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Based on high-spatiotemporal-resolution data, the authors perform a climatological study of strong rainfall events propagating from southeastern South America to the eastern slopes of the central Andes during the monsoon season. These events account for up to 70% of total seasonal rainfall in these areas. They are of societal relevance because of associated natural hazards in the form of floods and landslides, and they form an intriguing climatic phenomenon, because they propagate against the direction of the low-level moisture flow from the tropics. The responsible synoptic mechanism is analyzed using suitable composites of the relevant atmospheric variables with high temporal resolution. The results suggest that the low-level inflow from the tropics, while important for maintaining sufficient moisture in the area of rainfall, does not initiate the formation of rainfall clusters. Instead, alternating low and high pressure anomalies in midlatitudes, which are associated with an eastward-moving Rossby wave train, in combination with the northwestern Argentinean low, create favorable pressure and wind conditions for frontogenesis and subsequent precipitation events propagating from southeastern South America toward the Bolivian Andes.
We modeled the two most extreme highstands of Lake Naivasha during the last 175 k.y. to estimate potential precipitation/ evaporation changes in this basin. In a first step, the bathymetry of the paleolakes at f135 and 9 k.y. BP was reconstructed from sediment cores and surface outcrops. Second, we modeled the paleohydrologic budget during the highstands using a simplified coupled energy mass-balance model. Our results show that the hydrologic and hence the climate conditions at f135 and 9 k.y. BP were similar, but significantly different from today. The main difference is a f15% higher value in precipitation compared to the present. An adaptation and migration of vegetation in the cause of climate changes would result in a f30% increase in precipitation. The most likely cause for such a wetter climate at f135 and 9 k.y. BP is a more intense intertropical convergence and increased precipitation in East Africa.
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry
(2015)
In urban areas, leaf area index (LAI) is a key ecosystem structural attribute with implications for energy and water balance, gas exchange, and anthropogenic energy use. In this study, we estimated LAI spatially using airborne lidar in downtown Santa Barbara, California, USA. We implemented two different modeling approaches. First, we directly estimated effective LAI (LAIe) using scan angle- and clump-corrected lidar laser penetration metrics (LPM). Second, we adapted existing allometric equations to estimate crown structural metrics including tree height and crown base height using lidar. The latter approach allowed for LAI estimates at the individual tree-crown scale. The LPM method, at both high and decimated point densities, resulted in good linear agreement with estimates from ground-based hemispherical photography (r(2) = 0.82, y = 0.99x) using a model that assumed a spherical leaf angle distribution. Within individual tree crown segments, the lidar estimates of crown structure closely paralleled field measurements (e.g., r(2) = 0.87 for crown length). LAI estimates based on the lidar crown measurements corresponded well with estimates from field measurements (r(2) = 0.84, y = 0.97x + 0.10). Consistency of the LPM and allometric lidar methods was also strong at 71 validation plots (r(2) = 0.88) and at 450 additional sample locations across the entire study area (r(2) = 0.72). This level of correspondence exceeded that of the canopy hemispherical photography and allometric, ground-based estimates (r(2) = 0.53). The first-order alignment of these two disparate methods may indicate that the error bounds for mapping LAI in cities are small enough to pursue large scale, spatially explicit estimation. (C) 2015 Elsevier Inc All rights reserved.
The mechanisms by which climate and vegetation affect erosion rates over various time scales lie at the heart of understanding landscape response to climate change. Plot-scale field experiments show that increased vegetation cover slows erosion, implying that faster erosion should occur under low to moderate vegetation cover. However, demonstrating this concept over long time scales and across landscapes has proven to be difficult, especially in settings complicated by tectonic forcing and variable slopes. We investigate this problem by measuring cosmogenic Be-10-derived catchment-mean denudation rates across a range of climate zones and hillslope gradients in the Kenya Rift, and by comparing our results with those published from the Rwenzori Mountains of Uganda. We find that denudation rates from sparsely vegetated parts of the Kenya Rift are up to 0.13 mm/yr, while those from humid and more densely vegetated parts of the Kenya Rift flanks and the Rwenzori Mountains reach a maximum of 0.08 mm/yr, despite higher median hillslope gradients. While differences in lithology and recent land-use changes likely affect the denudation rates and vegetation cover values in some of our studied catchments, hillslope gradient and vegetation cover appear to explain most of the variation in denudation rates across the study area. Our results support the idea that changing vegetation cover can contribute to complex erosional responses to climate or land-use change and that vegetation cover can play an important role in determining the steady-state slopes of mountain belts through its stabilizing effects on the land surface.