dataesr/scientific-paragraphs-categorization huggingface dataset:
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). Author Response: Let us say a hypothetical hydropower reservoir has a (live) volume of 2, but a river carries an annual volume of 8 in a "normal" year. In this case, the "storable component" of river flow is equal to 2 (25%, or 2 out of 8), and the "non-storable component" of river flow is the remainder, i. e. 6 (75%). We have added this example to the text to clarify this idea. We hope that this wording sufficiently clarifies this concept and that a schematic is therefore not needed. In response to the comment that the referenced article is not available to the reviewer's institution, we are happy to report that the manual of the REVUB software, which was used in that work, contains the same discussion of "storable" and "non-storable" components, and is available in open-access on https://github.
com/VUB-HYDR/REVUB/blob/master/manual/REVUB_manual. pdf (see "Note 5"). 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. Author Response: The implementation of existing irrigation schemes in the SWAT+ model is fully described in the reference (Chawanda et al. , 2020), and section 2. 3 was only meant to repeat some of the most important elements of the simulations as described in the latter paper. However, we appreciate the reviewer's concern for clarity on the implementation of irrigation, and have added the following bullet point to the list of data sources cited in section 2.
Benoît Hingray Grenoble-INP, IGE, Université Grenoble Alpes, Grenoble, France Characterizing the potential for hydropower production at any location of the hydrological network is obviously key. This requires to estimate the mean resource but also its temporal variability at different temporal scales, including the seasonality and the interannual variability. The work of Sterl et al. aims to provide this characterisation for a large set of locations of the hydrological network in Africa, provided the catchment area of the drainage basin is large enough. To my knowledge, this characterisation was never proposed before. The characterization is based here on river flow time series reconstructed for a 37 years period (1980-2016) from hydrological simulations. River discharges are simulated with SWAT+ from weather pseudo-observations, that is temperature and precipitation reconstructions from different observational datasets.
As reported by the authors in the discussion, the AHA (African Hydropower Atlas) represents a first attempt to provide a comprehensive continent wide spatio-temporal dataset for Africa. This is obviously a very relevant contribution and the AHA is expected to be an important dataset for practitioners and policy makers. A large amount of data has been also collected to characterize water reservoirs along and beside the river network. At the present state however, the methodology used to develop the AHA is not described in enough detail and important clarifications are needed. The AHA is also subject to important limitations. The authors list a number of those. Other important limitations are missing and have to be mentioned and perhaps also discussed. It is very likely that the dataset will be widely used in the coming years, by many "non expert" engineers and policy makers; this definitively call for a better description of the AHA and of its quality.
For South Africa, Chawanda et al. also mention potentially critical limitations due to the low unavailability of reservoir management data; and to the limited information on agricultural management practices. These are expected to strongly modify 1) the water balance and then the available resource and 2) the seasonality of the river discharge downstream the dams. This has likely also to be discussed, at least mentioned. In a conventional publication for the academic world, the reader will guess how large is the uncertainty obtained with SWAT+ simulations in this scarce data context. The AHA is however to be used by practitioners / policy makers. The limitation associated to the different datasets used / produced here have clearly to be mentioned, especially for Africa, where the density of the meteorological / hydrological network is very low.
The reader can understand that there are many paths for improving the models and then the AHA dataset but I recommend that a fair evaluation of the performance of the modelling chain and of the hydrological model is presented. Finally, the concepts behind SWAT+ are rather simple. Other modelling approaches are possible and other have been proposed in the recent year. One important contribution here is the GloFas-ERA5 operational global river discharge reanalysis 1979-present (Harrigan et al. 2020)  1. The simulations are obtained with LISFLood a hydrological model with a long time development and evaluation worldwide. The authors should also cite this dataset and highly recommend practitioners to consider different "reanalysis" datasets to have an idea of the uncertainty / errors associated to hydrological data and simulations.
Metadata for the description of data / reservoirs The description of what has been done, with what data for which reservoir / location will be key for practitioners. A table with the description of all data / metadata provided for any given location is thus probably to be given in the manuscript. Is there any strategy to check the metadata already collected and processed in this work? For the reservoir description (P4. §4) for instance : Are the authors in contact the "Water Resource Authorities" of the different countries to check the exhaustivity / characteristics of the dams considered in the work. Then it would be interesting to precise the strategy retained to update the data / metadata of the AHA. For instance to update the metadata for the description of the different "existing" and future projects? Is there any reference institution that is committed to do this update? What is the contact for this ? Do the authors expect to produce / deliver improved releases of the AHA in the coming years? p7 §3 : "snapping" : is the hierarchy level considered for each plant in the AHA mentioned in the metadata of the AHA (this information is obviously key)? Data Availability I am not a typical enduser of such database but the author say that the AHA gives "-SWAT+ simulation results used to extract river flow profiles provided as text files (.
TXT). " The format of the data there is surprising and makes for me the file hard to exploit. All monthly time steps of the 1980 -2016 period are given in turn, with all 5438 hydrological units used in the SWAT+ model pooled for each time step in 5438 consecutive lines. We do not have thus access to the time series of simulated discharges for each hydrological unit individually. This could be likely improved sothat the enduser could access easily to the full time series (and not only to the yearly mean profiles) of each hydrological unit. I also did not find a note that describes what is contained in each file of the database. Can such a be given as a SM of the present note? Detailed comments P4. First § : the multi-annual mean discharges. Can you precise for which period? Based on which data? p7.
The authors list a number of those. Other important limitations are missing and have to be mentioned and perhaps also discussed. It is very likely that the dataset will be widely used in the coming years, by many "non expert" engineers and policy makers; this definitively call for a better description of the AHA and of its quality. The AHA has definitively to be indexed in a short time because of its high value for engineers and policy makers but, for the different reasons mentioned here, I recommend major corrections / clarification before the indexing of the manuscript. Author Response: We thank the reviewer for their positive words about our manuscript, and are grateful for the constructive and very extensive criticism which we have done our best to take into account.
The limitations of EWEMBI have also to be acknowledged. They are expected to be large in regions without or with scarce meteorological stations which is the case almost everywhere in Africa. Some reference with the relevant evaluation should be provided. Author Response: We thank the reviewer for this comment. We have added an eighth point of discussion in the "Discussion" section, which acknowledges the diversity of data of EWEMBI but notes also the wide scientific acceptance of the EWEMBI dataset. We note here also a few additional points as to why we consider the EWEMBI dataset to be the go-to dataset for this undertaking, specifically the fact that (i) despite the relative scarcity of observational stations on the African continent, reanalyses are well-constrained by the physical equations that govern weather dynamics, and (ii) the variables we are interested in integrate precipitation data over large regions and multiple weeks.
The AHA is however to be used by practitioners / policy makers. The limitation associated to the different datasets used / produced here have clearly to be mentioned, especially for Africa, where the density of the meteorological / hydrological network is very low. The reader can understand that there are many paths for improving the models and then the AHA dataset but I recommend that a fair evaluation of the performance of the modelling chain and of the hydrological model is presented. Finally, the concepts behind SWAT+ are rather simple. Other modelling approaches are possible and other have been proposed in the recent year. One important contribution here is the GloFas-ERA5 operational global river discharge reanalysis 1979-present (Harrigan et al. 2020)1. The simulations are obtained with LISFLood a hydrological model with a long time development and evaluation worldwide.
The authors should also cite this dataset and highly recommend practitioners to consider different "reanalysis" datasets to have an idea of the uncertainty / errors associated to hydrological data and simulations. Author Response: We thank the reviewer for this useful addition. In the revised version of the manuscript, we now cite the proposed datasets as potential alternatives to the SWAT+ dataset used in the present study, including the recommendation to undertake similar analyses in the future based on these datasets for a more comprehensive uncertainty assessment and the rationale behind this (low density of met/hydro station network on the African continent). We also note that an upcoming publication (Chawanda et al. , in prep. ) analyses the SWAT+ results in details and includes an uncertainty assessment.
While too late to be cited in the current AHA study, we hope that this information will be available to the scientific community in due course. Bias correction. P7 C2 § 3. The principle / interest / limitation of bias correction have to be clarified. If bias correction is applied on simulated discharges, observations are used therefore. What is then the added value of simulations as the characterisation of hydrological regime can be done with those observations only? Author Response: The added value of the simulations is to provide data on (i) seasonality and (ii) interannual variability of river flow, since the bias correction is based only on observed/cited long-term mean discharges. Clearly, one needs observations on seasonalities and interannual variabilities to infer long-term mean discharges, but the simple fact is that the latter are widely available in literature and public datasets, whereas the former are usually not.
We are interested in pursuing such a strategy in the future contingent upon budgetary possibilities. Then it would be interesting to precise the strategy retained to update the data / metadata of the AHA. For instance to update the metadata for the description of the different "existing" and future projects? Is there any reference institution that is committed to do this update? What is the contact for this ? Do the authors expect to produce / deliver improved releases of the AHA in the coming years? Author Response: Yes, it is expected that improved releases of the AHA will be published in the coming years as new information gets included in the database. The institution committed to do this update is the International Renewable Energy Agency (IRENA) in collaboration with the Vrije Universiteit Brussel.
The contact, at this moment, is the lead author of this paper. p7 §3 : "snapping" : is the hierarchy level considered for each plant in the AHA mentioned in the metadata of the AHA (this information is obviously key)? Yes, the AHA includes a column "source location" which describes the source of the coordinates as included. Data Availability I am not a typical end-user of such database but the author say that the AHA gives "-SWAT+ simulation results used to extract river flow profiles provided as text files (. TXT). " The format of the data there is surprising and makes for me the file hard to exploit. All monthly time steps of the 1980 -2016 period are given in turn, with all 5438 hydrological units used in the SWAT+ model pooled for each time step in 5438 consecutive lines. We do not have thus access to the time series of simulated discharges for each hydrological unit individually.
txt file. I also did not find a note that describes what is contained in each file of the database. Can such a be given as a SM of the present note? Author Response: In case the reviewer refers to the SWAT+ results when referring to the database, this information is given in the section "Data Availability". In case the reviewer refers to the AHA itself, this information is provided on the first worksheet "0 -Overview" of the spreadsheet file available on HydroShare. Detailed comments P4. First § : the multi-annual mean discharges. Can you precise for which period? Based on which data? Author Response: See our comment above (fourth response in the section "bias correction"). p7. C2 - § 1 : "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 time9 of more than one full year.
For these plants, instead of the 5th and 95th percentiles, the 10th and 90th percentiles were taken to account for this mitigation of dry and wet extremes on interannual timescales. " I do understand the rationale in the previous paragraph but I would suggest that the first estimate with the 5th and 95h percentiles is given for all reservoirs and that the 10th and 90th percentiles are given as additional information for the very large reservoirs. Is it also mentioned in the AHA what is the mean time required to fill in the reservoir from the zero level? Author Response: We thank the reviewer for this comment. While we see the rationale of the reviewer's suggestion, we prefer to leave the database as it is without adding additional percentile ranges, as this is likely to lead to confusion as to what our data suggests is representative of "very dry" and "very wet" years.
Author Response: We fully agree that this issue is important. During the revision of the paper, we developed the next version of the AHA (version 2. 0), which now includes a first iteration of climate change scenario results. Dedicated SWAT+ simulations were performed under three RCP-SSP combinations and the AHA results were recalculated based on this. Thus, the AHA v2. 0 contains now three extra sheets for these scenarios. We hope that this will add much additional value to the dataset and be to the reviewer's satisfaction. The following text was added to the revised paper: "A first iteration of the AHA under climate change scenarios has been included in its version 2. 0 (see "Data availability"). These results were obtained by forcing SWAT+ with simulation results obtained from four bias-adjusted global climate models (GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR and MIROC5).
Author Response 13 Dec 2021 Sebastian Sterl, Vrije Universiteit Brussel, Brussels, Belgium This article gives a clear description of the African Hydropower Atlas (AHA). The authors explain all the steps behind the creation of the data, and in general the methodology is very clear. The task of creating a complete and accurate dataset of all current and future hydropower projects in Africa is definitely challenging and I can say that the authors succeeded in this task. We thank the reviewer for their positive words about our manuscript and are grateful for the constructive criticism which we have done our best to take into account. However, I would like to mention a few points that can be addressed or discussed to improve the work: There are some hydropower plants that are built as a joint initiative of multiple countries which agree then to share the electricity generated.
African continent. Input data was obtained from the following sources: Digital elevation: A 90 × 90 m Digital Elevation Model (DEM) acquired from the Shuttle Radar Topography Mission 36 ; ➢ Land use: Data from the Land Use Harmonization (LUH2) dataset 37 at 0. 25° × 0. 25° resolution; ➢ Irrigated areas were obtained from Food and Agriculture Organisation (FAO) data at 0. 083° × 0. 083° resolution 38. Irrigation modelling was implemented as explained in ref. 35.
CORDEX climate projections. Climate Services. 2018; 11: 36-48 Publisher Full Text 7. Bichet A, Diedhiou A, Hingray B, Evin G, et al. : Assessing uncertainties in the regional projections of precipitation in CORDEX-AFRICA. Climatic Change. 2020; 162 (2): 583-601 Publisher Full Text 8. Sidibe M, Dieppois B, Eden J, Mahé G, et al. : Near-term impacts of climate variability and change on hydrological systems in West and Central Africa. Climate Dynamics. 2020; 54 (3-4): 2041-2070 Publisher Full Text Is the rationale for creating the dataset(s) clearly described? Yes Are the protocols appropriate and is the work technically sound? Yes Are sufficient details of methods and materials provided to allow replication by others? Partly Are the datasets clearly presented in a useable and accessible format? Partly Competing Interests: No competing interests were disclosed.
Section 2. 3, quality of SWAT+ simulations: The responses to comments by Benoît Hingray on this specific issues are in my view not satisfying. Hydrological modelling is in this study used to get reasonable estimates of seasonal and interannual variability of streamflow across the continent. I therefore strongly believe that an appropriate assessment of such properties is definitely required for one to be able to gain confidence in the AHA results. One may think of monthly NSE or monthly NSE with respect to the average regime, to check the ability of SWAT+ to get interannual variability right. This assessment should be included in the AHA database where available, i. e. where bias-correction has been done. More on the biascorrection below.
Acknowledgements The authors thank  A. Miketa ,  D. Gielen ,  R. Roesch ,  C. Ruiz ,  I. Gherboudj, M. S. Nababa ,  Y. Li & T. T. Bastianello  (IRENA),  S. Kuujala ,  J. Huhdanmäki ,  J. Leino & M. Jokinen  (Wärtsilä), and  A. Campbell ,  E. Rich ,  S. Law ,  O. McCue ,  & C. Diez Santos  ( International Hydropower Association ) for inspiring comments and discussions. The authors further thank  A. Carlino  ( Politecnico di Milano & IIASA ) for constructive criticism that helped to improve the database. The map in Figure 3 was created using the  Esri Satellite Basemap 32  and thus using the ArcGIS® software by  Esri. ArcGIS®  and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright ©Esri. All rights reserved. For more information about Esri® software, please visit www.
➢ African continent. Input data was obtained from the following sources: Digital elevation: A 90 × 90 m Digital Elevation Model (DEM) acquired from the Shuttle Radar Topography Mission 36 ; ➢ Land use: Data from the Land Use Harmonization (LUH2) dataset 37 at 0. 25° × 0. 25° resolution; ➢ Irrigated areas were obtained from Food and Agriculture Organisation (FAO) data at 0. 083° × 0. 083° resolution 38. Irrigation modelling was implemented as explained in ref. 35.
Publisher Full Text 25. Buontempo C, Booth B, Moufouma-Okia W: The climate of the Sahel. 2012; 58-72. Publisher Full Text 26. Vogel C, Steynor A, Manyuchi A: Climate services in Africa: Re-imagining an inclusive, robust and sustainable service. Clim Serv. 2019; 15: 100107. Publisher Full Text 27. Lehner B, Liermann CR, Revenga C, et al. : High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management. Front Ecol Environ. 2011; 9(9): 494-502. Publisher Full Text 28. FAO: Geo-referenced database on dams in Africa. 2016. Reference Source 29. IRENA: Renewable Capacity Statistics 2020. International Renewable Energy Agency, Abu Dhabi, 2020; Accessed: Apr. 06, 2018. Energy Strategy Rev. 2021; 33: 100606. Publisher Full Text 18. Linke S, Lehner B, Dallaire CO, et al.
Soil Profiles Database, version 1. 1: a compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa (with dataset). Africa Reference Source 42. Mishra SK, Singh VP: SCS-CN Method. In: Soil Conservation Service Curve Number (SCS-CN) Methodology. Water Science and Technology Library. Springer, Dordrecht, 2003; 42: 84-146. Publisher Full Text 43. Williams JR: Flood routing with variable travel time or variable storage coefficients. Transactions of the ASAE. 1969; 12(1): 100-103. Publisher Full Text 44. Chen J, Brissette FP, Chaumont D, et al. : Finding Soil Information Service (AfSIS) project, ISRIC. 2013. Publisher Full Text 40. Lange S: EartH2Observe, WFDEI and ERA-Interim data Merged and Biascorrected for ISIMIP (EWEMBI). GFZ Data Services, 2016. Publisher Full Text 41.
Hydropower versus irrigation-an analysis of global patterns Publisher Full Text 53. Dee DP, Uppala SM, Simmons A, et al. : The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc. 2011; 137(656): 553-97. Publisher Full Text 54. Lange S: Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth Syst Dynam. 2018; 9(2): 627-645. Publisher Full Text 55. Cucchi M, Weedon GP, Amici A, et al. : WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst Sci Data. 2020; 12(3): 2097-2120. Publisher Full Text 56. Harrigan S, Zsoter E, Alfieri L, et al. : GloFAS-ERA5 operational global river discharge reanalysis 1979-present. Earth Syst Sci Data. 2020; 12(3): 2043-2060. Publisher Full Text 57.
Section 2. 3, quality of SWAT+ simulations: The responses to comments by Benoît Hingray on this specific issues are in my view not satisfying. Hydrological modelling is in this study used to get reasonable estimates of seasonal and interannual variability of streamflow across the continent. I therefore strongly believe that an appropriate assessment of such properties is definitely required for one to be able to gain confidence in the AHA results. One may think of monthly NSE or monthly NSE with respect to the average regime, to check the ability of SWAT+ to get interannual variability right. This assessment should be included in the AHA database where available, i. e. where bias-correction has been done. More on the biascorrection below. ○ Section 2. 4, "a typical range of years": It should be specified here what kind of years are considered here.