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.

The hydroclimatology of Northern South America responds to strongly-coupled dynamics of oceanic and terrestrial surface-atmosphere exchange, as moisture evaporated from these sources interact to produce continental rainfall. However, the relative contributions of these two source types through the annual cycle have been described only in modeling studies, with no observational tools used to corroborate these predictions. The use of isotopic techniques to study moisture sources has been common in assessing changes in the water cycle and in climate dynamics, as isotopes allow tracking the connection between evaporation, transpiration, and precipitation, as well as the influence of large scale hydroclimatic phenomena, such as the seasonal Inter Tropical Convergence Zone migration. We characterize the isotopic composition of moisture sources becoming precipitation in the Andes and Caribbean regions of Colombia, using stable isotopes data (δ18O, δ2H) from the Global Network of Isotopes in Precipitation (1971-2016) and contrasting it with moisture trajectory tracking from the FLEXPART model, using input from ERA-Interim reanalysis to compute the relative contribution of oceanic and terrestrial sources through the annual cycle. Our results indicate that most precipitation in the region comes from terrestrial sources including recycling (>30 % for all months), Orinoco (up to 28 % monthly for April), and the northern Amazon (up to 17 % monthly for June, July, and August); followed by oceanic sources including the Tropical South Pacific (up to 30 % monthly in October, November, December) and Tropical North Atlantic (up to 30 % monthly for January). These outcomes highlight the utility of combining stable isotopes in precipitation and modeling techniques to discriminate terrestrial and oceanic sources of precipitation. Further, our results highlight the need to assess the hydrological consequences of land cover change in South America, particularly in a country like Colombia where water, food and energy security all depend directly on precipitation. .