Mejoras en la modelización hidrológica a escala de cuenca mediante el uso integrado de modelos de base física y técnicas de inteligencia artificial (TwinTagus)

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Rodríguez-Castellanos, José Manuel; Molina-Navarro, Eugenio, 2026, "Mejoras en la modelización hidrológica a escala de cuenca mediante el uso integrado de modelos de base física y técnicas de inteligencia artificial (TwinTagus)", https://doi.org/10.21950/RJAKVM, e-cienciaDatos, V1
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Description

Este dataset contiene los datos de acceso público necesarios para construir el modelo generado con SWAT+ en el proyecto (datos numéricos y coberturas geográficas), los datos necesarios para calibrar el modelo (aforos y valores de los parámetros) y los datos de salida del mismo, resultado de su calibración.

Los datos de entrada incluyen datos climáticos de libre disposición en la AEMET y tres coberturas geográficas: modelo digital de elevaciones y usos del suelo del SIOSE (CNIG), además de los tipos de suelo (DSOLMap, disponible en https://www.wateritech.com/data).

Los datos para calibrar incluyen datos de aforo en ríos de libre disposición en el Anuario de Aforos del CEDEX y los valores de los parámetros muestreados. Los datos de salida se componen de las distintas variables hidrológicas que estima el modelo SWAT+.

Subject Earth and Environmental Sciences
Keyword Calibración, Clima, Hidrología, Modelización, R, Río Tajo, SWAT+
Related Publication References: Rodríguez-Castellanos, J.M.; Sánchez-Gómez, A.; Bieger, K., Martínez-Pérez, S.; Senent-Aparicio, J., Molina-Navarro, E. 2025. Calibrating Streamflow and Hydrological Processes in Geological Regions Using a Combined Soft and Hard Calibration Approach. JAWRA 61(2), e70008.doi: https://doi.org/10.1111/1752-1688.70008
Notes

El proyecto TwinTagus ha supuesto un avance significativo en la mejora de la modelización hidrológica a escala de cuenca, contribuyendo directamente al reto de la gestión sostenible de los recursos hídricos y la protección del medio ambiente.

Durante su ejecución, se han desarrollado e integrado con éxito metodologías innovadoras basadas en la combinación de modelos hidrológicos de base física y técnicas avanzadas de inteligencia artificial.

El proyecto se ha desarrollado conjuntamente con la Universidad Católica de Murcia (UCAM). Este dataset recoge los datos del primer paquete de trabajo del proyecto, relativos a la implementación de un modelo hidrológico avanzado con SWAT+ en la cabecera del río Tajo, calibrado mediante enfoques multiestación y mejorado mediante la incorporación explícita de procesos de aguas subterráneas.

Este avance ha permitido representar de forma más realista la dinámica hidrológica en sistemas complejos, reduciendo la incertidumbre en la estimación de recursos hídricos.

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input_data.zip
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MD5: 66632da60d1b3e65a86a8930d2bacc84
Datos de precipitación en rejilla diario. Datos de temperatura en rejilla diarios; Mapa de usos del suelo; Modelo digital de elevaciones
model_calibration.zip
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Published May 13, 2026
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MD5: 9eef983158e018b538246b55ad63226a
Datos observados diarios de caudal y embalse. Archivos con parámetros muestreados en soft calibration, hard calibration y validación.
Readme-es_TwinTagus_Molina.txt
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Published May 13, 2026
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MD5: 42ad936f3cb9e2b9141853a99755c97a
Descripción del Dataset
Preview "Readme-es_TwinTagus_Molina.txt"
results.zip
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Published May 13, 2026
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MD5: 72dcb34e9a5fd76b6786531038e86b30
Archivos de variables hidrológicas simuladas
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