Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.

Understanding the motivations and benefits of citizen science (CS) participants is critical to the success of environmental science projects that rely on data collection from engaged citizens. Tailored communication with citizen scientists is essential, leading to the need to target specific societal groups for extensive and high-quality data sets. The purpose of the study was to apply marketing concepts such as stakeholder analysis, value proposition canvas (VPC), and key performance indicators (KPIs) to CS projects. The study examined the extent to which these strategies can be applied to CS projects and add value, resulting in improved recruitment and retention of participants, as well as improved project management. The dynamic landscape of CS projects requires adapted business-oriented approaches supporting ongoing participation with high motivation, acknowledging community needs, and recognizing institutional scientists, akin to targeting potential customers. The case study focuses on the CityCLIM initiative, an EU-funded project collecting urban climate data, especially air temperature, using mobile weather stations. The CityCLIM VPC, analyzing stakeholder groups, facilitated tailored communication strategies by analyzing stakeholder groups and highlighting the effectiveness of the CS cycling initiative within the “Stadtradeln” program. Impact KPIs served as a roadmap for strategic refinement, while data quality KPIs identified deficiencies, guiding adjustments to the campaign. Applied marketing tools improved project planning, engagement, and evaluation, demonstrating the potential of this concept. Adapting marketing tools to recruitment and communication strategies benefits CS projects by targeting specific groups. The holistic integration of stakeholder analysis, VPC, and KPIs streamlines project management and creates a framework for sustainable success. This adaptation forms a robust toolkit for CS project coordinators, contributing to effective communication, engagement, and impact assessment. Applying marketing tools significantly increases CS projects’ reach and impact, ensuring long-term success and meaningful scientific contributions.

Recent discussions in many scientific disciplines stress the necessity of “FAIR” data. FAIR data, however, does not necessarily include information on data trustworthiness, where trustworthiness comprises reliability, validity and provenience/provenance. This opens up the risk of misinterpreting scientific data, even though all criteria of “FAIR” are fulfilled. Especially applications such as secondary data processing, data blending, and joint interpretation or visualization efforts are affected. This paper intends to start a discussion in the scientific community about how to evaluate, describe, and implement trustworthiness in a standardized data evaluation approach and in its metadata description following the FAIR principles. It discusses exemplarily different assessment tools regarding soil moisture measurements, data processing and visualization and elaborates on which additional (metadata) information is required to increase the trustworthiness of data for secondary usage. Taking into account the perspectives of data collectors, providers and users, the authors identify three aspects of data trustworthiness that promote efficient data sharing: 1) trustworthiness of the measurement 2) trustworthiness of the data processing and 3) trustworthiness of the data integration and visualization. The paper should be seen as the basis for a community discussion on data trustworthiness for a scientifically correct secondary use of the data. We do not have the intention to replace existing procedures and do not claim completeness of reliable tools and approaches described. Our intention is to discuss several important aspects to assess data trustworthiness based on the data life cycle of soil moisture data as an example.

Greenhouse gas (GHG) emissions from rivers are globally relevant, but quantification of these emissions comes with considerable uncertainty. Quantification of ecosystem-scale emissions is challenged by both spatial and short-term temporal variability. We measured spatio-temporal variability of CO₂ and CH₄ fluxes from a 1 km long reach of the lowland river Elbe in Germany over 3 d to establish which factor is more relevant to be taken into consideration: small-scale spatial variability or short-term temporal variability of CO₂ and CH₄ fluxes. GHG emissions from the river reach studied were dominated by CO₂, and 90 % of total emissions were from the water surface, while 10 % of emissions were from dry fallen sediment at the side of the river. Aquatic CO₂ fluxes were similar at different habitats, while aquatic CH₄ fluxes were higher at the side of the river. Artificial structures to improve navigability (groynes) created still water areas with elevated CH₄ fluxes and lower CO₂ fluxes. CO₂ fluxes exhibited a clear diurnal pattern, but the exact shape and timing of this pattern differed between habitats. By contrast, CH₄ fluxes did not change diurnally. Our data confirm our hypothesis that spatial variability is especially important for CH₄, while diurnal variability is more relevant for CO₂ emissions from our study reach of the Elbe in summer. Continuous measurements or at least sampling at different times of the day is most likely necessary for reliable quantification of river GHG emissions.

Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km²), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.

Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapour being transported to a limited area. HPE rainfall rates and volumes cannot not be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Towards an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the added value of our observation strategy is exemplarily shown by its first implementation in the Mueglitz river basin (210 km²), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, initiation and development of HPE X-Band radar observations calibrated by ground based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge times series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways towards an integrative and scale-bridging understanding of event dynamics.