Generalization and evaluation of the process-based forest ecosystem model PnET-CN for other biomes

Terrestrial ecosystems play an important role in carbon, water, and nitrogen cycling. Process-based ecosystem models, including PnET-CN, have been widely used to simulate ecosystem processes during the last two decades. PnET-CN is a forest ecosystem model, originally designed to predict ecosystem carbon, water, and nitrogen dynamics of temperate forests under a variety of circumstances. Among terrestrial ecosystem models, PnET-CN offers unique benefits, including simplicity and transparency of its structure, reliance on data-driven parameterization rather than calibration, and use of generalizeable relationships that provide explicit linkages among carbon, water and nitrogen cycles. The objective of our study was to apply PnET-CN to non-forest biomes: grasslands, shrublands, and savannas. We determined parameter values for grasslands and shrublands using the literature and ecophysiological databases. To assess the usefulness of PnET-CN in these ecosystems, we simulated carbon and water fluxes for six AmeriFlux sites: two grassland sites (Konza Prairie and Fermi Prairie), two open shrubland sites (Heritage Land Conservancy Pinyon Juniper Woodland and Sevilleta Desert Shrubland), and two woody savanna sites (Freeman Ranch and Tonzi Ranch). Grasslands and shrublands were simulated using the biome-specific parameters, and savannas were simulated as mixtures of grasslands and forests. For each site, we used flux observations to evaluate modeled carbon and water fluxes: gross primary productivity (GPP), ecosystem respiration (ER), net ecosystem productivity (NEP), evapotranspiration (ET), and water yield. We also evaluated simulated water use efficiency (WUE). PnET-CN generally captured the magnitude, seasonality, and interannual variability of carbon and water fluxes as well as WUE for grasslands, shrublands, and savannas. Overall, our results show that PnET-CN is a promising tool for modeling ecosystem carbon and water fluxes for non-forest biomes (grasslands, shrublands, and savannas), and especially for modeling GPP in mature biomes. Limitations in model performance included an overestimation of seasonal variability in GPP and ET for the two shrubland sites and overestimation of early season ER for the two shrubland sites and Freeman Ranch. Future modifications of PnET-CN for non-forest biomes should focus on belowground processes, including water storage in dry shrubland soils, root growth and respiration in grasslands, and soil carbon fluxes for all biomes

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)

Background Large uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled. Results Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation. Conclusions The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty

ESTIMATION AND MODELING OF FOREST ATTRIBUTES ACROSS LARGE SPATIAL SCALES USING BIOMEBGC, HIGH-RESOLUTION IMAGERY, LIDAR DATA, AND INVENTORY DATA

The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest\u27s condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time. This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling

Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design

Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these additional degrees of freedom into fidelity assessment has become increasingly challenging. Here, we propose a new method to provide information on the model predictive skill in a concise way. The method is based on the conjoint use of a k-means clustering technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means algorithm and the assessment metrics reduce the number of model data points to be evaluated. The metrics evaluate either the model state accuracy or the skill of the model with respect to capturing emergent properties, such as the deep chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo observations as the sole evaluation data set ensures the accuracy of the data, as it is a homogenous data set with strict sampling methodologies and data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine Service. The model performance is evaluated using the model efficiency statistical score, which compares the model–observation misfit with the variability in the observations and, thus, objectively quantifies whether the model outperforms the BGC-Argo climatology. We show that, overall, the model surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and the mixed layers as well as silicate in the mesopelagic layer. However, there are still areas for improvement with respect to reducing the model–data misfit for certain variables such as silicate, pH, and the partial pressure of CO2 in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed here can also aid in refining the design of the BGC-Argo network, in particular regarding the regions in which BGC-Argo observations should be enhanced to improve the model accuracy via the assimilation of BGC-Argo data or process-oriented assessment studies. We strongly recommend increasing the number of observations in the Arctic region while maintaining the existing high-density of observations in the Southern Oceans. The model error in these regions is only slightly less than the variability observed in BGC-Argo measurements. Our study illustrates how the synergic use of modeling and BGC-Argo data can both provide information about the performance of models and improve the design of observing systems.</p

Sensitivity of ecosystem net primary productivity models to remotely sensed leaf area index in a montane forest environment

xii, 181 leaves : ill. ; 28 cm.Net primary productivity (NPP) is a key ecological parameter that is important in estimating carbon stocks in large forested areas. NPP is estimated using models of which leaf area index (LAI) is a key input. This research computes a variety of ground-based and remote sensing LAI estimation approaches and examines the impact of these estimates on modeled NPP. A relative comparison of ground-based LAI estimates from optical and allometric techniques showed that the integrated LAI-2000 and TRAC method was preferred. Spectral mixture analysis (SMA), accounting for subpixel influences on reflectance, outperformed vegetation indices in LAI prediction from remote sensing. LAI was shown to be the most important variable in modeled NPP in the Kananaskis, Alberta region compared to soil water content (SWC) and climate inputs. The variability in LAI and NPP estimates were not proportional, from which a threshold was suggested where first LAI is limiting than water availability

A New Concept for Simulation of Vegetated Land Surface Dynamics: The Event Driven Phenology Model Part I

Phenologies of the vegetated land surface are being used increasingly for diagnosis and prognosis of climate change consequences. Current prospective and retrospective phenological models stand far apart in their approaches to the subject. We report on an exploratory attempt to implement a phenological model based on a new event driven concept which has both diagnostic and prognostic capabilities in the same modeling framework. This Event Driven Phenological Model (EDPM) is shown to simulate land surface phenologies and phenophase transition dates in agricultural landscapes based on assimilation of weather data and land surface observations from spaceborne sensors. The model enables growing season phenologies to develop in response to changing environmental conditions and disturbance events. It also has the ability to ingest remotely sensed data to adjust its output to improve representation of the modeled variable. We describe the model and report results of initial testing of the EDPM using Level 2 flux tower records from the Ameri- flux sites at Mead, Nebraska, USA, and at Bondville, Illinois, USA. Simulating the dynamics of normalized difference vegetation index based on flux tower data, the predictions by the EDPM show good agreement (RMSE0.8) for maize and soybean during several growing seasons at different locations. This study presents the EDPM used in the companion paper (Kovalskyy and Henebry, 2011) in a coupling scheme to estimate daily actual evapotranspiration over multiple growing seasons

CARBON-WATER COUPLING OF TERRESTRIAL ECOSYSTEMS IN RESPONSE TO CLIMATE CHANGE AND CLIMATE VARIABILITY

Carbon and water cycles are two fundamental biophysical processes in terrestrial ecosystems. Rain use efficiency (RUE), defined as the ratio of ecosystem productivity to precipitation (PPT), and water use efficiency (WUE), defined as the ratio of ecosystem productivity to evapotranspiration (ET), are critical metrics of ecosystem function linking ecosystem carbon and water cycles. Under the context of global climate change and climate variability, much attention has been paid to the variation in RUE or WUE across biomes or species, and its responses to drought, elevated atmospheric CO2 concentration, and other environmental changes. However, due to differences in research method, study areas, and complexity in definitions, there is a lack of consensus on the coupling of carbon and water fluxes across different ecosystems and it’s responses to climate change and climate variability. Chapter 1 reviews the current status of carbon-water coupling studies and raises the major scientific questions that will be addressed in the dissertation. Chapter 2 examined the spatiotemporal variations in coupling of gross primary production (GPP) to PPT, and water fluxes (ET and T (transpiration)) at site and global scales. In-situ climate, and carbon and water fluxes datasets from 111 FLUXNET sites, global climate data, and remote sensing based GPP and ET data were combined to explore the relationships of GPP to PPT, ET, and T across different ecosystems and under different hydroclimatic conditions. Generally, GPP had a saturating relationship with PPT, and was linearly coupled with ET and T. This strong carbon-water flux coupling could be further improved by the incorporation of vapor pressure deficit (VPD) at site level. The sensitivity of GPP to PPT increased in severe drought years and decreased in pluvial years. There was no obvious change in the sensitivity of GPP to ET or T under altered climate conditions. Chapter 3 identified extreme drought events globally based on rain-use efficiency [RUE; GPP/PPT]. Ecosystem RUE is expected to increase with decreasing precipitation to a maximum (RUEmax) during moderate drought and will likely decline when water shortage is beyond the tolerance of vegetation, leading to a loss of ecosystem function. In this chapter, the PPT at the RUEmax was identified as a threshold of extreme drought condition, and the deviation of the RUE in drought condition from the norms in non-drought condition was further tested to determine if it exceeds the normal variability. Well-known extreme drought events were detected, e.g. 2003 drought in Europe, 2002 and 2011 drought in the U.S., and 2010 drought in Russia. Moreover, the reduced carbon uptake caused by extreme droughts (0.14±0.03 PgC/yr) could explain >70% of the GPP anomaly in drought-affected areas. Chapter 4 investigated the responses of WUE to environmental change in forests and grasslands in Northern Hemisphere. On the basis of Chapter 1, underlying water use efficiency (UWUE; GPP×VPD0.5/ET) incorporated the VPD effects on carbon assimilation and transpiration and hence provided an optimal indicator of carbon-water coupling in flux tower dataset. In this chapter, the interannual trend in UWUE and its responses to environmental factors were analyzed across 11 evergreen needleleaf forest (ENF) sites, 7 deciduous broadleaf forest (DBF) sites, and 9 grassland (GRA) sites. Results showed that, there was an obvious increase in UWUE in forests which was triggered by CO2 fertilization, increasing VPD, as well as the decreasing soil moisture in DBF. In GRA, the positive effect of CO2 fertilization on UWUE was offset by the negative effect of increasing soil moisture on UWUE, leading to no obvious trend in UWUE. Chapter 5 estimated the global ET at 8-day, 0.05° resolution from 2003 to 2015 based on GPP from Vegetation Photosynthesis Model (VPM), VPD estimated from Atmospheric Infrared Sounder (AIRS), and biome-level UWUE parameters. Biome-level UWUE was derived from the FLUXNET2015 dataset at 8-day timescale. The ET was calibrated and validated at the biome level against flux tower ET. The interannual trends in ET, GPP, and VPD were also analyzed at the global scale. There was an increasing trend in global ET over the study period (1.47 mm/yr). This ET product on the basis of carbon-water coupling showed better performance than a traditional approach, i.e. Penman-Monteith equation. Chapter 6 briefly summarizes the conclusions and perspectives from this dissertation

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)

Abstract Background Large uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled. Results Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation. Conclusions The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty. </jats:sec

Measuring and modelling carbon stocks in rubber (Hevea brasiliensis) dominated landscapes in Subtropical China

Rubber plantation has been rapidly expanded in Montane Mainland South East Asia in past decades. Limited by long-term monitoring data availability, the impacts of environmental change on rubber trees carbon stock development still not fully understood. Against global warming background, in order to better facilitate regional forest management, we applied synergetic approach combining field survey and modelling tools to improve predictions of dynamic carbon stock changes. The trade-off analysis regarding to rubber carbon stock and latex production optimization was further discussed in view of sustainable rubber cultivation. The first study explored the impact of regional land-use changes on landscape carbon balances. The Naban River Watershed National Nature Reserve (NRWNNR), Xishuangbanna, China, was selected as a case study location. Carbon stocks were evaluated using the Rapid Carbon Stock Appraisal (RaCSA) method based on tree, plot, land use and landscape level assessments of carbon stocks, integrating field sampling with remote sensing and GIS technology. The results showed that rubber plantations had larger time-averaged carbon stocks than non-forest land use types (agricultural crops, bush and grassland) but much lower than natural forest. During 23 years (1989-2012), the whole landscape of the nature reserve (26574 ha) gained 0.644 Tg C. Despite rubber expansion, the reforestation activities conducted in NRWNNR were able to enhance the carbon stocks. Regional evaluation of the carbon sequestration potential of rubber trees depends largely on the selection of suitable allometric equations and the biomass-to-carbon conversion factor. The second study developed generic allometric equations for rubber trees, covering rotation lengths of 4-35 years, within elevation gradient of 621-1,127 m, and locally used rubber tree clones (GT1, PRIM600, Yunyan77-4) in mountainous South Western China. Allometric equations for aboveground biomass (AGB) estimations considering diameter at breast height (DBH), tree height, and wood density were superior to other equations. We also tested goodness of fit for the recently proposed pan-tropical forest model. The results displayed that prediction of AGB by the model calibrated with the harvested rubber tree biomass and wood density was more accurate than the results produced by the pan-tropical forest model adjusted to local conditions. The relationships between DBH and height and between DBH and biomass were influenced by tapping, therefore biomass and C stock calculations for rubber have to be done using species-specific allometric equations. Based on the analysis of environmental factors acting at the landscape level, we noticed that above- and belowground carbon stocks were mostly affected by stand age, soil clay content, aspect, and planting density. The results of this study provide reference for reliable carbon accounting in other rubber-cultivated regions. In the last study, we explored how rubber trees growth and production response to climate change and regional management strategies (cultivation elevation, planting density). We applied the process-based Land Use Change Impact Assessment tool (LUCIA) calibrated with detailed ground survey data to model tree biomass development and latex yield in rubber plantations at the tree, plot and landscape level. Model simulation showed that during a 40-year rotation, lowland rubber plantations (< 900m) grew quicker and had larger latex yield than highland rubber (&#8807;900m). High planting density rubber plantations showed 5% higher above ground biomass than those at low- and medium-planting density. The mean total biomass and cumulative latex yield per tree over 40 years increased by 28% and 48%, respectively, when climate change scenarios were modelled from baseline to highest CO2 emission scenario (RCP 8.5). The same trend of biomass and latex yield increase with climate change was observed at plot level. Denser plantations had larger biomass, but the cumulative latex production decreased dramatically. The spatially explicit output maps produced during modelling could help maximize carbon stock and latex production of regional rubber plantations. Overall, rubber-based system required for appropriate monitoring scale in both temporal aspect (daily-, monthly-, and yearly-level) and in spatial aspect (pixel-, land use-, watershed-, and landscape- level). The findings from present study highlighted the important application of ecological modelling tools in nature resources management. The lessons learned here could be applicable for other rubber-cultivated regions, by updating with site-specific environmental variables. The significant role of rubber tree not limited in its nature latex production, it also lies in its great carbon sequestration potential. Our results here provided entry point for future developing comprehensive climate change adaption and mitigation strategies in South East Asia. By making use of interdisplinary cooperation, the sustainable rubber cultivation in Great Mekong Regions could be well realized.In den vergangenen Jahrzehnten wurde der Kautschukanbau in den Bergregionen des südostasiatischen Festlandes rasch ausgebaut. Die Auswirkungen von Umweltveränderungen auf die Entwicklung des Kohlenstoffbestandes von Kautschukbäumen sind durch die eingeschränkte Verfügbarkeit von Langzeit-Monitoring-Daten noch nicht vollständig geklärt. Vor dem Hintergrund der globalen Erwärmung und um die regionale Waldbewirtschaftung zu unterstützen, haben wir einen synergetischen Ansatz angewandt, der Feldmessungen und Modellierungswerkzeuge kombiniert, um die Vorhersage dynamischer Veränderungen der Kohlenstoffbestände zu verbessern. Die Kosten-Nutzen Abwägung für einen nachhaltigen Kautschukanbau bezüglich der Kautschuk-Kohlenstoffvorräte und der Optimierung der Latexproduktion wird im Weiteren diskutiert. Die erste Studie untersuchte die Auswirkungen regionaler Landnutzungsänderungen auf die Kohlenstoffbilanz der Landschaft. Das Naban River Watershed National Nature Reserve (NRWNNNR), Xishuangbanna, China, wurde als Fallstudienstandort ausgewählt. Die Bewertung der Kohlenstoffvorräte erfolgte mit der Rapid Carbon Stock Appraisal (RaCSA)-Methode. Diese basiert auf der Bewertung von Kohlenstoffvorräten auf dem Niveau von Bäumen, Grundstücken, Landnutzung und Landschaft, mit Einbindung von Feldprobennahme verbunden mit Fernerkundung und GIS-Technologie. Die Ergebnisse zeigten, dass Kautschukplantagen einen größeren zeitgemittelten Kohlenstoffvorrat hatten als nicht-forstliche Landnutzungsarten (Ackerland, Busch- und Grünland), aber viel weniger als natürliche Wälder. Während 23 Jahren (1989-2012) gewann das gesamte Gebiet des Naturschutzgebietes (26574 ha) 0,644 Tg C hinzu. Trotz Ausdehnung der Kautschukanbauflächen konnten die Aufforstungsaktivitäten in NRWNNR die Kohlenstoffvorräte erhöhen. Die regionale Bewertung des Kohlenstoffsequestrierungspotenzials von Kautschukbäumen hängt wesentlich von der Auswahl geeigneter allometrischer Gleichungen und des Biomasse-Kohlenstoff-Umwandlungsfaktors ab. Die zweite Studie entwickelte allgemeine allometrische Gleichungen für Kautschukbäume, basierend auf Daten aus Kautschukplantagen mit Umtriebszeiten von 4-35 Jahren, Höhenlagen von 621-1.127 m und lokal verwendeten Kautschukbaumklonen (GT1, PRIM600, Yunyan77-4) im bergigen Südwesten Chinas. Allometrische Gleichungen zur Berechnung der oberirdischen Biomasse (AGB), welche den Durchmesser in Brusthöhe (DBH), Baumhöhe und Holzdichte berücksichtigten, waren anderen Gleichungen überlegen. Wir haben auch die Anpassungsgüte des kürzlich vorgeschlagene pan-tropische Waldmodell getestet. Die Ergebnisse zeigten, dass die Vorhersage der AGB durch das mit der destruktiv bestimmten Biomasse und der Holzdichte kalibrierte Modell genauer war als die Ergebnisse des pan-tropischen Waldmodells, das an die lokalen Bedingungen angepasst wurde. Die Beziehungen zwischen DBH und Höhe, und DBH und Biomasse wurden durch die Anzapfung der Bäume beeinflusst. Aufgrund dessen müssen Biomasse- und C-Bestandsberechnungen für Kautschuk mit artspezifischen allometrischen Gleichungen durchgeführt werden. Basierend auf der Analyse von Umweltfaktoren, die auf Landschaftsebene wirken, stellten wir fest, dass die ober- und unterirdischen Kohlenstoffvorräte vor allem durch das Bestandsalter, den Tongehalt des Bodens, die Hanglage und die Pflanzdichte beeinflusst wurden. Die Ergebnisse dieser Studie liefern Anhaltspunkte für eine zuverlässige Kohlenstoffbilanzierung in anderen Kautschukanbaugebieten. In der letzten Studie haben wir untersucht, wie Kautschukbäume auf den Klimawandel und regionalen Managementstrategien (Anbauhöhe, Pflanzdichte) reagieren. Wir setzten das prozessbasierte Land Use Change Impact Assessment Tool (LUCIA) ein, das mit detaillierten Bodenuntersuchungsdaten kalibriert wurde, um die Entwicklung der Baumbiomasse und den Latexertrag in Kautschukplantagen auf Baum-, Parzelle- und Landschaftsebene zu modellieren. Die Modellsimulation zeigte, dass während einer 40-jährigen Rotationzeit die Flachland-Kautschukplantagen (< 900m) schneller wuchsen und eine höhere Latexausbeute hatten als die Hochland-Kautschukplantagen (&#8807;900m). Kautschukplantagen mit hoher Pflanzdichte zeigten eine um 5% höhere oberirdische Biomasse als solche mit niedriger und mittlerer Pflanzdichte. Der durchschnittliche Gesamtertrag an Biomasse und der kumulative Latexertrag pro Baum stieg in 40 Jahren um 28% bzw. 48%, wenn die Klimaszenarien vom Basisszenario bis zum höchsten CO2-Emissionsszenario (RCP 8. 5) durchsimuliert wurden. Dieser Trend der Zunahme der Biomasse- und Latexausbeute mit verstärktem Klimawandel wurde auch auf der Ebene der Parzelle beobachtet. Dichtere Plantagen hatten eine größere Biomasse, aber die kumulative Latexproduktion ging drastisch zurück. Die während der Modellierung erstellten räumlich expliziten Output-Karten könnten helfen, die Kohlenstoffvorräte und die Latexproduktion regionaler Kautschukplantagen zu maximieren. Allgemein ist für ein angemessenes Monitoring ein Kautschuk-basiertes System erforderlich, das sowohl in zeitlicher Hinsicht (Tages-, Monats- und Jahresebene) als auch in räumlicher Hinsicht (Pixel-, Landnutzungs-, Wassereinzugs- und Landschaftsebene) geeignet ist. Die Ergebnisse der vorliegenden Studie verdeutlichen die Bedeutung ökologischer Modellierungswerkzeuge im Naturressourcenmanagement. Die hier gemachten Erfahrungen könnten auch auf andere Kautschukanbaugebiete übertragen werden, indem sie mit standortspezifischen Umweltvariablen aktualisiert werden. Die bedeutende Rolle des Kautschukbaums ist nicht nur auf dieHerstellung von Naturlatex beschränkt, sondern liegt auch in seinem großen Potenzial zur Kohlenstoffbindung. Unsere Ergebnisse lieferen den Ausgangspunkt für die künftige Entwicklung umfassender Strategien zur Anpassung an den Klimawandel und zur Eindämmung des Klimawandels in Südostasien. Durch interdisziplinäre Zusammenarbeit könnte der nachhaltige Kautschukanbau in den Großen Mekong-Regionen realisiert werden