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2024 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Hydrological Processes Modelling and Data Analysis

A Primer

verfasst von: Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur

Verlag: Springer Nature Singapore

Buchreihe : Water Science and Technology Library

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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Über dieses Buch

This book provides a state-of-the-art overview of the concepts and methodologies of data and modelling-driven hydrological analyses and their wide range of practical applications. The book is driven by the realisation that science, technology, engineering, and mathematics (STEM) concepts are essential in engineering hydrology to produce well-trained hydrologists. Such hydrologists will be equipped to face future societal challenges that require enhanced information and communication technology tools and integration of technical and non-technical areas. The book contains 12 chapters that introduce the principles of hydrological data analysis and highlight the current and emerging tools and techniques for analysing hydrologic data. The book describes the types of data typically used in hydrological analyses. It highlights the revolutionary technological advancements made toward hydrological data collection, including the use of drones and smartphones. The foremost objective of the book is to present the hydrological data analysis procedures. It explains the steps involved in data analysis for easy understanding of the reader, including students and professionals. This book presents case studies that demonstrate step-by-step procedures involved in typical analysis problems and may guide students and professionals in planning and executing steps to analyse the problem at hand. Case study examples will guide them to understand the intricacies of hydrological data analysis. It provides the readers with a complete package to enrich their understanding of the hydrological data analysis tools and techniques. Subsequently, as well-trained hydrologists, they could execute their learning to meet any specific grand challenge of the twenty-first century.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Hydrology is the study of the hydrologic cycle. Providing a brief description of the components of the hydrologic cycle, this chapter discusses the elements of hydrologic modelling, including different kinds of models, parameter estimation, model calibration and validation, sensitivity analysis, and error analysis. Various kinds of hydrologic data needed for modelling are outlined. The chapter is concluded with the organisation of the book.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 2. Data Availability and Aquisition
Abstract
The hydrologic analysis relies on the availability of accurate and reliable data to effectively analyse the complex behaviour of hydrologic processes. However, the constraints and scarcity of data present significant challenges in achieving the desired accuracy and reliability. Researchers have leveraged various data sources to overcome data constraints, including ground-based observations, global and reanalysis datasets, and remote sensing information. Hydrologic analysis can be significantly enhanced by integrating these diverse data sources, enabling a deeper understanding of the complex phenomenon of water exchange between the soil-plan-atmosphere continuum. Furthermore, accurate hydrologic analyses facilitate improved water allocation, flood forecasting, drought management, and assessment of climate change impacts on water resources. The availability of reanalysis data, climate projections, and satellite-derived estimates from various sources provides valuable insights for researchers and practitioners involved in water resource management and climate change impact assessment. By capitalising on these rich data sources, researchers can develop robust models that support informed decision-making processes in water management and adaptation strategies. This chapter aims to explore the extensive range of data sources that can be readily availed to carry out hydrologic studies, focusing on addressing the challenges posed by limited data availability.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 3. Time Series Analysis
Abstract
Time series analysis of hydrologic data helps understand the behaviour of a hydrologic variable over time. It aids in identifying long-term patterns and trends in the data, which may enhance the prediction of future water availability and flood or drought risk. The chapter introduces different types of time series data and discusses the decomposition of a time series into its constituent components like trend, cycle, seasonality, and irregularity. Methods to test the stationarity of a time series, e.g., the Autocorrelation function (ACF) plot and Augmented Dickey-Fuller (ADF) test, and approaches to converting data into stationary are presented. Similarly, methods to analyse trend, seasonality and periodicity, e.g., the Mann–Kendall Test, Sen's Slope Estimator, and partial autocorrelation function (PACF) plots, are included. The chapter also introduces the time series models like Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models, which are typically used to model stochastic components of a time series.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 4. Remote Sensing and Geographic Information Systems Driven Data Analysis
Abstract
Remote sensing (RS) and Geographic Information Systems (GIS) are routinely used in hydrologic data monitoring, mapping and modelling. This chapter introduces the basic concepts of the RS and GIS. The earth observation satellites and missions, image processing techniques, and spectral indices are discussed. Similarly, the popular GIS spatial and attribute data models are presented. Data sources for hydrology and water resources modelling are highlighted. Besides, a few prevalent commercial and open-source GIS and remote sensing software are enlisted. The chapter includes the RS and GIS applications in flood management, drought monitoring, water quality monitoring and water body mapping.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 5. Climate Change Impact Analysis
Abstract
The pursuit of creating a developed world with high living standards has led to numerous human activities that have resulted in climate change. Despite improvements in living standards, the repercussions of climate change have threatened the world's sustainability. One significant impact is on water resources, as climate change alters water dynamics and distribution across space and time. Many organisations have developed state-of-the-art climate models to understand climate change better, and collective efforts are being made to improve our understanding of various Earth processes to refine existing models. With advancements in scientific understanding and computational power, new developments are being made in climate modelling and climate change studies. These include designing mechanisms for future trajectories in a warming world and regularly supplementing them with new information. The efforts of the Intergovernmental Panel on Climate Change (IPCC), from their First Assessment Report (FAR) to their most recent Sixth Assessment Report (AR6), are evidence of this scientific development. Understanding climate change and climate data handling is now a prerequisite for researchers in water resources. This chapter provides an overview of various concepts related to climate change, climate models, climate data downscaling, and the state and fate of water resources in the changing climate.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 6. Land Use Land Cover (LULC) Change Analysis
Abstract
The chapter presents an overview of the Land Use Land Cover (LULC) by providing the background, cause and trajectories, modelling, scenario formation, and popular modelling software. The chapter also attempts to identify the limitations, challenges, and novel opportunities in LULC modelling. The main recommendation from the chapter is the utilisation of integrated modelling systems for LULC changes, along with the consideration of uncertainty associated with it. The study highlights incorporating policy frameworks in the LULC models to make them helpful tools for policymakers and LULC planners.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 7. Integrated Modelling Systems
Abstract
The present chapter attempted to briefly overview integrated modelling systems and their applications for hydrologic sciences. Integrated modelling systems provide non-invasive ways of utilising the synergetic effect of two models. In conventional hydrologic research, integrated modelling focused on considering the surface and groundwater interactions (e.g., MIKE SHE) for accurate water balance estimation. However, in modern hydrology, integrated modelling is more inclined towards combining data-driven and process-based hydrologic modelling. In this aspect, ‘big data’ can further strengthen the credibility of the hybrid model by providing the opportunity to validate them on extensive, diverse data. The chapter also attempted to highlight the emerging techniques like explainable machine ML and physics-constraint ML that are useful in interpreting the black-box ML models and making the process opaque. Incorporation of these techniques can leverage the strengths of hybrid models.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 8. Extreme Event Analysis
Abstract
Floods and droughts are two severe climatic extremes that exert considerable impacts on both people and their environment. For hydrologists, it is imperative to comprehend the nature and types of these phenomena, as it enables them to simulate and develop appropriate modelling methods to study these extremes. Such modelling is vital for making informed decisions while devising policies and plans to manage and mitigate the risks associated with these extreme events. To effectively reduce the risks posed by floods and droughts, it is necessary to undertake comprehensive hazard and risk assessments. These assessments provide a more profound understanding of the potential impacts of these events, the level of risk they pose, and the measures that should be taken to mitigate their effects. The present chapter elucidates floods and droughts in detail, discusses the different methods used to model these extremes, and emphasises the significance of hazard and risk assessments in disaster risk reduction. Furthermore, this chapter describes the critical elements in conducting these assessments while providing an overview of real-time flood and drought forecasting to present the practical application of flood and drought modelling concepts.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 9. Machine Learning (ML) in Water Resources
Abstract
Machine Learning (ML) algorithms are gaining popularity as these can improve the understanding and analysis of hydrologic complexities. In addition, the interaction between ML methods and process-based models may lead to new routes in mechanistic modelling. This chapter includes the classification of ML algorithms, the history of ML, and day-to-day examples of the usage of ML techniques. Besides, the chapter briefly introduces a few ML techniques/algorithms. Among these, support vector machines (SVMs), convolutional neural networks (CNNs) and random forests appear to be the most actively investigated algorithms, but new ones are emerging.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 10. Uncertainty Analysis in Hydrologic Modelling
Abstract
Hydrologic models differ regarding hydrologic processes, their parameters, inputs and outputs. Consequently, the results of different models for a particular period for a study area also differ. Uncertainty sources of hydrologic predictions are divided into three groups: uncertainties due to model parameters, uncertainties due to model structure and uncertainties due to observation data (for input and calibration). Another source is the uncertainty generated from boundary conditions, i.e., scenario uncertainty. Different uncertainty analysis techniques are reviewed and summarised to identify which is more appropriate for analysing a particular hydrologic modelling uncertainty. Finally, research prospects on uncertainty analysis of hydrologic modelling are proposed.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 11. Emerging Fields in Hydrology
Abstract
Defining emerging fields in hydrology is challenging, partly because the scope of hydrology in the twenty-first century has expanded substantially during the past half a century. Several areas that had not taken birth earlier are now considered integral to hydrology. Examples are social hydrology, global hydrology, ecohydrology, remote sensing hydrology, hydrology of disasters, and nuclear hydrology. The emergence of some of the areas can be traced to the development of data science, tools of analysis, fields of application of hydrology, modelling technology, or the discovery of new concepts. This chapter provides a snapshot of fields deemed to be emerging in hydrology.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Chapter 12. Case Studies
Abstract
This chapter includes three case studies on climate change impact assessment from the drought characterisation perspective, land use land cover (LULC) impact analysis and uncertainty analysis. The case studies utilise widely used, state-of-the-art tools and techniques based on the theories discussed in the earlier chapters. Specifically, we discuss drought characterisation using Standardised Precipitation Evapotranspiration Index (SPEI) under changing climate for the Indian mainland, analyse water balance components under LULC change conditions using Soil and Water Assessment Tool (SWAT), and finally, uncertainty analysis of hydrologic model simulations of SWAT model using quantile mapping.
Vijay P. Singh, Rajendra Singh, Pranesh Kumar Paul, Deepak Singh Bisht, Srishti Gaur
Backmatter
Metadaten
Titel
Hydrological Processes Modelling and Data Analysis
verfasst von
Vijay P. Singh
Rajendra Singh
Pranesh Kumar Paul
Deepak Singh Bisht
Srishti Gaur
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Electronic ISBN
978-981-9713-16-5
Print ISBN
978-981-9713-15-8
DOI
https://doi.org/10.1007/978-981-97-1316-5

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