Assessing a data-driven approach for monthly runoff prediction in a mountain basin of the Central Andes of Argentina

Abstract

In the semi-arid Central Andes of Argentina, the water from snowmelt runoff plays a fundamental role as a provider of ecosystem services. Nowadays, the global climate change has an observable negative impact on this area, due, principally, to the decrease in both liquid and solid rainfall, with the consequent decrease in water availability. In this context, runoff prediction acquires vital importance for the integrated water resources management. The aim of this study is to investigate the performance of the Support Vector Regression (SVR) technique in predicting monthly discharges with 1-month lead-time in the Tupungato River basin in the Central Andes of Argentina. This methodology has never been applied before in this mountainous region. Different inputs, like meteorological data and satellite-based snow cover area estimates from MODIS, were analyzed in order to identify the suitable inputs predictors to forecast monthly streamflow. The results were compared against the results derived from a Classification and Regression Tree (CART) model and, also, against an Auto-regressive Integrated Moving-average (ARIMA) model. Different metrics were used to evaluate the performance of the SVR tests in reproducing streamflow observations at the basin outlet. The coefficient of determination for each of the analyzed tests lays between 0.75 and 0.89 in the validation set. The comparison with the other models showed a significant improvement in performance of SVR in respect of CART and ARIMA model. SVR models proved a promising approach to support water management and decision making for productive activities, potentially also in other basins in the region.

Keywords

• Support vector regression
• Runoff prediction
• Remote sensing
• Machine learning techniques
• Snow cover area