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Exploring drivers of the current distribution of tropical forest and savanna in Northern South America
Exploring drivers of the current distribution of tropical forest and savanna in Northern South America
We analyzed which factors best explain the transition in northern South America (Llanos ecoregion and northwestern Amazon), where common thresholds based on typical environmental factors (e.g., mean annual precipitation (MAP), wet season precipitation) fail to predict this transition. For instance, savannas in the Llanos occur at MAP levels (> 1500 mm) which are typical of forests in other tropical regions.
Our results highlight the importance of fire frequency and intra-seasonal precipitation in determining forest-savanna transition in northern South America. Furthermore, future studies should consider regional differences in the climatic space of forest and savanna to improve projections of global change impacts on these highly diverse ecosystems. Further details at Valencia et al. (2024).
Which remote sensing product best captures precipitation patterns across the topographic and climatic gradients of Colombia?
Which remote sensing product best captures precipitation patterns across the topographic and climatic gradients of Colombia?
Figure 1. General location of the study area. a) digital elevation model and natural regions of Colombia; b) location of 1,334 selected precipitation gauges; c) mean annual precipitation (MAP) from WorldClimv2.1.
We assessed the performance of nine global precipitation products (GPPs) in Colombia, using these 1,334 ground gauges as references, as detailed in Valencia et al. (2023). Figure 1 shows the spatial distribution of these gauges and their corresponfing time series are available HERE. For oficial request of hydroclimatological data in Colombia, we encorage to visit DHIME website.
Overall, CHIRPSv2 outperforms other GPPs throughout the country and its five natural regions for all elevation zones and temporal scales, followed closely by MSWEPv2.8 and IMERG. PERSIANN-CCS and ERA5 products had the lowest performance, mainly over the driest and higher-altitude regions, respectively.
How well does the SWAT model capture the Leaf Area Index dynamics in tropical Andean ecosystems? What are the implications for land use and land cover change simulations?
How well does the SWAT model capture the Leaf Area Index dynamics in tropical Andean ecosystems? What are the implications for land use and land cover change simulations?
Hydrological models such as the SWAT (Soil and Water Assessment Tool) are widely used tools to project the impacts of LULC (Land Use/Land Cover) change on water budget. However, their ability to produce reliable predictions depends on how accurately they represent the role of vegetation in the watershed’s water balance. In this study, we analyze how different representations of Leaf Area Index (LAI) affect streamflow responses to LULC change using the original SWAT model, SWAT-T (Alemayehu et al., 2017), and a proposed new variant (SWAT-Tb), which improves LAI bimodal representation for tropical regions. Further details at Valencia et al. (2024) . The SWAT-Tb executables are available at https://github.com/ValenciaSantiago/SWAT-Tb.
Fig. 2. (a-c) General location of the study area (South America, Colombia, and Antioquia Department, respectively). (d) Grande and Chico rivers watersheds including hydrometeorological gauges. (e) Digital Elevation Model (DEM), (f) land cover for the year 2015 (CORANTIOQUIA and UNAL, 2015), and (g) soil types (CORANTIOQUIA and UNAL, 2015). Land cover and soil types codes are: RYEG: pasture (51.2 % of CR watershed), FRST: native Andean forest (29.3 %), RYEB: shrubs (12.8 %), BROM: paramo vegetation (4.9 %), RYEE: pasture with secondary growth (1.6 %), PINE: planted forest (0.07 %), URMD: urban (0.13 %), AD: Andic Dystrudepts, AH: Andic Humudepts, AU: Andic Udifluvents, LH: Lithic Hapludand, TD: Typic Dystrudepts, TH: Typic Hapludands, and TM: typic melanudands.
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