Chapter 1 - Streamflow forecasting: overview of advances in data-driven techniques

Abstract

Reliable and realistic streamflow forecasting plays a crucial role in hydrology and water resources engineering as it can directly affect the dams operation and performance, groundwater recharge/exploitation, sediment conveyance capability of river, watershed management, etc. However, an accurate streamflow forecasting is not an easy task due to the high uncertainty associated with climate conditions and complexity of collecting and handling both spatial and nonspatial data. Therefore, hydrologists from all over the world have developed and adopted several types of data-driven techniques ranging from traditional stochastic time series modeling to modern hybrid artificial intelligence (AI) models for future prediction of streamflow. In literature, studies dealing with streamflow forecasting used a variety of techniques having dissimilar concepts and characteristics and streamflow datasets at different timescales such as daily, monthly, seasonal, and yearly, etc. This chapter first describes and classifies available data-driven techniques used in streamflow forecasting into suitable groups depending upon their characteristics. Then, growth of the salient data-driven models both single and hybrid such as time series models, artificial neural network models, and other AI models is discussed with their applications and comparisons as reported in studies on streamflow forecasting over time. Thereafter, current approaches used in the recent 5-year streamflow forecasting studies are briefly summarized. Also, challenges experienced by the researchers in applying data-driven techniques for streamflow forecasting are addressed. It is concluded that a vast scope exists for improving streamflow forecasts using emerging and modern tools and combining them with location-specific and in-depth knowledge of the physical processes occurring in the hydrologic system.