@article{StanleyUllmannLangeAthinodorou2021, author = {Stanley, Jean-Daniel and Ullmann, Tobias and Lange-Athinodorou, Eva}, title = {Holocene aridity-induced interruptions of human activity along a fluvial channel in Egypt's northern delta}, series = {Quaternary}, volume = {4}, journal = {Quaternary}, number = {4}, issn = {2571-550X}, doi = {10.3390/quat4040039}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-250285}, year = {2021}, abstract = {Geoarchaeological information presented here pertains to a subsidiary Nile channel that once flowed west of the main Sebennitic distributary and discharged its water and sediments at Egypt's then north-central deltaic coast. Periodical paleoclimatic episodes during the later Middle and Upper Holocene included decreased rainfall and increased aridity that reduced the Nile's flow levels and thus likely disrupted nautical transport and anthropogenic activity along this channel. Such changes in this deltaic sector, positioned adjacent to the Levantine Basin in the Eastern Mediterranean, can be attributed to climatic shifts triggered as far as the North Atlantic to the west, and African highland source areas of the Egyptian Nile to the south. Of special interest in a study core recovered along the channel are several sediment sequences without anthropogenic material that are interbedded between strata comprising numerous potsherds. The former are interpreted here as markers of increased regional aridity and reduced Nile flow which could have periodically disrupted the regional distribution of goods and nautical activities. Such times occurred ~5000 years B.P., ~4200-4000 years B.P., ~3200-2800 years B.P., ~2300-2200 years B.P., and more recently. Periods comparable to these are also identified by altered proportions of pollen, isotopic and compositional components in different radiocarbon-dated Holocene cores recovered elsewhere in the Nile delta, the Levantine region to the east and north of Egypt, and in the Faiyum depression south of the delta.}, language = {en} } @article{RoesslerWittIkonenetal.2021, author = {R{\"o}ßler, Sebastian and Witt, Marius S. and Ikonen, Jaakko and Brown, Ian A. and Dietz, Andreas J.}, title = {Remote sensing of snow cover variability and its influence on the runoff of S{\´a}pmi's rivers}, series = {Geosciences}, volume = {11}, journal = {Geosciences}, number = {3}, issn = {2076-3263}, doi = {10.3390/geosciences11030130}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-234261}, year = {2021}, abstract = {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).}, language = {en} } @techreport{RossiMaurelliUnnithanetal.2021, author = {Rossi, Angelo Pio and Maurelli, Francesco and Unnithan, Vikram and Dreger, Hendrik and Mathewos, Kedus and Pradhan, Nayan and Corbeanu, Dan-Andrei and Pozzobon, Riccardo and Massironi, Matteo and Ferrari, Sabrina and Pernechele, Claudia and Paoletti, Lorenzo and Simioni, Emanuele and Maurizio, Pajola and Santagata, Tommaso and Borrmann, Dorit and N{\"u}chter, Andreas and Bredenbeck, Anton and Zevering, Jasper and Arzberger, Fabian and Reyes Mantilla, Camilo Andr{\´e}s}, title = {DAEDALUS - Descent And Exploration in Deep Autonomy of Lava Underground Structures}, isbn = {978-3-945459-33-1}, issn = {1868-7466}, doi = {10.25972/OPUS-22791}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-227911}, pages = {188}, year = {2021}, abstract = {The DAEDALUS mission concept aims at exploring and characterising the entrance and initial part of Lunar lava tubes within a compact, tightly integrated spherical robotic device, with a complementary payload set and autonomous capabilities. The mission concept addresses specifically the identification and characterisation of potential resources for future ESA exploration, the local environment of the subsurface and its geologic and compositional structure. A sphere is ideally suited to protect sensors and scientific equipment in rough, uneven environments. It will house laser scanners, cameras and ancillary payloads. The sphere will be lowered into the skylight and will explore the entrance shaft, associated caverns and conduits. Lidar (light detection and ranging) systems produce 3D models with high spatial accuracy independent of lighting conditions and visible features. Hence this will be the primary exploration toolset within the sphere. The additional payload that can be accommodated in the robotic sphere consists of camera systems with panoramic lenses and scanners such as multi-wavelength or single-photon scanners. A moving mass will trigger movements. The tether for lowering the sphere will be used for data communication and powering the equipment during the descending phase. Furthermore, the connector tether-sphere will host a WIFI access point, such that data of the conduit can be transferred to the surface relay station. During the exploration phase, the robot will be disconnected from the cable, and will use wireless communication. Emergency autonomy software will ensure that in case of loss of communication, the robot will continue the nominal mission.}, subject = {Mond}, language = {en} } @article{RiyasSyedKumaretal.2021, author = {Riyas, Moidu Jameela and Syed, Tajdarul Hassan and Kumar, Hrishikesh and Kuenzer, Claudia}, title = {Detecting and analyzing the evolution of subsidence due to coal fires in Jharia coalfield, India using Sentinel-1 SAR data}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {8}, issn = {2072-4292}, doi = {10.3390/rs13081521}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-236703}, year = {2021}, abstract = {Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017-2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km\(^2\) area of coal fires. Results reveal that nearly 10\% of the coal fire area is newly formed, while 73\% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies.}, language = {en} } @article{OttingerBachoferHuthetal.2021, author = {Ottinger, Marco and Bachofer, Felix and Huth, Juliane and Kuenzer, Claudia}, title = {Mapping aquaculture ponds for the coastal zone of Asia with Sentinel-1 and Sentinel-2 time series}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {1}, issn = {2072-4292}, doi = {10.3390/rs14010153}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252207}, year = {2021}, abstract = {Asia dominates the world's aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.}, language = {en} } @article{MeisterGarbeTrappeetal.2021, author = {Meister, Julia and Garbe, Philipp and Trappe, Julian and Ullmann, Tobias and Es-Senussi, Ashraf and Baumhauer, Roland and Lange-Athinodorou, Eva and El-Raouf, Amr Abd}, title = {The Sacred Waterscape of the Temple of Bastet at Ancient Bubastis, Nile Delta (Egypt)}, series = {Geosciences}, volume = {11}, journal = {Geosciences}, number = {9}, issn = {2076-3263}, doi = {10.3390/geosciences11090385}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246129}, year = {2021}, abstract = {Sacred water canals or lakes, which provided water for all kinds of purification rites and other activities, were very specific and important features of temples in ancient Egypt. In addition to the longer-known textual record, preliminary geoarchaeological surveys have recently provided evidence of a sacred canal at the Temple of Bastet at Bubastis. In order to further explore the location, shape, and course of this canal and to find evidence of the existence of a second waterway, also described by Herodotus, 34 drillings and five 2D geoelectrical measurements were carried out in 2019 and 2020 near the temple. The drillings and 2D ERT surveying revealed loamy to clayey deposits with a thickness of up to five meters, most likely deposited in a very low energy fluvial system (i.e., a canal), allowing the reconstruction of two separate sacred canals both north and south of the Temple of Bastet. In addition to the course of the canals, the width of about 30 m fits Herodotus' description of the sacred waterways. The presence of numerous artefacts proved the anthropogenic use of the ancient canals, which were presumably connected to the Nile via a tributary or canal located west or northwest of Bubastis.}, language = {en} } @article{MayrKleinRutzingeretal.2021, author = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia}, title = {Systematic water fraction estimation for a global and daily surface water time-series}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {14}, issn = {2072-4292}, doi = {10.3390/rs13142675}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242586}, year = {2021}, abstract = {Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6\% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.}, language = {en} } @article{MayrKleinRutzingeretal.2021, author = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia}, title = {Determining temporal uncertainty of a global inland surface water time series}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {17}, issn = {2072-4292}, doi = {10.3390/rs13173454}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245234}, year = {2021}, abstract = {Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90\%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community.}, language = {en} } @article{KumarKhamzinaKnoefeletal.2021, author = {Kumar, Navneet and Khamzina, Asia and Kn{\"o}fel, Patrick and Lamers, John P. A. and Tischbein, Bernhard}, title = {Afforestation of degraded croplands as a water-saving option in irrigated region of the Aral Sea Basin}, series = {Water}, volume = {13}, journal = {Water}, number = {10}, issn = {2073-4441}, doi = {10.3390/w13101433}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-239626}, year = {2021}, abstract = {Climate change is likely to decrease surface water availability in Central Asia, thereby necessitating land use adaptations in irrigated regions. The introduction of trees to marginally productive croplands with shallow groundwater was suggested for irrigation water-saving and improving the land's productivity. Considering the possible trade-offs with water availability in large-scale afforestation, our study predicted the impacts on water balance components in the lower reaches of the Amudarya River to facilitate afforestation planning using the Soil and Water Assessment Tool (SWAT). The land-use scenarios used for modeling analysis considered the afforestation of 62\% and 100\% of marginally productive croplands under average and low irrigation water supply identified from historical land-use maps. The results indicate a dramatic decrease in the examined water balance components in all afforestation scenarios based largely on the reduced irrigation demand of trees compared to the main crops. Specifically, replacing current crops (mostly cotton) with trees on all marginal land (approximately 663 km\(^2\)) in the study region with an average water availability would save 1037 mln m\(^3\) of gross irrigation input within the study region and lower the annual drainage discharge by 504 mln m\(^3\). These effects have a considerable potential to support irrigation water management and enhance drainage functions in adapting to future water supply limitations.}, language = {en} } @phdthesis{Karama2021, author = {Karama, Alphonse}, title = {East African Seasonal Rainfall prediction using multiple linear regression and regression with ARIMA errors models}, doi = {10.25972/OPUS-25183}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-251831}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2021}, abstract = {The detrimental impacts of climate variability on water, agriculture, and food resources in East Africa underscore the importance of reliable seasonal climate prediction. To overcome this difficulty RARIMAE method were evolved. Applications RARIMAE in the literature shows that amalgamating different methods can be an efficient and effective way to improve the forecasts of time series under consideration. With these motivations, attempt have been made to develop a multiple linear regression model (MLR) and a RARIMAE models for forecasting seasonal rainfall in east Africa under the following objectives: 1. To develop MLR model for seasonal rainfall prediction in East Africa. 2. To develop a RARIMAE model for seasonal rainfall prediction in East Africa. 3. Comparison of model's efficiency under consideration In order to achieve the above objectives, the monthly precipitation data covering the period from 1949 to 2000 was obtained from Climate Research Unit (CRU). Next to that, the first differenced climate indices were used as predictors. In the first part of this study, the analyses of the rainfall fluctuation in whole Central- East Africa region which span over a longitude of 15 degrees East to 55 degrees East and a latitude of 15 degrees South to 15 degrees North was done by the help of maps. For models' comparison, the R-squared values for the MLR model are subtracted from the R-squared values of RARIMAE model. The results show positive values which indicates that R-squared is improved by RARIMAE model. On the other side, the root mean square errors (RMSE) values of the RARIMAE model are subtracted from the RMSE values of the MLR model and the results show negative value which indicates that RMSE is reduced by RARIMAE model for training and testing datasets. For the second part of this study, the area which is considered covers a longitude of 31.5 degrees East to 41 degrees East and a latitude of 3.5 degrees South to 0.5 degrees South. This region covers Central-East of the Democratic Republic of Congo (DRC), north of Burundi, south of Uganda, Rwanda, north of Tanzania and south of Kenya. Considering a model constructed based on the average rainfall time series in this region, the long rainfall season counts the nine months lead of the first principal component of Indian sea level pressure (SLP_PC19) and the nine months lead of Dipole Mode Index (DMI_LR9) as selected predictors for both statistical and predictive model. On the other side, the short rainfall season counts the three months lead of the first principal component of Indian sea surface temperature (SST_PC13) and the three months lead of Southern Oscillation Index (SOI_SR3) as predictors for predictive model. For short rainfall season statistical model SAOD current time series (SAOD_SR0) was added on the two predictors in predictive model. By applying a MLR model it is shown that the forecast can explain 27.4\% of the total variation and has a RMSE of 74.2mm/season for long rainfall season while for the RARIMAE the forecast explains 53.6\% of the total variation and has a RMSE of 59.4mm/season. By applying a MLR model it is shown that the forecast can explain 22.8\% of the total variation and has a RMSE of 106.1 mm/season for short rainfall season predictive model while for the RARIMAE the forecast explains 55.1\% of the total variation and has a RMSE of 81.1 mm/season. From such comparison, a significant rise in R-squared, a decrease of RMSE values were observed in RARIMAE models for both short rainfall and long rainfall season averaged time series. In terms of reliability, RARIMAE outperformed its MLR counterparts with better efficiency and accuracy. Therefore, whenever the data suffer from autocorrelation, we can go for MLR with ARIMA error, the ARIMA error part is more to correct the autocorrelation thereby improving the variance and productiveness of the model.}, subject = {Regression}, language = {en} }