Titlebook: Hydrological Processes Modelling and Data Analysis; A Primer Vijay P. Singh,Rajendra Singh,Srishti Gaur Textbook 2024 The Editor(s) (if app

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Emerging Fields in Hydrology,ed 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.

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Case Studies,nging 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.

Consequence

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Machine Learning (ML) in Water Resources,f 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.

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Integrated Modelling Systems,e 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.

Antioxidant

Textbook 2024nge 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

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Introduction,ements 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.

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Data Availability and Aquisition, 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 infor

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Time Series Analysis,nd 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

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Remote Sensing and Geographic Information Systems Driven Data Analysis,roduces 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 highl