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Acquiring knowledge is a life-long process; we constantly need to keep abreast of developments and progress in science and other disciplines. Embracing a scholarship of teaching and learning (SoTL) means practicing constant self-reflection, involving evaluation of the academic career and the ways in which strategies are designed to examine, interpret, and share learning about teaching. This practice not only yields benefits to the lecturer but also enriches the scholarly community in the discipline. In general, SoTL is regarded as a vibrant practice of ongoing self-criticism and sharing, which results in accumulated teaching experiences for teachers, students, and the teaching community at large. This book is a contribution from authors sharing their experiences, how their teaching portfolios reflect their personal development as teachers, and how their teaching experiences are embedded in the scholarship of teaching and learning.

sustainability --- Green Engineering --- curriculum development --- chemical education --- engineering education --- improving classroom teaching --- simulations --- teaching/learning strategies --- GIS --- learning tool --- open source software --- satellite data --- crystal system --- Bravais lattices --- spatial abilities --- didactic virtual resources --- didactic virtual tools --- design --- active methodology --- hidden curriculum --- engineering --- faculty --- professionalization --- mixed-methods --- critical theoretical frameworks --- anti-deficit approach --- engineering education research --- critical pedagogy --- inductive methods --- re-thinking the teaching --- viscometer --- systems engineering --- education --- role-play --- self-reflection --- reverse engineering --- active learning --- CDIO --- learning activity --- high school --- engineering curriculum --- STEM --- service-learning --- project-based learning --- underrepresented minorities --- outcomes --- ternary phase diagrams --- spatial visualization --- PDF-3D --- engineering education --- education --- engineering --- evaluation --- survey --- feedback --- moderation --- pass rate --- module

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Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies.

rainfall --- monsoon --- high resolution --- TRMM --- drought prediction --- APCC Multi-Model Ensemble --- seasonal climate forecast --- machine learning --- sparse monitoring network --- Fiji --- drought analysis --- ANN model --- drought indices --- meteorological drought --- SIAP --- SWSI --- hydrological drought --- discrete wavelet --- global warming --- statistical downscaling --- HBV model --- flow regime --- uncertainty --- reservoir inflow forecasting --- artificial neural network --- wavelet artificial neural network --- weighted mean analogue --- variation analogue --- streamflow --- artificial neural network --- simulation --- forecasting --- support vector machine --- evolutionary strategy --- heavy storm --- hyetograph --- temperature --- clausius-clapeyron scaling --- climate change --- the Cauca River --- climate variability --- ENSO --- extreme rainfall --- trends --- statistical downscaling --- random forest --- least square support vector regression --- extreme rainfall --- polynomial normal transform --- multivariate modeling --- sampling errors --- non-normality --- extreme rainfall analysis --- statistical analysis --- hydrological extremes --- stretched Gaussian distribution --- Hurst exponent --- INDC pledge --- precipitation --- extreme events --- extreme precipitation exposure --- non-stationary --- extreme value theory --- uncertainty --- flood regime --- flood management --- Kabul river basin --- Pakistan --- extreme events --- innovative methods --- downscaling --- forecasting --- compound events --- satellite data