Miracle-dz/UniFAR-FTUs huggingface dataset:
A globally applicable framework for compound flood hazard modeling

Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local-scale compound flood models provide state-of-the-art analyses but are hard to scale to other regions as these typically are based on local datasets. Hence, there is a need for globally applicable compound flood hazard modeling. We develop, validate, and apply a framework for compound flood hazard modeling that accounts for interactions between all drivers. It consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is automatically set up from global datasets and coupled with a global hydrodynamic river routing model and a global surge and tide model. To test the framework, we simulate two historical compound flood events, Tropical Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare the simulated flood extents to satellite-derived extents on multiple days for both events. Compared to the global CaMa-Flood model, the globally applicable model generally performs better in terms of the critical success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood depth maps are more realistic due to better floodplain connectivity and provide a more comprehensive picture as direct coastal flooding and pluvial flooding are simulated. Using the new framework, we determine the dominant flood drivers and transition zones between flood drivers. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to obtain a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large-scale compound flood hazard modeling.
Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff

Abstract. Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.
Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC)

Abstract. The RainGaugeQC scheme described in this paper is intended for real-time quality control of telemetric rain gauge data. It consists of several checks: detection of exceedance of the natural limit and climate-based threshold as well as checking of the conformity of rain gauge and radar observations, the consistency of time series from heated and unheated sensors, and the spatial consistency of adjacent gauges. The proposed approach is focused on assessing the reliability of individual rain gauge observations. A quantitative indicator of reliability, called the quality index (QI), describes the quality of each measurement as a number in the range from 0.0 (completely unreliable measurement) to 1.0 (perfect measurement). The QI of a measurement which fails any check is lowered, and only a measurement very likely to be erroneous is replaced with a “no data” value. The performance of this scheme has been evaluated by analysing the spatial distribution of the precipitation field and comparing it with precipitation observations and estimates provided by other techniques. The effectiveness of the RainGaugeQC scheme was also analysed in terms of the statistics of QI reduction. The quality information provided is very useful in further applications of rain gauge data. The scheme is used operationally by the Polish national meteorological and hydrological service (Institute of Meteorology and Water Management – National Research Institute).
Identifying the drivers of private flood precautionary measures in Ho Chi Minh City, Vietnam

Abstract. Private flood precautionary measures have proven to reduce flood damage effectively. Integration of these measures into flood response systems can improve flood risk management in high-risk areas such as Ho Chi Minh City (HCMC). Since uptake of such measures is voluntary, it is important to know what drives householders to implement precautionary measures. In this study, we developed a framework representing the uptake of private precautionary measures based on protection motivation theory and the transtheoretical model. Using empirical survey data collected from 1000 flood-prone households in HCMC, we implemented lasso and elastic-net regression to identify the drivers of private precaution. The measures were classified into structural measures and non-structural measures based on whether structural changes to the building were required. The households were classified into proactive and reactive households based on whether their decision to reduce risk (i.e. uptake of precautionary measures) was preceded by experiencing a flood. The data-driven model revealed that the household's level of education, the degree of belief in the government to implement regional flood protection measures and the degree of belief that in case of flooding one has to deal with the consequences of flooding by themselves positively influence the proactive uptake of non-structural measures. Among the households that experienced flooding before implementing the measures, the uptake was found to be driven by the severity of the experienced damage. For the same group of households, perceiving a high severity of future flood impacts was found to negatively influence the uptake of structural flood precautionary measures. These results highlight that efforts to improve the implementation of private precautionary measures should consider the socio-economic characteristics of the members of the household, their past flood experience and their perception of flood risk management for communicating flood risk and incentivizing private precautionary measures.
Comment on gmd-2021-361

In their study, Chien et al. present the results of the integration of the marine biogeochemistry model “Model of Oceanic Pelagic Stoichiometry” (MOPS) into the Earth system model “Flexible Ocean and Climate Infrastructure” (FOCI). A sufficient description of MOPS is given along with the references formerly presenting the two models. The simulated biogeochemical tracers and fluxes as well as their response to increasing atmospheric CO2 are extensively evaluated using observation-based datasets, models, and multiple statistical metrics. The manuscript is well written, well structured, well illustrated, and fits the scope of the GMD journal. FOCI-MOPS is a valuable input to the collection of already existing Earth system models including marine biogeochemistry. It will contribute to the model diversity of future climate model intercomparison projects.
Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)

Abstract. The impacts of the ice-crystal size distribution shape parameter (μi) were considered in the two-moment bulk cloud microphysics scheme of the Community Atmosphere Model Version 6 (CAM6). The μi's impact on the statistical mean radii of ice crystals can be analyzed based on their calculating formulas. Under the same mass (qi) and number (Ni), the ratios of the mass-weighted radius (Rqi, not related to μi) to other statistical mean radii (e.g., effective radiative radius) are completely determined by μi. Offline tests show that μi has a significant impact on the cloud microphysical processes owing to the μi-induced changes in ice-crystal size distribution and statistical mean radii (excluding Rqi). Climate simulations show that increasing μi would lead to higher qi and lower Ni in most regions, and these impacts can be explained by the changes in cloud microphysical processes. After increasing μi from 0 to 5, the longwave cloud radiative effect increases (stronger warming effect) by 5.58 W m−2 (25.11 %), and the convective precipitation rate decreases by −0.12 mm d−1 (7.64 %). In short, the impacts of μi on climate simulations are significant, and the main influence mechanisms are also clear. This suggests that the μi-related processes deserve to be parameterized in a more realistic manner.
Apoptosis and Improves Survival in Sepsis CD40 Receptor Decreases Lymphocyte Agonistic Monoclonal Antibody Against

Sepsis causes a marked apoptosis-induced depletion of lymphocytes. The degree of lymphocyte apoptosis during sepsis strongly correlates with survival. CD40, a member of the TNFR family, is expressed on APCs and has potent antiapoptotic activity. In this study we determined whether an agonistic Ab against CD40 could protect lymphocytes from sepsis-induced apoptosis. Secondly, we examined potential antiapoptotic mechanisms of the putative protection. Lastly, we aimed to determine whether anti-CD40 treatment could improve survival in sepsis. CD1 mice were made septic by the cecal ligation and puncture method and treated postoperatively with anti-CD40 Ab. Treatment with anti-CD40 completely abrogated sepsis-induced splenic B cell death and, surprisingly, decreased splenic and thymic T cell death as well ( p < 0.001). To investigate the mechanism of protection of anti-CD40 therapy on T cells, CD40 receptor expression was examined. As anticipated, the CD40 receptor was constitutively expressed on B cells, but, unexpectedly, splenic and thymic T cells were found to express CD40 receptor during sepsis. Furthermore, CD4 (cid:1) CD8 (cid:2) T cells were the predominant subtype of T cells expressing CD40 receptor during sepsis. Additionally, the antiapoptotic protein Bcl-x L was found to be markedly increased in splenic B and T cells as well as in thymic T cells after treatment with anti-CD40 Ab ( p < 0.0025). Lastly, mice that were made septic in a double injury model of sepsis had improved survival after treatment with anti-CD40 as compared with controls ( p (cid:3) 0.05). In conclusion, anti-CD40 treatment increases Bcl-x
Development of a broadband cavity-enhanced absorption spectrometer for simultaneous measurements of ambient NO3, NO2, and H2O

Abstract. We describe the characteristics and performances of our newly built broadband cavity-enhanced absorption spectrometer for measurements of nitrate radical (NO3), nitrogen dioxide (NO2), and water vapor (H2O). A customized vibration-resistance cavity layout incorporated with N2 purging on high-reflection mirror surfaces was implemented with a red light-emitting diode (LED) as a light source. In general, this system achieved over 40 km (up to 101.5 km) of effective light path length at 662 nm from a 0.52 m long cavity. For accurate NO3 measurement, the measured absorption spectrum of H2O was used for simultaneous concentration retrievals with the other species instead of being treated as interferences to be removed or corrected prior to NO3 detection. Synthesized N2O5 crystals under atmospheric pressure were used for performance tests of linear response and transmission efficiency. From the standard injection experiments of NO3, NO2, and H2O, high linearities were observed (R2≥0.9918). The total NO3 transmission efficiency through the system was determined to be 81.2 % (±2.9, 1σ) within the residence time of 2.59 s. The precisions (1σ) of NO3, NO2, and H2O in 1 Hz measurement from a single pixel on the charge-coupled device (CCD) were 1.41 pptv, 6.92 ppbv, and 35.0 ppmv with uncertainties of 10.8 %, 5.2 %, and ≥20.5 %, respectively, mainly from the errors in the literature absorption cross-section. The instrument was successfully deployed aboard the Korean icebreaker R/V Araon for an expedition conducted in the remote marine boundary layer in the Arctic Ocean during the summer of 2021.
Towards trustworthy explanations with gradient-based attribution methods

The low interpretability of deep neural networks (DNNs) remains a key barrier to their wide-spread adoption in the sciences. Attribution methods offer a promising solution, providing feature importance scores that serve as ﬁrst-order model explanations for a given input. In practice, gradient-based attribution methods, such as saliency maps, can yield noisy importance scores depending on model architecture and training procedure. Here we explore how various regularization techniques affect model explanations with saliency maps using synthetic regulatory genomic data, which allows us to quantitatively assess the efﬁcacy of attribution maps. Strikingly, we ﬁnd that generalization performance does not imply better saliency explanations; though unlike before, we do not observe a clear tradeoff. Interestingly, we ﬁnd that conventional regularization strategies, when tuned appropriately, can yield high generalization and interpretability performance, similar to what can be achieved with more sophisticated techniques, such as manifold mixup . Our work challenges the conventional knowledge that model selection should be based on test performance; another criterion is needed to sub-select models ideally suited for downstream post hoc interpretability for scientiﬁc discovery.
An Introduction to cpfa

Overview Package cpfa implements a k-fold cross-validation procedure to predict class labels using component loadings from a single mode of a Parallel Factor (Parafac) model-1 (Harshman, 1970; Harshman & Lundy, 1994), which is fit to a three-way or four-way data tensor. After fitting a Parafac model with package multiway via an alternating least squares algorithm (Helwig, 2019), estimated component loadings from one mode of this model are passed to one or several classification methods. These methods and the packages used to implement them include: penalized logistic regression (PLR) implemented through glmnet (Zou & Hastie, 2005; Friedman, Hastie, & Tibshirani, 2010); support vector machine (SVM) implemented through e1071 (Cortes & Vapnik, 1995; Meyer et al., 2021); random forest (RF) through randomForest (Breiman, 2001; Liaw & Wiener, 2002); and feed-forward neural network (NN) through nnet (Ripley, 1994; Venables & Ripley, 2002). For each method, a k-fold cross-validation is conducted to tune classification parameters using estimated Parafac component loadings, optimizing class label prediction. Multiple constraint options are available to impose on any mode of the Parafac model during the estimation step (see Helwig, 2017a). Multiple numbers of components can be considered over multiple Parafac models in the primary package function cpfa. This vignette describes the specific procedure implemented in cpfa and its usage in R (R Core Team, 2022).
Ice nucleating properties of the sea ice diatom Fragilariopsis cylindrus and its exudates

Abstract. In this study, we investigated the ice nucleation activity of the Antarctic sea ice diatom Fragilariopsis cylindrus. Diatoms are the main primary producers of organic carbon in the Southern Ocean, and the Antarctic sea ice diatom F. cylindrus is one of the predominant species. This psychrophilic diatom is abundant in open waters and within sea ice. It has developed several mechanisms to cope with the extreme conditions of its environment, for example, the production of ice-binding proteins (IBPs) and extracellular polymeric substances known to alter the structure of ice. Here, we investigated the ice nucleation activity of F. cylindrus using a microfluidic device containing individual sub-nanolitre (∼90 µm) droplet samples. The experimental method and a newly implemented Poisson-statistics-based data evaluation procedure applicable to samples with low ice nucleating particle concentrations were validated by comparative ice nucleation experiments with well-investigated bacterial samples from Pseudomonas syringae (Snomax®). The experiments reveal an increase of up to 7.2 ∘C in the ice nucleation temperatures for seawater containing F. cylindrus diatoms when compared to pure seawater. Moreover, F. cylindrus fragments also show ice nucleation activity, while experiments with the F. cylindrus ice-binding protein (fcIBP) show no significant ice nucleation activity. A comparison with experimental results from other diatoms suggests a universal behaviour of polar sea ice diatoms, and we provide a diatom-mass-based parameterization of their ice nucleation activity for use in models.
Contrastive latent variable models for neural text generation

Deep latent variable models such as variational autoencoders and energy-based models are widely used for neural text generation. Most of them focus on matching the prior distribution with the posterior distribution of the latent variable for text reconstruction. In addition to instance-level reconstruction, this paper aims to integrate contrastive learning in the latent space, forcing the latent variables to learn high-level semantics by exploring inter-instance relationships. Experiments on various text generation benchmarks show the effectiveness of our proposed method. We also empirically show that our method can mitigate the posterior collapse issue for latent variable based text generation models.
Long-Term Geomorphological Evolution of the Mouth Bar in the Modaomen Estuary of the Pearl River over the Last 55 Years (1964–2019)

: Based on mass bathymetric data and remote sensing data in the Modaomen Estuary, this study explored the long-term evolutionary characteristics of the mouth bar in the Modaomen Estuary of the Pearl River from 1964 to 2019. In the past 55 years, due to the impact of human activities, such as shoal reclamation and estuarine regulation in the Modaomen Estuary, the river mouth moved out of the shallow sea covered by several islands and faced the South China Sea directly. Therefore, the mouth bay became a siltation center in the estuarine region and expanded outwards, gradually evolving a geomorphic pattern with three shallow shoals and two distributary branches; a west branch as the main branch accompanied by a small east branch. Over the past decade, high-intensity sand dredging activities in the mouth bar have led to a considerable deepening of the water depth and a signiﬁcant reﬁnement of bed sediments, forming a discharge pattern of a wide and shallow channel ﬂowing into the sea. Therefore, the evolutionary characteristics of the mouth bar have become abnormal in recent years, so additional ﬁeld bathymetric data and hydrological data are required for further research regarding the subsequent evolution of the mouth bar, against the background of a signiﬁcant reduction of sediment discharge and high-intensity human activities.
Model Comparisons for Predicting Grassland Fire Occurrence Probability in Inner Mongolia Autonomous Region, China

Grassland fires threaten grassland ecosystem, human life and economic development greatly. However, there are limited researches focusing on grassland fire prediction, thereby it is necessary to find a better method to predict probability of grassland fire occurrence. Here we selected 16 environmental variables that may have impacts on fire occurrence, then built regression models of grassland fire probability based on historical fire points and variables in Inner Mongolia by three methods 15 to identify the grassland fire drivers and to predict fire probability. The three methods were global logistic regression, geographically weighted logistic regression and random forest. The results showed that random forest model had the best predictive effect. The influence of 9 variables selected by geographically weighted logistic regression model on grassland fire was unbalanced spatially. The three models all showed that meteorological factors were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurred in different areas had various responses to the influencing drivers, and 20 the areas with different natural and geographical condition had different fire prevention periods. Thus, the grassland fire management strategy based on local conditions should be advocated. This study analysed the temporal and spatial characteristics and driving factors of grassland fires in Inner Mongolia Autonomous Region from 2000 to 2018, and compared the prediction accuracy of global logistic regression, geographically weighted logistic regression and random forest models. The spatial distribution of historical fires in Inner Mongolia grassland was more in the east than in the central than in the west, and the fire high-incidence season was generally same as fire 440 prevention period regulated for Inner Mongolia forest and grassland. The fitting goodness of random forest model was the highest, and geographically weighted logistic regression models also had reference significance to some extent. The influence of 9 drivers selected by geographically weighted logistic regression model on grassland fire was spatially unbalanced. The random forest and geographically weighted logistic regression both showed that meteorological factors were of great importance to grassland fire. Inner Mongolia had a vast territory, and different areas had different sensitivities to different 445 drivers. Areas with different hydrothermal condition had different fire prevention periods, thus the grassland fire management strategy based on local conditions should be advocated.
Iterative and Adaptive Processing for Multiuser Communication Systems

The huge demand of wireless communications has driven the requirement for highly-efficient multiple-access communications schemes that can accommodate multiple simultaneous users, yet provide performance similar to single-user systems. Recently, iterative multiuser detection schemes have shown to provide this high level of performance at a manageable level of complexity. This thesis is concerned with iterative detection of two non-orthogonal asynchronous access schemes: codedivision multiple-access (CDMA); and interleave-division multiple-access (IDMA).