Study on the evolution law of performance of mid- to long-term streamflow forecasting based on data-driven models

Mid-to long-term streamflow forecasting is of great significance for the sustainable utilization and management of water resources. Currently, data-driven models are mainly used for mid-to long-term streamflow forecasting. However, the evolution law of prediction models' performance with increasing lead times has not been fully revealed due to too much attention on proposing a novel method to improve prediction accuracy. The main objective of this study was to explore the evolution law of the performance of ten-day streamflow forecasting models with the extension of lead time, including multiple linear regression, support Vector Regression, feed -forward neural network, and least-squares Boosting Decision Tree. The upper reaches of the Yangtze River in China were considered as the case study region. The results show that: (1) with the extension of the lead time, the performance of data-driven models first decreases and then gradually levels off; (2) the inclusion of tele-connection climatic factors can improve the performance of each model; and (3) the randomness in streamflow can weaken the prediction ability of the models. These findings can promote improvements in mid-to long-term streamflow forecasting accuracy.