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The Mesoproterozoic Aggeneys-Gamsberg ore district, South Africa, is one of the world´s largest sulfidic base metal concentrations and well-known as a prime example of Broken Hill-type base metal deposits, traditionally interpreted as metamorphosed SEDEX deposits. Within this district, the Gamsberg deposit stands out for its huge size and strongly Zn-dominated ore ( >14 Mt contained Zn). New electron microprobe analyses and element abundance maps of sulfides and silicates point to fluid-driven sulfidation during retrograde metamorphism. Differences in the chemistry of sulfide inclusions within zoned garnet grains reflect different degrees of interaction of sulfides with high metal/sulfur-ratio with a sulfur-rich metamorphic fluid. Independent evidence of sulfidation during retrograde metamorphism comes from graphic-textured sulfide aggregates that previously have been interpreted as quenched sulfidic melts, replacement of pyrrhotite by pyrite along micro-fractures, and sulfides in phyllic alteration zones. Limited availability of fluid under retrograde conditions caused locally different degrees of segregation of Fe-rich sphalerite into Zn-rich sphalerite and pyrite, and thus considerable heterogeneity in sphalerite chemistry. The invoked sulfur-rich metamorphic fluids would have been able to sulfidize base metal-rich zones in the whole deposit and thus camouflage a potential pre-metamorphic oxidation. These findings support the recently established hypothesis of a pre-Klondikean weathering-induced oxidation event and challenge the traditional explanation of Broken Hill-type deposits as merely metamorphosed SEDEX deposits. Instead, we suggest that the massive sulfide deposits experienced a complex history, starting with initial SEDEX-type mineralization, followed by near-surface oxidation with spatial metal separation, and then sulfidation of this oxidized ore during medium- to high-grade metamorphism.
During strong El Niño events, below-average rainfall is expected in large parts of southern Africa. The 1992 El Niño season was associated with one of the worst drought episodes in large parts of South Africa. Using reanalysis data set from NCEP-NCAR, this study examined circulation types (CTs) in Africa south of the equator that are statistically related to the El Niño signal in the southwest Indian Ocean and the implication of this relationship during the 1992 drought episode in South Africa. A statistically significant correlation was found between the above-average Nino 3.4 index and a CT that features widespread cyclonic activity in the tropical southwest Indian Ocean, coupled with a weaker state of the south Indian Ocean high-pressure. During the analysis period, it was found that the El Niño signal enhanced the amplitude of the aforementioned CT. The impacts of the El Niño signal on CTs in southern Africa, which could have contributed to the 1992 severe drought episode in South Africa, were reflected in (i) robust decrease in the frequency of occurrence of the austral summer climatology pattern of atmospheric circulation that favors southeasterly moisture fluxes, advected by the South Indian Ocean high-pressure; (ii) modulation of easterly moisture fluxes, advected by the South Atlantic Ocean high-pressure, ridging south of South Africa; (iii) and enhancement of the amplitude of CTs that both enhances subsidence over South Africa, and associated with the dominance of westerlies across the Agulhas current. Under the ssp585 scenario, the analyzed climate models suggested that the impact of radiative heating on the CT significantly related to El Niño might result in an anomalous increase in surface pressure at the eastern parts of South Africa.
Atmospheric circulation is a vital process in the transport of heat, moisture, and pollutants around the globe. The variability of rainfall depends to some extent on the atmospheric circulation. This paper investigates synoptic situations in southern Africa that can be associated with wet days and dry days in Free State, South Africa, in addition to the underlying dynamics. Principal component analysis was applied to the T-mode matrix (variable is time series and observation is grid points at which the field was observed) of daily mean sea level pressure field from 1979 to 2018 in classifying the circulation patterns in southern Africa. 18 circulation types (CTs) were classified in the study region. From the linkage of the CTs to the observed rainfall data, from 11 stations in Free State, it was found that dominant austral winter and late austral autumn CTs have a higher probability of being associated with dry days in Free State. Dominant austral summer and late austral spring CTs were found to have a higher probability of being associated with wet days in Free State. Cyclonic/anti-cyclonic activity over the southwest Indian Ocean, explained to a good extent, the inter-seasonal variability of rainfall in Free State. The synoptic state associated with a stronger anti-cyclonic circulation at the western branch of the South Indian Ocean high-pressure, during austral summer, leading to enhanced low-level moisture transport by southeast winds was found to have the highest probability of being associated with above-average rainfall in most regions in Free State. On the other hand, the synoptic state associated with enhanced transport of cold dry air, by the extratropical westerlies, was found to have the highest probability of being associated with (winter) dryness in Free State.
Die Covid-19-Pandemie gilt in vielen gesellschaftlichen Teilbereichen als Beschleuniger für Transformationsprozesse. Auch im Bereich der Organisation urbaner Logistik und Einzelhandelslandschaften etablieren sich neue Akteur*innen und Funktionen. Logistiker*innen integrieren lokale Onlinemarktplätze in ihre Profile und der stationäre Einzelhandel generiert Wettbewerbsfähigkeit gegenüber großen Onlinehändler*innen über die Nutzung lokaler Radlogistiknetzwerke, mittels derer Lieferungen noch am Tag der Bestellung (Same-Day-Delivery) verteilt werden können. Damit leisten die involvierten Akteur*innen potenziell auch einen Beitrag zur Nachhaltigkeitstransformation im Bereich urbaner Logistiksysteme. Im Fokus steht das Fallbeispiel WüLivery, ein Kooperationsprojekt des Stadtmarketingvereins, der Wirtschaftsförderung, Radlogistiker*innen sowie Einzelhändler*innen in Würzburg, welches während des zweiten coronabedingten Lockdowns im November 2020 umgesetzt wurde. Die entstehenden Dynamiken und Organisationsformen werden auf Basis von 11 Expert*inneninterviews dargestellt und analysiert. Es kann gezeigt werden, dass städtische Akteur*innen grundlegende Mediator*innen für Transformationsprozesse darstellen und Einzelhändler*innen und lokale Onlinemarktplätze als Katalysator*innen fungieren können. Das ist auch vor dem Hintergrund planerischer und politischer Kommunikationsprozesse zur Legitimation neuer Verkehrsinfrastrukturen nutzbar, da die einzelnen Akteur*innengruppen in Austausch kommen und ein gesteigertes Bewusstsein für die jeweiligen Bedarfe entsteht.
The Kunduz River is one of the main tributaries of the Amu Darya Basin in North Afghanistan. Many communities live in the Kunduz River Basin (KRB), and its water resources have been the basis of their livelihoods for many generations. This study investigates climate change impacts on the KRB catchment. Rare station data are, for the first time, used to analyze systematic trends in temperature, precipitation, and river discharge over the past few decades, while using Mann–Kendall and Theil–Sen trend statistics. The trends show that the hydrology of the basin changed significantly over the last decades. A comparison of landcover data of the river basin from 1992 and 2019 shows significant changes that have additional impact on the basin hydrology, which are used to interpret the trend analysis. There is considerable uncertainty due to the data scarcity and gaps in the data, but all results indicate a strong tendency towards drier conditions. An extreme warming trend, partly above 2 °C since the 1960s in combination with a dramatic precipitation decrease by more than −30% lead to a strong decrease in river discharge. The increasing glacier melt compensates the decreases and leads to an increase in runoff only in the highland parts of the upper catchment. The reduction of water availability and the additional stress on the land leads to a strong increase of barren land and a reduction of vegetation cover. The detected trends and changes in the basin hydrology demand an active management of the already scarce water resources in order to sustain water supply for agriculture and ecosystems in the KRB.
Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.
Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment.
Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016–2016/2017, 2016/2017–2017/2018 and 2017/2018–2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions.
Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6–8% for the LULC scenarios, whereas high flows increase by up to 84% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development.
Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.