Miracle-dz/UniFAR-FTUs huggingface dataset:
Global river flow data developed from surface runoff based on the Curve Number method

The availability of detailed surface runoff and river flow data across large geographic areas is needed for several 10 scientific applications, such as refined freshwater environmental risk assessments. Some limiting factors in developing detailed river flow datasets over large spatial scales have been paucity of detailed input spatial data and challenges in processing of these data. The well-established USDA Curve Number (CN) method was applied for spatially distributed hydrologic processing to estimate surface runoff. Publicly available global datasets for hydrologic soil groups, land cover, and precipitation were spatially processed by applying the CN equations to create a global mean annual surface runoff grid 15 of 50 meters. Runoff was spatially combined with global hydrology of catchments and rivers from publicly available datasets to estimate daily mean annual flow (MAF) across the globe. Estimated daily MAF were compared with measured gauge flow at rivers in several countries which showed good correlation (R 2 of 0.76 - 0.98). These flow estimates can be used for diverse applications at local watersheds to larger regions across the globe. The two spatial data products of this project representing MAF at the global scale are publicly available for download at 20 https://doi.org/10.6084/m9.figshare. 22694146 (Heisler, et al., 2023).
Sources and Radiative Impact of Carbonaceous Aerosols Using Four Years Ground-Based Measurements over the Central Himalayas

Climate and health in the pristine Himalayan region are largely impacted by the transport of carbonaceous aerosols from the polluted regions of Asia and Europe. Yet, there is a scarcity of source apportionment studies that can explain diurnal scale phenomena concerning various emission sources and radiative forcing. Here, we report the first simultaneous high-resolution delineation of primary organic carbon (POC) and secondary organic carbon (SOC) content and quantify the contributions of fossil fuel combustion and biomass burning over the Central Himalayas using four-year (2014–2017) online observations. Four different methods are employed to deconvolute organic carbon (OC) into POC and SOC. Unlike SOC, POC exhibits significant unimodal diurnal variations with higher values during daytime in all four methods. These methods show intra-annual variations in POC (56–80%) and SOC (20–44%) concentrations but they agree that overall POC (4.7–8 µ g m –3 ) dominates over SOC (2.4–3.9 µ g m –3 ). The role of crop residue burning in northern India and forest fires is shown to be dominant in spring while local heating-purpose emissions dominate in winter. Further, we show that the contribution of fossil fuel combustion (eBC ff ) is 3.5 times greater than that of biomass burning (eBC bb ). Monthly variations in mean diurnal amplitudes of eBC ff and eBC bb reveal that the differences in their amplitudes (9– 32%) is smallest during April–May, depicting the relative importance of biomass emissions at the diurnal scale during spring. The estimated daily radiative forcing shows that eBC ff contributes more (16.4%) atmospheric forcing than eBC bb . Atmospheric forcing from both eBC ff and eBC bb are higher (19.8 and 13.0 W m –2 , respectively) in the afternoon than morning. These findings underscore the need for high-resolution data when researching aerosol-radiation interaction over the Himalayan area and are vital for developing aerosol mitigation plans.
A high-frequency, long-term data set of hydrology and sediment yield: the alpine badland catchments of Draix-Bléone Observatory

Abstract. Draix-Bléone critical zone observatory was created in 1983 to study erosion processes in a mountainous badland region of the French Southern Alps. Six catchments of varying size (0.001 to 22 km2) and vegetation cover are equipped to measure water and sediment fluxes, both as bedload and suspended load. This paper presents the core dataset of the observatory, including rainfall and meteorology, high-frequency discharge and suspended-sediment concentration, and event-scale bedload volumes. The longest records span almost 40 years. Measurement and data-processing methods are presented, as well as data quality assessment procedures and examples of results. All the data presented in this paper are available on the open repository https://doi.org/10.17180/obs.draix (Draix-Bleone Observatory, 2015), and a 5-year snapshot is available for review at https://doi.org/10.57745/BEYQFQ (Klotz et al., 2023).
Reply to the comments of Referee #2 (RC2) on article gmd-2023-26

Most of the model improvements are of ”technical” nature. I am not computer scientist, hence I cannot completely judge these achievements. Still, the performance achievements seem great. However, I would have liked it even better, if the comparison with traditional bulk method would be made fairer. E.g. in the SDM and bin model there is no ”cold” microphysics (yet), but the bulk method contains these tracers. Hence, it is likely that the advection cost could be reduced to 4/10. Or in the time to solution comparison there is no discussion that with the bulk methods larger microphysical timesteps are possible and might be even sufficient to reach convergence. Considering that the SDM-new128 might be more costly than the bulk method. However, I still would want to apply it, as it makes, as stated in the paper, far less assumptions.
Finding geometric and topological similarities in building elements for large-scale pose updates in Scan-vs-BIM

Information-rich BIM models are rarely usable off-the-shelf for operations tasks. Change decisions made on the construction site can lead to significant differences between the as-designed and as-built state of buildings. The responsibility for keeping the digital representation in sync with its physical twin is not defined and will likely only fully be assigned when automatic methods facilitate the geometric update process. To this end, previous research succeeded in (1) identifying if an element was erected at the time and position it was initially designed, and (2) updating the parametric design geometry to fit its LiDAR-measured as-built state under a set of assumptions and threshold values. The research presented in this paper aims at updating the as-designed model in case of significant pose differences between the as-designed and as-built state. The method leverages graphs to encode the topological connectivity between geometric elements, once for the as-designed BIM model and once for the as-built point cloud. A similarity metric, namely the cosine distance, allows for a quantitative comparison of the topologically enriched point cloud clusters and their corresponding BIM element. The results show that a convincing type-wise similarity can be found in the feature space between the as-built point cloud clusters and the BIM elements. This similarity score becomes meaningful once the element’s topological arrangements are included. An instance-wise similarity score of above 90% is achieved for matching-pairs of free-standing columns and allows for a large-scale pose update in the as-designed BIM model.
Synthesis and Characterizations Tin Oxide Thick Films Developed by Screen Printing Technique

: In the industry today, demand for optical and solar cell technologies is booming. Numerous scientists are engaged in the synthesis of new nanomaterials for the development of optical devices as well as applications for energy conversion and storage. Metal oxide semiconductor (MOS) nanoparticles just revealed an exciting pathway for invention in practically all scientific fields. MOS nanoparticles are connected to contemporary research, and scientists anticipate their use in all branches of science and technological advancement in the future. Tin oxide (SnO 2 ) is the most promising material for solar cell as well as other applications form few decades. Due to its valence band gap, high mobility, and wide band gap SnO 2 is also the best material for development of perovskite solar cells. In the present investigation, we are focused on the synthesis of SnO 2 nanoparticles by precipitation method and developed SnO 2 Thick films by Screen Printing Technique. The electrical and optical properties of developed thick films were studied. The conductivity of pure SnO 2 thick films was found to be 2.78 × 10 -5 Ω -1 -m. The thickness of film was estimated 87 µm. The optical study was carried out using static photo response system. The light-dependent current variation of prepared SnO 2 thick films was investigated for an optical sensor application.
HIGH HEAT FLUX DAMAGE SIMULATIONS WITH AN ELECTROTHERMAL DISCHARGE DEVICE UNDERGOING INCREASING CURRENT AND PULSE LENGTH TIMES

Electrothermal (ET) discharge devices are a proposed method for testing materials that are being studied to be use in first wall applications of experimental nuclear fusion devices. The work done in this report utilized a code known as Electrothermal Flow (ETFLOW) in order simulate ET plasma discharges through a capillary ET device called HELIOS (Heat Exposure Limit Ion Outflow System). A study was conducted in which the current profile and shot pulse length were altered. When the current was increased and the pulse length was held constant, there was a uniform increase in all plasma parameters of interest. When the reverse was done, i.e. the current was held constant and the pulse length increased, the plasma parameter behavior varied, either increasing or decreasing based on the value of the applied current. Future work could improve upon this report by conducting physical experiments that mirror the simulated inputs to test the validity of the ETFLOW code.
The patterns of soil nitrogen stocks and C  :  N stoichiometry under impervious surfaces in China

Abstract. Accurate assessment of soil nitrogen (N) storage and carbon (C) : N stoichiometry under impervious surface areas (ISAs) is key to understanding the impact of urbanization on soil health and the N cycle. Based on 888 soil profiles from 148 sampling sites in 41 cities across China, we estimated the country's N stock (100 cm depth) in the ISA soil to be 98.74±59.13 Tg N with a mean N density (NISA) of 0.59±0.35 kg m−2, which was significantly lower (at all depths) than the soil N density (NPSA=0.83±0.46 kg m−2) under the reference permeable surface areas (PSAs). The NISA was also only about 53 %–69 % of the reported national mean soil N density, indicating that ISA expansion caused soil N loss. The C:N ratio of ISA (10.33±2.62) was 26 %–34 % higher than that of natural ecosystems (forests, grasslands, etc.) but close to the C:N of PSA. Moreover, there was a significant C–N correlation in ISA soil, showing no signs of C–N decoupling as suggested by the previous studies. The ISA had smaller variances in the C:N ratio than did the PSA at regional scale, indicating convergence of soil C:N stoichiometry due to ISA conversion. The eastern subregion of China had the highest NISA, although its natural soil N density was among the lowest in the country. Unlike the vertical pattern in natural permeable soils, whose N density declined faster in the upper soil layers than in the lower layers, NISA decreased linearly with depth. Similarly to natural soil N, NISA was negatively correlated with temperature; but unlike natural soil C:N which was positively correlated with temperature, the C:NISA was negatively correlated with temperature. NISA was not correlated with net primary productivity, but was significantly correlated with the soil N density of adjacent PSA and the urbanization rate. These findings indicate the ISA soil had a unique N distribution pattern, possibly as the result of intensive disturbances during land conversion. The dataset “Observations of soil nitrogen and soil organic carbon to soil nitrogen stoichiometry under the impervious surfaces areas (ISA) of China” is available from the National Cryosphere Desert Data Center (https://doi.org/10.12072/ncdc.socn.db2851.2023) (Ding et al., 2023).
Impact of UV – Vis light on the oxidation of bitumen in correlation to solar spectral irradiance data

The ageing behavior of bitumen is a complex phenomenon which is tackled by many studies in the laboratory and on the field. While ageing parameters like temperature, moisture, reactive gasses or UV light have been utilized in the laboratory, the impact of visible light has not been addressed yet. Thus, this study investigates the impact of UV – visible light in the range of 365 – 770 nm on the oxidation behavior of bitumen. Furthermore, a correlation to solar spectral data was conducted, which should link the ageing experiments run in the lab to the conditions on earth ’ s surface. FTIR spectroscopy on the sample surfaces was conducted to determine the extent of ageing. The results show that when considering solar spectral data, blue light in the range of 405 nm contributes significantly towards oxidation of bitumen. Additionally, an unexpected, characteristic oxidation profile shows higher oxidation levels around light in the blue (405 nm) and green (525 nm) wavelength ranges. This provides first evidence on how visible light can cause significant oxidation for bitumen in asphalt pavements. The obtained knowledge can be used to better understand which parameters contribute towards the ageing process in the field and to provide suitable solutions for laboratory ageing.
Surface dynamics and history of the calving cycle of the Astrolabe glacier (Antarctica) derived from optical imagery

The recent calving of the Astrolabe glacier (Terre Adélie, East Antarctica) in November 2021 presents an opportunity to better understand the processes leading to ice fracturing. Optical satellite imagery is used to retrieve the calving cycle of the glacier since 2000 by mapping the ice front location. A recent archive of high resolution optical images from Sentinel-2 is used to measure the ice motion and the ice strain rates for the period 2017-2021 in order to document fractures and rift evolution. These observations are compared with sea ice extent and concentration measurements. We found that a significant 5 change in the sea ice melting periodicity at the vicinity of the Astrolabe glacier occurred in the last decade (2011-2021) with respect to previous observations (1979-2011). After 2011, the occurrence of consecutive years of high sea-ice concentration at the vicinity of the glacier seems to have favored the ice tongue spatial extension. This lead to an unprecedentedly observed extension of the ice tongue until November 2021. The analysis of strain rate time series revealed that the glacier dislocated suddenly in June 2021 in the middle of the winter before releasing an iceberg of around 20 km 2 in November 2021 at the 10 onset of sea ice melting season. These observations suggest that although the presence of sea ice favors glacier extension, its buttressing effect may not be sufficient to prevent fracture opening.
Analysis of GEFS-Aerosols annual budget to better understand the aerosol predictions simulated in the model

GOCART), the aerosols in GEFS-Aerosols have a relatively short lifetime because of the faster removal processes in GEFS- 25 Aerosols. Meanwhile, in GEFS-Aerosols, aerosol emissions are the determining factor for the mass and composition of aerosols in the atmosphere. The size (bin) distribution of aerosol emissions is as important as its total emissions, especially in simulations of dust and sea salt. Also most importantly, the strong monthly and interannual variations in natural sources of Abstract. In September 2020, a global aerosol forecasting model was implemented as an ensemble member of the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting System (GEFS) v12.0. 1 (hereafter referred to as “GEFS-Aerosols”). In this study, GEFS-Aerosols simulation 20 results from September 1, 2019 to September 30, 2020 were evaluated using an aerosol budget analysis. These results were compared with results from other global models as well as reanalysis data. From this analysis, the global average lifetimes of black carbon (BC), organic carbon (OC), dust, sea salt, and sulfate are 4.06, 4.29, 4.59, 0.34 and 3.3 days, respectively, with the annual average loads of 0.135, 1.29, 4.52, 6.80 and 0.50 TG
An application of the Dulmage-Mendelsohn partition to the analysis of a discretized dynamic chemical looping reactor model

Full discretization of nonlinear differential-algebraic equation (DAE) systems is a popular approach for optimization of these systems due to the availability of general mathematical programming environments and nonlinear programming solvers, the fast solve times this approach may achieve, and its ability to handle constraints on state variables. The fully discretized DAE is a large-scale system of algebraic equations that may be handled efficiently by generic solvers. Due to its size, however, this system is not easy to analyze if an optimization solve fails to converge and a modeling error is suspected. To alleviate this difficulty, this work proposes to automatically partition a discretized DAE into differential and algebraic subsystems at each point in time and to apply a Dulmage-Mendelsohn partition to the bipartite graph of variables and equations of each of these subsystems. In this way, relatively small systems of equations can be analyzed for structural and numerical nonsingularity rather than the full, discretized DAE model. In the application to a chemical looping combustion reactor model from the IDAES process model library, the DAE model is found to have a structural singularity in the algebraic subsystems, and the Dulmage-Mendelsohn partition reveals the cause of this singularity. An updated version of the model is proposed to eliminate this singularity, and the performance of the two models in a dynamic optimization problem is compared.
Diversity and Social Justice in Technology Design

An interdisciplinary endeavour at the intersection of American Studies, Critical Diversity Studies, as well as Science and Technology Studies, this article scrutinises so-called diversity-aware technology. A diversity-aware system is a computer system whose designers a) account for differences between the system’s stakeholders, and/or b) draw on a normative notion of diversity like “inclusion” or “fairness” in its design. Diversity concepts embedded in technology carry contested values and have effects on the technology’s stakeholders. Therefore, it is vital to conduct a critical review of designs leveraging diversity concepts. In an exploration of three cases (diversity-aware datasets, machine learning fairness, and diversity-aware social media), the article sheds light on the shortcomings of mainstream or “individual-level” diversity-aware technology. Such technology leverages individual-level notions of diversity (demographics, personality, culture) to cater to users, thereby obscuring social inequalities among them. Inspired by Black feminism and critical race theory, the article offers a social-justice-oriented conceptualisation of diversity-aware technology. It develops a definition and criteria for critical or “structural-level” diversity-aware technology, where diversity concepts are linked to the visibility and redistribution of power. The article offers inspiration for researchers of technology and designers who work with diversity concepts.
Causality : from data to science

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On the importance of phenology in the evaporative process of the Miombo Woodland: Could it be why satellite-based evaporation estimates differ?

Complex African ecosystems such as the Miombo Woodland, with unique plant phenology, have evaporation dynamics that have not been investigated due to very few, if at all existent, flux tower observations. Furthermore, significant differences have been observed in satellite-based 20 evaporation estimates in the Miombo Woodland especially in the dry season. Therefore, deciding which sattelite evaporation product to use in this ecosystem is difficult, as these products vary in many respects. In this study, the actual evaporation estimates for six satellite-based evaporation estimates are compared across Miombo Woodland phenophases in the Luangwa Basin, in southern Africa. In the absence of basin scale field observations, the actual evaporation estimated using the general water balance is used as reference, to which the six satellite-based evaporation estimates have been compared. Our results show significant variation in actual evaporation estimates in the water limited, high temperature and lower forest canopy cover and leaf chlorophyll conditions in the dormant phenophase. Lowest variation is observed in water abundant, high temperature, high leaf chlorophyll
Adaptive Finetuning of 3D CNNs with Interpretation Uncertainty for Seismic Fault Prediction

3D CNNs can exploit the full extent of spatial information in seismic volumes to predict faults. They require large quantities of training data, but this issue has been mitigated by training such networks with large amounts of synthetic training data to apply afterwards on real datasets. Because of domain shift, pre-trained networks may fail to perform as expected, emphasizing the need to finetune the network with labels obtained on target data of interest. The mismatch in dimensions of network input and interpreter annotations (mostly 2D slices) poses a problem in this respect. In addition, there is a high degree of uncertainty attached to such labels, both on a coarse level as well as on a more fine-grained basis. On an image level, the interpreter may only annotate structures in a small, localized portion of the seismic volume. Furthermore, owing to uncertainty regarding the exact delineation of the endpoints of picked faults, interpreters may only label certain segments on the complete fault line. We propose a method whereby we demonstrate a procedure to finetune the pretrained 3D CNNs with sparse 2D labels on target datasets, resulting in the adaption of their weights to better pick faults in the new domains. Secondly, we devise means to incorporate interpretation uncertainty on labels produced for finetuning to generate better, more reliable fault estimations. We validate our findings on various real datasets and demonstrate improved network performance over the pretrained CNN alone. Additionally, we show that incorporating label uncertainty while finetuning leads to better interpretation performance compared to uncertainty-agnostic finetuning