The applicability of the FLEX-Topo and FIESTA models in simulating the impacts of land cover change on the streamflow dynamics in the tropical montane cloud forest of the Mestelá catchment, Alta Verapaz, Guatemala was investigated. Understanding these impacts is crucial for effective water resource management in regions vulnerable to deforestation, where cloud forests play a critical role in regulating hydrological processes, and in areas prone to flooding and droughts.

The FIESTA model was integrated to generate spatially distributed meteorological inputs, tailored to unique spatial characteristics of the tropical montane cloud forest region. These inputs informed the FLEX-Topo model, which was adapted to conceptualize the dominant hydrological processes in the study area for simulating streamflow in the Mestelá catchment. The model was calibrated to represent distinct land use classes within the catchment using multi-criteria calibration with different objective functions and constraints to account for parameter uncertainty. Scenario-based simulations, including deforestation, reforestation and the conversion of agricultural land to pine plantations, were conducted to quantify their effects on streamflow dynamics, water balance components and extreme hydrological
events.

The integration of the FIESTA model provided spatially distributed inputs, however further evaluation is needed to assess the accuracy of this distribution. Its spatial variability enabled the inclusion of fog interception into the water balance, representing a key hydrological process in cloud forests. Calibration of the FLEX-Topo model was achieved by optimizing parameters for distinct land use classes and using dynamic land use fractions. Scenario analysis revealed that deforestation potentially increased peak flows by 18.5% (±1.4%), while restoring forest cover reduced extreme flows by 39.5% (±1.9%), highlighting the role of reforestation in flood mitigation. Replacing agriculture with pine trees on steep slopes also reduced extreme flows, while additionally addressing landslide risks.

The combined application of the FLEX-Topo and FIESTA models offers valuable insights into hydrological responses to land use changes, particularly in cloud forest regions, highlighting their potential for informing policy decisions related to land conservation and water management in tropical montane cloud forests.

In a Ghana project, field measurements at the Black Volta aimed to enhance hydrological predictions for the Bui power dam. Validating devices and exploring floodplain contributions, such as with GNSS reflectometry and cameras, provides crucial insights for dam management and model improvement.

In this multidisciplinary project, conducted at the Black Volta in western Ghana, the focus is on addressing challenges related to data scarcity in hydrological predictions, particularly for the Bui power dam. The dam has faced issues during flood events, leading to spillages and subsequent damages. To enhance predictions, field measurements were carried out, employing innovative methods for remote areas within the TEMBO Africa project. The project aimed to validate devices measuring river parameters and improve the understanding of floodplain contributions to river discharge. Key methods included GNSS reflectometry for water level measurement and a camera-based approach for discharge determination. The GNSS-reflectometry device was successfully tested, validated, and installed at the Black Volta, automating water level measurements. The camera-based method demonstrated success on smaller streams but faced challenges on wider rivers. Despite this, it was installed at the Black Volta for daily discharge measurements, promising a reduction in rating curve uncertainty when combined with water level measurements. Field observations were utilized to enhance an existing hydraulic model, refining the floodplain's representation. The study also delved into determining the floodplain's roughness coefficient, involving manual measurements and attempts at automation using remote sensing techniques. The roughness coefficients were implemented into the Delft3D model, showing contributions of floodplains to river discharge. Despite challenges, including equipment installation timing and location disruptions due to bridge construction, the project provides valuable insights for improving hydrological models and preventing dam spillages in the future.