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This review provides a synthesis of current knowledge on the morphological and functional traits of testate amoebae, a polyphyletic group of protists commonly used as proxies of past hydrological changes in paleoecological investigations from peatland, lake sediment and soil archives. A trait-based approach to understanding testate amoebae ecology and paleoecology has gained in popularity in recent years, with research showing that morphological characteristics provide complementary information to the commonly used environmental inferences based on testate amoeba (morpho-)species data. We provide a broad overview of testate amoeba morphological and functional traits and trait-environment relationships in the context of ecology, evolution, genetics, biogeography, and paleoecology. As examples we report upon previous ecological and paleoecological studies that used trait-based approaches, and describe key testate amoebae traits that can be used to improve the interpretation of environmental studies. We also highlight knowledge gaps and speculate on potential future directions for the application of trait-based approaches in testate amoeba research.
Nature-based solutions (NBS) are inspired and supported by nature but designed by humans. Historically, governmental stakeholders have aimed to control nature using a top-down approach; more recently, environmental governance has shifted to collaborative planning. Polycentric governance and co-creation procedures, which include a large spectrum of stakeholders, are assumed to be more effective in the management of public goods than traditional approaches. In this context, NBS projects should benefit from strong collaborative governance models, and the European Union is facilitating and encouraging such models. While some theoretical approaches exist, setting-up the NBS co-creation process (namely co-design and co-implementation) currently relies mostly on self-organized stakeholders rather than on strategic decisions. As such, systematic methods to identify relevant stakeholders seem to be crucial to enable higher planning efficiency, reduce bottlenecks and time needed for planning, designing, and implementing NBS. In this context, this contribution is based on the analysis of 16 NBS and 359 stakeholders. Real-life constellations are compared to theoretical typologies, and a systematic stakeholder mapping method to support co-creation is presented. Rather than making one-fit-all statements about the "right" stakeholders, the contribution provides insights for those "in charge" to strategically consider who might be involved at each stage of the NBS project.
Contemporary drought impact assessments have been constrained due to data availability, leading to an incomplete representation of impact trends. To address this, we present a novel method for the comprehensive and near-real-time monitoring of drought socio-economic impacts based on media reports. We tested its application using the case of the exceptional 2018/19 German drought. By employing text mining techniques, 4839 impact statements were identified, relating to livestock, agriculture, forestry, fires, recreation, energy and transport sectors. An accuracy of 95.6% was obtained for their automatic classification. Furthermore, high levels of performance in terms of spatial and temporal precision were found when validating our results against independent data (e.g. soil moisture, average precipitation, population interest in droughts, crop yield and forest fire statistics). The findings highlight the applicability of media data for rapidly and accurately monitoring the propagation of drought consequences over time and space. We anticipate our method to be used as a starting point for an impact-based early warning system.
A growing focus is being placed on both individuals and communities to adapt to flooding as part of the Sendai Framework for Disaster Risk Reduction 2015-2030. Adaptation to flooding requires sufficient social capital (linkages between members of society), risk perceptions (understanding of risk), and self-efficacy (self-perceived ability to limit disaster impacts) to be effective. However, there is limited understanding of how social capital, risk perceptions, and self-efficacy interact. We seek to explore how social capital interacts with variables known to increase the likelihood of successful adaptation. To study these linkages we analyze survey data of 1010 respondents across two communities in Thua Tien-Hue Province in central Vietnam, using ordered probit models. We find positive correlations between social capital, risk perceptions, and self-efficacy overall. This is a partly contrary finding to what was found in previous studies linking these concepts in Europe, which may be a result from the difference in risk context. The absence of an overall negative exchange between these factors has positive implications for proactive flood risk adaptation.
Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.
Flood insurance coverage can enhance financial resilience of households to changing flood risk caused by climate change. However, income inequalities imply that not all households can afford flood insurance. The uptake of flood insurance in voluntary markets may decline when flood risk increases as a result of climate change. This increase in flood risk may cause substantially higher risk-based insurance premiums, reduce the willingness to purchase flood insurance, and worsen problems with the unaffordability of coverage for low-income households. A socio-economic tipping-point can occur when the functioning of a formal flood insurance system is hampered by diminishing demand for coverage. In this study, we examine whether such a tipping-point can occur in Europe for current flood insurance systems under different trends in future flood risk caused by climate and socio-economic change. This analysis gives insights into regional inequalities concerning the ability to continue to use flood insurance as an instrument to adapt to changing flood risk. For this study, we adapt the "Dynamic Integrated Flood and Insurance" (DIFI) model by integrating new flood risk simulations in the model that enable examining impacts from various scenarios of climate and socio-economic change on flood insurance premiums and consumer demand. Our results show rising unaffordability and declining demand for flood insurance across scenarios towards 2080. Under a high climate change scenario, simulations show the occurrence of a socio-economic tipping-point in several regions, where insurance uptake almost disappears. A tipping-point and related inequalities in the ability to use flood insurance as an adaptation instrument can be mitigated by introducing reforms of flood insurance arrangements.
Quantitative detection and attribution of groundwater level variations in the Amu Darya Delta
(2020)
In the past few decades, the shrinkage of the Aral Sea is one of the biggest ecological catastrophes caused by human activity. To quantify the joint impact of both human activities and climate change on groundwater, the spatiotemporal groundwater dynamic characteristics in the Amu Darya Delta of the Aral Sea from 1999 to 2017 were analyzed, using the groundwater level, climate conditions, remote sensing data, and irrigation information. Statistics analysis was adopted to analyze the trend of groundwater variation, including intensity, periodicity, spatial structure, while the Pearson correlation analysis and principal component analysis (PCA) were used to quantify the impact of climate change and human activities on the variabilities of the groundwater level. Results reveal that the local groundwater dynamic has varied considerably. From 1999 to 2002, the groundwater level dropped from -189 cm to -350 cm. Until 2017, the groundwater level rose back to -211 cm with fluctuation. Seasonally, the fluctuation period of groundwater level and irrigation water was similar, both were about 18 months. Spatially, the groundwater level kept stable within the irrigation area and bare land but fluctuated drastically around the irrigation area. The Pearson correlation analysis reveals that the dynamic of the groundwater level is closely related to irrigation activity within the irrigation area (Nukus: -0.583), while for the place adjacent to the Aral Sea, the groundwater level is closely related to the Large Aral Sea water level (Muynak: 0.355). The results of PCA showed that the cumulative contribution rate of the first three components exceeds 85%. The study reveals that human activities have a great impact on groundwater, effective management, and the development of water resources in arid areas is an essential prerequisite for ecological protection.
Floodplains have been degraded in Central Europe for centuries, resulting in less dynamic and less diverse ecosystems than in the past. They provide essential ecosystem services like nutrient retention to improve overall water quality and thus fulfill naturally what EU legislation demands, but this service is impaired by reduced connectivity patterns. Along the second-longest river in Europe, the Danube, restoration measures have been carried out and are planned for the near future in the Austrian Danube Floodplain National Park in accordance with navigation purposes. We investigated nutrient retention capacity in seven currently differently connected side arms and the effects of proposed restoration measures using two complementary modeling approaches. We modeled nutrient retention capacity in two scenarios considering different hydrological conditions, as well as the consequences of planned restoration measures for side arm connectivity. With existing monitoring data on hydrology, nitrate, and total phosphorus concentrations for three side arms, we applied a statistical model and compared these results to a semi-empirical retention model. The latter was originally developed for larger scales, based on transferable causalities of retention processes and set up for this floodplain with publicly available data. Both model outcomes are in a comparable range for NO3-N (77-198 kg ha(-1)yr(-1)) and TP (1.4-5.7 kg ha(-1)yr(-1)) retention and agree in calculating higher retention in floodplains, where reconnection allows more frequent inundation events. However, the differences in the model results are significant for specific aspects especially during high flows, where the semi-empirical model complements the statistical model. On the other hand, the statistical model complements the semi-empirical model when taking into account nutrient retention at times of no connection between the remaining water bodies left in the floodplain. Overall, both models show clearly that nutrient retention in the Danube floodplains can be enhanced by restoring lateral hydrological reconnection and, for all planned measures, a positive effect on the overall water quality of the Danube River is expected. Still, a frequently hydrologically connected stretch of national park is insufficient to improve the water quality of the whole Upper Danube, and more functional floodplains are required.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected "normal" behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the "stable" principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
The Fram Strait is an area with a relatively low and irregular distribution of diatom microfossils in surface sediments, and thus microfossil records are scarce, rarely exceed the Holocene, and contain sparse information about past richness and taxonomic composition. These attributes make the Fram Strait an ideal study site to test the utility of sedimentary ancient DNA (sedaDNA) metabarcoding. Amplifying a short, partial rbcL marker from samples of sediment core MSM05/5-712-2 resulted in 95.7 % of our sequences being assigned to diatoms across 18 different families, with 38.6 % of them being resolved to species and 25.8 % to genus level. Independent replicates show a high similarity of PCR products, especially in the oldest samples. Diatom sedaDNA richness is highest in the Late Weichselian and lowest in Mid- and Late Holocene samples. Taxonomic composition is dominated by cold-water and sea-ice-associated diatoms and suggests several reorganisations – after the Last Glacial Maximum, after the Younger Dryas, and after the Early and after the Mid-Holocene. Different sequences assigned to, amongst others, Chaetoceros socialis indicate the detectability of intra-specific diversity using sedaDNA. We detect no clear pattern between our diatom sedaDNA record and the previously published IP25 record of this core, although proportions of pennate diatoms increase with higher IP25 concentrations and proportions of Nitzschia cf. frigida exceeding 2 % of the assemblage point towards past sea-ice presence.