hydroGOF
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Description
hydroGOF is an R package developed to provide a rigorous and consistent framework for evaluating the performance of hydrological and environmental models. It implements a broad suite of widely used statistical and graphical goodness-of-fit metrics to compare simulatd values agains iits observed counterparts; such as the coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and percent bias (PBIAS); that support objective assessment of model behaviour during calibration, validation, and operational application.
The package is designed with practical modelling workflows in mind. Its functions facilitate transparent comparison between observed and simulated time series, enable systematic performance diagnostics, and handle common data challenges such as missing values in a controlled and reproducible manner. By standardising the computation of performance indicators, hydroGOF helps ensure that model evaluation remains methodologically consistent across studies and applications.
hydroGOF is widely used in research, teaching, and professional practice, which makes it particularly suitable for users who require dependable, well-documented tools to quantify model accuracy and communicate results with clarity. It provides a technically robust foundation for evidence-based model development, benchmarking, and decision support in hydrology and related environmental sciences.
hydroGOF R package.
I am an Associate Professor in the Department of Civil Engineering at the University of La Frontera. I hold a PhD in Environmental Engineering from the University of Trento (Italy) and completed postdoctoral training at the European Commission’s Joint Research Centre. I have more than 20 years of experience in water resources research and have previously served as an Associate Researcher at the Center for Climate and Resilience Research (CR)2 and as a member of the Earth Sciences Assessment Group of the Chilean National Research and Development Agency (ANID).
My research lies at the interface of hydrology, data science, and environmental sciences, with a particular focus on the use of gridded datasets and open-source tools to investigate droughts, extreme events, and water-related impacts of global change.
I work across spatial and temporal scales to improve the understanding of catchment-scale hydrological processes and to translate this knowledge into operational modelling, forecasting, and early-warning systems that support robust environmental decision-making.
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