This multidisciplinary project, undertaken in collaboration with Community Cloud Forest Conservation (CCFC) in Alta Verapaz, Guatemala, addresses the need for long-term meteorological and hydrological monitoring in the Mestelá River catchment. The tropical montane cloud forest in this region provides essential ecosystem services through canopy cloud water interception and regulation of streamflow, yet continuous, high-quality environmental data remain limited.

To support research and conservation efforts, a 13.5 m scaffolding tower was designed and constructed as a durable, safe, and adaptable measurement platform, engineered for future extension to 25 m. The structural design accounted for local wind loads, dynamic forces, foundation stability, and corrosion resistance, ensuring a projected operational lifespan of 15 years.

Beyond infrastructure, the project developed a hydrological monitoring set-up and a Python-based modelling framework to quantify the canopy water balance and hydrological cycle. Sensor selection, placement, and integration were tailored to capture key meteorological and hydrological variables, including rainfall, fog interception, throughfall, and soil moisture. Data acquisition and storage were configured to function as autonomously as possible under remote, high-humidity cloud forest conditions, while allowing for straightforward periodic maintenance of all components involved.

Recognising that sustainability extends beyond technical performance, the project incorporated cultural and institutional engagement. Workshops and collaborative activities with CCFC staff and local stakeholders were conducted to align the monitoring system with community values, build operational capacity, and foster local ownership. A comprehensive maintenance strategy and guidelines for potential expansion were developed to ensure the continued relevance and adaptability of the system, including options for biodiversity monitoring and additional research applications.

The resulting monitoring platform combines robust engineering, scientific instrumentation, and community integration. It establishes a foundation for long-term data collection that can inform hydrological modelling, climate adaptation strategies, and evidence-based conservation, while embedding the system within the local social and ecological context.

Cloud Forests play a critical role in regulating regional hydrology through fog interception, biodiversity support, and groundwater recharge. However, increasing deforestation and land‐use change threaten the functionality of these ecosystems, partially by altering cloud formation processes. This study investigates how different land‐use types affect cloud base height (CBH) in the Mestelá River catchment in Alta Verapaz, Guatemala. Using a distributed sensor network of temperature, humidity, and pressure sensors deployed across cloud forest, pine forest, and agricultural land, vertical profiles were collected over multiple field campaigns.

Cloud base height was estimated through a lifting condensation level (LCL) model based on local atmospheric measurements. Results show systematic differences between land‐use types: cloud forests exhibited cooler and more humid conditions, resulting in a lower CBH compared to pine forests and open agricultural areas. Open fields consistently showed the highest daytime temperatures and lowest relative humidity, producing the highest estimated cloud bases. Pine forests exhibited intermediate conditions.

These microclimatic differences were incorporated into the FIESTA fog interception model, improving spatial and temporal representation of fog occurrence and interception efficiency. It was shown that especially improving the temporal accuracy of FIESTA inputs by forcing a diurnal pattern led to more accurate results. In addition, a simplified canopy water balance model was applied to evaluate the hydrological contribution of fog events at stand level. The results confirm that land‐use change alters cloud immersion frequency and potentially reduces dry‐season water inputs in deforested areas.

This study demonstrates that deforestation influences atmospheric processes at local scales with direct hydrological consequences, underscoring the importance of cloud forest conservation for water security in mountainous regions. The deployed sensor network and modeling framework offer a scalable method for monitoring cloud dynamics and evaluating land‐use impacts in other tropical montane systems.

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.