dataesr/scientific-paragraphs-categorization huggingface dataset:
This agreement allowed to update the database at a daily time-step, up to 1980. Figure  1   [17]  shows the benefits of this agreement in the evolution of the rainfall database. From this set of data, a map of the mean annual rainfall over Western and Central Africa was drawn by L'Hote & Mahé  [18]. Water 2019, 11, 387 3 of 17 and were modified, kept or removed. During this process, every measurement point was linked to only one data set. Due to the state of technology at the time the data was collected, some measurement stations had no geographical referencing, and it was added later, when possible. A tripartite agreement between the Comité Interafricain d'Etudes Hydrauliques (CIEH) or the Interafrican Hydraulic studies Committee, the Agence pour la SECurité de la Navigation Aérienne (ASECNA) or Agency for the Safety of Aerial Navigation in Africa and Madagascar, and the ORSTOM, began in 1973, for the collection of data from thirteen countries of Western and Central Africa (Benin, Burkina Faso, Cameroon, Congo, Ivory Coast, Gabon, Mali, Mauritania, Niger, Central Africa, Senegal, Chad, and Togo) and ended in 1989, with the edition of the two rainfall data books, for each country, covering the years up to  1965 and 1966, to 1980 [5].
This agreement allowed to update the database at a daily time-step, up to 1980. Figure  1   [17]  shows the benefits of this agreement in the evolution of the rainfall database. From this set of data, a map of the mean annual rainfall over Western and Central Africa was drawn by L'Hote & Mahé  [18]. Up to 1999, the IRD Hydrology Laboratory in Montpellier gathered and managed all the climatological data archived in every country where ORSTOM hydrologists were present, mainly in Africa, as well as the rainfall data from all past programs. Since 2000, the HSM_SIEREM database was only used and enriched by the teams of HSM in Montpellier, with no institutional mandate for commitment to the management or the dissemination of this base. For Eastern and Southern Africa, HSM gathered data through its commitment in the IHP (International Hydrological Program) of UNESCO and mainly in the FRIEND programs (Flow Regimes from International Experimental and Network Data), as HSM managed the implementation and hosting of the databases of the different African FRIEND groups.
Many other international programs that the HSM collaborated on in Africa, allowed the IRD hydrologists access to more recent data. The CRU is specifically acknowledged, as they had developed a collaboration with the hydrologists of HSM which let them access the rainfall data in the areas where HSM did not have any access. However, both CRU and HSM databases where kept independent. The HSM_SIEREM database, thus, contains rainfall data for more than 6000 stations across Africa. Up to 1999, the IRD Hydrology Laboratory in Montpellier gathered and managed all the climatological data archived in every country where ORSTOM hydrologists were present, mainly in Africa, as well as the rainfall data from all past programs. Since 2000, the HSM_SIEREM database was only used and enriched by the teams of HSM in Montpellier, with no institutional mandate for commitment to the management or the dissemination of this base.
For Eastern and Southern Africa, HSM gathered data through its commitment in the IHP (International Hydrological Program) of UNESCO and mainly in the FRIEND programs (Flow Regimes from International Experimental and Network Data), as HSM managed the implementation and hosting of the databases of the different African FRIEND groups. Many other international programs that the HSM collaborated on in Africa, allowed the IRD hydrologists access to more recent data. The CRU is specifically acknowledged, as they had developed a collaboration with the hydrologists of HSM which let them access the rainfall data in the areas where HSM did not have any access. However, both CRU and HSM databases where kept independent. The HSM_SIEREM database, thus, contains rainfall data for more than 6000 stations across Africa.
The development of a continuous, quality-checked and reliable long-term database of monthly rainfall across Africa, was a key component of this project. The database contained numerous records, due to the fact that any incoming rainfall datum was always stored. According to Rouché et al. [20] , the result was that, for some stations, up to ten different data sets were stored (collected by different people, at different time-steps, data corrected for specific purposes, etc. ). Therefore, it was essential to eliminate redundant sets and choose the better series.
Constitution of the Reference Data Set The dataset was built for every African country. Records with missing geographical coordinates were removed, daily, and ten-day data were aggregated at the monthly time-step. Then, if several series for the same station still remained in the database, a "quality label" was given to the series (depending on the origin of the data, the reliability of the provider, and the length of the observation period), to select which one was to be kept. When the retained series had missing periods, it was filled with data from a different station of the same measurement point, when available. The aim of this process was to keep only one station per measurement point. The graph in Figure  2  shows the number of sets before and after the creation of the reference database, for every country.
Criticism of the Reference Data Set For every country, patterns were established to define: • the period of the rainy and dry seasons; and • a range of monthly rainfall values (minimum and maximum) that were not to be crossed. During this process, a series of automatic tests were run, and a flag was inserted if any value: • exceeded the previously defined values for the country; • was identical to the value of the same month of the previous or next year at the station; or • was identical to the value of the previous month of the same year. The flagged months were printed and checked by the HSM hydrologists, who determined if the flag led to the removal of the data or of part of the time-series. This step was the longest but the most important for the quality of the database. Thus, the time-series of each station was both automatically and manually checked, to reduce the risk of erroneous data in the reference database.
This limited our possibility to extend the grid to a period prior to 1940. However, this is also what makes a fundamental difference between the HSM_SIEREM and the CRU grids, i. e. , in the HSM_SIEREM database, each month, the grid was calculated only from the available observed data. All African National Meteorological services were then contacted to update the data series at a monthly time-step, especially from the last 20 years of the 20th century, but only a few of them sent recent monthly data. The number of months (Figure  4 ) with available data, therefore, varies from 4061 stations in 1975 to 1464 stations in 1999. This shows a decrease of the number of observed rain gauges; this trend started as soon as the 1980s and has been observed worldwide, but it affects Africa more severely.
However, the HSM_SIEREM database still has a sufficient number of points, compared to the Global Historical Climatology Network (GHCN) values-2500 gauges, worldwide, for the year 2000  [5] , compared to a little less than 1,500 for Africa alone, in our database, for December 1999. The scarcity of rainfall data was a recurrent problem, whatever the purpose  [5, 9, 21]. Due to its history, the HSM laboratory collected a large amount of data. However, it is not allowed, either by the National Meteorological Services or the WMO (World Meteorological Organization) to disseminate raw rainfall data, as they are kept as the property of these Services. It was then decided All African National Meteorological services were then contacted to update the data series at a monthly time-step, especially from the last 20 years of the 20th century, but only a few of them sent recent monthly data.
The number of months (Figure  4 ) with available data, therefore, varies from 4061 stations in 1975 to 1464 stations in 1999. This shows a decrease of the number of observed rain gauges; this trend started as soon as the 1980s and has been observed worldwide, but it affects Africa more severely. However, the HSM_SIEREM database still has a sufficient number of points, compared to the Global Historical Climatology Network (GHCN) values-2500 gauges, worldwide, for the year 2000  [5] , compared to a little less than 1,500 for Africa alone, in our database, for December 1999. that interpolated grids would be created, which would be disseminated for free to the community as the HSM_SIEREM database. This database has already been compared to the CRU for hydrological modeling in Western and Central Africa, and had given better results  [7].
Reference of the CRU Grid Constitution Our references for rainfall grids over the African continent were the CRU grids. These were built at a resolution of 0. 5 × 0. 5 degree. They concern seven climatological variables, namely precipitation, wet-day frequency, mean temperature, diurnal temperature range, vapor pressure, cloud cover, and ground frost frequency. For all data types, the construction of a monthly surface climate over global land areas, excluding Antarctica, covers the 1901/1996 time period (period of the first version, later update extended this period up to the 21st century). An "anomaly" approach was used, this technique attempted to maximize the available station data in space and time. "In this technique, grids of monthly anomalies relative to a standard normal period (1961/1990) (…) were first derived.
Three global station datasets were compiled by the CRU, from the basis of the construction of the gridded anomalies of primary variables. The precipitations and mean temperatures were compiled by the CRU, the diurnal temperature range dataset was based on the GHCN maximum and minimum temperature data. The interpolation method adopted to process the tiles was a thin-plate spline, a function of latitude, longitude, and elevation. "The technique is robust in areas with sparse or irregularly spaced The scarcity of rainfall data was a recurrent problem, whatever the purpose  [5, 9, 21]. Due to its history, the HSM laboratory collected a large amount of data. However, it is not allowed, either by the National Meteorological Services or the WMO (World Meteorological Organization) to disseminate raw rainfall data, as they are kept as the property of these Services.
Reference of the CRU Grid Constitution Our references for rainfall grids over the African continent were the CRU grids. These were built at a resolution of 0. 5 × 0. 5 degree. They concern seven climatological variables, namely precipitation, wet-day frequency, mean temperature, diurnal temperature range, vapor pressure, cloud cover, and ground frost frequency. For all data types, the construction of a monthly surface climate over global land areas, excluding Antarctica, covers the 1901/1996 time period (period of the first version, later update extended this period up to the 21st century). An "anomaly" approach was used, this technique attempted to maximize the available station data in space and time. "In this technique, grids of monthly anomalies relative to a standard normal period (1961/1990) (.
) were first derived. The anomaly grids were then combined with a high-resolution mean monthly climatology to arrive at fields of estimated monthly surface climate. " [8]. Rainfall anomalies are expressed in percentage. The standard period was chosen because of the good quantity of data available for this period. The consistency checks were somewhere similar to the ones we applied on our data sets, defining monthly minimum, mean, and maximum values, and checking the values out of these ranges for confirmation or removal. The anomalies over the standard normal period of rainfall were calculated, the CRU used tiles; 29 tiles were defined worldwide, with 4 tiles being defined for the African continent: tile 17 (20 Three global station datasets were compiled by the CRU, from the basis of the construction of the gridded anomalies of primary variables.
The precipitations and mean temperatures were compiled by the CRU, the diurnal temperature range dataset was based on the GHCN maximum and minimum temperature data. • W to 20 • E, 38 • N to 0 • ), tile 18 (20 • E to 52 • E, 38 • N to 0 • ), tile 19 (0 • to 52 • E, 0 • to 20 • S) The interpolation method adopted to process the tiles was a thin-plate spline, a function of latitude, longitude, and elevation. "The technique is robust in areas with sparse or irregularly spaced data points. The main advantage of splines over many other geostatistical methods is that prior estimation of the spatial autocovariance structure is not required"  [8]. The process was computed per tile at a step of 0. 5 × 0. 5 degrees, with the tiles overlapping by at least 5 × 5 degrees (the number of stations varied from 200 to 1000 per tile).
Choice of the Interpolation Process Options Compared to the CRU standard normal reference period, we had a sufficient number of observations for a longer period and could interpolate rainfall from the observed values at each monthly time-step (Figure  5 ). One file per month was created in the period 1940/1999 (i. e. , 720 files). All files of monthly data were imported in the ArcMap and a point shapefile was created. The following processes were computed, using the ArcInfo Workstation macro-command language (AML). This ensured a homogeneous process. Considering the size of the region to process and the heterogeneity of the distribution of rain gauge stations, and taking advantage of the CRU experience, the IDW (Inverse Distance Weighted) method was chosen. With the ArcInfo Workstation, the IDW interpolation method does not allow to take into account the altitude of the measurement points.
A few things can be discussed about this point. Due to the very different geographical situations across Africa, the question of altitude influence on precipitation cannot be dealt with one simple approach for the whole continent. For instance, altitude has no influence on rainfall over Western Africa (except in few specific locations, like near the Atakora mountains and close to the Guinea Mounts) and in the Sahel, covering several thousands of kilometers, therefore, the spline method gives the same results as the kriging method  [22]. The coast of Cameroon is particularly wet, and the highest rainfall amounts in Africa are recorded at the foot of the Mount Cameroon; the HSM_SIEREM database gives much higher rainfall in this area than the CRU one-an example of the kind of differences between the two grids.
Comparison between the CRU and HSM_SIEREM Grids The grids from the CRU are widely used in models. Paturel et al. [11]  compared the results of the GR2M hydrological model, using the rainfall grids from CRU and the ones from the previous versions of the HSM_SIEREM, over Western and Central Africa. The HSM_SIEREM database gave better results. Two mean annual grids over the 1940/1999 period were built, one with the HSM_SIEREM database and one with the CRU grids. The CRU monthly historical climate database, converted to the ESRI ASCII raster format by Jawoo Koo (HarvestChoice/IFPRI-Raw data), was This validation confirmed that in the presence of a dense network, different interpolation methods (such as IDW or OK) performed with a similar efficiency. Water 2019, 11, 387 10 of 17.
Comparison between the CRU and HSM_SIEREM Grids The grids from the CRU are widely used in models. Paturel et al. [11]  compared the results of the GR2M hydrological model, using the rainfall grids from CRU and the ones from the previous versions of the HSM_SIEREM, over Western and Central Africa. The HSM_SIEREM database gave better results. Two mean annual grids over the 1940/1999 period were built, one with the HSM_SIEREM database and one with the CRU grids. The CRU monthly historical climate database, converted to the ESRI ASCII raster format by Jawoo Koo (HarvestChoice/IFPRI-Raw data), was downloaded from http://badc. nerc. ac. uk/data/cru). Figure  7  shows that the main features of the African rainfall distribution were similar.
Comparison between the CRU and HSM_SIEREM Grids The grids from the CRU are widely used in models. Paturel et al. [11]  compared the results of the GR2M hydrological model, using the rainfall grids from CRU and the ones from the previous versions of the HSM_SIEREM, over Western and Central Africa. The HSM_SIEREM database gave better results. Two mean annual grids over the 1940/1999 period were built, one with the HSM_SIEREM database and one with the CRU grids. The CRU monthly historical climate database, converted to the ESRI ASCII raster format by Jawoo Koo (HarvestChoice/IFPRI-Raw data), was downloaded from http://badc. nerc. ac. uk/data/cru). Figure  7  shows that the main features of the African rainfall distribution were similar. We checked the differences between the two mean inter-annual grids of the African continent, over the period of 1940 to 1999.
In most of the Sahara the CRU grid showed near 0 values, while the HSM_SIEREM grid showed a significant amount of rainfall, which were coherent with the Dubief rainfall map  [25]. The CRU grid also showed too high a rainfall over the Saharan areas, in Egypt, North Chad, South-East Algeria and the Saharan border of the Atlas Mountains. Even in the regions where the difference in percent was the lowest between the two grids (-9% to +10%), there were many areas where the difference was over 50 or 100 millimeters per year, which could have a significant impact on water-balance issues. The differences between both grids could be due to the difference in the statistical approach of data processing and due to the density of observed stations. According to the figures given in Eklund et al. [26] , the number of stations in Africa in the CRU data set was twice lower than that in the HSM_SIEREM data set, between 1940 and 1985, and three times lower after 1985.
The maximum number of stations was over 4,000 for the HSM_SIEREM and less than 2000 for the CRU in the midseventies. It is quite likely that a greater number of stations will undergo a better precision in rainfall interpolation, at a grid scale. Even in the regions where the difference in percent was the lowest between the two grids (-9% to +10%), there were many areas where the difference was over 50 or 100 millimeters per year, which could have a significant impact on water-balance issues. The differences between both grids could be due to the difference in the statistical approach of data processing and due to the density of observed stations. According to the figures given in Eklund et al. [26] , the number of stations in Africa in the CRU data set was twice lower than that in the HSM_SIEREM data set, between 1940 and 1985, and three times lower after 1985.
Grid Production and Quality Assessment The interpolation process was run over 720 months; every month was exported as an ASCII grid format file, containing one rainfall value at every 0. 5 degree over the African continent; 6 zipped files, each containing 10 years of grids. Additionally, the NetCDF format array containing the 720 months of interpolated values, can be freely downloaded from the HSM website. This first assessment of the HSM_SIEREM grid content by comparison with the CRU grid gives interesting details about where the use of HSM_SIEREM grid led to substantial differences with the CRU grids. This comparison would worth being completed by a first look at the rainfall variability over Africa, to check whether the main climatic signals are well-depicted by the HSM_SIEREM database.
Statistical Tests on Mean Annual Rainfall These tests, checking the random character of the series, were gathered using the Khronostat software  [28] , which can be downloaded, free of charge, at http://www. hydrosciences. org/spip. php? article1000. The most widely used tests deal with the stationarity of the mean of the series, throughout its observation period  [29]. This test applied on the observed data confirmed a rupture in the series at three confidence levels, 99%, 95%, and 90% (Table  1 ). Table  1. Results of the Mann-Whitney test, modified by Pettitt  [29].
Discussion and Conclusion This paper is the result of a long duration of work on the collection, criticism, and assessment of a very rich and high-quality database of rainfall. This study could be carried out due to the quantity of data collected during a long period, mainly in Western and Central Africa, by the ORSTOM and the IRD, and due to the long and thorough criticism process that was applied to the data sets. It is especially precious, as the African continent has a particularly poor observation network, compared to the rest of the world. The HSM_SIEREM rainfall gridded data set showed a very good correlation with the observed data, whatever the interpolation method used. It was compared to the widely used CRU rainfall grid, and their differences have been mapped, to help determine their best use for future studies.
The HSM_SIEREM grid was built from twice the number of stations than the CRU grid, the number of stations was even three times higher for the period of 1985/1999, and the distribution of stations better covered the Sahara and some other parts of Africa. This gave the HSM_SIEREM grid more confidence in describing rainfall over Africa, with some regions showing very important differences, especially in the low-rainfall regions, in the Sahara and the Sahel, the Southwestern coast of Africa, and the horn of Africa. The results confirmed that the African continent was seriously affected by a rainfall deficit and a change in the rainfall regime at the end of the 20th century. The first assessment showed that the two main periods of rupture in the time-series was between 1969/1970 and 1979/1980, which are depicted in the global African rainfall times-series issued from the whole data set, in both, the gridded and observed time-series.
It seems that it was the first part of the year, from February to May, which registered the highest rainfall reduction, especially in the austral part of Africa (Figure  11 ). This reduction was several times higher than the rainfall reduction in the boreal part of Africa, at the same period. Even if part of these figures might be linked to the interpolation of unevenly distributed observed rainfall stations, this could have had strong incidences on the global climate of the area and requires further studies to analyze the regional variability of rainfall over the continent, between different databases. This study also showed that the use of direct observed data for interpolation gave different results and spatial representations than the use of rainfall patterns and anomalies to generate annual maps, which were used by the CRU and many other data centers.
The comparison between the spatially-averaged rainfall data and observed data, over several regions of Morocco  [32] , showed that averaging th data over large surfaces led to a reduced visibility of climatic variability and the main climatic signals, but the main signals remained visible whatever the size of the region used for averaging. This supports the idea that the results visible with, both, observed and gridded data sets are quite consistent. All monthly grids built during this study are available for free on the HydroSciences Montpellier This study also showed that the use of direct observed data for interpolation gave different results and spatial representations than the use of rainfall patterns and anomalies to generate annual maps, which were used by the CRU and many other data centers.