Se planteó una estrategia metodológica que solventa la escasez de datos para la modelación 2D de inundaciones en un delta. Digitalizando la elevación disponible en Google Earth^TM e integrada con secciones levantadas en el río y datos topográficos, se obtuvo una nube de puntos topográfica. Esta fue posteriormente interpolada a una red de triángulos irregular, y finalmente, la generación de un Modelo Digital del Terreno. Se simuló bidimensionalmente (IBER v2.3.2) un evento de inundación y una medida estructural (dique) para proteger una comunidad vulnerable. El desbordamiento se presentó solamente en el tramo final del cauce, por tanto, no influyó sobre el área urbana de la ciudad de Riohacha. El dique lograría evitar artificialmente las inundaciones en la zona protegida, pero aumentando el riesgo por inundación ante posibles fallas debido a crecidas de mayor magnitud. De acuerdo a los resultados, la metodología planteada es útil para el cálculo de áreas inundables en zonas deltaicas y apoyo a la toma decisiones en regiones con escases de datos.

In a previous paper, we presented a very simple monitoring system. That system aimed at providing the basic information needed to set up a hydrological simulation model that would be used to understand the behavior of the system and to explore possible changes linked to future climates. After more than 1.5 years of observations, the gathered information was utilizable to test a 0D hydrological model. This exercise is presented here, together with a comparison with a more refined, and more demanding, 2D hydrodynamic model, enlightening strengths and weaknesses. Several details are revealed in this paper showing how even a simple case needs some type of art in order to overcome the unavoidable difficulties. Surprisingly nice answers have been obtained that illuminate the behavior of the lagoon and shed light on the key points to be improved both in monitoring and modelling. This exercise can be of help in several similar situations.

A reliable Digital Elevation Model (DEM) is a key input for land use planning and risk management, particularly in floodplains where low-resolution models often fail to represent subtle topographic variations. In many regions worldwide, high-precision elevation data are unavailable, necessitating the development of methods to enhance existing global digital elevation models (DEM). This study proposes a practical and replicable methodology to improve the vertical accuracy of global DEMs in flat terrains with limited data availability. The approach is based on correcting the altimetric differences between the DEM and GNSS-RTK-surveyed topographic points, incorporating land cover classification to refine adjustments. The methodology was tested in the Ranchería River delta in Riohacha, La Guajira, Colombia, using four global DEMs: FABDEM, SRTM, ASTER, and ALOS. Results showed a significant reduction in root mean square error (RMSE), with improvements of up to 76.691% for ASTER, 55.882% for FABDEM, 55.932% for SRTM, and 36.842% for ALOS. The proposed method requires minimal computational resources and no advanced programming. Due to minimal data requirements, it makes it a scalable and replicable solution for similar floodplain environments. These enhancements in local altimetric accuracy could help to improve the reliability of hydrodynamic modeling, with direct implications for flood risk management and decision-making in vulnerable flatland areas.

We assessed the vertical accuracy of six global digital elevation models—FABDEM (SRTM-enhanced), SRTM, ASTER GDEM, ALOS AW3D30, DeltaDTM and GEDTM—against a local photogrammetry-derived DEM as a benchmark in a flat coastal plain of the Colombian Caribbean. Using GNSS-RTK ground points and a high-accuracy reference DEM, we computed BIAS, RMSE, and MAE. Errors were analyzed by land cover class and along transverse profiles relative to the reference DEM. We also evaluated hydrologic suitability by comparing flow accumulation and drainage patterns derived from each model, treating the photogrammetry-derived model as the control and the global DEMs as treatments to gauge their ability to represent hydraulic/hydrologic behavior. DeltaDTM, GEDTM and FABDEM showed the best overall performance, with the lowest vertical error (particularly in non-urban areas with sparse vegetation) and the highest drainage agreement, along with their flood extent sensitivity to a 0.5 m water level rise, all of which were comparable to the benchmark. These results provide practical guidance for selecting and preprocessing topographic models for risk management and territorial planning in flat regions.

In a previous paper [1], we presented a very simple monitoring system. That system aimed at providing the basic information needed to set up a hydrological simulation model which would be used to explore and understand the behavior of the system and explore possible changes linked to future climates. After more than 1.5 years of observations, the gathered information was utilizable to test a 0D hydrological model. This exercise is presented here together with a comparison with a more refined, and more demanding, 2D hydrodynamic model, enlightening strengths and weaknesses. Several details are revealed in this paper showing how even a simple case needs some type of art in order to overcome the unavoidable difficulties. Surprisingly nice answers have been obtained which illuminate on the behavior of the lagoon and shed light on the key points to be improved both in monitoring and modelling. This exercise can be of help in several similar situations.

A reliable Digital Elevation Model (DEM) is a key input for land use planning and risk management, particularly in floodplains where low-resolution models often fail to represent subtle topographic variations. In many regions worldwide, high-precision elevation data are unavailable, necessitating the development of methods to enhance existing global digital elevation models (DEM). This study proposes a practical and replicable methodology to improve the vertical accuracy of global DEMs in flat terrains with limited data availability. The approach is based on correcting the altimetric differences between the DEM and GNSS-RTK-surveyed topographic points, incorporating land cover classification to refine adjustments. The methodology was tested in the Ranchería River delta in Riohacha, La Guajira, Colombia, using four global DEMs: FABDEM, SRTM, ASTER, and ALOS. Results showed a significant reduction in root mean square error (RMSE), with improvements of up to 76.691% for ASTER, 55.882 % for FABDEM, 55.932 % for SRTM, and 36.842 % for ALOS. The proposed method requires minimal computational resources and no advanced programming. Due to minimal data requirements, it makes it a scalable and replicable solution for similar floodplain environments. These enhancements in local altimetric accuracy could help to improve the reliability of hydrodynamic modeling, with direct implications for flood risk management and decision-making in vulnerable flatland areas.