Future ecosystem services of temperate grasslands: bridging scales towards high-resolution spatio-temporal monitoring
Temperate grasslands cover approximately 38% of the European agricultural area and provide various ecosystem services such as forage production, biodiversity conservation and carbon sequestration. These ecosystem services strongly depend on the biomass productivity, which with future global changes...
free air carbon enrichment (FACE); extreme weather conditions; climate extremes; climate change; temperate grassland; aboveground biomass; futur
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|Summary:||Temperate grasslands cover approximately 38% of the European agricultural area and provide various ecosystem services such as forage production, biodiversity conservation and carbon sequestration. These ecosystem services strongly depend on the biomass productivity, which with future global changes remains uncertain. Above all, an increasing atmospheric CO2 concentration ([CO2 ]) is assumed to enhance biomass productivity (called the CO2 fertilization effect; CFE) in particular under dry and hot conditions, while such probable future environmental conditions rather decrease the grassland productivity in general. However, recent doubts about the classic view on the CFE call for in-depth analysis of the interacting effects of the CFE and varying environmental conditions on grassland productivity, which is usually done by CO2 enrichment studies. Here, Free Air Carbon dioxide Enrichment (FACE) experiments have proven to be the most suitable approaches due to their minimal invasive character. Consequently, this study uses the worldwide longest operating FACE experiment on grassland, the Giessen FACE facility (GiFACE), to improve the assessment of the potential future of ecosystem services under global
Initially, it was tested whether the CFE in the GiFACE grassland is reduced under more extreme average weather conditions and after single extreme climatic events. To cope with the real-world conditions, a specific approach, called moving subset analysis, was developed to enable the quantification of the CFE in dependence of average weather conditions under varying [CO2 ]s. Additionally, a time series analysis was developed to link single extreme climatic events (ECEs) with the strength of the CFE. It was found that the CFE was significant and strong under local average environmental conditions (defined by ±1 SD of long-term average conditions), but decreased under more extreme weather conditions. The strongest decrease in the CFE under ECEs was associated with intensive and long heat waves, and could be quantified to a large extent by calculating the Killing Degree Days (∼30% variance of the magnitude of the CFE).
Since the CFE was found to be reduced under unfavourable environmental conditions, the potential of future grassland productivity was assessed in a further step. Therefore, potential future climate regimes and statistical models of biomass were created using the long-term experimental observations. Biomass was predicted using climate variable alterations within the potential climate regimes. The comparison of the potential regimes with the climate model projections for the years with a similar [CO2 ] compared to enriched [CO2 ]s revealed that biomass is likely to be reduced in the mid of 21st century despite the increase in [CO2 ], and thus that the CFE cannot compensate yield losses due to unfavourable environmental conditions.
Short-term environmental changes such as ECEs were shown to affect the grassland productivity while their influence might be elusive to the traditional destructive sampling approaches at harvest dates. To overcome these sampling restrictions, in the final step of this study, the feasibility of the non-invasive hyperspectral monitoring of the GiFACE grassland on a high spatio-temporal resolution was investigated. Thus, methods were developed to work with hyperspectral data and the comprehensive statistical software CRAN R. The methods developed were used to derive transfer functions between hyperspectral measurements and various laboratory-derived grassland traits by applying machine learning approaches. Good to very good leave-one-out prediction results revealed that the most important ecosystem services can precisely be predicted by hyperspectral approaches. Hyperspectral predictions of the most important grassland traits during the vegetation period highlighted how remote sensing approaches can improve grassland management in future.
Alarmingly, the reduced CFE and biomass productivity in grasslands under unfavourable future environmental conditions as detected in this thesis, suggest decreasing ecosystem services such as carbon sequestration and related climate mitigation function in future. This may – in a vicious circle – lead to a further
aggravation of expected global changes and urgently calls for better mitigation and adaptation strategies. Measures necessary for this could be instructed and
monitored by remote sensing methods, as was shown by the present thesis.|
|Physical Description:||288 Pages|