Impact of Climate Change on Water Resources

This chapter provides information about atmospheric activities and impacts of climate change. Causes for climate variability, such as El Niño Southern Oscillation, and its two different phases, La Niña and El Niño for cooling and warming, are discussed. Teleconnections, climate feedback, and forcing mechanisms (radiative and non-radiative, periodic and random, and external and internal) are also parts of the chapter. Direct and indirect effects of aerosols that influence the visibility in the atmosphere are also discussed briefly but critically. Greenhouse gases and consequences of global warming such as variations in rainfall, ice caps and glacier melting, temperature, likelihood increase in frequency of floods and droughts, and acidification due to carbonic acid formation are also explained. In addition, importance of atmospheric chemistry, Palaeo records, monsoon variability, and Holocene is also stressed. Extensive discussion on Intergovernmental Panel on Climate Change (IPCC) scenarios which relate to demographic, economic, technological, and social changes, i.e., Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 and Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0, and 8.5 is also made. Impact of climate change on hydrology, water resources, urbanization, and hydrologic extremes is discussed extensively. In addition, climate change impacts on India are also covered in three aspects: What we know, what could happen, and what can be done? The reader is expected to understand various atmospheric processes/activities, impacts of climate change, and organization and utilization of the book by studying this chapter.

This chapter describes Global Climate Models (GCMs), limitations and uncertainties associated with the formulation of GCMs due to the effect of aerosols which are differently parameterized in GCMs, initial and boundary conditions for each GCM, parameters and model structure of GCMs, randomness, future greenhouse gas emissions, and scenarios leading to significant variability across model simulations of future climate. This chapter discussed the necessity of performance indicators for evaluating GCMs and explained mathematical description of these indicators. It also emphasized on normalization approach, weight computing techniques such as entropy and rating, ranking approaches, namely, compromise programming, cooperative game theory, TOPSIS, weighted average, PROMETHEE, and fuzzy TOPSIS. Spearman rank correlation which measures consistency in ranking pattern and group decision-making that aggregates individual rankings obtained by different techniques to form a single group preference is also part of this chapter. Ensembling methodology of GCMs is also discussed. Reader is expected to understand various uncertainties associated, role of decision-making techniques for ranking of GCMs by studying this chapter.

Describes downscaling techniques where GCM outputs are interpolated to the scale of hydrological modeling or local scale requirement. Statistical downscaling techniques that facilitate statistical relationships that metamorphose large-scale atmospheric variables/predictors simulated by GCMs to local scale variables/predictand are discussed in detail. Techniques include Linear and Non-linear regression, Artificial Neural Networks, Statistical Downscaling Model (SDSM), Change Factor, Least-Square, and Standard Support Vector Machines. Detailed discussion about Artificial Neural Networks that includes information about preprocessing, weights, epoch, activation function, training, learning rate, momentum factor, weight updation procedures, and challenges are also presented. SDSM, combination of regression and conditional weather generator techniques, Change Factor, and Support Vector Machine are also briefed. Nested Bias Correction technique which addresses bias across prespecified multiple timescales is also part of this chapter. Reader is expected to understand various statistical downscaling techniques by studying this chapter.

This chapter presents data compression techniques, namely, cluster and fuzzy cluster analysis, Kohonen neural networks for clustering GCMs and principal component analysis for transforming a set of observations of possible correlation into a set of linearly uncorrelated variables applying an orthogonal transformation. F--statistic test which can be used as the basis for finding optimal clusters is also discussed. Trend detection techniques, namely, Kendall’s rank correlation and turning point test along with mathematical background are also briefed with the objective to ascertain the quality of the hydrological or climatological records. In addition, optimization techniques, namely, linear and non-linear programming and genetic algorithms along with mathematical description are also discussed. The reader is expected to understand various statistical and optimization techniques along with their applicability by studying this chapter.

This chapter describes basic definitions, classification of models into various categories, with procedures for solving water resources engineering problems using hydrological models. Storm Water Management Model (SWMM), Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and Variable Infiltration Capacity (VIC) are discussed with mathematical background. Brief information about MIKE-based models are also part of the chapter. The reader is expected to understand various hydrological models along with their applicability by studying this chapter.

This chapter presents various real-world global case studies in AR3 and AR5 perspective that are related to the evaluation of GCMs for maximum and minimum temperatures for India, intercomparison of statistical downscaling methods for projection of extreme precipitation in Europe, downscaling of climate variables using Support Vector Machine, Multiple Linear Regression for Malaprabha and Lower Godavari Basins, India and applicability of large-scale climate Teleconnections and Artificial Neural Networks for Regional Rainfall Forecasting for Orissa, India. In addition, the impact of climate change on semi-arid catchment water balance using an ensemble of GCMs for Malaprabha catchment, India; streamflow in four large African river basins; projection of rainfall–runoff for Murray–Hotham catchment of Western Australia; future changes in Mekong River hydrology are also parts of the chapter. The reader is expected to understand the impact studies through various case studies by studying this chapter.