RcamelsCL
My Caption
Description
RcamelsCL is an R package developed to provide streamlined and reliable access to the Catchment Attributes and Meteorology for Large Sample Studies – Chile dataset (CAMELS-CL), a widely used benchmark resource for large-sample hydrology and comparative catchment analysis. This package focuses on simplifying the acquisition, organisation, and handling of both spatial and temporal data required for hydrological research and modelling across diverse climatic and physiographic regions of Chile.
Designed to support reproducible scientific workflows, RcamelsCL offers a consistent interface for downloading and managing hydrometeorological time series and catchment attributes directly from the official data repository. By standardising data access and preprocessing steps, the package reduces the time and effort typically required to prepare datasets for analysis, allowing users to focus on model development, hypothesis testing, and large-sample hydrological investigations.
Importantly, RcamelsCL preserves the integrity of the original dataset by providing direct access to the original raw data, without altering their content. This approach ensures transparency and traceability in scientific applications, which is particularly relevant for studies involving benchmarking, model intercomparison, and regional hydrological assessment.
Well suited for research, teaching, and operational analysis, RcamelsCL provides a technically sound and efficient gateway to one of the most comprehensive hydrological datasets available for Chile. It is especially valuable for users seeking a reliable foundation for data-driven hydrological studies and reproducible environmental research.
RcamelsCL 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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