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This study investigates the projected precipitation changes of the 21st century in the Mediterranean area with a model ensemble of all available CMIP3 and CMIP5 data based on four different scenarios. The large spread of simulated precipitation change signals underlines the need of an evaluation of the individual general circulation models in order to give higher weights to better and lower weights to worse performing models. The models' spread comprises part of the internal climate variability, but is also due to the differing skills of the circulation models. The uncertainty resulting from the latter is the aim of our weighting approach. Each weight is based on the skill to simulate key predictor variables in context of large and medium scale atmospheric circulation patterns within a statistical downscaling framework for the Mediterranean precipitation. Therefore, geopotential heights, sea level pressure, atmospheric layer thickness, horizontal wind components and humidity data at several atmospheric levels are considered. The novelty of this metric consists in avoiding the use of the precipitation data by itself for the weighting process, as state-of-the-art models still have major deficits in simulating precipitation. The application of the weights on the downscaled precipitation changes leads to more reliable and precise change signals in some Mediterranean sub-regions and seasons. The model weights differ between sub-regions and seasons, however, a clear sequence from better to worse models for the representation of precipitation in the Mediterranean area becomes apparent.
A new ranking of the world's largest cities—Do administrative units obscure morphological realities?
(2019)
With 37 million inhabitants, Tokyo is the world's largest city in UN statistics. With this work we call this ranking into question. Usually, global city rankings are based on nationally collected population figures, which rely on administrative units. Sprawling urban growth, however, leads to morphological city extents that may surpass conventional administrative units. In order to detect spatial discrepancies between the physical and the administrative city, we present a methodology for delimiting Morphological Urban Areas (MUAs). We understand MUAs as a territorially contiguous settlement area that can be distinguished from low-density peripheral and rural hinterlands. We design a settlement index composed of three indicators (settlement area, settlement area proportion and density within the settlements) describing a gradient of built-up density from the urban center to the periphery applying a sectoral monocentric city model. We assume that the urban-rural transition can be defined along this gradient. With it, we re-territorialize the conventional administrative units. Our data basis are recent mapping products derived from multi-sensoral Earth observation (EO) data – namely the Global Urban Footprint (GUF) and the GUF Density (GUF-DenS) – providing globally consistent knowledge about settlement locations and densities. For the re-territorialized MUAs we calculate population numbers using WorldPop data. Overall, we cover the 1692 cities with >300,000 inhabitants on our planet. In our results we compare the consistently re-territorialized MUAs and the administrative units as well as their related population figures. We find the MUA in the Pearl River Delta the largest morphologically contiguous urban agglomeration in the world with a calculated population of 42.6 million. Tokyo, in this new list ranked number 2, loses its top position. In rank-size distributions we present the resulting deviations from previous city rankings. Although many MUAs outperform administrative units by area, we find that, contrary to what we assumed, in most cases MUAs are considerably smaller than administrative units. Only in Europe we find MUAs largely outweighing administrative units in extent.
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.
The production of commodities such as cocoa, rubber, oil palm and cashew, is the main driver of deforestation in West Africa (WA). The practiced production systems correspond to a land managment approach referred to as agroforestry systems (AFS), which consist of managing trees and crops on the same unit of land.Because of the ubiquity of trees, AFS reported as viable solution for climate mitigation; the carbon sequestrated by the trees could be estimated with remote sensing (RS) data and methods and reported as emission reduction efforts. However, the diversity in AFS in relation to their composition, structure and spatial distribution makes it challenging for an accurate monitoring of carbon stocks using RS. Therefore, the aim of this research is to propose a RS-based approach for the estimation of carbon sequestration in AFS across the climatic regions of WA. The main objectives were to (i) provide an accurate classification map of AFS by modelling the spatial distribution of the classification error; (ii) estimate the carbon stock of AFS in the main climatic regions of WA using RS data; (iii) evaluate the dynamic of carbon stocks within AFS across WA. Three regions of interest (ROI) were defined in Cote d'Ivoire and Burkina Faso, one in each climatic region of WA namely the Guineo-Congolian, Guinean and Sudanian, and three field campaigns were carried out for data collection. The collected data consisted of reference points for image classification, biometric tree measurements (diameter, height, species) for biomass estimation. A total of 261 samples were collected in 12 AFS across WA. For the RS data, yearly composite images from Sentinel-1 and -2 (S1 and S2), ALOS-PALSAR and GEDI data were used. A supervised classification using random forest (RF) was implemented and the classification error was assessed using the Shannon entropy generated from the class probabilities. For carbon estimation, different RS data, machine learning algorithms and carbon reference sources were compared for the prediction of the aboveground biomass in AFS. The assessment of the carbon dynamic was carried between 2017 and 2021. An average carbon map was genrated and use as reference for the comparison of annual carbon estimations, using the standard deviation as threshold. As far as the results are concerned, the classification accuracy was higher than 0.9 in all the ROIs, and AFS were mainly represented by rubber (38.9%), cocoa (36.4%), palm (10.8%) in the ROI-1, mango (15.2%) and cashew (13.4%) in ROI-2, shea tree (55.7%) and African locust bean (28.1%) in ROI-3. However, evidence of misclassification was found in cocoa, mango, and shea butter. The assessment of the classification error suggested that the error level was higher in the ROI-3 and ROI-1. The error generated from the entropy was able to reduced the level of misclassification by 63% with 11% of loss of information. Moreover, the approach was able to accuretely detect encroachement in protected areas. On carbon estimation, the highest prediction accuracy (R²>0.8) was obtained for a RF model using the combination of S1 and S2 and AGB derived from field measurements. Predictions from GEDI could only be used as reference in the ROI-1 but resulted in a prediction error was higher in cashew, mango, rubber and cocoa plantations, and the carbon stock level was higher in African locust bean (43.9 t/ha), shea butter (15 t/ha), cashew (13.8 t/ha), mango (12.8 t/ha), cocoa (7.51 t/ha) and rubber (7.33 t/ha). The analysis showed that carbon stock is determined mainly by the diameter (R²=0.45) and height (R²=0.13) of trees. It was found that crop plantations had the lowest biodiversity level, and no significant relationship was found between the considered biodiversity indices and carbon stock levels. The assessment of the spatial distribution of carbon sources and sinks showed that cashew plantations are carbon emitters due to firewood collection, while cocoa plantations showed the highest potential for carbon sequestration. The study revealed that Sentinel data could be used to support a RS-based approach for modelling carbon sequestration in AFS. Entropy could be used to map crop plantations and to monitor encroachment in protected areas. Moreover, field measurements with appropriate allometric models could ensure an accurate estimation of carbon stocks in AFS. Even though AFS in the Sudanian region had the highest carbon stocks level, there is a high potential to increase the carbon level in cocoa plantations by integrating and/or maintaining forest trees.
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.
The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions.
Sufficient plant-available water is one of the most important requirements for vital, stable, and well-growing forest stands. In the face of climate change, there are various approaches to derive recommendations considering tree species selection based on plant-available water provided by measurements or simulations. Owing to the small-parcel management of Central European forests as well as small-spatial variation of soil and stand properties, in situ data collection for individual forest stands of large areas is not feasible, considering time and cost effort. This problem can be addressed using physically based modeling, aiming to numerically simulate the water balance. In this study, we parameterized, calibrated, and verified the hydrological multidimensional WaSiM-ETH model to assess the water balance at a spatial resolution of 30 m in a German forested catchment area (136.4 km2) for the period 2000–2021 using selected in situ data, remote sensing products, and total runoff. Based on the model output, drought-sensitive parameters, such as the difference between potential and effective stand transpiration (Tdiff) and the water balance, were deduced from the model, analyzed, and evaluated. Results show that the modeled evapotranspiration (ET) correlated significantly (R2 = 0.80) with the estimated ET using MODIS data (MOD16A2GFv006). Compared with observed daily, monthly, and annual runoff data, the model shows a good performance (R2: 0.70|0.77|0.73; Kling–Gupta efficiency: 0.59|0.62|0.83; volumetric efficiency: 0.52|0.60|0.83). The comparison with in situ data from a forest monitoring plot, established at the end of 2020, indicated good agreement between observed and simulated interception and soil water content. According to our results, WaSiM-ETH is a potential supplement for forest management, owing to its multidimensionality and the ability to model soil water balance for large areas at comparable high spatial resolution. The outputs offer, compared to non-distributed models (like LWF-Brook90), spatial differentiability, which is important for small-scale parceled forests, regarding stand structure and soil properties. Due to the spatial component offered, additional verification possibilities are feasible allowing a reliable and profound verification of the model and its parameterization.
Die mit dem Klimawandel einhergehenden Umweltveränderungen, wie steigende Temperaturen, Abnahme der Sommer- und Zunahme der Winterniederschläge, häufigere und längere Trockenperioden, zunehmende Starkniederschläge, Stürme und Hitzewellen betreffen besonders den Bodenwasserhaushalt in seiner zentralen Regelungsfunktion für den Landschaftswasserhaushalt. Von der Wasserverfügbarkeit im Boden hängen zu einem sehr hohen Grad auch die Erträge der Land- und Forstwirtschaft ab. Eine besonders große Bedeutung kommt dabei der Wasserspeicherkapazität der Böden zu, da während einer Trockenphase die effektiven Niederschläge den Wasserbedarf der Pflanzen nicht decken können und das bereits gespeicherte Bodenwasser das Überleben der Pflanzen sicherstellen kann. Für die land- und forstwirtschaftlichen Akteure sind in diesem Kontext quantitative und qualitative Aussagen zu den Auswirkungen des Klimawandels auf den Boden essenziell, um die notwendigen Anpassungsmaßnahmen für ihre Betriebe treffen zu können.
Zielsetzungen der vorliegenden Arbeit bestehen darin, die Dynamik der Bodenfeuchte in unterfränkischen Böden besser zu verstehen, die Datenlage zum Verlauf der Bodenfeuchte zu verbessern und die Auswirkungen von prognostizierten klimatischen Parametern abschätzen zu können. Hierzu wurden an sechs für ihre jeweiligen Naturräume und hinsichtlich ihrer anthropogenen Nutzung charakteristischen Standorten meteorologisch-bodenhydrologische Messstationen installiert. Die Messstationen befinden sich in einem Rigosol auf Buntsandstein in einem Weinberg bei Bürgstadt sowie auf einer Parabraunerde im Lössgebiet bei Herchsheim unter Ackernutzung. Am Übergang von Muschelkalk in Keuper befinden sich die Stationen in Obbach, wo eine Braunerde unter Ackernutzung vorliegt und im Forst des Universitätswalds Sailershausen werden die Untersuchungen in einer Braunerde-Terra fusca durchgeführt. Im Forst befinden sich auch die Stationen in Oberrimbach mit Braunerden aus Sandsteinkeuper und in Willmars mit Braunerden aus Buntsandstein. Der Beobachtungszeitraum dieser Arbeit reicht von Juli 2018 bis November 2022. In diesen Zeitraum fiel die dreijährige Dürre von 2018 bis 2020, das Jahr 2021 mit einem durchschnittlichen Witterungsverlauf und das Dürrejahr 2022.
Das Langzeitmonitoring wurde von umfangreichen Gelände- und Laboranalysen der grundlegenden bodenkundlichen Parameter der Bodenprofile und der Standorte begleitet. Die bodengeographischen-geomorphologischen Standortanalysen bilden zusammen mit den qualitativen Auswertungen der Bodenfeuchtezeitreihen die Grundlage für Einschätzungen zu den Auswirkungen des Klimawandels auf den Bodenwasserhaushalt. Verlässliche Aussagen zum Bodenwasserhaushalt können nur auf Grundlage von zeitlich und räumlich hoch aufgelösten Daten getroffen werden. Bodenfeuchtezeitreihen zusammen mit den bodenphysikalischen Daten lagen in dieser Datenqualität für Unterfranken bisher nur sehr vereinzelt vor.
Die vorliegenden Ergebnisse zeigen, dass die untersuchten Böden entsprechend den jeweiligen naturräumlichen Gegebenheiten sehr unterschiedliche bodenhydrologische Eigenschaften aufweisen. Während langer Trockenphasen können beispielsweise die Parabraunerden am Standort Herchsheim wegen ihrer höheren Wasserspeicherkapazität die Pflanzen länger mit Wasser versorgen als die sandigen Braunerden am Standort Oberrimbach. Die Bodenfeuchteregime im Beobachtungszeitraum waren sehr stark vom Witterungsverlauf einzelner Jahre abhängig. Das Bodenfeuchteregime bei einem durchschnittlichen Witterungsverlauf wie in 2021 zeichnet sich durch eine langsame Abnahme der Bodenfeuchte ab Beginn der Vegetationsperiode im Frühjahr aus. Regelmäßige Niederschläge im Frühjahr füllen den oberflächennahen Bodenwasserspeicher immer wieder auf und sichern den Bodenwasservorrat in der Tiefe bis in den Hochsommer. Im Hochsommer können Pflanzen dann während der Trockenphasen ihren Wasserbedarf aus den tieferen Horizonten decken. Im Gegensatz dazu nimmt die Bodenfeuchte in Dürrejahren wie 2018 bis 2020 oder 2022 bereits im Frühjahr bis in die untersten Horizonte stark ab. Die nutzbare Feldkapazität ist zum Teil schon im Juni weitgehend ausgeschöpft, womit für spätere Trockenphasen kein Bodenwasser mehr zur Verfügung steht. Die Herbst- und Winterniederschläge sättigen den Bodenwasservorrat wieder bis zur Feldkapazität auf. Bei tiefreichender Erschöpfung des Bodenwassers wurde die Feldkapazität erst im Januar oder Februar erreicht.
Im Zuge der land- und forstwirtschaftlichen Nutzung ist eine gute Datenlage zu den bodenkundlichen und standörtlichen Gegebenheiten für klimaadaptierte Anpassungsstrategien essentiell. Wichtige Zielsetzungen bestehen grundsätzlich in der Erhaltung der Bodenfunktionen, in der Verbesserung der Infiltrationskapazität und Wasserspeicherkapazität. Hier kommt dem Boden als interaktive Austauschfläche zwischen den Sphären und damit dem Bodenschutz eine zentrale Bedeutung zu. Die in Zukunft erwarteten klimatischen Bedingungen stellen an jeden Boden andere Herausforderungen, welchen mit standörtlich abgestimmten Bodenschutzmaßnahmen begegnet werden kann.
The development of retrogressive thaw slumps (RTS) is known to be strongly influenced by relief-related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump, located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify relationships between the spatiotemporal slump development and the influence of subsurface structures. Information on these were gained by means of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed by high-resolution satellite imagery and unmanned aerial vehicle–based digital elevation models (DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the slump area between August 2018 and August 2019. In addition, manual frost probing and GPR revealed a strong relationship between the active layer thickness, surface morphology, and hydrology. Detected furrows in permafrost table topography seem to affect the active layer hydrology and cause a canalization of runoff toward the slump. The three-dimensional ERT data revealed a partly unfrozen layer underlying a heterogeneous permafrost body. This may influence the local hydrology and affect the development of the RTS. The results highlight the complex relationships between slump development, subsurface structure, and hydrology and indicate a distinct research need for other RTSs.