Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality

We present a composite design methodology for the simulation and optimization of the solar cell performance. Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost); (f) Local, Global and “Glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based Identifiability Analysis is used to study the relationship between parameters. The results obtained show a noteworthy improvement with respect to the quantum efficiency of the reference cell demonstrating that the methodology presented is suitable for effective optimization of solar cell devices

Modelling sea level driven change of Macaronesian archipelago configurations since 120 kyr BP

The MacArthur and Wilson island biogeography theory relates species diversity on islands as the result of equilibrium between extinctions and colonization events which rates depend on island size and isolation. Although island size and isolation can be considered static on ecological timescales (<100 years) they are not static on longer time scales. Since the last million years sea levels fluctuate with a period of ca. 120 kyr between -120 m and up to +10 m MSL (Mean Sea Level). Due to these sea level changes islands have changed in size and ultimately may have drowned or emerged. The rate and degree of their drowning depends on island morphometry and the shape of the sea level change curve. We explore the effects of global sea level cycles on the configuration of archipelagos and volcanic islands of Macaronesia. The results indicate that the islands changed shape considerably during the last 120 kyr. Notably the period between 80 kyr and 15 kyr ago sea levels were at least 80 m lower than present and several islands now isolated were merged or were much larger than present. Recent shrinking of islands due to the sea level rise since the last glacial maximum period (20 kyr BP) led to more than 50% reductions in island size, significant loss of coastal habitat and a significant increase in isolation by the increase of distances between islands and island and continents. Island size reduction must have induced pressures especially on terrestrial insular ecosystems, inducing upward migrations and interspecies competitions, and probable extinctions. The splitting of merged islands must have led to separations of populations leading to gene flow losses for some biota. Present day islands are not representative for the mean island configurations during the last Myr but rather represent an anomaly. Islands at present are smallest and most isolated and this configuration makes the insular biota even more vulnerable to human impact

Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022

The production and evaluation of the analysis-ready and cloud-optimized (ARCO) data cube for continental Europe (including Ukraine, the UK, and Türkiye), derived from the Landsat analysis-ready dataset version 2 (ARD V2) produced by Global Land Analysis and Discovery (GLAD) team and covering the period from 2000 to 2022, is described. The data cube consists of 17 TB of data at a 30 m resolution and includes bimonthly, annual, and long-term spectral indices on various thematic topics, including surface reflectance bands, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), fraction of absorbed photosynthetically active radiation (FAPAR), normalized difference snow index (NDSI), normalized difference water index (NDWI), normalized difference tillage index (NDTI), minimum normalized difference tillage index (minNDTI), bare soil fraction (BSF), number of seasons (NOS), and crop duration ratio (CDR). The data cube was developed with the intention to provide a comprehensive feature space for environmental modeling and mapping. The quality of the produced time series was assessed by (1) assessing the accuracy of gap-filled bimonthly Landsat data with artificially created gaps; (2) visual examination for artifacts and inconsistencies; (3) plausibility checks with ground survey data; and (4) predictive modeling tests, examples with soil organic carbon (SOC) and land cover (LC) classification. The time series reconstruction demonstrates high accuracy, with a root mean squared error (RMSE) smaller than 0.05, and R2 higher than 0.6, across all bands. The visual examination indicates that the product is complete and consistent, except for winter periods in northern latitudes and high-altitude areas, where high cloud and snow density introduce significant gaps and hence many artifacts remain. The plausibility check further shows that the indices logically and statistically capture the processes. The BSF index showed a strong negative correlation (−0.73) with crop coverage data, while the minNDTI index had a moderate positive correlation (0.57) with the Eurostat tillage practice survey data. The detailed temporal resolution and long-term characteristics provided by different tiers of predictors in this data cube proved to be important for both soil organic carbon regression and LC classification experiments based on 60 723 LUCAS observations: long-term characteristics (tier 4) were particularly valuable for predictive mapping of SOC and LC, coming out on top of variable importance assessment. Crop-specific indices (NOS and CDR) provided limited value for the tested applications, possibly due to noise or insufficient quantification methods. The data cube is made available at https://doi.org/10.5281/zenodo.10776891 (Tian et al., 2024) under a CC-BY license and will be continuously updated.</p

NeuralHydrology -- Interpreting LSTMs in Hydrology

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.Comment: Pre-print of published book chapter. See journal reference and DOI for more inf

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at five spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc(Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000–2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR

Soil chemistry aspects of predicting future phosphorus requirements in Sub-Saharan Africa

Phosphorus (P) is a finite resource and critical to plant growth and therefore food security. Regional‐ and continental‐scale studies propose how much P would be required to feed the world by 2050. These indicate that sub‐Saharan Africa soils have the highest soil P deficit globally. However, the spatial heterogeneity of the P deficit caused by heterogeneous soil chemistry in the continental scale has never been addressed. We provide a combination of a broadly adopted P‐sorption model that is integrated into a highly influential, large‐scale soil phosphorus cycling model. As a result, we show significant differences between the model outputs in both the soil‐P concentrations and total P required to produce future crops for the same predicted scenarios. These results indicate the importance of soil chemistry for soil‐nutrient modelling and highlight that previous influential studies may have overestimated P required. This is particularly the case in Somalia where conventional modelling predicts twice as much P required to 2050 as our new proposed model. Plain language summary Improving food security in Sub‐Saharan Africa over the coming decades requires a dramatic increase in agricultural yields. Global yield increase has been driven by, amongst other factors, the widespread use of fertilisers including phosphorus. The use of fertilisers in Sub‐Saharan Africa is often prohibitively expensive and thus the most efficient use of phosphorus should be targeted. Soil chemistry largely controls phosphorus efficiency in agriculture, for example iron and aluminium which exist naturally in soil reduce the availability of phosphate to plants. Yet soil chemistry has not been included in several influential large‐scale modelling studies which estimate phosphorus requirements in Sub‐Saharan Africa to 2050. In this study we show that predictions of phosphorus requirement to feed the population of Sub‐Saharan Africa to 2050 can significantly change if soil chemistry is included (e.g. Somalia with up to 50% difference). Our findings are a new step towards making predictive decision‐making tool for phosphorus fertiliser management in Sub‐Saharan Africa considering the variability of soil chemistry