Modeling oil palm monoculture and its associated impacts on land-atmosphere carbon, water and energy fluxes in Indonesia

In dieser Studie wird ein neues Modul “CLM-Palm” für mehrjährige Nutzpflanzen zur Modellierung einer funktionellen Gruppe (plant functional type) für Ölpalmen im Rahmen des Community Land Models (CLM4.5) entwickelt, um die Auswirkungen der Transformation eines tropischen Waldes in eine Ölpalmenplantage auf die Kohlenstoff-, Wasser- und Energieflüsse zwischen Land und Atmosphäre zu quantifizieren. Um die Morphologie der Ölpalme möglichst detailgetreu darzustellen (das heißt, dass ungefähr 40 Phytomere einen mehrschichtigen Kronenraum formen), wird in dem Modul CLM-Palm eine phänologische und  physiologische Parametrisierung auf Skalen unterhalb des Kronraums eingeführt, so dass jedem Phytomer sein eigenes prognostisches Blattwachstum und seine Erntekapazität zugeordnet wird, während Stamm und Wurzeln gemeinsam genutzt werden. Das Modul CLM-Palm wurde ausschließlich für Ölpalmen getestet, ist aber auch für andere Palmarten (z. B. Kokospalmen) interessant.  Im ersten Kapitel dieser Arbeit werden Hintergrund und Motivation dieser Arbeit vorgestellt. In Kapitel 2 wird die Entwicklung des Haupt- bzw. Kernmodells beschrieben,  inklusive Phänologie und Allokationsfunktionen zur Simulation des Wachstums und des Ertrags der Palme PFT, wodurch die Basis zur Modellierung  der biophysikalischen und biogeochemicalischen Kreisläufe innerhalb dieser Monokultur bereitgestellt wird. Die neuen Parameter für die Phänologie und die Allokation wurden sorgfältig mit Feldmessungen des Blattflächenindexes (LAI), des Ertrags und der Nettoprimärproduktion (NPP) verschiedener Ölpalmenplantagen auf Sumatra (Indonesien) kalibriert und validiert. Die Validierung zeigte die Eignung von CLM-Palm zur adäquaten Vorhersage des mittleren Blattwachstums und Ertrags für verschiedene Standorte und repräsentiert in ausreichendem Maß die signifikante Variabilität bezüglich des Stickstoffs und Alters von Standort zu Standort.  In Kapitel 3 wird die weitere Modellentwicklung und die Implementierung eines Norman-Mehrschichtmodells für den Strahlungstransport vorgestellt, das an den  mehrschichtigen Kronenraum der Ölpalme angepasst ist. Dieses Norman-Mehrschichtmodell des Strahlungstransports zeigte im Vergleich zu dem in CLM4.5 implementierten Standardmodell (basierend auf großen Blättern) bei der Simulation der Licht-Photosynthese-Kurve leichte Verbesserungen und hat  lediglich marginale Vorteile gegenüber dem ebenfalls in CLM4.5 implementierten alternativen statistischen Mehrschichtmodell.  Dennoch liefert das Norman-Modell eine detailliertere und realistischere Repräsentation des Belaubungszustands wie etwa dem dynamischen LAI, der Blattwinkelverteilung in verschiedenen Höhen, und ein ausgewogeneres Profil der absorbierten photosynthetisch aktiven Strahlung (PAR). Die Validierung mit Hilfe der Eddy-Kovarianz Flussdaten zeigte die Stärke von CLM-Palm bei der Simulation der Kohlenstoffflüsse, offenbarte aber auch Abweichungen in der simulierten Evapotranspiration (ET), dem sensiblen und dem latenten Wärmefluss (H und LE). Eine Reihe von hydrologischen Messungen im Kronenraum wird in Kapitel 4 beschrieben. Dies beinhaltet eine Adaption des in CLM4.5 eingebauten Standardmodells für Niederschlag, Interzeption und Speicherfunktionen für die speziellen Merkmale eines Ölpalmen-Kronenraums. Die überarbeitete Hydrologie des Kronenraums behob die Probleme bei der Simulation der Wasserflüsse (ET und Transpiration im Kronenraum) und verbesserte die Energieaufteilung zwischen H und LE. Kapitel 5 dokumentiert die Implementierung eines neuen dynamischen Modells für Stickstoff (nitrogen, N) in CLM-Palm zur Verbesserung der Simulation der C- und N-Dynamik, insbesondere mit Bezug auf den N-Düngeeffekte in landwirtschaftlich genutzten Systemen. Das dynamische N-Modell durchbricht die Limitierung des Standardmodells in CLM4.5, mit fixierter C-N-Stöchiometrie und erlaubt die Variation des C:N-Verhältnisses in lebendem Gewebe in Abhängigkeit der N-Verfügbarkeit und dem N-Bedarf der Pflanze.  Eine Reihe von Tests bezüglich der Düngung zeigte beispielhaft die Vorteile des dynamischen N-Modells, wie zum Beispiel die Verbesserung des Netto-Ökosystemaustauschs (net ecosystem exchange, NEE), ein realistischeres C:N-Verhältnis im Blatt, eine verbesserte Repräsentation der Effizienz des Stickstoffeinsatzes (nitrogen-use efficiency, NUE), sowie der Effekte von Düngung auf Wachstum und Ertrag. Abschließend wird in Kapitel 6 eine Anwendungsstudie gezeigt, in der die zentralen Modellentwicklungen aus den vorangegangenen Kapiteln verwendet werden. Eine junge und eine  erntereife Ölpalmenplantage sowie ein Primärregenwald wurden simuliert und verglichen. Sie wiesen klare Unterschiede in den C-Flüssen und in den biophysikalischen Merkmalen (z.B. ET und Oberflächentemperatur) auf. Ölpalmenplantagen können durch Wachstumsentwicklung (im Alter von etwa 4 Jahren)  ebenso hohe und darüber hinausgehende C-Assimilation und Wassernutzungsraten erreichen wie Regenwälder, haben jedoch im Allgemeinen eine höhere Oberflächentemperatur als eine bewaldete Fläche – dies gilt auch für erntereife Plantagen. Eine Simulation des Übergangs, die zwei Rotationsperioden mit Neubepflanzungen alle 25 Jahre umspannt, zeigte dass der Anbau von Ölpalmen auf längeren Zeitskalen lediglich in etwa die Hälfte des ursprünglichen C-Speichers der bewaldeten Fläche vor dem Kahlschlag  rückspeichern kann. Das im Boden gespeicherte C nimmt in einer bewirtschafteten Plantage aufgrund des begrenzten Streurücklaufs langsam und graduell ab. Insgesamt reduziert die Umwandlung eines Regenwaldes in eine Ölpalmenplantage die langfristigen C-Speicher und die Kapazität der Fläche zur C-Sequestrierung und trägt potentiell zur Erwärmung der Landoberfläche bei – trotz des schnellen Wachstums und der hohen C-Assimilationsrate einer stark gedüngten Plantage. Zur Einschätzung der regionalen und globalen Effekte der Ausbreitung der Kultivierung von Ölpalmen auf die Austauschprozesse zwischen Land und Atmosphäre und auf das Klima ist es notwendig eine Upscaling-Studie durchzuführen

Pathogen manipulation of chloroplast function triggers a light-dependent immune recognition

In plants and animals, nucleotide-binding leucine-rich repeat (NLR) proteins are intracellular immune sensors that recognize and eliminate a wide range of invading pathogens. NLR-mediated immunity is known to be modulated by environmental factors. However, how pathogen recognition by NLRs is influenced by environmental factors such as light remains unclear. Here, we show that the agronomically important NLR Rpi-vnt1.1 requires light to confer disease resistance against races of the Irish potato famine pathogen Phytophthora infestans that secrete the effector protein AVRvnt1. The activation of Rpi-vnt1.1 requires a nuclear-encoded chloroplast protein, glycerate 3-kinase (GLYK), implicated in energy production. The pathogen effector AVRvnt1 binds the full-length chloroplast-targeted GLYK isoform leading to activation of Rpi-vnt1.1. In the dark, Rpi-vnt1.1-mediated resistance is compromised because plants produce a shorter GLYK-lacking the intact chloroplast transit peptide-that is not bound by AVRvnt1. The transition between full-length and shorter plant GLYK transcripts is controlled by a light-dependent alternative promoter selection mechanism. In plants that lack Rpi-vnt1.1, the presence of AVRvnt1 reduces GLYK accumulation in chloroplasts counteracting GLYK contribution to basal immunity. Our findings revealed that pathogen manipulation of chloroplast functions has resulted in a light-dependent immune response

The interaction of Solar Radiation Modification and Earth System Tipping Elements

The avoidance of hitting tipping points is often considered a key benefit of Solar Radiation Modification (SRM) techniques, however, the physical science underpinning this has thus far not been comprehensively assessed. This review assesses the available evidence for the interaction of SRM with a number of earth system tipping elements in the cryosphere, the oceans, the atmosphere and the biosphere , with a particular focus on the impact of SAI. We review the scant available literature directly addressing the interaction of SRM with the tipping elements or for closely related proxies to these elements. However, given how limited this evidence is, we also identify and describe the drivers of the tipping elements, and then assess the available evidence for the impact of SRM on these. We then briefly assess whether SRM could halt or reverse tipping once feedbacks have been initiated. Finally, we suggest pathways for further research. We find that SRM mostly reduces the risk of hitting tipping points relative to same emission pathway scenarios without SRM, although this conclusion is not clear for every tipping element, and large uncertainties remain

NorCPM1 and its contribution to CMIP6 DCPP

The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol-cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of SST and T/S-profile observations using the Ensemble Kalman Filter (EnKF).publishedVersio

Implementing a new rubber plant functional type in the Community Land Model (CLM5) improves accuracy of carbon and water flux estimation

Rubber plantations are an economically viable land-use type that occupies large swathes of land in Southeast Asia that have undergone conversion from native forest to intensive plantation forestry. Such land-use change has a strong impact on carbon, energy, and water fluxes in ecosystems, and uncertainties exist in the modeling of future land-use change impacts on these fluxes due to the scarcity of measured data and poor representation of key biogeochemical processes. In this current modeling effort, we utilized the Community Land Model Version 5 (CLM5) to simulate a rubber plant functional type (PFT) by comparing the baseline parameter values of tropical evergreen PFT and tropical deciduous PFT with a newly developed rubber PFT (focused on the parameterization and modification of phenology and allocation processes) based on site-level observations of a rubber clone in Indonesia. We found that the baseline tropical evergreen and baseline tropical deciduous functions and parameterizations in CLM5 poorly simulate the leaf area index, carbon dynamics, and water fluxes of rubber plantations. The newly developed rubber PFT and parametrizations (CLM-rubber) showed that daylength could be used as a universal trigger for defoliation and refoliation of rubber plantations. CLM-rubber was able to predict seasonal patterns of latex yield reasonably well, despite highly variable tapping periods across Southeast Asia. Further, model comparisons indicated that CLM-rubber can simulate carbon and energy fluxes similar to the existing rubber model simulations available in the literature. Our modeling results indicate that CLM-rubber can be applied in Southeast Asia to examine variations in carbon and water fluxes for rubber plantations and assess how rubber-related land-use changes in the tropics feedback to climate through carbon and water cycling

Modeled microbial dynamics explain the apparent temperature sensitivity of wetland methane emissions

Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites, there is only a small temperature effect. We hypothesize that microbial dynamics are a major driver of the seasonal cycle and that they can explain this apparent discrepancy. We introduce a relatively simple model of methanogenic growth and dormancy into a wetland methane scheme that is used in an Earth system model. We show that this addition is sufficient to reproduce the observed seasonal dynamics of methane emissions in fully saturated wetland sites, at the same time as reproducing the annual mean emissions. We find that a more complex scheme used in recent Earth system models does not add predictive power. The sites used span a range of climatic conditions, with the majority in high latitudes. The difference in apparent temperature sensitivity seasonally versus spatially cannot be recreated by the non‐microbial schemes tested. We therefore conclude that microbial dynamics are a strong candidate to be driving the seasonal cycle of wetland methane emissions. We quantify longer‐term temperature sensitivity using this scheme and show that it gives approximately a 12% increase in emissions per degree of warming globally. This is in addition to any hydrological changes, which could also impact future methane emissions

Tree crown mortality associated with roads in the Lake Tahoe Basin: a remote sensing approach

Tree crown mortality along highways in the Lake Tahoe Basin has been a concern for decades. Increased tree damage near roads is believed to be associated with de-icing compounds used to increase driving safety during winter. Several field studies have concluded that de-icing salts applied on the roads are a potential factor for roadside tree crown mortality, but the spatial pattern and temporal trend of de-icing salt damage has not been quantitatively measured. Moreover, most previous studies were based on field observations at sampling sites and were limited by temporal and spatial scale. An efficient large-scale approach is needed that can be repeatedly applied to retrieve historical dynamics, or to monitor future occurrence, of road-related tree crown mortality. Remote sensing provides a means for assessing potential road-related effects on tree crown mortality in a large-scale and long-term context. This study used remote sensing methods to quantify tree crown mortality, expressed as declines in leaf area index (LAI) at the scale of 4-m pixels for the Nevada portion of the Lake Tahoe Basin, and at the scale of 30-m pixels for the whole basin. The mortality data together with data for salt application, precipitation, traffic, and topography were then statistically analyzed to reveal the component of crown mortality that is road-related. Several relatively novel approaches were developed in this study for processing remote sensing images and utilizing GIS data. The Sun-Crown-Sensor (+C) topographic correction approach was developed to correct radiometric distortion caused by terrain variability in forest images. LiDAR data were utilized to aid in orthorectifying IKONOS images and extracting projected tree crown shapes from complex earth surface features. Several vegetation indices were compared and the normalized difference vegetation index (NDVI) was consistently found to be the best indicator for LAI. Interannual change in LAI was also found to be an appropriate measurement of tree crown mortality, defined as the loss of photosynthetic material in tree crowns. A field dataset of LAI was collected at 30 plots comprising 120 subplots of 30×30m, which was used to calibrate and transform remote sensing data into LAI that is physically meaningful. A dataset of yearly change detection results as measured by quantitative LAI change was generated using Landsat TM images from 1990 to 2010. A 4-year change in LAI from 2005 to 2009 was generated using a pair of IKONOS images, for which mortality was also defined based on LAI change thresholds. IKONOS derived mortality was used in fine-scale spatial analysis to assess the effects of de-icing salt through aerial deposition and flow accumulation mechanisms, which were represented by two spatial proxy variables constructed using high-resolution topographical data. Landsat derived mortality was used in both broad-scale spatial analysis and long-term temporal analysis. The broad-scale spatial analysis confirmed IKONOS fine-scale spatial analysis results. The long-term temporal analysis provided concrete evidence of how roadside mortality was related to variation in de-icing salt application. A clear trend of increasing mortality with increasing aerial deposition of de-icing salt was revealed in the fine-scale spatial analysis. Aerial deposition played a major role in mortality within 30m of the road and its overall effect was much stronger than that of flow accumulation, although the effect zone of the latter had the potential to extend to 100m from road. The temporal analysis revealed that mortality was strongly correlated with salt application from 1990 to 2010. De-icing salt effects (as suggested by a trend of increased crown mortality closer to the roads) were most distinct in wet years when de-icing salt application was high and other damaging factors were weak. The spatial analysis and temporal analysis together provided convincing evidence that de-icing salt was a significant factor for roadside tree crown mortality. In order to protect the roadside forests from degradation and preserve their aesthetic value to drivers, road management should decrease the amount of de-icing salt used as much as possible, plant salt-resistant species within the 0-30m salt-susceptible zones, and plant taller trees on concave slopes in order to minimize the aerial deposition effect

Preparation of Edible Non-wettable Coating with Soybean Wax for Repelling Liquid Foods with Little Residue

Liquid food adhesion on containers has increased food waste and pollution, which could be effectively alleviated with a superhydrophobic surface. In this research, the superhydrophobic coating was fabricated with edible soybean wax on different substrates by a spraying method. The coated surface showed excellent superhydrophobicity due to its microstructure formed by self-roughening, which could repel a variety of viscous liquid food with the apparent contact angle of 159 ± 2°. The coated surface was still liquid-repellent after hot water immersion (45 °C), abrasion test with sandpaper, water impact, finger touch and immersion into yogurt. The liquid-repellent coating with soybean wax, which is natural and green, is promising for application in the food industry to reduce waste

Inequal responses of drylands to radiative forcing geoengineering methods

Climate geoengineering has the potential to reduce global warming. However, the nonlinear responses of Earth's large‐scale circulation to climate geoengineering can exacerbate regional climate change, with potential inequality risks. We show noticeable inequality in the responses of drylands when three radiative forcing geoengineering (RFG) methodologies—cirrus cloud thinning (CCT), marine sky brightening (MSB), and stratospheric aerosol injection (SAI)—individually reduce the radiative forcing of the representative concentration pathway 8.5 scenario using a set of the Norwegian Earth system model (NorESM1‐ME) experiments. In North America, CCT and SAI alleviate drylands expansion, whereas drylands expand further under MSB. CCT induces significantly wetter conditions over the western Sahel. Wetting over Australia is enhanced and prevented by MSB and SAI, respectively. Our results suggest spatially inequal distributions of benefits and harms of individual RFGs on the projected distribution of drylands, which should be considered before any real‐world application of such RFGs.acceptedVersio

A Path Planning Algorithm with a Guaranteed Distance Cost in Wireless Sensor Networks

Navigation with wireless sensor networks (WSNs) is the key to provide an effective path for the mobile node. Without any location information, the path planning algorithm generates a big challenge. Many algorithms provided efficient paths based on tracking sensor nodes which forms a competitive method. However, most previous works have overlooked the distance cost of the path. In this paper, the problem is how to obtain a path with minimum distance cost and effectively organize the network to ensure the availability of this path. We first present a distributed algorithm to construct a path planning infrastructure by uniting the neighbors’ information of each sensor node into an improved connected dominating set. Then, a path planning algorithm is proposed which could produce a path with its length at most c times the shortest Euclidean length from initial position to destination. We prove that the distributed algorithm has low time and message complexity and c is no more than a constant. Under different deployed environments, extensive simulations evaluate the effectiveness of our work. The results show that factor c is within the upper bound proved in this paper and our distributed algorithm achieves a smaller infrastructure size