eChapters

Introduction

Climate variability can have significant impacts on agricultural production, affecting both the demand and supply sides of farming. As climate change continues to negatively impact our ecosystem, food production, and human livelihood, this topic has gained widespread attention from researchers and academics worldwide. Given that agriculture is highly dependent on factors such as precipitation and temperature, it is considered one of the most climate sensitive sectors (Sagar et al., 2022).

Climatic data is complex and nonlinear, with significant fluctuation across time components, resulting in greater volatility. This means that stochastic climatic variables, including rainfall, temperature, relative humidity, and other weather parameters, often exhibit conditional heteroscedasticity, leading to irregular residual mean and variance across time series.

Introduction

Drought is an evolutionary process often triggered by precipitation deficiencies and may result in enormous economic losses. Effective early drought warnings and predictions may be beneficial for the farmers. To determine drought and its severity level, meteorological drought indices such as the standardized precipitation index, effective drought index etc. are utilized. Drought events can be foreknown using prediction models and by predicting these indices. Many studies on prediction have primarily focused on statistical approaches such as Holt-Winters, auto-regressive integrated moving average (ARIMA), etc. These statistical techniques are insufficient for providing long-term predictions. However, with improvements in machine learning techniques, there seems to be a mechanism to forecast drought in the long run with high accuracy. Frequently used machine learning algorithms in drought forecasting are artificial neural network (ANN), support vector machine, etc. Machine learning techniques have a powerful and versatile data-driven algorithm for capturing and representing complex input and output relationships.

Introduction

Rainfall is a crucial component of the Earth’s climatic system that affects various aspects of human life. It has a direct impact on the ecosystems, water resources, agricultural production, and overall water management. Dependence on rainfall is particularly high in regions where it is the primary source of water, making it essential to have an accurate understanding of future rainfall patterns. Given its importance, rainfall forecasting is a critical concern for a wide range of stakeholders, including governments, businesses, risk management organizations, and the scientific community. However, predicting rainfall is a complex task as it is a random occurrence influenced by numerous factors (Samantaray et al., 2020). Even in similar weather conditions, the possibility of rainfall may vary. This makes historical data and predictive analytics crucial in forecasting future rainfall and taking necessary actions to manage its effects (Avinash et al., 2022). The impact of rainfall extends beyond water management to affect various human activities, including power generation, construction, tourism, and forestry, among others. Therefore, it is crucial to accurately forecast and analyze rainfall patterns to help mitigate the risks and consequences associated with it (Hernandez et al., 2016).

Introduction

Time series data is data that has been collected in a specific pattern with time stamps. (Nayak and Ghosh, 2013). This type of time-oriented data is collected at regular intervals, such as hourly, daily, monthly, or weekly. Time series data can be used to make accurate predictions in a variety of fields and domains, such as foreign currency rates, stock market trends, energy consumption estimates, and climate change. Machine learning and data mining techniques can be used to uncover hidden patterns in historical data and forecast future trends (Aftab et al. 2018a, 2018b, Mishra et al. 2017).

Weather forecasting based on historical data is a complex but very beneficial task that comes with several issues that must be resolved to achieve optimal results (Wu and Chau, 2013). Weather data includes attributes or features such as temperature, pressure, humidity, and wind speed. Machine learning techniques are used to forecast future weather conditions by identifying hidden patterns and relationships in historical weather data (Aftab et al. 2018b). Prediction of precipitation is an important part of the weather forecasting process. The use of machine learning and artificial intelligence techniques is regarded as a critical component in farmer services and policy formulation. Farmers require weather forecasting to plan their activities following the predicted weather. Accurate and timely rainfall prediction in farming, in particular, can be extremely beneficial in planning and security measures for operations, agricultural tasks, water reservoir systems, and construction and transportation activities (Chau and Wu, 2010). Forecasting rainfall will become increasingly important in the future.

Introduction

Drought occurs when a region experiences below average precipitation, resulting in prolonged shortages in its water supply, which can have a significant impact on agriculture and the ecosystem. To reduce the effect of drought on farm production and stabilize farm income, farmers must take systematic measures called drought coping mechanisms. These measures can be categorized into two broad categories: Ex-ante coping mechanisms, which are taken before the occurrence of drought, and Ex-post coping mechanisms, which are taken after the occurrence of drought. Ex-ante coping mechanisms include activities such as crop, spatial, and varietal diversification, income diversification, and the use of savings. Ex-post coping mechanisms include changing consumption patterns, borrowing money, liquidating assets, seeking government drought relief funds, and migration. Drought can have a significant impact on crop and livestock production, causing farmers to adopt drought coping mechanisms to stabilize their farm income. This study goals to identify the factors that determine the adoption of such coping mechanisms, to mitigate the effects of drought on the livelihoods of farmers.

Introduction

Drought is defined as “when a region receives below average precipitation,  resulting in prolonged shortages in its water supply, whether surface  or ground water or atmospheric. It can have a substantial impact on the  ecosystem and agriculture of the affected region.” When drought arises in  a particular area, obviously it affects the crop and livestock production. In  order to reduce the effect of drought on farm productivity and to stabilize farm  income, farmers have to take some systematic measures, and such measures  are referred to as drought coping mechanisms. The objective of the study was  to identify the determinants of the adoption of drought-coping mechanisms to  balance and stabilize the farm income of the stakeholders. The study also helps  to know how to mitigate the effect of drought on farmer’s livelihoods.

The existing literature on the adoption of drought coping mechanisms is  quite meager. Akwango et al., (2016), Chandan Kumar Jha et al., (2017),  Emmanuella Ellis (2017), Ronald Twongyirwe et al., (2019) assessed the adoption strategies by considering multinomial logistic regression and the  results state that the use of different tree planting, crop varieties, soil and water  conservation, early and late planting, and furrow irrigation. The data related to  the adoption of any agriculture technology have qualitative response variables  with two or more categories. Most of the studies used qualitative response  models such as the logit model, probit model, and multivariate techniques like  discriminant analysis to measure the farmer’s perception toward the adoption  of any agriculture technology. In this study, we chose machine learning models  such as Decision tree, to assess the classification ability and to determine the  factors influencing adoption.

Introduction

Agriculture is backbone of India’s economy and is largely dependent on the monsoon. Rainfall plays a dominant role in agricultural production and productivity, despite advancement in many technological fronts. Forecast of rainfall is essential for planning and management of water resources especially in an agriculture-based country like India. About 65 per cent of the total cultivated land in India is under the influence of the rainfed agriculture system. Especially, monthly and seasonal rainfall forecasts provide useful information for water resource management, agricultural planning and its associated crop insurance application (Garbrecht et al., 2010).

The early prediction of rainfall is essential which help farmers to make decisions on crop production and harvesting, as well as help prevent flooding, protect human lives and resources. Rainfall forecasting is very challenging due to its uncertain nature and dynamically changing climate. It is an application of science and technology to predict precipitation in advance. It’s always been a challenging task for meteorologists. Prediction of precipitation is categorized into short-range prediction and long-range prediction. Forecasting is done through the collection and analysis of weather and climate data. Avinash et al. (2022) studied temporal variation of land use pattern in Karnataka through indices and concluded that due to fluctuations in the rainfall the forest land is degrading year by year.

Introduction

Artificial intelligence (AI) is a field that mainly focuses on creating intelligent technologies that can easily perform tasks requiring human intelligence. One subfield is machine learning (ML), which enables machines to learn from data without being explicitly programmed. There are two main types of ML: supervised and unsupervised learning. Supervised learning trains machines on labeled data to predict output, while unsupervised learning trains machines on unlabeled data to find patterns. ML includes techniques such as regression, classification, clustering, and deep learning. ML has the potential to revolutionize many fields but also raises ethical, social, and legal issues. Developing trustworthy, responsible, and ethical ML systems is crucial. The MuCulloch and Pitts Model, proposed by Warren MuCulloch and Walter Pitts in 1943, is the first formal model of ML techniques. It is also known as the linear threshold gate (Fig. 1).

Introduction

The study of spatiotemporal time series forecasting has gained significant attention in recent years, as it involves analyzing records of events that occur over both space and time. Spatiotemporal models group time- and location-specific discrete variables and are used in various sectors, including agriculture, transportation, meteorology, economics, disease mapping, sociology, environmental sciences, and ecology. However, the popular Autoregressive Integrated Moving Average (ARIMA) (Box and Jenkins, 1970) model has two major flaws: it assumes linearity, which is not always the case in time series data that may exhibit nonlinear interactions, and it is a univariate model that cannot capture spatial linkages. Hence, when investigating time series data that are not perfectly linear and involve spatial correlation, the ARIMA model should not be used. Other multivariate time series models like VARMA have also been used to represent spatiotemporal data. However, VARMA models require more parameters, and the STARMA model introduced by Cliff and Ord has fewer parameters (Cliff and Ord, 1975). In previous literatures, the STARMA model was commonly used for modeling different spatiotemporal time series data (Kamarianakis, 2003). The STARMA model was previously employed to forecast the spatiotemporal data in different fields such as economics, disease mapping, climate modeling, agriculture, etc. (Pfeifer and Bodily, 1990; Lee, 2005; Rathod et al., 2018; Saha et al., 2020 and Rathod et al., 2021).

Introduction

The term “Remote Sensing” was coined in 1950s by Ms. Evelyn Pruitt, a geographer with the U.S. Office of Naval Research, and the term is now commonly used to describe as the science and art of identifying, observing, and measuring an object on the earth surface, without having any direct contact. This process involves the detection and measurement of radiation of different wavelengths reflected or emitted from distant objects or materials through which they may be identified and categorized by class/type, substance, and spatial distribution (National Aeronautics and Space Administration—NASA). Remote sensing technology has been widely applied in various applications such as water resources, agriculture, geological studies, forestry, land use, resource exploration, etc.

Elements of Remote Sensing

The remote sensing process involves an interaction between the incident radiation and the targets of interest, exemplified by the use of imaging systems in which the following seven elements are involved; however, remote sensing also involves sensing of emitted energy and using non-imaging sensors

Introduction

Watershed management has emerged as an important research area in recent years. Understanding the morphology of the catchment is one of the widely used methods for watershed management. Morphometry is the measurement and mathematical analysis of the configuration of the earth’s surface, shape, and dimension of its landforms (Agarwal 1998; Obi Reddy et al., 2002). The study of watershed is very useful in drainage basin evaluation, silt erosion control, flood frequency analysis, watershed prioritization, natural resources management, and conservation (Waikar and Nilawar, 2014). The significance of morphometric analysis is highlighted in the basins that are either un-gauged or difficult to access. The morphometry is usually analyzed through measurement of linear, aerial, relief, gradient of channel network, and contributing ground slope of the basin (Magesh et al., 2012). The hydrological behavior of a basin can be easily determined through the areal parameters (Mahala, 2020.) The evolutionary history of any basin can be best understood through the implication of different relief morphometric measures of drainage basin (Sharma and Sarma, 2013).

GIS and RS techniques are proven convenient methods for morphometric characterization of sub-watersheds and prioritization of watersheds. As the satellite images provide a synoptic view of a large area, the RS and GIS methods become very useful in the analysis of drainage basin morphometry (Rai et al., 2017). Thus, the morphometric analysis using spatial techniques has emerged to be the fast and efficient method as compared to most of the conventional methods (Rao et al., 2010) such as manual extraction of drainage network and assigning the stream order from a published Survey of India topographic map. The present study aims at using remote sensing and GIS technology to compute various parameters of morphometric characteristics of the Chaliyar River basin.

Introduction

Groundwater resources are under stress globally due to various reasons including increasing water demand, impending climate change, industrialization, land use/land cover changes, etc. Adequate sustainable development strategies have to be adopted for the management of groundwater resources. Irrespective of the developmental status of countries all over the world, groundwater serves as a crucial resource for meeting fundamental needs. However, increasing population, water demand, surface water contamination, and recent patterns of climate unpredictability have caused increasing reliance on groundwater as reported by several researchers (Adimalla and Taloor 2020; Aladejana et al. 2020; Cuthbert et al. 2019; Green 2016; Hamed et al. 2018; Jasrotia et al. 2019; Khalaj et al. 2019; Li 2016; McGill et al. 2019; Morsy et al. 2017; Taloor et al. 2020).

Even in India, water scarcity has been reported in many parts, though the country receives relatively higher rainfall. However, the rainfall pattern has changed over the last few years. The country has been experiencing significant spatiotemporal changes in rainfall patterns, leading to fewer rainy days and more extreme rainfall, which many studies have attributed to global climate change and anthropogenic influences. The rainfall variability has substantial impacts on water availability, agricultural productivity, and welfare of the 1.2 billion people in India because agriculture in India feeds about 17.2% of the global population and more than 56% of the agricultural land is rain-fed. Rapid and heavy patterns of rainfall result in the draining of most of the rainfall as surface runoff particularly on higher slopes, and reaches the surface water bodies, thereby reducing the infiltration and subsequent groundwater recharge. These variations in rainfall have ultimately affected the groundwater behavior and availability in many river basins.

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.

Introduction

Rainfall is one of the climatic variables that varies very highly spatially and temporally. Understanding this micro-level variation is a crucial indicator for planning future water requirements and preparing for management strategies. Numerous researchers have delved into the distribution, variability, and trends of rainfall across diverse spatial scales, ranging from global to regional and basin levels. Nair et al. (2014) analyzed the rainfall trend and seasonality index for Kerala state in India over the last 100 years. The rainfall trend was found to be decreasing significantly in almost all the regions of the state. Nischitha et al. (2013) provide insights into the spatial and temporal variability of daily monsoon rainfall in the Tunga and Bhadra River basins in Karnataka state. Paradkar et al. (2019) evaluated the pattern of weekly rainfall by using statistical parameters such as mean, standard deviation, skewness, and kurtosis for five stations in Pratapgarh district.

Geographical Information System (GIS) proved to be an effective tool in handling information and analyzing rainfall patterns, providing visual representations, and facilitating the decision-making process (Kadir et al. (2016) and Rao et al. (2017)). Anand et al. (2020) analyzed the long-term spatial and temporal trends of rainfall in the Lower Bhavani basin in Tamil Nadu, India using GIS and statistical methods. Earls et al. (2007) compared different interpolation methods such as Inverse Distance Weighting, Spline, and Kriging (ordinary and universal) to generate a 30 m representation of the rainfall data. Spatial interpolation techniques perform differently and their evaluation should not rely solely on quantitative assessment. Wu & Hung (2016) compared the performance of three spatial interpolation techniques i.e., IDW, kriging, and spline using quantitative assessment, 2D visualization, and 3D visualization. IDW and kriging performed similarly in terms of quantitative assessment, while spline performed worse. IDW and spline performed similarly in 2D visualization, while kriging showed different behavior. Kriging was observed to be an inexact interpolation in 3D visualization, while spline tended to create extreme values along the edges of the study area. Some studies discovered that the accuracy of Kriging in the study area is higher than that of IDW or Thiessen interpolations (Earls et al. 2007; Song et al. 2008; Chen et al. 2010). GIS applications have the capability to integrate spatial data and non-spatial data like rainfall and to visualize, model, and analyze how rainfall distribution varies across space and over time, providing insights into climate change, water resource management, agricultural planning, and disaster management. The present study aims to analyze annual rainfall data pertaining to Kerala state in the GIS environment.

Introduction to GIS

QGIS (Quantum GIS) is an open-source Geographic Information System (GIS) software has been used to showcase various exercises pertaining to viewing, editing, analyzing, and printing of the geospatial data. QGIS helps to allow the users to easily access and manage geographic data stored in folders on local disks or relational databases available on the user’s network. QGIS supports raster, vector, and mesh layers. QGIS also allows using data from external sources with the help of web services such as Web Map Service (WMS) and Web Feature Service. QGIS is a constantly developing software as anyone can add new features or update existing tools. Newer versions of QGIS are frequently released on the official website. It is a cross-platform software that can be installed in Windows, Linux, and MacOS.

1.1 Introduction

Water scarcity is seen as a major constraint to intensify agriculture in a sustainable manner as an attempt to meet the food requirements of a rapidly growing human population. The ever-increasing human population, climate change due to increased emissions of greenhouse gases (GHGs), and intensification of agriculture, are putting severe pressure on the world’s two major non-renewable resources of soil and water, and thus pose a big challenge to produce sufficient food to meet the current food demand. The present world population of 7.3 billion people is predicted to grow to over 9 billion by 2050, with the majority of this population increase occurring in developing countries, most of which already face food shortages. A 70% increase in current agricultural productivity will be required to produce sufficient food if these human population growth predictions prove to be correct. In this context, concerted efforts are being made globally to improve the effectiveness of water which will be used for enhancing the production of irrigated crops. Additionally, efforts are also being made to improve water harvesting and water conservation in rain-fed agriculture.

The injudicious use of saline/brackish water is all too often associated with the development of soil salinity, sodicity, ion toxicity, and groundwater pollution. Because of these negative effects, it is important to have a better understanding of exactly how the quality of water influences the management of irrigated agriculture, especially in arid and semi-arid regions.

2.1 Introduction

Water is the most precious gift of the nature and it is also the most crucial element for sustainability of the life in the earth. India is the largest user of freshwater in the world. India’s total water consumption alone is the higher than any other continents. The Agricultural sector is the largest consumer of water followed by the domestic sector and the industrial sector. The role of irrigation water as one of the most essential inputs for crop cultivation can’t be denied. As a traditional productive input, it ensures production by acting as an agent of insurance against inadequate and inconsistent monsoon. Ultimately the outcome provides agricultural production stability. Thus, irrigation is of prime importance in cultivation of vegetable crops as it ensures favourable water balance within the root zone in addition to natural precipitation. It fulfils the crop- water demand and improves the crop production and effectiveness of other agricultural inputs. It is also an important limiting factor of crop yield, because of its association with several factors of plant environment, which directly influence the crop growth and development (Yaghi et al., 2013). The various irrigation methods under different system of irrigation differ with regard to extent of control, timeliness and adequacy of supply of irrigation water for crop cultivation. Consequently, the economic benefits and the costs due to these irrigation methods vary among different irrigation systems. The dominant method of irrigation practiced in large parts of the country is surface irrigation (basin, border and furrow) where the entire soil surface is almost f looded without considering the actual consumptive requirements of the crops. Frequent over or under irrigation create the problems of water stress or water logging leading to reduced irrigation efficiency (less than 30%).

3.1 Introduction

Groundwater constitutes about 89% of the total fresh water resources in the planet. But in recent years, due to over exploitation of ground water and erratic nature of monsoon, there has been depletion of ground water across the world. Depletion of ground water has reached to the extent that it is virtually impossible to get the water table back. Even though there is a possibility of recharge of water from the other areas, the process is very slow and may take one year to replenish one meter. In view of this management of ground water has become one of the most significant issues in recent times. Added to it, there are also environmental problems such as aqua for mining, salt water intrusion, stream base flow reduction etc. For several reasons the efficient management of ground water resources through market mechanism has become difficult. Against this context the present article attempts to analyze the need for sustainable ground water management in India. The article also briefly discusses the concept of sustainable ground water management, factors affecting ground water availability, different approaches towards developing and using available ground water without adversely affecting the hydro geological balance. Further, the paper highlights strategies for sustainable groundwater management, including development of aquifers, rainwater harvesting and artificial recharge methods. The article offers some relevant policy recommendations for sustainable groundwater management in India.

4.1 Introduction

Water pollution is the contamination of water by an excess amount of a substance that can cause harm to human beings and/or the ecosystem. The level of water pollution depends on the abundance of the pollutant, the ecological impact of the pollutant, and the use of the water. Pollutants are derived from biological, chemical, or physical processes. Although natural processes such as volcanic eruptions or evaporation sometimes can cause water pollution, most pollution is derived from human, land-based activities (Figure 4.2). Water pollutants can move through different water reservoirs, as the water carrying them progresses through stages of the water cycle (Figure 4.1). Water residence time (the average time that a water molecule spends in a water reservoir) is very important to pollution problems because it affects pollution potential. Water in rivers has a relatively short residence time, so pollution usually is there only briefly. Of course, pollution in rivers may simply move to another reservoir, such as the ocean, where it can cause further problems. Groundwater is typically characterized by slow flow and longer residence time, which can make groundwater pollution particularly problematic. Finally, pollution residence time can be much greater than the water residence time because a pollutant may be taken up for a long time within the ecosystem or absorbed onto sediment.

5.1 Introduction

Out of the many climatic events that influence the earth’s environmental fabric, drought is perhaps the one that is most linked with desertification. Drought is a natural hazard originating from a deficiency of precipitation that results in a water shortage for some activities or some groups and is often associated with other climatic factors (such as high temperatures, high winds and low relative humidity) that can aggravate the severity of the event. Drought differs from aridity in that the latter is restricted to low rainfall regions and is a permanent feature of the climate. Drought occurrences are common in virtually all climatic regimes.

Widespread and severe drought conditions in Asia, Latin America and the Caribbean in 2000 have raised serious concerns about the continuing vulnerability of the world community to extended periods of droughts and water shortages. In 2000, major droughts affected much of south-eastern Europe, the Middle East, and the area through central Asia to northern China. Especially hard hit was Afghanistan, Bulgaria, Iraq and the Islamic Republic of Iran and parts of China. In North America, months of above-average temperature coincided with below-normal precipitation through northern Mexico and much of the southern and western regions of the USA, leading to one of the worst wildfires in the past 50 years.

By August 2001, much of Western Asia, Central Asia, and the Middle East was suffering the third year of a continuing drought that severely reduced many countries’ crop yields. The countries most affected were Afghanistan, India, Islamic Republic of Iran, Pakistan, and Tajikistan.