the fast depleting water table on the other side. As the ancient techniques have failed to give precise models on groundwater targeting, he was to invent and employ newer technologies. Under this scenario the recently invented spatial technology has emerged as a very powerful tool in groundwater targeting and management. The vistas and virtues of such spatial technology in groundwater management have been discussed in this overview paper by citing some case studies from Tamil Nadu, India.

Abstract

The coastal zones are very fragile owing to the reason that these form the junction between the continents and the oceans. Further the multivariate tectonic, fluvial, marine and aeolian processes, which vary independently in their degrees and duration in space and time, act in different permutations and combinations with each other and keep on constructing and destroying the landforms along the coasts. Again, as coastal zones are heavily populated; it adds further pressure on the coastal systems. The paper deals about the hierarchy of issues faced by the coastal zones like tectonic emergence and subsidence and it‘s impacts and the ways, means and needs for measuring the rate of such movements; marine regression and the need for the time series models on the land-ocean boundaries for drawing the sea level curve for India, the Quaternary deltas and the essentiality for understanding the mixture of fluvial and marine forces for the possible future forecasting models; back water modifications, the related issues and the demands for the sediment-chemo dynamics modelling; disaster vulnerabilities and mapping methodologies etc; all using the unexplored vistas of Geomatics technology. The concept has been explained by taking Tamilnadu coast as the reference region.

Introduction

The coastal zones are always fragile as these are formed by the tectonic, fluvial, marine and aeolian processes, which act independently in varying degrees and duration and occur cumulatively in different permutations and combinations along the different parts of the coasts. In addition to such inherent fragility, the coastal zones stand vulnerably exposed to various natural and anthropogenic disasters like seismicity and earth movements, tsunamis, floods, sea level rise, cyclones, coastal erosion etc. Further, owing to the enriched land and water resources, enjoyable eco systems and avenues for intra and intercontinental trades, more than 70% of the global population and the related infrastructure are clustered in the coastal zones. Hence the scientists from all over the world have started showing more attention towards the coastal regions (Ahmed, 1972). The advent of Geomatics technology comprising multi spectral and hyper spectral remote sensing, GIS, GPS, Interferometry, etc. have opened up exhaustive newer vistas in unfurling the hidden complexities of the coastal zones and to provide solutions. From amongst many, the remote sensing based studies carried out by Shailesh Nayak and his team (2004) have brought out many first time information to light about the Indian coasts. But still we will have to go a long way, as the global coasts in general and the Indian coasts in particular are confronted with a hierarchy of challenges and the Geomatics technology can provide answers for these challenging issues owing to its unique virtues. The paper narrates the unique issues of the Indian coasts and how effectively the geomatics technology can be used in solving them. This has been done by taking Tamil Nadu coast as the test area.

The principle of common but differentiated responsibilities is the core of the many climate agreements arrived at so far, including the Kyoto Protocol (1997) and the Bali Plan of Action (2007).  The differentiated responsibilities aim to meet the special needs of developing countries for accelerated and equitable economic development.  Both at Copenhagen and Cancun, the industrialised countries proposed limiting the rise in mean temperature to 2ºC above normal.  Even this seems to be unattainable in the context of the present rate of emission of greenhouse gases (GHG).  Hence, the principle of common but differentiated impact of the 2º change in mean temperature is essential for prioritising climate victims. For example, small islands like Tuvalu in the Pacific Ocean, Maldives, Lakshadweep, and Andaman and Nicobar, as well as the Sunderbans in West Bengal, Kuttanad in Kerala and many other coastal locations will all face the prospect of submergence.  Floods will become more serious and frequent in the Indo-Gangetic plains.  Drought-induced food and water scarcity will become more acute. South Asia, sub-Saharan Africa and the small islands will be the worst victims.  In contrast, countries in the northern latitudes will benefit due to longer growing seasons and higher yields.

8.1 Climate Change

Climate change is a broad term of global negative phenomena as created predominantly by burning of fossil fuels, which add heat-trapping gases to earth’s atmosphere. The over accumulation of greenhouse gases in the atmosphere lead to increased temperature and this is called as global warming. As a result, sea-level rise; ice mass loss in Greenland, Antarctica, Arctic and mountain glaciers worldwide; shifts in flowering/ plant blooming; and extreme weather events occur.

Coordinate Reference System (CRS) is a framework of coordinate based local, regional or global reference system used to represent the locations of geographic features. Coordinate Reference Systems (CRS) provide a standardized way to define the real-world geographic locations which further enable us to use common locations of geographic datasets for integration. The datasets from different sources and CRS are transformed to a common CRS in order to perform various integrated analytical operations like overlaying of data layers. Coordinate Reference Systems are mainly of two types:

1. Geographic Coordinate System: This is global or spherical coordinate system in which locations of features on earth are defined in terms of latitude and longitude values. Latitude and longitude are angles based on a point at the centre of the earth. In this system, locations are defined by degrees, therefore, distances cannot be measured accurately. The most common geographic coordinate system is the World Geodetic System 84 (WGS84).
2. Projected Coordinate System: In this system, locations of features are defined using cartesian x, y coordinates on a flat, two – dimensional surface. This system is based on sphere or ellipsoid projected on flat surface. Projected Coordinate System are generally referred as projec- tion. It is required for preparing maps on paper or computer screen. Un- like Geographic Coordinate System, projected coordinate system has ac- curate measurements of distance, angles and areas across the two dimen- sions. However, there is alwaystrade-offs between area, direction, shape, and distance in this system due to distortions created while representing ellipsoid or spheroid on a flat map. So, the projection type is selected based on its application. Most common projections are Universal Trans- verse Mercator (UTM), Lambert Conformal Conic and Albers Equal Area.

Introduction

Crop discrimination is the process of using remote-sensing technologies to identify and characterize different types of crops based on their electromagnetic behavior at different wavelengths. Each crop has a unique spectral signature that reflects its response to the incident radiation. However, the spectral signature can also vary depending on the season, the angle of the sensor, crop’s features, illumination intensity, weather phenomenon, and topography among other external factors (Parida et al., 2023).

Abstract

This paper portray the generation of spectral signature library for semi arid area crops using hyperspectral data and the scope of the application of the generated digital library for the classification of the satellite imagery of similar regions.

Hypersprctral data have narrow bands which help to provide additional information related to observed features. In case of this agriculture study crop related information are gathered using hyperspectral sensors The variation in the observations are due to physic-chemical characteristics such as biochemical composition, physical structure, water content and plant ecophysical status. Using the hand held spectroradiometer the reflectance is captured for the bajra, moong, moth crops from the Rajasthan, India’s semi arid region. The observations have been taken for different dates considering the phenology of the crops. Hyperion data from EO-1 satellite have been used to implement the classification operation based on the spectral signature library of bajra. It is observed that the image with the crops can be classified according to the phenology difference.

The subject on micrometeorology deals with small scale meteorology of near ground surface with its related elements of both physical and biological processes. When this micrometeorology is integrated for crop production purposes, then it becomes crop micrometeorology, which is very important in the science of agricultural climatology. This crop micrometeorology gets varied with the architecture of the crops grown, available soil moisture, agronomical practices followed, soil texture, topography of the area, the nature of climate and weather that prevails etc. The soil surface heat, exerts a profound frictional effect on wind movements and hence it is different from the air layers at higher levels. These special features constitute the microclimate, which is modified compared to a flat bare surface by factors like topography, nature of soil surface, presence of vegetation and type of vegetative cover. The presence of crop canopy leads to distinct differences in the thermal and humidity regimes of the soil, the air layers immediately above it and the distribution of radiation and wind profiles inside the canopy. The study on the physical exchanges of the transfer of heat, momentum and moisture in the microclimatic region is called as micrometeorology (Venkataraman and Krishnan, 1992).

Radha Krishna Murthy (2002) reported that the micrometeorology is the micro scale of both the physical and meteorological factors that are taking place within the ecosphere. The ecosphere means the area between just above the crop canopy and just below the root zone of a crop. Further he was on the opinion that the microclimate of valley is important as compared to the entire climate of the mountain for the bio-life of valley. For the crops purpose, the microclimate is the climate of the ground surface to top of the crop canopy or otherwise it can be called as crop’s climate, a modified one from the Stevenson’s Screen climate/weather observation.

Introduction 
The preceding chapter introduced the foundational concept of the systems approach, emphasizing the interconnectedness of components within complex entities and the power of simulation to understand their dynamic behavior. Building upon this understanding, This chapter delves into one of the most vital applications of system simulation in agriculture: crop models. These sophisticated computational tools stand as the cornerstone for quantitatively representing the intricate interplay between the soil, the plant, and the atmosphere, providing a dynamic lens through which to examine agricultural productivity and resource use efficiency. Crop models are essentially mathematical descriptions that mimic the growth, development, and yield of agricultural crops under various environmental conditions and management practices. They encapsulate our scientific understanding of how plants absorb light, water, and nutrients, how they convert these resources into biomass, and how they respond to stresses like drought, heat, or nutrient deficiency. Far from being mere statistical regressions, these are often process-based models, meaning they attempt to simulate the underlying biological, physical, and chemical mechanisms that govern crop performance. By integrating knowledge from disciplines such as plant physiology, soil science, meteorology, and agronomy, crop models provide a holistic and dynamic representation of the entire agricultural system. The evolution of crop modeling has mirrored advancements in computational power and scientific understanding. From early empirical models that relied heavily on statistical relationships between yield and a few environmental factors, the field has progressed to complex mechanistic models that simulate detailed physiological processes on a daily or even hourly basis. This evolution has transformed crop models from purely research-oriented tools into powerful instruments for real-world decision-making. Their ability to conduct virtual experiments—simulating hundreds or thousands of growing seasons

Introduction 
The preceding chapter elucidated the concepts of potential and achievable crop production, highlighting the significant gap that often exists between a crop‘s theoretical maximum yield and what is realized in the field. While genetic potential and optimal climatic conditions define the upper limits, the vast majority of agricultural systems globally operate under less-than-ideal circumstances. Among the most pervasive and impactful constraints on crop productivity are moisture (water) and nutrient limitations. These two factors, either individually or in complex interaction, are frequently the primary drivers of the yield gap, preventing crops from expressing their full genetic potential and significantly impacting farm profitability and regional food security. Water, as the universal solvent and a direct participant in photosynthesis and countless metabolic processes, is indispensable for plant life. However, its availability in agricultural systems is highly variable and often insufficient. From the rainfed fields dependent on increasingly erratic rainfall patterns to irrigated lands facing dwindling water resources, moisture stress – encompassing both drought and, paradoxically, waterlogging – poses an immense challenge. Drought alone accounts for a substantial portion of global crop losses, directly affecting plant physiological functions, growth, and ultimately, yield. Conversely, excessive moisture can lead to anaerobic soil conditions, nutrient leaching, and root damage, equally debilitating for crop performance. Parallel to moisture, the availability and judicious management of essential plant nutrients are foundational to productive agriculture. Plants require a suite of macro- and micronutrients for healthy growth, enzymatic activity, and the synthesis of biomass. Soils, over time and with continuous cropping, often become depleted of these vital elements, necessitating external application through fertilizers or the enhancement of natural nutrient cycling processes. However, nutrient management in the field is rarely perfect. Deficiencies of

5.1 Introduction

A Crop Simulation Model is a mathematical model which describes crop growth and development processes as a function of environment, soil conditions, and crop management. Simulation model is a schematic representation which works on set of underlying assumptions, equations and algorithms, closely replicating the behaviour of the agricultural systems. A crop growth simulation model predicts the harvestable yield. Apart from this final state of crop production, the model also provides a host of quantitative information about major processes involved in the various stages of crop development. The information covers each component of the crop such as leaves, stems, roots and harvestable products as they change over time, including the changes in soil moisture and nutrient status.

An increase in the population, demands an increase in agricultural production with available resources. Efficient management of available resources under varying weather conditions is important for increasing agricultural productivity. Besides that, the emphasis of agricultural production changes from quantity to quality and sustainability (Aggarwal et al., 2000). Solution of these new challenges lies in understanding the interaction of the numerous elements affecting the plant growth and generating a crop growth model. This extremely complicated information needs to be processed and interpreted on a spatial scale. Information technology which has unleashed new frontiers in data analysis and automation is the driving force behind this model generation. These powerful computer programmes simulate the crop growth or yield of crops under various management regimes and provide near real time information to help farmers to make strategic decision in the management of their crops.

Introduction 
Building upon our foundational understanding of how weather intrinsically shapes crop development, from the micro-scale physiological responses to the broader patterns revealed by Indigenous Technical Knowledge, we now transition to a chapter dedicated to a highly practical and actionable synthesis of this critical interrelationship: Crop Weather Calendars. These calendars are far more than mere chronological charts; they represent systematically organized and dynamic compilations of meteorological information meticulously integrated with precise crop-specific data, designed to serve as an indispensable guide for optimizing agricultural practices. In this chapter, we will embark on a comprehensive exploration of Crop Weather Calendars, moving beyond the often informal or culturally embedded ecological calendars previously discussed. We will delve into how these powerful tools systematically integrate historical weather patterns, which involves analyzing longterm meteorological data to establish reliable averages and identify probabilities of specific weather events, such as the typical onset of monsoons, average frost dates, or periods of peak solar radiation. They also incorporate real-time meteorological data, bringing in current weather observations to track actual conditions against historical norms and initial forecasts. Furthermore, Crop Weather Calendars utilize future weather forecasts, drawing on short-, medium-, and long-range meteorological predictions to anticipate upcoming conditions (Fig.12.1). This integrated weather information is then critically aligned with the precise phenological stages of a given crop cultivar, recognizing that a crop’s sensitivity and requirements vary significantly throughout its life cycle. Our exploration will detail the essential components that constitute a robust crop weather calendar, including the identification of crucial growth periods, pinpointing the most sensitive and influential developmental stages of the crop like germination, tillering, flowering, and grain filling, where weather impacts are most pronounced. It also involves the establishment of weather thresholds, which means defining the optimal, critical, and damaging ranges of key weather

1.1 Weather Parameters

The atmosphere is a gaseous component surrounding the earth as an integral part and its interaction with ocean (water bodies), other planets of the solar system and land (Earth) generates one physical product namely weather elements (temperature, pressure, wind, humidity, precipitation, etc.,) and these form the weather and climate of the Earth. The process of generation of weather and climate of the region is purely natural and cannot be modified by the human being. But they can do adaptation to the existing climate and weather for their sustainable life. The weather can be defined as day to day change in the atmosphere in terms of heat and moisture exchange, while the climate is average of long period weather covering larger area or a region.

The particular climate of a region dictates/permits the genesis of a particular bio-organism in that domain and this selected organism’s growth and multiplication are triggered by the prevailing weather of that particular climate. Hence weather parameters of the atmosphere are very important to human being and other organisms of the Earth. The details on weather elements are given in question and answer section of this book (Annexure-I)

Abstract

A large tsunami triggered due to a massive earthquake of 9.3M in the offshore of Sumatra on 26^th December 2004 created havoc in several countries of the Indian Ocean. The deadly tsunami waves lashed the towns adjoining the coastline of several countries including Indonesia, Thailand, Malaysia, India and Sri Lanka causing more than 1,50,000 deaths. The earthquake occurred along the plate boundary is marked by a subduction zone between the Indian plate and the Burmese micro plate. In Andhra Pradesh, six coastal Districts were affected and amongst them four south coastal districts experienced maximum damage. In the present study, Tuppalapalem (vagarru revenue village) of Vakadu mandal, Nellore District was selected as a case study for discussion in the article. The objective of the study was to make an inventory of the damages to the natural and physical resources, by preparing pre and post-tsunami maps on geomorphology, land use / land cover, tsunami inundation and finally the tsunami risk zonation map on 1:8,000 scale. In this process, water and soil samples of the affected regions were also collected and chemical analysis was carried out. The data on damages were collected using a specially formulated data sheet. High resolution IKONOS data of January 2000 and IRS data of January 2005 were used for the generation of respectively the pre and post-tsunami geomorphology and land use / land cover maps. Ground truth data were also incorporated in these thematic maps.

The study has revealed that the water in all the bore wells became brackish after tsunami. About 80 country boats, 40 fibre boats and nets were lost with an estimated loss of about one crore. The tidal waters damaged the bunds of the aqua-farms and deposited the transported sediments in them. The extensive casuarina plantations, were reduced by the onslaught of tsunami and the casuarina trees became brown due to their contact with tidal waters. From amongst the various geomorphic units mapped in the study area, the beach, spits, shoals, creeks, swales, palaeo creeks were inundated by tsunami where as beach ridges, dune complexes and stabilized sand dunes were not affected and hence inferred as barriers. Geomorphology, land use / land cover and the tsunami inundation maps, tsunami risk zonation map were prepared showing very high, high and moderate zones of tsunami risks. It is recommended to take up casuarina plantations extensively along the coast as a biowall, which will also provide employment to the local people. The shallow aquifers of the coastal environment were contaminated by the tidal waves. Hence, drinking water should be supplied through protected water supply schemes. As a part of the community development, proper awareness must be created through training and appraisal to the fisherman community to face such natural disasters. The coastal regulation zone act, which prohibits any construction activity within 500m of the high tide line, should be strictly enforced. Keeping in view of the experience, a holistic tsunami protection plan is needed to safe guard the Indian coasts.

Abstract

The tsunami has struck many coastal countries of South Asia on 26 December 2004 and caused greater devastation along Andaman - Nicobar and Indian coasts. In India, Tamil Nadu coast is worst affected in Kanyakumari, Nagapattinam and Cuddalore districts. Subsequently, damage assessment, in respect of natural and physical resources, was done using Remote Sensing and GIS in parts of Cuddalore and Pondicherry coasts. The structural damages were studied village wise and categorized as maximum, moderate and least affected areas. As far as the natural resources are concerned, the changes in geomorphology, land use/ land cover, soil and water have been studied. On the basis of the same, the risk maps were also generated in GIS environment.

Keywords: Tsunami, Damage Assessment, Remote Sensing, GIS, Cuddalore, Pondicherry