eChapters

Abstract

Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on regional scales. Large historical datasets are required in drought studies to understand the complex inter-relationship between the climatic and meteorological data. Extraction of valuable information from such large data archives demands an automated and efficient way. Data mining is answer to above problem as it has the potential to search for hidden pattern and identify the relationship between the datasets. For the current research, rainfall data from 1951-2006 was used to compute Standardized Precipitation Index (SPI) and NOAA-AVHRR NDVI images for the period 1981-2006 was used to calculate Vegetation Condition Index (VCI). SPI and VCI were used as input parameters in the Apriori Algorithm for generating the association rules. Interesting rules were developed identifying the relationship between user-specified target episodes and meteorological events. Therefore, spatial and temporal pattern of drought was achieved with 88% confidence.

Introduction

The first step of geostatistical analysis in R is the creation of dataset which is acceptable in R environment by using the data collected from field survey or laboratory experiments. Different type of data is collected during experiments e.g. sand content, silt content, organic carbon content, geographical location of data points, soil type, land use type etc. Here, we will discuss how these data are converted into a acceptable dataset format for R environment and how we manage these data. In R this task is performed in two steps. First, a data structure is created to hold the data and second is the entering or exporting the data into data structure.

Introduction

Hydrological data visualization plays a crucial role in analyzing and understanding the behavior and characteristics of water resources. By visualizing hydrological data, we can gain insight into the distribution, variability, and trends of different hydrological variables such as river flow, precipitation, groundwater levels, and water quality parameters. This information can help decision-making in areas such as water resources management, flood forecasting, and climate adaptation.

R Language is a popular and versatile tool for hydrological data visualization, offering several powerful packages for data analysis and visualization. Each of these packages has its own strengths and limitations, which can be leveraged depending on the specific needs of a given analysis. For example, ggplot2 is an extensively used package for creating publication-quality static plots that are customizable and visually appealing. This package is based on the grammar of graphics, which provides a new plotting system that combines the benefits of both base and lattice graphics. ggplot2 can be used to create a wide range of plot types, including time-series plots, spatial maps, and scatterplots. It provides several options for customizing plot elements such as axis labels, legends, and plot themes. On the other hand plotly offers interactive and dynamic plots that can be easily shared and embedded in web applications. With plotly, we can create plots that allow for zooming, panning, and hovering over data points to display additional information. This package is mainly useful for exploring relationships between hydrological variables. In addition to these packages, hydroTSM provides functions for reading, manipulating, and plotting time-series data. hydroTSM can help us create time-series plots of variables such as river f low, precipitation, and temperature, with options for adding trend lines error bars, and annotations.

Introduction

Data input or capture is the operation of encoding the data and incorporating in the database. The creation of a clean error free digital database is a complex and critical task determining the very usefulness of a GIS. GIS data represent not only where the graphic or cartographic features occur, but also the associated attributes of them.

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

(b) Entering the non-spatial, associated attributes and

(c) Linking the spatial to the non-spatial data.

At each stage necessary verification and checking procedures shall be deployed to ensure an error free database.

Introduction

Data are the most critical element of a GIS. It is reported that more than two thirds of the time and expenditure to develop a GIS is spent on data collection and organization. Understanding and appreciating the criticality of data is of paramount significance to practice GIS. This chapter is complementary to database concepts chapter discussed in Level II and deals with the fundamentals to start with and the concepts of building an information organization.

Data - A Definition and Certain Points to Ponder

“Data” are defined as a body of facts or figures, which have been gathered systematically for one or more specific purposes.”

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

4.1 INTRODUCTION

Database management system is a computer program to store, search, manipulate retrieve and give the result. Vast amount of spatial data are available and it is necessary to arrange them, store them for retrieval and analysis. Attribute data adds intelligent to GIS. Without database, a GIS will be just a Cad software. Cad softwares do not have database. GIS softwares have their own databases besides it has the potential to link with external database like Oracle, MS Access, MS Excel, Informix, INGRESS, Sybase and dbase. The data available in paper has to be digitally stored in a proper order. Previously the data were stored in a a flat file format where the data was stored without any structure. But to search and retrieve and analyse the database, database structures were developed. There are of 4 types of database structures namely Hierarchical, Network, Relational database and Object orientation.

Tables are made of rows and columns. Rows represent a map feature. Columns represents characteristic of map feature. Row is called a record or a tupule column is a field or an item. Table 4.1 is a table where rows 1, 2 and 3 represent the locations with the soil types. A column soil type represents various soil types.

Soil Organic Matter

Soil organic matter plays an important role in the maintenance and improvement of soil properties. It is a dynamic material and is one of the major sources of nutrient elements for plants. Soil organic matter is derived to a large extent from residues and remains of the plants together with the small quantities of animal remains excreta, and microbial tissues. Soil organic matter is composed of three major components i.e. plant residues, animal remains and dead remains of microorganisms. Soil organic matter comprises residues of plants and animals and these compounds occur in soil in close combination with inorganic substances. Animals and plant residues are made up of complex carbohydrates, simple sugars, starch, cellulose, hemicellulose, pectins, gums, mucilage, proteins fats, oils, waxes, resins, alcohols, aldehydes, ketones, organic acids, lignin, phenols, tannins, hydrocarbons, alkaloids, pigments, etc.

The organic matter subjected to microbial decay in the soil comes from several sources like plant remains, forest litter, incorporation of plant tissues, animal tissues, and excretory products. The chemistry of organic matter is very complex, and investigations of the transformations and the responsible organisms have therefore been extremely interesting. The organic constituents of the plants are commonly divided into six categories.

Soil organic matter is among the most complex materials existing in nature. In addition to the organic constituents present in undecayed plant and animal tissues, soil organic matter contains living and dead microbial cells, microbially synthesized compounds and an endless array of derivatives of these materials produced as the result of microbial activity. Soil organic matter probably contains most of the naturally occurring organic compounds. Some components of soil organic matter are no doubt distinctive to the soil environment, particularly those involving inorganic-organic complexes.

6.1. Composition of Soil Organic Matter

The composition of soil organic fraction is as follows:

1. Components of organic residues undergoing decomposition

Deep learning enables automated analysis of agricultural images for crop classification, disease detection, weed identification, yield prediction and soil monitoring. This chapter examines Convolutional Neural Networks, Recurrent Neural Networks and Vision Transformers applied to imagery collected by satellites, drones and ground-based sensors. These models automatically identify patterns and features that conventional methods may overlook. Integration with precision agriculture can improve decision-making, increase crop productivity and reduce resource waste. Challenges include shortages of annotated datasets, limited interpretability and high computational requirements. Transfer learning, data augmentation, edge computing and real-time processing are discussed as approaches for enabling practical deployment in remote and resource-constrained agricultural environments.

Deep learning emerged as a serious contender in the field from a 10-year explosive computational development. Deep learning is therefore a special form of machine learning, the algorithms of which are based on the structure and function of the human brain.

Machine learning VS Deep learning

Deep learning is arguably the most efficient form of machine learning. This is so important because they know the best way to solve the problem and how to fix everything. The following is a description of deep learning and machine Learning:

Biological activity or index of a soil is the function of number of organisms present in soil and their physiological efficiency. The rate of respiration can be used as an index of the biological activity of soil as it reflects the physiological efficiency of the organisms. Monitoring of dehydrogenase, which are respiratory enzymes and integral part of all soil organisms, will give a measure of biological activity of soil at a given time.

During respiration biological oxidation of reduced compounds occur which is catalyzed by dehydrogenase. In the process TTC gets reduced to a pink coloured compound Triphenyl formazan (TPF) which can be quantitatively extracted by methanol and measured colorimeterically. The soil dehydrogenase activity in the current experiment was measured as per the methods of Cassida, (1977).

Abstract

Population of Rajasthan as per Census 2011 is 68,621,012 persons which is much higher than census 2001 that records 56,507,188. It includes 12 districts of Rajasthan. Western Rajasthan covers about 61% area of the total geographical area and 39.51% population of the total population of the Rajasthan. The growth rate of population is much higher than that of the Rajasthan State. In the census 1981 Western Rajasthan had registered a phenomenal increase of 35.48 per cent over 1971 population. Regional pattern of growth rate in western Rajasthan shows that extreme hot arid region district (Barmer) is on top having 32.55 per cent growth rate whereas Indira Gandhi Canal Region (Ganganagar) district has least population growth of 10.06 per cent only. In 1981, the average urban growth rate had been 54.52 per cent. In 2001, the rural density of population was 83 persons per sq. km.

3.1. Determination of Bulk Density of Soil

3.1.1. Determination of Bulk Density of an Undisturbed Soil

Bulk density of soil is expressed as the ratio of the mass (weight) of soil particle to their total volume including the pore space between the soil particles. Alternately, it is the weight of unit volume of dry soil. It is usually expressed in the unit of gram per cubic centimeter (g/cc) or mega gram per cubic meter (Mg/m³). In fine textured soil bulk density varies from 1.00 to 1.60 g/cc, whereas in coarse textured soil from 1.20 to 1.80 g/cc.

Introduction

Desert soils are the soils of arid regions that remain dry for most part of the year and support sparse xerophytes. The amount of water received by precipitation is far less than what is lost by evapotranspiration. Most deserts receive an average precipitation of less than 400 mm. Because of extreme imbalance between evapotranspiration and precipitation, many desert soils contain salts.

Distribution

Deserts occupy about one-third of the areas of Africa and Australia, 11% of Asia and about 8% of the Americas (Boul et al. 1997). Desert soils include both cold and hot desert. In India, desert soils occur in three agro-ecological regions (AER). The cold arid desert of extreme north (Leh, Ladakh region of Jammu & Kashmir ), the hot arid desert of north western India (Rajasthan, southern parts of Haryana and Punjab and northern parts of Gujarat) and the tropical arid desert of the Deccan plateau in South India represents AER 1, 2 and 3, respectively. The three different desert regions in India have characteristic climatic and soil conditions.

Abstract

Conversion of an already arid land into a desert through indiscriminate human actions magnified by droughts. Such actions include overgrazing, repeated burning, intensive farming, and stripping of vegetation for firewood. One quarter of the earth’s surface is threatened by desertification - an area of over 3.6 billion hectares. Since 1990, Six million hectares of productive land are lost every year due to land degradation. Desertification threatens the liveihoods of more than one billion people and has already made over 135 million people homeless. Desertification does not occur in linear, easily mappable patterns. Deserts advance erratically, forming patches on their borders. Areas far from natural deserts can degrade quickly to barren soil, rock, or sand through poor land management.

Concept and Definition

Desertification is a major problem in the drylands of India, affecting the way of life for its inhabitants. The problem is more severe in the arid lands in the northwestern part of the country. There have been a few numbers of definitions of the word “Desertification”. However for simplicity of its origin and meaning we would refer to two of the frequently used ones; (1) proposed in 1991 by UNEP: “Land degradation in arid, semi-arid and dry sub-humid areas resulting mainly from adverse human impact and (2) prepared for the Earth Summit at Rio, held in June 1992, for inclusion in Agenda 21, as “Land degradation in arid, semi-arid and dry sub-humid areas resulting from climatic variations and human activities. (Anonymous 1992).

Comparing two of these initial definitions, it would show that first one indicated desertification as a state of natural resources like soil and vegetation, mainly resulting from human activity while the second one included climate as a major cause. However, it reduced the emphasis of human activities as being the primary responsible factor and tried to link the possible impact of climate change on desertification. But the simplicity of the 1992 definition of desertification helped researchers to bring in sharp focus on the special environmental and socio-economic problems of the drylands.

So over the years, the key words that explains the term “desertification” has been changing or are being amended for specific purposes. This would at least mean that the problem of desertification is still debated world-wide, and its impact is not limited to some nations/regions as was presumed and discussed in first of UNCCD platforms. Therefore, as a precaution and combating this process, few catchy words like non-sustainable management, productivity, over-exploitation of natural resources, dryland livelihood, droughts are found to surround the word desertification. Earlier, some, people equated desertification with advancing boundaries of the existing desert and in India, examples of expansion / contraction of geographical boundary of Thar desert was a public debate among various researchers and planners. In the same time, some of the definitions tried to broaden its geographical scope so that greater populations suffering from adverse effects of land degradation would benefit out of various remedial measures.

1.0 General

 The design of canal involves following three major aspects:

 A Irrigation and drainage canal network planning.

 B Determination of discharge / capacities of  canals and drainages.

 C Determination of shape and size i.e. geometrical dimensions of the proposed canal section, generally

 known as design of canal section. More appropriately it can be called as design of internal section

 of canal. It can be further divided in three following parts viz.

 i. Design of unlined canals:

 ii. Design of lined canals: or a canal may be partly lined and partly unlined.

 iii. Choice between the unlined and lined canals. This aspect should be distinguished from the selection of type of lining. However, the scope of choice of lining may include both the aspects.

 The design of canal section remains incomplete without the design of its embankments, either in lined or unlined canal, so the next aspect is

 iv. Design of canal embankments. Still to complete the design of a canal one more aspect is required i.e.

 v. Canal aesthetics and canal arboriculture.

Designing an earthen dam requires the coordinated evaluation of catchment runoff, reservoir capacity, site conditions, foundation properties, expected service life, and the consequences of failure. A selected return period is used to estimate extreme rainfall and runoff, while adequate spillway capacity and freeboard protect the embankment against overtopping. The dam must possess low permeability, stable upstream and downstream slopes, and sufficient resistance to seepage, settlement, erosion, and earthquake forces. This chapter presents design criteria, top width, dam height, side slopes, freeboard, seepage-control components, slope-protection measures, and stability considerations. It also explains procedures needed to create a safe, economical, and functional embankment.

5.1 Irrigation Channels
Canal or channel is an artificially made conduit through which water is supplied to the field from river, tank, reservoir or wells. The open channel is the most common type of water conveyance system. If the pipes and drains are partially full behave like open channel. The irrigation channel may be lined or unlined and the flow is either uniform or varied.

The canal may be classified in following ways.

a) Classification based on the source of supply i) Permanent or perennial canal ii) Inundation canal

A canal is said to be permanent when it is fed by a permanent source of supply and has permanent networks of distribution of supplies. Inundation canals draw their supplies from river usually when water stage exceeds a predetermined level. Headworks are not provided for diversion of river water to canal. However, canal head regulators are provided to regulate the canal head as required time to time to accommodate the changing pattern of river course.