Data mining methods for hydroclimatic forecasting

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize hydrosystem benefits. In this study, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river–reservoir systems. In a case study of the Lower Colorado River system in central Texas, a number of potential predictors are evaluated for forecasting seasonal streamflow, including large-scale climate indices related to the El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and others. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas.

► We use tree-structured data mining models for seasonal streamflow forecasting.
► Two techniques are verified and compared in a case study in central Texas.
► Tree-structured models are shown to capture nonlinear features in the data.
► Forecasts have potential for improving water management in winter and spring.