Description: Alluvial rivers aggrade, incise, and adjust their sediment-transport rates in response to changing sediment and water supply. Fluvial landforms, such as river terraces, and downstream stratigraphic archives may therefore record information about past environmental change. Using a physically based model describing sediment transport and long-profile evolution of alluvial rivers, we explore how their responses to environmental change depend on distance downstream, forcing timescales, and whether sediment or water supply is varied. We show that amplitudes of aggradation and incision, and therefore the likelihood of terrace formation, are greater upstream and in shorter and/or wetter catchments. Aggradation and incision, and therefore terrace ages, may also lag behind environmental change. How sediment-transport rates evolve depends strongly on whether water or sediment supply is varied. Diverse responses to environmental change could arise in natural alluvial valleys, controlled by their geometry and hydrology, with important implications for paleo-environmental interpretations of fluvial archives.

Description: Since 1963, the International Heat Flow Commission has been fostering the compilation of the Global Heat Flow Database to provide reliable heat-flow data. Over time, techniques and methodologies evolved, calling for a reorganization of the database structure and for a reassessment of stored heat-flow data. Here, we provide the results of a collaborative, community-driven approach to set-up a new, quality-approved global heat-flow database. We present background information on how heat-flow is determined and how this important thermal parameter could be systematically evaluated. The latter requires appropriate documentation of metadata to allow the application of a consistent evaluation scheme. The knowledge of basic data (name and coordinates of the site, depth range of temperature measurements, etc.), details on temperature and thermal-conductivity data and possible perturbing effects need to be given. The proposed heat-flow quality evaluation scheme can discriminate between different quality aspects affecting heat flow: numerical uncertainties, methodological uncertainties, and environmental effects. The resulting quality codes allow the evaluation of every stored heat-flow data entry. If mandatory basic data are missing, the entry is marked accordingly. In cases where more than one heat-flow determination is presented for one specific site, and all of them are considered for the site, the poorest evaluation score is inherited to the site level. The required data and the proposed scheme are presented in this paper. Due to the requirements of the newly developed evaluation scheme, the database structure as presented in 2021 has been updated and is available in the appendix of this paper. The new quality scheme will allow a comprehensible evaluation of the stored heat-flow data for the first time.

Description: Earth system science is an interdisciplinary effort to understand the fundamentals and interactions of environmental processes. Interdisciplinary research is challenging since it demands the integration of scientific schemes and practices from different research fields into a collaborative work environment. This paper introduces the framework F4ESS that supports this integration. F4ESS provides methods and technologies that facilitate the development of integrative work environments for Earth system science. F4ESS enables scientists a) to outline structured and summarized descriptions of scientific procedures to facilitate communication and synthesis, b) to combine a large variety of distributed data analysis software into seamless data analysis chains and workflows, c) to visually combine and interactively explore the manifold spatiotemporal data and results to support understanding and knowledge creation. The F4ESS methods and technologies are generic and can be applied in various scientific fields. We discuss F4ESS in the context of the interdisciplinary investigation of flood events.

Description: Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of 'transparency, accuracy, completeness, consistency and comparability'. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.

Description: For more than ten years, re3data, a global registry of research data repositories (RDRs), has been helping scientists, funding agencies, libraries, and data centers with finding, identifying, and referencing RDRs. As the world’s largest directory of RDRs, re3data currently describes over 3,000 RDRs on the basis of a comprehensive metadata schema. The service allows searching for RDRs of any type and from all disciplines, and users can filter results based on a wide range of characteristics. The re3data RDR descriptions are available as Open Data accessible through an API and are utilized by numerous Open Science services. re3data is engaged in various initiatives and projects concerning data management and is mentioned in the policies of many scientific institutions, funding organizations, and publishers. This article reflects on the ten-year experience of running re3data and discusses ten key issues related to the management of an Open Science service that caters to RDRs worldwide.

Description: Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

Description: This report documents the drilling operations of the Early Jurassic Earth System and Timescale scientific drilling project (JET, ICDP Project: 5065). The wells 5065_1_A, 5065_1_B, 5065_1_A were drilled in 2019-2021 with the support of the International Continental Scientific Drilling Program (ICDP) and the UK Natural Environment Research Council (NERC). Alternatively, the site is known as Prees 2 (Holes A – C). Prees 1 was a nearby hydrocarbon exploration well drilled by Trend Petroleum in 1972–1973. The project aims to construct a fully integrated and astronomically calibrated timescale for the Early Jurassic, a time in Earth history during which important physical, chemical, and biological elements of the modern Earth system were initiated. The JET drilling campaign supplements the earlier Llanbedr (Mochras Farm) borehole (1967 – 1969) in NW Wales – usually known as Mo-chras – which recovered a 1.3 km thick succession comprising the Rhaetian (Upper Triassic), Hettangian, Sinemurian, Pliensbachian and Toarcian (Lower Jurassic) stages (Woodland, 1971; Hesselbo et al., 2013). Using the combined framework of Prees and Mochras, internal and ex-ternal forcing factors on the Earth system will be documented and quantified for major palaeo-environmental events, such as the Late Triassic mass extinction and the Early Toarcian Oceanic Anoxic Event, and for the more stable ‘background’ state.

Description: Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change ( AGB) products has increased in recent years. Here we assessed the past decade net AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels 10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall AGB map cross-correlation between maps varied in the range 0.11–0.29 (r). Reported AGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that AGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, AGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.

Description: BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained from a joint analysis of separated and independent geophysical parameters. This paper discusses the application of a neural network approach to derive rock properties and seismic vulnerability from horizontal-to-vertical seismic ratio and seismic wave velocity data recorded in Majalengka-West Java, Indonesia.METHODS: Seismic microtremors were recorded at 54 locations and additionally multichannel analyses of surface wave experiments were performed at 18 locations because the multichannel analyses of surface wave experiment needs more effort and space. From the two methods, the values of the average shear wave velocity for the upper 30 meters, peak amplitudes and the dominant frequency between the measurement points were obtained from the interpolation of those geophysical data. Neural network was then applied to adaptively cluster and map the geophysical parameters. Four learning model clusters were developed from the three input seismic parameters: shear wave velocity, peak amplitude, and dominant frequency. FINDINGS: Generally, the values of the horizontal to vertical spectral ratios in the west of the study area were low (less than 5) compared with those in the southeastern part. The dominant frequency values in the west were mostly low at around 0.1–3 Hertz, associated with thick sedimentary layer. The pattern of the shear wave velocity map correlates with that of the horizontal to vertical spectral ratio map as the amplification is related to the soil or rock rigidity represented by the shear wave velocity. The combination of the geophysical data showed new features which is not found on the geological map such as in the eastern part of the study area. CONCLUSION: The application of the neural network based clustering analysis to the geophysical data revealed four rock types which are difficult to observe visually. The four clusters classified based on the variation of the geophysical parameters show a good correlation to rock types obtained from previous geological surveys. The clustering classified safe and vulnerable regions although detailed investigation is still required for confirmation before further development. This study demonstrates that low-cost geophysical experiments combined with neural network-based clustering can provide additional information which is important for seismic hazard mitigation in densely populated areas.

Description: As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001).