Tropical ecosystems are among the most vulnerable to climate change. Understanding climate impacts on these ecosystems is a primary challenge for policy makers, ecologists, and conservationists today. We analyzed the vulnerability of ecosystems in a very heterogeneous tropical region in southern Ecuador, selected because of its exceptional biodiversity and its ecosystem services provided to people of southern Ecuador and northern Peru. The vulnerability assessment focused on three components: exposure, sensitivity, and adaptive capacity. For the first two components, we identified stressors or drivers of change that negatively influence ecosystems. For the third component, we identified existing and potential buffers that reduce impacts. This process was developed in workshops and by expert elicitation. Representative Concentration Pathway (RCP) scenarios were used, considering RCP 2.6 and RCP 8.5 for a time horizon to 2050. Under the RCP 2.6 scenario, the components of overall vulnerability in the southern region of Ecuador showed very low to moderate vulnerability for most areas, particularly in semi-deciduous forest ecosystems, Amazon semi-deciduous forest, Amazon rainforest, and mangrove forests. These areas had high vulnerability under the RCP 8.5 scenario. A variety of conservation strategies (e.g., protected areas) were shown to increase the adaptive capacity of ecosystems and reduce their vulnerability. We therefore recommend improving these conservation initiatives in ecosystems like dry forests, where the greatest vulnerability is evident.

The prediction of river discharge using hydrological models (HMs) is of utmost importance, especially in basins that provide drinking water or serve as recreation areas, to mitigate damage to civil structures and to prevent the loss of human lives. Therefore, different HMs must be tested to determine their accuracy and usefulness as early warning tools, especially for extreme precipitation events. This study simulated the river discharge in an Andean watershed, for which the distributed HM Runoff Prediction Model (RPM) and the semi-distributed HM Hydrologic Modelling System (HEC-HMS) were applied. As precipitation input data for the RPM model, high-resolution radar observations were used, whereas the HEC-HMS model used the available meteorological station data. The obtained simulations were compared to measured discharges at the outlet of the watershed. The results highlighted the advantages of distributed HM (RPM) in combination with high-resolution radar images, which estimated accurately the discharges in magnitude and time. The statistical analysis showed good to very good accordance between observed and simulated discharge for the RPM model (R2: 0.85–0.92; NSE: 0.77–0.82), whereas for the HEC-HMS model accuracies were lower (R2: 0.68–0.86; NSE: 0.26–0.78). This was not only due to the application of means values for the watershed (HEC-HMS), but also to limited rain gauge information. Generally, station network density in tropical mountain regions is poor, for which reason the high spatiotemporal precipitation variability cannot be detected. For hydrological simulation and forecasting flash floods, as well as for environmental investigations and water resource management, meteorological radars are the better choice. The greater availability of cost-effective systems at the present time also reduces implementation and maintenance costs of dense meteorological station networks.