dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and methods The AHA, which is herewith made freely available to the research community, is designed to be a comprehensive resource containing technical, spatial, and temporal data on existing and future hydropower plants across Africa. It covers all continental African countries which together constitute the major African Power Pools (respectively the North, West, Central, Eastern, and Southern African Power Pool), as well as the island nation of Madagascar. The AHA is collated into a single spreadsheet-based file which contains both inputs and results of the calculations carried out to establish the atlas. An overview of the calculation flow performed to obtain the full dataset is provided in Figure  1. Each of the elements of this workflow are described in a separate subsection hereafter.
Database of technical characteristics of African hydropower plants The technical information for each hydropower plant includes the rated capacity (in MW), the reservoir size (in million m 3 wherever applicable), the multiannual mean discharge of the river section upon which the plant is located (in m 3 /s), the design discharge wherever known (in m 3 /s), the earliest expected year of entry into service, and the multiannual average capacity factor of the plant wherever known from previous research (in %). Here, the capacity factor of any power plant is taken to refer to the electricity that it generated over a certain time period (e. g. hour, day, season, year. ) divided by the power that would have been generated if the plant had run at full capacity over that entire period. In cases where the latter value was unknown, it was assumed to be 50% based on typical values observed for hydropower plants around the world  2.
This data was collated from a wide array of available information. Globally, the data sources can be divided into three categories: (i) existing hydro databases, such as the Global Reservoir and Dam (GRanD) database 27 , the FAO's Dams in Africa dataset  28  , and the West African Renewable Power Database (WARPD) 9 ; (ii) bespoke information, pertaining to individual hydropower projects, from technical project overview sheets, environmental impact assessments, white papers, scientific papers, and other technical modelling studies; and (iii) online news articles on hydropower projects. The consultation and selection of data sources happened strictly according to the hierarchy (i)-(ii)-(iii), with sources from category (i) forming the default, being supplemented by categories (ii) and (iii) wherever necessary.
All used data sources are referenced in the AHA. The processing of this data to calculate temporal hydropower availability profiles is explained further below, in section 2. 5. The database includes both existing (active) hydropower plants, as well as future plants. The term "future" is relatively broad and may encompass, for example, projects under construction or in the pipeline, projects in need of financing, or projects in the pre-feasibility phase. In many cases, distinguishing between these categories is not straightforward. Based on the abovementioned data sources, the AHA distinguishes between three categories of future projects in descending order of concreteness: committed, planned, and candidate. For any future plant where no specific information was identified regarding its status (as of the writing of this paper), the categorization was set to "candidate" by default.
In those cases, the "first year" parameter was left empty. Projects in this category may either be currently unlikely to obtain financing, have been shelved, or have never gone beyond pre-feasibility studies. We note that we constrained the entries to the current version of the atlas by the criterion that the data should be available in publicly consultable sources. Thus, the atlas could be improved if presently undisclosed information available in, for example, internal documents of planning agencies were to be made publicly available. We therefore eagerly invite all relevant stakeholders to review and submit corrections and/or missing data to the author team, since the goal is for the database to be regularly updated. This particularly concerns the list of future projects, which can likely be expanded much beyond its current state and of which we do not claim full comprehensiveness.
Currently, the AHA contains a total of 633 entries on hydropower plants, of which 266 are existing, 60 committed, 44 planned and 263 candidates. Their total capacity amounts to 132 GW, of which 24% is existing (approximately 32 GW, lining up well with other statistics on existing plants  29  ), 19% committed, 6% planned, and the remaining 51% candidate. The division of the total capacity by category and by country is shown in Figure  2. We note that hydropower plants have been allocated to the country of their coordinates, notwithstanding that, in some cases, a part of the produced electricity would be allocated for exports (e. g. hydropower plants in some river basins are shared among all riparian countries). In the cases of hydropower plants located on rivers forming country borders (11 cases in total in the AHA), their capacity was allocated equally among the countries in question, thus forming separate entries in the database.
Database of geospatial coordinates of African hydropower plants The geo-referencing of hydropower plants was done according to a hierarchy of data choices, depending on the status of each plant. Firstly, all existing plants were georeferenced using satellite imagery; the coordinates given in the AHA correspond to the location of the dam and/or powerhouse as identifiable via Google Maps. Secondly, all hydropower plants that are not yet servicing the grid but are clearly identifiable as being under construction on satellite imagery, were similarly georeferenced. Thirdly, the locations of all other committed, planned and candidate hydropower plants were identified as best possible from specific project information available in any of the consulted sources. This last category of data could take on a variety of specificity: in some cases, georeferenced coordinates of the intended location of the planned plant were provided in the consulted document(s) as referenced in the AHA; in others, the information remained less precise (e.
River flow dataset for the African continent To estimate hydropower generation profiles for each of the identified locations under the given technical plant characteristics, estimations of river flow at monthly resolution on the African continent were obtained from dedicated simulations with SWAT+ (Soil and Water Assessment Tool 30 ). A previous version of this dataset has been used for hydropower potential assessment in West Africa before (refs. 9,31); the updated version used for this paper is available through the repository in In SWAT+, watersheds are delineated into sub-basins from which hydrologic response units (HRUs, which are distinct areas of a sub-basin with a unique combination of land use, soil type and slope class) are defined. For the SWAT+ model used for the AHA, sub-basins were delineated using 3,500 km 2 as threshold, yielding 5,700 sub-basins and 461,829 HRUs across the  ➢ Soil: Data from the Africa Soil Information Service (AfSIS) dataset 39 resampled at 0.
25° × 0. 25°; ➢ Meteorological forcing: Data from the EWEMBI dataset 40 at 0. 5° × 0. 5°. Further, the following methodologies were employed to estimate evapotranspiration and surface runoff and perform flow routing: ➢ Evapotranspiration: Using the Penman-Monteith method 41 ; ➢ Surface runoff: Using the Soil Conservation Service curve number method 42 ; ➢ Flow routing: Using the variable storage routing method  43. Temporally, the simulations were carried out at daily resolution across the 37-year period 1980-2016. For the reposited dataset, results were averaged to monthly timescales to reduce file size. The first eight years of the simulation were considered as spin-up time and left out of the analysis. Spatially, each river section of the modelled river network is designated by a unique identifier (ID) as provided in the reposited dataset, to which hydropower plant coordinates could be mapped (see next section).
First, the flow profile for a "normal year" was defined as the monthly median of the dataset. Subsequently, the flow profile for "very dry" and "very wet" years was taken to be the "normal year" profile multiplied by a corrective factor, calculated as the ratio of the 5 th (very dry) and 95 th (very wet) percentile value of average annual flow to the multiannual average flow. To account for the fact that some few hydropower plants with very large reservoirs are capable of buffering water on interannual timescales and thus mitigate interannual variability, an exception in the calculation was made for those plants with a typical filling time 9 of more than one full year. For these plants, instead of the 5 th and 95 th percentiles, the 10 th and 90 th percentiles were taken to account for this mitigation of dry and wet extremes on interannual timescales.
Third, the seasonal river flow profile thus obtained (for very dry, normal, and very wet years), each characterised as a time series of twelve values representing the months of the year, was normalised to dimensionless values by dividing each time series by the simulated multiyear average flow as simulated (a single number). In this way, the (normalized) seasonality was obtained for each plant in the AHA for which a match of geospatial coordinates with SWAT+ simulated river stretches could be performed. Fourth, wherever possible, the three resulting dimensionless time series of normalised river inflow to each hydropower plant were multiplied again with a bias-correction factor (simple scaling 44 ), equal to the multiannual mean river discharge value collected from existing databases and literature (again, a single number; see section 2.
Calculation of representative seasonal hydropower availability profiles for energy modelling The final step in the calculations was to convert the typical river inflow datasets (whether bias-corrected or not) for each reservoir to typical power output profiles. A distinction was made between (i) run-of-river hydropower plants, (ii) reservoir hydropower plants, and (iii) hydropower plants in a cascade. For each of these, typical profiles of outflow (e. g. of turbined water) were calculated from inflow profiles as described below, before these were further converted to typical seasonal capacity factors.
Data coverage. With these procedures, seasonal availability profiles could be calculated for 550 out of 633 hydropower plant entries in the AHA (87%). For the remaining 83 entries -mostly small existing plants for which the snapping to the simulated river network could not be performed with confidence (see section 2. 4), and "candidates" with unclear locations -the profiles could not be calculated from the present version of the AHA. Future iterations of the database and the simulations may make it possible to further close this gap.
Such plants can be dispatched flexibly up to a certain extent, for example, to follow demand or to aid VRE integration 9 , constrained by typical (sub)-hourly ramping rates which are different from case to case. In such cases, the modelling should be set up in such a way as to ensure that the power plants are represented as dispatchable technologies but constrained by average seasonal availability profiles as given by the AHA. It is important to note that the AHA represents a first attempt at providing a comprehensive, continent-wide spatiotemporal dataset for Africa. As such, it is subject to various limitations which must be considered. The most important limitations are summarised below. First, the river flow profiles presented in this paper were obtained from simulations representing a historical period.
Seventh, in its current form, the AHA covers the African mainland and Madagascar. However, there is potential for small hydropower plants on other, small African island nations such as São Tomé & Príncipe and the Comoros. These are currently not covered by the hydrological simulations used for the AHA. However, these countries will be integrated into the AHA in the future, contingent upon more exhaustive river flow data becoming available. Eighth, we note that hydrological data are here obtained via simulation with a hydrological model forced with EWEMBI model forcing data, which may introduce additional uncertainties. The EWEMBI dataset was compiled to support the bias-correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)  47  which contributed to the 2018 IPCC special report on the impacts of global warming of 1.
5°C above pre-industrial levels and related global greenhouse gas emission pathways, and has as such been widely accepted by the scientific community. EWEMBI is based on the meteorological dataset ERA-Interim 53 , which provides estimates of past weather conditions through numerical weather prediction models, constrained by observations from meteorological or hydrological stations, satellite data, and other sources. While uncertainties in the EWEMBI dataset are indeed larger over the African continent due to sparser observational networks as compared to elsewhere, such reanalyses are still well-constrained by the physical equations that govern weather dynamics, especially for the variable we are interested in, i. e. river discharge, which integrates precipitation over large regions and multiple weeks  47, 54.
We also note that the EWEMBI dataset has meanwhile been superseded by the WFDE5 dataset, produced for phase 3 of ISIMIP  55. Ninth, we note that ideally, all river flow profiles extracted from the SWAT+ simulations would be bias-corrected to data covering the same time period as the simulation period (cf. section 2. 4), which is currently not the case. However, we note that an observational data product covering a substantial amount of the locations and a large part of the modelling period considered in this study does, to the authors' knowledge, not exist at this point. This led the authors to consult a wide range of data sources for the bias-correction, whose time periods are not necessarily consistent with one another. Thus, it is important to note that due to the spatiotemporal limitations of the bias-correction as it was performed, this translates into related spatiotemporal inconsistencies in the quality of the AHA as well.
We hope that this can be resolved in future iterations of the database. Related to the above, we note here that other river flow datasets exist which may be used as alternative or as complement to SWAT+. Relying on a single river flow dataset may have limitations, especially for the African continent where the density of the meteorological and hydrological network is relatively low. For this reason, the authors of this paper recommend practitioners to also consider such datasets as input to AHA-type assessments to more comprehensively clarify the uncertainty associated to hydrological data and simulations. An example of a complementary dataset may be the the GloFas-ERA5 operational global river discharge reanalysis (1979-present) 56.
Conclusions and outlook This paper describes a new African Hydropower Atlas, which marks the first, continent-wide spatiotemporal database of hydropower generation profiles for existing and future hydropower plants. The aim of the AHA is to provide estimates of monthly constraints on capacity factors of hydropower plants to the energy modelling community at a plant-by-plant resolution, taking the differences between moderately dry, normal, and moderately wet years into account. The data set is made freely available in a spreadsheet-based format; in the future, it may be integrated into a web-based interface to allow interactive visualization of the results and promote more widespread diffusion of the resource. By helping energy modellers to better represent hydropower plants' contribution to electricity mixes across Africa, the AHA may support more informed prioritisation of future hydropower projects to be developed.
References It is therefore of high interest for continental-scale energy modelling. The revised version of the manuscript has been much improved following the first-round comments of reviewers, and future projections are welcome. However, there are still places in the text and specific points that lack clarity, and some methodological issues would also require clarifications and additions to the text and the database. They are detailed below. ○ Section 2. 4, "First, the flow profile…": here, in spite of modifications made after the first round of comments, I am still unclear on how the final monthly regimes of normal, dry and wet are obtained. More precisely it is not clear what is the "monthly median of the dataset". I would tend to understand that you took the median interannual value for each month of the year and use it to build a "normal" 12-value regime.
Main issues ○ Section 2. 4, bias-correction: I believe that the answers made to Benoît Hingray on this issue are only partly satisfying. Indeed, while I understand and approve the need for biascorrecting (for this specific application), there are two points that require actions on the manuscript. First, the bias-correction (scaling) is made to existing data on multi-annual mean river discharge, but nothing is said on the period used for this bias-correction, and specifically whether, for a given location, the period for comparing observed and simulated multi-annual averages is the same. This is particularly important in (some parts of) Africa where the multidecadal variability is very high and could therefore lead to unwanted bias-○ correction effects. Clarifications on the methodology in the text about this point are essential, and the corresponding period should be added to the database.
Second, the consequences of this bias-correction for only specific locations should be discussed in the manuscript as it generates de facto possible longitudinal (but limited as I understand form the responses) but clearly spatial inconsistencies in the quality of simulations and thus in corresponding AHA products. This comment should be added to the manuscript. Section 2. 5. 2, storable component: I am sorry to say that in spite of my careful reading of the responses to Benoît Hingray and the modifications made to the manuscript, the intraannual behaviour of "storable component" hypothesized here still quite unclear. Here again, as asked by the previous reviewer, a schematic would be greatly appreciated. This is all the more important for the end-users of this database, for the hypotheses (which are very strong here for this separation between storable and non-storable components) to be correctly understood and apprehended.
This is all the more important that the article referred to by the authors on this point is not in Open Access, and not available to my institution (Sterl et al. , 2020 1 ). ○ Section 3, irrigation modelling: I was quite surprised to learn here that the hydrological simulations take (in some way) irrigation into account while nothing is said about it in the appropriate section 2. 3! Such an important information should be given (as well as appropriate references) in the description of the river flow dataset.
" Additionally, the opening sentence of the following paragraph, "Fourth, wherever possible, the three resulting time series of river inflow to each hydropower plant were additionally bias-corrected (using the simple scaling technique) to the multiannual mean river discharge value collected from existing databases and literature (see section 2. 1). " was changed to "Fourth, wherever possible, the three resulting dimensionless time series of normalised river inflow to each hydropower plant were multiplied again with a bias-correction factor (simple scaling), equal to the multiannual mean river discharge value collected from existing databases and literature (again, a single number; see section 2. 1). " Section 2. 4, bias-correction: I believe that the answers made to Benoît Hingray on this issue are only partly satisfying.
Indeed, while I understand and approve the need for bias-correcting (for this specific application), there are two points that require actions on the manuscript. First, the bias-correction (scaling) is made to existing data on multi-annual mean river discharge, but nothing is said on the period used for this bias-correction, and specifically whether, for a given location, the period for comparing observed and simulated multi-annual averages is the same. This is particularly important in (some parts of) Africa where the multidecadal variability is very high and could therefore lead to unwanted bias-correction effects. Clarifications on the methodology in the text about this point are essential, and the corresponding period should be added to the database. Second, the consequences of this bias-correction for only specific locations should be discussed in the manuscript as it generates de facto possible longitudinal (but limited as I understand form the responses) but clearly spatial inconsistencies in the quality of simulations and thus in corresponding AHA products.
This comment should be added to the manuscript. Author Response: We appreciate this comment from the reviewer, and we agree with them. It is important to note here that a bias-correction approach that would allow (i) all locations in the dataset to undergo bias-correction, for (ii) the same time period in each case, simply does not exist, this due to the strongly varying periods for which observations are available across the African reference stations. A bias-correction for a limited subset of data points, using time periods that are not necessarily all of the same length and not necessarily consistent with the modelling period, was considered by the authors to be the "next best option", despite clearly not being ideal. It is important to understand that the sources consulted to obtain these data were not necessarily from scientific literature.
In many cases, the consulted sources were technical project documents or articles which simply state an average flow rate "as is", without providing the specific period to which this pertains. We agree that this is not ideal, but it is also the "best we have", in many cases. For this reason, we do not provide the "corresponding period" explicitly in the database. However, all the data sources consulted for obtaining the average flow rate for biascorrection are documented in the database. We have adapted the sentence "We note that this step is not crucial for the data product and serves mostly for refinement of final numbers, as monthly availability curves of hydropower plants could be readily estimated without performing the bias-correction step, i. e. for the remaining 40% of cases (see section 2.
This led the authors to consult a wide range of data sources for the bias-correction, whose time periods are not necessarily consistent with one another. Thus, it is important to note that due to the spatiotemporal limitations of the bias-correction as it was performed, this translates into related spatiotemporal inconsistencies in the quality of the AHA as well. We hope that this can be resolved in future iterations of the database. " Section 2. 5. 2, storable component: I am sorry to say that in spite of my careful reading of the responses to Benoît Hingray and the modifications made to the manuscript, the intra-annual behaviour of "storable component" hypothesized here still quite unclear. Here again, as asked by the previous reviewer, a schematic would be greatly appreciated. This is all the more important for the end-users of this database, for the hypotheses (which are very strong here for this separation between storable and non-storable components) to be correctly understood and apprehended.