dataesr/scientific-paragraphs-categorization huggingface dataset:
Triple-collocation analysis applied to rainfall observations In this study, TC analysis  (Stoffelen, 1998)  was applied to estimate the correlation and the error of the rainfall estimates when a reliable reference is missing like in Africa. Here we present a summary of the theory behind TC, while the reader interested in more details can refer to  Massari et al. (2017a). Suppose we have three measurement systems X i , observing the true variable t characterized by an additive error model X i = X i + ε i = α i + β i t + ε i , (7) where the variables X i (i = 1, 2, 3) are collocated measurement systems linearly related to the true underlying value t with additive random errors ε i , respectively, while α i and β i are the ordinary least-squares intercepts and slopes. Assuming that the errors from the independent sources have zero mean (E(ε i ) = 0) and are uncorrelated with each other (Cov(ε i , ε j ) = 0, with i = j ) and with t (Cov(ε i , t) = 0), the variance of the error of each dataset can be expressed as  (Mc-Coll et al.
Results Both the calibration of SM2RAIN and the OLC implementation need a calibration dataset as described in Sect. 3. 3 (i. e. Y REF ). The choice of this dataset is strategic for obtaining a good-quality integrated product. Section 4. 1 describes the process of the selection of Y REF considering different potential candidates. Section 4. 1. 1 and 4. 1. 2 describe the validation over US, IN, AU and EU by using the hydrometeorological networks described in Sect. 2. 1 and the validation in Africa and South America by using TC (Sect. 3. 4. 2), respectively.
Calibration dataset selection The choice of a calibration dataset is strategic for both the SM2RAIN parameters selection and the OLC coefficients calculation. Thus, it has to be carefully selected based on (i) accuracy (i. e. low error and high correlation with "true" rainfall), (ii) homogeneous performance in time and space, and (iii) continuous spatial and temporal coverage (as well as spatial and temporal resolution closer to the one of the rainfall to be estimated). Potential candidates are: 1. GPCC. This has potentially high accuracy and low biases where the rain gauge coverage is good but can be unreliable when the rain gauge distribution is scarce (e. g. Africa and South America). It might also suffer from time dependence performance as a function of rain gauge availability. 2. ERA5.
GPCC, ERA5 and SM2RAIN-ASCAT* (triplet C). Note that SM2RAIN-ASCAT* above is not the one used in the integration, but it was produced using constant parameters a, b and Z all over the world (i. e. it is not regionally calibrated) as in  Massari et al. (2017a)  to avoid a potential violation of the TC assumptions. On the other hand, even using SM2RAIN-ASCAT (the calibrated dataset), similar results were obtained (not shown), as we found a negligible effect of the calibration on TC results (in terms of TC correlations). Table  2  shows results for triplet A, B and C. Different configurations of the triplets provide similar results, suggesting that TC can be considered reliable. In particular, ERA5 performs the best among all, but it also suffers from significant uncertainty over convection-dominated systems like in western Africa and the Sahel (see Fig.
The uneven rain gauge spatial distribution seems to significantly impact the GPCC quality and in turn can potentially cause sub-optimal performance if used as a calibration dataset. ERA5 relies less on observation density and shows a more homogeneous performance pattern with respect to GPCC. Thus, ERA5 was selected as Y REF. This selection does not guarantee optimal solutions, but it is the best we can do with the available datasets considering that other potential candidates can be affected from other or similar issues, which could result in a very different global precipitation estimate  (Herold et al. , 2016). The solution to this problem is not straightforward, but a possible way forward would be the integration of GPCC and ERA5 or the use of available integrated products  (Beck et al.
2. 1 as references. The short-latency products (less than 2-3 d) are shown in light blue, whereas the long-latency ones (larger than 2 months) are left white. The integrated product P R+SM outperforms both IMERG-ER (significantly) and in some cases long-latency products in terms of R and RMSE (see for instance P R+SM correlations in Australia, Europe and India vs. ERA5 correlations). This suggests that the selection of the calibration dataset is not necessarily a major limiting factor in the proposed framework, as satellite SM contains inherent information about rainfall, as long as its quality is sufficiently high. However, this is not always true for all the scores. For instance, in terms of bias, results are not optimal in India. Here, ERA5-based climatological correction is probably the reason for the suboptimal performance of the integrated product due to the relatively high bias of ERA5 over India.
P R+SM refers to the integrated product. Asterisks refer to short-latency products, while values in bold denote the best performing product in the region according to the specific score on the left. provements are obtained in terms of RMSE, which in some cases (i. e. CONUS) are larger than 40 %. In terms of R, the improvement spans from 5 % to 15 % with larger values obtained for Europe and Australia. There are also spots over north-western CONUS characterized by deterioration. We attributed this to the low agreement between stage IV data and ERA5 data (used as calibration dataset), which can be also found in  Beck et al. (2019). Note this is a challenging area for Stage IV data, as also demonstrated by  Tian et al. (2007) , who found significant performance differences of the Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall product in north-western CONUS when compared either to the CPC (Climate Prediction Center) Unified Gauge-based Analysis of Global Daily Precipitation  (Higgins et al.
6  for CONUS). Land cover information comes from the ECOCLIMAP dataset (a global database of land-surface parameters at 1 km resolution  Champeaux et al. , 2005) , provided at 1 km spatial resolution. We have simplified the original land use classes into eight categories: bare land, rocks, urban, forest, wooded grassland, shrubland, grassland and crop. Except for urban, rock and bare soil (with a percentage of pixels within CONUS of less than 0. 5 %), the integrated product performs better over shrubland, grassland and crop, whereas lower performance is obtained over forests. This result is also expected as the qual- ity of the satellite SM product can be highly impacted by the presence of dense vegetation for the difficulty of the retrieval in separating the effect of the soil water content from the water contained in leaves.
Discussion and conclusions In this study, we have developed a procedure to obtain a short-latency (less than 2-3 d), daily 25 km satellite-based rainfall product based on the integration of IMERG-ER with SM2RAIN-based rainfall estimates derived from three different satellite SM products (i. e. SMOS, SMAP and ASCAT). With this latency -potentially reduced to about 1 d via the use of L2 products -the product targets agricultural and wa-ter resource management applications over data-scarce regions like Africa, South America and central Asia. To merge SM2RAIN-based rainfall estimates with IMERG-ER, we used the OLC approach previously used by  Bishop and Abramowitz (2013)  to combine different climate model estimates. The procedure optimally merges multiple estimates of the same variable by minimizing the error with a calibration dataset.
The choice of this calibration dataset was discussed and analysed in detail by applying triple-collocation analysis to different candidates leading to the choice of the ERA5 reanalysis rainfall product. In the procedure, no gauge information was directly used either in the calibration of SM2RAIN or in the integration of estimates via OLC; therefore the developed product is totally independent from ground-based observations of rainfall (except the inherent gauge information contained in IMERG-ER). The integrated product was cross-validated with highquality ground-based rainfall observations in Australia, India, Europe and the conterminous United States and crosscompared in the same regions against long-latency products (i. e. released with a time span of 1-2 months and thus not suited for operational applications).
The validation entailed different continuous and categorical scores and was carried https://doi. org/10. 5194/hess-24-2687-2020 Hydrol. Earth Syst. Sci. , 24, 2687-2710, 2020 out for different land cover classes and as a function of the topographic complexity. In this respect, we found the following: 1. The integrated product performed relatively well and often better than the long-latency products, which are designed to obtain best performance, as they ingest many observations and use gauges (often the same used here for validation). The best product in regions with highdensity rain gauge observations was found to be GPCC (although this product is obviously correlated with the ground reference). An interesting feature was the better performance of the integrated product with respect to the calibration dataset which highlights the high value of information provided by SM.
These results are relevant given that the integrated product can be potentially released within 2-3 d. 2. The improvement of IMERG-ER was relevant and ranged from 10 % to 15 % in terms of correlation and up to 40 % in terms of RMSE. A smaller impact of the integration was obtained over very dense forests and complex terrain given the inherent limitations of satellite-based observations over these areas. We also observed deterioration in correlation in some areas of north-western CONUS and India which need further analysis. 3. An ability to reduce the variability ratio which was too high was observed in the IMERG-ER product. One of the reasons for this was also related to the lower variability of SM2RAIN-based rainfall estimates, which were produced by minimizing the RMSE with the calibration dataset (i.
e. ERA5). Despite being beneficial in this case, this issue can be relevant and could also impact the ability in the prediction of extreme values and a modification of the true rainfall distribution. However, a closer look at the distributions of the reference and the estimated rainfall (not shown) suggests that the integrated product was not impacted too much form this issue. 4. An improvement of the KGE score as a consequence of the improvement of the correlation (mainly) and the variability ratio was found in all cases except India. Here, despite the better correlation, the integrated prod- products derived from the setup during the calibration period in South America. When low correlation was found between the reference dataset (i. e. GPCC) and the SM2RAIN product, the latter was excluded from the analysis, and only IMERG-ER was retained.
uct was characterized by a higher bias and lower variability, which drew KGE to values lower than the ones of IMERG-ER. 5. An additional validation, totally independent from the calibration, was carried out in Africa and South America. Here, due to the lack of a reliable benchmark dataset, we adopted TC analysis (after having validated it) to calculate error and correlation of the integrated product, IMERG-ER, GPCC and ERA5. Results confirm the values of those obtained via classical validation with the integrated product outperforming IMERG-ER. Moreover, in data-scarce regions, the integrated product outperforms GPCC and provides similar performance to ERA5 (better in the Sahel region). Despite the good performance achieved by the product, several aspects need further investigation. 1. The short time records of some of the satellitebased observations used in the integration (i.
In this respect, IMERG-ER, with its 4-5 h of latency, is the only satellite product potentially providing rainfall observations that could be used for such applications, although in that case not only the latency is important but also the spatial resolution. Future work should focus on the integration of L2 satellite SM products with IMERG-ER also using alternative integration schemes and products with respect to those used in this study. 6. The record length of the product is restricted to the GPM and SMAP eras (i. e. 2015 onward). This potentially limits the use of the products for drought and flood https://doi. org/10. 5194/hess-24-2687-2020 Hydrol. Earth Syst. Sci. , 24, 2687-2710, 2020 frequency analysis. However, the integration procedure does not rely upon the availability of the above products but can be applied to any other long-term rainfall and soil moisture dataset available.
The head, the left front leg, the right middle leg and the right hind leg are missing. Box Blattodea 59. There is a ♀ specimen in the NHMUK (BMNH(E)876462) with a data label "♀ Moyoapan, Mexique, env. Sumichrast" [handwritten on ruled white card] which is presumably a syntype although not labelled as such in their online database Holocompsa azteca Saussure, 1862 CATALOGUE apacha  Saussure, 1893: 296 [Homoeogamia]  Mexico; Chihuahua. Unspecifi ed number of ♂. The MHNG collection contains three specimens. A ♂ specimen (MHNG-ARTO-20574) with labels: "Chihuahua, Amer. Cent. Mr Corcelle" [handwritten on ruled white card]; "Chihuahua, Mr Corcelle" [handwritten on white paper]; "Homoeogamia apacha Sauss. ♂" [handwritten on green paper]; "Syntypus" [printed on red paper]. The specimen is directly pinned, with the wings spread.
Box Blattodea 59. There is a ♀ specimen in the NHMUK (BMNH(E)879466) with a data label "S. Geronimo, Guatemala, Champion" [printed on white card] and referred to as a syntype in their online database. Holocompsa binotata Saussure & Zehntner, 1894 bolliana  Saussure, 1893: 296 [Homoeogamia]  Texas. Unspecifi ed number of ♂. The MHNG collection has two specimens. A ♂ specimen (MHNG-ARTO-20572) with labels: "Dallas, Texas ♂" [Handwritten on ruled white card]; "Homoeogamia Bolliana ♂ Sauss. " [handwritten on green paper]; Syntypus" [printed on red paper]. The specimen is directly pinned, with the wings spread. The antennae and the tarsi of the left front leg are lost. An immature ♂ specimen (MHNG-ARTO-20573) with labels: "Dallas, Texas, l. ♂" [handwritten on ruled white card]; "Homoeogamia Bolliana larva ♂ Sauss.
No specimens found in the MHNG collection. There is a ♂ specimen in the NHMUK (BMNH(E)876600) with a data label "S. Geronimo, Guatemala, Champion" [printed on white card] and referred to as the holotype in their online database. Compsodes cucullatus  (Saussure & Zehntner, 1894)  delicatula  Saussure & Zehntner,1894: 112, pl. 5, fi g. 10 [Latindia]  Guatemala; Zapote (Champion). Unspecifi ed series. No specimens found in the MHNG collection. There is a ♂ specimen in the NHMUK (BMNH(E)876601) with a data label "Zapote, Guatemala, G. C. Champion" [printed on white card] and referred to as the holotype in their online database. Compsodes delicatulus  (Saussure & Zehntner, 1894)  dohrniana  Saussure, 1893: 309 [Heterogamia]  Sina septentrionalis. One ♂. No specimens found in the MHNG collection.
Much of Dohrn's collection is currently in the MIZW, but no specimens of this species were found there and the type material is apparently lost. A junior synonym of Eupolyphaga sinensis  (Walker, 1868)  dohrniana  Saussure & Zehntner, 1894: 111, pl. 5, fi g. 7 [Latindia]  Guatemala (coll. Dohrn). Unspecifi ed number of ♀. No specimens found in the MHNG collection. Much of Dohrn's collection is currently in the MIZW, but no specimens of this species were found there and the type material is apparently lost. Latindia dohrniana Sussure & Zehntner, 1894 doriana  Saussure, 1895: 84-85 [Hemilatindia]  Assab, 1879 (Marq. Doria; voyage de l' "Esploratore"). Unspecifi ed number of ♂. No specimens found in the MHNG collection. There is a ♂ specimen with corresponding data in the MCSN labelled as the holotype.
Tivia doriana  (Saussure, 1895)  fenestrina Saussure 1864a: 324 [Hypercompsa] Brasilia. Unspecifi ed series. Saussure (1864b) mentioned the ♀ and stated that the type series was in the SMF. No specimens found in the MHNG collection. The type series is presumably in the SMF although no database for their Blattodea collection exists. Hypercompsa fenestrina Saussure, 1864 gestroiana  Saussure, 1895: 81-82 [Heterogamia]  Boran Galla, Auata (mois de Mai, Bottego). Unspecifi ed number of ♂. No specimens found in the MHNG collection. There is a ♂ specimen with corresponding data in the MCSN labelled as the holotype. Hemelytroblatta gestroiana  (Saussure, 1895)  gueriniana  Saussure, 1863: 140, fi g. 7 [Corydia]  Les Indes orientales; Sangor dans l'intérieur des Indes. Unspecifi ed number of ♂. The MHNG collection contains one ♂ specimen (MHNG-ARTO-20470) with labels: "540 11, Sangor, Indes Or.
)" [handwritten on blue paper]: "Possible syntype of P. syriaca Saussure Hollier 2019" [handwritten on red paper]. The specimen is directly pinned, with the wings folded. There is insect feeding damage to the base of the abdomen and the femur of the left hind leg. Four tarsal segments of the left front leg and the entire right hind leg are missing. This specimen was placed with many others in the MHNG collection under the name Arenivaga africana africana (L. ). The only other ♂ among them has a data label with an acquisition number showing that it entered the collection as "Heterogamia ursina Burm. " from the collection of Guérin-Méneville. The very simple data label of specimen MHNG-ARTO-20595 suggests that it may be a syntype of Polyphaga syriaca Saussure. Box Blattodea 61. A junior synonym of Hemelytroblatta africana africana  (Linnaeus, 1758)  tamerlana  Saussure, 1893: 316 [Anisogamia]  Turcomania.
Unspecifi ed number of ♀. The MHNG contains one ♀ specimen (MHNG-ARTO-20465) with labels: "Turkestan, M. König" [handwritten on ruled white card]; "Turkestan König" [handwritten on yellow paper]; "Anisogamia tamerlana ♀ Sauss. " [handwritten on yellow paper]; "Anisogamia tamerlana Sss. [handwritten on blue paper]; "Syntypus" [printed on red paper]. Most of both antennae are missing. Because the type series was not defi ned, this specimen must be considered a syntype. Box Blattodea 59. Anisogamia tamerlana Saussure, 1893 tolteca  Saussure & Zehntner, 1894: 109, pl. 3, fi g. 35 [Holocompsa]  Guatemala; Purula in Vera Paz (Champion). Unspecifed number of ♀ (one?). No specimens found in the MHNG collection. There is a ♀ specimen in the NHMUK (BMNH(E)876463) with a data label "Purula, Vera Paz, Champion" [printed on white card] and referred to as the holotype in their online database.
No specimens found in the MHNG collection. There is a ♂ specimen with corresponding data in the MCSN labelled as the holotype. Hemelytroblatta pilosella  (Saussure, 1895)  pusilla  Saussure & Zehntner, 1894: 112 [Latindia]  Peru; Tarma. Unspecifi ed number of ♂. No specimens found in the MHNG collection. The whereabouts of the type specimen(s) is unknown. Latindia pusilla  Saussure & Zehntner, 1894  sinensis  Saussure, 1869: 282-283 [Homoeogamia]  Nord de la Chine; Pekin. Two ♂. The MHNG collection contains one ♂ specimen (MHNG-ARTO-20468) with labels: "♂ Pekin, Chine, M. H de Saussure" [handwritten on ruled white card]; "Heterogamia sinensis Sauss. ♂" [handwritten on yellow paper]; Heterog. sinensis Sauss. " [handwritten on blue paper]; "Syntypus" [printed on red paper]. The specimen is directly pinned with the left wings spread and the right wings folded.
There is insect feeding damage to the head, and the left antenna, the last tarsal segment of the right middle leg, the last tarsal segment of the left hind leg and all of the tarsi of the right hind leg are missing. Box Blattodea 59. The whereabouts of the other syntype is unknown. This species is not listed in the CSF database. Kirby (1903: 379)  considered Polyphaga sinensis Mexique, env. Sumichrast" [handwritten on ruled white card; "Latindia tolteca Sauss. " [handwritten on green paper], "Holotypus" [printed on red card]. The specimen is directly pinned, with the wings folded. The specimen has insect feeding damage, and the antennae, three tarsal segments of both front legs, the tibia and tarsi of both middle legs, all of both hind legs and the abdomen are missing. Box Blattodea 59. A junior synonym of Latindia mexicanus Saussure, 1868 zapoteca  Saussure & Zehntner 1894: 109, pl.
3 fi g. 33 [Holocompsa]  Guatemala; Zapote (Champion). Unspecifi ed number of ♂. No specimens found in the MHNG collection. There is a ♂ specimen in the NHMUK (BMNH(E) 876464) with a data label "Zapote, Guatemala, G. C. Champion" [printed on white card] and referred to as the holotype in their online database. Holocompsa zapoteca  Saussure & Zehntner, 1894.
ACKNOWLEDGEMENTS Thanks are due to  Anita Hollier  and  Peter Schwendinger  for comments on the manuscript. Special thanks are due to  Emmanuelle Werlen  for entering the MHNG specimens into the MHNG database. We are grateful to  Ben Price  for exporting data from the NHMUK database to facilitate searching, to  Roberto Poggi  for information about the holdings of the MCSN, to  Joachim Holstein  for information about the holdings of the SMNS, to  Wolfgang Nässig  for information about the holdings of the SMF and to  David Szymroszczyk  for information about the holdings of the MIZW.
Sampling procedure, analyses and data treatment Peat core samples were collected in August, when the depth of unfrozen layer was at its maximum (i. e. , see  Raudina et al. , 2017). Based on measurements by temperature loggers over the summer period, the in-situ temperature of studied soil profile ranged from 155°C in the top soil (0-20 cm) to 42°C at the permafrost boundary (40-80 cm). The physical, chemical and https://doi. org/10. 5194/bg-2019-483 Preprint. Discussion started: 28 January 2020 c Author(s) 2020. CC BY 4. 0 License. botanical properties of several peat cores collected in the homogeneous palsa region in the north and ridgre-ryam complex in the south are highly similar among different peat mounds  (Velichko et al. , 2011; Stepanova et al. , 2015). Peat cores were extracted using a Russian sediment/peat corer and the frozen part was sampled using a motorized Russian peat corer (UKB-12/25 I, Russia) with a 4-cm diameter corer sterilized with 40% ethanol prior to each extraction.
All statistical tests used a significance level of 95% ( = 0. 05). Spearman rank order correlations (significant at p < 0. 05) were performed to characterize the link of Hg with C, N and other major and trace elements. The differences in Hg concentration between the active-and frozen peat layer were tested using the Mann-Whitney U test for paired data at a significance level of 0. 05. Hugelius et al. (2014)  were divided into two categories, organic and mineral soils. Histosols and Histels were defined as organic soils. Turbels and Orthels were considered as organic soils for the 0 -0. 3 m interval and as mineral soils for the 0. 3 -3m interval. All other soils were considered as mineral soils. To estimate the northern soil Hg pool, C pools were multiplied with the respective RHgC derived for organic (>20% C) and mineral (<20% C) soil data from north America (excluding Alaska) and Eurasia.
Acknowledgements: This work was mainly supported by the state task of the  Ministry of Science and Higher Education of the Russian Federation , grant no. 1. 8195. 2017/P220. Partial support from the  Russian Fund for Basic Research , via grant no. 19-29-05209-mk  and  19-55-15002-NCNI_a , and from the  CNRS Chantier Arctique Français , via the  PARCS  project, and the  H2020 ERA-PLANET (689443 ) iGOSP and iCUPE programmes are acknowledged. M. J. acknowledges funding by the  Swiss National Science Foundation  (grant no. PZ00P2_174101 ). We are thankful to  Luiz D. Lacerda  for sharing Hg and C data from Brazil.