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Surrogate models are widely used to improve the computational efficiency in various geophysical simulation problems by reducing the number of model runs. Conventional one-layer surrogate representations are based on global (e.g. polynomial chaos expansion, PCE) or on local kernels (e.g., Gaussian process emulator, GPE). Global representations omit some details, while local kernels require more model runs. The existing multi-resolution PCE is a promising hybrid: it is a global representation with local refinement. However, it can not (yet) estimate the uncertainty of the resulting surrogate, which techniques like the GPE can do. We propose to join multi-resolution PCE and GPE s into a joint surrogate framework to get the best out of both worlds. By doing so, we correct the surrogate bias and assess the remaining uncertainty of the surrogate itself. The resulting multi-resolution emulator offers a pathway for several active learning strategies to improve the surrogate at acceptable computational costs, compared to the existing PCE-kriging approach it adds the multi-resolution aspect. We analyze the performance of a multi-resolution emulator and a plain GPE using didactic test cases and a CO2 benchmark, that is representative of many alike problems in the geosciences. Both approaches show similar improvements during the active learning, but our multi-resolution emulator leads to much more stable results than the GPE. Overall, our suggested emulator can be seen as a generalization of multi-resolution PCE and GPE concepts that offers the possibility for active learning.

The increase in the Land Surface Temperature (LST) caused by global warming and extreme weather events is signifcantly increasing Urban Hot Spots (UHS) while impacting the environmental quality within urban areas. In this research, with the help of Landsat 5, 7, and 8 satellite images, the evolution of Land Use/Cover (LULC), LST, and Surface Urban Heat Island (SUHI) in the Turkish Republic of Northern Cyprus (TRNC) between the years 1985 and 2020 is examined. The main aim is to evaluate how these variables, together with the Normalized Diference Built-up Index (NDBI), Urban Index (UI), Normalized Diference Vegetation Index (NDVI), Normalized Diference Water Index (NDWI), Built Index, and Propor tion Vegetation (PV) have infuenced the variability of the UHS and the level of thermal comfort according to the Thermal Field Variance Index (UTFVI). The results report signifcant average rises of the variables: LST (6.62 °C), SUHI (0.97 °C), UHS (8.61%) of the UI and NDBI indices and class 6 of UTFVI (88.53%) that are related to the variability of the LULC corroborated by statistical analysis. Our results provide valuable information on the future development of urban areas in Northern Cyprus to make them more resilient and sustainable to rising temperatures, Funding for open access publishing: Universidad de Granada/ CBUA

This work focuses on the development of a two-step field-split nonlinear preconditioner to accelerate the convergence of two-phase flow and transport in heterogeneous porous media. We propose a field-split algorithm named Field-Split Multiplicative Schwarz Newton (FSMSN), consisting in two steps: first, we apply a preconditioning step to update pressure and saturations nonlinearly by solving approximately two subproblems in a sequential fashion; then, we apply a global step relying on a Newton update obtained by linearizing the system at the preconditioned state. Using challenging test cases, FSMSN is compared to an existing field-split preconditioner, Multiplicative Schwarz Preconditioned for Inexact Newton (MSPIN), and to standard solution strategies such as the Sequential Fully Implicit (SFI) method or the Fully Implicit Method (FIM). The comparison highlights the impact of the upwinding scheme in the algorithmic performance of the preconditioners and the importance of the dynamic adaptation of the subproblem tolerance in the preconditioning step. Our results demonstrate that the two-step nonlinear preconditioning approach-and in particular, FSMSN-results in a faster outer-loop convergence than with the SFI and FIM methods. The impact of the preconditioners on computational performance-i.e., measured by wall-clock time-will be studied in a subsequent publication.

The extraction of objects from large-scale historical maps has been examined in several studies. With the aim to research urban changes over time, semi-automated and transferable holistic approaches remain to be investigated. We apply a combination of object-based image analysis and vectorization methods on three different historical maps. By further matching and georeferencing an appropriate current geodataset, we provide a concept for analyzing and comparing those valuable sources from the past. With minor adjustments, our end-to-end workflow was transferable to other large-scale maps. The findings revealed that the extraction and spatial assignment of objects, such as buildings or roads, enable the comparison of maps from different times and form a basis for further historical analysis. Performing an affine transformation between the datasets, an absolute offset of no more than 72 m was achieved. The outcomes of this paper, therefore, facilitate the daily work of urban researchers or historians. However, it should be emphasized that specific knowledge is required for the presented subjective methodology.

This qualitative analysis of symbolic elements in nautical cartography aims to provide an interdisciplinary insight into some aspects of early modern cartographic representations of the Adriatic Sea. The nautical charts were supplemented with compass roses, a graphic structure that facilitates orientation and correlates with rhumb lines. The research objective focusses primarily on the variety of signs for the cardinal directions north and east, additionally considering some rare but innovative and avant-garde pragmatic uses of compass roses to indicate magnetic declination. On the majority of the selected nautical charts, most of which were created in Mediterranean and Western European cultural centres, decorative elements such as the fleur-de-lis and the cross were used in compass roses to determine the cardinal points. The compass roses of these nautical charts often served as symbolic evidence of the social and economic belonging of the Adriatic to the European part of the Mediterranean geographical system, as well as to the Christian sphere of tradition and influence. The study has thus proved the existence of significant symbolic communication capacity of a compass rose, which was not only a utilitarian but also an artistic element.

International audience; The metamodel-based approach (also referred to as the surrogate approach) is commonly applied to overcome the computational burden of numerical models that are used to simulate the evolution of reservoir fluids and pressures in response to any production scheme. In this study, we propose an adaptation of this approach for aquifer thermal energy storage (ATES) systems. ATES systems are characterized by cyclic loading/unloading production schemes, which result in a strong similarity in the dynamics of the intercyclic evolution of variables such as the temperature at the producer well. Instead of training several metamodels, i.e., one per cycle (“independent” metamodelling approach), we take advantage of the intercyclic similarity to train a single metamodel within the setting of multifidelity cokriging (“multicyclic” metamodelling approach). To explore the predictive performance of this approach, we applied a random subsampling validation approach multiple times to 300 simulation results of a realistic ATES system in the Paris basin by considering three characteristics, i.e., the minimum and maximum temperature, and the rate of temperature decrease at each cycle. Numerical experiments with varying training dataset sizes (from 33 to 66% of the total number of results) and using 100 test samples show that (1) the predictive error of the multicyclic metamodelling reaches lower levels (by 20–50%) than that of the independent approach; (2) this higher predictive performance is achieved while saving computational time cost because the training phase only needs a few tens of “complete” simulations (run over all cycles) together with a few hundreds of “partial” simulations (stopped at the first cycle); the latter simulations are less expensive to evaluate because of shorter simulated time.

A well-known problem with choropleth maps is the cognitively induced effect that larger regions are perceived as more dominant. Consequently, unsatisfactory detection rates for small areas can result, which becomes relevant when important spatial features or patterns are explored (e.g., regions with maximum values). One possible approach to avoiding the area size bias is the use of cartograms. While there is already some work on Equal Are Unit Maps, little or no research has been done on the possibility of applying the concept of Value By Area Maps to transform the reference area maps. One goal of this article is to introduce the concept of so-called “Sponge Maps”, which distort the base maps independently of thematic attributes, but depending on the need to show or emphasize certain areas of interest. The second goal of the article is to answer the overall research question whether Sponge Maps actually reduce the area size bias and improve the detectability of maximum value regions. A user study was able to verify the effectiveness of the Sponge Map approach in particular. However, it also became clear that not only the area size bias plays a role in the detection of important regions—dependencies on the absolute position (top-down bias), compactness or conspicuous shape (shape bias), the familiarity (awareness bias), the color intensity (darkness bias) and not least by the distortions in the Sponge Maps as such (distortion bias) are shown. Furthermore, special aspects of detecting minimum value regions are revealed (including the so-called inverse area size bias).

Crop yield improvement during the last decades has relied on increasing the ratio of the economic organ to the total aboveground biomass, known as the harvest index (HI). In most crop models, HI is set as a parameter; this empirical approach does not consider that HI not only depends on plant genotype, but is also affected by the environment. An alternative is to simulate allocation mechanistically, as in the LPJ-GUESS crop model, which simulates HI based on daily growing conditions and the crop development stage. Simulated HI is critical for agricultural research due to its economic importance, but it also can validate the robust representation of production processes. However, there is a challenge to constrain parameter values globally for the allocation processes. Therefore, this paper aims to evaluate the sensitivity of yield and HI of wheat and maize simulated with LPJ-GUESS to eight production allocation-related parameters and identify the most suitable parameter values for global simulations. The nitrogen demand reduction after anthesis, the minimum leaf carbon to nitrogen ratio (C:N) and the range of leaf C:N strongly affected carbon assimilation and yield, while the retranslocation of labile stem carbon to grains and the retranslocation rate of nitrogen and carbon from vegetative organs to grains after anthesis mainly influenced HI. A global database of observed HI for both crops was compiled for reference to constrain simulations before calibrating parameters for yield against reference data. Two high- and low-yielding maize cultivars emerged from the calibration, whilst spring and winter cultivars were found appropriate for wheat. The calibrated version of LPJ-GUESS improved the simulation of yield and HI at the global scale for both crops, providing a basis for future studies exploring crop production under different climate and management scenarios.

While the potential of biochar (BC) to immobilize potentially toxic elements (PTEs) in contaminated soils has been studied and reviewed, no review has focused on the potential use of BC for enhancing the phytoremediation efficacy of PTE-contaminated soils. Consequently, the overarching purpose in this study is to critically review the effects of BC on the mobilization, phytoextraction, phytostabilization, and bioremediation of PTEs in contaminated soils. Potential mechanisms of the interactions between BC and PTEs in soils are also reviewed in detail. We discuss the promises and challenges of various approaches, including potential environmental implications, of BC application to PTE-contaminated soils. The properties of BC (e.g., surface functional groups, mineral content, ionic content, and π-electrons) govern its impact on the (im)mobilization of PTEs, which is complex and highly element-specific. This review demonstrates the contrary effects of BC on PTE mobilization and highlights possible opportunities for using BC as a mobilizing agent for enhancing phytoremediation of PTEs-contaminated soils.