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The study of the environmental factors that control evapotranspiration and the components of evapotranspiration leads to a better understanding of the actual evapotranspiration (ET) process that links the functioning of the soil, water and atmosphere. It also improves local, regional and global ET modelling. Globally, few studies so far focussed on the controls and components of ET in alpine grasslands, especially in mountainous sites such as the tussock grasslands located in the páramo biome (above 3300 m a.s.l.). The páramo occupies 35 000 km 2 and provides water resources for many cities in the Andes. In this article, we unveiled the controls on ET and provided the first insights on the contribution of transpiration to ET. We found that the wet páramo is an energy-limited region and net radiation (Rn) is primarily controlling ET. ET was on average 1.7 mm/day. The monthly average evaporative fraction (ET/Rn) was 0.47 and it remained similar for wet and dry periods. The secondary controls on ET were wind speed, aerodynamic resistance and surface resistance that appeared more important for dry periods, where significantly higher ET rates were found (20% increase). During dry events, transpiration was on average 1.5 mm/day (range 0.7-2.7 mm/day), similar to other tussock grasslands in New Zealand (range 0.6-3.3 mm/day). Evidence showed interception contributes more to ET than transpiration. This study sets a precedent towards a better understanding of the evapotranspiration process and will ultimately lead to a better landatmosphere fluxes modelling in the tropics.

Andean ecosystems provide important ecosystem services including streamflow regulation and carbon sequestration, services that are controlled by the water retention properties of the soils. Even though these soils have been historically altered by pine afforestation and grazing, little research has been dedicated to the assessment of such impacts at local or regional scales. To partially fill this knowledge gap, we present an evaluation of the impacts of pine plantations and grazing on the soil hydro-physical properties and soil organic matter (SOM) of high montane forests and páramo in southern Ecuador, at elevations varying between 2705 and 3766 m a.s.l. In total, seven study sites were selected and each one was parceled into undisturbed and altered plots with pine plantation and grazing. Soil properties were characterized at two depths, 0–10 and 10–25 cm, and differences in soil parameters between undisturbed and disturbed plots were analyzed versus factors such as ecosystem type, sampling depth, soil type, elevation, and past/present land management. The main soil properties affected by land use change are the saturated hydraulic conductivity (Ksat), the water retention capacity (pF 0 to 2.52), and SOM. The impacts of pine afforestation are dependent on sampling depth, ecosystem type, plantation characteristics, and previous land use, while the impacts of grazing are primarily dependent on sampling depth and land use management (grazing intensity and tilling activities). The site-specific nature of the found relations suggests that extension of findings in response to changes in land use in montane Andean ecosystems is risky; therefore, future evaluations of the impact of land use change on soil parameters should take into consideration that responses are or can be site specific.

Accurate determination of the water retention curve (WRC) of a soil is essential for the understanding and modelling of the subsurface hydrological, ecological, and biogeochemical processes. Volcanic ash soils with andic properties (Andosols) are recognized as important providers of ecological and hydrological services in mountainous regions worldwide due to their large fraction of small size particles (clay, silt, and organic matter)

During the 1980s, reforestation programs using exotic species (Pinus spp.) were established in the páramo ecosystem of Ecuador. The aims of this study were: (1) to compare the natural regeneration between pine plantations (Pi) and natural grassland (NG) across an elevational gradient and (2) to identify the attributes of Pi and soil properties that were influencing herbaceous and woody plant composition and their plant cover. In total, six independent Pinus patula (Schltdl. & Cham. plantations (two per each elevation) were selected and distributed in an elevational range (3200-3400, 3400-3600, 3600-3800 m a.s.l.). Adjacent to Pi, plots in NG were established for recording natural regeneration. Both, namely the attributes and the soil samples, were measured in Pi. The results showed that natural regeneration differs significantly between both types of vegetation. As expected, NG holds more plant diversity than Pi; the elevational range showed a clear tendency that there was more herbaceous richness when elevation range increases, while the opposite was found for woody species. Moreover, attributes of Pi influenced herbaceous and woody vegetation, when saturated hydraulic conductivity (Ksat) in the soil, basal area (BA) and canopy density (CD) increased, herbaceous species richness and its cover decreseased; and when Ksat and the acidity in the soil increased, woody plants richness and its cover decreased. The plantations have facilitated the establishment of shade tolerant species. More studies are needed to evaluate if removal with adequate management of pine plantations can improve the restoration and conservation of the native vegetation of the páramo ecosystem.Forests 2019, 10, 745 2 of 30 diversity of the ecological conditions linked to the glacial geomorphology that has resulted in a large number of different plant associations, each one with their typical species [6].Elevation is an important factor that shapes plant diversity in the páramo. The elevational gradients combine sets of environmental conditions such as: temperature, wind velocity, atmospheric gas composition, water availability, nutrient deposition and cycling, soil weathering and solar radiation, all of which determine the composition and structure of vegetation [7]. Based on the influence of these factors and vegetation structure, the páramo has been divided into three zones, from lowest to highest: subpáramo, páramo (páramo grassland) and superpáramo [8]. The subpáramo, also called páramo forest, shrubby páramo, subpáramo woodland and subpáramo elfin forest [9], is the transition zone (ecotone) between the forest (upper montane cloud forest) and the páramo grassland [8][9][10][11]. The subpáramo is usually an entangle of shrubs and small dispersed trees, gradually reduced in size, that gives way to grasses and herbs [9]. The páramo vegetation zone, also called grass páramo or páramo grassland, is characterized by tussock grasses dominated by species of Calamagrostis and/or Festuca. Finally, above the páramo, there is the superpáramo, which i...

No abstract

<p>Accurate determination of the water retention curve (WRC) of a soil is essential for the understanding and modelling of the subsurface hydrological, ecological, and biogeochemical processes. Volcanic ash soils with andic properties (Andosols) are recognized as important providers of ecological and hydrological services in mountainous regions worldwide due to their outstanding water holding capacity. Previous comparative analyses of in situ (field) and standard laboratory (hydrostatic equilibrium based) methods for the determination of the WRC of Andosols showed contrasting results. Based on an extensive analysis of laboratory, experimental, and field measured WRCs of Andosols in combination with data extracted from the published literature we show that standard laboratory methods using small soil sample volumes (≤300 cm<sup>3</sup>) mimic the WRC of these soils only partially. The results obtained by the latter resemble only a small portion of the wet range of the Andosols’ WRC (from saturation up to -5 kPa, or pF 1.7), but overestimate substantially their water content for higher matric potentials. The disagreement limits our capacity to infer correctly subsurface hydrological behavior, as illustrated through the analysis of long-term soil moisture and matric potential data from an experimental site in the tropical Andes. These findings imply that results reported in past research should be used with caution and that future research should focus on determining laboratory methods that allow obtaining a correct characterization of the WRC of Andosols.</p>

<p>The paramo biome, located above 3300 m a.s.l. and covered mainly by tussock grasslands, provides ecosystem services for Andean cities, especially water resources used for drinking water, agriculture, hydropower generation and sustaining aquatic ecosystems. Even though research about the main components of the water cycle has increased substantially in the last decade, evaporation has remained unknown. In this study, we quantified for the first time daily, monthly and annual evaporation, its components (i.e. interception and transpiration) at event scale and its climatic drivers at a representative páramo catchment in Southern Ecuador (Figure 1). We used the eddy-covariance method to quantify evaporation. We additionally compared those measurements with lysimeters, water balance, energy balance, hydrological models (HBV-light and PDM) and the calibration of the Penman-Monteith equation in order to find easier and cheaper alternatives for estimating evaporation at the páramo (Figure 2).</p><p>Our results show that the paramo biome had a relatively low evaporation rate (annual ETa/P = 0.5, 1.7 mm/day) and is an energy-limited site, where net radiation is the primary control of evaporation (annual ETa/Rn = 0.47) (Figure 3). The secondary controls were wind speed, surface and aerodynamic conductance, especially important during dry periods. The maximum capacity of tussock grasslands to intercept water was 2 mm. During small events (P < 2 mm), between 100 and 80 % of precipitation was intercepted and released back to the atmosphere; while during large events (P > 2 mm), interception decreased from 80 to 10 %. During dry periods, transpiration rates ranged between 0.7 and 2.7 mm/day and on top, the fog and dew harvested by the vegetation contributed to the evaporation in around 30 %. When comparing eddy-covariance measurements with other methods, we found that the two hydrological models and the calibrated Penman-Monteith equation are robust alternatives for daily estimation of evaporation at the páramo. This study contributed to the understanding of the water cycle at the most important Andean ecosystem for its water resources.</p><p><img src="data:image/jpeg;base64, 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