dataesr/scientific-paragraphs-categorization huggingface dataset:
This criterion removes solutions for two events: the 2018 M 6. 1 Western Iran (709189) earthquake, because MF > 0:5; and the 2019 M 6. 1 Luzon, Philippines (760623) earthquake, because the FinDer centroid is too far off from the epicenter. For other events (e. g. , 802155), certain updates at 10, 20, or 30 min may be suppressed. E Q -T A R G E T ; t To evaluate the performance of FinDer for the felt reports, we compare the strike values of the FinDer line-source models Θ FinDer and reference values Θ ref in the literature (Table  1 ). We preferably take Θ ref from the finite-source model online Finite-Source Rupture Model Database (SRCMOD) database (see Data and Resources). SRCMOD collects models from different authors, and, therefore, captures the variability across different methodologies  (Mai and Thingbaijam, 2014).
Uncertainty estimates of fault geometries are usually not provided. If we cannot locate a given earthquake in the SRCMOD database, we search the USGS National Earthquake Information Center (NEIC) finite-fault catalog instead (see Data and Resources). These models are generally computed from a kinematic finite-fault inversion approach  (Hayes, 2017)  based on the method of  Ji et al. (2002). Finally, if we cannot find a model in either database, we use the Global Centroid Moment Tensor (Global CMT) catalog solution instead (see Data and Resources). In this case we have two solutions for Θ ref , because of the fault plane ambiguity. To provide a unique solution, we determine manually, whenever possible, the most probable rupture plane from the aftershock distributions in the EMSC catalog (see  Data and Resources).
Results To simulate the development of the collection of felt reports over time, we compute in this retrospective study FinDer line-source models from reports that were collected by the EMSC at 10, 20, and 30 min after the origin of each earthquake in our dataset. We limit our time window here to the first half of an hour, because we are interested in providing information fast. Furthermore, as will be discussed later, extending the time window beyond 30 min usually does not improve the models any further. Figure  2  shows the histograms of the differences in the strike angles, ΔΘ, computed for the FinDer line-source models Θ FinDer and the reference strike values Θ ref in Table  1  for all published FinDer models, which are 31 after 10 min and 20 min, respectively, and 32 after 30 min. For 50% of the events ΔΘ is less than 30°, and for 75% less than 55°.
MF and C do not correlate directly with ΔΘ (Table  1 ). Figure  3  decomposes the ΔΘ values as a function of earthquake magnitude and depth. As expected, because of their smaller source dimensions, the largest errors typically occur for events with M < 6:5. However, also two out of four earthquakes with M > 7:0 show ΔΘ > 50°. Both events have source depths of >30 km. However, overall Figure  3  does not suggest a systematic correlation between ΔΘ and Z for the range of depths we are looking at (Z < 50 km). The FinDer templates used in this study were calculated for a fixed source depth of 10 km  (Böse et al. , 2018). The performance could potentially be improved in the future, if templates for various depths were generated and used. FinDer line-source solutions for the four largest earthquakes in our dataset derived from felt reports that were available at the EMSC 30 min of their origin are plotted in Figure  4.
Evaluating the FinDer line-source models purely based on ΔΘ is best suited for strike-slip earthquakes, but does not capture their full potential for events with other source mechanisms. FinDer strike estimates actually agree better with the major rupture extension direction rather than the real strike of the event. The limited number of large earthquakes (M > 7:0) in our dataset prohibits a statistical statement, on whether these events need a larger time window to estimate rupture length. This is expected, because of the potentially devastating impact of these events on the affected population and the resulting delay in the submission of felt reports  (Bossu, Landès, et al. , 2018). As an example, Figure  6  compares the temporal evolution of the FinDer models for the 2015 M 7. 8 and 7. 3 Nepal earthquakes.
In previous studies,  Bossu et al. (2017)  and  Wald and Quitoriano (personal comm. , 2020)  found a good correlation between EMSC felt reports, manually derived macroseismic datasets, and DYFI responses. The latter uses a conversion between modified Mercalli intensity (MMI) to PGA  (Worden et al. , 2012) , supporting the suitability of our selected GMICE. For the subset of felt reports used in this study, we compare in Figure  S3  the reported intensities with those predicted by various GMPEs  (Faccioli and Cauzzi, 2006; Allen et al. , 2012)  and GMICEs  (Faccioli and Cauzzi, 2006; Worden et al. , 2012; Caprio et al. , 2015)  for different intensity, PGA, and distance bins. Overall,  Allen et al. (2012)  and  Worden et al. (2012)  show the highest correlation with the EMSC felt reports. The European-Mediterranean GMPE and GMICE of  Faccioli and Cauzzi (2006)  probably work less well in our application, because they use different intensity scales and building stocks and were derived from field-survey intensities.
We were able to demonstrate in this feasibility study that felt reports carry the information about fault rupture geometry that is needed to estimate damage and fatalities in large global earthquakes within the first ten to few tens of minutes of their occurrence. Furthermore, as described in this article and illustrated in Table  1 , for many significant earthquakes the world, it is not possible to find fault geometry information in any database (SCRMOD, USGS NEIC). Our proposed method not only provides faster results, but also helps to fill a general information gap that exists today. We have limited the evaluation of the resulting models only to the agreement with the predicted rupture strike. Determining if the fault rupture extends uni-or bilaterally relative to the epicenter, however, is similarly important.
Data and Resources Source parameters in the European-Mediterranean Seismological Centre (EMSC) catalog (Table  1 ) are either reported by an authoritative agency or computed by the EMSC. A list of data providers for each earthquake can be found at https://www. emsc-csem. org (last accessed June 2020). Reference strike values in Table  1  are taken either from the Finite-Source Rupture Model Database (SRCMOD) online database  (Mai and Thingbaijam, 2014 ; http://equake-rc. info/srcmod/, last assessed June 2020), the U. S. Geological Survey (USGS) National Earthquake Information Center (NEIC) finite-fault catalog  (Hayes, 2017;  https://earthquake. usgs. gov/data/finitefault/, last assessed June 2020), or the Global Centroid Moment Tensor (Global CMT) catalog  (Dziewonski et al. , 1981; Ekström et al.
ref are taken from the SRCMOD, USGS, or Global CMT databases, respectively (see Data and Resources). Felt reports datasets are characterized by the number of reports at close (100 km) and far distances, N felt close and N felt , their azimuthal gap around the epicenter, and maximum intensity maxI felt. All values are referring to reports that were collected by the EMSC within 30 min of earthquake origin. The FinDer line-source models are characterized by the rupture length L FinDer , the ground-motion misfit MF (see equation 3), and the spatial correlation coefficient C (see equation 2). The three columns on the right give the difference in the strike ΔΘ of the FinDer line source determined from the felt reports at 10, 20, and 30 min from event origin, relative to θ ref. See the Data and Processing section for details.
EMSC, European-Mediterranean Seismological Centre; FinDer, Finite-Fault Rupture Detector; Global CMT, Global Centroid Moment Tensor; SRCMOD, Finite-Source Rupture Model Database; USGS, U. S. Geological Survey. *Reference strike θ ref taken from Global CMT. † Reference strike θ ref taken from SRCMOD. ‡ Reference strike θ ref from USGS National Earthquake Information Center (NEIC) catalog. §One of the strike angles has been preferred considering the aftershocks distribution. ‖The strikes angles are close enough (<30°) to consider the average as the reference. (Continued next page. ).
EMSC, European-Mediterranean Seismological Centre; FinDer, Finite-Fault Rupture Detector; Global CMT, Global Centroid Moment Tensor; SRCMOD, Finite-Source Rupture Model Database; USGS, U. S. Geological Survey. *Reference strike θ ref taken from Global CMT. † Reference strike θ ref taken from SRCMOD. ‡ Reference strike θ ref from USGS National Earthquake Information Center (NEIC) catalog. §One of the strike angles has been preferred considering the aftershocks distribution. ‖The strikes angles are close enough (<30°) to consider the average as the reference.
Acknowledgments This article was partially funded by the  European Union 's  (EU) Horizon 2020 research and innovation program  under grant agreement Real-time earthquake rIsk reduction for a reSilient  Europe (RISE)  project (http://www. rise-eu. org)number  821115. Opinions expressed in this article solely reflect the authors' view; the  EU  is not responsible for any use that may be made of information it contains. The authors would like to thank  Editor-in-Chief Allison Bent ,  Martin Mai , and two anonymous reviewers for their thoughtful comments and suggestions that helped to improve this article.
θ ref are taken from the SRCMOD, USGS, or Global CMT databases, respectively (see Data and Resources). Felt reports datasets are characterized by the number of reports at close (100 km) and far distances, N felt close and N felt , their azimuthal gap around the epicenter, and maximum intensity maxI felt. All values are referring to reports that were collected by the EMSC within 30 min of earthquake origin. The FinDer line-source models are characterized by the rupture length L FinDer , the ground-motion misfit MF (see equation 3), and the spatial correlation coefficient C (see equation 2). The three columns on the right give the difference in the strike ΔΘ of the FinDer line source determined from the felt reports at 10, 20, and 30 min from event origin, relative to θ ref. See the Data and Processing section for details.
33 0. 88 807751 2019/11/26 10. 0 T02:54:11 Mindanao, 6. 73, 125. 15, 6. 8 319* , § 165/187 59 8 33 0. 36 0. 88 812196 2019/12/15 Philippines 20. 0 T06:11:52 θ Reference strike values and N felt , their azimuthal gap around the epicenter, and maximum intensity maxI felt. FinDer , the ground-motion misfit MF (see equation 3), and the spatial correlation coefficient C (see equation 2). The three columns on the right give the difference in the strike ΔΘ of the FinDer line source determined from the felt reports at 10, 20, and 30 min from event origin, relative to θ ref. See the Data and Processing section for details. EMSC, European-Mediterranean Seismological Centre; FinDer, Finite-Fault Rupture Detector; Global CMT, Global Centroid Moment Tensor; SRCMOD, Finite-Source Rupture Model Database; USGS, U.
Proxy data From the 79 ice core snow accumulation records included in the database of  Thomas et al. (2017) , we only keep the annually-resolved records (48 out of 79) as in  Medley and Thomas (2019). Medley and Thomas (2019)  provide a spatially complete annual Antarctic snow accumulation over the past two centuries using this ice core snow accumulation database (Fig. 1 ). This excludes a majority of snow accumulation records in the continental Dronning Maud Land region. As in  Medley and Thomas (2019) , we also add the B40 ice core record of snow accumulation  (Medley et al. , 2018) , which was published after the compilation of  Thomas et al. (2017). Additionally, we also employ the information from the ratio of stable isotopes oxygen in the ice core (δ 18 O). Stenni et al. (2017)  provide a compilation of the Antarctic precipitation-weighted δ 18 O records (n=112) covering the last two millennia.
We only keep the 29 annually-resolved δ 18 O records. It is worth noting that the majority of the ice cores (both snow accumulation and δ 18 O records) are located in West Antarctica (WAIS; Fig. 1 ). In addition to the Antarctic proxies, we also utilize the continental annuallyresolved proxies situated in the Southern Hemisphere from the global database of the PAGES2k working group that includes all the proxy records suitable for reconstructing the global temperature over the last two millennia (PAGES2k  Consortium et al. , 2013; Emile-Geay et al. , 2017). Among all the continental proxies included in the global database for the Southern Hemisphere, only the tree-ring width (hereafter TRW) are annually-resolved (in addition to some Antarctic ice cores already included in our study). A total of 18 sites presenting TRW time-series over past centuries are located in the Southern Hemisphere : three in New Zealand, three in Tasmania and 12 in South America.
Among the 12 South American sites, only six are positively correlated with climate (either near-surface air temperature or precipitation) according to our dataset (see section 2. 4) over the 1941-1990 CE period, and thus included. The geographical distribution of the selected sites is presented in Fig. 1. Most proxy records cover the last two centuries, but their number starts to decline from around 1990 CE (Fig. S1 ). Therefore, the period analyzed throughout the study is 1800-2000 CE. This provides a good compromise in terms of the number of records and the overlap with the satellite period (starting in 1979 CE) that is used for the evaluation of the reconstruction (see section 3. 1). This period was also used in the study of  Medley and Thomas (2019)  who reconstructed snow accumulation in Antarctica using the same ice core snow accumulation records as here.
Parameters of the Eqs. (  4 ) and (  5 ) are estimated using the ordinary least squares method. Similarly to the Last Millennium Reanalysis  (Tardif et al. , 2019) , we use the near-surface air temperature and precipitation as explanatory variables (X 1 and X 2 ). In addition to the annual mean of near-surface air temperature and precipitation, the climate variables are also averaged over different months during the calendar year (January to December), in order to take into account the seasonal response of trees: JJA and OND. All the possible combinations are tested. The calibration of the PSM is performed over the 1941-1990 CE period using the Global Meteorological Forcing Dataset for land surface modeling (v2; http://hydrology. princeton. edu/data. php, last access: 22 June 2020; hereafter GMF)  (Sheffield et al.
To this end, we assimilate the near-surface air temperatures from the Automatic Weather Stations (AWSs) over the Antarctic continent  (Turner et al. , 2004 ) (n=14) starting from 1958 CE (see Fig. 1  for their geographical localization). The model is also constrained by the snow accumulation records from ice cores, which are the best estimate of long-term snow accumulation, since AWSs do not record this variable. Additionally, TRW records are replaced by the near-surface air temperatures from the gridded dataset used for the calibration of the TRW PSM (i. e. , GMF; only where TRW sites are available). This reconstruction is referred to as the instrumental reconstruction. The observation error on the near-surface air temperature from AWSs is calculated similarly to the snow accumulation error (see section 2.
However, several studies (e. g. ,  Agosta et al. , 2019; van Wessem et al. , 2018)  showed a weaker spatial variability of near-surface air temperature compared to snow accumulation (e. g. ,  Agosta et al. , 2019; van Wessem et al. , 2018). Therefore, we assume that the factor applied to take into account the unresolved processes at the RACMO scale is smaller than for snow accumulation (see section 2. 6) and multiply the local observation error of near-surface air temperature for the Antarctic AWSs by a two-factor. Finally, the situation is very different for the assimilated nearsurface air temperature of the gridded dataset GMF associated with the TRW sites as it is a gridded dataset at a resolution similar to the one of the model that is thus expected to represent processes at a similar scale.
Validation of the instrumental paleo reconstructions over the last decades Before analyzing the long-term changes using the paleo-reconstruction over the past centuries, we first need to ensure that our data assimilation method works. We establish this by testing the skill of the instrumental and paleo reconstructions at reproducing interannual climate variability and trends over the last decades, in particular the atmospheric circulation, near-surface continental climate (temperature and snow accumulation) and sea-ice cover. To this end, Figure  2  presents the spatial correlation coefficients between our instrumental and paleo reconstructions and the latest ECMWF atmospheric reanalysis ERA5  (Hersbach et al. , 2020)  over the 1979-2000 CE period for the 500-hPa geopotential height, near-surface air temperature and snow accumulation, as well as sea-ice concentration using the dataset from the National Snow and Ice Data Center (NSIDC)  (Parkinson, 2019).
The degradation of the performance between the instrumental and paleo reconstructions is very limited for snow accumulation compared to near-surface air temperature, which is expected as we use the same observational dataset in the two reconstructions of snow accumulation. Moreover, our paleo-reconstruction displays a 18. 04 Gt year -1 per decade linear trend for the West Antarctic-wide snow accumulation over 1958-2000 CE, which is very close to  Medley and Thomas (2019) (18. 48  Gt year -1 per decade over the same period).
This index is then Fig. 4  (a) The Amundsen Sea Low (ASL) and (b) Southern Annular Mode (SAM; b) indexes derived from our instrumental (red) and paleo (blue) reconstructions correlated with ERA5 data (in black) for the  ASL over 1979 -2000 CE and the Marshall Index (Marshall, 2003)  (in black) for the SAM over 1958-2000 CE. For all the datasets plotted, the linear trends are displayed (expressed in decade -1 ) and the standard deviation (std) of the timeseries (unitless) over the  1979 -2000 CE and 1958 -2000 CE periods for the ASL and SAM, respectively. (c-d)  Comparison between our instrumental (red) and paleo (blue) sea-ice extent (10 6 km 2 ) reconstructions (in anomalies) and the observed sea-ice extent from the National Snow and Ice Data Center (NSIDC; in black; only available from 1979 CE) for the Bellingshausen/Amundsen Sea (130 • W-60 • W) and Ross Sea (160 • E-130 • W) sectors over the 1958-2000 CE period.
Consequently, the sea-ice concentration in that region cannot explain the variability of the signal recorded in ice cores (e. g. , snow accumulation and δ 18 O). Therefore, given that the relationship between the ice core signal and sea-ice concentration in that region is weak, the sea-ice concentration in that region cannot be successfully reconstructed. In order to quantify the sea-ice extent (i. e. , oceanic surface covered by at least 15% of ice) at the regional scale, we divide the West Antarctic sector into two sectors following the geographical definitions of  Parkinson (2019) : the Bellingshausen/Amundsen Sea sector (130 • W-60 • E) and Ross Sea sector (160 • E-130 • W). It is important to stress that the overlapped period of 22 years between the NSIDC dataset and our reconstructions is short to evaluate in detail our sea-ice extent reconstruction.
Therefore, this limits our ability to assess the quality of our reconstruction in terms of trends over the past decades. Our instrumental reconstruction displays a correlation coefficient r of 0. 43 (p-value<0. 05) and 0. 51 (p-value<0. 05) with the NSIDC dataset over the 1979-2000 CE period for the Bellingshausen/Amundsen Sea and Ross Sea sectors, respectively (Fig. 4c, d ). Regarding our paleo-reconstruction, we observe an overall reduction of the local skill compared to our instrumental reconstruction (Fig. 2 ). However, our paleo-reconstruction still displays high correlation coefficients for both the Ross Sea and Bellingshausen/Amundsen Sea sectors when compared to the NSIDC dataset (r =0. 45 (p-value<0. 05) and r =0. 35 (p-value>0. 05), respectively; Fig. 4c, d ). Additionally, the sea-ice extent for these two regions are highly correlated between the two reconstructions over the  1958 -2000 CE period (r =0.
However, this result is not surprising since both our instrumental reconstruction and Nicolas and Bromwich (2014) used the same near-surface air temperature dataset for the assimilation. Although the skill metrics are lower for the AP compared to the WAIS, our reconstruction correctly displays the largest warming in AP (0. 38 • C per decade) as in Nicolas and Bromwich (2014)  (0. 46 • C per decade). Comparing our paleo-reconstruction to the instrumental reconstruction shows that using paleo records instead of actual observations degrades the skill of the overall reconstruction, which is expected (Figs. 2 and 3a,b). However, near-surface air temperatures in the paleo-reconstruction still remain highly correlated with ERA5. This is in line with the good agreement with the nearsurface air temperature reconstruction from Nicolas and Bromwich (2014)  for the AP (r =0.
It is worth noting that the goal of Fogt et al. (2019) was not to reconstruct the ASL variability over the past decades. Furthermore, Fogt et al. (2019) mention that their reconstruction has a poor skill in the Amundsen region mainly because of the sparse observational network in West Antarctica. In particular, prior to 1958 CE, this reconstruc-tion only relies on pressure observations situated in the mid-latitudes, and thus only depends on the mid latitudes-Antarctic connections. However, the comparison with Fogt et al. (2019) is still valuable since this is the only Antarctic pressure dataset based on instrumental observations available before 1979 CE. The ASL index derived from Fogt et al. (2019) displays a correlation coefficient r of 0. 75 (p-value<0. 05) with ERA5 over 1979-2000 CE.
techniques and management of human and animal waste. Finally, ongoing and future studies on N 1 deposition are very important and should be communicated to policy makers to manage the unique 2 natural resource represented by the Lake Victoria and its catchment and to mitigate nutrient budgets Annual Development Plan (ADP) 2020/2021. (Homa Bay County: Department of Finance, Page 27 of 42 AUTHOR SUBMITTED MANUSCRIPT -ERL-110179. R1 3 21 32 A c c e p t e d M in the great African lakes ecosystems. 4 5 Acknowledgements: This study was supported by the United Nations Environmental Program (GEF-6 UNEP) International Nitrogen Management System (INMS) project, and by the French program Cycle 7 de l'Azote entre la Surface et l'Atmosphere en afriQUE (CASAQUE) funded by Les Enveloppes 8 Fluides et Environnement (LEFE) / Ecosphère Continentale et Côtière (EC2CO) of the Institut 9 project has received funding from the European Union's Horizon 2020 research and innovation Economic planning and service delivery, Homa Bay government.
The version 3 of the 15 IASI-NH3 dataset was created by the SQUARES group at ULB (Université libre de Bruxelles (ULB), 16 Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Brussels, 17 Belgium). We would like to thank Martin Van Damme, Lieven Clarisse, and Pierre Francois Coheur 18 for the NH3 dataset. 19 ADP 2019 27 Andreae M O, Byrd J T and Froehlich P N 1983 Arsenic, antimony, germanium, and tin in the Tejo 28 estuary, Portugal: modeling a polluted estuary Environ. Sci. Technol. 17 731-7 29 Avila A, Alarcón M and Queralt I 1998 The chemical composition of dust transported in red rains-30 its contribution to the biogeochemical cycle of a holm oak forest in Catalonia (Spain) Atmos. 31 Environ. 32 179-91 32 Avila A, Queralt-Mitjans I and Alarc´on, M 1997 Minerological composition of Africa dust delivered 33 by red rains over northeastern J.