Projecting the potential impacts of LULC (Land Use/Land Cover) change on watershed hydrological response is critical for water management decisions in a changing environment. An improved representation of vegetation dynamics is needed to improve the capability of several hydrological models to produce reliable projections of these impacts. Here we in troduce a modification in the plant growth module of SWAT (Soil Water Assessment Tool) to improve the representation of the bimodal seasonality of LAI (Leaf Area Index), which is particularly important for tropical watersheds with bimodal precipitation regimes. The new SWAT-Tb variant that we propose here reproduces not only observed streamflow, but also the bimodal seasonal pattern of LAI in a tropical mountain watershed of the Andes. In contrast, standard SWAT is inherently unable to reproduce this bimodality, although it can be calibrated to reproduce streamflow. Differences between models in the representation of LAI seasonality can lead to significantly different results about LULC change impacts on streamflow. SWAT-Tb results show that deforestation impacts on streamflow are more pronounced for seasonal than for annual streamflow, and indicate that forests can play a crucial role in enhancing water availability during dry seasons. The seasonality of streamflow anomalies is switched due to forest-to-pasture conversion, implying that while forest expansion increases water availability in dry seasons, forest conversion into pasture decreases it. Due to its poor representation of LAI seasonality, standard SWAT largely underestimates this role of forest, which can be misleading for decision making about water security and forest conservation

The savanna - forest transition in the tropics has a large and complex variation in vegetation structure both vertically and horizontally. 3D-imaging technologies provide detailed high-resolution measurements of the vegetation structure. However, the use of these observations globally faces practical challenges due to spatio-temporal gaps and operational restrictions, mainly in tropical regions. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is the first quasi-global LiDAR (light detection and ranging) observations of 3D vegetation structure at a footprint resolution of 25 m. Here we use GEDI data (GEDI02_Bv001) to analyze vegetation structure in the savanna - tropical forest transition of northern South America, using canopy height, canopy cover, total Plant Area Index (PAI), maximum Plant Area volume Density (PAVD), and vertical profile of PAI and PAVD as vegetation structure descriptors. Despite contrasts between savanna (open-canopy) and forest (closed-canopy), our results show a gradual variation along the transition in canopy height, canopy cover, total PAI, and maximum PAVD. Our results support that the savanna- forest transition in tropical regions can be described as a grassland - forest continuum. Results also indicate that GEDI data allow a better characterization of vegetation lower than 5 m in height, mainly in savanna, an improvement from other global databases (e.g. MODIS). Further, our study illustrates the potential of GEDI data to advance in the characterization of large-scale patterns of vegetation structure in tropics, key for supporting biogeography, and macroecology studies relevant in the phase of current ecosystem changes.