Dear Reviewer 1,

We appreciate the time and effort you dedicated to thoroughly reviewing our manuscript and offering suggestions for improvement. Below, we outline how we plan to address the raised issues. We reference your comments followed by our corresponding responses. The changes that would be applied to the manuscript text are highlighted in blue and are visible in the uploaded PDF version of this text.

COMMENT 1    

L40. Why information on hydraulic properties is often lacking. Particularly, now that the EU Commission proposes the Soil Monitoring Law and such attributes can be important for soil health. By the way, you did not mention anything in your manuscript about soil health. How those attributes are linked to Soil health and how to be used to estimate soil health?

ANSWER 1    

1.1 We will revise that sentence in the following way:

“The basic soil properties, i.e., soil organic carbon content, particle size distribution, in most cases are locally available at high resolution (< 100 m), but information on bulk density, albedo, soil erodibility factor, soil hydraulic properties, and soil nutrient content is often lacking.”

The reason for the above is that some of these properties are difficult to measure and/or too dynamic (like soil nutrients) and/or not relevant enough in some common applications (e.g. soil albedo is not important in daily agricultural management).

1.2 Soil hydraulic properties are important determinants of soil health as they influence water availability, soil structure, nutrient transport, gas exchange, and surface runoff. For soil health, the threshold values of its indicators might be interdependent. This needs an in depth description which is beyond the scope of this MS. Therefore we might not go into details, but mention soil health in the abstract and under conclusions to show that analysed soil properties are important to assess soil health. 

We will highlight soil health in the first sentence of the abstract:

“To effectively guide agricultural management planning strategies and policy, it is important to simulate water quantity and quality patterns and quantify the impact of land use and climate change on soil functions, soil health, hydrological, and other underlying processes.”

In the last paragraph of the Conclusions we will add the following text:

“The workflows and findings presented in the study offer practical guidance for model setup and data preprocessing in various modelling endeavours across Europe, such as hydrological simulations, assessment of soil health, land evaluation, crop modelling, and analysis of soil erosion risk among others.”

COMMENT 2    

Table 1 is not easily readable as proposed. I would propose to have a landscape format and add also a column as Reference. Practically, transfer the reference from first column to the new one. Some corrections are also necessary:

2.1 It is European Union (EU). Of course if you state Member states is similar but more precise to refer to the EU.

2.2 In Soil hydraulic or Physical data , you can add (as there are new datasets that have not been taken into account in your analysis ; such as the Bulk density in the EU and the Global K-factor: 

1    https://esdac.jrc.ec.europa.eu/content/topsoil-physical-properties-europe-based-lucas-topsoil-data   *** Clay, silt and sand content; coarse fragments; bulk density; USDA soil textural class; available water capacity. Resolution 500m. as in Ballabio et al., 2016

2    https://esdac.jrc.ec.europa.eu/content/chemical-properties-european-scale-based-lucas-topsoil-data *** pH, pH (CaCl), Cation Exchange Capacity (CEC), Calcium carbonates (CaCO3), C:N ratio, Nitrogen (N), Phosphorus (P) and Potassium (K)  as in Ballabio et al., 2019

3    https://esdac.jrc.ec.europa.eu/content/soil-bulk-density-europe ****** Soil Bulk density in Europe as in Panagos et al., 2024

4    https://esdac.jrc.ec.europa.eu/content/global-soil-erodibility ***** The Global K-factor of Gupta et al. 2024

Those are different that LUCAS. You mentioned LUCAS but those datasets are derived through LUCAS with Machine learning. 

ANSWER 2    

Thank you for the suggestions, we will format it to landscape and move references into a separate column.

2.1 We will correct it.

2.2 Thank you for the suggestions we will add these four datasets to Table 1.

COMMENT 3    

L116-117: in this place and other places of the manuscript. You mentioned this sentence but you have to admit that also application of PTF has a huge uncertainty and it not proper for all different pedo-climatic regions of Europe. EU is so diverse that the just one PTR is not the valid approach for the whole EU. Your statements such as this one as so strong and negative towards other estimations or assessments. In general, I would suggest a more multi-model approach where assessments based on machine learning or interpolations can be compared or assessed together with assessments of PTF. 

ANSWER 3    

Here we intended to draw attention on not combining local basic soil data with continental or global soil hydraulic maps, but use local basic soil data and derive the missing properties from that to keep consistency in the data (locally available and the derived one).

We will modify lines 116-118:

“Where local soil maps with soil layering, organic carbon content, clay, silt, and sand content are available, it is suggested that missing soil properties, such as bulk density, soil hydraulic properties, and albedo are estimated from the locally available basic soil properties to ensure consistency.”

We agree that PTFs have uncertainty, especially when those are applied on soils which have specific soil characteristics, e.g.: specific clay mineralogy, high exchangeable sodium content, high organic carbon content, which overrides the influence of basic soil properties on soil hydraulic characteristics. We will add the following text to highlight it:

“The predictions are subject to uncertainty, which depends on the similarity between the training data used for the selected prediction method and the target area in terms of soil physical and chemical characteristics (Román Dobarco et al., 2019; Tranter et al., 2009).”

We will modify line 129 :

“For the selection of the prediction approaches, three requirements had to be fulfilled: i) the prediction algorithm had to be trained on temperate soils and should not be specific to a particular soil reference group, …”

Our aim is to provide workflows which could be easily applied anywhere in Europe. Using ensemble approach or geostatistical methods is out of the scope of this study, but we will add under conclusions (before the last sentence) the following text:

“The presented workflows could be further improved by using a multi-model approach and applying geostatistical methods.”

COMMENT 4    

The evaluation of your results takes place using the EU-HYDI . This database as you state is not publicly available. This is really odd and not transparent. Others do European assessments (Soil Grids, ESDAC) or Global assessments but the point data (LUCAS, global point data) are available. Therefore, the approaches are transparent and everybody can test them.

ANSWER 4    

The reason for using EU-HYDI for this study is that it is the most representative soil hydraulic dataset for Europe that we could use for this study. The internal use and no external openness of the dataset has been requested by the data providers during the establishment of EU-HYDI, which was initiated and coordinated by EC Joint Research Centre (JRC) in 2013. Some contributors have given the JRC licence to distribute their raw data publicly on the European Soil Data Centre. Based on information from JRC it will soon be available from ZENODO. Information about data availability is provided here: https://github.com/melwey/euhydi_public . We will add this information under “Data availability” section as soon as the link will be available from JRC:

“6,583 samples of 1999 soil profiles, summing up to 35 % of the EU-HYDI dataset is available from ZENODO DOI LINK. The entire dataset cannot be made publicly available due to its legal restrictions.”

And add information about the LUCAS dataset:

“LUCAS TOPSOIL data can be accessed through European Soil Data Centre (ESDAC) (European Commission Joint Research Centre, 2024; Panagos et al., 2012, 2022). Local measured topsoil phosphorus data is private, only results of analysis and derived information can be published.”

COMMENT 5    

In Bulk density, the BD of Hollis is not so simple. Hollis has proposed different PTF per different land uses. Therefore, please pay attention in this use. In addition, as mentioned before, you ignored the recent public assessment of EU Bulk density with 6,000 points available (to download). 

ANSWER 5    

Thank you for pointing out that Hollis derived more PTFs, we will use the PTFs derived for “cultivated topsoils”, “all other mineral horizons” and “all organic horizons” based on the suggestion of Hollis et al. (2012). We will rerun the BD PTF analysis on the LUCAS point BD dataset, as well.

COMMENT 6    

For K-factor, the most used function is the by Wischmeier and Smith (1978) and Renard et al. (1997) (as described in Panagos et al., in Eq. (1)). 

You use a different equation and then you try to compare your results with the ones which have used the Renard equation. This is a little bit odd. 

ANSWER 6    

We would like to compare soil erodibility factor values which are derived in different ways, i.e. with the equation and retrieved from the European map to analyse the difference. We believe that this is informative for the readers who are not familiar with the computation of soil erodibility and very interesting to see the differences. The differences in the results can highlight the importance to use the appropriate equation for the computation or treat the erodibility factor as a parameter that has to be further tuned in the model calibration.

Our aim was to use only those equations which can be readily applied for the soil properties most frequently available and not use the ones that require non-easily available soil properties, such as soil structure or permeability. The Renard et al. 1997 equation fits into this logic, therefore we will add K factor computed with it (K_computed_Lenard) and compare its result with the methods already included. 

COMMENT 7    

In 2.4, for P it is not only the fertilization which plays a role. The available P in soils is a combination of P inputs (Fertilizers, manure, atmospheric deposition, chemical weathering) and outputs (plant uptake, plant residues , erosion). Therefore P level is not influenced only by fertilization. Please be careful and change as appropriate!

ANSWER 7    

Thank you for the suggestion. We will modify it with the following text:

“The available P level in agricultural soils is influenced by the P inputs – fertilizers, manure, atmospheric deposition, chemical weathering – and outputs – plant uptake and erosion.  The agricultural management practices (Tóth et al., 2014) are determined by factors such as the country's economy, climate, tillage practices, and crop production characteristics.”

COMMENT 8    

In soil chemical parameters, authors do not explain why not N and K?

ANSWER 8    

Organic nitrogen could be computed from soil organic carbon, but its inorganic part is variable in space and time, therefore it is complex to predict it. We have a paragraph on N at the end of section 2.4. There we explain why we do not consider inorganic N. Potassium is not typically included or computed in environmental models, therefore we did not add information on K.

We will modify line 325 of the manuscript:

“Organic nitrogen can be estimated from soil organic carbon content (Amorim et al., 2022; Liu et al., 2016; Pu et al., 2012; Zhai et al., 2019) if measured data are not available. The concentration of inorganic nitrogen in soil is …”

COMMENT 9    

In section 3.1, the performance of BD PTF is not valid as I explained my problematic for the Hollis eq (which is not used properly). 

In addition, why you do not test the PTFs against the LUCAS 6000 measured data which are publicly available?

ANSWER 9    

Thank you to point it out. We have received the data from EC JRC and will perform the analysis.

COMMENT 10    

The problematics on 3.3 have also described above as your results tend to compare non comparable stuff (different equations used!!!).

ANSWER 10    

Our aim is to compare different methods for deriving soil erodibility data. We believe it would be informative for readers to see the variation in derived soil erodibility values when using either an available soil erodibility map or an equation. This information will show that these predicted values could be used as preliminary approximations and need for K calibration.

We will add the following modification of line 425-427:

“While both can be used for environmental modelling, i) European soil erodibility map could be linked with LUCAS topsoil dataset and maps, ii) employing Eq. (17) might offer greater consistency with the other local basic and physical soil data, aligning more seamlessly with the modelling process.“ 

COMMENT 11    

In 618-619: you refer to something that it is too obvious. IF there are local data, of course they are better. The case is how to cover the data gaps in case local data are not available? That is why I have proposed a multi-model or multi-data source assessment?

ANSWER 11    

We will delete the sentence with that obvious statement (starting with “In summary, …”). As mentioned under our ANSWER 3, the aim of this study is to present easy to apply methods. Combining P map derived based on European data with locally measured data fits the idea of multi-data source solution, therefore we will add it to line 622:

“Where available, it is recommended to use measured data to overwrite the geometric mean values, creating a multi-data source solution that reflects the spatial pattern of nutrient content within arable land areas.” 

COMMENT 12    

Similar in your  conclusions L653. It is too obvious!

ANSWER 12    

We agree, therefore we will delete that sentence and modify lines 654-655 in the following way:

“Local data tend to retain finer soil details, hence it is recommended that users prioritise the utilisation of local (national) soil databases when it is deemed representative and reliable.”

COMMENT 13    

L675-685: you cannot propose this as the only way forward without making available your reference dataset (EU-HYDI)!!!

ANSWER 13    

We intended to write that the methodology can be applied on other databases as well. We will modify the sentence in the following way:

“The study’s methodology can be applied for soil databases not only in Europe but also in other regions or global datasets, …”

COMMENT 14    

L16:  which are the underlying processes?

ANSWER 14    

We will add some examples for the underlying processes:

“ … it is important to simulate water quantity and quality patterns and to quantify the impact of land use and climate change on soil functions, soil health, hydrological, and other underlying processes.”

COMMENT 15    

L28-29: why there an significant increase of available datasets?

ANSWER 15    

We will modify that sentence in the following way:

“The availability of raw and derived soil datasets, specifically soil hydraulic data, has increased significantly in Europe over the last 10 years as a result of the Soil Strategy and Soil Monitoring Law proposed by the EU Commission.”

COMMENT 16    

L159: Not only different methods but also through different ISO protocols, depths, etc and in different laboratories which sometimes is impossible to compare.!!

ANSWER 16    

We agree. 

We will add ISO protocols in L159-160:

“Different countries and institutions measure sand, silt, and clay content using different ISO protocols and methods by recognizing different cutoff limits and classification standards.”

COMMENT 17    

L325: It is nitrogen

ANSWER 17    

Here we would like to refer to inorganic nitrogen forms, which are more soluble in water, therefore are more susceptible to leaching and loss through processes like denitrification, volatilization, and leaching. We will modify lines 325 and 326:

“The concentration of inorganic nitrogen in soil is highly variable in space and time and the dynamic of its amount is significantly influenced by leaching, denitrification, volatilization, and nitrogen fertilization (Zhu et al., 2021).

REFERENCES ADDED IN THE DOCUMENT:

Amorim, H.C.S., Hurtarte, L.C.C., Souza, I.F., Zinn, Y.L., 2022. C:N ratios of bulk soils and particle-size fractions: Global trends and major drivers. Geoderma 425, 116026. https://doi.org/10.1016/j.geoderma.2022.116026

Liu, M., Ussiri, D.A.N., Lal, R., 2016. Soil Organic Carbon and Nitrogen Fractions under Different Land Uses and Tillage Practices. Commun. Soil Sci. Plant Anal. 47, 1528–1541. https://doi.org/10.1080/00103624.2016.1194993

Pu, X., Cheng, H., Shan, Y., Chen, S., Ding, Z., Hao, F., 2012. Factor controlling soil organic carbon and total nitrogen dynamics under long-term conventional cultivation in seasonally frozen soils. Acta Agric. Scand. Sect. B Soil Plant Sci. 62, 749–764. https://doi.org/10.1080/09064710.2012.700318

Román Dobarco, M., Cousin, I., Le Bas, C. and Martin, M. P.: Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty, Geoderma, 336(April 2018), 81–95, doi:10.1016/J.GEODERMA.2018.08.022, 2019.

Tranter, G., McBratney,  a. B. and Minasny, B.: Using distance metrics to determine the appropriate domain of pedotransfer function predictions, Geoderma, 149(3–4), 421–425, doi:10.1016/j.geoderma.2009.01.006, 2009.

Zhai, X., Liu, K., Finch, D.M., Huang, Di., Tang, S., Li, S., Liu, H., Wang, K., 2019. Stoichiometric characteristics of different agroecosystems under the same climatic conditions in the agropastoral ecotone of northern China. Soil Res. 57, 875–882. https://doi.org/10.1071/SR18355

Dear Diana Vieira,

Thank you for the time and effort you have put into the detailed review and suggestions on how to improve our manuscript. Please find below how we would address the raised issues. First, we cite your comment, then provide our response. The changes that would be applied to the manuscript text are highlighted in blue and are visible in the uploaded PDF version of this text.

GENERAL COMMENT:

This study compiles a series of datasets of soil properties and pedotransfer functions available for catchment scale modeling, evaluate them and provide an R open script for soil data derivation. I believe this is a valuable piece of work, however, I have several major concerns about its suitability for publication in its current shape but I believe it could be significantly improved. The main concerns is the fact that there is no data openness in this work, the main data used is not available to assess reproducibility and another smaller dataset (independent dataset provided by agricultural company) is not even described. If the authors are willing to share the data and correct the identified problems, I believe this could be an excellent contribution.

ANSWER FOR GENERAL COMMENT:

1) The reason for using EU-HYDI for this study is that it is the most representative soil hydraulic dataset for Europe that we could use for this study. The internal use and no external openness of the dataset has been requested by the data providers during the establishment of EU-HYDI, which was initiated and coordinated by the EC Joint Research Centre in 2013. Some contributors have given the JRC a licence to make their raw data publicly available on the European Soil Data Centre. Based on information from JRC it will soon be available from ZENODO. Information about data availability is provided here: https://github.com/melwey/euhydi_public . We will add this information to the “Data availability” section of the manuscript as soon as the link will be available from JRC:

“6,583 samples of 1999 soil profiles, summing up to 35 % of the EU-HYDI dataset, are available from ZENODO DOI LINK. The entire dataset cannot be made publicly available due to its legal restrictions.”

2) Thank you for pointing out the missing description of the dataset on topsoil phosphorus content. We will add the following text on it under section 2.1 “Evaluation of the methods” in line 143:

“The LUCAS Topsoil dataset (Orgiazzi et al., 2018; Tóth et al., 2013) of 2009 was used for the computation of nutrient content of the surface soil layer. For the assessment of the topsoil phosphorus maps, we used locally measured data obtained from an agricultural company. This dataset includes soil phosphorus content measured at a depth of 30 cm using the acid ammonium acetate lactate extraction (AL-P) method (Egnér et al., 1960) for 34 agricultural parcels in the year 2009. As the phosphorus content was required according to the Olsen method (Olsen-P) (Olsen et al., 1954), we applied the equation of Sárdi et al. (2009) for converting AL-P into Olsen-P. Table 2 shows the descriptive statistics of this database.”

SPECIFIC COMMENTS

COMMENT 1:

Lines 39 -58 – The introduction could approach more deeply the importance/availability of all the soil properties mentioned in the abstract (Lines 23-25), however the examples provided seemed disconnected between each other. Alternatively, further explanation could be provided to justify giving those examples and not other.

ANSWER 1:

1.1 We will modify lines 39-44 in the following way:

“The basic soil properties, i.e., soil organic carbon content, particle size distribution, in most cases are locally available at high resolution (< 100 m), but information on bulk density, albedo, soil erodibility factor, soil hydraulic properties, and soil nutrient content is often lacking. There are many PTFs available in the literature that can be used to calculate soil physical (Abbaspour et al. 2019) and hydrological (Bouma and van Lanen, 1987; Van Looy et al., 2017) parameters from basic soil properties, but determining the most suitable one might not be obvious.”

1.2 Lines 50-58 will be moved before lines 45-58:

“Information on soil nutrient properties often essential for environmental modelling, such as plant-available soil phosphorus or soil nitrate content, is seldom accessible at a catchment or regional scale. In the absence of measured data on nutrient content, estimating highly mobile nutrients like nitrate poses a challenge due to seasonal fluctuations influenced by factors such as fertilizer application, rainfall, plant nutrient uptake, and microbial activity. Regarding plant-available phosphorus, its levels typically exhibit minimal variation throughout a year. Therefore, approximating its quantity could rely on land use type and area-specific phosphorus fertilization loads (Ballabio et al., 2019). Nevertheless, multiple methods are employed across Europe to measure plant-available soil phosphorus content, potentially requiring conversions between these methods for broader-scale applications. A comprehensive review on conversion equations is available specifically for European studies in Steinfurth et al. (2021).”

1.3 Under lines 45-58 those soil properties are included which are more challenging to retrieve. Thanks for the suggestion, we will add the following explanation to the text:

“Often those soil properties are required as model input data as well, which are rarely available. One example is the data on soil cracking. Cracking intensity and number of cracks are determined by i) soil mineralogy, specifically the amount and type of clay minerals, ii) type of strength that forms soil structure (Lal and Shukla, 2004) and iii) human activity, e.g. tillage, plant spacing. The aperture and closure of cracks can be dynamically related to soil water content (Xing et al., 2023). The data that could describe the variability of cracking is also not easily available, therefore prediction of this parameter is limited at catchment scale.”

COMMENT 2:

Lines 64-67 – What would this allow the scientific community to do? Support modeling studies? Assist researchers in the decision for methodological approaches? Please provide a statement.

ANSWER 2:

We will modify lines 64-67 accordingly:

“Therefore, in this study we support soil data retrieval for environmental modelling across Europe by i) systemizing information on open access datasets and PTFs applicable for Europe, ii) demonstrating and quantifying the difference between some PTFs and prediction approaches to cover missing soil properties based on the point data of EU-HYDI, and iii) providing a comprehensive workflow and accompanying open-source R script and library for the derivation of missing soil data.”

COMMENT 3:

Materials and Methods

I was hoping to read here how you determine the compilation in table 1. How to ensure this dataset is complete?

ANSWER 3:

We will add the following text in line 111 to highlight the continuous improvement of datasets and most important sites where information on new dataset or updates is expected to be available in the future:

The availability of datasets is continuously improving. The following data sites include most of the updates:

• European Soil Data Centre, which includes soil datasets from Europe and information on EU Soil Observatory (https://esdac.jrc.ec.europa.eu/),
• ISRIC Soil Data Hub, which hosts soil data from around the world (https://data.isric.org/geonetwork/srv/eng/catalog.search#/home),
• soil related layers of the GAEZ Data Portal developed by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) (https://data.apps.fao.org),
• soil related layers of the OpenLandMap, which shares open geographical and geoscientific data (https://openlandmap.org).

However, these sources do not include products from specific institutes, such as http://globalchange.bnu.edu.cn/research. The datasets included in Table 1 might be appropriate for regional and continental modelling.

COMMENT 4:

Lines 92 -109 – These are the “soil properties most frequently required by environmental models” based on what? How did you determined this list?

ANSWER 4:

We will modify lines 92-93 in the following way:

“Soil properties most frequently required as static parameters by the environmental models – e.g. (Abbaspour et al., 2019; Dam et al., 2008; Dang et al., 2022; DHI, 2023; Hansen et al., 2012; Šimůnek et al., 2012; Yu et al., 2020) are:”

COMMENT 5:

Lines 112 – Regarding the statement “Local and national datasets provide more accurate input information”, I would say depends. If is a spatially explicit modeled database I would say definitely yes, if is point data with precise coordinates of the source of the data (e.g. LUCAS) I would say no. Of course, in this condition is not possible to address quantity of information (i.e. density of points) but one would expect local data to present higher data availability, but this is not mentioned in the text. Please be more precise on this matter.

ANSWER 5:

Thank you for the suggestion, we will modify lines 111-112 accordingly:

“However, for catchment scale and national studies, local and national spatially explicit modelled datasets provide more accurate input information.”

COMMENT 6:

Line 114-119 – I read this text several times, I understand you want to make the case regarding the previous sentence, but is not entirely clear please rephrase it.

ANSWER 6:

We will modify lines 112-119 lines to improve clarity:

When a certain local dataset is selected to be used as basic soil information, it is more consistent to compute the missing soil properties from this local data source rather than using other data sources. This allows to maintain consistency between the different soil properties. For example, it is not recommended to combine a local soil property map at 100 m resolution with soil hydraulic properties retrieved from EU-SoilHydroGrids at 250 m resolution (Hengl et al., 2017). Where local soil maps with soil layering, organic carbon content, clay, silt and sand content are available, it is suggested that missing soil properties such as bulk density, soil hydraulic properties, and albedo are estimated from the locally available basic soil properties to ensure consistency.

COMMENT 7:

Line 119 – 121 – Perhaps there is an expert on soil cracking in this team, but I have to admit I never used such variable. I had to search SWAT documentation, and in the EU only a small amount of soils are classified as Vertisoils. Therefore, I question the authors to justify the importance of soil cracking in comparison with all the other properties listed.

ANSWER 7:

We agree that the importance of soil cracking is significantly lower. It is included because the SWAT model requires this input and we decided to follow the structure of the SWAT+ usersoil table, but we will exclude this text because consideration of soil cracks is optional in SWAT+.

COMMENT 8:

Lines 122 – 126 – These are suggestions and not materials and methods, please move this to an appropriate section, or rephrase it if means you took those considerations when analyzing data.

ANSWER 8:

Thank you for the suggestion, we will move these recommendations under “4 Conclusions” section in line 661 in a separate paragraph, before the sentence starting with “When retrieving or deriving …”.

COMMENT 9:

Lines 127 -134 – same as before.

ANSWER 9:

We will delete the first sentence of that section and move the rest to line 67.

COMMENT 10:

Lines 139 – 141 – Not clear. I understand there is partial data availability for comparison, but I don’t understand what is the consequence of such approach. Please clarify the text.

ANSWER 10:

We will add the following clarification in line 142:

“This approach aimed to facilitate a more accurate and fair comparison among different PTFs, but decreased the number of samples used for the analysis.”

COMMENT 11:

Line 138 -139 – All right so this EU-HYDI is you data for validation right? It is reasonable to say this dataset hasn’t been updated since 2013, and considering the report is actual, should I ask if you only considered measured values? Because I see in the report that part of this dataset contains estimated values. In addition to that, could you point the readers to the data itself? The possibility to reproduce the same analysis is necessary.

ANSWER 11:

Yes, we considered only measured values. The total number of samples in EU-HYDI is 18,682. For our analysis, the number of samples varied between 1,591 and 11,287 depending on which soil property was analysed. Please find information regarding the link to the data in the “ANSWER FOR GENERAL COMMENT”.

COMMENT 12:

Line 152 -333– I honestly don’t understand why you wrote this as a protocol format, please reformulate to describe the data analysis that you produced. This is written as a textbook that is not the purpose right? Moreover, out of the sudden I realized that there are datasets that have not been used (e.g depth to water table) and others that have been, could you synthesize that information?

ANSWER 12:

Thank you for the suggestion on reformulation.

12.1 We will add the following section above line 150:

“2.2 Analysed soil properties

We analysed soil physical, hydraulic, and chemical parameters. Under soil physical parameters, we addressed bulk density, porosity, albedo, and soil erodibility factor. For soil hydraulic parameters, we examined water retention, saturated hydraulic conductivity and hydrological soil groups. Regarding soil nutrient content, we focused on topsoil phosphorus content and described the challenges of retrieving soil nitrate content. Hereinafter information about the analysis by soil properties is provided.”

12.2 We will reformulate the lines 152-303 in the following way:

1) move lines 151-174 under “3.9 Suggested workflow to derive soil input parameters” section.

2) reorganize level of subtitles: “Soil physical parameters”, “Soil hydraulic parameters” and “Soil Chemical parameters” will go under “2.2 Analysed soil properties”. There would be no numbered subtitles, only the name of the soil property in the case of bulk density, porosity, albedo, soil erodibility factor, and water retention and saturated hydraulic conductivity.

3) move lines 176-180 and 189-198 under “3.9 Suggested workflow to derive soil input parameters” section. We will keep the following text under “Bulk density section”:

“Table 2 lists the PTFs that were tested on point data in EU-HYDI dataset. We selected the bulk density PTFs – derived on soils of the temperate region – based on previous works (Casanova et al., 2016; Hossain et al., 2015; Palladino et al., 2022; Xiangsheng et al., 2016) that tested the prediction performance of several methods.”

4) delete line 200 and move lines 209-212 above Table 3.

5) rephrase lines 246-247 in the following way:

“Soil water retention and hydraulic conductivity can be computed from the parameters of the widely used van Genuchten model (VG) (van Genuchten, 1980):”

6) move lines 257-265 above Table 4.

7) delete “This approach is recommended for the computation of FC.” from line 268.

8) move text on Hydrological groups (lines 285-303) without subtitle to “3.9 Suggested workflow to derive soil input parameters” section above sentence starting with “Figures 18-21 …”.

COMMENT 13:

Line 312 – How can we know about local fertilization schemes?

ANSWER 13:

We will add the following information in line 312:

"Selection of LUCAS Topsoil samples (EUROSTAT, 2015; Orgiazzi et al., 2018) from the adequate year and an agroclimatic zone (Ceglar et al., 2019) similar to the target area, preferably in the same country (NUTS region). Additional criteria for the data selection could be comparable soil types and fertilization systems. If this information is not known, the NUTS2 phosphorus map of the European cropland areas (Tóth et al., 2014) might be useful in the data selection ."

COMMENT 14:

Line 319 - Locally independent measured dataset?? Provided by an agricultural company? How many samples? When samples were taken? Laboratorial methods? Statistical analysis? This is clearly an insufficient methods description.

ANSWER 14:

Thank you for pointing it out, we will add the text inserted under point 2) of ANSWER FOR GENERAL COMMENT in line 143.

COMMENT 15:

Porosity – Lines 396-399 – so what have you done regarding this? If 43% of the samples presented errors, was this data excluded? (explain for all parameters)

ANSWER 15:

Finding discrepancy between porosity and saturated water content is common in international soil hydraulic databases. This rests in the complexity of their measurement when it comes to small laboratory practicalities, as well as assumptions that are frequently made. In terms of measuring water content, samples often drain some of the water by the time the technician has a chance to raise it from the water bath and put the sample onto the scale. It can also happen that some water is still ponding on top of the sample when the measurement is taken. At the same time, there are known error sources in the determination of particle-density, but this property is often only assumed to be 2.65g/cm3 without measuring. The same applies to measuring bulk density: different standards and/or routines are known to exist worldwide (e.g. measuring on clods vs. ring samples, or measuring at a dry state vs. at an equilibrated moisture level), and the determination of this property is also very sensitive to sample quality. Samples can often over or underfill the rings. It is also customary that data reporters just equate porosity and saturated water content without measuring one of them.

When both properties are reported, discrepancies are often seen due to the above reasons – and beyond. The independent user is typically not equipped to judge which one to trust and which one not to. Therefore, it is a routine procedure to cross-check them and report on data quality, but to only be concerned about the value-pairs that show large discrepancies. The proportion of the suspicious samples in the further analysis is low, 39 out of 1591. Therefore, we did not decrease the number of samples used for the analysis.

We will precise lines 396-397 in the following way:

“Figure 5 shows the relationship between porosity and saturated water content for 391 EU-HYDI samples with measured values of both parameters. Among these samples, 56.5% have a total porosity larger or equal to the saturated water content. For the samples where the saturated water content is higher than the total porosity (N = 170), the reason may be the uncertainties in the measurement of both parameters. It is possible that free water could have pounded on top of the sample when its saturated weight was measured, and errors in the measurement of particle density used to compute porosity may have also contributed (Kutílek and Nielsen, 1994; Nimmo, 2004), resulting in a lower porosity.”

COMMENT 16:

Soil erodibility – I thought the Renard et al 1997 was the most used version of the RUSLE model (almost 5 thousand citations according to semantic scholar), but in case I am not right would you provide an information about it?

Predicting Soil Erosion by Water: A Guide to Conservation Planning With the Revised Universal Soil Loss Equation (RUSLE) (usda.gov) page 65.

Either way, why testing only one equation?

ANSWER 16:

Thanks you for the suggestion! We wanted to use only those equations which can be readily applied for the soil properties most frequently available and not use the ones that require non-easily available soil properties, such as soil structure or permeability. The Renard et al. 1997 equation fits into the logic, therefore we will add K factor computed with it (K_computed_Lenard) and compare its result with the methods already included.

COMMENT 17:

Field capacity – Would be worth to make a reference to table 4 early in the beginning. (for all parameters)

ANSWER 17:

Thank you for the suggestion, we will add the followings:

in line 452: “The FC defined (see abbreviations in Table 4) based on …”

in line 495: “Calculating WP (see abbreviations in Table 4) from …”

in line 522: “If only AWC (see abbreviations in Table 4) is required …”

in line 560-561: “Figure 14 shows the relationship between measured KS and computed with Eq. (22) based on the fitted VG parameters (KS_VG) (see abbreviation in Table 4).”

COMMENT 18:

Wilting point – I don’t understand why the number of points available to assess VG (table 11) differs from the ptf (table 12)

ANSWER 18:

The number of samples differs because Table 11 shows the performances on the VG test set of the EU-HYDI, Table 12 includes performances analysed on the WP test set of the EU-HYDI. Analysis of direct WP prediction (pred_WP) was added to show the difference in accuracy between pred_WP_VG and pred_WP. To increase clarity, we will add the following modifications in the captions of Table 11 and 12:

“Table 11. Prediction performance of wilting point (cm³ cm^-3) derived with the VG model, computed by pedotransfer functions on the VG test set of the EU-HYDI dataset. Observed variable is the WP value computed based on the fitted parameters of the VG model. …”

“Table 12. Prediction performance of wilting point (cm³ cm^-3) computed by pedotransfer functions on the WP test set of the EU-HYDI dataset. Observed variable is the measured WP value. …”

COMMENT 19:

Saturated hydraulic conductivity – found strange you didn’t use this database ESSD - SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications (copernicus.org) for comparisons also, any justification for that?

ANSWER 19:

Our aim was to use EU-HYDI dataset, because that is the most representative soil hydraulic dataset for Europe, therefore we would not add further datasets for the KS analysis.

COMMENT 20:

Line 625 - So these workflow are the result of your analysis, whereas you described the most efficient workflow for better data quality. Right?

ANSWER 20:

Yes, thank you for the suggestion on describing the aim of this section. We will add in line 626 the following:

“Based on the above results, we describe the most efficient workflow to retrieve the soil input parameters for European environmental modelling.”

Then we will continue with the “protocol type” text that we move from the material and methods – mentioned under “ANSWER 12”:

“Initially, the data source of the most relevant soil basic properties, such as soil layering, rooting depth, organic carbon content, clay, silt, and sand content, must be selected. …”

COMMENT 21:

Line 653 – “Key findings underscored the significance of local soil data over global or large-scale datasets in environmental modelling”, I’m afraid you haven’t provided hard evidence on this. Besides this Figure 17, you presented zero information about the sampling, the timing of the year, the laboratory analysis of this procedure, among many other details. Please provide the sufficient info in order to assess if such assessment is even comparable.

ANSWER 21:

Thank you for highlighting it. We agree with you, we will delete this conflicting sentence, because recommendation on density and timing of sampling, type of laboratory methods and other related topics is out of the scope of this manuscript.

COMMENT 22:

Data availability – I find this justification rather poor considering we are not talking about personal information, nor information that could reduce the value of the land. There is a report online, and this work was paid with taxpayer’s money already more than 10 years ago. In the meanwhile, many things changed in science, and the open data is the new reality. Making this data available, in an open data journal as this one, would help the scientific community to overcome many obstacles in the hydrological modelling.

ANSWER 22:

Please find our answer in “ANSWER FOR GENERAL COMMENT”. Please note that EC JRC covered the cost of harmonising the data structure and meetings on creating EU-HYDI, but data collection and laboratory analysis was covered by the participating institutions. EU-HYDI is not the property of any of the authors of this manuscript. Two of the authors were collaborators during the construction of EU-HYDI, therefore the dataset could be accessed for the presented analysis according to its licensing. Accommodating the opening of EU-HYDI dataset is beyond the task and reach of these authors.

We will add the information included in “ANSWER FOR GENERAL COMMENT” and the following text to “Data availability” section:

“LUCAS TOPSOIL data can be accessed through European Soil Data Centre (ESDAC) (European Commission Joint Research Centre, 2024; Panagos et al., 2012, 2022). Local measured topsoil phosphorus data is private, only results of analysis and derived information can be published.”

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