This contributed volume describes management practices based on interdisciplinary and convergence science approaches from different disciplines of agricultural science to enhance the resilience of dryland agriculture. The main focus of this book is to address the current issues and trends along with future prospects and challenges in adopting salient agricultural management practices in drylands globally under a climate-change scenario. Climate change and global warming have profound repercussions on increasing frequency, severity, and duration of droughts and/or floods, which may have implications for future productivity of dryland agriculture, e.g., more water shortages or abundances and high or low runoff rates, diminished crop yields, and reduced water productivity. In past few years, many technological advancements and management strategies have been evolved to tackle the climate-induced risks of dryland agriculture considering interdisciplinary and convergence approaches that integrate knowledge from multi-disciplines. This book is an attempt to bridge the gap in literature by unraveling controversies and characteristics of dryland ecosystems under the changing climate and dealing with detailed procedures of applying the advanced practices adapted to climate change for management of dryland agriculture. This edited book is of interest to ecologists, economists, environmentalists, geologists, horticulturalists, hydrologists, soil scientists, social scientists, natural resource conservationists and policy makers dealing with dryland agriculture. This book offers a broad understanding of dryland agriculture and assists the reader to identify both the current as well as the probable future state of dryland agriculture in a global context. 

Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties. This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting. This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest. This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions.