R Package | Dr. Mauricio Zambrano-Bigiarini

hydroMOPSO

Thu, 15 Jan 2026 00:00:00 +0000

R package.

Description

is an R package designed to support robust multi-objective optimisation of complex environmental and engineering models. It implements a state-of-the-art Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm, tailored to address the practical challenges commonly encountered in hydrological modelling, such as non-linearity, non-smooth response surfaces, computationally intensive simulations, and competing performance criteria.

is built to integrate seamlessly with real-world modelling workflows. It can optimise models written in R as well as external simulation models executed from the system console—such as distributed hydrological or water quality models—by communicating through standard input and output files. This architecture allows users to perform advanced optimisation without modifying model source code, preserving model integrity while enabling systematic calibration across multiple parameters, variables, and time periods.

Designed with flexibility and computational efficiency in mind, supports parallel execution on multi-core machines and computing clusters, making it suitable for large-scale calibration and decision-support applications. Its configurable optimisation settings and multi-objective capabilities enable users to explore trade-offs among performance metrics and identify parameter sets that balance competing modelling goals.

is widely applicable to hydrology and other environmental sciences, providing a technically rigorous and operationally practical framework for global optimisation. It is particularly well suited for researchers and practitioners who require transparent, reproducible, and scalable tools to calibrate complex models and support evidence-based analysis.

RcamelsCL

Wed, 27 Aug 2025 00:00:00 +0000

Description

is an R package developed to provide streamlined and reliable access to the Catchment Attributes and Meteorology for Large Sample Studies – Chile dataset ( ), 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, 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, 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, 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.

R package.

RFmerge

Fri, 22 May 2020 00:00:00 +0000

R package.

Description

is an R package (currently not on CRAN, but working with the terra package on Github) designed to generate more reliable environmental datasets by combining information from gridded datasets and ground-based observations. It implements the Random Forest Merging Procedure (RF-MEP) (Baez-Villanueva et al., 2020), a machine-learning approach developed to improve the spatial and temporal representation of environmental variables—particularly precipitation—by leveraging the complementary strengths of different data sources.

The package addresses a persistent challenge in hydrology and Earth system sciences: no single dataset provides a complete and unbiased representation of environmental conditions. Rain gauges offer accurate point measurements but limited spatial coverage, while satellite products provide broad spatial information that may contain systematic errors. By integrating these sources within a unified statistical framework, produces merged datasets that better capture variability, reduce bias, and enhance the reliability of environmental analyses, especially in data-scarce regions.

Built with operational applications in mind, provides a transparent and reproducible workflow for dataset merging that can be adapted to a wide range of variables beyond precipitation, including temperature, soil moisture, or other gridded datasets. It is particularly well suited for researchers and practitioners who require spatially consistent datasets to support hydrological modelling, climate analysis, and water resources assessment.

Grounded in peer-reviewed research and real-world applications, offers a technically robust and methodologically sound foundation for transforming heterogeneous environmental observations into coherent, analysis-ready datasets.

Reference

Baez-Villanueva, O.M.; Zambrano-Bigiarini, M.; Beck, H.; McNamara, I.; Ribbe, L.; Nauditt, A.; Birkel, C.; Verbist, K.; Giraldo-Osorio, J.D.; Thinh, N.X. (2020). , Remote Sensing of Environment, 239, 111610. doi:10.1016/j.rse.2019.111606.

hydroPSO

Fri, 13 Apr 2012 00:00:00 +0000

Description

is an R package developed to provide a robust and flexible framework for the global optimisation and calibration of environmental and engineering models. It implements state-of-the-art variants of the Particle Swarm Optimisation (PSO) algorithm, designed to efficiently explore complex parameter spaces commonly associated with non-linear, non-smooth, and computationally demanding models.

The package was conceived with practical modelling workflows in mind. It is fully model-independent, allowing users to couple the optimisation engine with virtually any simulation model, whether implemented in R or executed externally, without requiring modifications to the model’s internal code. This architecture makes particularly suitable for systematic calibration of hydrological and environmental models, where reproducibility, transparency, and flexibility are essential.

To support rigorous model evaluation, includes advanced diagnostic and sensitivity analysis capabilities, as well as comprehensive graphical summaries that facilitate interpretation of optimisation results. Its parallel computing support enables efficient use of multi-core machines and computing clusters, helping to reduce the computational burden associated with large-scale or high-resolution simulations.

Widely used in research, teaching, and applied modelling, provides a technically sound and operationally reliable optimisation environment. It is especially well suited for users who require a scalable, transparent, and methodologically robust tool to calibrate complex models and support evidence-based analysis in hydrology and related environmental sciences.

Reference

Zambrano-Bigiarini, M. and Rojas, R. (2013). , Environmental Modelling & Software, 43, 5-25, doi:10.1016/j.envsoft.2013.01.004.

R package.

hydroGOF

Mon, 11 Oct 2010 00:00:00 +0000

Description

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, helps ensure that model evaluation remains methodologically consistent across studies and applications.

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.

R package.

hydroTSM

Mon, 11 Oct 2010 00:00:00 +0000

Description

is an R package designed to support the practical workflow of hydrologists and environmental scientists who routinely work with time series data. It provides a comprehensive and coherent set of tools for the management, quality control, analysis, interpolation, and visualization of hydrological and environmental time series, with particular emphasis on tasks commonly encountered in hydrological modelling and water resources assessment.

prioritises reliability, transparency, and functional breadth, reflecting the operational realities of applied hydrology, where reproducible data handling and robust diagnostics are often more critical than marginal computational gains. Its functions are built to integrate naturally into analytical pipelines, facilitating consistent preprocessing and exploration of observational datasets prior to modelling or decision-making.

Developed with the daily needs of practitioners in mind, has been widely used in research, teaching, and professional applications. It is especially suitable for users who require dependable, well-documented tools to support routine hydrological analysis while maintaining full control over data processing steps within the R environment.

R package.