dataesr/scientific-paragraphs-categorization huggingface dataset:
The database is named Global Applications of Soil Erosion Modelling Tracker (GASEMT). In the case of studies reporting multiple model applications or numerous study sites, authors created multiple individual data entries in the GASEMT database. Each entry in the database reports information on the 42 attributes listed above (unless the reviewers did not find the required details and therefore reported the term 'unknown'). The term 'NA' is used as the acronym of 'not applicable'. Notably, the database only considers and reports on studies presenting soil-erosion modelling applications with spatially and temporally defined boundary conditions. In the database, we did not include data from articles that exclusively reported technical descriptions of models, refinement of individual model parameters, or methodological improvement without practical applications.
Results A literature search in the Elsevier's Scopus bibliographic database resulted in 8471 articles potentially reporting soil-erosion modelling applications. The further review process revealed that 6042 articles (71%) were not relevant for the study, as they did not report actual soil-erosion modelling applications. The number of articles not in English language or not accessible totalled 513 (6%) and 241 (3%), respectively. The resulting number of suitable articles was 1697 (20%), representing 3030 data entries in GASEMT, each equal to an individual modelling application.
An inter-country analysis based on their number of records in GASEMT shows that the United States of America (537) and China (450) have the highest number of records in the GASEMT database, followed by India (161). Considering the European Union (EU-28, including the United Kingdom) as a single geographical entity, it would show the highest number of modelling applications, totalling 841 entries. In the EU, the highest frequencies are observed in Mediterranean countries such as Italy (n = 173), Spain (n = 125), and Greece (n = 84). In contrast, few model applications are available for large sectors of South America, Western and Central Africa, and North/Central Asia, with further decreased coverage in non-desert continental interiors. Overall, we noted a general tendency of the studies to be located within the main global cropland districts, as corroborated by further observations carried out and reported in the Discussion section.
Temporal trends A total of 1697 articles applying soil-erosion models at local/ regional/national or larger scales were published within the 24 years  covered by GASEMT, with an average publication rate of 70 articles per year. Splitting the database into 4-year time windows reveals an increasing trend of publications (Fig. 2 ), except for the 4-year period from 2010 to 2013. The last evaluated year (2017) recorded the highest number of annual publications (158 articles, 340 modelling applications). Studies on soil erosion by water dominate all 4-year time windows. In the first distinguished period  (1994) (1995) (1996) (1997) , all 55 modelling applications reported in the database addressed soil erosion by water. During this period, 51 of these studies were performed within the three distinguished major spatial clusters i.
, 1989) , Water Erosion Prediction Model (WEPP;  Flanagan and Nearing, 1995) , the Limburg Soil Erosion Model (LISEM; De  Roo et al. , 1996)  and the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS;  Beasley et al. , 1980). In time windows post-1997, modelling applications investigating wind, tillage (downslope sediment redistribution due to tillage activity), and harvest erosion (export of sediments with harvested plants due to soil attaching to roots or machine parts) become apparent in the GASEMT database. In most time periods, the number of applications modelling this suite of processes are below ten. Only in 2006-2009 and 2014-2017 did tillage (n = 30) and wind erosion (n = 41) exceed ten database entries, respectively. Within 2014-2017, wind erosion models show an evident increase reaching a level of applications higher than observed in the previous 20 years.
Erosion processes and type of predictions The GASEMT database has a marked dominance of water-erosion studies, constituting 94. 6% of all entries. Roughly 0. 9% of the data entries reported combined estimates of water and wind erosion, while individual simulations of wind (2. 3%), tillage (1. 8%), and harvest erosion (0. 4%) also contributed small parts of the database (Fig. 2 ). The vast majority of the model applications estimate only sheet and rill erosion processes (~54%), with a smaller proportion estimating sediment yields (~27%) and sediment budgets (net erosion/deposition) (~10%). The remaining 10% of modelling applications can be classified as stream bank erosion (1%), mass movement (0. 6%), rill (0. 5%) and gully (0. 3%), or more generally as sensitivity mapping (2. 8%), soil displacement due to wind erosion (2.
We identified 67 (~2%) national-scale modelling applications, mostly applied in Europe (n = 34), Asia (n = 12), and North America (n = 9). Except for three wind erosion studies in the USA and Spain  (Baade and Rekolainen, 2006; Hansen et al. , 2002) , all other quantitative (n = 48) and qualitative (n = 3) applications focus on water erosion. Of these, the USA (n = 6), Czech Republic (n = 4), and Hungary (n = 4) are examples of countries with higher modelling applications. About 14% (n = 418) of the recorded modelling exercises fall into regional-scale applications (x̃= 6131 km 2 ). Although smaller in size, the watershed-scale applications have the largest share of entries in the database (~59%), also including some very large study areas (x̃= 128. 5 km 2 ). The three remaining small-scale application categories are hillslope (~10%; x̃= 1 km 2 ), farm/landscape (~0.
7%), or both (8. 4%) represent most of the entries in the database (i. e. , 87. 5%). Although less common, studies providing either future or combined present and future projections of soil erosion still cover a relevant share of the entries with 3. 8% and 5. 9%, respectively. For the remaining entries (~2. 8%), the modelling application's temporal frame was not specified (classified as 'unknown' in GASEMT). More than half of the modelling applications estimate soil erosion considering all types of land uses/land covers present in the investigated area (n = 1575; ~54. 4%). Agricultural areas in general, and exclusively arable land, are modelled specifically in only about 13. 6% and 9. 3% of the cases, respectively. The remaining modelling applications address forests (5. 1%), grassland/rangeland (4.
Models, input data and outcomes Overall, 435 distinct models and model variants are listed in the GASEMT database, although several cases indicated that different nomenclature referred to the same modelling approaches. Table  2  lists the 25 most applied models and offers an example of the issue related to the heterogeneous model nomenclature (e. g. , USLE, RUSLE and USLE-SDR, RUSLE-SDR, SEDD). In their different forms and applications, the models belonging to the (R)USLE-family are by far the most widely applied soil erosion prediction models globally, with over 1200 applications (~41% of the total). These numbers would be higher if USLE-type models such as WaTEM/SEDEM, EPIC, SWAT, and USPED were to be counted as members of the (R)USLE group. Modelling approaches independent from the USLE such as the process-based WEPP (n = 224; 7.
2% and 11. 9% of the total. In most cases, these models are applied at the watershed, hillslope, and plot scales, although there are also a few national-scale applications (n = 10) and a pan-European one. Medium (> 25 m and ≤ 150 m) and moderate cell size (> 150 m and ≤ 300 m) outcomes were used for about 19. 8% and 1. 6% of the records, respectively. The remaining model applications (~3%) predicted soil-erosion rates with a coarse cell size between 330 and 60,000 m. Temporal analysis of the database shows a trend of decreasing cell sizes in modelled study areas at the watershed scale and below. Affinities between model type and grid-scale were not present except for in large-scale applications. These are mainly performed using empirical models of the (R)USLE and (R)WEQ families for water and wind erosion, respectively.
These two categories are too broad and generic when considered a posteriori and should be better defined in future versions of the database. An extensive set of techniques are included in the validation/evaluation group, ranging from volumetric loss measurement (e. g. , pins, cross-sections, contour gauge, and terrestrial laser scanning) to qualitative observations performed through field observations and remote sensing. About one-third of the entries reported model calibration. The models with the highest shares of calibration are SWAT, LISEM, WaTEM/SEDEM, and MMF. Specific information about the calibration techniques was not collected, as these were found to be highly variable and difficult to classify given the extensive range of models considered. Some level of field-based data collection exists in over half of the modelling application cases.
Statistical analysis Overall, the model area covers an approximated total surface of 48. 3 million km 2. This area covers about 32% of the World's land area assuming (i) a total area of 149 million km 2 and (ii) a marginal overlap between the modelled areas contributing to the GASEMT database. The predicted annual soil erosion totals 80. 4 billion Mg yr -1 , with an average area-specific soil erosion rate of 16. 6 Mg ha -1 yr -1 (x̃= 7. 4 Mg ha -1 yr -1 ; σ = 39. 8 Mg ha -1 yr -1 ). As expected, a significant difference between median values of gross (x̃= 10 Mg ha -1 yr -1 ) and net (x̃= 5. 4 Mg ha -1 yr -1 ) erosion is observed. In the gross erosion category, all modelling applications that did not consider re-deposition are included (e. g. , traditional (R)USLE-based models)). In contrast, the net erosion category includes modelling applications that predict sediment yield from a plot, hillslope, or watershed.
Approximately 18% of the modelling applications reported in the GASEMT database aimed at land use or climate changes as the modelling objective. Comparing soil-erosion rates by land cover/use types (Fig. 6 ), we observe a substantial decline in soil-erosion rates (reported in mm yr -1 assuming an average bulk density of 1. 35 g cm -3 ) from bare soil (x̃= 1. 2 mm yr -1 ) to agricultural areas (generic, x̃= 0. 3 mm yr -1 ; arable land, x̃= 0. 5 mm yr -1 ; agroforestry, x̃= 0. 1 mm yr -1 ), forests (x̃= 0. 2 mm yr -1 ) and other forms of semi-natural vegetation (x̃= 0. 2 mm yr -1 ). When all land uses are modelled, we obtain a median value of 0. 75 mm yr -1. This distribution of soil erosion rates among the different land cover/use units fit those reported by  Montgomery (2007)  and  Borrelli et al.
Cerdan et al. (2010)  hypothesized that field measurements in arable lands could be biased towards areas known to be exposed to erosion processes. Similarly, our analysis results lead us to hypothesize that the modelling applications explicitly addressing forestland and grasslands could be biased towards areas experiencing human-induced disturbances. The boxplots in Fig. 7  illustrate the key descriptive statistics of the soil-erosion estimates derived from the nine most commonly encountered models in the GASEMT database. Soil erosion rates predicted by models classified within the 'net erosion' group (and thus evaluating the budget between soil erosion and deposition either on plot scale or as net sediment transfer to downslope locations), such as AnnAGNPS (x̃= 3. 3 Mg ha -1 yr -1 ), LISEM (x̃= 3.
Discussion The collaboration of 67 scientists from 25 countries representing all continents (except Antarctica) allowed the creation of GASEMT. The database is composed of 3030 individual modelling records (applied in 126 countries), retrieved from 1697 articles that were thoroughly reviewed. The database contains information on most of the existing Fig. 6. Comparison of modelled erosion rates under different land covers. Note that the outliers >8 mm yr -1 are excluded in the graphic. The boxplots display the interquartile range (grey boxes), the median (horizontal bold black lines), the 10th and 90th percentile (horizontal black lines) and outliers (dots). Fig. 7. Comparison of the predicted soil erosion rates of the nine models most commonly occurring in the GASEMT database. Note that the outliers >100 Mg ha -1 yr -1 are excluded in the graphic.
The large number of records of this database makes it a highly practical source of information and a powerful tool for further research. Here, our attention and interest mainly address the observation and description of the general aspects of the soil-erosion modelling applications worldwide. However, we believe that GASEMT can be useful to comprehensively target a number of further in-depth studies and observations. Further analysis could disaggregate the information reported in GASEMT to address specific erosion agents and processes, methodologies, or geographical regions. We provided all details in the Supplementary Information (Appendix A) of this study. Bezak et al. (2021)  provide a practical example of the GaSEM database's further use, investigating the relationship between soil erosion modelling and bibliometric characteristics (applying a generalized boosted regression tree model).
, 2006; Rezaei et al. , 2016; Zhang and McBean, 2016). In addition, the 37% of the GASEMT records reporting wind and tillage erosion predicted high soil erosion rates (x̃= 10. 2 Mg ha -1 yr -1 ) that may locally represent a threat to agricultural productivity and the sustainability of the Earth's natural resources. Broad spatiotemporal trends in model applications are evident globally. In their different forms and applications, models belonging to the (R)USLE-family are by far the most widely applied soil-erosion models globally. They cover ~41% of the total records in the database. This value could increase to ~55% if USLE-based models such as WaTEM/ SEDEM, EPIC, SWAT were included in the same category. In line with the observation of  Alewell et al. (2019) , we also found a strong rising trend (R 2 0.
This situation contrasts with a general understanding of the geography of soilerosion processes emerging from field observations, indicating that tropical zones are more prone to erosion  (Boardman, 2006). A phenomenon also confirmed by global expert-based qualitative assessments such as (GLASOD;  Oldeman, 1994)  and quantitative modelling descriptions of major soil-erosion drivers  (Chappell and Webb, 2016; Panagos et al. , 2017) , which indicate tropical regions as being highly susceptible to erosion  (Labrière et al. , 2015). The GASEMT database demonstrates the lower incidence of studies in the tropics and subtropical regions, while latitudinal trends indicate higher soil erosion on average in the tropics (Fig. 8 ). The gradually increasing erosion rates from the subpolar zones to the temperate, the subtropical, and finally the tropics are paired with decreasing investigation intensity and thus a noticeable lack of knowledge.
A comparison of the spatial patterns of the soil erosion rate measurements collected by García-Ruiz et al. (  2015 ) (Fig. 9 ) and the modelling applications gathered in this study (Fig. 8 ) indicates that model applications have a more even distribution globally. Although a significant spatial agreement between the two datasets can be observed in North and South America, Western Europe, and Eastern Africa, models appear to be more applied in regions that rarely report field measurements such as India, China, and Southeast Asia. While it is generally agreed in the scientific world that models should be validated/evaluated with measured data, erosion measurements are often as uncertain as modelling  (Batista et al. , 2019; Alewell et al. , 2019) , and are not in existence in many areas of the world.
The erosion process is divided into two distinct phases. First, a sediment phase which determines soil loss due to raindrop impact and runoff detachment, and a water phase that reflects the transport of soil particles by runoff. A soil erodibility factor mainly influences the detachment rate. Simultaneously, the runoff's annual transport capacity is a function of water volume, slope steepness, and a crop cover factor. WEPP is the most applied and comprehensive physically-based model listed in GASEMT. It has been structured to be applicable for an extensive range of geographic, land-use, and land-management conditions. WEPP can be applied at parcel, hillslope, road, and watershed-scale providing event or daily sediment yield, runoff, and subsurface flow. Daily rainfall conditions are described using a stochastic weather generation sub-module.
GASEMT database: data availability and limitations All data supporting the findings of this study can be extracted from the Supplementary Information file (Appendix A: GASEMT database). Our evaluation of the GASEMT database revealed some shortcomings. First, some missing data (reported as unknown in the database) are due to the unavailability of information in the reviewed publications. Although this affects only some attributes, key information such as predicted soil-erosion rates, coordinates of the study area, and the study area's size can be missing. A meta-regression analysis using GASEMT data could not be performed due to restrictions imposed by the heterogeneity of the records and, more importantly, due to a lack of detailed georeferenced information about the study areas. Due to capacity bottlenecks (our project relied on a volunteer participatory approach without funding), the scientific team refrained from defining the shape and perimeter for each study area.
This situation limited the possibility of compiling variables for the modelled sites and investigating relationships between predicted erosion rates and environmental factors (e. g. , climate data, slope, vegetation cover) through exploratory analysis. A further shortcoming relates to potential inconsistencies in the database. Although a harmonization procedure has been carried out to identify and rectify most inconsistencies and misclassifications among experts, some of these may still affect some records in this publication's final database. The limited number of studies compiled dealing with spatial modelling of gully, and other erosion processes (e. g. , harvest erosion, tillage erosion, piping) may also have been diminished by the Scopus search criteria. Our decision to consider only research work validated/evaluated through peer-review excluded all grey literature from GASEMT, such as scientific reports, government publications, proceedings, conference abstracts, and national associations journals.
We recognize that this decision could be influenced by scientific communication practices and 'epistemic cultures'  (Cetina, 1991)  that are more oriented towards peer-reviewed journals. The exclusion of grey and non-English peer-reviewed literature in GASEMT may have affected the geographical representation of some regions in the database where publishing in local and national language/ literature is more frequent. However, in light of the 419,000 results obtained searching for 'soil erosion modelling' via Google Scholar, we recognize that this large amount of literature would have been an unrealistic amount to evaluate without aids such as artificial intelligence. An internal debate among the authors also highlighted whether the use of Elsevier's Scopus bibliographic database, instead of Thomson Reuters' Web of Science (WoF), or both, may have contributed to bias in the geographical distribution of the GASEMT records.
Conclusions This systematic literature review to compile the GASEMT database and it's posterior analysis have allowed data-driven insights into the global state-of-the-art in soil erosion modelling for the first time. Statistical analysis of GASEMT showed that models tend to predict erosion rates that peak in the tropics and decrease towards higher latitudes. The frequency of model applications inversely relates to this erosion severity pattern, with greater numbers of studies in temperate and Mediterranean zones. The results of this analysis suggest that industrialised and highly developed countries, generally in temperate latitudes with lower erosion rates, show a higher incidence of studies. The database reports fewer studies in less-developed, tropical, and subtropical countries, where modelling findings suggest greater exposure to erosive processes.
Data collection and GASEMT database. 4 2. 2. Statistical analysis. 4 3. Results. 5 3. 1. Geography of the modelling applications. 5 3. 2. Temporal trends. 5 3. 3. Erosion processes and type of predictions. 6 3. 4. Spatial scale. 6 3. 5. Aims of the modelling application. 7 3. 6. Models, input data and outcomes. 7 3. 7. Statistical analysis. 8 4. Discussion. 10 5.
Acknowledgement. 16 References. 16  Acknowledgement Jae E. Yang  and  Pasquale Borrelli  are funded by the  EcoSSSoil Project ,  Korea Environmental Industry & Technology Institute (KEITI) ,  Korea  (Grant No. 2019002820004 ). Diana Vieira  is funded by national funds (OE), through  FCT -Fundação para a Ciência e a Tecnologia, I. P. , in the scope of the framework contract foreseen - DL57/2016  ( CDL-CTTRI-97-ARH/2018 -REF. 191-97-ARH/2018 ), and acknowledges CESAM financial support of through ( UIDP/50017/2020+UIDB/50017/2020 ). Walter Chen  is funded by the  Ministry of Science and Technology (Taiwan) Research Project  (Grant Number  MOST 109-2121-M-027-001 ). Nejc Bezak  and  Matjaž Mikoš  would like to acknowledge the support of the  Slovenian Research Agency  through grant  P2-0180.
Data collection and GASEMT database. 4 2. 2. Statistical analysis. 4 3. Results. 5 3. 1. Geography of the modelling applications. 5 3. 2. Temporal trends. 5 3. 3. Erosion processes and type of predictions. 6 3. 4. Spatial scale. 6 3. 5. Aims of the modelling application. 7 3. 6. Models, input data and outcomes. 7 3. 7. Statistical analysis. 8 4. Discussion. 10 5. 2. 1.
We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2. 0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dustradiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth.
The Unified NCEP/NCAR/AFWA Noah  (Ek et al. , 2003)  land surface model is used for the computation of heat and moisture surface fluxes. The gas-phase chemistry in MONARCH solves the Carbon Bond 2005 chemical mechanism (CB05;  Yarwood et al. , 2005)  extended with toluene and chlorine chemistry. The CB05 is well formulated for urban to remote tropospheric conditions, and it considers 51 chemical species and solves 156 reactions. The photolysis rates are computed with the Fast-J scheme  (Wild et al. , 2000)  considering the physics of each model layer (e. g. , aerosols, clouds, absorbers such as ozone). The aerosol module in MONARCH describes the life cycle of dust, sea salt, black carbon, organic matter (both primary and secondary), sulfate, and nitrate aerosols  (Spada, 2015). While a sectional approach is used for dust and sea salt, a bulk description of the other aerosol species is currently adopted  (Spada, 2015).