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The Universal Soil Loss Equation (USLE) was initially developed to support the implementation of conservation measures to minimize soil loss by water erosion, i.e. sheet and rill erosion, on a local scale and in the context of agricultural land use. The approach was refined over decades and became the Revised Universal Soil Loss Equation (RUSLE). At the same time the scope of applications has grown considerably. Due to its rather simple structure and relatively low demand for input data, it has been used for the assessment of soil loss from water erosion for ever growing spatial entities, i.e. regional scale catchments or whole countries. Recently this has been applied on a global scale in order to identify global hotspots of soil erosion. This coherent approach for a global comparison is most welcome against the background of the large number of country-specific assessments which are rather difficult to compare.However, there are two issues of concern. First, one needs to remember that RUSLE-derived soil loss assessments do not account for gully erosion, which might not be linearly scaled with sheet and rill erosion. Furthermore, information on the uncertainties of RUSLE based erosion assessments are not frequently reported. This especially concerns the impact of specific combinations of varying unique input data on the results.In this contribution we compare in high spatial resolution the results of two RUSLE-based soil loss by water erosion assessments conducted for six 100 by 100 km large study sites in South Africa. The first assessment was conducted about two decades ago and was based on the then available data covering the whole of South Africa. The second assessment is a revision, which includes the latest input data for rainfall erosivity, soil erodibility, the topographic factor as well as land cover and management. When compared, the results of the current soil loss estimates are an order of magnitude lower than the previous estimates. Does this difference represent a temporal trend or just the inherent uncertainties reflecting different input data and slightly different data processing? This is the question discussed in this contribution.

Climate change and the associated rise in air temperature have affected the Tibetan Plateau to a significantly stronger degree than the global average over the past decades. This has caused deglaciation, increased precipitation and permafrost degradation. The latter in particular is associated with increased slope instability and an increase in mass-wasting processes, which pose a danger to infrastructure in the vicinity. Interferometric synthetic aperture radar (InSAR) analysis is well suited to study the displacement patterns driven by permafrost processes, as they are on the order of millimeters to decimeters. The Nyainqêntanglha range on the Tibetan Plateau lacks high vegetation and features relatively thin snow cover in winter, allowing for continuous monitoring of those displacements throughout the year. The short revisit time of the Sentinel-1 constellation further reduces the risk of temporal decorrelation, making it possible to produce surface displacement models with good spatial coverage. We created three different surface displacement models to study heave and subsidence in the valleys, seasonally accelerated sliding and linear creep on the slopes. Flat regions at Nam Co are mostly stable on a multiannual scale but some experience subsidence. We observe a clear cycle of heave and subsidence in the valleys, where freezing of the active layer followed by subsequent thawing cause a vertical oscillation of the ground of up to a few centimeters, especially near streams and other water bodies. Most slopes of the area are unstable, with velocities of 8 to 17 mm yr−1. During the summer months surface displacement velocities more than double on most unstable slopes due to freeze–thaw processes driven by higher temperatures and increased precipitation. Specific landforms, most of which have been identified as rock glaciers, protalus ramparts or frozen moraines, reach velocities of up to 18 cm yr−1. Their movement shows little seasonal variation but a linear pattern indicating that their displacement is predominantly gravity-driven.

Due to the high variability of climate variables under climate change, the assessment of the climate impacts on water management, ecosystems restoration, as well as climate change adaptation requires very detailed climate information regionally and ideally at a local scale. State-of-the-art coupled land-atmosphere numerical models incorporate the water and energy exchange processes in the soil–vegetation–atmosphere continuum in a physically consistent way, thereby their simulations capture the complete evolution of state variables and provide the complex linkages across compartmental boundaries in the Earth system. As an effort to contribute to climate- and water-related research in South Africa, we present a high spatial and temporal resolution climatological atmosphere–land surface–hydrology analysis dataset covering the period 2000-2020. This analysis dataset is dynamically downscaled from ERA5 reanalysis using the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro). This dataset covers the territory of South Africa with a grid resolution of 4 km and a time interval of 1 hour.As a result, a comprehensive analysis dataset is provided, including the land surface and atmosphere state conditions, as well as the water flux components for the joint atmospheric-terrestrial water balance. The model performance is evaluated based on in-situ measurement records and remote sensing results. For instance, we evaluate the soil moisture and soil temperature using continuous in-situ measurement over six South Africa locations following a climate gradient, and the spatiotemporal trends of soil moisture are further evaluated using a newly developed radar-retrieved Surface Moisture Index (SurfMI). Biases of simulation results have been identified that should be taken into account in any application.

Mapping reservoir siltation is an often-used method for assessing sediment yield and soil erosion from catchments. An advantage of this approach is that measurements can potentially provide mean values that represent timeframes of several decades and thus overcome the bias induced by climate fluctuations, especially in semi-arid and arid regions. Furthermore, reservoir siltation mapping can be performed repeatedly, and thus repeated sediment yield trends over time can be derived. There are several studies that report sediment yield estimates based on reservoir siltation surveys, however, information on the uncertainties involved in these measurements is not frequently reported.In October 2019 and March 2020, we conducted reservoir siltation surveys of eight mid-size (~ 10 mio m³ water storage capacity), filled and dried-out reservoirs in South Africa. The water-filled reservoirs were surveyed using single beam, single frequency echosounders mounted to a boat. The dried-out reservoirs were surveyed using differential GNSS and a Terrestrial Laser-Scanner (TLS) with a scanning range of up to 1 km deployed at multiple scanning positions.In this contribution we present survey results, report on the issues encountered during the surveys and the uncertainties observed in the results. For the water-filled reservoirs we derived depth measurement uncertainties from the survey leg intersection points. Here, the mean measurement error is in the order of 0.1 m (p= 0.05). When this uncertainty of the volume estimation is applied to the water storage capacity of the dams, the resulting uncertainties are inthe order of a few percent, only. However, if this volume estimation uncertainty refers to the volume of the sediment at the bottom of the reservoirs, the relative error is can be in the order of a few ten percent. From this we conclude, that depending on the sediment inflow, it may take several decades before a repeated survey can establish a meaningful trend in sediment yield from the catchment beyond the measurement uncertainties involved.

Southern Africa is particularly sensitive to climate change, due to both ecological and socio-economic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities. We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scale-specific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.

Water temperature and the related thermal structure and stratification of a lake are very important to lake ecosystems because of their significant effects on the vertical exchanges of dissolved and particulate matter. In this study, we present high resolution, seasonal variations in water temperature at different depths of a large deep lake on the central Tibetan Plateau. The results show that Nam Co is a typical dimictic lake whose thermal stratification begins and ends in early June and early November, respectively. Increases in the water temperature during spring and the establishment of thermal stratification in the eastern small sub-basin occur approximately one month prior to the main basin which is likely caused by the different morphometry, different water transparency during spring, and the possible presence of a spring thermal bar. The Schmidt stability of the water column is directly controlled by surface water temperature. During the ice-covered period, the homogeneous water temperature exhibits a continuous increasing trend from approximately 0.5 °C to 3.5 °C. The daily mean surface water temperature of the main open lake area is highly correlated to the air temperature but shows a hysteresis effect of approximately 38 days, which shows the significant heat storage in such a large deep lake. Nam Co is a typical lake in this area in terms of its altitude, water depth and climatic conditions, so our results have broader significance for limnological and paleolimnological studies of similar lakes on the Tibetan Plateau.

Swartvlei is one of the most thoroughly investigated lacustrine coastal systems in South Africa. However, studies regarding the most recent past (i.e., the last 30–40 years) examining anthropogenic impacts on sediment deposition are missing completely. This study, covering the past 400 years, provides evidence for distinct environmental changes during this time most likely related to anthropogenic activities such as farming or water abstraction. A decrease in marine influence is observed starting somewhat earlier but was potentially supported by human management activities. In this respect age-depth-modelling is an important issue and needs further investigations in many coastal geoarchives of South Africa. Therefore, a multi-dating approach using several methods is suggested since errors in the chronology distinctly impact paleoenvironmental reconstructions (timing, flux rates etc.). In this context this is the first time that paleomagnetic secular variation data obtained from South African sediment records are used for dating purposes which is the only approach to establish a reliable chronology for recent sediments in this system.

Wer heute studieren will, muss effizient sein und selbstständig arbeiten können. Dabei gilt es, sowohl inhaltlichen als auch formalen Anforderungen gerecht zu werden. Dieses Buch bietet eine umfassende, leicht verständliche Anleitung für das wissenschaftliche Arbeiten. Es thematisiert den Umgang mit Literatur, die Gestaltung von schriftlichen Arbeiten und verschiedene Präsentationstechniken. Mit Tipps, Anregungen, vielen Beispielen und weiterführenden Literaturangaben ist das Buch ein verlässlicher Begleiter für Studierende (nicht nur) der Geographie – vom ersten Referat bis zur Abschlussarbeit.

For many decades the problem of land degradation has been an issue in South Africa. This is mainly due to the high variability of the mostly semi-arid climatic conditions providing a challenging environmental setting. Strong population growth and resulting socio-economic pressure on land resources aggravate the situation. Thus, reaching a number of Sustainable Development Goals (SDGs), like achieving food security (#2), access to clean water (#6), and the sustainable use of terrestrial (#15) and marine (#14) resources represents a challenge.In South Africa, land degradation has been linked to the terms veld degradation and soil degradation and has been addressed by numerous measures over the past decades. However, there is still uncertainty on the extent of human induced land degradation as compared to periodic climate induced land surface property changes. In cooperation with South African institutions and stakeholders the overarching goal of SALDi is to implement novel, adaptive, and sustainable tools for assessing land degradation in multi-use landscapes. Building upon the state of the art in land degradation assessments, the project aims to advance current methodologies by innovatively incorporating inter-annual and seasonal variability in a spatially explicit approach. SALDi takes advantage of the emerging availability of high spatio-temporal resolution Earth observation data (e.g. Copernicus Sentinels, DLR TanDEM-X, NASA/USGS Landsat), growing sources of in-situ data and advancements in modelling approaches.SALDi focusses on six study sites representing a major climate gradient from the (humid) winter-rainfall region in the SW across the (semi-arid) year-round rainfall to the (very humid) summer-rainfall region in the NE. The sites cover also different geological conditions and different agricultural practices. These include commercial, rain-fed and irrigated cropland, free-range cattle and sheep farming as well as communal and subsistence farming. Protected areas within our study regions represent benchmark sites, providing a foundation for baseline trend scenarios, against which climate-driven ecosystem-service dynamics of multi-used landscapes (cropland, rangeland, forests) will be evaluated.The aim of this presentation is to provide an overview of recent activities and advancements in the three thematic fields addressed by the project:i) to develop an automated system for high temporal frequency (bi-weekly) and spatial resolution (10 to 30 m) change detection monitoring of ecosystem service dynamics,ii) to develop, adapt and apply a Regional Earth System Model (RESM) to South Africa and investigate the feedbacks between land surface properties and the regional climate,iii) to advance current soil degradation process assessment tools for soil erosion.A number of additional SALDi team member presentations will provide detailed information on current developments.

Global biodiversity and ecosystem services are under high pressure of human impact. Although avoiding, reducing and reversing the impacts of human activities on ecosystems should be an urgent priority, the loss of biodiversity continues. One of the main drivers of biodiversity loss is land use change and land degradation. In South Africa land degradation has a long history and is of great concern. The SPACES II project SALDi (South African Land Degradation Monitor) aims for developing new, adaptive and sustainable tools for assessing land degradation by addressing the dynamics and functioning of multi-use landscapes with respect to land use change and ecosystem services. SPACES II is a German-South African “Science Partnerships for the Adaptation to Complex Earth System Processes”. Within SALDi ready-to-use earth observation (EO) data cubes are developed. EO data cubes are useful and effective tools using earth observations to deliver decision-ready products. By accessing, storing and processing of remote sensing products and time-series in data cubes, the efficient monitoring of land degradation can therefore be enabled. The SALDi data cubes from optical and radar satellite data include all necessary pre-processing steps and are generated to monitor vegetation dynamics of five years for six focus areas. Intra- and interannual variability in both, a high spatial and temporal resolution will be accounted to monitor land degradation. Therefore, spatial high resolution earth observation data from 2016 to 2021 from Sentinel-1 (C-Band radar) and Sentinel-2 (multispectral) will be integrated in the SALDi data cube for six research areas of 100 x 100 km. Additionally, a number of vegetation indices will be implemented to account for explicit land degradation and vegetation monitoring. Spatially explicit query tools will enable users of the system to focus on specific areas, like hydrological catchments or blocks of fields.

Land degradation with its direct impact on vegetation, surface soil layers and land surface albedo, has great relevance with the climate system. Assessing the climatic and ecological effects induced by land degradation requires a precise understanding of the interaction between the land surface and atmosphere. In coupled land-atmosphere modeling, the low boundary conditions impact the thermal and hydraulic exchanges at the land surface, therefore regulates the overlying atmosphere by land-atmosphere feedback processes. However, those land-atmosphere interactions are not convincingly represented in coupled land-atmosphere modeling applications. It is partly due to an approximate representation of hydrological processes in land surface modeling. Another source of uncertainties relates to the generalization of soil physical properties in the modeling system. This study focuses on the role of the prescribed physical properties of soil in high-resolution land surface-atmosphere simulations over South Africa. The model used here is the hydrologically-enhanced Weather Research and Forecasting (WRF-Hydro) model. Four commonly used global soil datasets obtained from UN Food and Agriculture Organization (FAO) soil database, Harmonized World Soil Database (HWSD), Global Soil Dataset for Earth System Model (GSDE), and SoilGrids dataset, are incorporated within the WRF-Hydro experiments for investigating the impact of soil information on land-atmosphere interactions. The simulation results of near-surface temperature, skin temperature, and surface energy fluxes are presented and compared to observational-based reference dataset. It is found that simulated soil moisture is largely influenced by soil texture features, which affects its feedback to the atmosphere.

The project ‘South African Land Degradation Monitor (SALDi)’ contributes to the German-South African Science Program SPACES by addressing the dynamics and functioning of multi-use landscapes with respect to land use, land cover change, water fluxes, and implications for habitats and ecosystem services. Particularly, SALDi aims: i) to develop an automated system for high temporal (bi-weekly) and spatial resolution (10 to 30 m) change detection monitoring of ecosystem service dynamics, ii) to develop, adapt and apply a Regional Earth System Model (RESM) to South Africa and investigate the feedbacks between land surface properties and the regional climate, iii) to advance current soil degradation process assessment tools as a limiting factor for ecosystem services. Protected areas (SANParks and other) within our six study regions represent benchmark sites, providing a foundation for baseline trend scenarios, against which climate-driven ecosystem service dynamics of multi-used landscape (cropland, rangeland, forests) are evaluated. Our study regions follow a climatic SW-NE transect: 1-Overberg, 2-Kai !Garib/Augrabies Falls, 3-Sol Plaatje/Kimberley, 4-Mantsopa/Ladybrand, 5-Bojanala Platinum/Pilanesberg, 6-Ehlanzeni /Mpumalanga.We are utilizing Sentinel-1A/B C-Band VV/VH-SAR time series with a 10 m resolution. The revisit time is 12 days on average for South Africa. Pre-processing is done using pyroSAR, a Python framework for large-scale SAR-processing providing processing utilities in ESA’s Sentinel Application Platform (SNAP) as well as GAMMA Remote Sensing software. The first two analytical approaches for the evaluation of the Sentinel-1 time series to detect surface changes, are based on the recognition of irregularities in the radar backscatter or coherence dynamics. Sentinel-2A/B data were pre-processed to L2A and used to calculate a wide range of vegetation indices (e.g. NDVI, EVI, SAVI, REIP) using DLR’s Sen2Cor-processor. The time frame starts with the first Sentinel-1 and -2 acquisitions and continues. The analysis-ready data, that is, harmonized, standardized, interoperable, radiometrically and geometrically consistent data, is being ingested in the SALDi Data Cube. Algorithms and models for developing products such as land degradation indicators are being developed using Jypiter notebooks. SANSA in collaboration with SARAO (South African Radio Astronomy Observatory), is developing the open data cube Digital Earth South Africa (DESA) based on SPOT data. Other datasets from different sensors will be ingested at a later stage. SALDi’s Data Cube will be open access to make it available to the wider scientific community, and also for teaching and training purposes. The application/use of the individual development stages should be possible on the fly for the partners in South Africa. The SASSCAL platform shall be used for distribution of the finalised SALDi Data Cube.This presentation demonstrates results from hyper-temporal Sentinel-1 and -2 timeseries concerning woody cover mapping and breakpoint analyses of the complex savanna systems, invasive slangbos (Seriphium plumosum) bush encroachment in grassland areas and regional soil moisture retrievals. Validation has been performed by cross-comparisons with VHR airborne DMC surface products, field trips and permanently installed soil moisture networks and interaction with local South African stakeholders.

Land degradation is a human-induced process deteriorating ecosystem functioning and services including soil fertility or biological productivity and, usually, it is accompanied by a loss of biodiversity. Land degradation causes on-site and off-site damages like a profound change or removal of vegetation cover and soil erosion on one hand as well as flooding of receiving streams and siltation of reservoirs one the other hand. Thus, land degradation poses a threat to a number of Sustainable Development Goals (SDG) including foremost sustainable life on land and under water, the provision of clean water and eventually the eradication of poverty and hunger on Earth.Often, land cover change is a valid indicator of land degradation providing the opportunity to take advantage of the increasing geometrically and temporally high-resolution remote sensing capabilities to identify and monitor land degradation. However, especially in semi-arid regions like savanna environments, globally driven inter-annual and decadal climate variations cause as well profound land cover dynamics which might be mistaken for land degradation.Assessing and combating land degradation has already a long scientific, socio-economic and political history. Based on this, the aim of this session is to explore the wide range of methodological approaches to assess land degradation, its dynamics over all spatial and temporal scales as well as the implications for society and the interaction with the different spheres of the Earth including the anthroposphere, atmosphere, biosphere, hydrosphere and pedosphere. Contributions to this session can be based on field work, remote sensing approaches or modelling exercises, they can also focus on specific physical and socio-economic aspects of land degradation like land management, land cover change or soil erosion or discuss land degradation in a broader societal context. The aim of this contribution is to provide a concise overview of the thematic framework, current activities, research questions and advancements.

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be major factors driving land degradation. During the last decades woody cover encroachment has increased over large areas in southern Africa inducing environmental, land cover as well as land use changes. The goal of this study is to synergistically combine SAR (Sentinel-1) and optical (Sentinel-2) earth observation information to monitor the slangbos encroachment on arable land in the Free State province, South Africa, between 2015 and 2020. Both, optical and radar satellite data are sensitive to different land surface and vegetation properties caused by sensor specific scattering or reflection mechanisms they rely on. This study focuses on mapping the slangbos aka bankrupt bush (Seriphium plumosum) encroachment in a selected test region in the Free State province of South Africa. Though being indigenous to South Africa, the slangbos has been documented to be the main encroacher on the grassvelds (South African grassland biomes) and thrive in poorly maintained cultivated lands. The shrub reaches a height and diameter of up to 0.6 m and the root system reaches a depth of up to 1.8 m. Slangbos has small light green leaves unpalatable to grazers due to their high oil content and is better adapted to long dry periods compared to grass communities.We used the random forest approach to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were based on expert knowledge and field information from the Department of Agriculture, Forestry and Fisheries (DAFF). Several input variables have been tested according to their model performance, e.g. backscatter, backscatter ratio, interferometric coherence as well as optical indices (e.g. NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), EVI (Enhanced Vegetation Index), etc.). We found that the Sentinel-1 VH backscatter (vertical–horizontal/cross-polarization) and the Sentinel-2 SAVI time series information have the highest importance for the random forest classifier among all input parameters. The estimation of the model accuracy was accomplished via spatial-cross validation and resulted in an overall accuracy of above 80 % for each time step, with the slangbos class being close to or above 90 %. Currently we are developing a prototype application to be tested in cooperation with local stakeholders to bring this approach to the farmers level. Once field work in southern Africa is possible again, further ground truthing and interaction with farmers will be carried out.

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step.

This study explores the potential of Landsat-8 and Sentinel-2 imagery for annual grass biomass mapping in savannas. To this end, three wet season image mosaics based on Landsat-8 and Sentinel-2 were created for 2016, 2017 and 2018 over Kruger National Park (KNP), South Africa. For the purpose of calibration and validation, use was made of in situ fuel biomass values measured as part of the yearly veld condition assessment (VCA) in KNP. The satellite and reference data were fed into a random forests machine learning approach to make park-wide predictions of grass biomass and to assess the performance of Landsat-8 and Sentinel-2 predictors (i.e., surface reflectance and the normalized difference vegetation index, NDVI). Examples of the data sets used and biomass maps produced are provided together with the obtained error statistics. The latter suggest that wet season NDVI mosaics from Landsat-8 and Sentinel-2 data enable the creation of fairly reliable, annual maps of fuel biomass for KNP. These new biomass estimates represent a slight improvement over recent mapping efforts based on Sentinel-1 data [1].

Reservoir siltation surveys facilitate the quantification of the mean area-specific sediment yield (SSY) of catchments for decadal and longer time spans. This requires information on the volume (VS) and dry bulk density (dBD) of reservoir deposits, the period of time (TR) during which they were accumulated, the reservoir sediment trap efficiency (TE) and the catchment area (A). For the calculation of the catchment-wide average rate of erosion by water (E), the sediment delivery ratio (SDR) of the catchment needs to be estimated. Each step in this workflow represents a potential source of uncertainty. Here, we explore the extent to which individual error sources contribute to uncertainties in SSY and E values. Mean SSY and E values are inferred for small to medium-sized catchments (≤100 km2) of 15 small (≤350 × 103 m3) intermittently dry reservoirs located in the southern Kruger National Park and observation periods of 30 to 65 yr. Mean relative uncertainties of resulting SSY and E values amount to ±21% and ±46% at the 95% confidence level, respectively. Uncertainties in SSY values arise mainly from the TE estimation (mean fractional uncertainty contribution of 64%), while the SDR estimation is the major cause (79%) for uncertain E values. Uncertainties in the determination of VS and dBD values are rather unimportant, contributing together

This study investigates Holocene sediments from Eilandvlei, a coastal lake located within the Wilderness embayment at the southern Cape coast of South Africa. The evolution of the present estuarine/coastal lake system is reconstructed based on seismic data as well as a multi-proxy approach on a 30.5 m sediment core spanning the last ~8.9 kyr. Geochemical (Ca, TOC/S, Br/TOC) and micropalaeontological data (diatoms, foraminifera) reflect changes in the degree of marine influence at the core site. The embayment likely developed via distinct phases of connectivity to the Indian Ocean caused by sea level changes and dune progradation. Marine conditions prevailed at the core site from ~8900 to 4700 cal BP. The rapid sea level rise during the early Holocene caused the inundation of a palaeovalley that most likely had formed at lower sea levels during the Pleistocene. Towards the mid-Holocene the sea level exceeded its present height around ~7500 cal BP creating a marine embayment. At ~4700 cal BP, the embayment became distinctly more disconnected from the ocean turning into a lagoon system that persisted until ~1200 cal BP. Subsequently, the marine influence further decreased and the present estuarine/coastal lake system was established. Grain size and geochemical data (Fe, Si/Al, chemical index of alteration (CIA)) further reflect changes in the deposition of terrigenous sediments at the core site. While the sedimentation of fine-grained (

The assessment of sediment yield from reservoir siltation requires knowledge of the reservoir's sediment trap efficiency (TE). Widely used approaches for the estimation of the long-term mean TE rely on the ratio of the reservoir's storage capacity (C) to its catchment size (A) or mean annual inflow (I). These approaches have been developed from a limited number of reservoirs (N ≤ 40), most of them located in temperate climate regions. Their general applicability to reservoirs receiving highly variable runoff such as in semi-arid areas has been questioned. Here, we examine the effect of ephemeral inflow on the TE of ten small (≤280 × 103 m3), intermittently dry reservoirs located in the Kruger National Park. Field work was conducted to determine the storage capacity of the reservoir basins. The frequency and magnitude of spillage events was simulated with the daily time step Pitman rainfall-runoff model. Different runoff scenarios were established to cope with uncertainties arising from the lack of runoff records and imperfect input data. Scenarios for the relationship between water and sediment discharge were created based on sediment rating curves. Taking into account uncertainties in hydrological modelling, uncertainties of mean TE estimates, calculated from all scenarios (N = 9), are moderate, ranging from ±6% to ±11% at the 95% confidence level. By comparison, estimating TE from the storage capacity to catchment area (C/A) ratio induces high uncertainty (ranges of 35% to 65%), but this uncertainty can be confined (15% to 33%) when the latter approach is combined with hydrological modelling. Established methods relying on the storage capacity to mean annual inflow (C/I) ratio most probably lead to an overestimation of the TE for the investigated reservoirs. The approach presented here may be used instead to estimate the TE of small, intermittently dry reservoirs in semi-arid climate regions.

Citizen Science (CS) operates at the interface of engineering, natural and social sciences. The topic is currently gaining importance, which, from a political perspective, is based, among other things, on the hope of increasing the acceptance of science and scientific knowledge among the general public. The involvement of non-specialists in the conception and implementation of research projects enables and requires the development of innovative educational concepts that integrate knowledge transfer and added value to science, for example through citizen-based data acquisition. This win-win situation of active learning and the generation of research-relevant data can be implemented in educational institutions in particular by expanding didactic concepts with the integration of citizen science.As an example, the project of the DLR in cooperation with the Friedrich Schiller University Jena will be presented. The campaign took place at site 15 km to the SE of Jena featuring planted and intensively managed forest. During the past two years the forest was affected by several stressors such as storm events, long drought periods (spring 2018 and 2019, summer 2018), and bark beetle attacks. Thus, forest management activities were conducted in June 2019 to remove stressed and infected trees. Two CS campaigns were conducted: one before (May) and one after (July) the management action (cross validation, check which trees were logged). The aim was to collect the stem circumference, the species, and other describing parameters. The citizens were “gathered“ from a university lecture for forthcoming Geography teachers. During the campaign a new approach for improved positioning under challenging GNSS conditions was tested (offset correction using Bluetooth low energy beacons – BLE).

South Africa is greatly affected by land degradation, partly due to the high variability of its climatic conditions, the strong population growth and resulting economic demands. Thus reaching a number of SDGs, like achieving food security (#2), access to clean water (#6), and the sustainable use of terrestrial (#15) and marine (#14) resources represents a clear challenge under the present global change pressures. Land degradation has been linked in South Africa to the terms veld degradation and soil degradation and has been addressed by numerous measures. But there is still uncertainty on the extent of human induced land degradation as compared to periodic climate induced land surface property changes.In cooperation with South African institutions and stakeholders (ARC-ISCW, SAEON, SANParks, SANSA, Stellenbosch University and University of the Free State, Equispectives Research and Consulting Services, Nuwejaars Wetlands SMA), the overarching goal of SALDi is to implement novel, adaptive, and sustainable tools for assessing land degradation in multi-use landscapes in South Africa. Building upon the state of the art in land degradation assessments, the project aims to advance current methodologies for multi-use landscapes by innovatively incorporating inter-annual and seasonal variability in a spatially explicit approach. SALDi takes advantage of the emerging availability of high spatio-temporal resolution Earth observation data (e.g. Copernicus Sentinels, DLR TanDEM-X, NASA/USGS Landsat program), growing sources of in-situ data and advancements in modelling approaches. Particularly, SALDi aims to:i) develop an automated system for high temporal frequency (bi-weekly) and spatial resolution (10 to 30 m) change detection monitoring of ecosystem service dynamics, ii) develop, adapt and apply a Regional Earth System Model (RESM) to South Africa and investigate the feedbacks between land surface properties and the regional climate, iii) advance current soil degradation process assessment tools for soil erosion, as this process represents an intrinsic limiting factor for biomass production and other regulating, supporting and provisioning ecosystem services, like providing clean water.The aim of this presentation is to introduce this new cooperative research project to the EGU Community and to seek new opportunities for collaboration.

Soil moisture is the dynamic link between climate, soil and vegetation and the dynamics and variation are affected by several often interrelated factors such as soil texture, soil structural parameters (soil organic carbon) and vegetation parameters (e.g. belowground- and aboveground biomass). For the characterization of soil moisture, including its variability and the resulting water and matter fluxes, the knowledge of the relative importance of these factors is of major challenge. Because of the spatial heterogeneity of its drivers soil moisture varies strongly over time and space. Our objective was to assess the spatio-temporal variability of soil moisture and factors which could explain that variability, like soil properties and vegetation cover, in in a long term biodiversity experiment (Jena Experiment).The Jena Experiment consist 86 plots on which plant species richness (0, 1, 2, 4, 8, 16, and 60) and functional groups (legumes, grasses, tall herbs, and small herbs) were manipulated in a factorial design Soil moisture measurements were performed weekly April to September 2003-2005 and 2008-2013 in 0.1, 0.2, 0.3, 0.4, and 0.6 m soil depth using Delta T theta probe.The analysis showed that both plant species richness and the presence of particular functional groups affected soil water content, while functional group richness per se played no role. Plots containing grasses was consistently drier than average at the soil surface in all observed years while plots containing legumes comparatively moister, but only up to the year 2008.Interestingly, plant species richness led to moister than average subsoil at the beginning of the experiment (2003 and 2004), which changed to lower than average up to the year 2010 in all depths.Shortly after establishment, increased topsoil water content was related to higher leaf area index in species‐rich plots, which enhanced shading. In later years, higher species richness increased topsoil organic carbon, likely improving soil aggregation. Improved aggregation, in turn, dried topsoils in species‐rich plots due to faster drainage of rainwater.Our decade‐long experiment shows that besides abiotic factors like texture, soil water patterns are consistently affected by biotic factors such as species diversity and plant functional types, but also properties that originate from biotic-abiotic interactions such as soil structure. Especially the effect of plant species richness propagated to deeper soil layers 8 years after the establishment of the experiment, and while originally caused by shading it was later related to altered soil physical characteristics in addition to modification of water uptake depth. Functional groups affected soil water distribution, likely due to plant traits affecting root water uptake depths, shading, or water‐use efficiency. Our results highlight the role of vegetation composition for soil processes and emphasize the need for long-term experiments to discover diversity effects in slow reacting systems like soil.