Coupling SWAT+ and machine learning-Enhanced global climate models for seasonal hydrological prediction

The Segura River Basin, which supplies water for agriculture, receives water from the Upper Tagus River Basin (UTRB) through the Tagus-Segura water transfer, involving two reservoirs: Entrepeñas and Buendía. Accurate reservoir inflow forecasts, particularly seasonal ones, are crucial for making better and more reliable water transfer decisions. This study introduces a methodology for seasonal forecasting using ensemble weather forecasts from climate models, with a focus on the SEAS5 model from the European Centre for Medium-Range Weather Forecasts (ECMWF). Initially, by combining the global climate model with machine learning algorithms, bias correction of daily precipitation and temperature forecasts is achieved. The Soil and Water Assessment Tool (SWAT+) hydrological model is employed to simulate inflows to the Entrepeñas and Buendía reservoirs, calibrated against observed inflows. The first five years from 1995 to 1999 are used for warm-up, the period from 2000 to 2009 for calibration, and from 2010 to 2019 for validation. The calibrated SWAT+ model is then forced with bias-corrected meteorological data forecasts to predict reservoir inflows for the upcoming months. The SWAT+ model's performance during calibration and validation was very good, with monthly NSE values exceeding 0.7 and PBIAS values below 14% for both reservoirs. When the model was forced with bias-corrected hydrological forecasts, it performed well, demonstrating the effectiveness of bias-corrected forecasted meteorological data in predicting reservoir inflows. This work was supported by the Spanish Ministry of Science and Innovation, under grants PID2021-128126OA-I00.

Keywords: SWAT+, machine learning, coupled modelling, streamflow simulation, seasonal hydrological forecasting