Laboratory for
Climatology and Remote Sensing

Abstract:

The meteorological measurements in Brno, Czech Republic, is among the world's oldest measurements, operating since 1799. Like many others, station was initially installed in the city center, relocated several times, and currently operates at an airport outside the city. These geographical changes potentially bias the temperature record due to different station surroundings and varying degrees of urban heat island effects. Here, we assess the influence of land cover on spatial temperature variations in Brno, capitol of Moravia and the second largest city of the Czech Republic. We therefore use a unique dataset of half-hourly resolved measurements from 11 stations spanning a period of more than 3.5 years and apply this information to reduce relocation biases in the long-term temperature record from 1799 to the present. Regression analysis reveals a significant warming influence from nearby buildings and a cooling influence from vegetation, explaining up to 80{\%} of the spatial variability within our network. The influence is strongest during the warm season and for land cover changes between 300 and 500 m around stations. Relying on historical maps and recent satellite data, it was possible to capture the building densities surrounding the past locations of the meteorological station. Using the previously established land cover--temperature relation, the anthropogenic warming for each measurement site could be quantified and hence eliminated from the temperature record accordingly, thereby increasing the long-term warming trend.

Abstract:

With 450,000?km2Kobresia (syn. Carex) pygmaea dominated pastures in the eastern Tibetan highlands are the world's largest pastoral alpine ecosystem forming a durable turf cover at 3000–6000?m?a.s.l. Kobresia's resilience and competitiveness is based on dwarf habit, predominantly below-ground allocation of photo assimilates, mixture of seed production and clonal growth, and high genetic diversity. Kobresia growth is co-limited by livestock-mediated nutrient withdrawal and, in the drier parts of the plateau, low rainfall during the short and cold growing season. Overstocking has caused pasture degradation and soil deterioration over most parts of the Tibetan highlands and is the basis for this man-made ecosystem. Natural autocyclic processes of turf destruction and soil erosion are initiated through polygonal turf cover cracking, and accelerated by soil-dwelling endemic small mammals in the absence of predators. The major consequences of vegetation cover deterioration include the release of large amounts of C, earlier diurnal formation of clouds, and decreased surface temperatures. These effects decrease the recovery potential of Kobresia pastures and make them more vulnerable to anthropogenic pressure and climate change. Traditional migratory rangeland management was sustainable over millennia, and possibly still offers the best strategy to conserve and possibly increase C stocks in the Kobresia turf.

Abstract:

Information about clouds is important for observing and predicting weather and climate as well as for generating and distributing solar power. Most existing approaches extract cloud information from satellite data by classifying individual pixels instead of using closely integrated spatial information, ignoring the fact that clouds are highly dynamic, spatially continuous entities. This paper proposes a novel cloud classification method based on deep learning. Relying on a Convolutional Neural Network (CNN) architecture for image segmentation, the presented Cloud Segmentation CNN (CS-CNN), classifies all pixels of a scene simultaneously rather than individually. We show that CS-CNN can successfully process multispectral satellite data to classify continuous phenomena such as highly dynamic clouds. The proposed approach produces excellent results on Meteosat Second Generation (MSG) satellite data in terms of quality, robustness, and runtime compared to other machine learning methods such as random forests. In particular, comparing CS-CNN with the CLAAS-2 cloud mask derived from MSG data shows high accuracy (0.94) and Heidke Skill Score (0.90) values. In contrast to a random forest, CS-CNN produces robust results and is insensitive to challenges created by coast lines and bright (sand) surface areas. Using GPU acceleration, CS-CNN requires only 25 ms of computation time for classification of images of Europe with 508  508 pixels.

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:

In tropical agriculture, the vigorously growing Bracken fern causes severe problems by invading pastures and out-competing the common pasture grasses. Due to infestation by that weed, pastures are abandoned after a few years, and as a fatal consequence, the biodi- versity-rich tropical forest is progressively cleared for new grazing areas. Here we present a broad physiological comparison of the two plant species that are the main competitors on the pastures in the tropical Ecuadorian Andes, the planted forage grass Setaria sphacelata and the weed Bracken (Pteridium arachnoideum).With increasing elevation, the competitive power of Bracken increases as shown by satellite data of the study region. Using data obtained from field measurements, the annual biomass production of both plant species, as a measure of their competitive strength, was modeled over an elevational gradient from 1800 to 2800 m. The model shows that with increasing elevation, biomass production of the two species shifts in favor of Bracken which, above 1800 m, is capable of outgrowing the grass. In greenhouse experiments, the effects on plant growth of the presumed key vari- ables of the elevational gradient, temperature and UV radiation, were separately analyzed. Low temperature, as well as UV irradiation, inhibited carbon uptake of the C4-grass more than that of the C3-plant Bracken. The less temperature-sensitive photosynthesis of Bracken and its effective protection from UV radiation contribute to the success of the weed on the highland pastures. In field samples of Bracken but not of Setaria, the content of flavo- noids as UV-scavengers increased with the elevation. Combining modeling with measure- ments in greenhouse and field allowed to explain the invasive growth of a common weed in upland pastures. The performance of Setaria decreases with elevation due to suboptimal photosynthesis at lower temperatures and the inability to adapt its cellular UV screen

Abstract:

The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of ?uctuating weather-dependent energy sources into the grid already has amajor impact on its load ?ows. As a result, the interest in forecasting wind and solar radiation with a suf?cient accuracy over short time periods (<4 h) has grown. In this study, the short-term forecast of the effective cloud albedo based on optical ?ow estimation methods is investigated. The optical ?ow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the clouds are displaced, the solar surface radiation will be calculated with SPECMAGIC NOW, which computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation can be forecasted from 5 min up to 4 h with a resolution of 0.05. The validation results of this method are very promising, and the RMSE of the 30-min, 60-min, 90-min and 120-min forecast equals 10.47%, 14.28%, 16.87% and 18.83%, respectively. The paper gives a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently, evaluation results will be presented and discussed.

Abstract:

Biological soil crusts (BSC) encompassing green algae, cyanobacteria, lichens, bryophytes, heterotrophic bacteria and microfungi are keystone species in arid environments because of their role in nitrogen- and carbon-fixation, weathering and soil stabilization, all depending on the photosynthesis of the BSC. Despite their importance, little is known about the BSCs of the Atacama Desert, although especially crustose chlorolichens account for a large proportion of biomass in the arid coastal zone, where photosynthesis is mainly limited due to low water availability. Here, we present the first hyperspectral reflectance data for the most wide-spread BSC species of the southern Atacama Desert. Combining laboratory and field measurements, we establish transfer functions that allow us to estimate net photosynthesis rates for the most common BSC species. We found that spectral differences among species are high, and differences between the background soil and the BSC at inactive stages are low. Additionally, we found that the water absorption feature at 1420 nm is a more robust indicator for photosynthetic activity than the chlorophyll absorption bands. Therefore, we conclude that common vegetation indices must be taken with care to analyze the photosynthesis of BSC with multispectral data.

Abstract:

Temperate grasslands play globally an important role, for example, for biodiversity conservation, livestock forage production, and carbon storage. The latter two are primarily controlled by biomass production, which is assumed to decrease with lower amounts and higher variability of precipitation, while increasing air temperature might either foster or suppress biomass production. Additionally, a higher atmospheric CO2 concentration ([CO2]) is supposed to increase biomass productivity either by directly stimulating photosynthesis or indirectly by inducing water savings (CO2 fertilization effect). Consequently, future biomass productivity is controlled by the partially contrasting effects of changing climatic conditions and [CO2], which to date are only marginally understood. This results in high uncertainties of future biomass production and carbon storage estimates. Consequently, this study aims at statistically estimating mid-21st century grassland aboveground biomass (AGB) based on 18 years of data (1998–2015) from a free air carbon enrichment experiment. We found that lower precipitation totals and a higher precipitation variability reduced AGB. Under drier conditions accompanied by increasing air temperature, AGB further decreased. Here AGB under elevated [CO2] was partly even lower compared to AGB under ambient [CO2], probably because elevated [CO2] reduced evaporative cooling of plants, increasing heat stress. This indicates a higher susceptibility of AGB to increased air temperature under future atmospheric [CO2]. Since climate models for Central Europe project increasing air temperature and decreasing total summer precipitation associated with an increasing variability, our results suggest that grassland summer AGB will be reduced in the future, contradicting the widely expected positive yield anomalies from increasing [CO2].

Abstract:

Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter.

Abstract:

The Atacama Desert is well known for the high occurrence of large-scale fog (spatial extents: hundreds of kilometers) emerging as low stratus (LST) decks over the Pacific Ocean. By contrast, the small-scale and heterogeneous occurrence of small-scale fog (hundreds of meters) particularly during summers is widely unconsidered. However, these events are important for the local vegetation and particularly for the biological soil crusts (BSC) that are widely distributed in this extreme ecosystem. Consequently, a case study in a typical fog oasis in the Pan de Azúcar National Park was conducted to test the feasibility combining field measurements, drone profiling, remote sensing and numerical modeling (i) to investigate fog-type specific differences regarding dynamics, physical properties and formation, (ii) to test the applicability of remote sensing technology for fog monitoring based on existing low-resolution and a proposed new high-resolution product and (iii) to estimate the related fog water input to BSCs. Two types of fog were observed. The well-known fog/LST deck emerging from the Pacific Ocean with high water path and large spatial extent was the first type. Fog of the second type was patchier, small-scale and not necessarily connected to the LST over the ocean. Instead, fog formation of the second type was related to thermal breeze systems, which produced shallow clouds containing less water than those of type 1. In general, such small-scale fog events were not captured well by existing remote sensing products but could be detected with the proposed new high-resolution product which provided promising results. Both fog types were important water resources for the BSCs, with approximately 8% to 24% of the fog water flux available to the BSCs at the surface. The results indicated the feasibility of the proposed methods’ pool to estimate the water budget of BSCs with a high spatial resolution in the future.

Abstract:

Land-use and climate change are major threats to biodiversity and ecosystem functions. Most of the current biodiversity monitoring systems are based on periodic records of the populations of a set of threatened or popular ‘?agship’ indicator species. In contrast to the abundance-based monitoring of species, also speci?c indicators of processes and functional interactions in an ecosystem may become targets of a more functional monitoring which can unveil early responses of an ecosystem to environmental changes at different spatial and temporal scales. The contributions of this Special Issue present such functional indicators for assessing and predicting responses to environmental changes of ecosystem functions in a hotspot of tropical biodiversity.

Abstract:

Environmental stressors and changes in land use have led to rapid and dramatic species losses. As such, we need effective monitoring programs that alert us not only to biodiversity losses, but also to functional changes in species assemblages and associated ecosystem processes. Ants are important components of terrestrial food webs and a key group in food web interactions and numerous ecosystem processes. Their sensitive and rapid response to environmental changes suggests that they are a suitable indicator group for the monitoring of abiotic, biotic, and functional changes. We tested the suitability of the incidence (i.e. the sum of all species occurrences at 30 baits), species richness, and functional richness of ants as indicators of ecological responses to environmental change, forest degradation, and of the ecosystem process predation on herbivorous arthropods. We sampled data along an elevational gradient (1000–3000 m a.s.l.) and across seasons (wetter and drier period) in a montane rainforest in south- ern Ecuador. The incidence of ants declined with increasing elevation but did not change with forest degradation. Ant incidence was higher during the drier season. Species richness was highly correlated with incidence and showed comparable results. Functional richness also declined with increasing ele- vation and did not change with forest degradation. However, a null-model comparison revealed that the functional richness pattern did not differ from a pattern expected for ant assemblages with ran- domly distributed sets of traits across species. Predation on arti?cial caterpillars decreased along the elevational gradient; the pattern was not driven by elevation itself, but by ant incidence (or inter- changeable by ant richness), which positively affected predation. In spite of lower ant incidence (or ant richness), predation was higher during the wetter season and did not change with forest degrada- tion and ant functional richness. We used path analysis to disentangle the causal relationships of the environmental factors temperature (with elevation as a proxy), season, and habitat degradation with the incidence and functional richness of ants, and their consequences for predation. Our results would suggest that the forecasted global warming might support more active and species-rich ant assem- blages, which in turn would mediate increased predation on herbivorous arthropods. However, this prediction should be made with reservation, as it assumes that the dispersal of ants keeps pace with the climatic changes as well as a one-dimensional relationship between ants and predation within a food-web that comprises species interactions of much higher complexity. Our results also suggested that degraded forests in our study area might provide suitable habitat for epigaeic, ground-dwelling ant assemblages that do not differ in incidence, species richness, functional richness, composition, or predation on arthropods from assemblages of primary forests. Most importantly, our results suggest that the occurrence and activity of ants are important drivers of ecosystem processes and that changes in the incidence and richness of ants can be used as effective indicators of responses to temperature changes and of predation within mega-diverse forest ecosystems

Abstract:

The effects of an increasing moisture on trees of the tropical species-rich mountain rain forest in the South Ecuadorian Andes was investigated, using the daily total water consumption (TWC) and the instantaneous water use ef?ciency (WUE, ratio of photosynthetic CO2 uptake per water loss by transpiration) as eco- physiological indicators. Two canopy and one sub-canopy tree species, (Vismia tomentosa, Clusiaceae, an as of yet unknown Lauracee, and Spirotheca rosea, Bombacaceae) were the experimental objects. Seasonal changes as well as a long-term (18 months) trend of increasing precipitation caused an inverse reaction of the TWC of the trees. Because of a rather unlimited water supply to the trees from a permanently high water content of the soil, transpiration followed mainly the atmospheric demand of water vapor, and increasing moisture hence reduced water loss by transpiration. It was hypothesized that in spite of the reduction in transpiratory water loss photosynthetic carbon acquisition would be not or less affected due to an increase in water use ef?ciency. Concomitant measurements of photosynthetic net CO2 uptake showed the expected increase of WUE in V. tomentosa and S. rosea, but no clear reaction of the Lauracee. Accompanying measurements of stem extension growth con?rmed an undiminished growth of V. tomen- tosa and S. rosea but showed also suspended growth of the Lauracee during the wettest months. While TWC can be continuously monitored with the heat dissipation technique, WUE is determined by leaf porometry in campaigns for which access to the canopy is required. Simultaneous recordings of the gas exchange of leaves at 4 different positions in the crown of one of the experimental trees (V. tomentosa) showed the usability of the trait WUE in combination with the total daily water consumption as indicator set for assessing the response of trees to a subtly changing climate. However, not all tree species appear as likewise useful indicator trees