Responses and adaptation strategies of terrestrial ecosystems to climate change

Terrestrial ecosystems are likely to be affected by climate change, as climate change-induced shift of water and heat stresses patterns will have significant impacts on species composition, habitat distribution, and ecosystem functions, and thereby weaken the terrestrial carbon (C) sink and threaten global food security and biofuel production. This thesis investigates the responses of terrestrial ecosystems to climate change and is structured in four main chapters.;The first chapter of the thesis is directed towards the impacts of snow variation on ecosystem phenology. Variations in seasonal snowfall regulate regional and global climatic systems and vegetation growth by changing energy budgets of the lower atmosphere and land surface. We investigated the effects of snow on the start of growing season (SGS) of temperate vegetation in China. Across the entire temperate region in China, the winter snow depth increased at a rate of 0.15 cm•yr-1 (p=0.07) during the period 1982-1998, and decreased at a rate of 0.36 cm•yr-1 (p=0.09) during the period 1998-2005. Correspondingly, the SGS advanced at a rate of 0.68 d•yr-1 (p\u3c0.01) during 1982 to 1998, and delayed at a rate of 2.13 d•yr-1 (p=0.07) during 1998 to 2005, against a warming trend throughout the entire study period of 1982-2005. Spring air temperature strongly regulated the SGS of both deciduous broad-leaf and coniferous forests; whilst the winter snow had a greater impact on the SGS of grassland and shrubs. Snow depth variation combined with air temperature contributed to the variability in the SGS of grassland and shrubs, as snow acted as an insulator and modulated the underground thermal conditions. Additionally, differences were seen between the impacts of winter snow depth and spring snow depth on the SGS; as snow depths increased, the effect associated went from delaying SGS to advancing SGS. The observed thresholds for these effects were snow depths of 6.8 cm (winter) and 4.0 cm (spring). The results of this study suggest that the response of the vegetation\u27s SGS to seasonal snow change may be attributed to the coupling effects of air temperature and snow depth associated with the soil thermal conditions.;The second chapter further addresses snow impacts on terrestrial ecosystem with focus on regional carbon exchange between atmosphere and biosphere. Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying micro-climate, but the impacts of snow cover change on the annual C budget at the large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Here, we used site-based eddy covariance flux data to investigate the relationship between snow cover depth and ecosystem respiration (Reco) during winter. We then used the Biome-BGC model to estimate the effect of reductions in winter snow cover on C balance of Northern forests in non-permafrost region. According to site observations, winter net ecosystem C exchange (NEE) ranged from 0.028-1.53 gC•m-2•day-1, accounting for 44 +/- 123% of the annual C budget. Model simulation showed that over the past 30 years, snow driven change in winter C fluxes reduced non-growing season CO2 emissions, enhancing the annual C sink of northern forests. Over the entire study area, simulated winter ecosystem respiration (Reco) significantly decreased by 0.33 gC•m-2•day -1•yr-1 in response to decreasing snow cover depth, which accounts for approximately 25% of the simulated annual C sink trend from 1982 to 2009. Soil temperature was primarily controlled by snow cover rather than by air temperature as snow served as an insulator to prevent chilling impacts. A shallow snow cover has less insulation potential, causing colder soil temperatures and potentially lower respiration rates. Both eddy covariance analysis and model-simulated results showed that both Reco and NEE were significantly and positively correlated with variation in soil temperature controlled by variation in snow depth. Overall, our results highlight that a decrease in winter snow cover restrains global warming through emitting less C to the atmosphere.;The third chapter focused on assessing drought\u27s impact on global terrestrial ecosystems. Drought can affect the structure, composition and function of terrestrial ecosystems, yet the drought impacts and post-drought recovery potential of different land cover types have not been extensively studied at a global scale. Here, we evaluated drought impacts on gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) of different global terrestrial ecosystems, as well as the drought-resilience of each ecosystem type during the period of 2000 to 2011. We found the rainfall and soil moisture during drought period were dramatically lower than these in non-drought period, while air temperatures were higher than normal during drought period with amplitudes varied by land cover types. The length of recovery days (LRD) presented an evident gradient of high (\u3e 60 days) in mid- latitude region and low (\u3c 60 days) in low (tropical area) and high (boreal area) latitude regions. As average GPP increased, the LRD showed a significantly decreasing trend among different land covers (R 2=0.53, p\u3c0.0001). Moreover, the most dramatic reduction of the drought-induced GPP was found in the mid-latitude region of north Hemisphere (48% reduction), followed by the low-latitude region of south Hemisphere (13% reduction). In contrast, a slightly enhanced GPP (10%) was showed in the tropical region under drought impact. Additionally, the highest drought-induced reduction of ET was found in the Mediterranean area, followed by Africa. The water use efficiency, however, showed a pattern of decreasing in the north Hemisphere and increasing in the south Hemisphere.;The last chapter compared the differences of performance in trading water for carbon in planted forest and natural forest, with specific focus on China. Planted forests have been widely established in China as an essential approach to improving the ecological environment and mitigating climate change. Large-scale forest planting programs, however, are rarely examined in the context of tradeoffs between carbon sequestration and water yield between planted and natural forests. We reconstructed evapotranspiration (ET) and gross primary production (GPP) data based on remote-sensing and ground observational data, and investigated the differences between natural and planted forests, in order to evaluate the suitability of tree-planting activity in different climate regions where the afforestation and reforestation programs have been extensively implemented during the past three decades in China. While the differences changed with latitude (and region), we found that, on average, planted forests consumed 5.79% (29.13mm) more water but sequestered 1.05% (-12.02 gC m-2 yr -1) less carbon than naturally generated forests, while the amplitudes of discrepancies varied with latitude. It is suggested that the most suitable lands in China for afforestation should be located in the moist south subtropical region (SSTP), followed by the mid-subtropical region (MSTP), to attain a high carbon sequestration potential while maintain a relatively low impact on regional water balance. The high hydrological impact zone, including the north subtropical region (NSTP), warm temperate region (WTEM), and temperate region (TEM) should be cautiously evaluated for future afforestation due to water yield reductions associated with plantations

Impact of the North Sea–Caspian pattern on meteorological drought and vegetation response over diverging environmental systems in western Eurasia

Emerging drought stress on vegetation over western Eurasia is linked to varying teleconnection patterns. The North Sea–Caspian Pattern (NCP) is a relatively less studied Eurasian teleconnection pattern, which has a role on drought conditions and the consequence of changing conditions on vegetation. Between 1981 and 2015, we found that the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) have different trend patterns over various parts of western Eurasia. Specifically, the vegetation greenness is linked with wetter conditions over Scandinavia, and vegetation cover decreases over a drying central Asia. However, western Russia and Franceare paradoxically becoming greener under drier conditions. Using the Budyko framework, such paradoxical patterns are found in energy-limited environmental systems, where vegetation growth is primarily promoted by warmer temperatures. While most studies focused on the impacts of the North Atlantic Oscillation (NAO), we test whether the NCP explains better the variability of meteorological drought and vegetation response over western Eurasia. We hypothesised that the positive phases of the NCP are correlated to high pressure anomalies over the North Sea, which can be associated with weakening onshore moisture advection, leading to warmer and dryness conditions. These conditions are driving vegetation greening, as western Eurasia is mainly energy limited. However, we show that as the climate is warming along with the teleconnection impacts, the future ecosystem over western Eurasia will be transferred from energy-limited to water-limited systems. This suggests that the observed vegetation greening over past three decades is unlikely to sustain in the future

Assessment of Drought in Grasslands: Spatio – Temporal Analyses of Soil Moisture and Extreme Climate Effects in Southwestern Mongolia

Soil moisture plays an essential key role in the assessment of hydrological and meteorological droughts that may affect a wide area of the natural grassland and the groundwater resource. The surface soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological, and agricultural applications, especially in water-limited or drought-prone regions. However, gauging soil moisture is challenging because of its high variability. While point-scale in-situ measurements are scarce, the remote sensing tools remain the only practical means to obtain regional and global-scale soil moisture estimates. A Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to gauge the Earth’s surface soil moisture (SM) at the near-daily time scales. This work aims to evaluate the spatial and temporal patterns of SMOS soil moisture, determine the effect of the climate extremes on the vegetation growth cycle, and demonstrate the feasibility of using our drought model (GDI) the Gobi Drought Index. The GDI is based on the combination of SMOS soil moisture and several products from the MODIS satellite. We used this index for hydro-meteorological drought monitoring in Southwestern Mongolia. Firstly, we validated bias-corrected SMOS soil moisture for Mongolia by the in-situ soil moisture observations 2000 to 2015. Validation shows satisfactory results for assessing drought and water-stress conditions in the grasslands of Mongolia. The correlation analysis between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems shows a high correlation between the bias-corrected, monthly-averaged SMOS and NDVI data (R2 > 0.81). Further analysis of the SMOS and in situ SM data revealed a good match between spatial SM distribution and the rainfall events over Southwestern Mongolia. For example, during dry 2015, SM was decreased by approximately 30% across the forest-steppe and steppe areas. We also notice that both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. The second part of this research, analyzed several dzud (specific type of climate winter disaster) events (2000, 2001, 2002, and 2010) related to drought, to comprehend the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. We determined how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), NDVI, and livestock mortality data. The NDVI is used as an indicator of vegetation activity and growth. Its spatial and temporal pattern is expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in arid areas. We found that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The snowy winters can cause a 10 to 20-day early peak in NDVI and the following increase in vegetation growth. However, during a year with dry winter conditions, the vegetation growth phase begins later due to water deficiency and the entire year has a weaker vegetation growth. Generally, livestock loss and the reduction of grazing pressure was played a crucial role in vegetation recovery after extreme climatic events in Mongolia. At the last stage of our study, we develop an integrated Gobi drought index (GDI), derived from SMOS and LST, PET, and NDVI MODIS products. GDI can incorporate both, the meteorological and soil moisture drought patterns and sufficiently well represent overall drought conditions in the arid lands. Specifically, the monthly GDI and 1-month standardized precipitation index SPI showed significant correlations. Both of them are useful for drought monitoring in semi-arid lands. But, the SPI requires in situ data that are sparse, while the GDI is free from the meteorological network restriction. Consequently, we compared the GDI with other drought indices (VSWI, NDDI, NDWI, and in-situ SM). Comparison of these drought indices with the GDI allowed assessing the droughts’ behavior from different angles and quantified better their intensity. The GDI maps at fine-scale (< 1km) permit extending the applicability of our drought model to regional and local studies. These maps were generated from 2000 to 2018 across Southwestern Mongolia. Fine-scale GDI drought maps are currently limited to the whole territory for Mongolia but the algorithm is dynamic and can be transported to any region. The GDI drought index can be served as a useful tool for prevention services to detect extremely dry soil and vegetation conditions posing a risk of drought and groundwater resource depletion. It was able to detect the drought events that were underestimated by the National Drought Watch System in Mongolia. In summary, with the help of satellite, climatological, and geophysical data, the integrated GDI can be beneficial for vegetation drought stress characterization and can be a useful tool to monitor the effectiveness of pasture land restoration management practices for Mongolian livelihoods. The future application of the GDI can be extended to monitor potential impacts on water resources and agriculture in Mongolia, which have been impacted by long periods of drought

Satellite Observations of Regional Drought Severity in the Continental United States Using GRACE-Based Terrestrial Water Storage Changes

Drought monitoring is important for characterizing the timing, extent, and severity of drought for effective mitigation and water management. Presented here is a novel satellite-based drought severity index (DSI) for regional monitoring derived using time-variable terrestrial water storage changes from the Gravity Recovery and Climate Experiment (GRACE). The GRACE-DSI enables drought feature comparison across regions and periods, it is unaffected by uncertainties associated with soil water balance models and meteorological forcing data, and it incorporates water storage changes from human impacts including groundwater withdrawals thatmodify land surface processes and impact water management. Here, the underlying algorithm is described, and the GRACEDSI performance in the continental United States during 2002–14 is evaluated. It is found that the GRACE-DSI captures documented regional drought events and shows favorable spatial and temporal agreement with the monthly Palmer Drought Severity Index (PDSI) and the U.S. Drought Monitor (USDM). The GRACE-DSI also correlateswellwith a satellite-based normalized difference vegetation index (NDVI), indicating sensitivity to plantavailable water supply changes affecting vegetation growth. Because the GRACE-DSI captures changes in total terrestrial water storage, it complements more traditional drought monitoring tools such as the PDSI by providing information about deeper water storage changes that affect soil moisture recharge and drought recovery. The GRACE-DSI shows potential for globally consistent and effective drought monitoring, particularly where sparse ground observations (especially precipitation) limit the use of traditional drought monitoring methods

Terrestrial vegetation-water interactions in observations and models

Im Zusammenhang mit dem globalen Klimawandel ist die Vegetation besonders wichtig, da sie die anthropogenen CO2-Emissionen aufnehmen und den Wasser- und Energiekreislauf regulieren kann. Während frühere Forschungsarbeiten wertvolle Einblicke in langfristige Veränderungen des Grüns der Vegetation und in Bezug auf die Reaktion der Vegetation auf steigende Temperaturen und atmosphärisches CO2 lieferten, sind die Wechselwirkungen zwischen Vegetation und Wasser noch immer nicht vollständig verstanden. Tatsächlich hat die Dynamik der Bodenfeuchte in der Wurzelzone einen grundlegenden Einfluss auf die Veränderung des Grüns und die Produktivität der Vegetation. Dennoch sind weder die die Empfindlichkeit der Vegetationsproduktivität gegenüber der Bodenwasserversorgung noch die funktionelle Reaktion der Vegetation (d. h. Photosynthese und Transpiration) auf Bodentrockenheitsepisoden auf globaler Ebene vollständig geklärt worden. Forschungsengpässe sind fehlende globale Beobachtungen von Vegetationsfunktion und Bodenwasservariabilität. Außerdem werden die statistischen Instrumente für die Analyse umfangreicher und vielschichtiger Daten nur unzureichend genutzt, was ein besseres Verständnis der globalen Reaktion der Vegetation auf Wasser verhindert. Gleichzeitig trägt eine bessere Kenntnis der Reaktion der Vegetation auf die Wasserversorgung zu einem besseren Verständnis des terrestrischen Wasserkreislaufs bei. Hydrologische Extremereignisse schädigen die Infrastruktur, können das menschliche Wohlergehen beeinträchtigen und treten Berichten zufolge in vielen Regionen der Welt immer häufiger und intensiver auf. Während ein Konsens über die Bedeutung meteorologischer Faktoren für die Regulierung des Wasserkreislaufs und der damit verbundenen Extremereignisse besteht, ist die Rolle der Vegetationsdynamik und -eigenschaften noch nicht ausreichend erforscht. Ihre stärkere Berücksichtigung in hydrologischen Studien bietet das Potenzial, die Prozesse, die hydrologische Extreme antreiben, genauer zu verstehen. Dadurch kann ein besseres Verständnis der Wechselwirkungen zwischen Vegetation und Wasser im Hinblick auf die Wasserempfindlichkeit der Vegetation und die Rückkopplung der Vegetation auf Klimaextreme die Genauigkeit der Landoberflächenmodellierung verbessern, was für die Verbesserung der Klimaprojektionen unerlässlich ist. Dank der jüngsten Entwicklungen im Bereich der Erdbeobachtung und der Anwendbarkeit leistungsfähiger statistischer Analysewerkzeuge ist es nun möglich, globale Wechselwirkungen zwischen Vegetation und Wasser mit noch nie dagewesener Genauigkeit zu untersuchen. In diesem Zusammenhang stützt sich diese Arbeit insbesondere auf (i) neuartige Datenprodukte wie sonneninduzierte Chlorophyllfluoreszenz oder globale Bodenfeuchte und Evapotranspiration, die aus der Hochskalierung von Stationsmessungen mit Algorithmen des maschinellen Lernens gewonnen wurden, (ii) längere Aufzeichnungen und aktualisierte Aufbereitungen etablierter Datenprodukte wie Blattflächenindex und terrestrische Wasserspeicherung und (iii) die Entwicklung erklärbarer Methoden des maschinellen Lernens, mit denen Informationen effizient aus multivariaten Datenströmen abgeleitet werden können und die darüber hinaus leicht implementier- und in ökohydrologischen Studien anwendbar sind. Basierend auf diesen Datensätzen und Werkzeugen, wird in dieser Arbeit die Empfindlichkeit der globalen Vegetation gegenüber der Bodenwasserversorgung über Raum und Zeit hinweg neu untersucht.:Summary 7 Zusammenfassung 11 1 Introduction 15 1.1 Motivation 16 1.2 Terrestrial vegetation and its relationship with water supply 18 1.2.1 Vegetation functioning 18 1.2.2 Hydro-meteorological drivers of evaporation and vegetation productivity 19 1.2.3 Vegetation structure and physiology 21 1.3 Terrestrial water cycle and its relationship with vegetation 24 1.3.1 Water balance 24 1.3.2 Vegetation regulating the water cycle 26 1.3.3 The relevance of vegetation on hydrological extremes 27 1.4 Advances in observations and models 30 1.4.1 Spaceborne remote sensing 30 1.4.2 Data-driven and physical-based models 34 1.5 Research questions and thesis outline 37 1.5.1 What is the relationship between vegetation productivity and water supply? 37 1.5.2 Can vegetation regulate hydrological extremes? 38 1.5.3 Can land surface models capture vegetation-water interplay? 40 1.5.4 Thesis outline 40 2 Global vegetation controls using multi-layer soil moisture 41 2.1 Introduction 42 2.2 Data and methods 43 2.3 Results and discussion 45 2.4 Conclusions 53 2.A Appendix 54 3 Widespread increasing vegetation sensitivity to soil moisture 70 3.1 Introduction 71 3.2 Data and methods 72 3.3 Results and discussion 78 3.4 Conclusions 85 3.A Appendix 86 4 The drought effect on vegetation physiology inferred from space 101 4.1 Introduction 102 4.2 Data and methods 104 4.3 Results and discussion 111 4.4 Conclusions 122 4.A Appendix 123 5 Drought propagation into the terrestrial water cycle 136 5.1 Introduction 137 5.2 Data and methods 139 5.3 Results and discussion 145 5.4 Conclusions 155 5.A Appendix 157 6 Drivers of high river flows in European near-natural catchments 171 6.1 Introduction 172 6.2 Data and methods 173 6.3 Results and discussion 179 6.4 Conclusion 184 6.A Appendix 186 7 Synthesis 193 7.1 What is the relationship between vegetation productivity and water supply? 194 7.2 Can vegetation regulate hydrological extremes? 197 7.3 Can land surface models capture the observed vegetation-water interplay? 199 7.4 Limitations 200 7.4.1 Difficulties in predicting SIF in tropical regions 200 7.4.2 Observing terrestrial photosynthesis and evaporation 201 7.4.3 Methods related to variable importance quantification 202 7.5 Outlook 202 7.5.1 Vegetation sensitivity to soil moisture and its implications 203 7.5.2 Vegetation functioning and related structure and physiology 203 7.5.3 Extreme events: floods and drought 204 References 206 Statement of authorship contributions 238 Acknowledgements 239 Curriculum Vitae 241 Scientific publications 242 IMPRS certificate 244In the context of global climate change, vegetation is particularly relevant as it can take up anthropogenic CO2 emissions and regulate water and energy cycling. While previous research provided valuable insights into long-term changes in vegetation greenness and in terms of the vegetation response to increasing temperature and atmospheric CO2, vegetation-water interactions are still not fully understood. In fact, root-zone soil moisture dynamics have a fundamental influence on modulating vegetation greenness and productivity. Nevertheless, neither the sensitivity of vegetation productivity to soil water supply nor the vegetation functional response (i.e., photosynthesis and transpiration) to soil drought episodes have been fully resolved at the global scale. Missing global observations of vegetation functioning and terrestrial water variability are bottlenecks, and statistical tools for analyzing large and multi-stream data are poorly exploited, preventing a better understanding of global vegetation water response. At the same time, a better knowledge of the vegetation response to the water supply in turn advances the understanding of the terrestrial water cycle. Hydrological extremes are damaging infrastructure and can affect human well-being, and have been reported to become more frequent and intense in many regions around the world. While a consensus exists regarding the importance of meteorological drivers for regulating the water cycle and related extreme events, the role of vegetation dynamics and characteristics is understudied. Its greater consideration in hydrological studies offers the potential to more accurately understand the processes driving hydrological extremes. Thereby, a better understanding on vegetation-water interactions in terms of vegetation water sensitivity and vegetation feedbacks on climate extremes can advance the accuracy of land surface modelling which is essential to improve climate projections. Thanks to recent developments in Earth observations and in the applicability of powerful statistical analyses tools, investigating global vegetation-water interactions is now possible with unprecedented accuracy. In this context, this thesis builds particularly on (i) novel data products such as Sun-induced chlorophyll fluorescence or global gridded soil moisture and evapotranspiration products obtained from upscaling station measurements with machine learning algorithms, (ii) longer records and updated processing of established data products such as leaf area index and terrestrial water storage, and (iii) the development of explainable machine learning methods which can efficiently derived information from multivariate data streams, and are furthermore implemented and readily applicable in ecohydrological studies. With these datasets and tools, this thesis revisits the sensitivity of global vegetation to soil water supply across space and time.:Summary 7 Zusammenfassung 11 1 Introduction 15 1.1 Motivation 16 1.2 Terrestrial vegetation and its relationship with water supply 18 1.2.1 Vegetation functioning 18 1.2.2 Hydro-meteorological drivers of evaporation and vegetation productivity 19 1.2.3 Vegetation structure and physiology 21 1.3 Terrestrial water cycle and its relationship with vegetation 24 1.3.1 Water balance 24 1.3.2 Vegetation regulating the water cycle 26 1.3.3 The relevance of vegetation on hydrological extremes 27 1.4 Advances in observations and models 30 1.4.1 Spaceborne remote sensing 30 1.4.2 Data-driven and physical-based models 34 1.5 Research questions and thesis outline 37 1.5.1 What is the relationship between vegetation productivity and water supply? 37 1.5.2 Can vegetation regulate hydrological extremes? 38 1.5.3 Can land surface models capture vegetation-water interplay? 40 1.5.4 Thesis outline 40 2 Global vegetation controls using multi-layer soil moisture 41 2.1 Introduction 42 2.2 Data and methods 43 2.3 Results and discussion 45 2.4 Conclusions 53 2.A Appendix 54 3 Widespread increasing vegetation sensitivity to soil moisture 70 3.1 Introduction 71 3.2 Data and methods 72 3.3 Results and discussion 78 3.4 Conclusions 85 3.A Appendix 86 4 The drought effect on vegetation physiology inferred from space 101 4.1 Introduction 102 4.2 Data and methods 104 4.3 Results and discussion 111 4.4 Conclusions 122 4.A Appendix 123 5 Drought propagation into the terrestrial water cycle 136 5.1 Introduction 137 5.2 Data and methods 139 5.3 Results and discussion 145 5.4 Conclusions 155 5.A Appendix 157 6 Drivers of high river flows in European near-natural catchments 171 6.1 Introduction 172 6.2 Data and methods 173 6.3 Results and discussion 179 6.4 Conclusion 184 6.A Appendix 186 7 Synthesis 193 7.1 What is the relationship between vegetation productivity and water supply? 194 7.2 Can vegetation regulate hydrological extremes? 197 7.3 Can land surface models capture the observed vegetation-water interplay? 199 7.4 Limitations 200 7.4.1 Difficulties in predicting SIF in tropical regions 200 7.4.2 Observing terrestrial photosynthesis and evaporation 201 7.4.3 Methods related to variable importance quantification 202 7.5 Outlook 202 7.5.1 Vegetation sensitivity to soil moisture and its implications 203 7.5.2 Vegetation functioning and related structure and physiology 203 7.5.3 Extreme events: floods and drought 204 References 206 Statement of authorship contributions 238 Acknowledgements 239 Curriculum Vitae 241 Scientific publications 242 IMPRS certificate 24

Drought, Heat, and the Carbon Cycle: a Review

Purpose of the Review Weather and climate extremes substantially affect global- and regional-scale carbon (C) cycling, and thus spatially or temporally extended climatic extreme events jeopardize terrestrial ecosystem carbon sequestration. We illustrate the relevance of drought and/or heat events (“DHE”) for the carbon cycle and highlight underlying concepts and complex impact mechanisms. We review recent results, discuss current research needs and emerging research topics. Recent Findings Our review covers topics critical to understanding, attributing and predicting the effects of DHE on the terrestrial carbon cycle: (1) ecophysiological impact mechanisms and mediating factors, (2) the role of timing, duration and dynamical effects through which DHE impacts on regional-scale carbon cycling are either attenuated or enhanced, and (3) large-scale atmospheric conditions under which DHE are likely to unfold and to affect the terrestrial carbon cycle. Recent research thus shows the need to view these events in a broader spatial and temporal perspective that extends assessments beyond local and concurrent C cycle impacts of DHE. Summary Novel data streams, model (ensemble) simulations, and analyses allow to better understand carbon cycle impacts not only in response to their proximate drivers (drought, heat, etc.) but also attributing them to underlying changes in drivers and large-scale atmospheric conditions. These attribution-type analyses increasingly address and disentangle various sequences or dynamical interactions of events and their impacts, including compensating or amplifying effects on terrestrial carbon cycling.publishedVersio

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The datasets used in this paper include the Noah land surface modelbased SM estimations, AtmosphereLandExchangeInverse modelbased Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEMderived rootmeansquareerrors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSEweighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010Russia, 2011USA, 2013New Zealand droughts and other reported agricultural drought occurrences over the 20092014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts

Impacts of Climate Extremes on Terrestrial Productivity

Terrestrial biosphere absorbs approximately 28% of anthropogenic CO2 emissions. This terrestrial carbon sink might become saturated in a future climate regime. To explore the issues associated with this topic, an accurate estimate of gross primary production (GPP) of global terrestrial ecosystems is needed. A major uncertainty in modeling global terrestrial GPP is the parameter of light use efficiency (LUE). Most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this study, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated the tower-based LUE estimates with key environmental and biological variables at 0.5º × 0.5º grid-cell resolutions, using a random forest regression (RFR) approach. Then we developed a RFR-LUE-GPP model using the grid-cell LUE data. In order to calibrate the LUE model, we developed a data-driven RFR-GPP model using random forest regression method only. Our results showed LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.23±0.03 gC m-2 MJ-1 APAR, which led to an estimate of global gross primary production (GPP) of 107.5±2.5 Gt C /year from 2001 to 2005. Large uncertainties existed in GPP estimations over sparsely vegetated areas covered by savannas and woody savannas at middle to low latitude (i.e. 20ºS to 40ºS and 5ºN to 40ºN) due to the lack of available data. Model results were improved by incorporating Köppen climate types to represent climate/meteorological information in machine learning modeling. This brought a new understanding to the recognized problem of climate-dependence of spring onset of photosynthesis and the challenges in accurately modeling the biome GPP of evergreen broad leaf forests (EBF). The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echo the necessity of modeling GPP separately by latitudes. We also used a perfect-deficit approach to identify forest canopy photosynthetic capacity (CPC) deficits and analyze how they correlate to climate extremes, based on observational data measured by the eddy covariance method at 27 forest sites over 146 site-years. We found that droughts severely affect the carbon assimilation capacities of evergreen broadleaf forest and deciduous broadleaf forest. The carbon assimilation capacities of Mediterranean forests were highly sensitive to climate extremes, while marine forest climates tended to be insensitive to climate extremes. Our estimates suggest an average global reduction of forest canopy photosynthetic capacity due to unfavorable climate extremes of 6.3 Pg C (~5.2% of global gross primary production) per growing season over 2001-2010, with evergreen broadleaf forests contributing 52% of the total reduction. At biome-scale, terrestrial carbon uptake is controlled mainly by weather variability. Observational data from a global monitoring network indicate that the sensitivity of terrestrial carbon sequestration to mean annual temperature (T) breaks down at a threshold value of 16oC, above which terrestrial CO2 fluxes are controlled by dryness rather than temperature. Here we show that since 1948 warming climate has moved the 16oC T latitudinal belt poleward. Land surface area with T \u3e16oC and now subject to dryness control rather than temperature as the regulator of carbon uptake has increased by 6% and is expected to increase by at least another 8% by 2050