Showing papers by "Helmholtz Centre for Environmental Research - UFZ published in 2022"

A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector

Abstract: Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected on a 0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.

COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors

Abstract: Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).

Contrasting altitudinal patterns and co-occurrence networks of soil bacterial and fungal communities along soil depths in the cold-temperate montane forests of China

Abstract: Soil bacterial and fungal communities with different key ecological functions play important roles in boreal forest ecosystems. Although several studies have reported on the altitudinal distribution patterns of microbes, our understanding of the characteristics of the microbial community and the core composition of the microbiome in cold-temperate montane forests is still limited. In this study, Illumina MiSeq sequencing was used to investigate the changes in soil bacterial and fungal communities in surface and subsurface soils along an altitudinal gradient (from 830 m to 1300 m) on Oakley Mountain. The diversity of the bacterial and fungal communities showed a monotonic decrease and a monotonic increase with altitude, respectively. The influence of altitude on the bacterial and fungal community composition was stronger than that of soil depth. The variations in pH and dissolved organic nitrogen content at different altitudes were the main factors influencing the bacterial and fungal community structures, respectively. There was no obvious difference between the network structures of the surface and subsurface soil fungal communities, while the network of the subsurface soil bacterial community was more complex and intricate than that of the surface soil bacterial community. The network nodes mainly belonging to Proteobacteria and Actinobacteria were the key bacterial taxa in the two soil layers. Although the main drivers of microbial community structure are consistent for whole and sub-nerwork communities, the subnetwork community analysis revealed other important drivers (i.e. soil temperature and nitrate nitrogen) that do not capture by whole community analysis. Thus, the more comprehensive picture of the important factors shaping microbial community structure can be achieved by combining whole and subnetwork community analyses. Our results demonstrated that altitude had a stronger influence on soil bacterial and fungal communities than soil depth and that bacterial and fungal communities showed divergent patterns with altitude and soil depth.

Knowledge sharing for shared success in the decade on ecosystem restoration

Abstract: The Decade on Ecosystem Restoration aims to provide the means and incentives for upscaling restoration efforts worldwide. Although ecosystem restoration is a broad, interdisciplinary concept, effective ecological restoration requires sound ecological knowledge to successfully restore biodiversity and ecosystem services in degraded landscapes. We emphasize the critical role of knowledge and data sharing to inform synthesis for the most robust restoration science possible. Such synthesis is critical for helping restoration ecologists better understand how context affects restoration outcomes, and to increase predictive capacity of restoration actions. This predictive capacity can help to provide better information for evidence-based decision-making, and scale-up approaches to meet ambitious targets for restoration. We advocate for a concerted effort to collate species-level, fine-scale, ecological community data from restoration studies across a wide range of environmental and ecological gradients. Well-articulated associated metadata relevant to experience and social or landscape contexts can further be used to explain outcomes. These data could be carefully curated and made openly available to the restoration community to help to maximize evidence-based knowledge sharing, enable flexible re-use of existing data and support predictive capacity in ecological community responses to restoration actions. We detail how integrated data, analysis and knowledge sharing via synthesis can support shared success in restoration ecology by identifying successful and unsuccessful outcomes across diverse systems and scales. We also discuss potential interdisciplinary solutions and approaches to overcome challenges associated with bringing together subfields of restoration practice. Sharing this knowledge and data openly can directly inform actions and help to improve outcomes for the Decade on Ecosystem Restoration.

Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture

Abstract: The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities.By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production.Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.

The role of cover crops for cropland soil carbon, nitrogen leaching, and agricultural yields – a global simulation study with LPJmL (V. 5.0-tillage-cc)

Abstract: Abstract. Land management practices can reduce the environmental impact of agricultural land use and production, improve productivity, and transform cropland into carbon sinks. In our study we assessed the biophysical and biogeochemical impacts and the potential contribution of cover crop practices to sustainable land use. We applied the process-based, global dynamic vegetation model LPJmL (Lund–Potsdam–Jena managed Land) V. 5.0-tillage-cc with a modified representation of cover crops to simulate the growth of grasses on cropland in periods between two consecutive main crops' growing seasons for near-past climate and land use conditions. We quantified simulated responses of agroecosystem components to cover crop cultivation in comparison to bare-soil fallowing practices on global cropland for a period of 50 years. For cover crops with tillage, we obtained annual global median soil carbon sequestration rates of 0.52 and 0.48 t C ha−1 yr−1 for the first and last decades of the entire simulation period, respectively. We found that cover crops with tillage reduced annual nitrogen leaching rates from cropland soils by medians of 39 % and 54 % but also the productivity of the following main crop by an average of 1.6 % and 2 % for the 2 analyzed decades. The largest reductions in productivity were found for rice and modestly lowered ones for maize and wheat, whereas the soybean yield revealed an almost homogenously positive response to cover crop practices replacing bare-soil fallow periods. The obtained simulation results of cover crop with tillage practices exhibit a good ability of the model version to reproduce observed effects reported in other studies. Further, the results suggest that having no tillage is a suitable complementary practice to cover crops, enhancing soil carbon sequestration and the reduction in nitrogen leaching, while reducing potential trade-offs with the main-crop productivity due to their impacts on soil nitrogen and water dynamics. The spatial heterogeneity of simulated impacts of cover crops on the variables assessed here was related to the time period since the introduction of the management practice as well as to environmental and agronomic conditions of the cropland. This study supports findings of other studies, highlighting the substantial potential contribution of cover crop practices to the sustainable development of arable production.

Adipsin Serum Concentrations and Adipose Tissue Expression in People with Obesity and Type 2 Diabetes

Abstract: (1) Adipsin is an adipokine that may link increased fat mass and adipose tissue dysfunction to obesity-related cardiometabolic diseases. Here, we investigated whether adipsin serum concentrations and adipose tissue (AT) adipsin mRNA expression are related to parameters of AT function, obesity and type 2 diabetes (T2D). (2) Methods: A cohort of 637 individuals with a wide range of age and body weight (Age: 18-85 years; BMI: 19-70 kg/m2) with (n = 237) or without (n = 400) T2D was analyzed for serum adipsin concentrations by ELISA and visceral (VAT) and subcutaneous (SAT) adipsin mRNA expression by RT-PCR. (3) Results: Adipsin serum concentrations were significantly higher in patients with T2D compared to normoglycemic individuals. We found significant positive univariate relationships of adipsin serum concentrations with age (r = 0.282, p < 0.001), body weight (r = 0.264, p < 0.001), fasting plasma glucose (r = 0.136, p = 0.006) and leptin serum concentrations (r = 0.362, p < 0.001). Neither VAT nor SAT adipsin mRNA expression correlated with adipsin serum concentrations after adjusting for age, sex and BMI. Independent of T2D status, we found significantly higher adipsin expression in SAT compared to VAT (4) Conclusions: Our data suggest that adipsin serum concentrations are strongly related to obesity and age. However, neither circulating adipsin nor adipsin AT expression reflects parameters of impaired glucose or lipid metabolism in patients with obesity with or without T2D.

Effects of heat and drought stress on the health status of six urban street tree species in Leipzig, Germany

Abstract: Trees are one of the most important elements of green infrastructure in cities. Climate change is specifically affecting trees in many European cities. Trees are experiencing negative impacts from the increase in heat waves and droughts, both of which begin, in some cases, early in the year and continue through the growing season. Current studies on the regionalization of climate change indicate that important water reservoirs such as soil and tree canopies have been drying out for years/decades, and these impacts can be observed in various parts of Europe. Trees react to stress as they age through mechanisms such as crown defoliation, early wilting, shedding of branches and, ultimately, lowered resistance to pests. As a result, massive tree death, both in park trees and street trees, can be observed in many cities. The present study provides a current inventory of street tree damage caused by heat and drought in the city of Leipzig, Germany, in 2020, the third extreme dry year after 2018 and 2019. The field maps focus on different age groups of Quercus, Tilia, Aesculus, Platanus, Fraxinus and Acer along a periurban-urban gradient. The results are clear: significant damage was found in all tree species. Older trees and newly planted trees are most likely to die as a result of extreme conditions, while younger trees with narrow trunks and crowns that have not yet expanded cope better with both heat and drought. Four out of five mapped street trees showed recognizable damage, indicating severe impacts of climate change on important elements of green infrastructure in cities.

Characterizing Catchment‐Scale Nitrogen Legacies and Constraining Their Uncertainties

Abstract: Improving nitrogen (N) status in European water bodies is a pressing issue. N levels depend not only on current but also past N inputs to the landscape, that have accumulated through time in legacy stores (e.g., soil, groundwater). Catchment-scale N models, that are commonly used to investigate in-stream N levels, rarely examine the magnitude and dynamics of legacy components. This study aims to gain a better understanding of the long-term fate of the N inputs and its uncertainties, using a legacy-driven N model (ELEMeNT) in Germany's largest national river basin (Weser; 38,450 km2) over the period 1960–2015. We estimate the nine model parameters based on a progressive constraining strategy, to assess the value of different observational data sets. We demonstrate that beyond in-stream N loading, soil N content and in-stream N concentration allow to reduce the equifinality in model parameterizations. We find that more than 50% of the N surplus denitrifies (1480–2210 kg ha−1) and the stream export amounts to around 18% (410–640 kg ha−1), leaving behind as much as around 230–780 kg ha−1 of N in the (soil) source zone and 10–105 kg ha−1 in the subsurface. A sensitivity analysis reveals the importance of different factors affecting the residual uncertainties in simulated N legacies, namely hydrologic travel time, denitrification rates, a coefficient characterizing the protection of organic N in source zone and N surplus input. Our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce the equifinality in water quality modeling.

Modeling neighborhood-scale shallow geothermal energy utilization: a case study in Berlin

Abstract: Abstract Nowadays, utilizing shallow geothermal energy for heating and cooling buildings has received increased interest in the building sector. Among different technologies, large borehole heat exchanger arrays are widely employed to supply heat to various types of buildings. Recently, a 16-borehole array was constructed to extract shallow geothermal energy to provide heat to a newly-developed public building in Berlin. To guarantee the quality of the numerical model and reveal its sensitivity to different subsurface conditions, model simulations were conducted for 25 years with two finite element simulators, namely the open-source code OpenGeoSys and the widely applied commercial software FEFLOW. Given proper numerical settings, the simulation results from OpenGeoSys and FEFLOW are in good agreement. However, further analysis reveals differences with respect to borehole inflow temperature calculation implemented in the two software. It is found that FEFLOW intrinsically uses the outflow temperature from the previous time step to determine the current inflow temperature, which makes it capable of much faster simulation by avoiding iterations within a single time step. In comparison, OpenGeoSys always updates the inflow and outflow temperature based on their current time step values. Because the updates are performed after each iteration, it delivers more accurate results with the expense of longer simulation time. Based on this case study, OpenGeoSys is a valid alternative to FEFLOW for modeling ground source heat pump systems. For modellers working in this field, it is thus recommended to adopt small enough time step size, so that potential numerical error can be avoided.

Na-β-Al₂O₃ stabilized Fe₂O₃ oxygen carriers for chemical looping water splitting: correlating structure with redox stability

Abstract: Chemical looping is an emerging technology to produce high purity hydrogen from fossil fuels or biomass with the simultaneous capture of the CO2 produced at the distributed scale. This process requires the availability of stable Fe2O3-based oxygen carriers. Fe2O3-Al2O3 based oxygen carriers exhibit a decay in the H2 yield with cycle number, due to the formation of FeAl2O4 that possesses a very low capacity for water splitting at typical operating conditions of conventional chemical looping schemes (700-1000 °C). In this study, the addition of sodium (via a sodium salt) in the synthesis of Fe2O3-Al2O3 oxygen carriers was assessed as a means to counteract the cyclic deactivation of the oxygen carrier. Detailed insight into the oxygen carrier's structure was gained by combined X-ray powder diffraction (XRD), X-ray absorption spectroscopy (XAS) at the Al, Na and Fe K-edges and scanning transmission electron microscopy/energy-dispersive X-ray spectroscopy (STEM/EDX) analyses. The addition of sodium prevented the formation of FeAl2O4 and stabilized the oxygen carrier via the formation of a layered structure, Na-β-Al2O3 phase. The material, i.e. Na-β-Al2O3 stabilized Fe2O3, showed a stable H2 yield of ca. 13.3 mmol g-1 over 15 cycles.