Laboratory for
Climatology and Remote Sensing

Abstract:

This study addresses transpiration in a tropical evergreen mountain forest in the Ecuadorian Andes from the leaf to the stand level, with emphasis on nocturnal plant-water relations. The stand level: Evapotranspiration (ET) measured over 12 months with the Eddy-Covariance (ECov) technique proved as the major share (79%) of water received from precipitation. Irrespective of the humid climate, the vegetation transpired day and night. On average, 15.3% of the total daily ET were due to nocturnal transpiration. Short spells of drought increased daily ET, mainly by enhanced nighttime transpiration. Following leaf transpiration rather than air temperature and atmospheric water vapor deficit, ET showed its maximum already in the morning hours. The tree level: Due to the humid climate, the total water consumption of trees was generally low. Nevertheless, xylem sap flux measurements separated the investigated tree species into a group showing relatively high and another one with low sap flux rates. The leaf level: Transpiration rates of Tapirira guianensis, a member of the high-flux-rate group, were more than twice those of Ocotea aciphylla, a representative of the group showing low sap flux rates. Representatives of the Tapirira group operated at a relatively high leaf water potential but with a considerable diurnal amplitude, while the leaves of the Ocotea group showed low water potential and small diurnal fluctuations. Overall, the Tapirira group performed anisohydrically and the Ocotea group isohydrically. Grouping of the tree species by their water relations complied with the extents of the diurnal stem circumference fluctuations. Nighttime transpiration and hydrological type: In contrast to the isohydrically performing trees of the Ocotea group, the anisohydric trees showed considerable water vapour pressure deficit (VPD)-dependent nocturnal transpiration. Therefore, we conclude that nighttime ET at the forest level is mainly sourced by the tree species with anisohydric performance.

Abstract:

Tropical forests are highly diverse at many spatial scales. In these forests, small-sized canopy organisms can form species-rich communities already within a few cm2. Understanding how species numbers increase when expanding the sampling along the tree and the forest is critical for evaluating the processes maintaining biodiversity. We therefore studied epiphytic bryophyte diversity in tree crowns and along trunks across spatial scales in a tropical lowland forest in Amazonian Ecuador, sampling bryophytes in 100-cm2 quadrats on 24 trees (15–22 quadrats each) using a spatially hierarchical design, analyzing alpha and beta diversity at different spatial grains and extents. At the smallest grain, tree crowns held more bryophyte species than trunks, but at the largest grain the trunks held most species (93 vs. 77), as beta diversity was higher among trunks than among crowns. However, except for trunks at the largest extent (all 24 trees), the highest beta diversity among quadrats was always found between crowns and trunks. Species turnover strongly dominated beta diversity at all spatial scales. This and the high species richness resulted in highly unpredictable species compositions, especially in trunk communities. These patterns suggest different controls of diversity in crowns than on trunks and an important role for chance processes in shaping these communities. The high beta diversity within trees, in combination with the large effort involved in climbing trees, implies that diversity sampling of small canopy organisms is most efficient using an intensive (many plots on few trees) rather than extensive (many trees across a large area) sampling.

Abstract:

Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-min temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.

Abstract:

The thermal structure of a megadiverse mountain ecosystem in southern Ecuador is examined on the basis of temperature measurements inside the natural mountain forest and at open sites along an altitudinal gradient from 1700m to 3200m. The data is used to calculated maps of monthly average minimum, mean and maximum 2m air temperature for the period 1999-2007, needed by ecological projects. The maps are generated by combining the straightforward detrending technique by using a Digital Elevation Model and satellite-based land cover classification. The results reveal a clear thermal differentiation over the year, partly triggered by the change of synoptic weather situation but also by land cover effects. Thermal amplitudes are particularly low during the main rainy season when cloudiness and air humidity are high but markedly pronounced in the relative dry season when daily irradiance and outgoing nocturnal radiation causes distinct differences between the land cover units. Particularly the lower pasture areas gained by slash and burn of the natural forest exhibit the most extreme thermal conditions while the atmosphere inside the mountain forest is slightly cooler due to the regulating effects of the dense vegetation. Thus, clearing the forest clearly reduces the thermal regulation function (regulating ecosystem services) of the ecosystem which might become problematic under future global warming.

Abstract:

Global biodiversity is threatened by climate and land cover change. The research unit "Biodiversity and Sustainable Management of a Megadiverse Mountain Ecosystem in South Ecuador" (FOR 816) funded by the German research council (Deutsche Forschungsgemeinschaft, DFG) is working in one of the hottest hotspots of biodiversity of the world. In this region the pressure from the local population on the environment is severe resulting in a high deforestation rate. Sustainable management systems have to be developed on a regional scale to counteract the loss of livelihood of the local population. Numerical models are capable to investigate the changes of the mentioned future land cover changes and its response to climatic and hydrologic variability. The chance to test numerously land use scenarios without interfering into the real environment offers the possibility to investigate and to evaluate the proposed management strategies. The presented work targets at an analysis of the impact of the predicted land cover changes in respect of the ecosystem services of climate and water regulation. Therefore a state-of-the-art land surface model called "Community Land Model" (CLM) is setup in a regional scale. The parametrization of the vegetation is implemented using plant functional types (PFT). The PFTs are defined a priori with vegetation classes based on ecological field surveys. Three central hypotheses are formulated to support the parametrization of the model. The completed work offers a regionalized model setup to analyze different land cover developments in reference to energy and water fluxes between the soil, the vegetation and the atmosphere under changing climatic conditions. Besides the appraisal of the stated hypotheses other innovative contributions are made. The new values for the pre-installed CLM-PFT of tropical evergreen trees add to the current improvements made to the CLM.

Abstract:

A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution fo Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It wa developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieva technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

Abstract:

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems

Abstract:

Abstract Conversion of tropical forests is among the primary causes of global environmental change. The loss of their important environmental services has prompted calls to integrate ecosystem services (ES) in addition to socio-economic objectives in decision-making. To test the effect of accounting for both ES and socio-economic objectives in land-use decisions, we develop a new dynamic approach to model deforestation scenarios for tropical mountain forests. We integrate multi-objective optimization of land allocation with an innovative approach to consider uncertainty spaces for each objective. These uncertainty spaces account for potential variability among decision-makers, who may have different expectations about the future. When optimizing only socio-economic objectives, the model continues the past trend in deforestation (1975–2015) in the projected land-use allocation (2015–2070). Based on indicators for biomass production, carbon storage, climate and water regulation, and soil quality, we show that considering multiple ES in addition to the socio-economic objectives has heterogeneous effects on land-use allocation. It saves some natural forest if the natural forest share is below 38%, and can stop deforestation once the natural forest share drops below 10%. For landscapes with high shares of forest (38%–80% in our study), accounting for multiple ES under high uncertainty of their indicators may, however, accelerate deforestation. For such multifunctional landscapes, two main effects prevail: (a) accelerated expansion of diversified non-natural areas to elevate the levels of the indicators and (b) increased landscape diversification to maintain multiple ES, reducing the proportion of natural forest. Only when accounting for vascular plant species richness as an explicit objective in the optimization, deforestation was consistently reduced. Aiming for multifunctional landscapes may therefore conflict with the aim of reducing deforestation, which we can quantify here for the first time. Our findings are relevant for identifying types of landscapes where this conflict may arise and to better align respective policies.

Abstract:

Atmospheric carbon (CO2) exchange, evapotranspiration (ET) processes, and their interactions with climatic drivers across tropical alpine grasslands are poorly understood. This lack of understanding is particularly evident for the páramo, the highest vegetated frontier in the northern Andes, the main source of water for inter-Andean cities, and a large carbon storage area. Studies of CO2 and ET fluxes via the standard Eddy Covariance (EC) technique have never been applied to this region, limiting the understanding of diurnal / nocturnal exchanges and budget estimations. In this paper, we report the first EC analysis conducted on the Andean páramo (3765?m a.s.l.); this analysis measured CO2, ET, and micrometeorological variables over two years (2016–2018) to understand their interactions with climatic / biophysical controls. The páramo was found to be a source of CO2 and exhibited a net positive exchange (mean = +99?±?30 gC m?2 per year). The light-responses of net CO2 exchange and the primary productivity were correlated and model-parameterized. Evapotranspiration was 635?±?9?mm per year (51% of the annual rainfall total), and we obtained crop coefficients for the dominant vegetation (Tussock grass) based on reference-ET models FAO56 and ASCE-ERWI (0.90 and 0.78, respectively). We also compared our results to those from other high-altitude (alpine) and high-latitude grasslands (tundra). Finally, we demonstrate that our measurement period is representative of the páramo’s longer-term climate dynamics. Our investigation contributes to the body of knowledge on the land surface-atmosphere processes of the tropical Andes and supports decision-making about ecosystem services management and the preservation of this vulnerable biome.

Abstract:

Reference evapotranspiration (ETo) is often calculated using the Penman-Monteith (FAO 56 PM; Allen et al 1998) method, which requires data on temperature, relative humidity, wind speed, and solar radiation. But in high-mountain environments, such as the Andean páramo, meteorological monitoring is limited and high-quality data are scarce. Therefore, the FAO 56 PM equation can be applied only through the use of an alternative method suggested by the same authors that substitutes estimates for missing data. This study evaluated whether the FAO 56 PM method for estimating missing data can be effectively used for páramo landscapes in the high Andes of southern Ecuador. Our investigation was based on data from 2 automatic weather stations at elevations of 3780 m and 3979 m. We found that using estimated wind speed data has no major effect on calculated ETo but that if solar radiation data are estimated, ETo calculations may be erroneous by as much as 24%; if relative humidity data are estimated, the error may be as high as 14%; and if all data except temperature are estimated, errors higher than 30% may result. Our study demonstrates the importance of using high-quality meteorological data for calculating ETo in the wet páramo landscapes of southern Ecuador.

Abstract:

Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the páramo. To overcome this lack of knowledge, we implemented the energy-balance model METRIC with Landsat 7 ETM+ and MODIS-Terra imagery for a páramo catchment. The implementation contemplated adjustments for complex terrain in order to obtain daily, monthly and annual ET maps (between 2013 and 2014). In addition, we compared our results to the global ET product MOD16. Finally, a rigorous validation of the outputs was conducted with residual ET from the water balance. ET retrievals from METRIC (Landsat-based) showed good agreement with the validation-related ET at monthly and annual steps (mean bias error <8 mm·month?1 and annual deviation <17%). However, METRIC (MODIS-based) outputs and the MOD16 product were revealed to be unsuitable for our study due to the low spatial resolution. At last, the plausibility of METRIC to obtain spatial ET retrievals using higher resolution satellite data is demonstrated, which constitutes the first contribution to the understanding of spatially-explicit ET over an alpine catchment in the neo-tropical Andes.

Abstract:

Human activities during the last decades provoked a notable reduction in global forest cover. Knowing that forest stands act as stock and sinks for carbon and other greenhouse gases, it is important to determine the existing forest cover at country level and to calculate annual deforestation rates. This work uses NOAA satellite images in a resolution of 1 km x 1 km to classify the surface of continental Ecuador in “forest” – “non-forest” pixels and to estimate the annual deforestation rate from 1986 to 2001 as well as from 2001 to 2008. The method is based on a decision tree algorithm that includes different spectral bands of the NOAA-AVHRR sensor and additional topographic and meteorological parameters. The results show that the total forest cover of continental Ecuador was reduced from 48.1 % in 1986 to 36.8 % in 2008. The calculated annual deforestation rates indicate that forest reduction increased during the last decade. The most affected area is the Coastal Lowland, due to the enhanced population pressure, followed by the Amazon Basin, not only caused by the governmental supported oil and mining industry, but also due to the uncontrolled timber extraction. The Andean Highland has been less affected, because the major parts of this region were deforested before, during the Pre-Columbian-Era.

Abstract:

Mountain regions worldwide present a pronounced spatiotemporal precipitation variability, which added to scarce monitoring networks limits our understanding of the generation processes involved. To improve our understanding of clouds and precipitation dynamics and cross-scale generation processes in mountain regions, we analyzed spatiotemporal rainfall patterns using satellite cloud products (SCP) in the Paute basin (900–4200m a.s.l. and 6481 km2) in the Andes of Ecuador. Precipitation models, using SCP and GIS data, reveal the spatial extension of three regimes: a three-modal (TM) regime present across the basin, a bimodal (BM) regime, along sheltered valleys, and a unimodal (UM) regime at windward slopes of the eastern cordillera. Subsequently, the spatiotemporal analysis using synoptic information shows that the dry season of the BM regime during boreal summer is caused by strong subsidence inhibiting convective clouds formation.Meanwhile, in UMregions, low advective shallow cap clouds mainly cause precipitation, influenced by water vapor from the Amazon and enhanced easterlies during boreal summer. TM regions are transition zones fromUMto BMand zones on the windward slopes of the western cordillera. These results highlight the suitability of satellite and GIS data-driven statistical models to study spatiotemporal rainfall seasonality and generation processes in complex terrain, as the Andes.