Markus Tum

Large‐scale variation in boreal and temperate forest carbon turnover rate related to climate

Vegetation carbon turnover processes in forest ecosystems and their dominant drivers are far from being understood at a broader scale. Many of these turnover processes act on long time-scales and include a lateral dimension and thus can hardly be investigated by plot-level studies alone. Making use of remote sensing based products of net primary productivity (NPP) and biomass, here we show that spatial gradients of carbon turnover rate (k) in Northern Hemisphere boreal and temperate forests are explained by different climate-related processes depending on the ecosystem. k is related to frost damage effects and the trade-off between growth and frost adaptation in boreal forests, while drought stress and climate effects on insects and pathogens can explain an elevated k in temperate forests. By identifying relevant processes underlying broad-scale patterns in k, we provide the basis for a detailed exploration of these mechanisms in field studies, and ultimately the improvement of their representations in global vegetation models (GVMs).

How sensitive are estimates of carbon fixation in agricultural models to input data

BackgroundProcess based vegetation models are central to understand the hydrological and carbon cycle. To achieve useful results at regional to global scales, such models require various input data from a wide range of earth observations. Since the geographical extent of these datasets varies from local to global scale, data quality and validity is of major interest when they are chosen for use. It is important to assess the effect of different input datasets in terms of quality to model outputs. In this article, we reflect on both: the uncertainty in input data and the reliability of model results. For our case study analysis we selected the Marchfeld region in Austria. We used independent meteorological datasets from the Central Institute for Meteorology and Geodynamics and the European Centre for Medium-Range Weather Forecasts (ECMWF). Land cover / land use information was taken from the GLC2000 and the CORINE 2000 products.ResultsFor our case study analysis we selected two different process based models: the Environmental Policy Integrated Climate (EPIC) and the Biosphere Energy Transfer Hydrology (BETHY/DLR) model. Both process models show a congruent pattern to changes in input data. The annual variability of NPP reaches 36% for BETHY/DLR and 39% for EPIC when changing major input datasets. However, EPIC is less sensitive to meteorological input data than BETHY/DLR. The ECMWF maximum temperatures show a systematic pattern. Temperatures above 20°C are overestimated, whereas temperatures below 20°C are underestimated, resulting in an overall underestimation of NPP in both models. Besides, BETHY/DLR is sensitive to the choice and accuracy of the land cover product.DiscussionThis study shows that the impact of input data uncertainty on modelling results need to be assessed: whenever the models are applied under new conditions, local data should be used for both input and result comparison.

Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests.

Abstract Turnover concepts in state‐of‐the‐art global vegetation models (GVMs) account for various processes, but are often highly simplified and may not include an adequate representation of the dominant processes that shape vegetation carbon turnover rates in real forest ecosystems at a large spatial scale. Here, we evaluate vegetation carbon turnover processes in GVMs participating in the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP, including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combination of remote sensing based products of biomass and net primary production (NPP). We find that current model limitations lead to considerable biases in the simulated biomass and in k (severe underestimations by all models except JeDi and VISIT compared to observation‐based average k), likely contributing to underestimation of positive feedbacks of the northern forest carbon balance to climate change caused by changes in forest mortality. A need for improved turnover concepts related to frost damage, drought, and insect outbreaks to better reproduce observation‐based spatial patterns in k is identified. As direct frost damage effects on mortality are usually not accounted for in these GVMs, simulated relationships between k and winter length in boreal forests are not consistent between different regions and strongly biased compared to the observation‐based relationships. Some models show a response of k to drought in temperate forests as a result of impacts of water availability on NPP, growth efficiency or carbon balance dependent mortality as well as soil or litter moisture effects on leaf turnover or fire. However, further direct drought effects such as carbon starvation (only in HYBRID4) or hydraulic failure are usually not taken into account by the investigated GVMs. While they are considered dominant large‐scale mortality agents, mortality mechanisms related to insects and pathogens are not explicitly treated in these models. &NA; We evaluate vegetation carbon turnover processes in global vegetation models (GVMs) participating in the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP, including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combination of remote sensing based products of biomass and net primary production (NPP). We find that current model limitations lead to considerable biases in the simulated biomass and in k (severe underestimations by all models except JeDi and VISIT compared to observation‐based average k), likely contributing to underestimation of positive feedbacks of the northern forest carbon balance to climate change caused by changes in forest mortality. A need for improved turnover concepts related to frost damage, drought, and insect outbreaks to better reproduce observation‐based spatial patterns in k and biomass is identified. Figure. No caption available.

Global Gap-Free MERIS LAI Time Series (2002–2012)

This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R2 : 0.88) of agreement on a global scale.

A Process-Based Vegetation Model for Estimating Agricultural Bioenergy Potentials

We present an approach to estimate sustainable straw energy potentials by means of a modelled net primary productivity (NPP) product validated against empirical data on the managed area and mean yields of the main crops in Germany. We used the Biosphere Energy Transfer Hydrology Model (BETHY/DLR) as a theoretical framework for estimating the NPP of agricultural areas in Germany. The BETHY/DLR was driven by remote sensing data from SPOT-VEGETATION, meteorological data from the European Centre for Medium-Range Weather Forecast (ECMWF) and additional static datasets such as land cover information (GLC2000), a soil map (ISRIC-WISE) and an elevation model (ETOP05). The output of the BETHY/DLR, i.e. the yearly accumulated NPP, was first converted into straw potentials through simple allocation rules (root-to-shoot and yield-to-straw ratios). Thereafter it was converted into energy potentials through species-specific lower heating values. The 2006 and 2007 results were compared with data from the literature. Using this method for estimating sustainable bioenergy potentials, we found good compatibility between the established approaches with only little overestimations (up to 12 %) and high correlations with the R2 of up to 0.78. Our analysis shows that the presented approach fills an important gap in estimating energy potentials from the modelled NPP. The estimated straw biomass energy potentials play an important role in the sustainable energy debate.

Sustainable Bioenergy Potentials for Europe and the Globe

Sustainable Bioenergy Potentials for Europe and the Globe In the framework of the EU FP7 project EnerGEO (Earth Observation for Monitoring and Assessment of the Environmental Impact of Energy Use) sustainable energy potentials for agricultural and forest areas were estimated by applying three different model approaches. The EPIC (Environmental Policy Including Climate) yield forecast model was used to estimate crop yields. The Global Forest Model (G4M) was applied to estimate global woody biomass growth.

Exploiting Big Earth Data from Space – First Experiences with the TimeScan Processing Chain

Abstract The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring. They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data ( 250 m). However, images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions. Hence, this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of (private or public) computing clusters. The TimeScan framework covers solutions for data access to arbitrary mission archives (with different data provisioning policies) and data ingestion into a processing environment (EO2Data module), mission specific pre-processing of multi-temporal data collections (Data2TimeS module), and the generation of a final TimeScan baseline product (TimeS2Stats module) providing a spectrally and temporally harmonized representation of the observed surfaces. Technically, a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest (i.e. up to hundreds of TBs or even PBs of data) into a higher level product with significantly reduced volume. In first test, the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat–8 scenes acquired from 2013 to 2015, a global data-set of 25,550 Envisat ASAR radar images collected 2010–2012, and regional Sentinel–1 and Sentinel–2 collections of 1500 images acquired from 2014 to 2016. The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics.

Comparing results of a remote sensing driven interception-infiltrationmodel for regional to global applications with ECMWF data

We present results of a remote sensing based modelling approach to simulate the 1D water transport in the vadose zone of unsaturated soils on a daily basis, which can be used for regional to global applications. To calculate the hydraulic conductivity our model is driven by van Genuchten parameters, which we calculated for Bavaria (South-East-Germany), which we choose as area of investigation, using the ISRIC-WISE Harmonized Global Soil Profile Dataset Ver. 3.1 and the Rosetta programme. Soil depth and layering of up to six layers were defined independently for each soil. Interception by vegetation is also considered by using Leaf Area Index (LAI) time series from SPOT-VEGETATION. Precipitation is based on daily time series from the European Centre for Medium-Range Weather Forecasts (ECMWF). The model was applied to the Biosphere Energy Transfer Hydrology (BETHY/DLR) vegetation model, driven at the German Aerospace Center (DLR), to discuss the possibility of regionalization of a global model concept, regarding the soil water budged. Furthermore we compare our results with ECMWF data and discuss the results for the state of Bavaria. We found a good agreement for the general characteristics of our results with this dataset, especially for soils which are close to the standard characteristics of the ECMWF. Disagreements were found for shallow soils and soils under stagnant moisture, which are not considered in the ECMWF modelling scheme, but are distinguished in our approach.

New Approaches for Biomass Estimation and Monitoring

The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words on the biomass potential that can be assessed from regional to global scale. Since certain biomass fractions have a low energy density, their spatial distribution is a crucial economical and ecological factor. For other biomass fractions a super-regional or global market is envisaged. Thus spatial and temporal information is vital for the future expansion of bioenergy use. This paper discuss the limits of the traditional biomass potential estimation approaches - material flow balance, site examinations and statistical surveys – in their accuracy and spatial localisation. Moreover, it shows new approaches in the potential estimation via GIS analysis (Geographic Information System) and remote sensing (RS). In addition, first results of German biomass potential estimated with help of these new approaches are shown and discussed.