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Dans le Niger oriental, des phénomenes karstiques sont fréquents dans les roches siliceuses: gres, silcretes, croûtes ferrugineuses, roches cristallines. A partir des études géomorphologiques et micromorphologiques, on peut conclure a une kartsification, au sense de production de formes par dissolution. Les résultats permettent de dater du Tertiaire inférieur la principale période de karstification. La répartition régionale des formes induites par cette karstification indique une dépendance probable des conditions paléoclimatiques. Actuellement le karst influe encore sur le développement des autres formes de relief.
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
Pre‐Klondikean oxidation prepared the ground for Broken Hill‐type mineralization in South Africa
(2021)
New Cu isotope data obtained on chalcopyrite from the Black Mountain and the Broken Hill deposits in the medium‐ to high‐grade metamorphic Aggeneys‐Gamsberg ore district (South Africa) require a revision of our understanding of the genesis of metamorphic Broken Hill‐type massive sulphide deposits. Chalcopyrite from both deposits revealed unusually wide ranges in δ\(^{65}\)Cu (−2.41 to 2.84‰ NIST 976 standard) in combination with distinctly positive mean values (0.27 and 0.94‰, respectively). This is interpreted to reflect derivation from various silicate and oxide precursor minerals in which Cu occurred in higher oxidation states. Together with the observation of a typical supergene base metal distribution within the deposits and their spatial association with an unconformity only meters above the ore horizon, our new data are best explained by supergene oxidation of originally possibly SEDEX deposits prior to metamorphic sulphide formation, between the Okiepian (1,210–1,180 Ma) and Klondikean (1,040–1,020 Ma) orogenic events.
Calc-silicate rocks occur as elliptical bands and boudins intimately interlayered with eclogites and high-pressure gneisses in the Munchberg gneiss complex of NE Bavaria. Core assemblages of the boudins consist of grossular-rich garnet, diopside, quartz, zoisite, clinozoisite, calcite, rutile and titanite. The polygonal granoblastic texture commonly displays mineral relics and reaction textures such as postkinematic grossular-rich garnet coronas. Reactions between these mineral phases have been modelled in the CaO-Al203-Si02-C02-H2 0 system with an internally consistent thermodynamic data base. High-pressure metamorphism in the calc-silicate rocks has been estimated at a minimum pressure of 31 kbar at a temperature of 630°C with X^oSQ.Gi. Small volumes of a C02-N2-rich fluid whose composition was buffered on a local scale were present at peak-metamorphic conditions. The P-T conditions for the onset of the amphibolite facies overprint are about 10 kbar at the same temperature. A'co., of the H20-rich fluid phase is regarded to have been <0.03 during amphibolite facies conditions. These P-T estimates are interpreted as representing different stages of recrystallization during isothermal decompression. The presence of multiple generations of mineral phases and the preservation of very high-pressure relics in single thin sections preclude pervasive post-peak metamorphic fluid flow as a cause of a re-equilibration within the calc-silicates. The preservation of eclogite facies, very high-pressure relics as well as amphibolite facies reactions textures in the presence of a fluid phase is in agreement with fast, tectonically driven unroofing of these rocks.
Der größte Teil des zentralsaharischen Reliefs ist unter dem Einfluß nicht-arider Klimate gebildet worden. Andererseits gab es im Pleistozän äolisch geprägte aride Phasen, die extremer als heutige gewesen sind. Der Kenntnisstand zur Reliefgeschichte erlaubt es, daraus die Grundzüge der Klimageschichte eines großen Teils der südlich-zentralen Sahara abzuleiten.
Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change.
Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people's physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.
The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR's Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).
Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.
Despite the widespread application of landslide susceptibility analyses, there is hardly any information about whether or not the occurrence of recent landslide events was correctly predicted by the relevant susceptibility maps. Hence, the objective of this study is to evaluate four landslide susceptibility maps retrospectively in a landslide-prone area of the Swabian Alb (Germany). The predictive performance of each susceptibility map is evaluated based on a landslide event triggered by heavy rainfalls in the year 2013. The retrospective evaluation revealed significant variations in the predictive accuracy of the analyzed studies. Both completely erroneous as well as very precise predictions were observed. These differences are less attributed to the applied statistical method and more to the quality and comprehensiveness of the used input data. Furthermore, a literature review of 50 peer-reviewed articles showed that most landslide susceptibility analyses achieve very high validation scores. 73% of the analyzed studies achieved an area under curve (AUC) value of at least 80%. These high validation scores, however, do not reflect the high uncertainty in statistical susceptibility analysis. Thus, the quality assessment of landslide susceptibility maps should not only comprise an index-based, quantitative validation, but also an additional qualitative plausibility check considering local geomorphological characteristics and local landslide mechanisms. Finally, the proposed retrospective evaluation approach cannot only help to assess the quality of susceptibility maps and demonstrate the reliability of such statistical methods, but also identify issues that will enable the susceptibility maps to be improved in the future.
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.
Satellite-derived land surface temperature dynamics in the context of global change — a review
(2023)
Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.
The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.
Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped slums in the optical data: QuickBird image obtains 86–88% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55% and from 79 to 85% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.
Silicate and iron crust karst pits and sinkholes in eastern Niger are filled with reworked lateritic sediments or with unconsolidated palaeosoils and aeolian deposits. The fillings facies depend on the environmental conditions during deposition. Geomorphological and sedimentological studies on the karst fillings and the interpretation of various karst/filling associations allow an approach to the chronology of landscape development in eastern Niger plateaus.
Projected climate changes for the 21st century may cause great uncertainties on the hydrology of a river basin. This study explored the impacts of climate change on the water balance and hydrological regime of the Jhelum River Basin using the Soil and Water Assessment Tool (SWAT). Two downscaling methods (SDSM, Statistical Downscaling Model and LARS-WG, Long Ashton Research Station Weather Generator), three Global Circulation Models (GCMs), and two representative concentration pathways (RCP4.5 and RCP8.5) for three future periods (2030s, 2050s, and 2090s) were used to assess the climate change impacts on flow regimes. The results exhibited that both downscaling methods suggested an increase in annual streamflow over the river basin. There is generally an increasing trend of winter and autumn discharge, whereas it is complicated for summer and spring to conclude if the trend is increasing or decreasing depending on the downscaling methods. Therefore, the uncertainty associated with the downscaling of climate simulation needs to consider, for the best estimate, the impact of climate change, with its uncertainty, on a particular basin. The study also resulted that water yield and evapotranspiration in the eastern part of the basin (sub-basins at high elevation) would be most affected by climate change. The outcomes of this study would be useful for providing guidance in water management and planning for the river basin under climate change.